From cbca7cf3ceda428e92b6cbe4ba715f72deac1669 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:37:11 +0200 Subject: [PATCH 01/73] Start writing dds in python. Very raw version --- spotpy/examples/dds.py | 20361 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 20361 insertions(+) create mode 100644 spotpy/examples/dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py new file mode 100644 index 00000000..383a457e --- /dev/null +++ b/spotpy/examples/dds.py @@ -0,0 +1,20361 @@ +import numpy as np +import random + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=[ + 0.05584386259223517, + 0.2535410132532582, + 0.3555753282652794, + 0.9767060208907836, + 0.46324425193900864, + 0.3950042024070052, + 0.5889712334602943, + 0.274782124811474, + 0.46535661133090267, + 0.2384618204031883, + 0.9016519630874807, + 0.2764917577582132, + 0.3478041353510165, + 0.20491028154260693, + 0.6414137312302646, + 0.08968343622461994, + 0.39692730727476133, + 0.9967532542326776, + 0.841419416942886, + 0.6618842788308826, + 0.31882636417540466, + 0.8354743646674859, + 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self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise Exception("ERROR END ind my_randN") + if len(x) == 1: + return x[0] + else: + return x + + def my_randi + + +f_rand = FixedRandomizer() +print(f_rand.my_rand(10)) +print(np.random.rand(10)) +exit() + +#todo read in users own initial soultion, no we just generate it self + +def ackley(vector): + firstSum = 0.0 + secondSum = 0.0 + for c in range(len(vector)): + firstSum += c ** 2.0 + secondSum += np.cos(2.0 * np.pi * vector[c]) + n = float(len(vector)) + return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + + +def get_objfunc(x): + return ackley(x)[0] + + +def dds(sinitial,its,to_max,fraction1,maxiter): + + # ! name,LowerB,UpperB,Integer? + # 1 -2.0 2.0 0 + # 2 -2.0 2.0 0 + # 3 -2.0 2.0 0 + # 4 -2.0 2.0 0 + # 5 -2.0 2.0 0 + # 6 -2.0 2.0 0 + # 7 -2.0 2.0 0 + # 8 -2.0 2.0 0 + # 9 -2.0 2.0 0 + # 10 -2.0 2.0 0 + + # TODO read in from somewhere + + + + S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting + S_max = np.array(10*[2.0]) # dec variable maximums - get from starting + + + + + Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + # zzz = [] # temporary flag + num_dec = len(S_min) # num_dec is the number of decision variables + solution = np.array((maxiter) * [(3+num_dec) * [0]]) + + + # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour + # + # dvn_count = 0 + # new_value = 0 + # dec_var = 0 + + stest = [] + sbest = [] + # Jtest = [] + Jbest = [] + + S_range = S_max - S_min + + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); + ileft = maxiter - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + # raise error error('#Initialization samples >= Max # function evaluations.') + pass + + for i in range(its): + if Discrete_flag == 0: # continuous variable + stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + else: # discrete case + for j in range(num_dec): + stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + + Jtest = to_max * get_objfunc(stest) # get obj function value + + + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest=its # needed to initialize variable and avoid code failure when small # iterations + trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution + + # END First inititlisation + + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + randnums = np.random.rand(num_dec) + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + # TODO more efficient! + stest[dec_var] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) + + #print(Jtest,Jbest) + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + + + print("trial_initial: ",trial_initial) + + print("sbest: ", sbest) + + + #print([solution, it_sbest, sbest, ]) + + + + +def neigh_value_continuous(s,s_min,s_max,fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + np.random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = np.random.rand() + + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + +def neigh_value_discrete(s,s_min,s_max,fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = np.random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = np.random.rand() + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + +def neigh_value_mixed(s,s_min,s_max,fraction1,j): + # todo discrete flagg as a class field + Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + + if Discrete_flag: + return neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return neigh_value_discrete(s,s_min,s_max,fraction1) + + +#1 for minimization +#farction is r_val +# maxiter is line 6 -> 1000 +# np.random.seed(150) +for runs in range(2): + maxiter = 1000 + num_samples=np.max([5,round(0.005*maxiter)]) + Initial_solution = [] # TODO if user had seom, read it in + + dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file From 9367e525b6ff9ab27f70f63036dddcaa89f544b4 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:56:22 +0200 Subject: [PATCH 02/73] short before substitution with own random handler --- spotpy/examples/dds.py | 49 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 45 insertions(+), 4 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index 383a457e..aa58aeac 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -20025,18 +20025,59 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END ind my_randN") + raise Exception("ERROR END in my_rand") if len(x) == 1: return x[0] else: return x - def my_randi + def my_randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise Exception("ERROR END in my_randint") + + def my_randn(self,loc,scale): + + if loc != 0 or scale != 1: + raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! + + if self.normal_counter < self.max_normal_counter: + x = self.normal_list[self.normal_counter] + self.normal_counter += 1 + return x + + else: + raise Exception("ERROR END in my_randn") + + # TODO: better Exceptions + + + + +# TODO Convert this to unittest f_rand = FixedRandomizer() -print(f_rand.my_rand(10)) -print(np.random.rand(10)) +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +print(np.random.normal(0, 1)) +print(f_rand.my_randn(0,1)) + exit() #todo read in users own initial soultion, no we just generate it self From cce2dbf08adfe3f25cb31a12df6c9670a13b5037 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 21:13:43 +0200 Subject: [PATCH 03/73] Working version, which is same as Matlab, extended with own random funcs --- spotpy/examples/dds.py | 106 +++++++++++++++++++++++++++++++---------- 1 file changed, 80 insertions(+), 26 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index aa58aeac..8ff63fd3 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -10009,9 +10009,9 @@ def __init__(self): 0.715363917574095 ] - + self.uniform_list*=3 self.max_normal_counter = 10000 - self.max_uniform_counter = 10000 + self.max_uniform_counter = 30000 self.normal_list = [ 0.9723088366617443, @@ -20025,7 +20025,7 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END in my_rand") + raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) if len(x) == 1: return x[0] else: @@ -20066,7 +20066,7 @@ def my_randn(self,loc,scale): # TODO Convert this to unittest -f_rand = FixedRandomizer() +# f_rand = FixedRandomizer() # print(f_rand.my_rand(10)) # print(np.random.rand(10)) @@ -20075,10 +20075,9 @@ def my_randn(self,loc,scale): # print(np.random.randint(1,101010)) # print("----------------------") -print(np.random.normal(0, 1)) -print(f_rand.my_randn(0,1)) +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) -exit() #todo read in users own initial soultion, no we just generate it self @@ -20092,11 +20091,22 @@ def ackley(vector): return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1=sum1+vector[i]**2 + sum2=sum2+np.cos(2*np.pi*vector[i]) + return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) + + def get_objfunc(x): - return ackley(x)[0] + return ackley10(x) -def dds(sinitial,its,to_max,fraction1,maxiter): +def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): # ! name,LowerB,UpperB,Integer? # 1 -2.0 2.0 0 @@ -20157,10 +20167,13 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(its): if Discrete_flag == 0: # continuous variable - stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + stest = S_min + S_range * f_rand.my_rand(num_dec) + else: # discrete case for j in range(num_dec): - stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) Jtest = to_max * get_objfunc(stest) # get obj function value @@ -20199,32 +20212,58 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(ileft): # remaining F evals after initialization # Determine variable selected as neighbour - Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = np.random.rand(num_dec) + + + #TODO back: randnums = np.random.rand(num_dec) + randnums = f_rand.my_rand(num_dec) + + # print(stest) + # print(randnums); + # print(Pn); + # choosed_nums = [] for j in range(num_dec): if randnums[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) + # choosed_nums+=[j] # TODO make this method!! # TODO more efficient!! stest[j] = new_value # change relevant dec var value in stest + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) + + + new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) # TODO more efficient! - stest[dec_var] = new_value # change relevant dec var value in stest + + stest[dec_var-1] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) - #print(Jtest,Jbest) + # if (i+1)%100 == 0: + # print(Jtest) + + #print([Jtest, Jbest]); + #print(stest) if Jtest <= Jbest: + Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found @@ -20282,7 +20321,10 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): s_range = s_max - s_min - snew = s + np.random.normal(0, 1) * fraction1 * s_range + #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range + snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -20291,7 +20333,8 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() if snew < s_min: # works for any pos or neg s_min @@ -20345,10 +20388,13 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): # s_range is the range of the real variable (s_max-s_min) s_range = s_max - s_min - delta = np.random.normal(0, 1) * fraction1 * s_range + #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range + delta = f_rand.my_randn(0, 1) * fraction1 * s_range snew = s + delta - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() + if snew < s_min - 0.5: # works for any pos or neg s_min if P_Abs_or_Ref <= 0.5: # with 50% chance reflect snew = (s_min - 0.5) + ((s_min - 0.5) - snew) @@ -20373,7 +20419,12 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): snew = np.round(snew) # New value must be integer if snew == s: # pick a number between s_max and s_min by a Uniform distribution - sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) + print(sample) + print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) + exit() + if sample < s: snew = sample else: # must increment option number by one @@ -20382,9 +20433,9 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): def neigh_value_mixed(s,s_min,s_max,fraction1,j): # todo discrete flagg as a class field - Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - if Discrete_flag: + if Discrete_flag == 0: return neigh_value_continuous(s, s_min, s_max, fraction1) else: return neigh_value_discrete(s,s_min,s_max,fraction1) @@ -20394,9 +20445,12 @@ def neigh_value_mixed(s,s_min,s_max,fraction1,j): #farction is r_val # maxiter is line 6 -> 1000 # np.random.seed(150) + +f_rand = FixedRandomizer() + for runs in range(2): maxiter = 1000 num_samples=np.max([5,round(0.005*maxiter)]) Initial_solution = [] # TODO if user had seom, read it in - dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file + dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file From 9d1e04bbf87c7ddaa25a7cb736881516dba8b2d2 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:02:25 +0200 Subject: [PATCH 04/73] Add new class FixedrRandom to control randomized algorithms in Matlab vs Python --- spotpy/__init__.py | 1 + spotpy/tools/__init__.py | 1 + spotpy/tools/fixedrandom.py | 100 + spotpy/tools/normal_list.txt | 10000 ++++++++++++++++++++++++++++++++ spotpy/tools/uniform_list.txt | 10000 ++++++++++++++++++++++++++++++++ 5 files changed, 20102 insertions(+) create mode 100644 spotpy/tools/__init__.py create mode 100644 spotpy/tools/fixedrandom.py create mode 100644 spotpy/tools/normal_list.txt create mode 100644 spotpy/tools/uniform_list.txt diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 89f811d0..aa2829ff 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -38,5 +38,6 @@ from . import examples # Contains tutorials how to use SPOTPY from . import describe # Contains some helper functions to describe smaplers and setups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures +from . import tools __version__ = '1.3.30' \ No newline at end of file diff --git a/spotpy/tools/__init__.py b/spotpy/tools/__init__.py new file mode 100644 index 00000000..0abf9cd7 --- /dev/null +++ b/spotpy/tools/__init__.py @@ -0,0 +1 @@ +from .fixedrandom import * \ No newline at end of file diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py new file mode 100644 index 00000000..8fd1200e --- /dev/null +++ b/spotpy/tools/fixedrandom.py @@ -0,0 +1,100 @@ +''' +Copyright (c) 2018 by Benjamin Manns +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Benjamin Manns +''' + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +import os + +# replaces numpy.random module in a way + +class FixedRandomizerEndOfDataException(Exception): + pass + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) + + self.uniform_list*=3 + self.max_normal_counter = 10000 + self.max_uniform_counter = 30000 + + self.normal_list = list(np.loadtxt(os.path.dirname(__file__)+"/normal_list.txt")) + + def rand(self,dim_x=1,dim_y=1): + #x = np.array(dim_y * [dim_x * [0]]) + x = dim_x * [0] + for i in range(dim_x): + #for j in range(dim_y): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) + if len(x) == 1: + return x[0] + else: + return x + + def randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise FixedRandomizerEndOfDataException("No more data left.") + + def normal(self,loc,scale,size=1): + x = [] + for j in range(size): + if self.normal_counter < self.max_normal_counter: + x.append(self.normal_list[self.normal_counter]*scale + loc) + self.normal_counter += 1 + + else: + raise FixedRandomizerEndOfDataException("No more data left.") + if len(x) == 1: + return x[0] + else: + return x + + +# TODO UNITEST irgendwie + + #print(f_rand.normal(12,1,12)) + #print(np.random.normal(12,1,12)) + + #f_rand.normal(12,1,12)- + # print(np.var(np.random.normal(12,1,12)-np.random.normal(12,1,12))) + # print("-------------------------") + + + + +# TODO Convert this to unittest +# f_rand = FixedRandomizer() +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) + diff --git a/spotpy/tools/normal_list.txt b/spotpy/tools/normal_list.txt new file mode 100644 index 00000000..106ad330 --- /dev/null +++ b/spotpy/tools/normal_list.txt @@ -0,0 +1,10000 @@ +0.9723088366617443 +-1.5793809997742225 +1.667272128859827 +0.5956093579250138 +-0.21465028974841896 +0.0890394326818669 +0.3967447818935998 +0.6617353321641583 +-1.207298903627786 +0.7261802902895028 +-1.6904643439817781 +1.818238762180847 +0.5674817772592261 +2.5637647688819505 +1.388917534231378 +-0.6004552119062644 +-0.39965695397882667 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+0.856638434144594 +0.8082832739328184 +0.7235164671806334 +0.1581037615721247 +0.4979856141070268 +0.6628804575269793 +0.8292933407663836 +0.9934713877904263 +0.1091060261935286 +0.9106798366593588 +0.5623164088484348 +0.5319991192467912 +0.8471715784682357 +0.5472039337392008 +0.33549763790012943 +0.3335582633537695 +0.6821765450184556 +0.9289596195165171 +0.4723780405402096 +0.5213915826938492 +0.6022551170828936 +0.04297716653285144 +0.2536747977268964 +0.715363917574095 \ No newline at end of file From 017d34a90d5129fbc9843ff0a8d28021eb307f8f Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:05:21 +0200 Subject: [PATCH 05/73] Add a version unready version of DDS algorithm. *datawriter* and *objectivefunction* support still open. --- spotpy/algorithms/__init__.py | 3 +- spotpy/algorithms/dds.py | 351 ++++++++++++++++++++++++++++++++++ 2 files changed, 353 insertions(+), 1 deletion(-) create mode 100644 spotpy/algorithms/dds.py diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index f59834a3..7579a1ff 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -30,4 +30,5 @@ from .abc import abc # Artificial Bee Colony from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis -from .list import list # Samples from given spotpy database \ No newline at end of file +from .list import list # Samples from given spotpy database +from .dds import DDS \ No newline at end of file diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py new file mode 100644 index 00000000..ea3f28ef --- /dev/null +++ b/spotpy/algorithms/dds.py @@ -0,0 +1,351 @@ +import numpy as np +from spotpy.tools.fixedrandom import * +from . import _algorithm + +class DDS(_algorithm): + """ + http://www.civil.uwaterloo.ca/btolson/software.aspx + Paper: + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + """ + + def __init__(self, *args, **kwargs): + """ + Input + ---------- + spot_setup: class + model: function + Should be callable with a parameter combination of the parameter-function + and return an list of simulation results (as long as evaluation list) + parameter: function + When called, it should return a random parameter combination. Which can + be e.g. uniform or Gaussian + objectivefunction: function + Should return the objectivefunction for a given list of a model simulation and + observation. + evaluation: function + Should return the true values as return by the model. + + dbname: str + * Name of the database where parameter, objectivefunction value and simulation results will be saved. + + dbformat: str + * ram: fast suited for short sampling time. no file will be created and results are saved in an array. + * csv: A csv file will be created, which you can import afterwards. + + parallel: str + * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu. + * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os). + + save_sim: boolean + * True: Simulation results will be saved + * False: Simulation results will not be saved + """ + + super(DDS, self).__init__(*args, **kwargs) + + # self.np_random = np.random + self.np_random = FixedRandomizer() + + def __set_np_random(self,f_rand): + self.np_random = f_rand + + def sample(self, repetitions,obj_func,fraction1,trials = 1): + """ + --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- + """ + + result_list = [] + sinitial, its, to_max = [], np.max([5, round(0.005 * repetitions)]), 1 + + #print(self.objectivefunction([121312,12,12],[1214123,34,34])) + #exit() + + self.set_repetiton(repetitions) + + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + num_dec = len(self.min_bound) # num_dec is the number of decision variables + + for trial in range(trials): + + solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) + + stest = [] + sbest = [] + Jbest = [] + + s_range = self.max_bound-self.min_bound + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + # own initial solution: + # sinitial = self.parameter()['random'] + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + print('Finding best starting point for trial '+str(trial)+' using '+str(its)+' random samples.') + ileft = repetitions - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') + + for i in range(its): + if self.discrete_flag == 0: # continuous variable + stest = self.min_bound + s_range * self.np_random.rand(num_dec) + + else: # discrete case + for j in range(num_dec): + stest[j] = self.np_random.randint(self.min_bound[j], self.max_bound[j] + 1) + + Jtest = to_max * obj_func(stest) # get obj function value + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest = its # needed to initialize variable and avoid code failure when small # iterations + trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution + + + # + # # A generator that produces parametersets if called + # param_generator = ((rep, self.parameter()['random']) + # for rep in range(int(repetitions))) + # for rep, randompar, simulations in self.repeat(param_generator): + # # A function that calculates the fitness of the run and the manages the database + # self.postprocessing(rep, randompar, simulations) + # self.final_call() + + + + #param_generator = ((rep, 1.0 - np.log(rep + 1) / np.log(ileft), np_random.rand(num_dec)) for rep in range(int(ileft))) + + # TODO implement like this! + # import pprint + # pprint.pprint(list(param_generator)) + # exit() + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i + 1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + + randnums = self.np_random.rand(num_dec) + + + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j + 1) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE + # TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) + + new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, + dec_var - 1) + # TODO more efficient! + + stest[dec_var - 1] = new_value # change relevant dec var value in stest + + # get ojective function value + + Jtest = to_max * obj_func(stest) + + # print([Jtest, Jbest]); + # print(stest) + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + # [list(solution), it_sbest, sbest, trial_initial] + + result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) + return result_list + + + def neigh_value_continuous(self,s, s_min, s_max, fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + self.np_random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = self.np_random.rand() + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + def neigh_value_discrete(self,s, s_min, s_max, fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = self.np_random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = self.np_random.rand() + + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + + def neigh_value_mixed(self,s, s_min, s_max, fraction1, j): + if self.discrete_flag == 0: + return self.neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return self.neigh_value_discrete(s, s_min, s_max, fraction1) + + + + # TODO: getestet werden sollten alle 5 Ergebnisvektoren + + + + From 9e73b372812806c4baec897e4e621556b57a7f0c Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:06:11 +0200 Subject: [PATCH 06/73] Add first unittest for DDS to compare Matlab vs Python output --- spotpy/examples/dds.py | 20456 ------------------- spotpy/examples/spot_setup_dds.py | 26 + spotpy/examples/tutorial_dds.py | 41 + spotpy/unittests/DDS_references/run_1.json | 19 + spotpy/unittests/DDS_references/run_2.json | 19 + spotpy/unittests/DDS_references/run_3.json | 19 + spotpy/unittests/DDS_references/run_4.json | 19 + spotpy/unittests/DDS_references/run_5.json | 29 + spotpy/unittests/test_dds.py | 90 + 9 files changed, 262 insertions(+), 20456 deletions(-) delete mode 100644 spotpy/examples/dds.py create mode 100644 spotpy/examples/spot_setup_dds.py create mode 100644 spotpy/examples/tutorial_dds.py create mode 100644 spotpy/unittests/DDS_references/run_1.json create mode 100644 spotpy/unittests/DDS_references/run_2.json create mode 100644 spotpy/unittests/DDS_references/run_3.json create mode 100644 spotpy/unittests/DDS_references/run_4.json create mode 100644 spotpy/unittests/DDS_references/run_5.json create mode 100644 spotpy/unittests/test_dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py deleted file mode 100644 index 8ff63fd3..00000000 --- a/spotpy/examples/dds.py +++ /dev/null @@ -1,20456 +0,0 @@ -import numpy as np -import random - - -class FixedRandomizer(): - def __init__(self): - self.uniform_counter = 0 - self.normal_counter = 0 - self.uniform_list=[ - 0.05584386259223517, - 0.2535410132532582, - 0.3555753282652794, - 0.9767060208907836, - 0.46324425193900864, - 0.3950042024070052, - 0.5889712334602943, - 0.274782124811474, - 0.46535661133090267, - 0.2384618204031883, - 0.9016519630874807, - 0.2764917577582132, - 0.3478041353510165, - 0.20491028154260693, - 0.6414137312302646, - 0.08968343622461994, - 0.39692730727476133, - 0.9967532542326776, - 0.841419416942886, - 0.6618842788308826, - 0.31882636417540466, - 0.8354743646674859, - 0.028589608077657536, - 0.6079846845146457, - 0.6335957316411811, - 0.41559355020526556, - 0.30770270978552716, - 0.027064337038376518, - 0.7477269802923528, - 0.1502196251407184, - 0.8105389307758969, - 0.2184395961255544, - 0.8632392886169169, - 0.5468106182968853, - 0.6821964128103708, - 0.6281853256117305, - 0.8491284581827278, - 0.26182629209071273, - 0.6517490926530392, - 0.23627743562210546, - 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0.010466942524873549, - 1.0105377516892615, - -0.051870128281102525, - -0.5243040829263796, - -0.20276337726918783, - 0.05811174019879224, - 0.6221996966541786, - 0.46242904765338133, - -1.2985231897722824, - -0.5162081560822431, - -0.7553807158854496, - 0.35823893652515626, - 2.370608856072925, - -0.30360437519425315, - 1.2139469312964672, - 0.5203344997331647, - 1.2583371304217381, - 0.20634917734795732, - -0.06438169964327194 - ] - - def my_rand(self,dim_x=1,dim_y=1): - #x = np.array(dim_y * [dim_x * [0]]) - x = dim_x * [0] - for i in range(dim_x): - #for j in range(dim_y): - if self.uniform_counter < self.max_uniform_counter: - x[i] = self.uniform_list[self.uniform_counter] - self.uniform_counter = self.uniform_counter + 1 - else: - raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) - if len(x) == 1: - return x[0] - else: - return x - - def my_randint(self,x_from,x_to): - vals = [j for j in range(x_from,x_to)] - vals_size = len(vals) - if vals_size == 0: - raise ValueError("x_to >= x_from") - fraq = 1 / vals_size - if self.uniform_counter < self.max_uniform_counter: - q_uni = self.uniform_list[self.uniform_counter] - pos = np.int(np.floor(q_uni / fraq)) - self.uniform_counter += 1 - return vals[pos] - else: - raise Exception("ERROR END in my_randint") - - def my_randn(self,loc,scale): - - if loc != 0 or scale != 1: - raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! - - if self.normal_counter < self.max_normal_counter: - x = self.normal_list[self.normal_counter] - self.normal_counter += 1 - return x - - else: - raise Exception("ERROR END in my_randn") - - # TODO: better Exceptions - - - - - - -# TODO Convert this to unittest -# f_rand = FixedRandomizer() -# print(f_rand.my_rand(10)) -# print(np.random.rand(10)) - -# for k in range(100): -# print(f_rand.my_randint(1,101010)) -# print(np.random.randint(1,101010)) -# print("----------------------") - -# print(np.random.normal(0, 1)) -# print(f_rand.my_randn(0,1)) - - -#todo read in users own initial soultion, no we just generate it self - -def ackley(vector): - firstSum = 0.0 - secondSum = 0.0 - for c in range(len(vector)): - firstSum += c ** 2.0 - secondSum += np.cos(2.0 * np.pi * vector[c]) - n = float(len(vector)) - return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] - - - -def ackley10(vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1=sum1+vector[i]**2 - sum2=sum2+np.cos(2*np.pi*vector[i]) - return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) - - -def get_objfunc(x): - return ackley10(x) - - -def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): - - # ! name,LowerB,UpperB,Integer? - # 1 -2.0 2.0 0 - # 2 -2.0 2.0 0 - # 3 -2.0 2.0 0 - # 4 -2.0 2.0 0 - # 5 -2.0 2.0 0 - # 6 -2.0 2.0 0 - # 7 -2.0 2.0 0 - # 8 -2.0 2.0 0 - # 9 -2.0 2.0 0 - # 10 -2.0 2.0 0 - - # TODO read in from somewhere - - - - S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting - S_max = np.array(10*[2.0]) # dec variable maximums - get from starting - - - - - Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) - - # zzz = [] # temporary flag - num_dec = len(S_min) # num_dec is the number of decision variables - solution = np.array((maxiter) * [(3+num_dec) * [0]]) - - - # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour - # - # dvn_count = 0 - # new_value = 0 - # dec_var = 0 - - stest = [] - sbest = [] - # Jtest = [] - Jbest = [] - - S_range = S_max - S_min - - - - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= - - - - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); - ileft = maxiter - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: - # raise error error('#Initialization samples >= Max # function evaluations.') - pass - - for i in range(its): - if Discrete_flag == 0: # continuous variable - #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples - stest = S_min + S_range * f_rand.my_rand(num_dec) - - else: # discrete case - for j in range(num_dec): - # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); - stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) - - Jtest = to_max * get_objfunc(stest) # get obj function value - - - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest - - - - it_sbest=its # needed to initialize variable and avoid code failure when small # iterations - trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution - - # END First inititlisation - - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour - dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) - - - #TODO back: randnums = np.random.rand(num_dec) - randnums = f_rand.my_rand(num_dec) - - # print(stest) - # print(randnums); - # print(Pn); - # choosed_nums = [] - for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) - - # choosed_nums+=[j] - # TODO make this method!! - # TODO more efficient!! - - stest[j] = new_value # change relevant dec var value in stest - - # print(choosed_nums) - # print(stest) - # print("--------------------") - - - if dvn_count == 0: # no DVs selected at random, so select ONE - #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) - - - new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) - # TODO more efficient! - - stest[dec_var-1] = new_value # change relevant dec var value in stest - - - - # get ojective function value - - Jtest = to_max * get_objfunc(stest) - - - # if (i+1)%100 == 0: - # print(Jtest) - - #print([Jtest, Jbest]); - #print(stest) - if Jtest <= Jbest: - - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found - - - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - - # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest - - - # end DDS function loop - - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - - - print("trial_initial: ",trial_initial) - - print("sbest: ", sbest) - - - #print([solution, it_sbest, sbest, ]) - - - - -def neigh_value_continuous(s,s_min,s_max,fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) - - s_range = s_max - s_min - - #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range - snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range - - - - # NEED to deal with variable upper and lower bounds: - # Originally bounds in DDS were 100# reflective - # But some times DVs are right on the boundary and with 100# reflective - # boundaries it is hard to detect them. Therefore, we decided to make the - # boundaries reflective with 50# chance and absorptive with 50# chance. - # M. Asadzadeh and B. Tolson Dec 2008 - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past s_max then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max: - snew = s_min - - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max - - return snew - - -def neigh_value_discrete(s,s_min,s_max,fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) - s_range = s_max - s_min - #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range - delta = f_rand.my_randn(0, 1) * fraction1 * s_range - snew = s + delta - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past (s_max+0.5) then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min - - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max - - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution - #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value - sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) - print(sample) - print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) - exit() - - if sample < s: - snew = sample - else: # must increment option number by one - snew = sample + 1 - return snew - -def neigh_value_mixed(s,s_min,s_max,fraction1,j): - # todo discrete flagg as a class field - Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - - if Discrete_flag == 0: - return neigh_value_continuous(s, s_min, s_max, fraction1) - else: - return neigh_value_discrete(s,s_min,s_max,fraction1) - - -#1 for minimization -#farction is r_val -# maxiter is line 6 -> 1000 -# np.random.seed(150) - -f_rand = FixedRandomizer() - -for runs in range(2): - maxiter = 1000 - num_samples=np.max([5,round(0.005*maxiter)]) - Initial_solution = [] # TODO if user had seom, read it in - - dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py new file mode 100644 index 00000000..a62a773e --- /dev/null +++ b/spotpy/examples/spot_setup_dds.py @@ -0,0 +1,26 @@ +import spotpy +from spotpy.parameter import Uniform +from spotpy.objectivefunctions import rmse +import numpy as np + + +class spot_setup(object): + + def __init__(self): + self.params = [Uniform(str(j),-2, 2, 1.5, 3.0, -2, 2, doc=str(j)+' value of Rosenbrock function') + for j in range(10)] + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + x = np.array(vector) + simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + return simulations + + def evaluation(self): + observations = [0] + return observations + + def objectivefunction(self, simulation, evaluation): + objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py new file mode 100644 index 00000000..cf511646 --- /dev/null +++ b/spotpy/examples/tutorial_dds.py @@ -0,0 +1,41 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +import numpy as np +from spotpy.describe import describe + +from spotpy.examples.spot_setup_dds import spot_setup + + +#Create samplers for every algorithm: +results=[] +spot_setup=spot_setup() +rep=1000 +timeout=10 #Given in Seconds + + + +Initial_solution = [] # TODO if user had seom, read it in + + +parallel = "seq" +dbformat = "csv" + +sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +#print(describe(sampler)) +sampler.sample(rep) +#results.append(sampler.getdata()) + + + +print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +evaluation = spot_setup.evaluation() diff --git a/spotpy/unittests/DDS_references/run_1.json b/spotpy/unittests/DDS_references/run_1.json new file mode 100644 index 00000000..a3d42f83 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_1.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1000, + "trial_runs" : 2, + "r_val" : 0.2, + "results":[ + { + "sbest" : [0.0067187,-0.0267234,-0.0113414,0.0123613,0.0274250,0.0044262,0.0038885,-0.0233492,-0.0034915,-0.0074681], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.642849 + }, + { + "sbest" : [-0.0106570,-0.0445861,0.0014575,0.0013610,0.0328764,-0.0190379,-0.0067921,0.0061256,0.0324258,-0.0161818], + "trial_initial": [-1.329892,1.146982,-0.055018,-0.439524,1.080476,1.260820,0.727994,-1.718272,-0.359212,1.787397], + "objfunc_val":-22.603228 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_2.json b/spotpy/unittests/DDS_references/run_2.json new file mode 100644 index 00000000..f151431a --- /dev/null +++ b/spotpy/unittests/DDS_references/run_2.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1000, + "trial_runs" : 2, + "r_val" : 0.1, + "results":[ + { + "sbest" : [0.00061643,-0.012349,-0.00059654,-0.022133,-0.012122,0.00097649,-0.00057045,-0.0069949,0.0031554,0.0027167], + "trial_initial": [-1.7766,-0.98584,-0.5777,1.9068,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.0462], + "objfunc_val":-22.676681 + }, + { + "sbest" : [-0.0019426,0.004766,-0.0011191,0.0011356,0.0091296,0.014486,0.007099,-0.0092478,-0.01621,-0.0073524], + "trial_initial": [-1.3299,1.147,-0.055018,-0.43952,1.0805,1.2608,0.72799,-1.7183,-0.35921,1.7874], + "objfunc_val":-22.678947 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_3.json b/spotpy/unittests/DDS_references/run_3.json new file mode 100644 index 00000000..b32153ae --- /dev/null +++ b/spotpy/unittests/DDS_references/run_3.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 458, + "trial_runs" : 2, + "r_val" : 0.1, + "results":[ + { + "sbest" : [-0.0118273,0.0517943,-0.0596296,-0.0092867,0.0245581,-0.0173006,0.0268898,0.0025346,-0.0084837,-0.0451151], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.537145 + }, + { + "sbest" : [-2.4989e-02,-8.2942e-02,2.9951e-02,-6.0965e-04,-2.7539e-02,2.0719e-02,5.5417e-03,4.6295e-02,6.6750e-03,-1.2331e-02], + "trial_initial": [0.15979,1.13943,0.88842,1.10456,1.89854,0.59761,-0.69949,0.83442,0.28254,-0.96890], + "objfunc_val":-22.517933 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_4.json b/spotpy/unittests/DDS_references/run_4.json new file mode 100644 index 00000000..30e50b6b --- /dev/null +++ b/spotpy/unittests/DDS_references/run_4.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1100, + "trial_runs" : 2, + "r_val" : 0.7584, + "results":[ + { + "sbest" : [-0.0122048,-0.0695413,-0.0274150,0.0247516,-0.1724304,-0.0940382,-0.0076873,-0.0091148,0.0456905,-0.0245715], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.214618 + }, + { + "sbest" : [0.1060041,-0.0012436,-0.0559713,0.0337600,-0.0136549,0.1186215,-0.0190572,-0.0607619,0.0630677,-0.0578622], + "trial_initial": [0.9393077,0.0084414,0.2428478,1.3114781,1.0642522,-1.2121538,1.5810095,-0.9043814,-0.4694569,-0.7822007], + "objfunc_val":-22.257303 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_5.json b/spotpy/unittests/DDS_references/run_5.json new file mode 100644 index 00000000..1030c9c3 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_5.json @@ -0,0 +1,29 @@ +{ + "objfunc" : "ackley", + "evatrials" : 250, + "trial_runs" : 4, + "r_val" : 0.29, + "results":[ + { + "sbest" : [0.0031109,0.0027083,0.0669848,0.2031390,0.0802268,0.0690243,-0.0883651,0.0188589,0.2928081,-0.0507384], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-21.630197 + }, + { + "sbest" : [-0.076650,0.011593,0.098371,0.077140,-0.302584,-0.109039,-0.022255,-0.020336,0.073031,-0.281894], + "trial_initial": [0.58720,0.27984,-0.16666,-1.52057,0.69041,0.34202,-1.55106,-1.06755,-1.13758,-1.04445], + "objfunc_val":-21.384372 + }, + { + "sbest" : [-0.1296543,0.2273927,-0.8694331,0.0361375,-0.0168509,0.0066662,0.1133868,-0.0617312,0.0405173,-0.0208061], + "trial_initial": [0.380940,0.023070,1.846857,1.315453,-1.510769,-0.144067,0.791034,-1.443670,1.278489,0.133970], + "objfunc_val":-21.122759 + }, + { + "sbest" : [-5.5878e-02,5.7219e-02,2.3842e-02,-1.4539e-01,-4.8674e-04,1.4931e-01,-2.9320e-03,-8.2249e-02,-1.4800e-02,1.1134e-02], + "trial_initial": [0.912602,-0.775228,0.097104,1.270168,0.725063,0.945704,0.104205,1.548895,0.201936,0.036866], + "objfunc_val":-22.141054 + } + ] +} + diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py new file mode 100644 index 00000000..afe2f4d0 --- /dev/null +++ b/spotpy/unittests/test_dds.py @@ -0,0 +1,90 @@ +import unittest +import sys + +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy + +import numpy as np +import os +from spotpy.examples.spot_setup_dds import spot_setup +from pprint import pprint +import json + + +class TestDDS(unittest.TestCase): + def setUp(self): + self.spot_setup = spot_setup() + self.rep = 1000 + self.timeout = 1 # Given in Seconds + + + def ackley10(self,vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + + + def json_helper(self, run): + with open(os.path.dirname(__file__)+"/DDS_references/run_"+str(run)+".json") as f: + data = json.load(f) + + return data + + def func_switcher(self,name): + if name == "ackley": + return self.ackley10 + + def test_run_1(self): + self.run_a_dds(1) + + def test_run_2(self): + self.run_a_dds(2) + + def test_run_3(self): + self.run_a_dds(3) + + def test_run_4(self): + self.run_a_dds(4) + + def test_run_5(self): + self.run_a_dds(5) + + def run_a_dds(self,run): + original_result = self.json_helper(run) + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + + + results = sampler.sample(original_result["evatrials"],self.func_switcher(original_result['objfunc']),original_result["r_val"],original_result["trial_runs"]) + + + for t in range(original_result["trial_runs"]): + #pprint(results) + #pprint(original_result) + # +self.assertEqual(203, len(results)) + print(results[t]["objfunc_val"],original_result["results"][t]["objfunc_val"]) + self.assertTrue(np.abs(results[t]["objfunc_val"]-original_result["results"][t]["objfunc_val"]) < 0.000001) + py_sbest = results[t]["sbest"] + matlb_sbest = original_result["results"][t]["sbest"] + for k in range(len(py_sbest)): + print(py_sbest[k],matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k],matlb_sbest[k],delta=0.00001) + + py_trial_initial = results[t]["trial_initial"] + matlb_trial_initial = original_result["results"][t]["trial_initial"] + for k in range(len(py_sbest)): + print(py_trial_initial[k], matlb_trial_initial[k]) + self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) + + + + +if __name__ == '__main__': + unittest.main() From b3a36cbc680419e50b90ce12133873fa93d900fe Mon Sep 17 00:00:00 2001 From: bees4ever Date: Tue, 18 Sep 2018 21:47:33 +0200 Subject: [PATCH 07/73] implement distinct parameters sampling in DDS with spotpy's parameter module --- spotpy/algorithms/dds.py | 114 ++++++--------------- spotpy/examples/spot_setup_dds.py | 53 +++++++++- spotpy/examples/tutorial_dds.py | 18 ++-- spotpy/parameter.py | 2 + spotpy/unittests/DDS_references/run_6.json | 19 ++++ spotpy/unittests/test_dds.py | 43 +++----- 6 files changed, 129 insertions(+), 120 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_6.json diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index ea3f28ef..6f4d02aa 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -48,60 +48,58 @@ def __init__(self, *args, **kwargs): # self.np_random = np.random self.np_random = FixedRandomizer() - def __set_np_random(self,f_rand): + def __set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions,obj_func,fraction1,trials = 1): + def sample(self, repetitions, fraction1, trials=1): """ --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- """ result_list = [] - sinitial, its, to_max = [], np.max([5, round(0.005 * repetitions)]), 1 - - #print(self.objectivefunction([121312,12,12],[1214123,34,34])) - #exit() + sinitial, its, to_max = [], np.int(np.max([5, round(0.005 * repetitions)])), 1 self.set_repetiton(repetitions) self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + self.discrete_flag = [u.is_distinct for u in self.setup.params] num_dec = len(self.min_bound) # num_dec is the number of decision variables for trial in range(trials): solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) - - stest = [] sbest = [] Jbest = [] - s_range = self.max_bound-self.min_bound - + s_range = self.max_bound - self.min_bound # ================================================================================================= # INITIAL SOLUTION # ================================================================================================= - # own initial solution: + # # todo implement own initial solution # sinitial = self.parameter()['random'] if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - print('Finding best starting point for trial '+str(trial)+' using '+str(its)+' random samples.') + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + its) + ' random samples.') ileft = repetitions - its # use this to reduce number of fevals in DDS loop if ileft <= 0: raise ValueError('# Initialization samples >= Max # function evaluations.') for i in range(its): - if self.discrete_flag == 0: # continuous variable + stest = [] + if not self.discrete_flag[i]: # continuous variable stest = self.min_bound + s_range * self.np_random.rand(num_dec) else: # discrete case for j in range(num_dec): - stest[j] = self.np_random.randint(self.min_bound[j], self.max_bound[j] + 1) + stest.append( + self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1)) - Jtest = to_max * obj_func(stest) # get obj function value + # TODO how to use the simulation in this case + Jtest = to_max * self.objectivefunction(stest, []) # get obj function value if i == 0: Jbest = Jtest @@ -118,7 +116,7 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): else: # know its=1, using a user supplied initial solution. Calculate obj func value. ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value + Jtest = self.objectivefunction(stest, []) # get obj function value Jbest = Jtest sbest = list(stest) solution[0, 0] = 1 @@ -126,12 +124,9 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): solution[0, 2] = to_max * Jtest solution[0, 3:3 + num_dec] = stest - - it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution - # # # A generator that produces parametersets if called # param_generator = ((rep, self.parameter()['random']) @@ -141,87 +136,52 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): # self.postprocessing(rep, randompar, simulations) # self.final_call() + param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(ileft))) + for rep, randompar, simulations in self.repeat(param_generator): + self.postprocessing(rep, randompar, simulations) - #param_generator = ((rep, 1.0 - np.log(rep + 1) / np.log(ileft), np_random.rand(num_dec)) for rep in range(int(ileft))) - - # TODO implement like this! - # import pprint - # pprint.pprint(list(param_generator)) - # exit() - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i + 1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(rep + 1) / np.log(ileft) dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = self.np_random.rand(num_dec) - - + # TODO: maybe loop with np vectors / array to be faster... for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour + if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j + 1) - - # TODO make this method!! - # TODO more efficient!! - + new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j) stest[j] = new_value # change relevant dec var value in stest - # print(choosed_nums) - # print(stest) - # print("--------------------") - if dvn_count == 0: # no DVs selected at random, so select ONE - # TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) - - new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, - dec_var - 1) - # TODO more efficient! + new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], fraction1, + dec_var - 1) stest[dec_var - 1] = new_value # change relevant dec var value in stest - # get ojective function value + Jtest = to_max * self.objectivefunction(stest, simulations) - Jtest = to_max * obj_func(stest) - - # print([Jtest, Jbest]); - # print(stest) if Jtest <= Jbest: Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - # accumulate results + solution[i + its, 0] = i + its solution[i + its, 1] = to_max * Jbest solution[i + its, 2] = to_max * Jtest solution[i + its, 3:3 + num_dec] = stest - # end DDS function loop + # end DDS function loop print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - # [list(solution), it_sbest, sbest, trial_initial] - result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) - return result_list + return result_list - def neigh_value_continuous(self,s, s_min, s_max, fraction1): + def neigh_value_continuous(self, s, s_min, s_max, fraction1): # select a RANDOM neighbouring real value of a SINGLE decision variable # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT @@ -275,7 +235,7 @@ def neigh_value_continuous(self,s, s_min, s_max, fraction1): return snew - def neigh_value_discrete(self,s, s_min, s_max, fraction1): + def neigh_value_discrete(self, s, s_min, s_max, fraction1): # Created by B.Tolson and B.Yung, June 2006 # Modified by B. Tolson & M. Asadzadeh, Sept 2008 # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) @@ -336,16 +296,8 @@ def neigh_value_discrete(self,s, s_min, s_max, fraction1): snew = sample + 1 return snew - def neigh_value_mixed(self,s, s_min, s_max, fraction1, j): - if self.discrete_flag == 0: + def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): + if not self.discrete_flag[j]: return self.neigh_value_continuous(s, s_min, s_max, fraction1) else: return self.neigh_value_discrete(s, s_min, s_max, fraction1) - - - - # TODO: getestet werden sollten alle 5 Ergebnisvektoren - - - - diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index a62a773e..37e302bd 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -4,11 +4,55 @@ import numpy as np +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + + +def griewank10(vector): + sum1 = 0 + term2 = 1 + term3 = 1 + + for i in range(len(vector)): + sum1 = sum1 + (vector[i] ** 2) / 4000 + term2 = term2 * np.cos(vector[i] / (i + 1) ** 0.5) + + return sum1 - term2 + term3 + + class spot_setup(object): + """ + Setup for a simple example to run DDS Algorithm + """ def __init__(self): - self.params = [Uniform(str(j),-2, 2, 1.5, 3.0, -2, 2, doc=str(j)+' value of Rosenbrock function') - for j in range(10)] + self.params = None + self.objfunc = None + + def _objfunc_switcher(self, name): + """ + Set new parameter and objective function while setup is instanced in a test case + :param name: function name which overwrites initial objective function + :return: + """ + + if name == "ackley": + self.objfunc = ackley10 + self.params = [Uniform(str(j), -2, 2, 1.5, 3.0, -2, 2, doc=str(j) + ' value of Rosenbrock function') + for j in range(10)] + elif name == "griewank": + self.objfunc = griewank10 + self.params = [Uniform('d' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'distinc parameter within a boundary', distinct=True) + for j in range(2)] + [Uniform('c' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'continuous parameter within a boundary') + for j in range(8)] def parameters(self): return spotpy.parameter.generate(self.params) @@ -23,4 +67,7 @@ def evaluation(self): return observations def objectivefunction(self, simulation, evaluation): - objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) + if self.objfunc is None: + print("Please choose an objectivefunction with '_objfunc_switcher' method") + else: + return self.objfunc(simulation) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index cf511646..b19a8396 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -10,11 +10,11 @@ sys.path.append(".") import spotpy -import numpy as np + from spotpy.describe import describe from spotpy.examples.spot_setup_dds import spot_setup - +from spotpy.examples.spot_setup_dds import ackley10 #Create samplers for every algorithm: results=[] @@ -27,15 +27,21 @@ Initial_solution = [] # TODO if user had seom, read it in + + parallel = "seq" dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) -#print(describe(sampler)) -sampler.sample(rep) -#results.append(sampler.getdata()) + +print(describe(sampler)) +sampler.sample(rep,fraction1=0.2,trials=2) + +#print(sampler.getdata()) + +results.append(sampler.getdata()) -print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +#print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x evaluation = spot_setup.evaluation() diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 1a096b8a..cac7d064 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -212,6 +212,8 @@ def __init__(self, rndfunc, rndfuncname, *args, **kwargs): self.maxbound = 0.0 self.description = arghelper.get('doc') + + self.is_distinct = not not arghelper.get("distinct") arghelper.check_complete() def __call__(self, **kwargs): diff --git a/spotpy/unittests/DDS_references/run_6.json b/spotpy/unittests/DDS_references/run_6.json new file mode 100644 index 00000000..1210e7b7 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_6.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "griewank", + "evatrials" : 1000, + "trial_runs" : 1, + "r_val" : 0.2, + "results":[ + { + "sbest" : [-3.00000,0.00000,-4.72715 ,0.84169,9.53635,1.67684,2.72483,2.59443,3.42391,-8.47452], + "trial_initial": [-433,-196,-73,673,56,-26,207,-170,58,-214], + "objfunc_val":1.023480 + }, + { + "sbest" : [-7.00000,-2.00000,0.16090,2.97275,10.80989,-9.10410,2.74935,-8.67317,3.29054,-9.10145], + "trial_initial": [-299,444,83,-32,424,479,319,-416,-8,637], + "objfunc_val":1.109736 + } + ] +} + diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index afe2f4d0..03f4ecd6 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -10,7 +10,6 @@ import numpy as np import os from spotpy.examples.spot_setup_dds import spot_setup -from pprint import pprint import json @@ -20,27 +19,12 @@ def setUp(self): self.rep = 1000 self.timeout = 1 # Given in Seconds - - def ackley10(self,vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1 = sum1 + vector[i] ** 2 - sum2 = sum2 + np.cos(2 * np.pi * vector[i]) - return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) - - def json_helper(self, run): - with open(os.path.dirname(__file__)+"/DDS_references/run_"+str(run)+".json") as f: + with open(os.path.dirname(__file__) + "/DDS_references/run_" + str(run) + ".json") as f: data = json.load(f) return data - def func_switcher(self,name): - if name == "ackley": - return self.ackley10 - def test_run_1(self): self.run_a_dds(1) @@ -56,26 +40,27 @@ def test_run_4(self): def test_run_5(self): self.run_a_dds(5) - def run_a_dds(self,run): + def test_run_6(self): + self.run_a_dds(6) + + def run_a_dds(self, run): original_result = self.json_helper(run) - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) + self.spot_setup._objfunc_switcher(original_result['objfunc']) - results = sampler.sample(original_result["evatrials"],self.func_switcher(original_result['objfunc']),original_result["r_val"],original_result["trial_runs"]) + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) for t in range(original_result["trial_runs"]): - #pprint(results) - #pprint(original_result) - # +self.assertEqual(203, len(results)) - print(results[t]["objfunc_val"],original_result["results"][t]["objfunc_val"]) - self.assertTrue(np.abs(results[t]["objfunc_val"]-original_result["results"][t]["objfunc_val"]) < 0.000001) + print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) + self.assertAlmostEqual(results[t]["objfunc_val"] , original_result["results"][t]["objfunc_val"],delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] for k in range(len(py_sbest)): - print(py_sbest[k],matlb_sbest[k]) - self.assertAlmostEqual(py_sbest[k],matlb_sbest[k],delta=0.00001) + print(py_sbest[k], matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k], matlb_sbest[k], delta=0.00001) py_trial_initial = results[t]["trial_initial"] matlb_trial_initial = original_result["results"][t]["trial_initial"] @@ -84,7 +69,5 @@ def run_a_dds(self,run): self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) - - if __name__ == '__main__': unittest.main() From 8464e4f9149fe34082902d0eafe1c0935fecdae9 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 11:19:11 +0200 Subject: [PATCH 08/73] Add more tests, reorganize pytest for DDS --- spotpy/examples/spot_setup_dds.py | 17 ++++- spotpy/unittests/DDS_references/run_6.json | 13 ++-- spotpy/unittests/DDS_references/run_7.json | 89 ++++++++++++++++++++++ spotpy/unittests/test_dds.py | 9 ++- 4 files changed, 115 insertions(+), 13 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_7.json diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 37e302bd..8b2a0bf5 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -55,19 +55,28 @@ def _objfunc_switcher(self, name): for j in range(8)] def parameters(self): + if self.params is None: + self.params = [ + Uniform("0", -10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function'), + Uniform("1", -10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function'), + Uniform("z", -10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function')] return spotpy.parameter.generate(self.params) def simulation(self, vector): x = np.array(vector) - simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + simulations = x * np.random.rand(len(vector)) + # print(simulations) return simulations def evaluation(self): - observations = [0] + # observations = [0] + observations = [2, 3, 4] return observations def objectivefunction(self, simulation, evaluation): if self.objfunc is None: - print("Please choose an objectivefunction with '_objfunc_switcher' method") + print(simulation, evaluation) + return -rmse(evaluation, simulation) else: - return self.objfunc(simulation) + return self.objfunc(evaluation) diff --git a/spotpy/unittests/DDS_references/run_6.json b/spotpy/unittests/DDS_references/run_6.json index 1210e7b7..7bcb41c5 100644 --- a/spotpy/unittests/DDS_references/run_6.json +++ b/spotpy/unittests/DDS_references/run_6.json @@ -5,14 +5,11 @@ "r_val" : 0.2, "results":[ { - "sbest" : [-3.00000,0.00000,-4.72715 ,0.84169,9.53635,1.67684,2.72483,2.59443,3.42391,-8.47452], - "trial_initial": [-433,-196,-73,673,56,-26,207,-170,58,-214], - "objfunc_val":1.023480 - }, - { - "sbest" : [-7.00000,-2.00000,0.16090,2.97275,10.80989,-9.10410,2.74935,-8.67317,3.29054,-9.10145], - "trial_initial": [-299,444,83,-32,424,479,319,-416,-8,637], - "objfunc_val":1.109736 + "sbest" : [ -3.000000000000000,0.000000000000000,-5.036757578611908,11.733566302568235,9.429452816289487, + -5.549942605772495,2.490307284756468,2.332983597833255,3.851841900847800,0.390134503633089], + "trial_initial": [-433.0000000000000,-196.0000000000000,-73.3096060816648,672.0472250689404,55.8931023268103, + -25.9949571115938,206.7654801523532,-170.2614502262312,58.4279335970832,-213.8458155161740], + "objfunc_val":1.049515 } ] } diff --git a/spotpy/unittests/DDS_references/run_7.json b/spotpy/unittests/DDS_references/run_7.json new file mode 100644 index 00000000..1f70f3b6 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_7.json @@ -0,0 +1,89 @@ +{ + "objfunc": "griewank", + "evatrials": 550, + "trial_runs": 3, + "r_val": 0.37, + "results": [ + { + "sbest": [ + 11.00000000000000, + 19.00000000000000, + 2.03662487437028, + -1.23426415183106, + -5.24719291922387, + 25.07730145065261, + -12.02999926055234, + -22.00167804376221, + -3.14697244811857, + 29.56346268517814 + ], + "trial_initial": [ + -433.0000000000000, + -196.0000000000000, + -73.3096060816648, + 672.0472250689404, + 55.8931023268103, + -25.9949571115938, + 206.7654801523532, + -170.2614502262312, + 58.4279335970832, + -213.8458155161740 + ], + "objfunc_val": 1.664190 + }, + { + "sbest": [ + 7.00000000000000, + 10.00000000000000, + -6.39484002324608, + -46.76336537365788, + 9.58467601113504, + 38.38930958584108, + -6.09867128065270, + -45.82775862964041, + 33.63691393959370, + 2.93087800411658 + ], + "trial_initial": [ + -190.000000000000, + -288.000000000000, + 263.958476836666, + 269.302313621106, + 131.174425938139, + 633.973326660225, + 316.115885867790, + 296.859343133407, + -181.234821869745, + -344.830402395067 + ], + "objfunc_val": 2.802375 + }, + { + "sbest": [ + 12.00000000000000, + 12.00000000000000, + 32.85859327725726, + 7.08140986453067, + 5.86773597147155, + -1.02258063794287, + 3.75986146463776, + -17.85588284586939, + 30.57592135814601, + 1.34518680862905 + ], + "trial_initial": [ + -219.00000000000000, + -344.00000000000000, + 534.86798334588570, + 303.76611823508085, + 106.05989330870204, + 4.88429904716634, + 160.48210213748337, + -277.59019421646974, + -227.26786514527339, + 151.34888971727344 + ], + "objfunc_val": 1.645369 + } + ] +} diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 03f4ecd6..d9414247 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,6 +1,8 @@ import unittest import sys +from spotpy.tools import FixedRandomizer + try: import spotpy except ImportError: @@ -18,6 +20,7 @@ def setUp(self): self.spot_setup = spot_setup() self.rep = 1000 self.timeout = 1 # Given in Seconds + self.f_random = FixedRandomizer() def json_helper(self, run): with open(os.path.dirname(__file__) + "/DDS_references/run_" + str(run) + ".json") as f: @@ -43,6 +46,9 @@ def test_run_5(self): def test_run_6(self): self.run_a_dds(6) + def test_run_7(self): + self.run_a_dds(7) + def run_a_dds(self, run): original_result = self.json_helper(run) @@ -50,6 +56,7 @@ def run_a_dds(self, run): sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) + sampler._set_np_random(self.f_random) results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) @@ -66,7 +73,7 @@ def run_a_dds(self, run): matlb_trial_initial = original_result["results"][t]["trial_initial"] for k in range(len(py_sbest)): print(py_trial_initial[k], matlb_trial_initial[k]) - self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) + self.assertAlmostEqual(py_trial_initial[k],matlb_trial_initial[k], delta=0.0001) if __name__ == '__main__': From df285952cf8840ecc39ca020780f9cc3b90b54f2 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 11:20:04 +0200 Subject: [PATCH 09/73] little bugfixes more descriptions --- spotpy/algorithms/dds.py | 319 ++++++++++++++++++++------------------- 1 file changed, 162 insertions(+), 157 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 6f4d02aa..af0e737a 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -2,12 +2,18 @@ from spotpy.tools.fixedrandom import * from . import _algorithm + class DDS(_algorithm): """ + Implements the Dynamically dimensioned search algorithm for computationally efficient watershed model + calibration + by + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient + watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based + selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + http://www.civil.uwaterloo.ca/btolson/software.aspx - Paper: - Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. - Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. """ def __init__(self, *args, **kwargs): @@ -45,32 +51,48 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) - # self.np_random = np.random - self.np_random = FixedRandomizer() + self.np_random = np.random + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + + if hasattr(self.setup,"params"): + self.discrete_flag = [u.is_distinct for u in self.setup.params] + else: + self.discrete_flag = [False] * len(self.max_bound) - def __set_np_random(self, f_rand): + def _set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions, fraction1, trials=1): + def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): """ - --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- + Samples from the DDS Algorithm + :param repetitions: Maximum number of runs. + :type repetitions: int + :param fraction1: value between 0 and 1 + :type fraction1: float + :param trials: amount of runs DDS algorithm will be performed + :param s_initial: set an initial trial set + :return: """ result_list = [] - sinitial, its, to_max = [], np.int(np.max([5, round(0.005 * repetitions)])), 1 + to_max = 1 self.set_repetiton(repetitions) - self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.discrete_flag = [u.is_distinct for u in self.setup.params] - num_dec = len(self.min_bound) # num_dec is the number of decision variables + if len(s_initial) == 0: + its = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(s_initial) != num_dec: + raise ValueError("User specified 's_initial' has not the same length as available parameters") + else: + its = 1 + for trial in range(trials): - solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) - sbest = [] - Jbest = [] + # solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) + s_best = [] + j_best = [] s_range = self.max_bound - self.min_bound @@ -78,128 +100,115 @@ def sample(self, repetitions, fraction1, trials=1): # INITIAL SOLUTION # ================================================================================================= - # # todo implement own initial solution - # sinitial = self.parameter()['random'] - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( its) + ' random samples.') - ileft = repetitions - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: + i_left = repetitions - its # use this to reduce number of fevals in DDS loop + if i_left <= 0: raise ValueError('# Initialization samples >= Max # function evaluations.') - for i in range(its): - stest = [] - if not self.discrete_flag[i]: # continuous variable - stest = self.min_bound + s_range * self.np_random.rand(num_dec) - - else: # discrete case - for j in range(num_dec): - stest.append( - self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1)) - - # TODO how to use the simulation in this case - Jtest = to_max * self.objectivefunction(stest, []) # get obj function value - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = self.objectivefunction(stest, []) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + self.discrete_flag[j] else self.min_bound[j] + s_range[j] * self.np_random.rand() for j in + range(int(num_dec))]) for rep in range(int(its))) + + for rep, s_test, simulations in self.repeat(starting_generator): + j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + + if rep == 0: + j_best = j_test + s_best = list(s_test) + + if j_test <= j_best: + j_best = j_test + s_best = list(s_test) + + # solution[rep, 0] = rep + # solution[rep, 1] = to_max * j_best + # solution[rep, 2] = to_max * j_test + # solution[rep, 3:3 + num_dec] = s_test + + else: # now its=1, using a user supplied initial solution. Calculate obj func value. + i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + s_test = s_initial # get from the inputs + j_test = self.objectivefunction(s_test, self.simulate((trial, s_initial))) # get obj function value + j_best = j_test + s_best = list(s_test) + # solution[0, 0] = 1 + # solution[0, 1] = to_max * j_best + # solution[0, 2] = to_max * j_test + # solution[0, 3:3 + num_dec] = s_test it_sbest = its # needed to initialize variable and avoid code failure when small # iterations - trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution - - # - # # A generator that produces parametersets if called - # param_generator = ((rep, self.parameter()['random']) - # for rep in range(int(repetitions))) - # for rep, randompar, simulations in self.repeat(param_generator): - # # A function that calculates the fitness of the run and the manages the database - # self.postprocessing(rep, randompar, simulations) - # self.final_call() + trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution - param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(ileft))) + param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) for rep, randompar, simulations in self.repeat(param_generator): self.postprocessing(rep, randompar, simulations) - Pn = 1.0 - np.log(rep + 1) / np.log(ileft) + Pn = 1.0 - np.log(rep + 1) / np.log(i_left) dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) + s_test = list(s_best) # define s_test initially as current (s_best for greedy) - # TODO: maybe loop with np vectors / array to be faster... for j in range(num_dec): if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j) - stest[j] = new_value # change relevant dec var value in stest + + new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], fraction1, + j) + s_test[j] = new_value # change relevant dec var value in stest if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) - new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], + dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) + new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, dec_var - 1) - stest[dec_var - 1] = new_value # change relevant dec var value in stest + s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - Jtest = to_max * self.objectivefunction(stest, simulations) + j_test = to_max * self.objectivefunction(simulations,s_test) - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found + if j_test <= j_best: + j_best = j_test + s_best = list(s_test) + it_sbest = rep + its # iteration number best solution found # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest + # solution[rep + its, 0] = rep + its + # solution[rep + its, 1] = to_max * j_best + # solution[rep + its, 2] = to_max * j_test + # solution[rep + its, 3:3 + num_dec] = s_test # end DDS function loop - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) + print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') + result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list def neigh_value_continuous(self, s, s_min, s_max, fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) + """ + select a RANDOM neighbouring real value of a SINGLE decision variable + CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + variables: + s_range is the range of the real variable (s_max-s_min) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param fraction1: is the neighbourhood parameter (replaces V parameter~see not + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = fraction1 * s_range + for comparison: variance (V) = (fraction1 * s_range)^2 + :return: + """ s_range = s_max - s_min - snew = s + self.np_random.normal(0, 1) * fraction1 * s_range + s_new = s + self.np_random.normal(0, 1) * fraction1 * s_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -208,93 +217,89 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = self.np_random.rand() + p_abs_or_ref = self.np_random.rand() - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) + if s_new < s_min: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50%chance reflect + s_new = s_min + (s_min - s_new) else: # with 50% chance absorb - snew = s_min + s_new = s_min # if reflection goes past s_max then value should be s_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if snew > s_max: - snew = s_min + if s_new > s_max: + s_new = s_min - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) + elif s_new > s_max: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = s_max - (s_new - s_max) else: # with 50% chance absorb - snew = s_max + s_new = s_max # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max + if s_new < s_min: + s_new = s_max - return snew + return s_new def neigh_value_discrete(self, s, s_min, s_max, fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) + """ + Created by B.Tolson and B.Yung, June 2006 + Modified by B. Tolson & M. Asadzadeh, Sept 2008 + Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + 2- Round the new value at the end of generation. + select a RANDOM neighbouring integer value of a SINGLE decision variable + discrete distribution is approximately normal + alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param fraction1: fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = fraction1 * s_range + for comparison: variance (V) = (fraction1 * s_range)^2 + :return: + """ + s_range = s_max - s_min delta = self.np_random.normal(0, 1) * fraction1 * s_range - snew = s + delta + s_new = s + delta - P_Abs_or_Ref = self.np_random.rand() + p_abs_or_ref = self.np_random.rand() - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + if s_new < s_min - 0.5: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_min - 0.5) + ((s_min - 0.5) - s_new) else: # with 50% chance absorb - snew = s_min + s_new = s_min # if reflection goes past (s_max+0.5) then value should be s_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min + if s_new > s_max + 0.5: + s_new = s_min - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + elif s_new > s_max + 0.5: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_max + 0.5) - (s_new - (s_max + 0.5)) else: # with 50% chance absorb - snew = s_max + s_new = s_max # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max + if s_new < s_min - 0.5: + s_new = s_max - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution + s_new = np.round(s_new) # New value must be integer + if s_new == s: # pick a number between s_max and s_min by a Uniform distribution sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) if sample < s: - snew = sample + s_new = sample else: # must increment option number by one - snew = sample + 1 - return snew + s_new = sample + 1 + return s_new def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): if not self.discrete_flag[j]: From b42398e280a3daa3aa57de570c14374639457dca Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 15:20:33 +0200 Subject: [PATCH 10/73] use get_fitness to calculate objective function the spotpy way --- spotpy/algorithms/dds.py | 19 +++++++++++++++---- spotpy/examples/spot_setup_dds.py | 8 ++++---- spotpy/examples/tutorial_dds.py | 26 +++++++++++++------------- spotpy/unittests/test_dds.py | 5 +++-- 4 files changed, 35 insertions(+), 23 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index af0e737a..2c14f99d 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -87,6 +87,11 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): raise ValueError("User specified 's_initial' has not the same length as available parameters") else: its = 1 + s_initial = np.array(s_initial) + print(np.all(s_initial <= self.max_bound)) + print(np.all(s_initial >= self.min_bound)) + if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): + raise ValueError("User specified 's_initial' but the values are not within the parameter range") for trial in range(trials): @@ -113,7 +118,9 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): range(int(num_dec))]) for rep in range(int(its))) for rep, s_test, simulations in self.repeat(starting_generator): - j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + like = self.postprocessing(rep,s_test,simulations) # get obj function value + #j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + j_test = to_max * like if rep == 0: j_best = j_test @@ -131,7 +138,10 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop s_test = s_initial # get from the inputs - j_test = self.objectivefunction(s_test, self.simulate((trial, s_initial))) # get obj function value + nr, inpt, sims = self.simulate((trial, s_initial)) + + j_test = self.getfitness(sims, s_test) # get obj function value + j_best = j_test s_best = list(s_test) # solution[0, 0] = 1 @@ -145,7 +155,7 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) for rep, randompar, simulations in self.repeat(param_generator): - self.postprocessing(rep, randompar, simulations) + Pn = 1.0 - np.log(rep + 1) / np.log(i_left) dvn_count = 0 # counter for how many decision variables vary in neighbour @@ -167,7 +177,8 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - j_test = to_max * self.objectivefunction(simulations,s_test) + like = self.postprocessing(rep, s_test, simulations, chains=trial) + j_test = to_max * like if j_test <= j_best: j_best = j_test diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 8b2a0bf5..ee17610a 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -66,7 +66,6 @@ def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] simulations = x * np.random.rand(len(vector)) - # print(simulations) return simulations def evaluation(self): @@ -74,9 +73,10 @@ def evaluation(self): observations = [2, 3, 4] return observations - def objectivefunction(self, simulation, evaluation): + def objectivefunction(self, simulation, evaluation, params): + if self.objfunc is None: - print(simulation, evaluation) return -rmse(evaluation, simulation) else: - return self.objfunc(evaluation) + pars, names = params + return self.objfunc(pars) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index b19a8396..4ab21f3f 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -19,29 +19,29 @@ #Create samplers for every algorithm: results=[] spot_setup=spot_setup() -rep=1000 +rep=3000 timeout=10 #Given in Seconds - -Initial_solution = [] # TODO if user had seom, read it in - - - - parallel = "seq" dbformat = "csv" - sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) - print(describe(sampler)) -sampler.sample(rep,fraction1=0.2,trials=2) +sampler.sample(rep, trials=1,fraction1=0.1) +results.append(sampler.getdata()) + -#print(sampler.getdata()) +sampler=spotpy.algorithms.dream(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +print(describe(sampler)) +sampler.sample(rep) results.append(sampler.getdata()) -#print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x -evaluation = spot_setup.evaluation() +#algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream'] +spotpy.analyser.plot_parametertrace_algorithms(results,algorithmnames=["DDS","DREAM"],parameternames=['0','1']) + + +print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +#evaluation = spot_setup.evaluation() diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index d9414247..6e93c85b 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,7 +1,7 @@ import unittest import sys -from spotpy.tools import FixedRandomizer + try: import spotpy @@ -9,7 +9,8 @@ sys.path.append(".") import spotpy -import numpy as np +from spotpy.tools import FixedRandomizer + import os from spotpy.examples.spot_setup_dds import spot_setup import json From 30e1d642a92470d87cdfcba1b97fadca8ddcad2d Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 17:31:30 +0200 Subject: [PATCH 11/73] finish user's parameter initialization and write unittests --- spotpy/algorithms/dds.py | 9 ++- .../DDS_references/run_own_input_1.json | 47 ++++++++++++ .../DDS_references/run_own_input_2.json | 74 +++++++++++++++++++ spotpy/unittests/test_dds.py | 33 +++++++-- 4 files changed, 154 insertions(+), 9 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_own_input_1.json create mode 100644 spotpy/unittests/DDS_references/run_own_input_2.json diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 2c14f99d..afeefdb5 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -88,8 +88,6 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: its = 1 s_initial = np.array(s_initial) - print(np.all(s_initial <= self.max_bound)) - print(np.all(s_initial >= self.min_bound)) if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") @@ -138,9 +136,12 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop s_test = s_initial # get from the inputs - nr, inpt, sims = self.simulate((trial, s_initial)) - j_test = self.getfitness(sims, s_test) # get obj function value + single_generator = ( (i,s_test) for i in range(1) ) + rep, s_test_param, simulations = next(self.repeat(single_generator)) + + j_test = self.postprocessing(rep,s_test,simulations) + j_best = j_test s_best = list(s_test) diff --git a/spotpy/unittests/DDS_references/run_own_input_1.json b/spotpy/unittests/DDS_references/run_own_input_1.json new file mode 100644 index 00000000..b1798ba7 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_own_input_1.json @@ -0,0 +1,47 @@ +{ + "objfunc": "griewank", + "evatrials": 1000, + "trial_runs": 1, + "r_val": 0.2, + "s_initial": [ + -1, + 3, + -2, + 4, + 6, + 9, + -3, + 6.7, + 2.2, + 10 + ], + "results": [ + { + "sbest": [ + -1.00000000000000, + 3.00000000000000, + -2.00000000000000, + 4.00000000000000, + 6.00000000000000, + -3.14440865613332, + -5.15170165876658, + 6.70000000000000, + 2.20000000000000, + 10.00000000000000 + ], + "trial_initial": [ + -1.00000000000000, + 3.00000000000000, + -2.00000000000000, + 4.00000000000000, + 6.00000000000000, + 9.00000000000000, + -3.00000000000000, + 6.70000000000000, + 2.20000000000000, + 10.00000000000000 + ], + "objfunc_val": 1.060679 + } + ] +} diff --git a/spotpy/unittests/DDS_references/run_own_input_2.json b/spotpy/unittests/DDS_references/run_own_input_2.json new file mode 100644 index 00000000..b9a486d3 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_own_input_2.json @@ -0,0 +1,74 @@ +{ + "objfunc": "ackley", + "evatrials": 1000, + "trial_runs": 2, + "r_val": 0.23, + "s_initial": [ + -0.6998811, + -1.95230402, + -0.77098336, + -1.78082543, + -0.93364598, + 0.05985113, + -1.2270304, + 1.32375772, + -1.97949944, + -1.94960125 + ], + "results": [ + { + "sbest": [ + -0.01085857370638122, + -0.02253069124037839, + 0.01502826748703164, + -0.03056730293640454, + 0.05340255845087846, + -0.00489005220163242, + -0.00714949428369765, + -0.00685105529011589, + -0.00155646134117859, + 0.02910863259046434 + ], + "trial_initial": [ + -0.6998811000000000, + -1.9523040199999999, + -0.7709833600000000, + -1.7808254299999999, + -0.9336459800000000, + 0.0598511300000000, + -1.2270304000000001, + 1.3237577199999999, + -1.9794994400000001, + -1.9496012500000000 + ], + "objfunc_val": -22.594014 + }, + { + "sbest": [ + 0.01638165987817117, + 0.00714059038361719, + 0.00638449094200643, + -0.02265515934058741, + 0.03554349803571422, + -0.00268569499221568, + -0.01012133140550065, + 0.01938318407346291, + 0.01757040721020015, + -0.03600477373134182 + ], + "trial_initial": [ + -0.6998811000000000, + -1.9523040199999999, + -0.7709833600000000, + -1.7808254299999999, + -0.9336459800000000, + 0.0598511300000000, + -1.2270304000000001, + 1.3237577199999999, + -1.9794994400000001, + -1.9496012500000000 + ], + "objfunc_val": -22.613691 + } + ] +} diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 6e93c85b..24d03bc3 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,7 +1,6 @@ import unittest import sys - - +import numpy as np try: import spotpy @@ -50,6 +49,12 @@ def test_run_6(self): def test_run_7(self): self.run_a_dds(7) + def test_run_own_initial_1(self): + self.run_a_dds("own_input_1") + + def test_run_own_initial_2(self): + self.run_a_dds("own_input_2") + def run_a_dds(self, run): original_result = self.json_helper(run) @@ -59,11 +64,18 @@ def run_a_dds(self, run): sim_timeout=self.timeout) sampler._set_np_random(self.f_random) - results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) + if original_result.get("s_initial") is not None: + # if a parameter initialisation is given, test this: + results = sampler.sample(original_result["evatrials"], original_result["r_val"], + original_result["trial_runs"], s_initial=original_result["s_initial"]) + else: + results = sampler.sample(original_result["evatrials"], original_result["r_val"], + original_result["trial_runs"]) for t in range(original_result["trial_runs"]): print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) - self.assertAlmostEqual(results[t]["objfunc_val"] , original_result["results"][t]["objfunc_val"],delta=0.000001) + self.assertAlmostEqual(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"], + delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] for k in range(len(py_sbest)): @@ -74,7 +86,18 @@ def run_a_dds(self, run): matlb_trial_initial = original_result["results"][t]["trial_initial"] for k in range(len(py_sbest)): print(py_trial_initial[k], matlb_trial_initial[k]) - self.assertAlmostEqual(py_trial_initial[k],matlb_trial_initial[k], delta=0.0001) + self.assertAlmostEqual(py_trial_initial[k], matlb_trial_initial[k], delta=0.0001) + + def test_own_initial_out_of_borders_ackley_1(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + + try: + sampler.sample(1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) + self.assertTrue(False, "ValueError was not thrown") + except ValueError as e: + self.assertTrue(True, "a ValueError was thrown and this is expected") if __name__ == '__main__': From d3cc3c065b969f9d676b76383b1756b38510c0e2 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 18:12:30 +0200 Subject: [PATCH 12/73] Some more unittests concerning own parameter initialization --- spotpy/unittests/test_dds.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 24d03bc3..143becf6 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -92,12 +92,19 @@ def test_own_initial_out_of_borders_ackley_1(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) + self.assertRaises(ValueError,sampler.sample,1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) - try: - sampler.sample(1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) - self.assertTrue(False, "ValueError was not thrown") - except ValueError as e: - self.assertTrue(True, "a ValueError was thrown and this is expected") + def test_own_initial_too_lees(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 9))) + + def test_own_initial_too_much(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) if __name__ == '__main__': From e5418623a3368ebc5033728ea1a4cdccaa759f1c Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 13:03:34 +0100 Subject: [PATCH 13/73] Refactor loop to postprocess correct simulation and parameters --- spotpy/algorithms/dds.py | 117 ++++++++++++++++-------------- spotpy/examples/spot_setup_dds.py | 3 +- spotpy/tools/fixedrandom.py | 7 ++ 3 files changed, 73 insertions(+), 54 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index afeefdb5..9c34f219 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -54,28 +54,46 @@ def __init__(self, *args, **kwargs): self.np_random = np.random self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - if hasattr(self.setup,"params"): + if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: self.discrete_flag = [False] * len(self.max_bound) + # self.generator_repetitions will be set in `sample` and is needed to generate a generator which sends back actual parameter s_test + self.generator_repetitions = -1 + + # holds currents best parameter + self.next_s_best = [] + def _set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): + def get_next_s_test(self): + for rep in range(self.generator_repetitions - 1): + yield rep + 1, self.next_s_best + + def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): """ - Samples from the DDS Algorithm + Samples from the DDS Algorithm. User can define an own 's_initial' parameter configuration set. If not `s_initial` + is set, the algorithm defines an own + :param repetitions: Maximum number of runs. :type repetitions: int :param fraction1: value between 0 and 1 :type fraction1: float :param trials: amount of runs DDS algorithm will be performed :param s_initial: set an initial trial set + :param to_max: 1 to minimize objective function, -1 maximized objective function :return: """ + self.fraction1 = fraction1 + + # Check if `to_max` is correct + if to_max != 1 and to_max != -1: + raise ValueError("please specify `to_max` as 1 or -1") + result_list = [] - to_max = 1 self.set_repetiton(repetitions) @@ -91,17 +109,16 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") + # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): - - # solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) s_best = [] j_best = [] s_range = self.max_bound - self.min_bound - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= + # Calculate the initial Solution, if `its` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating its times to warm um the algorithm by trying which + # randomized generated input matches best (has minimal / maximum likelihood) if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( @@ -116,8 +133,8 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): range(int(num_dec))]) for rep in range(int(its))) for rep, s_test, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep,s_test,simulations) # get obj function value - #j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + like = self.postprocessing(rep, s_test, simulations) # get obj function value + j_test = to_max * like if rep == 0: @@ -128,57 +145,31 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): j_best = j_test s_best = list(s_test) - # solution[rep, 0] = rep - # solution[rep, 1] = to_max * j_best - # solution[rep, 2] = to_max * j_test - # solution[rep, 3:3 + num_dec] = s_test - else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - s_test = s_initial # get from the inputs + s_test = list(s_initial) # get from the inputs - single_generator = ( (i,s_test) for i in range(1) ) + single_generator = ((i, s_test) for i in range(1)) rep, s_test_param, simulations = next(self.repeat(single_generator)) - j_test = self.postprocessing(rep,s_test,simulations) - + j_test = self.postprocessing(rep, s_test, simulations) j_best = j_test s_best = list(s_test) - # solution[0, 0] = 1 - # solution[0, 1] = to_max * j_best - # solution[0, 2] = to_max * j_test - # solution[0, 3:3 + num_dec] = s_test it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution + self.next_s_best = list(s_best) - param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) - - for rep, randompar, simulations in self.repeat(param_generator): - - - Pn = 1.0 - np.log(rep + 1) / np.log(i_left) - dvn_count = 0 # counter for how many decision variables vary in neighbour - s_test = list(s_best) # define s_test initially as current (s_best for greedy) + # important to set this field `generator_repetitions` so that method `get_next_s_test` can generate exact paremters + self.generator_repetitions = i_left - for j in range(num_dec): - if randompar[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 + self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, 0) - new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], fraction1, - j) - s_test[j] = new_value # change relevant dec var value in stest - - if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) - new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], fraction1, - dec_var - 1) - - s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + for rep, s_test, simulations in self.repeat(self.get_next_s_test()): like = self.postprocessing(rep, s_test, simulations, chains=trial) + j_test = to_max * like if j_test <= j_best: @@ -186,20 +177,40 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): s_best = list(s_test) it_sbest = rep + its # iteration number best solution found - # accumulate results - - # solution[rep + its, 0] = rep + its - # solution[rep + its, 1] = to_max * j_best - # solution[rep + its, 2] = to_max * j_test - # solution[rep + its, 3:3 + num_dec] = s_test - # end DDS function loop + s_test = list(s_best) + self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, rep) print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list + def calculate_next_s_test(self, num_dec, s_test, s_best, rep): + randompar = self.np_random.rand(num_dec) + + Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + dvn_count = 0 # counter for how many decision variables vary in neighbour + # s_test = list(s_best) # define s_test initially as current (s_best for greedy) + + for j in range(num_dec): + if randompar[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + + new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], self.fraction1, + j) + s_test[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) + new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], self.fraction1, + dec_var - 1) + + s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + + return s_test + def neigh_value_continuous(self, s, s_min, s_max, fraction1): """ select a RANDOM neighbouring real value of a SINGLE decision variable diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index ee17610a..e4d972ec 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -65,7 +65,8 @@ def parameters(self): def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] - simulations = x * np.random.rand(len(vector)) + #simulations = x * np.random.rand(len(vector)) + simulations = x * np.sum(vector) return simulations def evaluation(self): diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py index 8fd1200e..0b150cd8 100644 --- a/spotpy/tools/fixedrandom.py +++ b/spotpy/tools/fixedrandom.py @@ -19,6 +19,7 @@ class FixedRandomizerEndOfDataException(Exception): class FixedRandomizer(): def __init__(self): + self.debug = False self.uniform_counter = 0 self.normal_counter = 0 self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) @@ -37,6 +38,8 @@ def rand(self,dim_x=1,dim_y=1): if self.uniform_counter < self.max_uniform_counter: x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 + if self.debug: + print("fixrand::rand() counter = "+str(self.uniform_counter)) else: raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) if len(x) == 1: @@ -54,6 +57,8 @@ def randint(self,x_from,x_to): q_uni = self.uniform_list[self.uniform_counter] pos = np.int(np.floor(q_uni / fraq)) self.uniform_counter += 1 + if self.debug: + print("fixrand::randint() counter = " + str(self.uniform_counter)) return vals[pos] else: raise FixedRandomizerEndOfDataException("No more data left.") @@ -64,6 +69,8 @@ def normal(self,loc,scale,size=1): if self.normal_counter < self.max_normal_counter: x.append(self.normal_list[self.normal_counter]*scale + loc) self.normal_counter += 1 + if self.debug: + print("fixrand::normal() counter = " + str(self.normal_counter)) else: raise FixedRandomizerEndOfDataException("No more data left.") From 58c610f9402d62c78bce596160dc7f468c1712b0 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 14:26:37 +0100 Subject: [PATCH 14/73] Refactor sample method and clean up comments --- spotpy/algorithms/__init__.py | 2 +- spotpy/algorithms/dds.py | 74 +++++++++++++++++++++---------- spotpy/examples/spot_setup_dds.py | 4 +- spotpy/unittests/test_dds.py | 6 +++ 4 files changed, 59 insertions(+), 27 deletions(-) diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index 7579a1ff..8109af2e 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -31,4 +31,4 @@ from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis from .list import list # Samples from given spotpy database -from .dds import DDS \ No newline at end of file +from .dds import DDS # Dynamically Dimensioned Search algorithm by Bryan Tolson. \ No newline at end of file diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 9c34f219..0f79db8e 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -14,6 +14,16 @@ class DDS(_algorithm): selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. http://www.civil.uwaterloo.ca/btolson/software.aspx + + Method: + "The DDS algorithm is a novel and simple stochastic single-solution based heuristic global search + algorithm that was developed for the purpose of finding good global solutions + (as opposed to globally optimal solutions) within a specified maximum function (or model) evaluation limit." + (Page 3) + + The DDS algorithm is a simple greedy algorithm, always using the best solution (min or max) from the current + point of view. This may not lead to the global optimization. + """ def __init__(self, *args, **kwargs): @@ -59,18 +69,23 @@ def __init__(self, *args, **kwargs): else: self.discrete_flag = [False] * len(self.max_bound) - # self.generator_repetitions will be set in `sample` and is needed to generate a generator which sends back actual parameter s_test + # self.generator_repetitions will be set in `sample` and is needed to generate a + # generator which sends back actual parameter s_test self.generator_repetitions = -1 # holds currents best parameter - self.next_s_best = [] + self.next_s_test = [] def _set_np_random(self, f_rand): self.np_random = f_rand def get_next_s_test(self): + """ + Fake a generator to run self.repeat to use multiprocessing + """ + # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions - 1): - yield rep + 1, self.next_s_best + yield rep + 1, self.next_s_test def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): """ @@ -84,7 +99,7 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): :param trials: amount of runs DDS algorithm will be performed :param s_initial: set an initial trial set :param to_max: 1 to minimize objective function, -1 maximized objective function - :return: + :return: a key-value set of all parameter combination which has been used. May changed in future. """ self.fraction1 = fraction1 @@ -159,12 +174,12 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution - self.next_s_best = list(s_best) - # important to set this field `generator_repetitions` so that method `get_next_s_test` can generate exact paremters + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters self.generator_repetitions = i_left - self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, 0) + self.next_s_test = self.calculate_next_s_test(s_best, 0) for rep, s_test, simulations in self.repeat(self.get_next_s_test()): @@ -177,39 +192,50 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): s_best = list(s_test) it_sbest = rep + its # iteration number best solution found - # end DDS function loop - s_test = list(s_best) - self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, rep) + # prepare next s_test parameter based on s_best + self.next_s_test = self.calculate_next_s_test(s_best, rep) - print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') + print('Best solution found has obj function value of ' + str(to_max * j_best) + ' at ' + + str(it_sbest) + '\n\n') result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list - def calculate_next_s_test(self, num_dec, s_test, s_best, rep): - randompar = self.np_random.rand(num_dec) + def calculate_next_s_test(self, previous_s_tests, rep): + """ + Needs to run in side `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. + + :param previous_s_tests: A set of parameters + :param rep: Position in DDS loop + :return: next parameter set + """ + amount_params = len(previous_s_tests) + + new_s_test = list(previous_s_tests) # define new_s_test initially as current (previous_s_tests for greedy) + + randompar = self.np_random.rand(amount_params) Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) dvn_count = 0 # counter for how many decision variables vary in neighbour - # s_test = list(s_best) # define s_test initially as current (s_best for greedy) - for j in range(num_dec): + for j in range(amount_params): if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], self.fraction1, - j) - s_test[j] = new_value # change relevant dec var value in stest + new_value = self.neigh_value_mixed(previous_s_tests[j], self.min_bound[j], self.max_bound[j], + self.fraction1, j) + new_s_test[j] = new_value # change relevant dec var value in stest if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) - new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) + new_value = self.neigh_value_mixed(previous_s_tests[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], self.fraction1, dec_var - 1) - s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + new_s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - return s_test + return new_s_test def neigh_value_continuous(self, s, s_min, s_max, fraction1): """ @@ -226,7 +252,7 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): normal random number/s_range. Eg: std dev desired = fraction1 * s_range for comparison: variance (V) = (fraction1 * s_range)^2 - :return: + :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min @@ -284,7 +310,7 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): normal random number/s_range. Eg: std dev desired = fraction1 * s_range for comparison: variance (V) = (fraction1 * s_range)^2 - :return: + :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index e4d972ec..e6577b4c 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -65,8 +65,8 @@ def parameters(self): def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] - #simulations = x * np.random.rand(len(vector)) - simulations = x * np.sum(vector) + simulations = x * np.random.rand(len(vector)) + #simulations = x * np.sum(vector) return simulations def evaluation(self): diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 143becf6..583ad7a5 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -106,6 +106,12 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) + def test_wrong_min_max_input(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) + if __name__ == '__main__': unittest.main() From 38a1f1f425df5ea698e5c694a1c13af514741abe Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 16:42:12 +0100 Subject: [PATCH 15/73] DDS: Refactoring variable names and use BestValue classs --- spotpy/algorithms/dds.py | 267 ++++++++++++++++++++------------ spotpy/examples/tutorial_dds.py | 2 +- spotpy/unittests/test_dds.py | 12 +- 3 files changed, 172 insertions(+), 109 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 0f79db8e..eb584910 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -1,6 +1,33 @@ import numpy as np from spotpy.tools.fixedrandom import * from . import _algorithm +import spotpy.parameter + +class BestValue(object): + def __init__(self, parameters, obj_value): + # self.parameters = spotpy.parameter.Gamma() + # self.best_obj_val = spotpy.parameter.Gamma() + self.parameters = parameters + self.best_obj_val = obj_value + self.best_rep = 0 + + # TODO Docstring + def update(self,curr_parameters, curr_obj_val, curr_rep): + """ + Update on maximize + :param curr_parameters: + :param curr_obj_val: + :return: + """ + # TODO MAXIMAIZE + if self.best_obj_val is None or self.best_obj_val >= curr_obj_val: + self.best_obj_val = curr_obj_val + self.parameters = curr_parameters + self.best_rep = curr_rep + + def init(self,parameters,obj_value): # fake init # todo change that + self.parameters = parameters + self.best_obj_val = obj_value class DDS(_algorithm): @@ -57,13 +84,24 @@ def __init__(self, *args, **kwargs): save_sim: boolean * True: Simulation results will be saved * False: Simulation results will not be saved + :param r: neighborhood size perturbation parameter (r) that defines the random perturbation size standard + deviation as a fraction of the decision variable range. Default is 0.2. + :type r: float + """ + try: + self.r = kwargs.pop("r") + except KeyError: + self.r = 0.2 # default value + super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.best_value = BestValue(self.parameter(), 0) # TODO set obj_value in a better way + if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: @@ -87,157 +125,188 @@ def get_next_s_test(self): for rep in range(self.generator_repetitions - 1): yield rep + 1, self.next_s_test - def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): + def sample(self, repetitions, trials=1, s_initial=[]): """ - Samples from the DDS Algorithm. User can define an own 's_initial' parameter configuration set. If not `s_initial` - is set, the algorithm defines an own + Samples from the DDS Algorithm. + + DDS is a greedy type of algorithm since the current solution, also the best solution identified so far, + is never updated with a solution that has an inferior value of the objective function. + + That means in detail: + The DDS Algorithm starts with an initial phase: + If the user does not defines an own initial configuration The DDS algorithm start with searching a parameter + configuration in between the given parameter bounds. + + The next phase is the dds algorithm itself which runs in a loop `repetion` times: + Based on the parameter configuration x_new the algorithm run the model and simulation with the current parameter set + and calculates the objective function value called F_curr. + + If F_curr > F_best, where F_best is the current max value objective function value, we set x_best = x_curr and + F_best = F_curr. + + Select k of all parameters to include them in the neighborhood calculation. This is performed by calcualating a + threshold Pn (probability in neighbourhood). + + The neighbourhood calculation perturb x_best on standard normal distribution and reflect the result if it + breaks the parameter boundary. + The updated parameter configuration is called x_curr :param repetitions: Maximum number of runs. :type repetitions: int - :param fraction1: value between 0 and 1 - :type fraction1: float :param trials: amount of runs DDS algorithm will be performed - :param s_initial: set an initial trial set - :param to_max: 1 to minimize objective function, -1 maximized objective function + :param s_initial: set an initial trial set as a first parameter configuration. If the set is empty the algorithm + select an own initial parameter configuration :return: a key-value set of all parameter combination which has been used. May changed in future. """ - self.fraction1 = fraction1 - - # Check if `to_max` is correct - if to_max != 1 and to_max != -1: - raise ValueError("please specify `to_max` as 1 or -1") - - result_list = [] + debug_results = [] # every iteration a map of all relevant values is stored, only for debug purpose. + # Spotpy will not need this values. self.set_repetiton(repetitions) - num_dec = len(self.min_bound) # num_dec is the number of decision variables + number_of_parameters = len(self.min_bound) # number_of_parameters is the amount of parameters if len(s_initial) == 0: - its = np.int(np.max([5, round(0.005 * repetitions)])) - elif len(s_initial) != num_dec: + initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(s_initial) != number_of_parameters: raise ValueError("User specified 's_initial' has not the same length as available parameters") else: - its = 1 + initial_iterations = 1 s_initial = np.array(s_initial) if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): - s_best = [] - j_best = [] + # repitionno_best saves on which iteration the best parameter configuration has been found + repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations + repetions_left, f_best, trial_initial = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, s_initial) + + # TODO use ParameterSet Class + + #x_best = list(trial_initial) + self.best_value.init(trial_initial, f_best) + + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters + self.generator_repetitions = repetions_left - s_range = self.max_bound - self.min_bound + self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - # Calculate the initial Solution, if `its` > 1 otherwise the user defined a own one. - # If we need to find an initial solution we iterating its times to warm um the algorithm by trying which - # randomized generated input matches best (has minimal / maximum likelihood) + for rep, x_curr, simulations in self.repeat(self.get_next_s_test()): + f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( - its) + ' random samples.') - i_left = repetitions - its # use this to reduce number of fevals in DDS loop - if i_left <= 0: - raise ValueError('# Initialization samples >= Max # function evaluations.') + self.best_value.update(x_curr,f_curr,rep + initial_iterations) - starting_generator = ( - (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if - self.discrete_flag[j] else self.min_bound[j] + s_range[j] * self.np_random.rand() for j in - range(int(num_dec))]) for rep in range(int(its))) + # TODO MAXIMIZE! + # if f_curr <= f_best: + # f_best = f_curr + # x_best = list(x_curr) + # repitionno_best = rep # iteration number best solution found - for rep, s_test, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep, s_test, simulations) # get obj function value + # prepare next x_curr parameter based on x_best + self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) - j_test = to_max * like + print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + + str(repitionno_best) + '\n\n') + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_initial, "objfunc_val": self.best_value.best_obj_val}) - if rep == 0: - j_best = j_test - s_best = list(s_test) + return debug_results - if j_test <= j_best: - j_best = j_test - s_best = list(s_test) + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, s_initial): + parameter_bound_range = self.max_bound - self.min_bound + number_of_parameters = len(parameter_bound_range) - else: # now its=1, using a user supplied initial solution. Calculate obj func value. - i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - s_test = list(s_initial) # get from the inputs + # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm + # by trying which randomized generated input matches best + x_best = [] + f_best = [] + if initial_iterations > 1: # initial_iterations is the number of function evaluations to initialize the DDS algorithm solution + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + initial_iterations) + ' random samples.') + repetions_left = repetitions - initial_iterations # use this to reduce number of fevals in DDS loop + if repetions_left <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') - single_generator = ((i, s_test) for i in range(1)) - rep, s_test_param, simulations = next(self.repeat(single_generator)) + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + self.discrete_flag[j] else self.min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + for j in + range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) - j_test = self.postprocessing(rep, s_test, simulations) + for rep, x_curr, simulations in self.repeat(starting_generator): + like = self.postprocessing(rep, x_curr, simulations) # get obj function value - j_best = j_test - s_best = list(s_test) + f_curr = like - it_sbest = its # needed to initialize variable and avoid code failure when small # iterations - trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution + if rep == 0: + f_best = f_curr + x_best = list(x_curr) - # important to set this field `generator_repetitions` so that - # method `get_next_s_test` can generate exact parameters - self.generator_repetitions = i_left + if f_curr <= f_best: + f_best = f_curr + x_best = list(x_curr) - self.next_s_test = self.calculate_next_s_test(s_best, 0) + else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. + repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + x_curr = list(s_initial) # get from the inputs - for rep, s_test, simulations in self.repeat(self.get_next_s_test()): + rep, s_test_param, simulations = self.simulate((0, x_curr)) - like = self.postprocessing(rep, s_test, simulations, chains=trial) + f_curr = self.postprocessing(rep, x_curr, simulations) - j_test = to_max * like + f_best = f_curr + x_best = list(x_curr) - if j_test <= j_best: - j_best = j_test - s_best = list(s_test) - it_sbest = rep + its # iteration number best solution found + return repetions_left, f_best, x_best - # prepare next s_test parameter based on s_best - self.next_s_test = self.calculate_next_s_test(s_best, rep) + def calculate_next_s_test(self, previous_x_curr, rep, r): + """ + Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. - print('Best solution found has obj function value of ' + str(to_max * j_best) + ' at ' - + str(it_sbest) + '\n\n') - result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) + `probability_neighborhood` is a threshold at which level a parameter is added to neighbourhood calculation. - return result_list + Using a normal distribution + The decision variable - def calculate_next_s_test(self, previous_s_tests, rep): - """ - Needs to run in side `sample` method. Calculate the next set of parameters based on a given set. - This is greedy algorithm belonging to the DDS algorithm. + `dvn_count` counts how many parameter configuration has been exchanged with neighbourhood values. + If no parameters has been exchanged just one will select and exchanged with it's neighbourhood value. - :param previous_s_tests: A set of parameters + :param previous_x_curr: A set of parameters :param rep: Position in DDS loop + :param r: neighbourhood size perturbation parameter :return: next parameter set """ - amount_params = len(previous_s_tests) + amount_params = len(previous_x_curr) - new_s_test = list(previous_s_tests) # define new_s_test initially as current (previous_s_tests for greedy) + new_x_curr = list(previous_x_curr) # define new_x_curr initially as current (previous_x_curr for greedy) randompar = self.np_random.rand(amount_params) - Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) dvn_count = 0 # counter for how many decision variables vary in neighbour + # TODO simplify this with np.arrays for j in range(amount_params): - if randompar[j] < Pn: # then j th DV selected to vary in neighbour + if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(previous_s_tests[j], self.min_bound[j], self.max_bound[j], - self.fraction1, j) - new_s_test[j] = new_value # change relevant dec var value in stest + new_value = self.neigh_value_mixed(previous_x_curr[j], self.min_bound[j], self.max_bound[j], r, j) + new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_s_tests[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], self.fraction1, + new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], r, dec_var - 1) - new_s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test - return new_s_test + return new_x_curr - def neigh_value_continuous(self, s, s_min, s_max, fraction1): + def neigh_value_continuous(self, s, s_min, s_max, r): """ select a RANDOM neighbouring real value of a SINGLE decision variable CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT @@ -247,17 +316,17 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): :param s: is a current SINGLE decision variable VALUE :param s_min: is the min of variable s :param s_max: is the max of variable s - :param fraction1: is the neighbourhood parameter (replaces V parameter~see not + :param r: is the neighbourhood parameter (replaces V parameter~see not It is defined as the ratio of the std deviation of the desired normal random number/s_range. Eg: - std dev desired = fraction1 * s_range - for comparison: variance (V) = (fraction1 * s_range)^2 + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min - s_new = s + self.np_random.normal(0, 1) * fraction1 * s_range + s_new = s + self.np_random.normal(0, 1) * r * s_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -292,7 +361,7 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): return s_new - def neigh_value_discrete(self, s, s_min, s_max, fraction1): + def neigh_value_discrete(self, s, s_min, s_max, r): """ Created by B.Tolson and B.Yung, June 2006 Modified by B. Tolson & M. Asadzadeh, Sept 2008 @@ -305,16 +374,16 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): :param s: is a current SINGLE decision variable VALUE :param s_min: is the min of variable s :param s_max: is the max of variable s - :param fraction1: fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + :param r: r is the neighbourhood parameter (replaces V parameter~see notes) It is defined as the ratio of the std deviation of the desired normal random number/s_range. Eg: - std dev desired = fraction1 * s_range - for comparison: variance (V) = (fraction1 * s_range)^2 + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min - delta = self.np_random.normal(0, 1) * fraction1 * s_range + delta = self.np_random.normal(0, 1) * r * s_range s_new = s + delta p_abs_or_ref = self.np_random.rand() @@ -350,8 +419,8 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): s_new = sample + 1 return s_new - def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): + def neigh_value_mixed(self, s, s_min, s_max, r, j): if not self.discrete_flag[j]: - return self.neigh_value_continuous(s, s_min, s_max, fraction1) + return self.neigh_value_continuous(s, s_min, s_max, r) else: - return self.neigh_value_discrete(s, s_min, s_max, fraction1) + return self.neigh_value_discrete(s, s_min, s_max, r) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index 4ab21f3f..6b2d69e3 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -27,7 +27,7 @@ dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) print(describe(sampler)) -sampler.sample(rep, trials=1,fraction1=0.1) +sampler.sample(rep, trials=1, r=0.1) results.append(sampler.getdata()) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 583ad7a5..06a0142d 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -61,15 +61,15 @@ def run_a_dds(self, run): self.spot_setup._objfunc_switcher(original_result['objfunc']) sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) + sim_timeout=self.timeout,r=original_result["r_val"]) sampler._set_np_random(self.f_random) if original_result.get("s_initial") is not None: # if a parameter initialisation is given, test this: - results = sampler.sample(original_result["evatrials"], original_result["r_val"], + results = sampler.sample(original_result["evatrials"], original_result["trial_runs"], s_initial=original_result["s_initial"]) else: - results = sampler.sample(original_result["evatrials"], original_result["r_val"], + results = sampler.sample(original_result["evatrials"], original_result["trial_runs"]) for t in range(original_result["trial_runs"]): @@ -106,12 +106,6 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) - def test_wrong_min_max_input(self): - self.spot_setup._objfunc_switcher("ackley") - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) - if __name__ == '__main__': unittest.main() From e298ba794ef489a7f1841fc93c1235086fecb535 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 17:47:17 +0100 Subject: [PATCH 16/73] DDS: refctor generator --- spotpy/algorithms/dds.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index eb584910..9186a6f2 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -117,13 +117,13 @@ def __init__(self, *args, **kwargs): def _set_np_random(self, f_rand): self.np_random = f_rand - def get_next_s_test(self): + def get_next_x_curr(self): """ Fake a generator to run self.repeat to use multiprocessing """ # We need to shift position and length of the sampling process - for rep in range(self.generator_repetitions - 1): - yield rep + 1, self.next_s_test + for rep in range(self.generator_repetitions): + yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.r) def sample(self, repetitions, trials=1, s_initial=[]): """ @@ -191,21 +191,17 @@ def sample(self, repetitions, trials=1, s_initial=[]): # method `get_next_s_test` can generate exact parameters self.generator_repetitions = repetions_left - self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) + #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - for rep, x_curr, simulations in self.repeat(self.get_next_s_test()): + for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) self.best_value.update(x_curr,f_curr,rep + initial_iterations) # TODO MAXIMIZE! - # if f_curr <= f_best: - # f_best = f_curr - # x_best = list(x_curr) - # repitionno_best = rep # iteration number best solution found # prepare next x_curr parameter based on x_best - self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) + #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') @@ -261,7 +257,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions return repetions_left, f_best, x_best - def calculate_next_s_test(self, previous_x_curr, rep, r): + def calculate_next_s_test(self, previous_x_curr, rep, all_rep, r): """ Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. This is greedy algorithm belonging to the DDS algorithm. From 3aa84445406030be3fde1a3131a3b67ca46ddb08 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:37:11 +0200 Subject: [PATCH 17/73] Start writing dds in python. Very raw version --- spotpy/examples/dds.py | 20361 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 20361 insertions(+) create mode 100644 spotpy/examples/dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py new file mode 100644 index 00000000..383a457e --- /dev/null +++ b/spotpy/examples/dds.py @@ -0,0 +1,20361 @@ +import numpy as np +import random + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=[ + 0.05584386259223517, + 0.2535410132532582, + 0.3555753282652794, + 0.9767060208907836, + 0.46324425193900864, + 0.3950042024070052, + 0.5889712334602943, + 0.274782124811474, + 0.46535661133090267, + 0.2384618204031883, + 0.9016519630874807, + 0.2764917577582132, + 0.3478041353510165, + 0.20491028154260693, + 0.6414137312302646, + 0.08968343622461994, + 0.39692730727476133, + 0.9967532542326776, + 0.841419416942886, + 0.6618842788308826, + 0.31882636417540466, + 0.8354743646674859, + 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0.07231989308449034, + -0.654969725627664, + -1.1885568435104552, + 0.745449087352912, + -0.11451494925492176, + -0.42053138348011143, + -2.180044685215999, + -0.5863400273711193, + 0.9733894116381485, + -1.5380051834695678, + -0.8934864797237319, + -1.8166433233884607, + -1.6753856720898266, + -0.7749818761500956, + 1.6550131388895926, + 0.010466942524873549, + 1.0105377516892615, + -0.051870128281102525, + -0.5243040829263796, + -0.20276337726918783, + 0.05811174019879224, + 0.6221996966541786, + 0.46242904765338133, + -1.2985231897722824, + -0.5162081560822431, + -0.7553807158854496, + 0.35823893652515626, + 2.370608856072925, + -0.30360437519425315, + 1.2139469312964672, + 0.5203344997331647, + 1.2583371304217381, + 0.20634917734795732, + -0.06438169964327194 + ] + + def my_rand(self,dim_x=1,dim_y=1): + #x = np.array(dim_y * [dim_x * [0]]) + x = dim_x * [0] + for i in range(dim_x): + #for j in range(dim_y): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise Exception("ERROR END ind my_randN") + if len(x) == 1: + return x[0] + else: + return x + + def my_randi + + +f_rand = FixedRandomizer() +print(f_rand.my_rand(10)) +print(np.random.rand(10)) +exit() + +#todo read in users own initial soultion, no we just generate it self + +def ackley(vector): + firstSum = 0.0 + secondSum = 0.0 + for c in range(len(vector)): + firstSum += c ** 2.0 + secondSum += np.cos(2.0 * np.pi * vector[c]) + n = float(len(vector)) + return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + + +def get_objfunc(x): + return ackley(x)[0] + + +def dds(sinitial,its,to_max,fraction1,maxiter): + + # ! name,LowerB,UpperB,Integer? + # 1 -2.0 2.0 0 + # 2 -2.0 2.0 0 + # 3 -2.0 2.0 0 + # 4 -2.0 2.0 0 + # 5 -2.0 2.0 0 + # 6 -2.0 2.0 0 + # 7 -2.0 2.0 0 + # 8 -2.0 2.0 0 + # 9 -2.0 2.0 0 + # 10 -2.0 2.0 0 + + # TODO read in from somewhere + + + + S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting + S_max = np.array(10*[2.0]) # dec variable maximums - get from starting + + + + + Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + # zzz = [] # temporary flag + num_dec = len(S_min) # num_dec is the number of decision variables + solution = np.array((maxiter) * [(3+num_dec) * [0]]) + + + # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour + # + # dvn_count = 0 + # new_value = 0 + # dec_var = 0 + + stest = [] + sbest = [] + # Jtest = [] + Jbest = [] + + S_range = S_max - S_min + + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); + ileft = maxiter - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + # raise error error('#Initialization samples >= Max # function evaluations.') + pass + + for i in range(its): + if Discrete_flag == 0: # continuous variable + stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + else: # discrete case + for j in range(num_dec): + stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + + Jtest = to_max * get_objfunc(stest) # get obj function value + + + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest=its # needed to initialize variable and avoid code failure when small # iterations + trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution + + # END First inititlisation + + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + randnums = np.random.rand(num_dec) + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + # TODO more efficient! + stest[dec_var] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) + + #print(Jtest,Jbest) + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + + + print("trial_initial: ",trial_initial) + + print("sbest: ", sbest) + + + #print([solution, it_sbest, sbest, ]) + + + + +def neigh_value_continuous(s,s_min,s_max,fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + np.random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = np.random.rand() + + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + +def neigh_value_discrete(s,s_min,s_max,fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = np.random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = np.random.rand() + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + +def neigh_value_mixed(s,s_min,s_max,fraction1,j): + # todo discrete flagg as a class field + Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + + if Discrete_flag: + return neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return neigh_value_discrete(s,s_min,s_max,fraction1) + + +#1 for minimization +#farction is r_val +# maxiter is line 6 -> 1000 +# np.random.seed(150) +for runs in range(2): + maxiter = 1000 + num_samples=np.max([5,round(0.005*maxiter)]) + Initial_solution = [] # TODO if user had seom, read it in + + dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file From bb7fad7dbe47add0ec36805a5da2b5417e8c47b3 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:56:22 +0200 Subject: [PATCH 18/73] short before substitution with own random handler --- spotpy/examples/dds.py | 49 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 45 insertions(+), 4 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index 383a457e..aa58aeac 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -20025,18 +20025,59 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END ind my_randN") + raise Exception("ERROR END in my_rand") if len(x) == 1: return x[0] else: return x - def my_randi + def my_randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise Exception("ERROR END in my_randint") + + def my_randn(self,loc,scale): + + if loc != 0 or scale != 1: + raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! + + if self.normal_counter < self.max_normal_counter: + x = self.normal_list[self.normal_counter] + self.normal_counter += 1 + return x + + else: + raise Exception("ERROR END in my_randn") + + # TODO: better Exceptions + + + + +# TODO Convert this to unittest f_rand = FixedRandomizer() -print(f_rand.my_rand(10)) -print(np.random.rand(10)) +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +print(np.random.normal(0, 1)) +print(f_rand.my_randn(0,1)) + exit() #todo read in users own initial soultion, no we just generate it self From e562c94f8b4a7b9145f8d812ea61634eda28bac1 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 21:13:43 +0200 Subject: [PATCH 19/73] Working version, which is same as Matlab, extended with own random funcs --- spotpy/examples/dds.py | 106 +++++++++++++++++++++++++++++++---------- 1 file changed, 80 insertions(+), 26 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index aa58aeac..8ff63fd3 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -10009,9 +10009,9 @@ def __init__(self): 0.715363917574095 ] - + self.uniform_list*=3 self.max_normal_counter = 10000 - self.max_uniform_counter = 10000 + self.max_uniform_counter = 30000 self.normal_list = [ 0.9723088366617443, @@ -20025,7 +20025,7 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END in my_rand") + raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) if len(x) == 1: return x[0] else: @@ -20066,7 +20066,7 @@ def my_randn(self,loc,scale): # TODO Convert this to unittest -f_rand = FixedRandomizer() +# f_rand = FixedRandomizer() # print(f_rand.my_rand(10)) # print(np.random.rand(10)) @@ -20075,10 +20075,9 @@ def my_randn(self,loc,scale): # print(np.random.randint(1,101010)) # print("----------------------") -print(np.random.normal(0, 1)) -print(f_rand.my_randn(0,1)) +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) -exit() #todo read in users own initial soultion, no we just generate it self @@ -20092,11 +20091,22 @@ def ackley(vector): return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1=sum1+vector[i]**2 + sum2=sum2+np.cos(2*np.pi*vector[i]) + return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) + + def get_objfunc(x): - return ackley(x)[0] + return ackley10(x) -def dds(sinitial,its,to_max,fraction1,maxiter): +def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): # ! name,LowerB,UpperB,Integer? # 1 -2.0 2.0 0 @@ -20157,10 +20167,13 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(its): if Discrete_flag == 0: # continuous variable - stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + stest = S_min + S_range * f_rand.my_rand(num_dec) + else: # discrete case for j in range(num_dec): - stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) Jtest = to_max * get_objfunc(stest) # get obj function value @@ -20199,32 +20212,58 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(ileft): # remaining F evals after initialization # Determine variable selected as neighbour - Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = np.random.rand(num_dec) + + + #TODO back: randnums = np.random.rand(num_dec) + randnums = f_rand.my_rand(num_dec) + + # print(stest) + # print(randnums); + # print(Pn); + # choosed_nums = [] for j in range(num_dec): if randnums[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) + # choosed_nums+=[j] # TODO make this method!! # TODO more efficient!! stest[j] = new_value # change relevant dec var value in stest + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) + + + new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) # TODO more efficient! - stest[dec_var] = new_value # change relevant dec var value in stest + + stest[dec_var-1] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) - #print(Jtest,Jbest) + # if (i+1)%100 == 0: + # print(Jtest) + + #print([Jtest, Jbest]); + #print(stest) if Jtest <= Jbest: + Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found @@ -20282,7 +20321,10 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): s_range = s_max - s_min - snew = s + np.random.normal(0, 1) * fraction1 * s_range + #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range + snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -20291,7 +20333,8 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() if snew < s_min: # works for any pos or neg s_min @@ -20345,10 +20388,13 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): # s_range is the range of the real variable (s_max-s_min) s_range = s_max - s_min - delta = np.random.normal(0, 1) * fraction1 * s_range + #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range + delta = f_rand.my_randn(0, 1) * fraction1 * s_range snew = s + delta - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() + if snew < s_min - 0.5: # works for any pos or neg s_min if P_Abs_or_Ref <= 0.5: # with 50% chance reflect snew = (s_min - 0.5) + ((s_min - 0.5) - snew) @@ -20373,7 +20419,12 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): snew = np.round(snew) # New value must be integer if snew == s: # pick a number between s_max and s_min by a Uniform distribution - sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) + print(sample) + print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) + exit() + if sample < s: snew = sample else: # must increment option number by one @@ -20382,9 +20433,9 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): def neigh_value_mixed(s,s_min,s_max,fraction1,j): # todo discrete flagg as a class field - Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - if Discrete_flag: + if Discrete_flag == 0: return neigh_value_continuous(s, s_min, s_max, fraction1) else: return neigh_value_discrete(s,s_min,s_max,fraction1) @@ -20394,9 +20445,12 @@ def neigh_value_mixed(s,s_min,s_max,fraction1,j): #farction is r_val # maxiter is line 6 -> 1000 # np.random.seed(150) + +f_rand = FixedRandomizer() + for runs in range(2): maxiter = 1000 num_samples=np.max([5,round(0.005*maxiter)]) Initial_solution = [] # TODO if user had seom, read it in - dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file + dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file From 218155e9e7733488eee3ef78381d929a4c5e6ae4 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:02:25 +0200 Subject: [PATCH 20/73] Add new class FixedrRandom to control randomized algorithms in Matlab vs Python --- spotpy/__init__.py | 1 + spotpy/tools/__init__.py | 1 + spotpy/tools/fixedrandom.py | 100 + spotpy/tools/normal_list.txt | 10000 ++++++++++++++++++++++++++++++++ spotpy/tools/uniform_list.txt | 10000 ++++++++++++++++++++++++++++++++ 5 files changed, 20102 insertions(+) create mode 100644 spotpy/tools/__init__.py create mode 100644 spotpy/tools/fixedrandom.py create mode 100644 spotpy/tools/normal_list.txt create mode 100644 spotpy/tools/uniform_list.txt diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 89f811d0..aa2829ff 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -38,5 +38,6 @@ from . import examples # Contains tutorials how to use SPOTPY from . import describe # Contains some helper functions to describe smaplers and setups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures +from . import tools __version__ = '1.3.30' \ No newline at end of file diff --git a/spotpy/tools/__init__.py b/spotpy/tools/__init__.py new file mode 100644 index 00000000..0abf9cd7 --- /dev/null +++ b/spotpy/tools/__init__.py @@ -0,0 +1 @@ +from .fixedrandom import * \ No newline at end of file diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py new file mode 100644 index 00000000..8fd1200e --- /dev/null +++ b/spotpy/tools/fixedrandom.py @@ -0,0 +1,100 @@ +''' +Copyright (c) 2018 by Benjamin Manns +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Benjamin Manns +''' + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +import os + +# replaces numpy.random module in a way + +class FixedRandomizerEndOfDataException(Exception): + pass + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) + + self.uniform_list*=3 + self.max_normal_counter = 10000 + self.max_uniform_counter = 30000 + + self.normal_list = list(np.loadtxt(os.path.dirname(__file__)+"/normal_list.txt")) + + def rand(self,dim_x=1,dim_y=1): + #x = np.array(dim_y * [dim_x * [0]]) + x = dim_x * [0] + for i in range(dim_x): + #for j in range(dim_y): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) + if len(x) == 1: + return x[0] + else: + return x + + def randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise FixedRandomizerEndOfDataException("No more data left.") + + def normal(self,loc,scale,size=1): + x = [] + for j in range(size): + if self.normal_counter < self.max_normal_counter: + x.append(self.normal_list[self.normal_counter]*scale + loc) + self.normal_counter += 1 + + else: + raise FixedRandomizerEndOfDataException("No more data left.") + if len(x) == 1: + return x[0] + else: + return x + + +# TODO UNITEST irgendwie + + #print(f_rand.normal(12,1,12)) + #print(np.random.normal(12,1,12)) + + #f_rand.normal(12,1,12)- + # print(np.var(np.random.normal(12,1,12)-np.random.normal(12,1,12))) + # print("-------------------------") + + + + +# TODO Convert this to unittest +# f_rand = FixedRandomizer() +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) + diff --git a/spotpy/tools/normal_list.txt b/spotpy/tools/normal_list.txt new file mode 100644 index 00000000..106ad330 --- /dev/null +++ b/spotpy/tools/normal_list.txt @@ -0,0 +1,10000 @@ +0.9723088366617443 +-1.5793809997742225 +1.667272128859827 +0.5956093579250138 +-0.21465028974841896 +0.0890394326818669 +0.3967447818935998 +0.6617353321641583 +-1.207298903627786 +0.7261802902895028 +-1.6904643439817781 +1.818238762180847 +0.5674817772592261 +2.5637647688819505 +1.388917534231378 +-0.6004552119062644 +-0.39965695397882667 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+0.856638434144594 +0.8082832739328184 +0.7235164671806334 +0.1581037615721247 +0.4979856141070268 +0.6628804575269793 +0.8292933407663836 +0.9934713877904263 +0.1091060261935286 +0.9106798366593588 +0.5623164088484348 +0.5319991192467912 +0.8471715784682357 +0.5472039337392008 +0.33549763790012943 +0.3335582633537695 +0.6821765450184556 +0.9289596195165171 +0.4723780405402096 +0.5213915826938492 +0.6022551170828936 +0.04297716653285144 +0.2536747977268964 +0.715363917574095 \ No newline at end of file From 0e1a9edd2f69f44ca2207b9c29aed13679f30317 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:05:21 +0200 Subject: [PATCH 21/73] Add a version unready version of DDS algorithm. *datawriter* and *objectivefunction* support still open. --- spotpy/algorithms/__init__.py | 3 +- spotpy/algorithms/dds.py | 351 ++++++++++++++++++++++++++++++++++ 2 files changed, 353 insertions(+), 1 deletion(-) create mode 100644 spotpy/algorithms/dds.py diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index f59834a3..7579a1ff 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -30,4 +30,5 @@ from .abc import abc # Artificial Bee Colony from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis -from .list import list # Samples from given spotpy database \ No newline at end of file +from .list import list # Samples from given spotpy database +from .dds import DDS \ No newline at end of file diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py new file mode 100644 index 00000000..ea3f28ef --- /dev/null +++ b/spotpy/algorithms/dds.py @@ -0,0 +1,351 @@ +import numpy as np +from spotpy.tools.fixedrandom import * +from . import _algorithm + +class DDS(_algorithm): + """ + http://www.civil.uwaterloo.ca/btolson/software.aspx + Paper: + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + """ + + def __init__(self, *args, **kwargs): + """ + Input + ---------- + spot_setup: class + model: function + Should be callable with a parameter combination of the parameter-function + and return an list of simulation results (as long as evaluation list) + parameter: function + When called, it should return a random parameter combination. Which can + be e.g. uniform or Gaussian + objectivefunction: function + Should return the objectivefunction for a given list of a model simulation and + observation. + evaluation: function + Should return the true values as return by the model. + + dbname: str + * Name of the database where parameter, objectivefunction value and simulation results will be saved. + + dbformat: str + * ram: fast suited for short sampling time. no file will be created and results are saved in an array. + * csv: A csv file will be created, which you can import afterwards. + + parallel: str + * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu. + * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os). + + save_sim: boolean + * True: Simulation results will be saved + * False: Simulation results will not be saved + """ + + super(DDS, self).__init__(*args, **kwargs) + + # self.np_random = np.random + self.np_random = FixedRandomizer() + + def __set_np_random(self,f_rand): + self.np_random = f_rand + + def sample(self, repetitions,obj_func,fraction1,trials = 1): + """ + --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- + """ + + result_list = [] + sinitial, its, to_max = [], np.max([5, round(0.005 * repetitions)]), 1 + + #print(self.objectivefunction([121312,12,12],[1214123,34,34])) + #exit() + + self.set_repetiton(repetitions) + + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + num_dec = len(self.min_bound) # num_dec is the number of decision variables + + for trial in range(trials): + + solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) + + stest = [] + sbest = [] + Jbest = [] + + s_range = self.max_bound-self.min_bound + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + # own initial solution: + # sinitial = self.parameter()['random'] + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + print('Finding best starting point for trial '+str(trial)+' using '+str(its)+' random samples.') + ileft = repetitions - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') + + for i in range(its): + if self.discrete_flag == 0: # continuous variable + stest = self.min_bound + s_range * self.np_random.rand(num_dec) + + else: # discrete case + for j in range(num_dec): + stest[j] = self.np_random.randint(self.min_bound[j], self.max_bound[j] + 1) + + Jtest = to_max * obj_func(stest) # get obj function value + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest = its # needed to initialize variable and avoid code failure when small # iterations + trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution + + + # + # # A generator that produces parametersets if called + # param_generator = ((rep, self.parameter()['random']) + # for rep in range(int(repetitions))) + # for rep, randompar, simulations in self.repeat(param_generator): + # # A function that calculates the fitness of the run and the manages the database + # self.postprocessing(rep, randompar, simulations) + # self.final_call() + + + + #param_generator = ((rep, 1.0 - np.log(rep + 1) / np.log(ileft), np_random.rand(num_dec)) for rep in range(int(ileft))) + + # TODO implement like this! + # import pprint + # pprint.pprint(list(param_generator)) + # exit() + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i + 1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + + randnums = self.np_random.rand(num_dec) + + + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j + 1) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE + # TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) + + new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, + dec_var - 1) + # TODO more efficient! + + stest[dec_var - 1] = new_value # change relevant dec var value in stest + + # get ojective function value + + Jtest = to_max * obj_func(stest) + + # print([Jtest, Jbest]); + # print(stest) + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + # [list(solution), it_sbest, sbest, trial_initial] + + result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) + return result_list + + + def neigh_value_continuous(self,s, s_min, s_max, fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + self.np_random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = self.np_random.rand() + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + def neigh_value_discrete(self,s, s_min, s_max, fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = self.np_random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = self.np_random.rand() + + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + + def neigh_value_mixed(self,s, s_min, s_max, fraction1, j): + if self.discrete_flag == 0: + return self.neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return self.neigh_value_discrete(s, s_min, s_max, fraction1) + + + + # TODO: getestet werden sollten alle 5 Ergebnisvektoren + + + + From be3ddedd0ac42e265a06c2812baf4e89110ae9d5 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:06:11 +0200 Subject: [PATCH 22/73] Add first unittest for DDS to compare Matlab vs Python output --- spotpy/examples/dds.py | 20456 ------------------- spotpy/examples/spot_setup_dds.py | 26 + spotpy/examples/tutorial_dds.py | 41 + spotpy/unittests/DDS_references/run_1.json | 19 + spotpy/unittests/DDS_references/run_2.json | 19 + spotpy/unittests/DDS_references/run_3.json | 19 + spotpy/unittests/DDS_references/run_4.json | 19 + spotpy/unittests/DDS_references/run_5.json | 29 + spotpy/unittests/test_dds.py | 90 + 9 files changed, 262 insertions(+), 20456 deletions(-) delete mode 100644 spotpy/examples/dds.py create mode 100644 spotpy/examples/spot_setup_dds.py create mode 100644 spotpy/examples/tutorial_dds.py create mode 100644 spotpy/unittests/DDS_references/run_1.json create mode 100644 spotpy/unittests/DDS_references/run_2.json create mode 100644 spotpy/unittests/DDS_references/run_3.json create mode 100644 spotpy/unittests/DDS_references/run_4.json create mode 100644 spotpy/unittests/DDS_references/run_5.json create mode 100644 spotpy/unittests/test_dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py deleted file mode 100644 index 8ff63fd3..00000000 --- a/spotpy/examples/dds.py +++ /dev/null @@ -1,20456 +0,0 @@ -import numpy as np -import random - - -class FixedRandomizer(): - def __init__(self): - self.uniform_counter = 0 - self.normal_counter = 0 - self.uniform_list=[ - 0.05584386259223517, - 0.2535410132532582, - 0.3555753282652794, - 0.9767060208907836, - 0.46324425193900864, - 0.3950042024070052, - 0.5889712334602943, - 0.274782124811474, - 0.46535661133090267, - 0.2384618204031883, - 0.9016519630874807, - 0.2764917577582132, - 0.3478041353510165, - 0.20491028154260693, - 0.6414137312302646, - 0.08968343622461994, - 0.39692730727476133, - 0.9967532542326776, - 0.841419416942886, - 0.6618842788308826, - 0.31882636417540466, - 0.8354743646674859, - 0.028589608077657536, - 0.6079846845146457, - 0.6335957316411811, - 0.41559355020526556, - 0.30770270978552716, - 0.027064337038376518, - 0.7477269802923528, - 0.1502196251407184, - 0.8105389307758969, - 0.2184395961255544, - 0.8632392886169169, - 0.5468106182968853, - 0.6821964128103708, - 0.6281853256117305, - 0.8491284581827278, - 0.26182629209071273, - 0.6517490926530392, - 0.23627743562210546, - 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0.010466942524873549, - 1.0105377516892615, - -0.051870128281102525, - -0.5243040829263796, - -0.20276337726918783, - 0.05811174019879224, - 0.6221996966541786, - 0.46242904765338133, - -1.2985231897722824, - -0.5162081560822431, - -0.7553807158854496, - 0.35823893652515626, - 2.370608856072925, - -0.30360437519425315, - 1.2139469312964672, - 0.5203344997331647, - 1.2583371304217381, - 0.20634917734795732, - -0.06438169964327194 - ] - - def my_rand(self,dim_x=1,dim_y=1): - #x = np.array(dim_y * [dim_x * [0]]) - x = dim_x * [0] - for i in range(dim_x): - #for j in range(dim_y): - if self.uniform_counter < self.max_uniform_counter: - x[i] = self.uniform_list[self.uniform_counter] - self.uniform_counter = self.uniform_counter + 1 - else: - raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) - if len(x) == 1: - return x[0] - else: - return x - - def my_randint(self,x_from,x_to): - vals = [j for j in range(x_from,x_to)] - vals_size = len(vals) - if vals_size == 0: - raise ValueError("x_to >= x_from") - fraq = 1 / vals_size - if self.uniform_counter < self.max_uniform_counter: - q_uni = self.uniform_list[self.uniform_counter] - pos = np.int(np.floor(q_uni / fraq)) - self.uniform_counter += 1 - return vals[pos] - else: - raise Exception("ERROR END in my_randint") - - def my_randn(self,loc,scale): - - if loc != 0 or scale != 1: - raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! - - if self.normal_counter < self.max_normal_counter: - x = self.normal_list[self.normal_counter] - self.normal_counter += 1 - return x - - else: - raise Exception("ERROR END in my_randn") - - # TODO: better Exceptions - - - - - - -# TODO Convert this to unittest -# f_rand = FixedRandomizer() -# print(f_rand.my_rand(10)) -# print(np.random.rand(10)) - -# for k in range(100): -# print(f_rand.my_randint(1,101010)) -# print(np.random.randint(1,101010)) -# print("----------------------") - -# print(np.random.normal(0, 1)) -# print(f_rand.my_randn(0,1)) - - -#todo read in users own initial soultion, no we just generate it self - -def ackley(vector): - firstSum = 0.0 - secondSum = 0.0 - for c in range(len(vector)): - firstSum += c ** 2.0 - secondSum += np.cos(2.0 * np.pi * vector[c]) - n = float(len(vector)) - return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] - - - -def ackley10(vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1=sum1+vector[i]**2 - sum2=sum2+np.cos(2*np.pi*vector[i]) - return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) - - -def get_objfunc(x): - return ackley10(x) - - -def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): - - # ! name,LowerB,UpperB,Integer? - # 1 -2.0 2.0 0 - # 2 -2.0 2.0 0 - # 3 -2.0 2.0 0 - # 4 -2.0 2.0 0 - # 5 -2.0 2.0 0 - # 6 -2.0 2.0 0 - # 7 -2.0 2.0 0 - # 8 -2.0 2.0 0 - # 9 -2.0 2.0 0 - # 10 -2.0 2.0 0 - - # TODO read in from somewhere - - - - S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting - S_max = np.array(10*[2.0]) # dec variable maximums - get from starting - - - - - Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) - - # zzz = [] # temporary flag - num_dec = len(S_min) # num_dec is the number of decision variables - solution = np.array((maxiter) * [(3+num_dec) * [0]]) - - - # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour - # - # dvn_count = 0 - # new_value = 0 - # dec_var = 0 - - stest = [] - sbest = [] - # Jtest = [] - Jbest = [] - - S_range = S_max - S_min - - - - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= - - - - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); - ileft = maxiter - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: - # raise error error('#Initialization samples >= Max # function evaluations.') - pass - - for i in range(its): - if Discrete_flag == 0: # continuous variable - #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples - stest = S_min + S_range * f_rand.my_rand(num_dec) - - else: # discrete case - for j in range(num_dec): - # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); - stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) - - Jtest = to_max * get_objfunc(stest) # get obj function value - - - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest - - - - it_sbest=its # needed to initialize variable and avoid code failure when small # iterations - trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution - - # END First inititlisation - - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour - dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) - - - #TODO back: randnums = np.random.rand(num_dec) - randnums = f_rand.my_rand(num_dec) - - # print(stest) - # print(randnums); - # print(Pn); - # choosed_nums = [] - for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) - - # choosed_nums+=[j] - # TODO make this method!! - # TODO more efficient!! - - stest[j] = new_value # change relevant dec var value in stest - - # print(choosed_nums) - # print(stest) - # print("--------------------") - - - if dvn_count == 0: # no DVs selected at random, so select ONE - #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) - - - new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) - # TODO more efficient! - - stest[dec_var-1] = new_value # change relevant dec var value in stest - - - - # get ojective function value - - Jtest = to_max * get_objfunc(stest) - - - # if (i+1)%100 == 0: - # print(Jtest) - - #print([Jtest, Jbest]); - #print(stest) - if Jtest <= Jbest: - - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found - - - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - - # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest - - - # end DDS function loop - - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - - - print("trial_initial: ",trial_initial) - - print("sbest: ", sbest) - - - #print([solution, it_sbest, sbest, ]) - - - - -def neigh_value_continuous(s,s_min,s_max,fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) - - s_range = s_max - s_min - - #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range - snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range - - - - # NEED to deal with variable upper and lower bounds: - # Originally bounds in DDS were 100# reflective - # But some times DVs are right on the boundary and with 100# reflective - # boundaries it is hard to detect them. Therefore, we decided to make the - # boundaries reflective with 50# chance and absorptive with 50# chance. - # M. Asadzadeh and B. Tolson Dec 2008 - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past s_max then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max: - snew = s_min - - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max - - return snew - - -def neigh_value_discrete(s,s_min,s_max,fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) - s_range = s_max - s_min - #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range - delta = f_rand.my_randn(0, 1) * fraction1 * s_range - snew = s + delta - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past (s_max+0.5) then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min - - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max - - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution - #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value - sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) - print(sample) - print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) - exit() - - if sample < s: - snew = sample - else: # must increment option number by one - snew = sample + 1 - return snew - -def neigh_value_mixed(s,s_min,s_max,fraction1,j): - # todo discrete flagg as a class field - Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - - if Discrete_flag == 0: - return neigh_value_continuous(s, s_min, s_max, fraction1) - else: - return neigh_value_discrete(s,s_min,s_max,fraction1) - - -#1 for minimization -#farction is r_val -# maxiter is line 6 -> 1000 -# np.random.seed(150) - -f_rand = FixedRandomizer() - -for runs in range(2): - maxiter = 1000 - num_samples=np.max([5,round(0.005*maxiter)]) - Initial_solution = [] # TODO if user had seom, read it in - - dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py new file mode 100644 index 00000000..a62a773e --- /dev/null +++ b/spotpy/examples/spot_setup_dds.py @@ -0,0 +1,26 @@ +import spotpy +from spotpy.parameter import Uniform +from spotpy.objectivefunctions import rmse +import numpy as np + + +class spot_setup(object): + + def __init__(self): + self.params = [Uniform(str(j),-2, 2, 1.5, 3.0, -2, 2, doc=str(j)+' value of Rosenbrock function') + for j in range(10)] + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + x = np.array(vector) + simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + return simulations + + def evaluation(self): + observations = [0] + return observations + + def objectivefunction(self, simulation, evaluation): + objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py new file mode 100644 index 00000000..cf511646 --- /dev/null +++ b/spotpy/examples/tutorial_dds.py @@ -0,0 +1,41 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +import numpy as np +from spotpy.describe import describe + +from spotpy.examples.spot_setup_dds import spot_setup + + +#Create samplers for every algorithm: +results=[] +spot_setup=spot_setup() +rep=1000 +timeout=10 #Given in Seconds + + + +Initial_solution = [] # TODO if user had seom, read it in + + +parallel = "seq" +dbformat = "csv" + +sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +#print(describe(sampler)) +sampler.sample(rep) +#results.append(sampler.getdata()) + + + +print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +evaluation = spot_setup.evaluation() diff --git a/spotpy/unittests/DDS_references/run_1.json b/spotpy/unittests/DDS_references/run_1.json new file mode 100644 index 00000000..a3d42f83 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_1.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1000, + "trial_runs" : 2, + "r_val" : 0.2, + "results":[ + { + "sbest" : [0.0067187,-0.0267234,-0.0113414,0.0123613,0.0274250,0.0044262,0.0038885,-0.0233492,-0.0034915,-0.0074681], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.642849 + }, + { + "sbest" : [-0.0106570,-0.0445861,0.0014575,0.0013610,0.0328764,-0.0190379,-0.0067921,0.0061256,0.0324258,-0.0161818], + "trial_initial": [-1.329892,1.146982,-0.055018,-0.439524,1.080476,1.260820,0.727994,-1.718272,-0.359212,1.787397], + "objfunc_val":-22.603228 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_2.json b/spotpy/unittests/DDS_references/run_2.json new file mode 100644 index 00000000..f151431a --- /dev/null +++ b/spotpy/unittests/DDS_references/run_2.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1000, + "trial_runs" : 2, + "r_val" : 0.1, + "results":[ + { + "sbest" : [0.00061643,-0.012349,-0.00059654,-0.022133,-0.012122,0.00097649,-0.00057045,-0.0069949,0.0031554,0.0027167], + "trial_initial": [-1.7766,-0.98584,-0.5777,1.9068,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.0462], + "objfunc_val":-22.676681 + }, + { + "sbest" : [-0.0019426,0.004766,-0.0011191,0.0011356,0.0091296,0.014486,0.007099,-0.0092478,-0.01621,-0.0073524], + "trial_initial": [-1.3299,1.147,-0.055018,-0.43952,1.0805,1.2608,0.72799,-1.7183,-0.35921,1.7874], + "objfunc_val":-22.678947 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_3.json b/spotpy/unittests/DDS_references/run_3.json new file mode 100644 index 00000000..b32153ae --- /dev/null +++ b/spotpy/unittests/DDS_references/run_3.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 458, + "trial_runs" : 2, + "r_val" : 0.1, + "results":[ + { + "sbest" : [-0.0118273,0.0517943,-0.0596296,-0.0092867,0.0245581,-0.0173006,0.0268898,0.0025346,-0.0084837,-0.0451151], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.537145 + }, + { + "sbest" : [-2.4989e-02,-8.2942e-02,2.9951e-02,-6.0965e-04,-2.7539e-02,2.0719e-02,5.5417e-03,4.6295e-02,6.6750e-03,-1.2331e-02], + "trial_initial": [0.15979,1.13943,0.88842,1.10456,1.89854,0.59761,-0.69949,0.83442,0.28254,-0.96890], + "objfunc_val":-22.517933 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_4.json b/spotpy/unittests/DDS_references/run_4.json new file mode 100644 index 00000000..30e50b6b --- /dev/null +++ b/spotpy/unittests/DDS_references/run_4.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1100, + "trial_runs" : 2, + "r_val" : 0.7584, + "results":[ + { + "sbest" : [-0.0122048,-0.0695413,-0.0274150,0.0247516,-0.1724304,-0.0940382,-0.0076873,-0.0091148,0.0456905,-0.0245715], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.214618 + }, + { + "sbest" : [0.1060041,-0.0012436,-0.0559713,0.0337600,-0.0136549,0.1186215,-0.0190572,-0.0607619,0.0630677,-0.0578622], + "trial_initial": [0.9393077,0.0084414,0.2428478,1.3114781,1.0642522,-1.2121538,1.5810095,-0.9043814,-0.4694569,-0.7822007], + "objfunc_val":-22.257303 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_5.json b/spotpy/unittests/DDS_references/run_5.json new file mode 100644 index 00000000..1030c9c3 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_5.json @@ -0,0 +1,29 @@ +{ + "objfunc" : "ackley", + "evatrials" : 250, + "trial_runs" : 4, + "r_val" : 0.29, + "results":[ + { + "sbest" : [0.0031109,0.0027083,0.0669848,0.2031390,0.0802268,0.0690243,-0.0883651,0.0188589,0.2928081,-0.0507384], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-21.630197 + }, + { + "sbest" : [-0.076650,0.011593,0.098371,0.077140,-0.302584,-0.109039,-0.022255,-0.020336,0.073031,-0.281894], + "trial_initial": [0.58720,0.27984,-0.16666,-1.52057,0.69041,0.34202,-1.55106,-1.06755,-1.13758,-1.04445], + "objfunc_val":-21.384372 + }, + { + "sbest" : [-0.1296543,0.2273927,-0.8694331,0.0361375,-0.0168509,0.0066662,0.1133868,-0.0617312,0.0405173,-0.0208061], + "trial_initial": [0.380940,0.023070,1.846857,1.315453,-1.510769,-0.144067,0.791034,-1.443670,1.278489,0.133970], + "objfunc_val":-21.122759 + }, + { + "sbest" : [-5.5878e-02,5.7219e-02,2.3842e-02,-1.4539e-01,-4.8674e-04,1.4931e-01,-2.9320e-03,-8.2249e-02,-1.4800e-02,1.1134e-02], + "trial_initial": [0.912602,-0.775228,0.097104,1.270168,0.725063,0.945704,0.104205,1.548895,0.201936,0.036866], + "objfunc_val":-22.141054 + } + ] +} + diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py new file mode 100644 index 00000000..afe2f4d0 --- /dev/null +++ b/spotpy/unittests/test_dds.py @@ -0,0 +1,90 @@ +import unittest +import sys + +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy + +import numpy as np +import os +from spotpy.examples.spot_setup_dds import spot_setup +from pprint import pprint +import json + + +class TestDDS(unittest.TestCase): + def setUp(self): + self.spot_setup = spot_setup() + self.rep = 1000 + self.timeout = 1 # Given in Seconds + + + def ackley10(self,vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + + + def json_helper(self, run): + with open(os.path.dirname(__file__)+"/DDS_references/run_"+str(run)+".json") as f: + data = json.load(f) + + return data + + def func_switcher(self,name): + if name == "ackley": + return self.ackley10 + + def test_run_1(self): + self.run_a_dds(1) + + def test_run_2(self): + self.run_a_dds(2) + + def test_run_3(self): + self.run_a_dds(3) + + def test_run_4(self): + self.run_a_dds(4) + + def test_run_5(self): + self.run_a_dds(5) + + def run_a_dds(self,run): + original_result = self.json_helper(run) + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + + + results = sampler.sample(original_result["evatrials"],self.func_switcher(original_result['objfunc']),original_result["r_val"],original_result["trial_runs"]) + + + for t in range(original_result["trial_runs"]): + #pprint(results) + #pprint(original_result) + # +self.assertEqual(203, len(results)) + print(results[t]["objfunc_val"],original_result["results"][t]["objfunc_val"]) + self.assertTrue(np.abs(results[t]["objfunc_val"]-original_result["results"][t]["objfunc_val"]) < 0.000001) + py_sbest = results[t]["sbest"] + matlb_sbest = original_result["results"][t]["sbest"] + for k in range(len(py_sbest)): + print(py_sbest[k],matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k],matlb_sbest[k],delta=0.00001) + + py_trial_initial = results[t]["trial_initial"] + matlb_trial_initial = original_result["results"][t]["trial_initial"] + for k in range(len(py_sbest)): + print(py_trial_initial[k], matlb_trial_initial[k]) + self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) + + + + +if __name__ == '__main__': + unittest.main() From 685caf0710de1817af23463652282db17d517d6c Mon Sep 17 00:00:00 2001 From: bees4ever Date: Tue, 18 Sep 2018 21:47:33 +0200 Subject: [PATCH 23/73] implement distinct parameters sampling in DDS with spotpy's parameter module --- spotpy/algorithms/dds.py | 114 ++++++--------------- spotpy/examples/spot_setup_dds.py | 53 +++++++++- spotpy/examples/tutorial_dds.py | 18 ++-- spotpy/parameter.py | 2 + spotpy/unittests/DDS_references/run_6.json | 19 ++++ spotpy/unittests/test_dds.py | 43 +++----- 6 files changed, 129 insertions(+), 120 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_6.json diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index ea3f28ef..6f4d02aa 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -48,60 +48,58 @@ def __init__(self, *args, **kwargs): # self.np_random = np.random self.np_random = FixedRandomizer() - def __set_np_random(self,f_rand): + def __set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions,obj_func,fraction1,trials = 1): + def sample(self, repetitions, fraction1, trials=1): """ --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- """ result_list = [] - sinitial, its, to_max = [], np.max([5, round(0.005 * repetitions)]), 1 - - #print(self.objectivefunction([121312,12,12],[1214123,34,34])) - #exit() + sinitial, its, to_max = [], np.int(np.max([5, round(0.005 * repetitions)])), 1 self.set_repetiton(repetitions) self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + self.discrete_flag = [u.is_distinct for u in self.setup.params] num_dec = len(self.min_bound) # num_dec is the number of decision variables for trial in range(trials): solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) - - stest = [] sbest = [] Jbest = [] - s_range = self.max_bound-self.min_bound - + s_range = self.max_bound - self.min_bound # ================================================================================================= # INITIAL SOLUTION # ================================================================================================= - # own initial solution: + # # todo implement own initial solution # sinitial = self.parameter()['random'] if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - print('Finding best starting point for trial '+str(trial)+' using '+str(its)+' random samples.') + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + its) + ' random samples.') ileft = repetitions - its # use this to reduce number of fevals in DDS loop if ileft <= 0: raise ValueError('# Initialization samples >= Max # function evaluations.') for i in range(its): - if self.discrete_flag == 0: # continuous variable + stest = [] + if not self.discrete_flag[i]: # continuous variable stest = self.min_bound + s_range * self.np_random.rand(num_dec) else: # discrete case for j in range(num_dec): - stest[j] = self.np_random.randint(self.min_bound[j], self.max_bound[j] + 1) + stest.append( + self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1)) - Jtest = to_max * obj_func(stest) # get obj function value + # TODO how to use the simulation in this case + Jtest = to_max * self.objectivefunction(stest, []) # get obj function value if i == 0: Jbest = Jtest @@ -118,7 +116,7 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): else: # know its=1, using a user supplied initial solution. Calculate obj func value. ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value + Jtest = self.objectivefunction(stest, []) # get obj function value Jbest = Jtest sbest = list(stest) solution[0, 0] = 1 @@ -126,12 +124,9 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): solution[0, 2] = to_max * Jtest solution[0, 3:3 + num_dec] = stest - - it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution - # # # A generator that produces parametersets if called # param_generator = ((rep, self.parameter()['random']) @@ -141,87 +136,52 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): # self.postprocessing(rep, randompar, simulations) # self.final_call() + param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(ileft))) + for rep, randompar, simulations in self.repeat(param_generator): + self.postprocessing(rep, randompar, simulations) - #param_generator = ((rep, 1.0 - np.log(rep + 1) / np.log(ileft), np_random.rand(num_dec)) for rep in range(int(ileft))) - - # TODO implement like this! - # import pprint - # pprint.pprint(list(param_generator)) - # exit() - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i + 1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(rep + 1) / np.log(ileft) dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = self.np_random.rand(num_dec) - - + # TODO: maybe loop with np vectors / array to be faster... for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour + if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j + 1) - - # TODO make this method!! - # TODO more efficient!! - + new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j) stest[j] = new_value # change relevant dec var value in stest - # print(choosed_nums) - # print(stest) - # print("--------------------") - if dvn_count == 0: # no DVs selected at random, so select ONE - # TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) - - new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, - dec_var - 1) - # TODO more efficient! + new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], fraction1, + dec_var - 1) stest[dec_var - 1] = new_value # change relevant dec var value in stest - # get ojective function value + Jtest = to_max * self.objectivefunction(stest, simulations) - Jtest = to_max * obj_func(stest) - - # print([Jtest, Jbest]); - # print(stest) if Jtest <= Jbest: Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - # accumulate results + solution[i + its, 0] = i + its solution[i + its, 1] = to_max * Jbest solution[i + its, 2] = to_max * Jtest solution[i + its, 3:3 + num_dec] = stest - # end DDS function loop + # end DDS function loop print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - # [list(solution), it_sbest, sbest, trial_initial] - result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) - return result_list + return result_list - def neigh_value_continuous(self,s, s_min, s_max, fraction1): + def neigh_value_continuous(self, s, s_min, s_max, fraction1): # select a RANDOM neighbouring real value of a SINGLE decision variable # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT @@ -275,7 +235,7 @@ def neigh_value_continuous(self,s, s_min, s_max, fraction1): return snew - def neigh_value_discrete(self,s, s_min, s_max, fraction1): + def neigh_value_discrete(self, s, s_min, s_max, fraction1): # Created by B.Tolson and B.Yung, June 2006 # Modified by B. Tolson & M. Asadzadeh, Sept 2008 # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) @@ -336,16 +296,8 @@ def neigh_value_discrete(self,s, s_min, s_max, fraction1): snew = sample + 1 return snew - def neigh_value_mixed(self,s, s_min, s_max, fraction1, j): - if self.discrete_flag == 0: + def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): + if not self.discrete_flag[j]: return self.neigh_value_continuous(s, s_min, s_max, fraction1) else: return self.neigh_value_discrete(s, s_min, s_max, fraction1) - - - - # TODO: getestet werden sollten alle 5 Ergebnisvektoren - - - - diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index a62a773e..37e302bd 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -4,11 +4,55 @@ import numpy as np +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + + +def griewank10(vector): + sum1 = 0 + term2 = 1 + term3 = 1 + + for i in range(len(vector)): + sum1 = sum1 + (vector[i] ** 2) / 4000 + term2 = term2 * np.cos(vector[i] / (i + 1) ** 0.5) + + return sum1 - term2 + term3 + + class spot_setup(object): + """ + Setup for a simple example to run DDS Algorithm + """ def __init__(self): - self.params = [Uniform(str(j),-2, 2, 1.5, 3.0, -2, 2, doc=str(j)+' value of Rosenbrock function') - for j in range(10)] + self.params = None + self.objfunc = None + + def _objfunc_switcher(self, name): + """ + Set new parameter and objective function while setup is instanced in a test case + :param name: function name which overwrites initial objective function + :return: + """ + + if name == "ackley": + self.objfunc = ackley10 + self.params = [Uniform(str(j), -2, 2, 1.5, 3.0, -2, 2, doc=str(j) + ' value of Rosenbrock function') + for j in range(10)] + elif name == "griewank": + self.objfunc = griewank10 + self.params = [Uniform('d' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'distinc parameter within a boundary', distinct=True) + for j in range(2)] + [Uniform('c' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'continuous parameter within a boundary') + for j in range(8)] def parameters(self): return spotpy.parameter.generate(self.params) @@ -23,4 +67,7 @@ def evaluation(self): return observations def objectivefunction(self, simulation, evaluation): - objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) + if self.objfunc is None: + print("Please choose an objectivefunction with '_objfunc_switcher' method") + else: + return self.objfunc(simulation) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index cf511646..b19a8396 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -10,11 +10,11 @@ sys.path.append(".") import spotpy -import numpy as np + from spotpy.describe import describe from spotpy.examples.spot_setup_dds import spot_setup - +from spotpy.examples.spot_setup_dds import ackley10 #Create samplers for every algorithm: results=[] @@ -27,15 +27,21 @@ Initial_solution = [] # TODO if user had seom, read it in + + parallel = "seq" dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) -#print(describe(sampler)) -sampler.sample(rep) -#results.append(sampler.getdata()) + +print(describe(sampler)) +sampler.sample(rep,fraction1=0.2,trials=2) + +#print(sampler.getdata()) + +results.append(sampler.getdata()) -print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +#print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x evaluation = spot_setup.evaluation() diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 1a096b8a..cac7d064 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -212,6 +212,8 @@ def __init__(self, rndfunc, rndfuncname, *args, **kwargs): self.maxbound = 0.0 self.description = arghelper.get('doc') + + self.is_distinct = not not arghelper.get("distinct") arghelper.check_complete() def __call__(self, **kwargs): diff --git a/spotpy/unittests/DDS_references/run_6.json b/spotpy/unittests/DDS_references/run_6.json new file mode 100644 index 00000000..1210e7b7 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_6.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "griewank", + "evatrials" : 1000, + "trial_runs" : 1, + "r_val" : 0.2, + "results":[ + { + "sbest" : [-3.00000,0.00000,-4.72715 ,0.84169,9.53635,1.67684,2.72483,2.59443,3.42391,-8.47452], + "trial_initial": [-433,-196,-73,673,56,-26,207,-170,58,-214], + "objfunc_val":1.023480 + }, + { + "sbest" : [-7.00000,-2.00000,0.16090,2.97275,10.80989,-9.10410,2.74935,-8.67317,3.29054,-9.10145], + "trial_initial": [-299,444,83,-32,424,479,319,-416,-8,637], + "objfunc_val":1.109736 + } + ] +} + diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index afe2f4d0..03f4ecd6 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -10,7 +10,6 @@ import numpy as np import os from spotpy.examples.spot_setup_dds import spot_setup -from pprint import pprint import json @@ -20,27 +19,12 @@ def setUp(self): self.rep = 1000 self.timeout = 1 # Given in Seconds - - def ackley10(self,vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1 = sum1 + vector[i] ** 2 - sum2 = sum2 + np.cos(2 * np.pi * vector[i]) - return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) - - def json_helper(self, run): - with open(os.path.dirname(__file__)+"/DDS_references/run_"+str(run)+".json") as f: + with open(os.path.dirname(__file__) + "/DDS_references/run_" + str(run) + ".json") as f: data = json.load(f) return data - def func_switcher(self,name): - if name == "ackley": - return self.ackley10 - def test_run_1(self): self.run_a_dds(1) @@ -56,26 +40,27 @@ def test_run_4(self): def test_run_5(self): self.run_a_dds(5) - def run_a_dds(self,run): + def test_run_6(self): + self.run_a_dds(6) + + def run_a_dds(self, run): original_result = self.json_helper(run) - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) + self.spot_setup._objfunc_switcher(original_result['objfunc']) - results = sampler.sample(original_result["evatrials"],self.func_switcher(original_result['objfunc']),original_result["r_val"],original_result["trial_runs"]) + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) for t in range(original_result["trial_runs"]): - #pprint(results) - #pprint(original_result) - # +self.assertEqual(203, len(results)) - print(results[t]["objfunc_val"],original_result["results"][t]["objfunc_val"]) - self.assertTrue(np.abs(results[t]["objfunc_val"]-original_result["results"][t]["objfunc_val"]) < 0.000001) + print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) + self.assertAlmostEqual(results[t]["objfunc_val"] , original_result["results"][t]["objfunc_val"],delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] for k in range(len(py_sbest)): - print(py_sbest[k],matlb_sbest[k]) - self.assertAlmostEqual(py_sbest[k],matlb_sbest[k],delta=0.00001) + print(py_sbest[k], matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k], matlb_sbest[k], delta=0.00001) py_trial_initial = results[t]["trial_initial"] matlb_trial_initial = original_result["results"][t]["trial_initial"] @@ -84,7 +69,5 @@ def run_a_dds(self,run): self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) - - if __name__ == '__main__': unittest.main() From d2ea7779d4d217688d773f6cf23490072d5d301c Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 11:19:11 +0200 Subject: [PATCH 24/73] Add more tests, reorganize pytest for DDS --- spotpy/examples/spot_setup_dds.py | 17 ++++- spotpy/unittests/DDS_references/run_6.json | 13 ++-- spotpy/unittests/DDS_references/run_7.json | 89 ++++++++++++++++++++++ spotpy/unittests/test_dds.py | 9 ++- 4 files changed, 115 insertions(+), 13 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_7.json diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 37e302bd..8b2a0bf5 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -55,19 +55,28 @@ def _objfunc_switcher(self, name): for j in range(8)] def parameters(self): + if self.params is None: + self.params = [ + Uniform("0", -10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function'), + Uniform("1", -10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function'), + Uniform("z", -10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function')] return spotpy.parameter.generate(self.params) def simulation(self, vector): x = np.array(vector) - simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + simulations = x * np.random.rand(len(vector)) + # print(simulations) return simulations def evaluation(self): - observations = [0] + # observations = [0] + observations = [2, 3, 4] return observations def objectivefunction(self, simulation, evaluation): if self.objfunc is None: - print("Please choose an objectivefunction with '_objfunc_switcher' method") + print(simulation, evaluation) + return -rmse(evaluation, simulation) else: - return self.objfunc(simulation) + return self.objfunc(evaluation) diff --git a/spotpy/unittests/DDS_references/run_6.json b/spotpy/unittests/DDS_references/run_6.json index 1210e7b7..7bcb41c5 100644 --- a/spotpy/unittests/DDS_references/run_6.json +++ b/spotpy/unittests/DDS_references/run_6.json @@ -5,14 +5,11 @@ "r_val" : 0.2, "results":[ { - "sbest" : [-3.00000,0.00000,-4.72715 ,0.84169,9.53635,1.67684,2.72483,2.59443,3.42391,-8.47452], - "trial_initial": [-433,-196,-73,673,56,-26,207,-170,58,-214], - "objfunc_val":1.023480 - }, - { - "sbest" : [-7.00000,-2.00000,0.16090,2.97275,10.80989,-9.10410,2.74935,-8.67317,3.29054,-9.10145], - "trial_initial": [-299,444,83,-32,424,479,319,-416,-8,637], - "objfunc_val":1.109736 + "sbest" : [ -3.000000000000000,0.000000000000000,-5.036757578611908,11.733566302568235,9.429452816289487, + -5.549942605772495,2.490307284756468,2.332983597833255,3.851841900847800,0.390134503633089], + "trial_initial": [-433.0000000000000,-196.0000000000000,-73.3096060816648,672.0472250689404,55.8931023268103, + -25.9949571115938,206.7654801523532,-170.2614502262312,58.4279335970832,-213.8458155161740], + "objfunc_val":1.049515 } ] } diff --git a/spotpy/unittests/DDS_references/run_7.json b/spotpy/unittests/DDS_references/run_7.json new file mode 100644 index 00000000..1f70f3b6 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_7.json @@ -0,0 +1,89 @@ +{ + "objfunc": "griewank", + "evatrials": 550, + "trial_runs": 3, + "r_val": 0.37, + "results": [ + { + "sbest": [ + 11.00000000000000, + 19.00000000000000, + 2.03662487437028, + -1.23426415183106, + -5.24719291922387, + 25.07730145065261, + -12.02999926055234, + -22.00167804376221, + -3.14697244811857, + 29.56346268517814 + ], + "trial_initial": [ + -433.0000000000000, + -196.0000000000000, + -73.3096060816648, + 672.0472250689404, + 55.8931023268103, + -25.9949571115938, + 206.7654801523532, + -170.2614502262312, + 58.4279335970832, + -213.8458155161740 + ], + "objfunc_val": 1.664190 + }, + { + "sbest": [ + 7.00000000000000, + 10.00000000000000, + -6.39484002324608, + -46.76336537365788, + 9.58467601113504, + 38.38930958584108, + -6.09867128065270, + -45.82775862964041, + 33.63691393959370, + 2.93087800411658 + ], + "trial_initial": [ + -190.000000000000, + -288.000000000000, + 263.958476836666, + 269.302313621106, + 131.174425938139, + 633.973326660225, + 316.115885867790, + 296.859343133407, + -181.234821869745, + -344.830402395067 + ], + "objfunc_val": 2.802375 + }, + { + "sbest": [ + 12.00000000000000, + 12.00000000000000, + 32.85859327725726, + 7.08140986453067, + 5.86773597147155, + -1.02258063794287, + 3.75986146463776, + -17.85588284586939, + 30.57592135814601, + 1.34518680862905 + ], + "trial_initial": [ + -219.00000000000000, + -344.00000000000000, + 534.86798334588570, + 303.76611823508085, + 106.05989330870204, + 4.88429904716634, + 160.48210213748337, + -277.59019421646974, + -227.26786514527339, + 151.34888971727344 + ], + "objfunc_val": 1.645369 + } + ] +} diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 03f4ecd6..d9414247 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,6 +1,8 @@ import unittest import sys +from spotpy.tools import FixedRandomizer + try: import spotpy except ImportError: @@ -18,6 +20,7 @@ def setUp(self): self.spot_setup = spot_setup() self.rep = 1000 self.timeout = 1 # Given in Seconds + self.f_random = FixedRandomizer() def json_helper(self, run): with open(os.path.dirname(__file__) + "/DDS_references/run_" + str(run) + ".json") as f: @@ -43,6 +46,9 @@ def test_run_5(self): def test_run_6(self): self.run_a_dds(6) + def test_run_7(self): + self.run_a_dds(7) + def run_a_dds(self, run): original_result = self.json_helper(run) @@ -50,6 +56,7 @@ def run_a_dds(self, run): sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) + sampler._set_np_random(self.f_random) results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) @@ -66,7 +73,7 @@ def run_a_dds(self, run): matlb_trial_initial = original_result["results"][t]["trial_initial"] for k in range(len(py_sbest)): print(py_trial_initial[k], matlb_trial_initial[k]) - self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) + self.assertAlmostEqual(py_trial_initial[k],matlb_trial_initial[k], delta=0.0001) if __name__ == '__main__': From 67d981f46aa33fc37032631bcb6279fd8a5d16e6 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 11:20:04 +0200 Subject: [PATCH 25/73] little bugfixes more descriptions --- spotpy/algorithms/dds.py | 319 ++++++++++++++++++++------------------- 1 file changed, 162 insertions(+), 157 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 6f4d02aa..af0e737a 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -2,12 +2,18 @@ from spotpy.tools.fixedrandom import * from . import _algorithm + class DDS(_algorithm): """ + Implements the Dynamically dimensioned search algorithm for computationally efficient watershed model + calibration + by + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient + watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based + selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + http://www.civil.uwaterloo.ca/btolson/software.aspx - Paper: - Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. - Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. """ def __init__(self, *args, **kwargs): @@ -45,32 +51,48 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) - # self.np_random = np.random - self.np_random = FixedRandomizer() + self.np_random = np.random + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + + if hasattr(self.setup,"params"): + self.discrete_flag = [u.is_distinct for u in self.setup.params] + else: + self.discrete_flag = [False] * len(self.max_bound) - def __set_np_random(self, f_rand): + def _set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions, fraction1, trials=1): + def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): """ - --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- + Samples from the DDS Algorithm + :param repetitions: Maximum number of runs. + :type repetitions: int + :param fraction1: value between 0 and 1 + :type fraction1: float + :param trials: amount of runs DDS algorithm will be performed + :param s_initial: set an initial trial set + :return: """ result_list = [] - sinitial, its, to_max = [], np.int(np.max([5, round(0.005 * repetitions)])), 1 + to_max = 1 self.set_repetiton(repetitions) - self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.discrete_flag = [u.is_distinct for u in self.setup.params] - num_dec = len(self.min_bound) # num_dec is the number of decision variables + if len(s_initial) == 0: + its = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(s_initial) != num_dec: + raise ValueError("User specified 's_initial' has not the same length as available parameters") + else: + its = 1 + for trial in range(trials): - solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) - sbest = [] - Jbest = [] + # solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) + s_best = [] + j_best = [] s_range = self.max_bound - self.min_bound @@ -78,128 +100,115 @@ def sample(self, repetitions, fraction1, trials=1): # INITIAL SOLUTION # ================================================================================================= - # # todo implement own initial solution - # sinitial = self.parameter()['random'] - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( its) + ' random samples.') - ileft = repetitions - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: + i_left = repetitions - its # use this to reduce number of fevals in DDS loop + if i_left <= 0: raise ValueError('# Initialization samples >= Max # function evaluations.') - for i in range(its): - stest = [] - if not self.discrete_flag[i]: # continuous variable - stest = self.min_bound + s_range * self.np_random.rand(num_dec) - - else: # discrete case - for j in range(num_dec): - stest.append( - self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1)) - - # TODO how to use the simulation in this case - Jtest = to_max * self.objectivefunction(stest, []) # get obj function value - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = self.objectivefunction(stest, []) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + self.discrete_flag[j] else self.min_bound[j] + s_range[j] * self.np_random.rand() for j in + range(int(num_dec))]) for rep in range(int(its))) + + for rep, s_test, simulations in self.repeat(starting_generator): + j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + + if rep == 0: + j_best = j_test + s_best = list(s_test) + + if j_test <= j_best: + j_best = j_test + s_best = list(s_test) + + # solution[rep, 0] = rep + # solution[rep, 1] = to_max * j_best + # solution[rep, 2] = to_max * j_test + # solution[rep, 3:3 + num_dec] = s_test + + else: # now its=1, using a user supplied initial solution. Calculate obj func value. + i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + s_test = s_initial # get from the inputs + j_test = self.objectivefunction(s_test, self.simulate((trial, s_initial))) # get obj function value + j_best = j_test + s_best = list(s_test) + # solution[0, 0] = 1 + # solution[0, 1] = to_max * j_best + # solution[0, 2] = to_max * j_test + # solution[0, 3:3 + num_dec] = s_test it_sbest = its # needed to initialize variable and avoid code failure when small # iterations - trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution - - # - # # A generator that produces parametersets if called - # param_generator = ((rep, self.parameter()['random']) - # for rep in range(int(repetitions))) - # for rep, randompar, simulations in self.repeat(param_generator): - # # A function that calculates the fitness of the run and the manages the database - # self.postprocessing(rep, randompar, simulations) - # self.final_call() + trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution - param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(ileft))) + param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) for rep, randompar, simulations in self.repeat(param_generator): self.postprocessing(rep, randompar, simulations) - Pn = 1.0 - np.log(rep + 1) / np.log(ileft) + Pn = 1.0 - np.log(rep + 1) / np.log(i_left) dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) + s_test = list(s_best) # define s_test initially as current (s_best for greedy) - # TODO: maybe loop with np vectors / array to be faster... for j in range(num_dec): if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j) - stest[j] = new_value # change relevant dec var value in stest + + new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], fraction1, + j) + s_test[j] = new_value # change relevant dec var value in stest if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) - new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], + dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) + new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, dec_var - 1) - stest[dec_var - 1] = new_value # change relevant dec var value in stest + s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - Jtest = to_max * self.objectivefunction(stest, simulations) + j_test = to_max * self.objectivefunction(simulations,s_test) - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found + if j_test <= j_best: + j_best = j_test + s_best = list(s_test) + it_sbest = rep + its # iteration number best solution found # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest + # solution[rep + its, 0] = rep + its + # solution[rep + its, 1] = to_max * j_best + # solution[rep + its, 2] = to_max * j_test + # solution[rep + its, 3:3 + num_dec] = s_test # end DDS function loop - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) + print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') + result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list def neigh_value_continuous(self, s, s_min, s_max, fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) + """ + select a RANDOM neighbouring real value of a SINGLE decision variable + CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + variables: + s_range is the range of the real variable (s_max-s_min) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param fraction1: is the neighbourhood parameter (replaces V parameter~see not + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = fraction1 * s_range + for comparison: variance (V) = (fraction1 * s_range)^2 + :return: + """ s_range = s_max - s_min - snew = s + self.np_random.normal(0, 1) * fraction1 * s_range + s_new = s + self.np_random.normal(0, 1) * fraction1 * s_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -208,93 +217,89 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = self.np_random.rand() + p_abs_or_ref = self.np_random.rand() - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) + if s_new < s_min: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50%chance reflect + s_new = s_min + (s_min - s_new) else: # with 50% chance absorb - snew = s_min + s_new = s_min # if reflection goes past s_max then value should be s_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if snew > s_max: - snew = s_min + if s_new > s_max: + s_new = s_min - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) + elif s_new > s_max: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = s_max - (s_new - s_max) else: # with 50% chance absorb - snew = s_max + s_new = s_max # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max + if s_new < s_min: + s_new = s_max - return snew + return s_new def neigh_value_discrete(self, s, s_min, s_max, fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) + """ + Created by B.Tolson and B.Yung, June 2006 + Modified by B. Tolson & M. Asadzadeh, Sept 2008 + Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + 2- Round the new value at the end of generation. + select a RANDOM neighbouring integer value of a SINGLE decision variable + discrete distribution is approximately normal + alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param fraction1: fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = fraction1 * s_range + for comparison: variance (V) = (fraction1 * s_range)^2 + :return: + """ + s_range = s_max - s_min delta = self.np_random.normal(0, 1) * fraction1 * s_range - snew = s + delta + s_new = s + delta - P_Abs_or_Ref = self.np_random.rand() + p_abs_or_ref = self.np_random.rand() - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + if s_new < s_min - 0.5: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_min - 0.5) + ((s_min - 0.5) - s_new) else: # with 50% chance absorb - snew = s_min + s_new = s_min # if reflection goes past (s_max+0.5) then value should be s_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min + if s_new > s_max + 0.5: + s_new = s_min - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + elif s_new > s_max + 0.5: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_max + 0.5) - (s_new - (s_max + 0.5)) else: # with 50% chance absorb - snew = s_max + s_new = s_max # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max + if s_new < s_min - 0.5: + s_new = s_max - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution + s_new = np.round(s_new) # New value must be integer + if s_new == s: # pick a number between s_max and s_min by a Uniform distribution sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) if sample < s: - snew = sample + s_new = sample else: # must increment option number by one - snew = sample + 1 - return snew + s_new = sample + 1 + return s_new def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): if not self.discrete_flag[j]: From 83ee09c96d13f034f155f9d960089ca03497b375 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 15:20:33 +0200 Subject: [PATCH 26/73] use get_fitness to calculate objective function the spotpy way --- spotpy/algorithms/dds.py | 19 +++++++++++++++---- spotpy/examples/spot_setup_dds.py | 8 ++++---- spotpy/examples/tutorial_dds.py | 26 +++++++++++++------------- spotpy/unittests/test_dds.py | 5 +++-- 4 files changed, 35 insertions(+), 23 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index af0e737a..2c14f99d 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -87,6 +87,11 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): raise ValueError("User specified 's_initial' has not the same length as available parameters") else: its = 1 + s_initial = np.array(s_initial) + print(np.all(s_initial <= self.max_bound)) + print(np.all(s_initial >= self.min_bound)) + if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): + raise ValueError("User specified 's_initial' but the values are not within the parameter range") for trial in range(trials): @@ -113,7 +118,9 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): range(int(num_dec))]) for rep in range(int(its))) for rep, s_test, simulations in self.repeat(starting_generator): - j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + like = self.postprocessing(rep,s_test,simulations) # get obj function value + #j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + j_test = to_max * like if rep == 0: j_best = j_test @@ -131,7 +138,10 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop s_test = s_initial # get from the inputs - j_test = self.objectivefunction(s_test, self.simulate((trial, s_initial))) # get obj function value + nr, inpt, sims = self.simulate((trial, s_initial)) + + j_test = self.getfitness(sims, s_test) # get obj function value + j_best = j_test s_best = list(s_test) # solution[0, 0] = 1 @@ -145,7 +155,7 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) for rep, randompar, simulations in self.repeat(param_generator): - self.postprocessing(rep, randompar, simulations) + Pn = 1.0 - np.log(rep + 1) / np.log(i_left) dvn_count = 0 # counter for how many decision variables vary in neighbour @@ -167,7 +177,8 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - j_test = to_max * self.objectivefunction(simulations,s_test) + like = self.postprocessing(rep, s_test, simulations, chains=trial) + j_test = to_max * like if j_test <= j_best: j_best = j_test diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 8b2a0bf5..ee17610a 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -66,7 +66,6 @@ def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] simulations = x * np.random.rand(len(vector)) - # print(simulations) return simulations def evaluation(self): @@ -74,9 +73,10 @@ def evaluation(self): observations = [2, 3, 4] return observations - def objectivefunction(self, simulation, evaluation): + def objectivefunction(self, simulation, evaluation, params): + if self.objfunc is None: - print(simulation, evaluation) return -rmse(evaluation, simulation) else: - return self.objfunc(evaluation) + pars, names = params + return self.objfunc(pars) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index b19a8396..4ab21f3f 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -19,29 +19,29 @@ #Create samplers for every algorithm: results=[] spot_setup=spot_setup() -rep=1000 +rep=3000 timeout=10 #Given in Seconds - -Initial_solution = [] # TODO if user had seom, read it in - - - - parallel = "seq" dbformat = "csv" - sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) - print(describe(sampler)) -sampler.sample(rep,fraction1=0.2,trials=2) +sampler.sample(rep, trials=1,fraction1=0.1) +results.append(sampler.getdata()) + -#print(sampler.getdata()) +sampler=spotpy.algorithms.dream(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +print(describe(sampler)) +sampler.sample(rep) results.append(sampler.getdata()) -#print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x -evaluation = spot_setup.evaluation() +#algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream'] +spotpy.analyser.plot_parametertrace_algorithms(results,algorithmnames=["DDS","DREAM"],parameternames=['0','1']) + + +print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +#evaluation = spot_setup.evaluation() diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index d9414247..6e93c85b 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,7 +1,7 @@ import unittest import sys -from spotpy.tools import FixedRandomizer + try: import spotpy @@ -9,7 +9,8 @@ sys.path.append(".") import spotpy -import numpy as np +from spotpy.tools import FixedRandomizer + import os from spotpy.examples.spot_setup_dds import spot_setup import json From a1b272d997001194a9b22cf0bb0db9bb9889e1aa Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 17:31:30 +0200 Subject: [PATCH 27/73] finish user's parameter initialization and write unittests --- spotpy/algorithms/dds.py | 9 ++- .../DDS_references/run_own_input_1.json | 47 ++++++++++++ .../DDS_references/run_own_input_2.json | 74 +++++++++++++++++++ spotpy/unittests/test_dds.py | 33 +++++++-- 4 files changed, 154 insertions(+), 9 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_own_input_1.json create mode 100644 spotpy/unittests/DDS_references/run_own_input_2.json diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 2c14f99d..afeefdb5 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -88,8 +88,6 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: its = 1 s_initial = np.array(s_initial) - print(np.all(s_initial <= self.max_bound)) - print(np.all(s_initial >= self.min_bound)) if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") @@ -138,9 +136,12 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop s_test = s_initial # get from the inputs - nr, inpt, sims = self.simulate((trial, s_initial)) - j_test = self.getfitness(sims, s_test) # get obj function value + single_generator = ( (i,s_test) for i in range(1) ) + rep, s_test_param, simulations = next(self.repeat(single_generator)) + + j_test = self.postprocessing(rep,s_test,simulations) + j_best = j_test s_best = list(s_test) diff --git a/spotpy/unittests/DDS_references/run_own_input_1.json b/spotpy/unittests/DDS_references/run_own_input_1.json new file mode 100644 index 00000000..b1798ba7 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_own_input_1.json @@ -0,0 +1,47 @@ +{ + "objfunc": "griewank", + "evatrials": 1000, + "trial_runs": 1, + "r_val": 0.2, + "s_initial": [ + -1, + 3, + -2, + 4, + 6, + 9, + -3, + 6.7, + 2.2, + 10 + ], + "results": [ + { + "sbest": [ + -1.00000000000000, + 3.00000000000000, + -2.00000000000000, + 4.00000000000000, + 6.00000000000000, + -3.14440865613332, + -5.15170165876658, + 6.70000000000000, + 2.20000000000000, + 10.00000000000000 + ], + "trial_initial": [ + -1.00000000000000, + 3.00000000000000, + -2.00000000000000, + 4.00000000000000, + 6.00000000000000, + 9.00000000000000, + -3.00000000000000, + 6.70000000000000, + 2.20000000000000, + 10.00000000000000 + ], + "objfunc_val": 1.060679 + } + ] +} diff --git a/spotpy/unittests/DDS_references/run_own_input_2.json b/spotpy/unittests/DDS_references/run_own_input_2.json new file mode 100644 index 00000000..b9a486d3 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_own_input_2.json @@ -0,0 +1,74 @@ +{ + "objfunc": "ackley", + "evatrials": 1000, + "trial_runs": 2, + "r_val": 0.23, + "s_initial": [ + -0.6998811, + -1.95230402, + -0.77098336, + -1.78082543, + -0.93364598, + 0.05985113, + -1.2270304, + 1.32375772, + -1.97949944, + -1.94960125 + ], + "results": [ + { + "sbest": [ + -0.01085857370638122, + -0.02253069124037839, + 0.01502826748703164, + -0.03056730293640454, + 0.05340255845087846, + -0.00489005220163242, + -0.00714949428369765, + -0.00685105529011589, + -0.00155646134117859, + 0.02910863259046434 + ], + "trial_initial": [ + -0.6998811000000000, + -1.9523040199999999, + -0.7709833600000000, + -1.7808254299999999, + -0.9336459800000000, + 0.0598511300000000, + -1.2270304000000001, + 1.3237577199999999, + -1.9794994400000001, + -1.9496012500000000 + ], + "objfunc_val": -22.594014 + }, + { + "sbest": [ + 0.01638165987817117, + 0.00714059038361719, + 0.00638449094200643, + -0.02265515934058741, + 0.03554349803571422, + -0.00268569499221568, + -0.01012133140550065, + 0.01938318407346291, + 0.01757040721020015, + -0.03600477373134182 + ], + "trial_initial": [ + -0.6998811000000000, + -1.9523040199999999, + -0.7709833600000000, + -1.7808254299999999, + -0.9336459800000000, + 0.0598511300000000, + -1.2270304000000001, + 1.3237577199999999, + -1.9794994400000001, + -1.9496012500000000 + ], + "objfunc_val": -22.613691 + } + ] +} diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 6e93c85b..24d03bc3 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,7 +1,6 @@ import unittest import sys - - +import numpy as np try: import spotpy @@ -50,6 +49,12 @@ def test_run_6(self): def test_run_7(self): self.run_a_dds(7) + def test_run_own_initial_1(self): + self.run_a_dds("own_input_1") + + def test_run_own_initial_2(self): + self.run_a_dds("own_input_2") + def run_a_dds(self, run): original_result = self.json_helper(run) @@ -59,11 +64,18 @@ def run_a_dds(self, run): sim_timeout=self.timeout) sampler._set_np_random(self.f_random) - results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) + if original_result.get("s_initial") is not None: + # if a parameter initialisation is given, test this: + results = sampler.sample(original_result["evatrials"], original_result["r_val"], + original_result["trial_runs"], s_initial=original_result["s_initial"]) + else: + results = sampler.sample(original_result["evatrials"], original_result["r_val"], + original_result["trial_runs"]) for t in range(original_result["trial_runs"]): print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) - self.assertAlmostEqual(results[t]["objfunc_val"] , original_result["results"][t]["objfunc_val"],delta=0.000001) + self.assertAlmostEqual(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"], + delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] for k in range(len(py_sbest)): @@ -74,7 +86,18 @@ def run_a_dds(self, run): matlb_trial_initial = original_result["results"][t]["trial_initial"] for k in range(len(py_sbest)): print(py_trial_initial[k], matlb_trial_initial[k]) - self.assertAlmostEqual(py_trial_initial[k],matlb_trial_initial[k], delta=0.0001) + self.assertAlmostEqual(py_trial_initial[k], matlb_trial_initial[k], delta=0.0001) + + def test_own_initial_out_of_borders_ackley_1(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + + try: + sampler.sample(1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) + self.assertTrue(False, "ValueError was not thrown") + except ValueError as e: + self.assertTrue(True, "a ValueError was thrown and this is expected") if __name__ == '__main__': From 5627488e56b32ccbfd6b20bd089e810f62b14974 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 18:12:30 +0200 Subject: [PATCH 28/73] Some more unittests concerning own parameter initialization --- spotpy/unittests/test_dds.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 24d03bc3..143becf6 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -92,12 +92,19 @@ def test_own_initial_out_of_borders_ackley_1(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) + self.assertRaises(ValueError,sampler.sample,1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) - try: - sampler.sample(1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) - self.assertTrue(False, "ValueError was not thrown") - except ValueError as e: - self.assertTrue(True, "a ValueError was thrown and this is expected") + def test_own_initial_too_lees(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 9))) + + def test_own_initial_too_much(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) if __name__ == '__main__': From ae3f5050299e2446679b7f706537d5d657af33bf Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 13:03:34 +0100 Subject: [PATCH 29/73] Refactor loop to postprocess correct simulation and parameters --- spotpy/algorithms/dds.py | 117 ++++++++++++++++-------------- spotpy/examples/spot_setup_dds.py | 3 +- spotpy/tools/fixedrandom.py | 7 ++ 3 files changed, 73 insertions(+), 54 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index afeefdb5..9c34f219 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -54,28 +54,46 @@ def __init__(self, *args, **kwargs): self.np_random = np.random self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - if hasattr(self.setup,"params"): + if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: self.discrete_flag = [False] * len(self.max_bound) + # self.generator_repetitions will be set in `sample` and is needed to generate a generator which sends back actual parameter s_test + self.generator_repetitions = -1 + + # holds currents best parameter + self.next_s_best = [] + def _set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): + def get_next_s_test(self): + for rep in range(self.generator_repetitions - 1): + yield rep + 1, self.next_s_best + + def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): """ - Samples from the DDS Algorithm + Samples from the DDS Algorithm. User can define an own 's_initial' parameter configuration set. If not `s_initial` + is set, the algorithm defines an own + :param repetitions: Maximum number of runs. :type repetitions: int :param fraction1: value between 0 and 1 :type fraction1: float :param trials: amount of runs DDS algorithm will be performed :param s_initial: set an initial trial set + :param to_max: 1 to minimize objective function, -1 maximized objective function :return: """ + self.fraction1 = fraction1 + + # Check if `to_max` is correct + if to_max != 1 and to_max != -1: + raise ValueError("please specify `to_max` as 1 or -1") + result_list = [] - to_max = 1 self.set_repetiton(repetitions) @@ -91,17 +109,16 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") + # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): - - # solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) s_best = [] j_best = [] s_range = self.max_bound - self.min_bound - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= + # Calculate the initial Solution, if `its` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating its times to warm um the algorithm by trying which + # randomized generated input matches best (has minimal / maximum likelihood) if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( @@ -116,8 +133,8 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): range(int(num_dec))]) for rep in range(int(its))) for rep, s_test, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep,s_test,simulations) # get obj function value - #j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + like = self.postprocessing(rep, s_test, simulations) # get obj function value + j_test = to_max * like if rep == 0: @@ -128,57 +145,31 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): j_best = j_test s_best = list(s_test) - # solution[rep, 0] = rep - # solution[rep, 1] = to_max * j_best - # solution[rep, 2] = to_max * j_test - # solution[rep, 3:3 + num_dec] = s_test - else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - s_test = s_initial # get from the inputs + s_test = list(s_initial) # get from the inputs - single_generator = ( (i,s_test) for i in range(1) ) + single_generator = ((i, s_test) for i in range(1)) rep, s_test_param, simulations = next(self.repeat(single_generator)) - j_test = self.postprocessing(rep,s_test,simulations) - + j_test = self.postprocessing(rep, s_test, simulations) j_best = j_test s_best = list(s_test) - # solution[0, 0] = 1 - # solution[0, 1] = to_max * j_best - # solution[0, 2] = to_max * j_test - # solution[0, 3:3 + num_dec] = s_test it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution + self.next_s_best = list(s_best) - param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) - - for rep, randompar, simulations in self.repeat(param_generator): - - - Pn = 1.0 - np.log(rep + 1) / np.log(i_left) - dvn_count = 0 # counter for how many decision variables vary in neighbour - s_test = list(s_best) # define s_test initially as current (s_best for greedy) + # important to set this field `generator_repetitions` so that method `get_next_s_test` can generate exact paremters + self.generator_repetitions = i_left - for j in range(num_dec): - if randompar[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 + self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, 0) - new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], fraction1, - j) - s_test[j] = new_value # change relevant dec var value in stest - - if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) - new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], fraction1, - dec_var - 1) - - s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + for rep, s_test, simulations in self.repeat(self.get_next_s_test()): like = self.postprocessing(rep, s_test, simulations, chains=trial) + j_test = to_max * like if j_test <= j_best: @@ -186,20 +177,40 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): s_best = list(s_test) it_sbest = rep + its # iteration number best solution found - # accumulate results - - # solution[rep + its, 0] = rep + its - # solution[rep + its, 1] = to_max * j_best - # solution[rep + its, 2] = to_max * j_test - # solution[rep + its, 3:3 + num_dec] = s_test - # end DDS function loop + s_test = list(s_best) + self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, rep) print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list + def calculate_next_s_test(self, num_dec, s_test, s_best, rep): + randompar = self.np_random.rand(num_dec) + + Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + dvn_count = 0 # counter for how many decision variables vary in neighbour + # s_test = list(s_best) # define s_test initially as current (s_best for greedy) + + for j in range(num_dec): + if randompar[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + + new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], self.fraction1, + j) + s_test[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) + new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], self.fraction1, + dec_var - 1) + + s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + + return s_test + def neigh_value_continuous(self, s, s_min, s_max, fraction1): """ select a RANDOM neighbouring real value of a SINGLE decision variable diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index ee17610a..e4d972ec 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -65,7 +65,8 @@ def parameters(self): def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] - simulations = x * np.random.rand(len(vector)) + #simulations = x * np.random.rand(len(vector)) + simulations = x * np.sum(vector) return simulations def evaluation(self): diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py index 8fd1200e..0b150cd8 100644 --- a/spotpy/tools/fixedrandom.py +++ b/spotpy/tools/fixedrandom.py @@ -19,6 +19,7 @@ class FixedRandomizerEndOfDataException(Exception): class FixedRandomizer(): def __init__(self): + self.debug = False self.uniform_counter = 0 self.normal_counter = 0 self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) @@ -37,6 +38,8 @@ def rand(self,dim_x=1,dim_y=1): if self.uniform_counter < self.max_uniform_counter: x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 + if self.debug: + print("fixrand::rand() counter = "+str(self.uniform_counter)) else: raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) if len(x) == 1: @@ -54,6 +57,8 @@ def randint(self,x_from,x_to): q_uni = self.uniform_list[self.uniform_counter] pos = np.int(np.floor(q_uni / fraq)) self.uniform_counter += 1 + if self.debug: + print("fixrand::randint() counter = " + str(self.uniform_counter)) return vals[pos] else: raise FixedRandomizerEndOfDataException("No more data left.") @@ -64,6 +69,8 @@ def normal(self,loc,scale,size=1): if self.normal_counter < self.max_normal_counter: x.append(self.normal_list[self.normal_counter]*scale + loc) self.normal_counter += 1 + if self.debug: + print("fixrand::normal() counter = " + str(self.normal_counter)) else: raise FixedRandomizerEndOfDataException("No more data left.") From b35a94da3b1cdc745a8fbcbad0c24064119eb2bf Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 14:26:37 +0100 Subject: [PATCH 30/73] Refactor sample method and clean up comments --- spotpy/algorithms/__init__.py | 2 +- spotpy/algorithms/dds.py | 74 +++++++++++++++++++++---------- spotpy/examples/spot_setup_dds.py | 4 +- spotpy/unittests/test_dds.py | 6 +++ 4 files changed, 59 insertions(+), 27 deletions(-) diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index 7579a1ff..8109af2e 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -31,4 +31,4 @@ from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis from .list import list # Samples from given spotpy database -from .dds import DDS \ No newline at end of file +from .dds import DDS # Dynamically Dimensioned Search algorithm by Bryan Tolson. \ No newline at end of file diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 9c34f219..0f79db8e 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -14,6 +14,16 @@ class DDS(_algorithm): selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. http://www.civil.uwaterloo.ca/btolson/software.aspx + + Method: + "The DDS algorithm is a novel and simple stochastic single-solution based heuristic global search + algorithm that was developed for the purpose of finding good global solutions + (as opposed to globally optimal solutions) within a specified maximum function (or model) evaluation limit." + (Page 3) + + The DDS algorithm is a simple greedy algorithm, always using the best solution (min or max) from the current + point of view. This may not lead to the global optimization. + """ def __init__(self, *args, **kwargs): @@ -59,18 +69,23 @@ def __init__(self, *args, **kwargs): else: self.discrete_flag = [False] * len(self.max_bound) - # self.generator_repetitions will be set in `sample` and is needed to generate a generator which sends back actual parameter s_test + # self.generator_repetitions will be set in `sample` and is needed to generate a + # generator which sends back actual parameter s_test self.generator_repetitions = -1 # holds currents best parameter - self.next_s_best = [] + self.next_s_test = [] def _set_np_random(self, f_rand): self.np_random = f_rand def get_next_s_test(self): + """ + Fake a generator to run self.repeat to use multiprocessing + """ + # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions - 1): - yield rep + 1, self.next_s_best + yield rep + 1, self.next_s_test def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): """ @@ -84,7 +99,7 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): :param trials: amount of runs DDS algorithm will be performed :param s_initial: set an initial trial set :param to_max: 1 to minimize objective function, -1 maximized objective function - :return: + :return: a key-value set of all parameter combination which has been used. May changed in future. """ self.fraction1 = fraction1 @@ -159,12 +174,12 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution - self.next_s_best = list(s_best) - # important to set this field `generator_repetitions` so that method `get_next_s_test` can generate exact paremters + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters self.generator_repetitions = i_left - self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, 0) + self.next_s_test = self.calculate_next_s_test(s_best, 0) for rep, s_test, simulations in self.repeat(self.get_next_s_test()): @@ -177,39 +192,50 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): s_best = list(s_test) it_sbest = rep + its # iteration number best solution found - # end DDS function loop - s_test = list(s_best) - self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, rep) + # prepare next s_test parameter based on s_best + self.next_s_test = self.calculate_next_s_test(s_best, rep) - print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') + print('Best solution found has obj function value of ' + str(to_max * j_best) + ' at ' + + str(it_sbest) + '\n\n') result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list - def calculate_next_s_test(self, num_dec, s_test, s_best, rep): - randompar = self.np_random.rand(num_dec) + def calculate_next_s_test(self, previous_s_tests, rep): + """ + Needs to run in side `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. + + :param previous_s_tests: A set of parameters + :param rep: Position in DDS loop + :return: next parameter set + """ + amount_params = len(previous_s_tests) + + new_s_test = list(previous_s_tests) # define new_s_test initially as current (previous_s_tests for greedy) + + randompar = self.np_random.rand(amount_params) Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) dvn_count = 0 # counter for how many decision variables vary in neighbour - # s_test = list(s_best) # define s_test initially as current (s_best for greedy) - for j in range(num_dec): + for j in range(amount_params): if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], self.fraction1, - j) - s_test[j] = new_value # change relevant dec var value in stest + new_value = self.neigh_value_mixed(previous_s_tests[j], self.min_bound[j], self.max_bound[j], + self.fraction1, j) + new_s_test[j] = new_value # change relevant dec var value in stest if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) - new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) + new_value = self.neigh_value_mixed(previous_s_tests[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], self.fraction1, dec_var - 1) - s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + new_s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - return s_test + return new_s_test def neigh_value_continuous(self, s, s_min, s_max, fraction1): """ @@ -226,7 +252,7 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): normal random number/s_range. Eg: std dev desired = fraction1 * s_range for comparison: variance (V) = (fraction1 * s_range)^2 - :return: + :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min @@ -284,7 +310,7 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): normal random number/s_range. Eg: std dev desired = fraction1 * s_range for comparison: variance (V) = (fraction1 * s_range)^2 - :return: + :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index e4d972ec..e6577b4c 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -65,8 +65,8 @@ def parameters(self): def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] - #simulations = x * np.random.rand(len(vector)) - simulations = x * np.sum(vector) + simulations = x * np.random.rand(len(vector)) + #simulations = x * np.sum(vector) return simulations def evaluation(self): diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 143becf6..583ad7a5 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -106,6 +106,12 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) + def test_wrong_min_max_input(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) + if __name__ == '__main__': unittest.main() From 5b20463b5acac6481d59a4a1ebb12aad073534d2 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 16:42:12 +0100 Subject: [PATCH 31/73] DDS: Refactoring variable names and use BestValue classs --- spotpy/algorithms/dds.py | 267 ++++++++++++++++++++------------ spotpy/examples/tutorial_dds.py | 2 +- spotpy/unittests/test_dds.py | 12 +- 3 files changed, 172 insertions(+), 109 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 0f79db8e..eb584910 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -1,6 +1,33 @@ import numpy as np from spotpy.tools.fixedrandom import * from . import _algorithm +import spotpy.parameter + +class BestValue(object): + def __init__(self, parameters, obj_value): + # self.parameters = spotpy.parameter.Gamma() + # self.best_obj_val = spotpy.parameter.Gamma() + self.parameters = parameters + self.best_obj_val = obj_value + self.best_rep = 0 + + # TODO Docstring + def update(self,curr_parameters, curr_obj_val, curr_rep): + """ + Update on maximize + :param curr_parameters: + :param curr_obj_val: + :return: + """ + # TODO MAXIMAIZE + if self.best_obj_val is None or self.best_obj_val >= curr_obj_val: + self.best_obj_val = curr_obj_val + self.parameters = curr_parameters + self.best_rep = curr_rep + + def init(self,parameters,obj_value): # fake init # todo change that + self.parameters = parameters + self.best_obj_val = obj_value class DDS(_algorithm): @@ -57,13 +84,24 @@ def __init__(self, *args, **kwargs): save_sim: boolean * True: Simulation results will be saved * False: Simulation results will not be saved + :param r: neighborhood size perturbation parameter (r) that defines the random perturbation size standard + deviation as a fraction of the decision variable range. Default is 0.2. + :type r: float + """ + try: + self.r = kwargs.pop("r") + except KeyError: + self.r = 0.2 # default value + super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.best_value = BestValue(self.parameter(), 0) # TODO set obj_value in a better way + if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: @@ -87,157 +125,188 @@ def get_next_s_test(self): for rep in range(self.generator_repetitions - 1): yield rep + 1, self.next_s_test - def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): + def sample(self, repetitions, trials=1, s_initial=[]): """ - Samples from the DDS Algorithm. User can define an own 's_initial' parameter configuration set. If not `s_initial` - is set, the algorithm defines an own + Samples from the DDS Algorithm. + + DDS is a greedy type of algorithm since the current solution, also the best solution identified so far, + is never updated with a solution that has an inferior value of the objective function. + + That means in detail: + The DDS Algorithm starts with an initial phase: + If the user does not defines an own initial configuration The DDS algorithm start with searching a parameter + configuration in between the given parameter bounds. + + The next phase is the dds algorithm itself which runs in a loop `repetion` times: + Based on the parameter configuration x_new the algorithm run the model and simulation with the current parameter set + and calculates the objective function value called F_curr. + + If F_curr > F_best, where F_best is the current max value objective function value, we set x_best = x_curr and + F_best = F_curr. + + Select k of all parameters to include them in the neighborhood calculation. This is performed by calcualating a + threshold Pn (probability in neighbourhood). + + The neighbourhood calculation perturb x_best on standard normal distribution and reflect the result if it + breaks the parameter boundary. + The updated parameter configuration is called x_curr :param repetitions: Maximum number of runs. :type repetitions: int - :param fraction1: value between 0 and 1 - :type fraction1: float :param trials: amount of runs DDS algorithm will be performed - :param s_initial: set an initial trial set - :param to_max: 1 to minimize objective function, -1 maximized objective function + :param s_initial: set an initial trial set as a first parameter configuration. If the set is empty the algorithm + select an own initial parameter configuration :return: a key-value set of all parameter combination which has been used. May changed in future. """ - self.fraction1 = fraction1 - - # Check if `to_max` is correct - if to_max != 1 and to_max != -1: - raise ValueError("please specify `to_max` as 1 or -1") - - result_list = [] + debug_results = [] # every iteration a map of all relevant values is stored, only for debug purpose. + # Spotpy will not need this values. self.set_repetiton(repetitions) - num_dec = len(self.min_bound) # num_dec is the number of decision variables + number_of_parameters = len(self.min_bound) # number_of_parameters is the amount of parameters if len(s_initial) == 0: - its = np.int(np.max([5, round(0.005 * repetitions)])) - elif len(s_initial) != num_dec: + initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(s_initial) != number_of_parameters: raise ValueError("User specified 's_initial' has not the same length as available parameters") else: - its = 1 + initial_iterations = 1 s_initial = np.array(s_initial) if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): - s_best = [] - j_best = [] + # repitionno_best saves on which iteration the best parameter configuration has been found + repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations + repetions_left, f_best, trial_initial = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, s_initial) + + # TODO use ParameterSet Class + + #x_best = list(trial_initial) + self.best_value.init(trial_initial, f_best) + + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters + self.generator_repetitions = repetions_left - s_range = self.max_bound - self.min_bound + self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - # Calculate the initial Solution, if `its` > 1 otherwise the user defined a own one. - # If we need to find an initial solution we iterating its times to warm um the algorithm by trying which - # randomized generated input matches best (has minimal / maximum likelihood) + for rep, x_curr, simulations in self.repeat(self.get_next_s_test()): + f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( - its) + ' random samples.') - i_left = repetitions - its # use this to reduce number of fevals in DDS loop - if i_left <= 0: - raise ValueError('# Initialization samples >= Max # function evaluations.') + self.best_value.update(x_curr,f_curr,rep + initial_iterations) - starting_generator = ( - (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if - self.discrete_flag[j] else self.min_bound[j] + s_range[j] * self.np_random.rand() for j in - range(int(num_dec))]) for rep in range(int(its))) + # TODO MAXIMIZE! + # if f_curr <= f_best: + # f_best = f_curr + # x_best = list(x_curr) + # repitionno_best = rep # iteration number best solution found - for rep, s_test, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep, s_test, simulations) # get obj function value + # prepare next x_curr parameter based on x_best + self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) - j_test = to_max * like + print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + + str(repitionno_best) + '\n\n') + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_initial, "objfunc_val": self.best_value.best_obj_val}) - if rep == 0: - j_best = j_test - s_best = list(s_test) + return debug_results - if j_test <= j_best: - j_best = j_test - s_best = list(s_test) + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, s_initial): + parameter_bound_range = self.max_bound - self.min_bound + number_of_parameters = len(parameter_bound_range) - else: # now its=1, using a user supplied initial solution. Calculate obj func value. - i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - s_test = list(s_initial) # get from the inputs + # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm + # by trying which randomized generated input matches best + x_best = [] + f_best = [] + if initial_iterations > 1: # initial_iterations is the number of function evaluations to initialize the DDS algorithm solution + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + initial_iterations) + ' random samples.') + repetions_left = repetitions - initial_iterations # use this to reduce number of fevals in DDS loop + if repetions_left <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') - single_generator = ((i, s_test) for i in range(1)) - rep, s_test_param, simulations = next(self.repeat(single_generator)) + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + self.discrete_flag[j] else self.min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + for j in + range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) - j_test = self.postprocessing(rep, s_test, simulations) + for rep, x_curr, simulations in self.repeat(starting_generator): + like = self.postprocessing(rep, x_curr, simulations) # get obj function value - j_best = j_test - s_best = list(s_test) + f_curr = like - it_sbest = its # needed to initialize variable and avoid code failure when small # iterations - trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution + if rep == 0: + f_best = f_curr + x_best = list(x_curr) - # important to set this field `generator_repetitions` so that - # method `get_next_s_test` can generate exact parameters - self.generator_repetitions = i_left + if f_curr <= f_best: + f_best = f_curr + x_best = list(x_curr) - self.next_s_test = self.calculate_next_s_test(s_best, 0) + else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. + repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + x_curr = list(s_initial) # get from the inputs - for rep, s_test, simulations in self.repeat(self.get_next_s_test()): + rep, s_test_param, simulations = self.simulate((0, x_curr)) - like = self.postprocessing(rep, s_test, simulations, chains=trial) + f_curr = self.postprocessing(rep, x_curr, simulations) - j_test = to_max * like + f_best = f_curr + x_best = list(x_curr) - if j_test <= j_best: - j_best = j_test - s_best = list(s_test) - it_sbest = rep + its # iteration number best solution found + return repetions_left, f_best, x_best - # prepare next s_test parameter based on s_best - self.next_s_test = self.calculate_next_s_test(s_best, rep) + def calculate_next_s_test(self, previous_x_curr, rep, r): + """ + Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. - print('Best solution found has obj function value of ' + str(to_max * j_best) + ' at ' - + str(it_sbest) + '\n\n') - result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) + `probability_neighborhood` is a threshold at which level a parameter is added to neighbourhood calculation. - return result_list + Using a normal distribution + The decision variable - def calculate_next_s_test(self, previous_s_tests, rep): - """ - Needs to run in side `sample` method. Calculate the next set of parameters based on a given set. - This is greedy algorithm belonging to the DDS algorithm. + `dvn_count` counts how many parameter configuration has been exchanged with neighbourhood values. + If no parameters has been exchanged just one will select and exchanged with it's neighbourhood value. - :param previous_s_tests: A set of parameters + :param previous_x_curr: A set of parameters :param rep: Position in DDS loop + :param r: neighbourhood size perturbation parameter :return: next parameter set """ - amount_params = len(previous_s_tests) + amount_params = len(previous_x_curr) - new_s_test = list(previous_s_tests) # define new_s_test initially as current (previous_s_tests for greedy) + new_x_curr = list(previous_x_curr) # define new_x_curr initially as current (previous_x_curr for greedy) randompar = self.np_random.rand(amount_params) - Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) dvn_count = 0 # counter for how many decision variables vary in neighbour + # TODO simplify this with np.arrays for j in range(amount_params): - if randompar[j] < Pn: # then j th DV selected to vary in neighbour + if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(previous_s_tests[j], self.min_bound[j], self.max_bound[j], - self.fraction1, j) - new_s_test[j] = new_value # change relevant dec var value in stest + new_value = self.neigh_value_mixed(previous_x_curr[j], self.min_bound[j], self.max_bound[j], r, j) + new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_s_tests[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], self.fraction1, + new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], r, dec_var - 1) - new_s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test - return new_s_test + return new_x_curr - def neigh_value_continuous(self, s, s_min, s_max, fraction1): + def neigh_value_continuous(self, s, s_min, s_max, r): """ select a RANDOM neighbouring real value of a SINGLE decision variable CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT @@ -247,17 +316,17 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): :param s: is a current SINGLE decision variable VALUE :param s_min: is the min of variable s :param s_max: is the max of variable s - :param fraction1: is the neighbourhood parameter (replaces V parameter~see not + :param r: is the neighbourhood parameter (replaces V parameter~see not It is defined as the ratio of the std deviation of the desired normal random number/s_range. Eg: - std dev desired = fraction1 * s_range - for comparison: variance (V) = (fraction1 * s_range)^2 + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min - s_new = s + self.np_random.normal(0, 1) * fraction1 * s_range + s_new = s + self.np_random.normal(0, 1) * r * s_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -292,7 +361,7 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): return s_new - def neigh_value_discrete(self, s, s_min, s_max, fraction1): + def neigh_value_discrete(self, s, s_min, s_max, r): """ Created by B.Tolson and B.Yung, June 2006 Modified by B. Tolson & M. Asadzadeh, Sept 2008 @@ -305,16 +374,16 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): :param s: is a current SINGLE decision variable VALUE :param s_min: is the min of variable s :param s_max: is the max of variable s - :param fraction1: fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + :param r: r is the neighbourhood parameter (replaces V parameter~see notes) It is defined as the ratio of the std deviation of the desired normal random number/s_range. Eg: - std dev desired = fraction1 * s_range - for comparison: variance (V) = (fraction1 * s_range)^2 + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min - delta = self.np_random.normal(0, 1) * fraction1 * s_range + delta = self.np_random.normal(0, 1) * r * s_range s_new = s + delta p_abs_or_ref = self.np_random.rand() @@ -350,8 +419,8 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): s_new = sample + 1 return s_new - def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): + def neigh_value_mixed(self, s, s_min, s_max, r, j): if not self.discrete_flag[j]: - return self.neigh_value_continuous(s, s_min, s_max, fraction1) + return self.neigh_value_continuous(s, s_min, s_max, r) else: - return self.neigh_value_discrete(s, s_min, s_max, fraction1) + return self.neigh_value_discrete(s, s_min, s_max, r) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index 4ab21f3f..6b2d69e3 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -27,7 +27,7 @@ dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) print(describe(sampler)) -sampler.sample(rep, trials=1,fraction1=0.1) +sampler.sample(rep, trials=1, r=0.1) results.append(sampler.getdata()) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 583ad7a5..06a0142d 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -61,15 +61,15 @@ def run_a_dds(self, run): self.spot_setup._objfunc_switcher(original_result['objfunc']) sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) + sim_timeout=self.timeout,r=original_result["r_val"]) sampler._set_np_random(self.f_random) if original_result.get("s_initial") is not None: # if a parameter initialisation is given, test this: - results = sampler.sample(original_result["evatrials"], original_result["r_val"], + results = sampler.sample(original_result["evatrials"], original_result["trial_runs"], s_initial=original_result["s_initial"]) else: - results = sampler.sample(original_result["evatrials"], original_result["r_val"], + results = sampler.sample(original_result["evatrials"], original_result["trial_runs"]) for t in range(original_result["trial_runs"]): @@ -106,12 +106,6 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) - def test_wrong_min_max_input(self): - self.spot_setup._objfunc_switcher("ackley") - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) - if __name__ == '__main__': unittest.main() From b6fc42dd9ae6ed93143a01d2b372733e71d5b8cd Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 17:47:17 +0100 Subject: [PATCH 32/73] DDS: refctor generator --- spotpy/algorithms/dds.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index eb584910..9186a6f2 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -117,13 +117,13 @@ def __init__(self, *args, **kwargs): def _set_np_random(self, f_rand): self.np_random = f_rand - def get_next_s_test(self): + def get_next_x_curr(self): """ Fake a generator to run self.repeat to use multiprocessing """ # We need to shift position and length of the sampling process - for rep in range(self.generator_repetitions - 1): - yield rep + 1, self.next_s_test + for rep in range(self.generator_repetitions): + yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.r) def sample(self, repetitions, trials=1, s_initial=[]): """ @@ -191,21 +191,17 @@ def sample(self, repetitions, trials=1, s_initial=[]): # method `get_next_s_test` can generate exact parameters self.generator_repetitions = repetions_left - self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) + #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - for rep, x_curr, simulations in self.repeat(self.get_next_s_test()): + for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) self.best_value.update(x_curr,f_curr,rep + initial_iterations) # TODO MAXIMIZE! - # if f_curr <= f_best: - # f_best = f_curr - # x_best = list(x_curr) - # repitionno_best = rep # iteration number best solution found # prepare next x_curr parameter based on x_best - self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) + #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') @@ -261,7 +257,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions return repetions_left, f_best, x_best - def calculate_next_s_test(self, previous_x_curr, rep, r): + def calculate_next_s_test(self, previous_x_curr, rep, all_rep, r): """ Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. This is greedy algorithm belonging to the DDS algorithm. From 2bf8910ac7d56a1fd9ad93a6dca1e0ec95f0286c Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:37:11 +0200 Subject: [PATCH 33/73] Start writing dds in python. Very raw version --- spotpy/examples/dds.py | 20361 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 20361 insertions(+) create mode 100644 spotpy/examples/dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py new file mode 100644 index 00000000..383a457e --- /dev/null +++ b/spotpy/examples/dds.py @@ -0,0 +1,20361 @@ +import numpy as np +import random + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=[ + 0.05584386259223517, + 0.2535410132532582, + 0.3555753282652794, + 0.9767060208907836, + 0.46324425193900864, + 0.3950042024070052, + 0.5889712334602943, + 0.274782124811474, + 0.46535661133090267, + 0.2384618204031883, + 0.9016519630874807, + 0.2764917577582132, + 0.3478041353510165, + 0.20491028154260693, + 0.6414137312302646, + 0.08968343622461994, + 0.39692730727476133, + 0.9967532542326776, + 0.841419416942886, + 0.6618842788308826, + 0.31882636417540466, + 0.8354743646674859, + 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0.07231989308449034, + -0.654969725627664, + -1.1885568435104552, + 0.745449087352912, + -0.11451494925492176, + -0.42053138348011143, + -2.180044685215999, + -0.5863400273711193, + 0.9733894116381485, + -1.5380051834695678, + -0.8934864797237319, + -1.8166433233884607, + -1.6753856720898266, + -0.7749818761500956, + 1.6550131388895926, + 0.010466942524873549, + 1.0105377516892615, + -0.051870128281102525, + -0.5243040829263796, + -0.20276337726918783, + 0.05811174019879224, + 0.6221996966541786, + 0.46242904765338133, + -1.2985231897722824, + -0.5162081560822431, + -0.7553807158854496, + 0.35823893652515626, + 2.370608856072925, + -0.30360437519425315, + 1.2139469312964672, + 0.5203344997331647, + 1.2583371304217381, + 0.20634917734795732, + -0.06438169964327194 + ] + + def my_rand(self,dim_x=1,dim_y=1): + #x = np.array(dim_y * [dim_x * [0]]) + x = dim_x * [0] + for i in range(dim_x): + #for j in range(dim_y): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise Exception("ERROR END ind my_randN") + if len(x) == 1: + return x[0] + else: + return x + + def my_randi + + +f_rand = FixedRandomizer() +print(f_rand.my_rand(10)) +print(np.random.rand(10)) +exit() + +#todo read in users own initial soultion, no we just generate it self + +def ackley(vector): + firstSum = 0.0 + secondSum = 0.0 + for c in range(len(vector)): + firstSum += c ** 2.0 + secondSum += np.cos(2.0 * np.pi * vector[c]) + n = float(len(vector)) + return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + + +def get_objfunc(x): + return ackley(x)[0] + + +def dds(sinitial,its,to_max,fraction1,maxiter): + + # ! name,LowerB,UpperB,Integer? + # 1 -2.0 2.0 0 + # 2 -2.0 2.0 0 + # 3 -2.0 2.0 0 + # 4 -2.0 2.0 0 + # 5 -2.0 2.0 0 + # 6 -2.0 2.0 0 + # 7 -2.0 2.0 0 + # 8 -2.0 2.0 0 + # 9 -2.0 2.0 0 + # 10 -2.0 2.0 0 + + # TODO read in from somewhere + + + + S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting + S_max = np.array(10*[2.0]) # dec variable maximums - get from starting + + + + + Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + # zzz = [] # temporary flag + num_dec = len(S_min) # num_dec is the number of decision variables + solution = np.array((maxiter) * [(3+num_dec) * [0]]) + + + # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour + # + # dvn_count = 0 + # new_value = 0 + # dec_var = 0 + + stest = [] + sbest = [] + # Jtest = [] + Jbest = [] + + S_range = S_max - S_min + + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); + ileft = maxiter - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + # raise error error('#Initialization samples >= Max # function evaluations.') + pass + + for i in range(its): + if Discrete_flag == 0: # continuous variable + stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + else: # discrete case + for j in range(num_dec): + stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + + Jtest = to_max * get_objfunc(stest) # get obj function value + + + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest=its # needed to initialize variable and avoid code failure when small # iterations + trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution + + # END First inititlisation + + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + randnums = np.random.rand(num_dec) + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + # TODO more efficient! + stest[dec_var] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) + + #print(Jtest,Jbest) + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + + + print("trial_initial: ",trial_initial) + + print("sbest: ", sbest) + + + #print([solution, it_sbest, sbest, ]) + + + + +def neigh_value_continuous(s,s_min,s_max,fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + np.random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = np.random.rand() + + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + +def neigh_value_discrete(s,s_min,s_max,fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = np.random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = np.random.rand() + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + +def neigh_value_mixed(s,s_min,s_max,fraction1,j): + # todo discrete flagg as a class field + Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + + if Discrete_flag: + return neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return neigh_value_discrete(s,s_min,s_max,fraction1) + + +#1 for minimization +#farction is r_val +# maxiter is line 6 -> 1000 +# np.random.seed(150) +for runs in range(2): + maxiter = 1000 + num_samples=np.max([5,round(0.005*maxiter)]) + Initial_solution = [] # TODO if user had seom, read it in + + dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file From 37bb488e20616fdbb6236ee5e68512c0e7dd21fb Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:56:22 +0200 Subject: [PATCH 34/73] short before substitution with own random handler --- spotpy/examples/dds.py | 49 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 45 insertions(+), 4 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index 383a457e..aa58aeac 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -20025,18 +20025,59 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END ind my_randN") + raise Exception("ERROR END in my_rand") if len(x) == 1: return x[0] else: return x - def my_randi + def my_randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise Exception("ERROR END in my_randint") + + def my_randn(self,loc,scale): + + if loc != 0 or scale != 1: + raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! + + if self.normal_counter < self.max_normal_counter: + x = self.normal_list[self.normal_counter] + self.normal_counter += 1 + return x + + else: + raise Exception("ERROR END in my_randn") + + # TODO: better Exceptions + + + + +# TODO Convert this to unittest f_rand = FixedRandomizer() -print(f_rand.my_rand(10)) -print(np.random.rand(10)) +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +print(np.random.normal(0, 1)) +print(f_rand.my_randn(0,1)) + exit() #todo read in users own initial soultion, no we just generate it self From e77fb61e931de390b0d35b81d38df9bc159077eb Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 21:13:43 +0200 Subject: [PATCH 35/73] Working version, which is same as Matlab, extended with own random funcs --- spotpy/examples/dds.py | 106 +++++++++++++++++++++++++++++++---------- 1 file changed, 80 insertions(+), 26 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index aa58aeac..8ff63fd3 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -10009,9 +10009,9 @@ def __init__(self): 0.715363917574095 ] - + self.uniform_list*=3 self.max_normal_counter = 10000 - self.max_uniform_counter = 10000 + self.max_uniform_counter = 30000 self.normal_list = [ 0.9723088366617443, @@ -20025,7 +20025,7 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END in my_rand") + raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) if len(x) == 1: return x[0] else: @@ -20066,7 +20066,7 @@ def my_randn(self,loc,scale): # TODO Convert this to unittest -f_rand = FixedRandomizer() +# f_rand = FixedRandomizer() # print(f_rand.my_rand(10)) # print(np.random.rand(10)) @@ -20075,10 +20075,9 @@ def my_randn(self,loc,scale): # print(np.random.randint(1,101010)) # print("----------------------") -print(np.random.normal(0, 1)) -print(f_rand.my_randn(0,1)) +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) -exit() #todo read in users own initial soultion, no we just generate it self @@ -20092,11 +20091,22 @@ def ackley(vector): return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1=sum1+vector[i]**2 + sum2=sum2+np.cos(2*np.pi*vector[i]) + return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) + + def get_objfunc(x): - return ackley(x)[0] + return ackley10(x) -def dds(sinitial,its,to_max,fraction1,maxiter): +def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): # ! name,LowerB,UpperB,Integer? # 1 -2.0 2.0 0 @@ -20157,10 +20167,13 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(its): if Discrete_flag == 0: # continuous variable - stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + stest = S_min + S_range * f_rand.my_rand(num_dec) + else: # discrete case for j in range(num_dec): - stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) Jtest = to_max * get_objfunc(stest) # get obj function value @@ -20199,32 +20212,58 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(ileft): # remaining F evals after initialization # Determine variable selected as neighbour - Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = np.random.rand(num_dec) + + + #TODO back: randnums = np.random.rand(num_dec) + randnums = f_rand.my_rand(num_dec) + + # print(stest) + # print(randnums); + # print(Pn); + # choosed_nums = [] for j in range(num_dec): if randnums[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) + # choosed_nums+=[j] # TODO make this method!! # TODO more efficient!! stest[j] = new_value # change relevant dec var value in stest + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) + + + new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) # TODO more efficient! - stest[dec_var] = new_value # change relevant dec var value in stest + + stest[dec_var-1] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) - #print(Jtest,Jbest) + # if (i+1)%100 == 0: + # print(Jtest) + + #print([Jtest, Jbest]); + #print(stest) if Jtest <= Jbest: + Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found @@ -20282,7 +20321,10 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): s_range = s_max - s_min - snew = s + np.random.normal(0, 1) * fraction1 * s_range + #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range + snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -20291,7 +20333,8 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() if snew < s_min: # works for any pos or neg s_min @@ -20345,10 +20388,13 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): # s_range is the range of the real variable (s_max-s_min) s_range = s_max - s_min - delta = np.random.normal(0, 1) * fraction1 * s_range + #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range + delta = f_rand.my_randn(0, 1) * fraction1 * s_range snew = s + delta - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() + if snew < s_min - 0.5: # works for any pos or neg s_min if P_Abs_or_Ref <= 0.5: # with 50% chance reflect snew = (s_min - 0.5) + ((s_min - 0.5) - snew) @@ -20373,7 +20419,12 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): snew = np.round(snew) # New value must be integer if snew == s: # pick a number between s_max and s_min by a Uniform distribution - sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) + print(sample) + print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) + exit() + if sample < s: snew = sample else: # must increment option number by one @@ -20382,9 +20433,9 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): def neigh_value_mixed(s,s_min,s_max,fraction1,j): # todo discrete flagg as a class field - Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - if Discrete_flag: + if Discrete_flag == 0: return neigh_value_continuous(s, s_min, s_max, fraction1) else: return neigh_value_discrete(s,s_min,s_max,fraction1) @@ -20394,9 +20445,12 @@ def neigh_value_mixed(s,s_min,s_max,fraction1,j): #farction is r_val # maxiter is line 6 -> 1000 # np.random.seed(150) + +f_rand = FixedRandomizer() + for runs in range(2): maxiter = 1000 num_samples=np.max([5,round(0.005*maxiter)]) Initial_solution = [] # TODO if user had seom, read it in - dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file + dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file From 5cf96425132046801b8c9ec0d32fa839e1f80669 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:02:25 +0200 Subject: [PATCH 36/73] Add new class FixedrRandom to control randomized algorithms in Matlab vs Python --- spotpy/__init__.py | 1 + spotpy/tools/__init__.py | 1 + spotpy/tools/fixedrandom.py | 100 + spotpy/tools/normal_list.txt | 10000 ++++++++++++++++++++++++++++++++ spotpy/tools/uniform_list.txt | 10000 ++++++++++++++++++++++++++++++++ 5 files changed, 20102 insertions(+) create mode 100644 spotpy/tools/__init__.py create mode 100644 spotpy/tools/fixedrandom.py create mode 100644 spotpy/tools/normal_list.txt create mode 100644 spotpy/tools/uniform_list.txt diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 89f811d0..aa2829ff 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -38,5 +38,6 @@ from . import examples # Contains tutorials how to use SPOTPY from . import describe # Contains some helper functions to describe smaplers and setups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures +from . import tools __version__ = '1.3.30' \ No newline at end of file diff --git a/spotpy/tools/__init__.py b/spotpy/tools/__init__.py new file mode 100644 index 00000000..0abf9cd7 --- /dev/null +++ b/spotpy/tools/__init__.py @@ -0,0 +1 @@ +from .fixedrandom import * \ No newline at end of file diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py new file mode 100644 index 00000000..8fd1200e --- /dev/null +++ b/spotpy/tools/fixedrandom.py @@ -0,0 +1,100 @@ +''' +Copyright (c) 2018 by Benjamin Manns +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Benjamin Manns +''' + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +import os + +# replaces numpy.random module in a way + +class FixedRandomizerEndOfDataException(Exception): + pass + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) + + self.uniform_list*=3 + self.max_normal_counter = 10000 + self.max_uniform_counter = 30000 + + self.normal_list = list(np.loadtxt(os.path.dirname(__file__)+"/normal_list.txt")) + + def rand(self,dim_x=1,dim_y=1): + #x = np.array(dim_y * [dim_x * [0]]) + x = dim_x * [0] + for i in range(dim_x): + #for j in range(dim_y): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) + if len(x) == 1: + return x[0] + else: + return x + + def randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise FixedRandomizerEndOfDataException("No more data left.") + + def normal(self,loc,scale,size=1): + x = [] + for j in range(size): + if self.normal_counter < self.max_normal_counter: + x.append(self.normal_list[self.normal_counter]*scale + loc) + self.normal_counter += 1 + + else: + raise FixedRandomizerEndOfDataException("No more data left.") + if len(x) == 1: + return x[0] + else: + return x + + +# TODO UNITEST irgendwie + + #print(f_rand.normal(12,1,12)) + #print(np.random.normal(12,1,12)) + + #f_rand.normal(12,1,12)- + # print(np.var(np.random.normal(12,1,12)-np.random.normal(12,1,12))) + # print("-------------------------") + + + + +# TODO Convert this to unittest +# f_rand = FixedRandomizer() +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) + diff --git a/spotpy/tools/normal_list.txt b/spotpy/tools/normal_list.txt new file mode 100644 index 00000000..106ad330 --- /dev/null +++ b/spotpy/tools/normal_list.txt @@ -0,0 +1,10000 @@ +0.9723088366617443 +-1.5793809997742225 +1.667272128859827 +0.5956093579250138 +-0.21465028974841896 +0.0890394326818669 +0.3967447818935998 +0.6617353321641583 +-1.207298903627786 +0.7261802902895028 +-1.6904643439817781 +1.818238762180847 +0.5674817772592261 +2.5637647688819505 +1.388917534231378 +-0.6004552119062644 +-0.39965695397882667 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+0.856638434144594 +0.8082832739328184 +0.7235164671806334 +0.1581037615721247 +0.4979856141070268 +0.6628804575269793 +0.8292933407663836 +0.9934713877904263 +0.1091060261935286 +0.9106798366593588 +0.5623164088484348 +0.5319991192467912 +0.8471715784682357 +0.5472039337392008 +0.33549763790012943 +0.3335582633537695 +0.6821765450184556 +0.9289596195165171 +0.4723780405402096 +0.5213915826938492 +0.6022551170828936 +0.04297716653285144 +0.2536747977268964 +0.715363917574095 \ No newline at end of file From bbef8830b855cef91999b2be6667dd1e38a62e75 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:05:21 +0200 Subject: [PATCH 37/73] Add a version unready version of DDS algorithm. *datawriter* and *objectivefunction* support still open. --- spotpy/algorithms/__init__.py | 3 +- spotpy/algorithms/dds.py | 351 ++++++++++++++++++++++++++++++++++ 2 files changed, 353 insertions(+), 1 deletion(-) create mode 100644 spotpy/algorithms/dds.py diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index a2fac161..e47a5f1c 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -30,4 +30,5 @@ from .abc import abc # Artificial Bee Colony from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis -from .list_sampler import list_sampler # Samples from given spotpy database \ No newline at end of file +from .list_sampler import list_sampler # Samples from given spotpy database +from .dds import DDS diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py new file mode 100644 index 00000000..ea3f28ef --- /dev/null +++ b/spotpy/algorithms/dds.py @@ -0,0 +1,351 @@ +import numpy as np +from spotpy.tools.fixedrandom import * +from . import _algorithm + +class DDS(_algorithm): + """ + http://www.civil.uwaterloo.ca/btolson/software.aspx + Paper: + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + """ + + def __init__(self, *args, **kwargs): + """ + Input + ---------- + spot_setup: class + model: function + Should be callable with a parameter combination of the parameter-function + and return an list of simulation results (as long as evaluation list) + parameter: function + When called, it should return a random parameter combination. Which can + be e.g. uniform or Gaussian + objectivefunction: function + Should return the objectivefunction for a given list of a model simulation and + observation. + evaluation: function + Should return the true values as return by the model. + + dbname: str + * Name of the database where parameter, objectivefunction value and simulation results will be saved. + + dbformat: str + * ram: fast suited for short sampling time. no file will be created and results are saved in an array. + * csv: A csv file will be created, which you can import afterwards. + + parallel: str + * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu. + * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os). + + save_sim: boolean + * True: Simulation results will be saved + * False: Simulation results will not be saved + """ + + super(DDS, self).__init__(*args, **kwargs) + + # self.np_random = np.random + self.np_random = FixedRandomizer() + + def __set_np_random(self,f_rand): + self.np_random = f_rand + + def sample(self, repetitions,obj_func,fraction1,trials = 1): + """ + --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- + """ + + result_list = [] + sinitial, its, to_max = [], np.max([5, round(0.005 * repetitions)]), 1 + + #print(self.objectivefunction([121312,12,12],[1214123,34,34])) + #exit() + + self.set_repetiton(repetitions) + + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + num_dec = len(self.min_bound) # num_dec is the number of decision variables + + for trial in range(trials): + + solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) + + stest = [] + sbest = [] + Jbest = [] + + s_range = self.max_bound-self.min_bound + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + # own initial solution: + # sinitial = self.parameter()['random'] + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + print('Finding best starting point for trial '+str(trial)+' using '+str(its)+' random samples.') + ileft = repetitions - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') + + for i in range(its): + if self.discrete_flag == 0: # continuous variable + stest = self.min_bound + s_range * self.np_random.rand(num_dec) + + else: # discrete case + for j in range(num_dec): + stest[j] = self.np_random.randint(self.min_bound[j], self.max_bound[j] + 1) + + Jtest = to_max * obj_func(stest) # get obj function value + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest = its # needed to initialize variable and avoid code failure when small # iterations + trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution + + + # + # # A generator that produces parametersets if called + # param_generator = ((rep, self.parameter()['random']) + # for rep in range(int(repetitions))) + # for rep, randompar, simulations in self.repeat(param_generator): + # # A function that calculates the fitness of the run and the manages the database + # self.postprocessing(rep, randompar, simulations) + # self.final_call() + + + + #param_generator = ((rep, 1.0 - np.log(rep + 1) / np.log(ileft), np_random.rand(num_dec)) for rep in range(int(ileft))) + + # TODO implement like this! + # import pprint + # pprint.pprint(list(param_generator)) + # exit() + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i + 1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + + randnums = self.np_random.rand(num_dec) + + + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j + 1) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE + # TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) + + new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, + dec_var - 1) + # TODO more efficient! + + stest[dec_var - 1] = new_value # change relevant dec var value in stest + + # get ojective function value + + Jtest = to_max * obj_func(stest) + + # print([Jtest, Jbest]); + # print(stest) + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + # [list(solution), it_sbest, sbest, trial_initial] + + result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) + return result_list + + + def neigh_value_continuous(self,s, s_min, s_max, fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + self.np_random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = self.np_random.rand() + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + def neigh_value_discrete(self,s, s_min, s_max, fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = self.np_random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = self.np_random.rand() + + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + + def neigh_value_mixed(self,s, s_min, s_max, fraction1, j): + if self.discrete_flag == 0: + return self.neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return self.neigh_value_discrete(s, s_min, s_max, fraction1) + + + + # TODO: getestet werden sollten alle 5 Ergebnisvektoren + + + + From e217536b066e730294e36be253a6212553679f5a Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:06:11 +0200 Subject: [PATCH 38/73] Add first unittest for DDS to compare Matlab vs Python output --- spotpy/examples/dds.py | 20456 ------------------- spotpy/examples/spot_setup_dds.py | 26 + spotpy/examples/tutorial_dds.py | 41 + spotpy/unittests/DDS_references/run_1.json | 19 + spotpy/unittests/DDS_references/run_2.json | 19 + spotpy/unittests/DDS_references/run_3.json | 19 + spotpy/unittests/DDS_references/run_4.json | 19 + spotpy/unittests/DDS_references/run_5.json | 29 + spotpy/unittests/test_dds.py | 90 + 9 files changed, 262 insertions(+), 20456 deletions(-) delete mode 100644 spotpy/examples/dds.py create mode 100644 spotpy/examples/spot_setup_dds.py create mode 100644 spotpy/examples/tutorial_dds.py create mode 100644 spotpy/unittests/DDS_references/run_1.json create mode 100644 spotpy/unittests/DDS_references/run_2.json create mode 100644 spotpy/unittests/DDS_references/run_3.json create mode 100644 spotpy/unittests/DDS_references/run_4.json create mode 100644 spotpy/unittests/DDS_references/run_5.json create mode 100644 spotpy/unittests/test_dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py deleted file mode 100644 index 8ff63fd3..00000000 --- a/spotpy/examples/dds.py +++ /dev/null @@ -1,20456 +0,0 @@ -import numpy as np -import random - - -class FixedRandomizer(): - def __init__(self): - self.uniform_counter = 0 - self.normal_counter = 0 - self.uniform_list=[ - 0.05584386259223517, - 0.2535410132532582, - 0.3555753282652794, - 0.9767060208907836, - 0.46324425193900864, - 0.3950042024070052, - 0.5889712334602943, - 0.274782124811474, - 0.46535661133090267, - 0.2384618204031883, - 0.9016519630874807, - 0.2764917577582132, - 0.3478041353510165, - 0.20491028154260693, - 0.6414137312302646, - 0.08968343622461994, - 0.39692730727476133, - 0.9967532542326776, - 0.841419416942886, - 0.6618842788308826, - 0.31882636417540466, - 0.8354743646674859, - 0.028589608077657536, - 0.6079846845146457, - 0.6335957316411811, - 0.41559355020526556, - 0.30770270978552716, - 0.027064337038376518, - 0.7477269802923528, - 0.1502196251407184, - 0.8105389307758969, - 0.2184395961255544, - 0.8632392886169169, - 0.5468106182968853, - 0.6821964128103708, - 0.6281853256117305, - 0.8491284581827278, - 0.26182629209071273, - 0.6517490926530392, - 0.23627743562210546, - 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0.010466942524873549, - 1.0105377516892615, - -0.051870128281102525, - -0.5243040829263796, - -0.20276337726918783, - 0.05811174019879224, - 0.6221996966541786, - 0.46242904765338133, - -1.2985231897722824, - -0.5162081560822431, - -0.7553807158854496, - 0.35823893652515626, - 2.370608856072925, - -0.30360437519425315, - 1.2139469312964672, - 0.5203344997331647, - 1.2583371304217381, - 0.20634917734795732, - -0.06438169964327194 - ] - - def my_rand(self,dim_x=1,dim_y=1): - #x = np.array(dim_y * [dim_x * [0]]) - x = dim_x * [0] - for i in range(dim_x): - #for j in range(dim_y): - if self.uniform_counter < self.max_uniform_counter: - x[i] = self.uniform_list[self.uniform_counter] - self.uniform_counter = self.uniform_counter + 1 - else: - raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) - if len(x) == 1: - return x[0] - else: - return x - - def my_randint(self,x_from,x_to): - vals = [j for j in range(x_from,x_to)] - vals_size = len(vals) - if vals_size == 0: - raise ValueError("x_to >= x_from") - fraq = 1 / vals_size - if self.uniform_counter < self.max_uniform_counter: - q_uni = self.uniform_list[self.uniform_counter] - pos = np.int(np.floor(q_uni / fraq)) - self.uniform_counter += 1 - return vals[pos] - else: - raise Exception("ERROR END in my_randint") - - def my_randn(self,loc,scale): - - if loc != 0 or scale != 1: - raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! - - if self.normal_counter < self.max_normal_counter: - x = self.normal_list[self.normal_counter] - self.normal_counter += 1 - return x - - else: - raise Exception("ERROR END in my_randn") - - # TODO: better Exceptions - - - - - - -# TODO Convert this to unittest -# f_rand = FixedRandomizer() -# print(f_rand.my_rand(10)) -# print(np.random.rand(10)) - -# for k in range(100): -# print(f_rand.my_randint(1,101010)) -# print(np.random.randint(1,101010)) -# print("----------------------") - -# print(np.random.normal(0, 1)) -# print(f_rand.my_randn(0,1)) - - -#todo read in users own initial soultion, no we just generate it self - -def ackley(vector): - firstSum = 0.0 - secondSum = 0.0 - for c in range(len(vector)): - firstSum += c ** 2.0 - secondSum += np.cos(2.0 * np.pi * vector[c]) - n = float(len(vector)) - return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] - - - -def ackley10(vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1=sum1+vector[i]**2 - sum2=sum2+np.cos(2*np.pi*vector[i]) - return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) - - -def get_objfunc(x): - return ackley10(x) - - -def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): - - # ! name,LowerB,UpperB,Integer? - # 1 -2.0 2.0 0 - # 2 -2.0 2.0 0 - # 3 -2.0 2.0 0 - # 4 -2.0 2.0 0 - # 5 -2.0 2.0 0 - # 6 -2.0 2.0 0 - # 7 -2.0 2.0 0 - # 8 -2.0 2.0 0 - # 9 -2.0 2.0 0 - # 10 -2.0 2.0 0 - - # TODO read in from somewhere - - - - S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting - S_max = np.array(10*[2.0]) # dec variable maximums - get from starting - - - - - Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) - - # zzz = [] # temporary flag - num_dec = len(S_min) # num_dec is the number of decision variables - solution = np.array((maxiter) * [(3+num_dec) * [0]]) - - - # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour - # - # dvn_count = 0 - # new_value = 0 - # dec_var = 0 - - stest = [] - sbest = [] - # Jtest = [] - Jbest = [] - - S_range = S_max - S_min - - - - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= - - - - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); - ileft = maxiter - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: - # raise error error('#Initialization samples >= Max # function evaluations.') - pass - - for i in range(its): - if Discrete_flag == 0: # continuous variable - #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples - stest = S_min + S_range * f_rand.my_rand(num_dec) - - else: # discrete case - for j in range(num_dec): - # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); - stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) - - Jtest = to_max * get_objfunc(stest) # get obj function value - - - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest - - - - it_sbest=its # needed to initialize variable and avoid code failure when small # iterations - trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution - - # END First inititlisation - - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour - dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) - - - #TODO back: randnums = np.random.rand(num_dec) - randnums = f_rand.my_rand(num_dec) - - # print(stest) - # print(randnums); - # print(Pn); - # choosed_nums = [] - for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) - - # choosed_nums+=[j] - # TODO make this method!! - # TODO more efficient!! - - stest[j] = new_value # change relevant dec var value in stest - - # print(choosed_nums) - # print(stest) - # print("--------------------") - - - if dvn_count == 0: # no DVs selected at random, so select ONE - #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) - - - new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) - # TODO more efficient! - - stest[dec_var-1] = new_value # change relevant dec var value in stest - - - - # get ojective function value - - Jtest = to_max * get_objfunc(stest) - - - # if (i+1)%100 == 0: - # print(Jtest) - - #print([Jtest, Jbest]); - #print(stest) - if Jtest <= Jbest: - - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found - - - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - - # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest - - - # end DDS function loop - - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - - - print("trial_initial: ",trial_initial) - - print("sbest: ", sbest) - - - #print([solution, it_sbest, sbest, ]) - - - - -def neigh_value_continuous(s,s_min,s_max,fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) - - s_range = s_max - s_min - - #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range - snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range - - - - # NEED to deal with variable upper and lower bounds: - # Originally bounds in DDS were 100# reflective - # But some times DVs are right on the boundary and with 100# reflective - # boundaries it is hard to detect them. Therefore, we decided to make the - # boundaries reflective with 50# chance and absorptive with 50# chance. - # M. Asadzadeh and B. Tolson Dec 2008 - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past s_max then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max: - snew = s_min - - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max - - return snew - - -def neigh_value_discrete(s,s_min,s_max,fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) - s_range = s_max - s_min - #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range - delta = f_rand.my_randn(0, 1) * fraction1 * s_range - snew = s + delta - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past (s_max+0.5) then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min - - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max - - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution - #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value - sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) - print(sample) - print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) - exit() - - if sample < s: - snew = sample - else: # must increment option number by one - snew = sample + 1 - return snew - -def neigh_value_mixed(s,s_min,s_max,fraction1,j): - # todo discrete flagg as a class field - Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - - if Discrete_flag == 0: - return neigh_value_continuous(s, s_min, s_max, fraction1) - else: - return neigh_value_discrete(s,s_min,s_max,fraction1) - - -#1 for minimization -#farction is r_val -# maxiter is line 6 -> 1000 -# np.random.seed(150) - -f_rand = FixedRandomizer() - -for runs in range(2): - maxiter = 1000 - num_samples=np.max([5,round(0.005*maxiter)]) - Initial_solution = [] # TODO if user had seom, read it in - - dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py new file mode 100644 index 00000000..a62a773e --- /dev/null +++ b/spotpy/examples/spot_setup_dds.py @@ -0,0 +1,26 @@ +import spotpy +from spotpy.parameter import Uniform +from spotpy.objectivefunctions import rmse +import numpy as np + + +class spot_setup(object): + + def __init__(self): + self.params = [Uniform(str(j),-2, 2, 1.5, 3.0, -2, 2, doc=str(j)+' value of Rosenbrock function') + for j in range(10)] + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + x = np.array(vector) + simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + return simulations + + def evaluation(self): + observations = [0] + return observations + + def objectivefunction(self, simulation, evaluation): + objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py new file mode 100644 index 00000000..cf511646 --- /dev/null +++ b/spotpy/examples/tutorial_dds.py @@ -0,0 +1,41 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +import numpy as np +from spotpy.describe import describe + +from spotpy.examples.spot_setup_dds import spot_setup + + +#Create samplers for every algorithm: +results=[] +spot_setup=spot_setup() +rep=1000 +timeout=10 #Given in Seconds + + + +Initial_solution = [] # TODO if user had seom, read it in + + +parallel = "seq" +dbformat = "csv" + +sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +#print(describe(sampler)) +sampler.sample(rep) +#results.append(sampler.getdata()) + + + +print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +evaluation = spot_setup.evaluation() diff --git a/spotpy/unittests/DDS_references/run_1.json b/spotpy/unittests/DDS_references/run_1.json new file mode 100644 index 00000000..a3d42f83 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_1.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1000, + "trial_runs" : 2, + "r_val" : 0.2, + "results":[ + { + "sbest" : [0.0067187,-0.0267234,-0.0113414,0.0123613,0.0274250,0.0044262,0.0038885,-0.0233492,-0.0034915,-0.0074681], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.642849 + }, + { + "sbest" : [-0.0106570,-0.0445861,0.0014575,0.0013610,0.0328764,-0.0190379,-0.0067921,0.0061256,0.0324258,-0.0161818], + "trial_initial": [-1.329892,1.146982,-0.055018,-0.439524,1.080476,1.260820,0.727994,-1.718272,-0.359212,1.787397], + "objfunc_val":-22.603228 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_2.json b/spotpy/unittests/DDS_references/run_2.json new file mode 100644 index 00000000..f151431a --- /dev/null +++ b/spotpy/unittests/DDS_references/run_2.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1000, + "trial_runs" : 2, + "r_val" : 0.1, + "results":[ + { + "sbest" : [0.00061643,-0.012349,-0.00059654,-0.022133,-0.012122,0.00097649,-0.00057045,-0.0069949,0.0031554,0.0027167], + "trial_initial": [-1.7766,-0.98584,-0.5777,1.9068,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.0462], + "objfunc_val":-22.676681 + }, + { + "sbest" : [-0.0019426,0.004766,-0.0011191,0.0011356,0.0091296,0.014486,0.007099,-0.0092478,-0.01621,-0.0073524], + "trial_initial": [-1.3299,1.147,-0.055018,-0.43952,1.0805,1.2608,0.72799,-1.7183,-0.35921,1.7874], + "objfunc_val":-22.678947 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_3.json b/spotpy/unittests/DDS_references/run_3.json new file mode 100644 index 00000000..b32153ae --- /dev/null +++ b/spotpy/unittests/DDS_references/run_3.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 458, + "trial_runs" : 2, + "r_val" : 0.1, + "results":[ + { + "sbest" : [-0.0118273,0.0517943,-0.0596296,-0.0092867,0.0245581,-0.0173006,0.0268898,0.0025346,-0.0084837,-0.0451151], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.537145 + }, + { + "sbest" : [-2.4989e-02,-8.2942e-02,2.9951e-02,-6.0965e-04,-2.7539e-02,2.0719e-02,5.5417e-03,4.6295e-02,6.6750e-03,-1.2331e-02], + "trial_initial": [0.15979,1.13943,0.88842,1.10456,1.89854,0.59761,-0.69949,0.83442,0.28254,-0.96890], + "objfunc_val":-22.517933 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_4.json b/spotpy/unittests/DDS_references/run_4.json new file mode 100644 index 00000000..30e50b6b --- /dev/null +++ b/spotpy/unittests/DDS_references/run_4.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "ackley", + "evatrials" : 1100, + "trial_runs" : 2, + "r_val" : 0.7584, + "results":[ + { + "sbest" : [-0.0122048,-0.0695413,-0.0274150,0.0247516,-0.1724304,-0.0940382,-0.0076873,-0.0091148,0.0456905,-0.0245715], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-22.214618 + }, + { + "sbest" : [0.1060041,-0.0012436,-0.0559713,0.0337600,-0.0136549,0.1186215,-0.0190572,-0.0607619,0.0630677,-0.0578622], + "trial_initial": [0.9393077,0.0084414,0.2428478,1.3114781,1.0642522,-1.2121538,1.5810095,-0.9043814,-0.4694569,-0.7822007], + "objfunc_val":-22.257303 + } + ] +} + diff --git a/spotpy/unittests/DDS_references/run_5.json b/spotpy/unittests/DDS_references/run_5.json new file mode 100644 index 00000000..1030c9c3 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_5.json @@ -0,0 +1,29 @@ +{ + "objfunc" : "ackley", + "evatrials" : 250, + "trial_runs" : 4, + "r_val" : 0.29, + "results":[ + { + "sbest" : [0.0031109,0.0027083,0.0669848,0.2031390,0.0802268,0.0690243,-0.0883651,0.0188589,0.2928081,-0.0507384], + "trial_initial": [-1.77662,-0.98584,-0.57770,1.90682,-0.14702,-0.41998,0.35588,-0.90087,-0.13857,-1.04615], + "objfunc_val":-21.630197 + }, + { + "sbest" : [-0.076650,0.011593,0.098371,0.077140,-0.302584,-0.109039,-0.022255,-0.020336,0.073031,-0.281894], + "trial_initial": [0.58720,0.27984,-0.16666,-1.52057,0.69041,0.34202,-1.55106,-1.06755,-1.13758,-1.04445], + "objfunc_val":-21.384372 + }, + { + "sbest" : [-0.1296543,0.2273927,-0.8694331,0.0361375,-0.0168509,0.0066662,0.1133868,-0.0617312,0.0405173,-0.0208061], + "trial_initial": [0.380940,0.023070,1.846857,1.315453,-1.510769,-0.144067,0.791034,-1.443670,1.278489,0.133970], + "objfunc_val":-21.122759 + }, + { + "sbest" : [-5.5878e-02,5.7219e-02,2.3842e-02,-1.4539e-01,-4.8674e-04,1.4931e-01,-2.9320e-03,-8.2249e-02,-1.4800e-02,1.1134e-02], + "trial_initial": [0.912602,-0.775228,0.097104,1.270168,0.725063,0.945704,0.104205,1.548895,0.201936,0.036866], + "objfunc_val":-22.141054 + } + ] +} + diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py new file mode 100644 index 00000000..afe2f4d0 --- /dev/null +++ b/spotpy/unittests/test_dds.py @@ -0,0 +1,90 @@ +import unittest +import sys + +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy + +import numpy as np +import os +from spotpy.examples.spot_setup_dds import spot_setup +from pprint import pprint +import json + + +class TestDDS(unittest.TestCase): + def setUp(self): + self.spot_setup = spot_setup() + self.rep = 1000 + self.timeout = 1 # Given in Seconds + + + def ackley10(self,vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + + + def json_helper(self, run): + with open(os.path.dirname(__file__)+"/DDS_references/run_"+str(run)+".json") as f: + data = json.load(f) + + return data + + def func_switcher(self,name): + if name == "ackley": + return self.ackley10 + + def test_run_1(self): + self.run_a_dds(1) + + def test_run_2(self): + self.run_a_dds(2) + + def test_run_3(self): + self.run_a_dds(3) + + def test_run_4(self): + self.run_a_dds(4) + + def test_run_5(self): + self.run_a_dds(5) + + def run_a_dds(self,run): + original_result = self.json_helper(run) + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + + + results = sampler.sample(original_result["evatrials"],self.func_switcher(original_result['objfunc']),original_result["r_val"],original_result["trial_runs"]) + + + for t in range(original_result["trial_runs"]): + #pprint(results) + #pprint(original_result) + # +self.assertEqual(203, len(results)) + print(results[t]["objfunc_val"],original_result["results"][t]["objfunc_val"]) + self.assertTrue(np.abs(results[t]["objfunc_val"]-original_result["results"][t]["objfunc_val"]) < 0.000001) + py_sbest = results[t]["sbest"] + matlb_sbest = original_result["results"][t]["sbest"] + for k in range(len(py_sbest)): + print(py_sbest[k],matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k],matlb_sbest[k],delta=0.00001) + + py_trial_initial = results[t]["trial_initial"] + matlb_trial_initial = original_result["results"][t]["trial_initial"] + for k in range(len(py_sbest)): + print(py_trial_initial[k], matlb_trial_initial[k]) + self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) + + + + +if __name__ == '__main__': + unittest.main() From 231afd43cb87ebe04da18884602be2f62a60378d Mon Sep 17 00:00:00 2001 From: bees4ever Date: Tue, 18 Sep 2018 21:47:33 +0200 Subject: [PATCH 39/73] implement distinct parameters sampling in DDS with spotpy's parameter module --- spotpy/algorithms/dds.py | 114 ++++++--------------- spotpy/examples/spot_setup_dds.py | 53 +++++++++- spotpy/examples/tutorial_dds.py | 18 ++-- spotpy/parameter.py | 2 + spotpy/unittests/DDS_references/run_6.json | 19 ++++ spotpy/unittests/test_dds.py | 43 +++----- 6 files changed, 129 insertions(+), 120 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_6.json diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index ea3f28ef..6f4d02aa 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -48,60 +48,58 @@ def __init__(self, *args, **kwargs): # self.np_random = np.random self.np_random = FixedRandomizer() - def __set_np_random(self,f_rand): + def __set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions,obj_func,fraction1,trials = 1): + def sample(self, repetitions, fraction1, trials=1): """ --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- """ result_list = [] - sinitial, its, to_max = [], np.max([5, round(0.005 * repetitions)]), 1 - - #print(self.objectivefunction([121312,12,12],[1214123,34,34])) - #exit() + sinitial, its, to_max = [], np.int(np.max([5, round(0.005 * repetitions)])), 1 self.set_repetiton(repetitions) self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + self.discrete_flag = [u.is_distinct for u in self.setup.params] num_dec = len(self.min_bound) # num_dec is the number of decision variables for trial in range(trials): solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) - - stest = [] sbest = [] Jbest = [] - s_range = self.max_bound-self.min_bound - + s_range = self.max_bound - self.min_bound # ================================================================================================= # INITIAL SOLUTION # ================================================================================================= - # own initial solution: + # # todo implement own initial solution # sinitial = self.parameter()['random'] if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - print('Finding best starting point for trial '+str(trial)+' using '+str(its)+' random samples.') + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + its) + ' random samples.') ileft = repetitions - its # use this to reduce number of fevals in DDS loop if ileft <= 0: raise ValueError('# Initialization samples >= Max # function evaluations.') for i in range(its): - if self.discrete_flag == 0: # continuous variable + stest = [] + if not self.discrete_flag[i]: # continuous variable stest = self.min_bound + s_range * self.np_random.rand(num_dec) else: # discrete case for j in range(num_dec): - stest[j] = self.np_random.randint(self.min_bound[j], self.max_bound[j] + 1) + stest.append( + self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1)) - Jtest = to_max * obj_func(stest) # get obj function value + # TODO how to use the simulation in this case + Jtest = to_max * self.objectivefunction(stest, []) # get obj function value if i == 0: Jbest = Jtest @@ -118,7 +116,7 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): else: # know its=1, using a user supplied initial solution. Calculate obj func value. ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value + Jtest = self.objectivefunction(stest, []) # get obj function value Jbest = Jtest sbest = list(stest) solution[0, 0] = 1 @@ -126,12 +124,9 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): solution[0, 2] = to_max * Jtest solution[0, 3:3 + num_dec] = stest - - it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution - # # # A generator that produces parametersets if called # param_generator = ((rep, self.parameter()['random']) @@ -141,87 +136,52 @@ def sample(self, repetitions,obj_func,fraction1,trials = 1): # self.postprocessing(rep, randompar, simulations) # self.final_call() + param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(ileft))) + for rep, randompar, simulations in self.repeat(param_generator): + self.postprocessing(rep, randompar, simulations) - #param_generator = ((rep, 1.0 - np.log(rep + 1) / np.log(ileft), np_random.rand(num_dec)) for rep in range(int(ileft))) - - # TODO implement like this! - # import pprint - # pprint.pprint(list(param_generator)) - # exit() - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i + 1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(rep + 1) / np.log(ileft) dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = self.np_random.rand(num_dec) - - + # TODO: maybe loop with np vectors / array to be faster... for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour + if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j + 1) - - # TODO make this method!! - # TODO more efficient!! - + new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j) stest[j] = new_value # change relevant dec var value in stest - # print(choosed_nums) - # print(stest) - # print("--------------------") - if dvn_count == 0: # no DVs selected at random, so select ONE - # TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) - - new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, - dec_var - 1) - # TODO more efficient! + new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], fraction1, + dec_var - 1) stest[dec_var - 1] = new_value # change relevant dec var value in stest - # get ojective function value + Jtest = to_max * self.objectivefunction(stest, simulations) - Jtest = to_max * obj_func(stest) - - # print([Jtest, Jbest]); - # print(stest) if Jtest <= Jbest: Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - # accumulate results + solution[i + its, 0] = i + its solution[i + its, 1] = to_max * Jbest solution[i + its, 2] = to_max * Jtest solution[i + its, 3:3 + num_dec] = stest - # end DDS function loop + # end DDS function loop print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - # [list(solution), it_sbest, sbest, trial_initial] - result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) - return result_list + return result_list - def neigh_value_continuous(self,s, s_min, s_max, fraction1): + def neigh_value_continuous(self, s, s_min, s_max, fraction1): # select a RANDOM neighbouring real value of a SINGLE decision variable # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT @@ -275,7 +235,7 @@ def neigh_value_continuous(self,s, s_min, s_max, fraction1): return snew - def neigh_value_discrete(self,s, s_min, s_max, fraction1): + def neigh_value_discrete(self, s, s_min, s_max, fraction1): # Created by B.Tolson and B.Yung, June 2006 # Modified by B. Tolson & M. Asadzadeh, Sept 2008 # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) @@ -336,16 +296,8 @@ def neigh_value_discrete(self,s, s_min, s_max, fraction1): snew = sample + 1 return snew - def neigh_value_mixed(self,s, s_min, s_max, fraction1, j): - if self.discrete_flag == 0: + def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): + if not self.discrete_flag[j]: return self.neigh_value_continuous(s, s_min, s_max, fraction1) else: return self.neigh_value_discrete(s, s_min, s_max, fraction1) - - - - # TODO: getestet werden sollten alle 5 Ergebnisvektoren - - - - diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index a62a773e..37e302bd 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -4,11 +4,55 @@ import numpy as np +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + + +def griewank10(vector): + sum1 = 0 + term2 = 1 + term3 = 1 + + for i in range(len(vector)): + sum1 = sum1 + (vector[i] ** 2) / 4000 + term2 = term2 * np.cos(vector[i] / (i + 1) ** 0.5) + + return sum1 - term2 + term3 + + class spot_setup(object): + """ + Setup for a simple example to run DDS Algorithm + """ def __init__(self): - self.params = [Uniform(str(j),-2, 2, 1.5, 3.0, -2, 2, doc=str(j)+' value of Rosenbrock function') - for j in range(10)] + self.params = None + self.objfunc = None + + def _objfunc_switcher(self, name): + """ + Set new parameter and objective function while setup is instanced in a test case + :param name: function name which overwrites initial objective function + :return: + """ + + if name == "ackley": + self.objfunc = ackley10 + self.params = [Uniform(str(j), -2, 2, 1.5, 3.0, -2, 2, doc=str(j) + ' value of Rosenbrock function') + for j in range(10)] + elif name == "griewank": + self.objfunc = griewank10 + self.params = [Uniform('d' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'distinc parameter within a boundary', distinct=True) + for j in range(2)] + [Uniform('c' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'continuous parameter within a boundary') + for j in range(8)] def parameters(self): return spotpy.parameter.generate(self.params) @@ -23,4 +67,7 @@ def evaluation(self): return observations def objectivefunction(self, simulation, evaluation): - objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) + if self.objfunc is None: + print("Please choose an objectivefunction with '_objfunc_switcher' method") + else: + return self.objfunc(simulation) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index cf511646..b19a8396 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -10,11 +10,11 @@ sys.path.append(".") import spotpy -import numpy as np + from spotpy.describe import describe from spotpy.examples.spot_setup_dds import spot_setup - +from spotpy.examples.spot_setup_dds import ackley10 #Create samplers for every algorithm: results=[] @@ -27,15 +27,21 @@ Initial_solution = [] # TODO if user had seom, read it in + + parallel = "seq" dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) -#print(describe(sampler)) -sampler.sample(rep) -#results.append(sampler.getdata()) + +print(describe(sampler)) +sampler.sample(rep,fraction1=0.2,trials=2) + +#print(sampler.getdata()) + +results.append(sampler.getdata()) -print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +#print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x evaluation = spot_setup.evaluation() diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 1a096b8a..cac7d064 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -212,6 +212,8 @@ def __init__(self, rndfunc, rndfuncname, *args, **kwargs): self.maxbound = 0.0 self.description = arghelper.get('doc') + + self.is_distinct = not not arghelper.get("distinct") arghelper.check_complete() def __call__(self, **kwargs): diff --git a/spotpy/unittests/DDS_references/run_6.json b/spotpy/unittests/DDS_references/run_6.json new file mode 100644 index 00000000..1210e7b7 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_6.json @@ -0,0 +1,19 @@ +{ + "objfunc" : "griewank", + "evatrials" : 1000, + "trial_runs" : 1, + "r_val" : 0.2, + "results":[ + { + "sbest" : [-3.00000,0.00000,-4.72715 ,0.84169,9.53635,1.67684,2.72483,2.59443,3.42391,-8.47452], + "trial_initial": [-433,-196,-73,673,56,-26,207,-170,58,-214], + "objfunc_val":1.023480 + }, + { + "sbest" : [-7.00000,-2.00000,0.16090,2.97275,10.80989,-9.10410,2.74935,-8.67317,3.29054,-9.10145], + "trial_initial": [-299,444,83,-32,424,479,319,-416,-8,637], + "objfunc_val":1.109736 + } + ] +} + diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index afe2f4d0..03f4ecd6 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -10,7 +10,6 @@ import numpy as np import os from spotpy.examples.spot_setup_dds import spot_setup -from pprint import pprint import json @@ -20,27 +19,12 @@ def setUp(self): self.rep = 1000 self.timeout = 1 # Given in Seconds - - def ackley10(self,vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1 = sum1 + vector[i] ** 2 - sum2 = sum2 + np.cos(2 * np.pi * vector[i]) - return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) - - def json_helper(self, run): - with open(os.path.dirname(__file__)+"/DDS_references/run_"+str(run)+".json") as f: + with open(os.path.dirname(__file__) + "/DDS_references/run_" + str(run) + ".json") as f: data = json.load(f) return data - def func_switcher(self,name): - if name == "ackley": - return self.ackley10 - def test_run_1(self): self.run_a_dds(1) @@ -56,26 +40,27 @@ def test_run_4(self): def test_run_5(self): self.run_a_dds(5) - def run_a_dds(self,run): + def test_run_6(self): + self.run_a_dds(6) + + def run_a_dds(self, run): original_result = self.json_helper(run) - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) + self.spot_setup._objfunc_switcher(original_result['objfunc']) - results = sampler.sample(original_result["evatrials"],self.func_switcher(original_result['objfunc']),original_result["r_val"],original_result["trial_runs"]) + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) for t in range(original_result["trial_runs"]): - #pprint(results) - #pprint(original_result) - # +self.assertEqual(203, len(results)) - print(results[t]["objfunc_val"],original_result["results"][t]["objfunc_val"]) - self.assertTrue(np.abs(results[t]["objfunc_val"]-original_result["results"][t]["objfunc_val"]) < 0.000001) + print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) + self.assertAlmostEqual(results[t]["objfunc_val"] , original_result["results"][t]["objfunc_val"],delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] for k in range(len(py_sbest)): - print(py_sbest[k],matlb_sbest[k]) - self.assertAlmostEqual(py_sbest[k],matlb_sbest[k],delta=0.00001) + print(py_sbest[k], matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k], matlb_sbest[k], delta=0.00001) py_trial_initial = results[t]["trial_initial"] matlb_trial_initial = original_result["results"][t]["trial_initial"] @@ -84,7 +69,5 @@ def run_a_dds(self,run): self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) - - if __name__ == '__main__': unittest.main() From 45a66fc93c3c77e63d3f866d35e01946dfa32473 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 11:19:11 +0200 Subject: [PATCH 40/73] Add more tests, reorganize pytest for DDS --- spotpy/examples/spot_setup_dds.py | 17 ++++- spotpy/unittests/DDS_references/run_6.json | 13 ++-- spotpy/unittests/DDS_references/run_7.json | 89 ++++++++++++++++++++++ spotpy/unittests/test_dds.py | 9 ++- 4 files changed, 115 insertions(+), 13 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_7.json diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 37e302bd..8b2a0bf5 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -55,19 +55,28 @@ def _objfunc_switcher(self, name): for j in range(8)] def parameters(self): + if self.params is None: + self.params = [ + Uniform("0", -10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function'), + Uniform("1", -10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function'), + Uniform("z", -10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function')] return spotpy.parameter.generate(self.params) def simulation(self, vector): x = np.array(vector) - simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + simulations = x * np.random.rand(len(vector)) + # print(simulations) return simulations def evaluation(self): - observations = [0] + # observations = [0] + observations = [2, 3, 4] return observations def objectivefunction(self, simulation, evaluation): if self.objfunc is None: - print("Please choose an objectivefunction with '_objfunc_switcher' method") + print(simulation, evaluation) + return -rmse(evaluation, simulation) else: - return self.objfunc(simulation) + return self.objfunc(evaluation) diff --git a/spotpy/unittests/DDS_references/run_6.json b/spotpy/unittests/DDS_references/run_6.json index 1210e7b7..7bcb41c5 100644 --- a/spotpy/unittests/DDS_references/run_6.json +++ b/spotpy/unittests/DDS_references/run_6.json @@ -5,14 +5,11 @@ "r_val" : 0.2, "results":[ { - "sbest" : [-3.00000,0.00000,-4.72715 ,0.84169,9.53635,1.67684,2.72483,2.59443,3.42391,-8.47452], - "trial_initial": [-433,-196,-73,673,56,-26,207,-170,58,-214], - "objfunc_val":1.023480 - }, - { - "sbest" : [-7.00000,-2.00000,0.16090,2.97275,10.80989,-9.10410,2.74935,-8.67317,3.29054,-9.10145], - "trial_initial": [-299,444,83,-32,424,479,319,-416,-8,637], - "objfunc_val":1.109736 + "sbest" : [ -3.000000000000000,0.000000000000000,-5.036757578611908,11.733566302568235,9.429452816289487, + -5.549942605772495,2.490307284756468,2.332983597833255,3.851841900847800,0.390134503633089], + "trial_initial": [-433.0000000000000,-196.0000000000000,-73.3096060816648,672.0472250689404,55.8931023268103, + -25.9949571115938,206.7654801523532,-170.2614502262312,58.4279335970832,-213.8458155161740], + "objfunc_val":1.049515 } ] } diff --git a/spotpy/unittests/DDS_references/run_7.json b/spotpy/unittests/DDS_references/run_7.json new file mode 100644 index 00000000..1f70f3b6 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_7.json @@ -0,0 +1,89 @@ +{ + "objfunc": "griewank", + "evatrials": 550, + "trial_runs": 3, + "r_val": 0.37, + "results": [ + { + "sbest": [ + 11.00000000000000, + 19.00000000000000, + 2.03662487437028, + -1.23426415183106, + -5.24719291922387, + 25.07730145065261, + -12.02999926055234, + -22.00167804376221, + -3.14697244811857, + 29.56346268517814 + ], + "trial_initial": [ + -433.0000000000000, + -196.0000000000000, + -73.3096060816648, + 672.0472250689404, + 55.8931023268103, + -25.9949571115938, + 206.7654801523532, + -170.2614502262312, + 58.4279335970832, + -213.8458155161740 + ], + "objfunc_val": 1.664190 + }, + { + "sbest": [ + 7.00000000000000, + 10.00000000000000, + -6.39484002324608, + -46.76336537365788, + 9.58467601113504, + 38.38930958584108, + -6.09867128065270, + -45.82775862964041, + 33.63691393959370, + 2.93087800411658 + ], + "trial_initial": [ + -190.000000000000, + -288.000000000000, + 263.958476836666, + 269.302313621106, + 131.174425938139, + 633.973326660225, + 316.115885867790, + 296.859343133407, + -181.234821869745, + -344.830402395067 + ], + "objfunc_val": 2.802375 + }, + { + "sbest": [ + 12.00000000000000, + 12.00000000000000, + 32.85859327725726, + 7.08140986453067, + 5.86773597147155, + -1.02258063794287, + 3.75986146463776, + -17.85588284586939, + 30.57592135814601, + 1.34518680862905 + ], + "trial_initial": [ + -219.00000000000000, + -344.00000000000000, + 534.86798334588570, + 303.76611823508085, + 106.05989330870204, + 4.88429904716634, + 160.48210213748337, + -277.59019421646974, + -227.26786514527339, + 151.34888971727344 + ], + "objfunc_val": 1.645369 + } + ] +} diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 03f4ecd6..d9414247 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,6 +1,8 @@ import unittest import sys +from spotpy.tools import FixedRandomizer + try: import spotpy except ImportError: @@ -18,6 +20,7 @@ def setUp(self): self.spot_setup = spot_setup() self.rep = 1000 self.timeout = 1 # Given in Seconds + self.f_random = FixedRandomizer() def json_helper(self, run): with open(os.path.dirname(__file__) + "/DDS_references/run_" + str(run) + ".json") as f: @@ -43,6 +46,9 @@ def test_run_5(self): def test_run_6(self): self.run_a_dds(6) + def test_run_7(self): + self.run_a_dds(7) + def run_a_dds(self, run): original_result = self.json_helper(run) @@ -50,6 +56,7 @@ def run_a_dds(self, run): sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) + sampler._set_np_random(self.f_random) results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) @@ -66,7 +73,7 @@ def run_a_dds(self, run): matlb_trial_initial = original_result["results"][t]["trial_initial"] for k in range(len(py_sbest)): print(py_trial_initial[k], matlb_trial_initial[k]) - self.assertTrue(np.abs(py_trial_initial[k] - matlb_trial_initial[k]) < 0.0001) + self.assertAlmostEqual(py_trial_initial[k],matlb_trial_initial[k], delta=0.0001) if __name__ == '__main__': From 4e61df5815b43575e5988dd3f5a87a99efe2c922 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 11:20:04 +0200 Subject: [PATCH 41/73] little bugfixes more descriptions --- spotpy/algorithms/dds.py | 319 ++++++++++++++++++++------------------- 1 file changed, 162 insertions(+), 157 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 6f4d02aa..af0e737a 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -2,12 +2,18 @@ from spotpy.tools.fixedrandom import * from . import _algorithm + class DDS(_algorithm): """ + Implements the Dynamically dimensioned search algorithm for computationally efficient watershed model + calibration + by + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient + watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based + selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + http://www.civil.uwaterloo.ca/btolson/software.aspx - Paper: - Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. - Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. """ def __init__(self, *args, **kwargs): @@ -45,32 +51,48 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) - # self.np_random = np.random - self.np_random = FixedRandomizer() + self.np_random = np.random + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + + if hasattr(self.setup,"params"): + self.discrete_flag = [u.is_distinct for u in self.setup.params] + else: + self.discrete_flag = [False] * len(self.max_bound) - def __set_np_random(self, f_rand): + def _set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions, fraction1, trials=1): + def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): """ - --- --- --- -- .--.--.-.--.---.---.-.-.-..-.-.-- + Samples from the DDS Algorithm + :param repetitions: Maximum number of runs. + :type repetitions: int + :param fraction1: value between 0 and 1 + :type fraction1: float + :param trials: amount of runs DDS algorithm will be performed + :param s_initial: set an initial trial set + :return: """ result_list = [] - sinitial, its, to_max = [], np.int(np.max([5, round(0.005 * repetitions)])), 1 + to_max = 1 self.set_repetiton(repetitions) - self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.discrete_flag = [u.is_distinct for u in self.setup.params] - num_dec = len(self.min_bound) # num_dec is the number of decision variables + if len(s_initial) == 0: + its = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(s_initial) != num_dec: + raise ValueError("User specified 's_initial' has not the same length as available parameters") + else: + its = 1 + for trial in range(trials): - solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) - sbest = [] - Jbest = [] + # solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) + s_best = [] + j_best = [] s_range = self.max_bound - self.min_bound @@ -78,128 +100,115 @@ def sample(self, repetitions, fraction1, trials=1): # INITIAL SOLUTION # ================================================================================================= - # # todo implement own initial solution - # sinitial = self.parameter()['random'] - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( its) + ' random samples.') - ileft = repetitions - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: + i_left = repetitions - its # use this to reduce number of fevals in DDS loop + if i_left <= 0: raise ValueError('# Initialization samples >= Max # function evaluations.') - for i in range(its): - stest = [] - if not self.discrete_flag[i]: # continuous variable - stest = self.min_bound + s_range * self.np_random.rand(num_dec) - - else: # discrete case - for j in range(num_dec): - stest.append( - self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1)) - - # TODO how to use the simulation in this case - Jtest = to_max * self.objectivefunction(stest, []) # get obj function value - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = repetitions - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = self.objectivefunction(stest, []) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + self.discrete_flag[j] else self.min_bound[j] + s_range[j] * self.np_random.rand() for j in + range(int(num_dec))]) for rep in range(int(its))) + + for rep, s_test, simulations in self.repeat(starting_generator): + j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + + if rep == 0: + j_best = j_test + s_best = list(s_test) + + if j_test <= j_best: + j_best = j_test + s_best = list(s_test) + + # solution[rep, 0] = rep + # solution[rep, 1] = to_max * j_best + # solution[rep, 2] = to_max * j_test + # solution[rep, 3:3 + num_dec] = s_test + + else: # now its=1, using a user supplied initial solution. Calculate obj func value. + i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + s_test = s_initial # get from the inputs + j_test = self.objectivefunction(s_test, self.simulate((trial, s_initial))) # get obj function value + j_best = j_test + s_best = list(s_test) + # solution[0, 0] = 1 + # solution[0, 1] = to_max * j_best + # solution[0, 2] = to_max * j_test + # solution[0, 3:3 + num_dec] = s_test it_sbest = its # needed to initialize variable and avoid code failure when small # iterations - trial_initial = list(sbest) # extra variable here to simplify code for tracking initial DDS solution - - # - # # A generator that produces parametersets if called - # param_generator = ((rep, self.parameter()['random']) - # for rep in range(int(repetitions))) - # for rep, randompar, simulations in self.repeat(param_generator): - # # A function that calculates the fitness of the run and the manages the database - # self.postprocessing(rep, randompar, simulations) - # self.final_call() + trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution - param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(ileft))) + param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) for rep, randompar, simulations in self.repeat(param_generator): self.postprocessing(rep, randompar, simulations) - Pn = 1.0 - np.log(rep + 1) / np.log(ileft) + Pn = 1.0 - np.log(rep + 1) / np.log(i_left) dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) + s_test = list(s_best) # define s_test initially as current (s_best for greedy) - # TODO: maybe loop with np vectors / array to be faster... for j in range(num_dec): if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(sbest[j], self.min_bound[j], self.max_bound[j], fraction1, j) - stest[j] = new_value # change relevant dec var value in stest + + new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], fraction1, + j) + s_test[j] = new_value # change relevant dec var value in stest if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec) * self.np_random.rand())) - new_value = self.neigh_value_mixed(sbest[dec_var - 1], self.min_bound[dec_var - 1], + dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) + new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], fraction1, dec_var - 1) - stest[dec_var - 1] = new_value # change relevant dec var value in stest + s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - Jtest = to_max * self.objectivefunction(stest, simulations) + j_test = to_max * self.objectivefunction(simulations,s_test) - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found + if j_test <= j_best: + j_best = j_test + s_best = list(s_test) + it_sbest = rep + its # iteration number best solution found # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest + # solution[rep + its, 0] = rep + its + # solution[rep + its, 1] = to_max * j_best + # solution[rep + its, 2] = to_max * j_test + # solution[rep + its, 3:3 + num_dec] = s_test # end DDS function loop - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - result_list.append({"sbest": sbest, "trial_initial": trial_initial, "objfunc_val": to_max * Jbest}) + print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') + result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list def neigh_value_continuous(self, s, s_min, s_max, fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) + """ + select a RANDOM neighbouring real value of a SINGLE decision variable + CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + variables: + s_range is the range of the real variable (s_max-s_min) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param fraction1: is the neighbourhood parameter (replaces V parameter~see not + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = fraction1 * s_range + for comparison: variance (V) = (fraction1 * s_range)^2 + :return: + """ s_range = s_max - s_min - snew = s + self.np_random.normal(0, 1) * fraction1 * s_range + s_new = s + self.np_random.normal(0, 1) * fraction1 * s_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -208,93 +217,89 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = self.np_random.rand() + p_abs_or_ref = self.np_random.rand() - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) + if s_new < s_min: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50%chance reflect + s_new = s_min + (s_min - s_new) else: # with 50% chance absorb - snew = s_min + s_new = s_min # if reflection goes past s_max then value should be s_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if snew > s_max: - snew = s_min + if s_new > s_max: + s_new = s_min - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) + elif s_new > s_max: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = s_max - (s_new - s_max) else: # with 50% chance absorb - snew = s_max + s_new = s_max # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max + if s_new < s_min: + s_new = s_max - return snew + return s_new def neigh_value_discrete(self, s, s_min, s_max, fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) + """ + Created by B.Tolson and B.Yung, June 2006 + Modified by B. Tolson & M. Asadzadeh, Sept 2008 + Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + 2- Round the new value at the end of generation. + select a RANDOM neighbouring integer value of a SINGLE decision variable + discrete distribution is approximately normal + alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param fraction1: fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = fraction1 * s_range + for comparison: variance (V) = (fraction1 * s_range)^2 + :return: + """ + s_range = s_max - s_min delta = self.np_random.normal(0, 1) * fraction1 * s_range - snew = s + delta + s_new = s + delta - P_Abs_or_Ref = self.np_random.rand() + p_abs_or_ref = self.np_random.rand() - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + if s_new < s_min - 0.5: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_min - 0.5) + ((s_min - 0.5) - s_new) else: # with 50% chance absorb - snew = s_min + s_new = s_min # if reflection goes past (s_max+0.5) then value should be s_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min + if s_new > s_max + 0.5: + s_new = s_min - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + elif s_new > s_max + 0.5: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_max + 0.5) - (s_new - (s_max + 0.5)) else: # with 50% chance absorb - snew = s_max + s_new = s_max # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max + if s_new < s_min - 0.5: + s_new = s_max - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution + s_new = np.round(s_new) # New value must be integer + if s_new == s: # pick a number between s_max and s_min by a Uniform distribution sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) if sample < s: - snew = sample + s_new = sample else: # must increment option number by one - snew = sample + 1 - return snew + s_new = sample + 1 + return s_new def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): if not self.discrete_flag[j]: From d019ac3ac4505ac7e8e3eeca7afb373873c03677 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 15:20:33 +0200 Subject: [PATCH 42/73] use get_fitness to calculate objective function the spotpy way --- spotpy/algorithms/dds.py | 19 +++++++++++++++---- spotpy/examples/spot_setup_dds.py | 8 ++++---- spotpy/examples/tutorial_dds.py | 26 +++++++++++++------------- spotpy/unittests/test_dds.py | 5 +++-- 4 files changed, 35 insertions(+), 23 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index af0e737a..2c14f99d 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -87,6 +87,11 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): raise ValueError("User specified 's_initial' has not the same length as available parameters") else: its = 1 + s_initial = np.array(s_initial) + print(np.all(s_initial <= self.max_bound)) + print(np.all(s_initial >= self.min_bound)) + if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): + raise ValueError("User specified 's_initial' but the values are not within the parameter range") for trial in range(trials): @@ -113,7 +118,9 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): range(int(num_dec))]) for rep in range(int(its))) for rep, s_test, simulations in self.repeat(starting_generator): - j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + like = self.postprocessing(rep,s_test,simulations) # get obj function value + #j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + j_test = to_max * like if rep == 0: j_best = j_test @@ -131,7 +138,10 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop s_test = s_initial # get from the inputs - j_test = self.objectivefunction(s_test, self.simulate((trial, s_initial))) # get obj function value + nr, inpt, sims = self.simulate((trial, s_initial)) + + j_test = self.getfitness(sims, s_test) # get obj function value + j_best = j_test s_best = list(s_test) # solution[0, 0] = 1 @@ -145,7 +155,7 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) for rep, randompar, simulations in self.repeat(param_generator): - self.postprocessing(rep, randompar, simulations) + Pn = 1.0 - np.log(rep + 1) / np.log(i_left) dvn_count = 0 # counter for how many decision variables vary in neighbour @@ -167,7 +177,8 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - j_test = to_max * self.objectivefunction(simulations,s_test) + like = self.postprocessing(rep, s_test, simulations, chains=trial) + j_test = to_max * like if j_test <= j_best: j_best = j_test diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 8b2a0bf5..ee17610a 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -66,7 +66,6 @@ def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] simulations = x * np.random.rand(len(vector)) - # print(simulations) return simulations def evaluation(self): @@ -74,9 +73,10 @@ def evaluation(self): observations = [2, 3, 4] return observations - def objectivefunction(self, simulation, evaluation): + def objectivefunction(self, simulation, evaluation, params): + if self.objfunc is None: - print(simulation, evaluation) return -rmse(evaluation, simulation) else: - return self.objfunc(evaluation) + pars, names = params + return self.objfunc(pars) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index b19a8396..4ab21f3f 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -19,29 +19,29 @@ #Create samplers for every algorithm: results=[] spot_setup=spot_setup() -rep=1000 +rep=3000 timeout=10 #Given in Seconds - -Initial_solution = [] # TODO if user had seom, read it in - - - - parallel = "seq" dbformat = "csv" - sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) - print(describe(sampler)) -sampler.sample(rep,fraction1=0.2,trials=2) +sampler.sample(rep, trials=1,fraction1=0.1) +results.append(sampler.getdata()) + -#print(sampler.getdata()) +sampler=spotpy.algorithms.dream(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +print(describe(sampler)) +sampler.sample(rep) results.append(sampler.getdata()) -#print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x -evaluation = spot_setup.evaluation() +#algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream'] +spotpy.analyser.plot_parametertrace_algorithms(results,algorithmnames=["DDS","DREAM"],parameternames=['0','1']) + + +print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x +#evaluation = spot_setup.evaluation() diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index d9414247..6e93c85b 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,7 +1,7 @@ import unittest import sys -from spotpy.tools import FixedRandomizer + try: import spotpy @@ -9,7 +9,8 @@ sys.path.append(".") import spotpy -import numpy as np +from spotpy.tools import FixedRandomizer + import os from spotpy.examples.spot_setup_dds import spot_setup import json From 45b04389e90891aa2fae05fb28921f8314bc9aa2 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 17:31:30 +0200 Subject: [PATCH 43/73] finish user's parameter initialization and write unittests --- spotpy/algorithms/dds.py | 9 ++- .../DDS_references/run_own_input_1.json | 47 ++++++++++++ .../DDS_references/run_own_input_2.json | 74 +++++++++++++++++++ spotpy/unittests/test_dds.py | 33 +++++++-- 4 files changed, 154 insertions(+), 9 deletions(-) create mode 100644 spotpy/unittests/DDS_references/run_own_input_1.json create mode 100644 spotpy/unittests/DDS_references/run_own_input_2.json diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 2c14f99d..afeefdb5 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -88,8 +88,6 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: its = 1 s_initial = np.array(s_initial) - print(np.all(s_initial <= self.max_bound)) - print(np.all(s_initial >= self.min_bound)) if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") @@ -138,9 +136,12 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop s_test = s_initial # get from the inputs - nr, inpt, sims = self.simulate((trial, s_initial)) - j_test = self.getfitness(sims, s_test) # get obj function value + single_generator = ( (i,s_test) for i in range(1) ) + rep, s_test_param, simulations = next(self.repeat(single_generator)) + + j_test = self.postprocessing(rep,s_test,simulations) + j_best = j_test s_best = list(s_test) diff --git a/spotpy/unittests/DDS_references/run_own_input_1.json b/spotpy/unittests/DDS_references/run_own_input_1.json new file mode 100644 index 00000000..b1798ba7 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_own_input_1.json @@ -0,0 +1,47 @@ +{ + "objfunc": "griewank", + "evatrials": 1000, + "trial_runs": 1, + "r_val": 0.2, + "s_initial": [ + -1, + 3, + -2, + 4, + 6, + 9, + -3, + 6.7, + 2.2, + 10 + ], + "results": [ + { + "sbest": [ + -1.00000000000000, + 3.00000000000000, + -2.00000000000000, + 4.00000000000000, + 6.00000000000000, + -3.14440865613332, + -5.15170165876658, + 6.70000000000000, + 2.20000000000000, + 10.00000000000000 + ], + "trial_initial": [ + -1.00000000000000, + 3.00000000000000, + -2.00000000000000, + 4.00000000000000, + 6.00000000000000, + 9.00000000000000, + -3.00000000000000, + 6.70000000000000, + 2.20000000000000, + 10.00000000000000 + ], + "objfunc_val": 1.060679 + } + ] +} diff --git a/spotpy/unittests/DDS_references/run_own_input_2.json b/spotpy/unittests/DDS_references/run_own_input_2.json new file mode 100644 index 00000000..b9a486d3 --- /dev/null +++ b/spotpy/unittests/DDS_references/run_own_input_2.json @@ -0,0 +1,74 @@ +{ + "objfunc": "ackley", + "evatrials": 1000, + "trial_runs": 2, + "r_val": 0.23, + "s_initial": [ + -0.6998811, + -1.95230402, + -0.77098336, + -1.78082543, + -0.93364598, + 0.05985113, + -1.2270304, + 1.32375772, + -1.97949944, + -1.94960125 + ], + "results": [ + { + "sbest": [ + -0.01085857370638122, + -0.02253069124037839, + 0.01502826748703164, + -0.03056730293640454, + 0.05340255845087846, + -0.00489005220163242, + -0.00714949428369765, + -0.00685105529011589, + -0.00155646134117859, + 0.02910863259046434 + ], + "trial_initial": [ + -0.6998811000000000, + -1.9523040199999999, + -0.7709833600000000, + -1.7808254299999999, + -0.9336459800000000, + 0.0598511300000000, + -1.2270304000000001, + 1.3237577199999999, + -1.9794994400000001, + -1.9496012500000000 + ], + "objfunc_val": -22.594014 + }, + { + "sbest": [ + 0.01638165987817117, + 0.00714059038361719, + 0.00638449094200643, + -0.02265515934058741, + 0.03554349803571422, + -0.00268569499221568, + -0.01012133140550065, + 0.01938318407346291, + 0.01757040721020015, + -0.03600477373134182 + ], + "trial_initial": [ + -0.6998811000000000, + -1.9523040199999999, + -0.7709833600000000, + -1.7808254299999999, + -0.9336459800000000, + 0.0598511300000000, + -1.2270304000000001, + 1.3237577199999999, + -1.9794994400000001, + -1.9496012500000000 + ], + "objfunc_val": -22.613691 + } + ] +} diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 6e93c85b..24d03bc3 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,7 +1,6 @@ import unittest import sys - - +import numpy as np try: import spotpy @@ -50,6 +49,12 @@ def test_run_6(self): def test_run_7(self): self.run_a_dds(7) + def test_run_own_initial_1(self): + self.run_a_dds("own_input_1") + + def test_run_own_initial_2(self): + self.run_a_dds("own_input_2") + def run_a_dds(self, run): original_result = self.json_helper(run) @@ -59,11 +64,18 @@ def run_a_dds(self, run): sim_timeout=self.timeout) sampler._set_np_random(self.f_random) - results = sampler.sample(original_result["evatrials"], original_result["r_val"], original_result["trial_runs"]) + if original_result.get("s_initial") is not None: + # if a parameter initialisation is given, test this: + results = sampler.sample(original_result["evatrials"], original_result["r_val"], + original_result["trial_runs"], s_initial=original_result["s_initial"]) + else: + results = sampler.sample(original_result["evatrials"], original_result["r_val"], + original_result["trial_runs"]) for t in range(original_result["trial_runs"]): print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) - self.assertAlmostEqual(results[t]["objfunc_val"] , original_result["results"][t]["objfunc_val"],delta=0.000001) + self.assertAlmostEqual(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"], + delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] for k in range(len(py_sbest)): @@ -74,7 +86,18 @@ def run_a_dds(self, run): matlb_trial_initial = original_result["results"][t]["trial_initial"] for k in range(len(py_sbest)): print(py_trial_initial[k], matlb_trial_initial[k]) - self.assertAlmostEqual(py_trial_initial[k],matlb_trial_initial[k], delta=0.0001) + self.assertAlmostEqual(py_trial_initial[k], matlb_trial_initial[k], delta=0.0001) + + def test_own_initial_out_of_borders_ackley_1(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + + try: + sampler.sample(1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) + self.assertTrue(False, "ValueError was not thrown") + except ValueError as e: + self.assertTrue(True, "a ValueError was thrown and this is expected") if __name__ == '__main__': From 82d08761144ea9f2742f51e2909c85818a4f6efa Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 18:12:30 +0200 Subject: [PATCH 44/73] Some more unittests concerning own parameter initialization --- spotpy/unittests/test_dds.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 24d03bc3..143becf6 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -92,12 +92,19 @@ def test_own_initial_out_of_borders_ackley_1(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) + self.assertRaises(ValueError,sampler.sample,1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) - try: - sampler.sample(1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) - self.assertTrue(False, "ValueError was not thrown") - except ValueError as e: - self.assertTrue(True, "a ValueError was thrown and this is expected") + def test_own_initial_too_lees(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 9))) + + def test_own_initial_too_much(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) if __name__ == '__main__': From 881881a7364f2fa58999b7b66b8504bf9ee1d6e6 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 13:03:34 +0100 Subject: [PATCH 45/73] Refactor loop to postprocess correct simulation and parameters --- spotpy/algorithms/dds.py | 117 ++++++++++++++++-------------- spotpy/examples/spot_setup_dds.py | 3 +- spotpy/tools/fixedrandom.py | 7 ++ 3 files changed, 73 insertions(+), 54 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index afeefdb5..9c34f219 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -54,28 +54,46 @@ def __init__(self, *args, **kwargs): self.np_random = np.random self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - if hasattr(self.setup,"params"): + if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: self.discrete_flag = [False] * len(self.max_bound) + # self.generator_repetitions will be set in `sample` and is needed to generate a generator which sends back actual parameter s_test + self.generator_repetitions = -1 + + # holds currents best parameter + self.next_s_best = [] + def _set_np_random(self, f_rand): self.np_random = f_rand - def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): + def get_next_s_test(self): + for rep in range(self.generator_repetitions - 1): + yield rep + 1, self.next_s_best + + def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): """ - Samples from the DDS Algorithm + Samples from the DDS Algorithm. User can define an own 's_initial' parameter configuration set. If not `s_initial` + is set, the algorithm defines an own + :param repetitions: Maximum number of runs. :type repetitions: int :param fraction1: value between 0 and 1 :type fraction1: float :param trials: amount of runs DDS algorithm will be performed :param s_initial: set an initial trial set + :param to_max: 1 to minimize objective function, -1 maximized objective function :return: """ + self.fraction1 = fraction1 + + # Check if `to_max` is correct + if to_max != 1 and to_max != -1: + raise ValueError("please specify `to_max` as 1 or -1") + result_list = [] - to_max = 1 self.set_repetiton(repetitions) @@ -91,17 +109,16 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") + # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): - - # solution = np.array(repetitions * [(3 + num_dec) * [0.0]]) s_best = [] j_best = [] s_range = self.max_bound - self.min_bound - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= + # Calculate the initial Solution, if `its` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating its times to warm um the algorithm by trying which + # randomized generated input matches best (has minimal / maximum likelihood) if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( @@ -116,8 +133,8 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): range(int(num_dec))]) for rep in range(int(its))) for rep, s_test, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep,s_test,simulations) # get obj function value - #j_test = to_max * self.objectivefunction(simulations, s_test) # get obj function value + like = self.postprocessing(rep, s_test, simulations) # get obj function value + j_test = to_max * like if rep == 0: @@ -128,57 +145,31 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): j_best = j_test s_best = list(s_test) - # solution[rep, 0] = rep - # solution[rep, 1] = to_max * j_best - # solution[rep, 2] = to_max * j_test - # solution[rep, 3:3 + num_dec] = s_test - else: # now its=1, using a user supplied initial solution. Calculate obj func value. i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - s_test = s_initial # get from the inputs + s_test = list(s_initial) # get from the inputs - single_generator = ( (i,s_test) for i in range(1) ) + single_generator = ((i, s_test) for i in range(1)) rep, s_test_param, simulations = next(self.repeat(single_generator)) - j_test = self.postprocessing(rep,s_test,simulations) - + j_test = self.postprocessing(rep, s_test, simulations) j_best = j_test s_best = list(s_test) - # solution[0, 0] = 1 - # solution[0, 1] = to_max * j_best - # solution[0, 2] = to_max * j_test - # solution[0, 3:3 + num_dec] = s_test it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution + self.next_s_best = list(s_best) - param_generator = ((rep, self.np_random.rand(num_dec)) for rep in range(int(i_left))) - - for rep, randompar, simulations in self.repeat(param_generator): - - - Pn = 1.0 - np.log(rep + 1) / np.log(i_left) - dvn_count = 0 # counter for how many decision variables vary in neighbour - s_test = list(s_best) # define s_test initially as current (s_best for greedy) + # important to set this field `generator_repetitions` so that method `get_next_s_test` can generate exact paremters + self.generator_repetitions = i_left - for j in range(num_dec): - if randompar[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 + self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, 0) - new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], fraction1, - j) - s_test[j] = new_value # change relevant dec var value in stest - - if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) - new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], fraction1, - dec_var - 1) - - s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + for rep, s_test, simulations in self.repeat(self.get_next_s_test()): like = self.postprocessing(rep, s_test, simulations, chains=trial) + j_test = to_max * like if j_test <= j_best: @@ -186,20 +177,40 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[]): s_best = list(s_test) it_sbest = rep + its # iteration number best solution found - # accumulate results - - # solution[rep + its, 0] = rep + its - # solution[rep + its, 1] = to_max * j_best - # solution[rep + its, 2] = to_max * j_test - # solution[rep + its, 3:3 + num_dec] = s_test - # end DDS function loop + s_test = list(s_best) + self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, rep) print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list + def calculate_next_s_test(self, num_dec, s_test, s_best, rep): + randompar = self.np_random.rand(num_dec) + + Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + dvn_count = 0 # counter for how many decision variables vary in neighbour + # s_test = list(s_best) # define s_test initially as current (s_best for greedy) + + for j in range(num_dec): + if randompar[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + + new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], self.fraction1, + j) + s_test[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) + new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], self.fraction1, + dec_var - 1) + + s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + + return s_test + def neigh_value_continuous(self, s, s_min, s_max, fraction1): """ select a RANDOM neighbouring real value of a SINGLE decision variable diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index ee17610a..e4d972ec 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -65,7 +65,8 @@ def parameters(self): def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] - simulations = x * np.random.rand(len(vector)) + #simulations = x * np.random.rand(len(vector)) + simulations = x * np.sum(vector) return simulations def evaluation(self): diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py index 8fd1200e..0b150cd8 100644 --- a/spotpy/tools/fixedrandom.py +++ b/spotpy/tools/fixedrandom.py @@ -19,6 +19,7 @@ class FixedRandomizerEndOfDataException(Exception): class FixedRandomizer(): def __init__(self): + self.debug = False self.uniform_counter = 0 self.normal_counter = 0 self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) @@ -37,6 +38,8 @@ def rand(self,dim_x=1,dim_y=1): if self.uniform_counter < self.max_uniform_counter: x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 + if self.debug: + print("fixrand::rand() counter = "+str(self.uniform_counter)) else: raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) if len(x) == 1: @@ -54,6 +57,8 @@ def randint(self,x_from,x_to): q_uni = self.uniform_list[self.uniform_counter] pos = np.int(np.floor(q_uni / fraq)) self.uniform_counter += 1 + if self.debug: + print("fixrand::randint() counter = " + str(self.uniform_counter)) return vals[pos] else: raise FixedRandomizerEndOfDataException("No more data left.") @@ -64,6 +69,8 @@ def normal(self,loc,scale,size=1): if self.normal_counter < self.max_normal_counter: x.append(self.normal_list[self.normal_counter]*scale + loc) self.normal_counter += 1 + if self.debug: + print("fixrand::normal() counter = " + str(self.normal_counter)) else: raise FixedRandomizerEndOfDataException("No more data left.") From 5cde3911c4d61f348b9581ea3f50dbac0462e481 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 14:26:37 +0100 Subject: [PATCH 46/73] Refactor sample method and clean up comments --- spotpy/algorithms/__init__.py | 2 +- spotpy/algorithms/dds.py | 74 +++++++++++++++++++++---------- spotpy/examples/spot_setup_dds.py | 4 +- spotpy/unittests/test_dds.py | 6 +++ 4 files changed, 59 insertions(+), 27 deletions(-) diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index e47a5f1c..ffde32c8 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -31,4 +31,4 @@ from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis from .list_sampler import list_sampler # Samples from given spotpy database -from .dds import DDS +from .dds import DDS # Dynamically Dimensioned Search algorithm by Bryan Tolson. diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 9c34f219..0f79db8e 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -14,6 +14,16 @@ class DDS(_algorithm): selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. http://www.civil.uwaterloo.ca/btolson/software.aspx + + Method: + "The DDS algorithm is a novel and simple stochastic single-solution based heuristic global search + algorithm that was developed for the purpose of finding good global solutions + (as opposed to globally optimal solutions) within a specified maximum function (or model) evaluation limit." + (Page 3) + + The DDS algorithm is a simple greedy algorithm, always using the best solution (min or max) from the current + point of view. This may not lead to the global optimization. + """ def __init__(self, *args, **kwargs): @@ -59,18 +69,23 @@ def __init__(self, *args, **kwargs): else: self.discrete_flag = [False] * len(self.max_bound) - # self.generator_repetitions will be set in `sample` and is needed to generate a generator which sends back actual parameter s_test + # self.generator_repetitions will be set in `sample` and is needed to generate a + # generator which sends back actual parameter s_test self.generator_repetitions = -1 # holds currents best parameter - self.next_s_best = [] + self.next_s_test = [] def _set_np_random(self, f_rand): self.np_random = f_rand def get_next_s_test(self): + """ + Fake a generator to run self.repeat to use multiprocessing + """ + # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions - 1): - yield rep + 1, self.next_s_best + yield rep + 1, self.next_s_test def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): """ @@ -84,7 +99,7 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): :param trials: amount of runs DDS algorithm will be performed :param s_initial: set an initial trial set :param to_max: 1 to minimize objective function, -1 maximized objective function - :return: + :return: a key-value set of all parameter combination which has been used. May changed in future. """ self.fraction1 = fraction1 @@ -159,12 +174,12 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): it_sbest = its # needed to initialize variable and avoid code failure when small # iterations trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution - self.next_s_best = list(s_best) - # important to set this field `generator_repetitions` so that method `get_next_s_test` can generate exact paremters + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters self.generator_repetitions = i_left - self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, 0) + self.next_s_test = self.calculate_next_s_test(s_best, 0) for rep, s_test, simulations in self.repeat(self.get_next_s_test()): @@ -177,39 +192,50 @@ def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): s_best = list(s_test) it_sbest = rep + its # iteration number best solution found - # end DDS function loop - s_test = list(s_best) - self.next_s_best = self.calculate_next_s_test(num_dec, s_test, s_best, rep) + # prepare next s_test parameter based on s_best + self.next_s_test = self.calculate_next_s_test(s_best, rep) - print('Best solution found has obj function value of ' + str(to_max * j_best) + ' \n\n') + print('Best solution found has obj function value of ' + str(to_max * j_best) + ' at ' + + str(it_sbest) + '\n\n') result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) return result_list - def calculate_next_s_test(self, num_dec, s_test, s_best, rep): - randompar = self.np_random.rand(num_dec) + def calculate_next_s_test(self, previous_s_tests, rep): + """ + Needs to run in side `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. + + :param previous_s_tests: A set of parameters + :param rep: Position in DDS loop + :return: next parameter set + """ + amount_params = len(previous_s_tests) + + new_s_test = list(previous_s_tests) # define new_s_test initially as current (previous_s_tests for greedy) + + randompar = self.np_random.rand(amount_params) Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) dvn_count = 0 # counter for how many decision variables vary in neighbour - # s_test = list(s_best) # define s_test initially as current (s_best for greedy) - for j in range(num_dec): + for j in range(amount_params): if randompar[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(s_best[j], self.min_bound[j], self.max_bound[j], self.fraction1, - j) - s_test[j] = new_value # change relevant dec var value in stest + new_value = self.neigh_value_mixed(previous_s_tests[j], self.min_bound[j], self.max_bound[j], + self.fraction1, j) + new_s_test[j] = new_value # change relevant dec var value in stest if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil(num_dec * self.np_random.rand())) - new_value = self.neigh_value_mixed(s_best[dec_var - 1], self.min_bound[dec_var - 1], + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) + new_value = self.neigh_value_mixed(previous_s_tests[dec_var - 1], self.min_bound[dec_var - 1], self.max_bound[dec_var - 1], self.fraction1, dec_var - 1) - s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + new_s_test[dec_var - 1] = new_value # change relevant dec var value in s_test - return s_test + return new_s_test def neigh_value_continuous(self, s, s_min, s_max, fraction1): """ @@ -226,7 +252,7 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): normal random number/s_range. Eg: std dev desired = fraction1 * s_range for comparison: variance (V) = (fraction1 * s_range)^2 - :return: + :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min @@ -284,7 +310,7 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): normal random number/s_range. Eg: std dev desired = fraction1 * s_range for comparison: variance (V) = (fraction1 * s_range)^2 - :return: + :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index e4d972ec..e6577b4c 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -65,8 +65,8 @@ def parameters(self): def simulation(self, vector): x = np.array(vector) # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] - #simulations = x * np.random.rand(len(vector)) - simulations = x * np.sum(vector) + simulations = x * np.random.rand(len(vector)) + #simulations = x * np.sum(vector) return simulations def evaluation(self): diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 143becf6..583ad7a5 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -106,6 +106,12 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) + def test_wrong_min_max_input(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) + if __name__ == '__main__': unittest.main() From 2997f43eacad728c804f0d5f4e6757ea588c0af3 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 16:42:12 +0100 Subject: [PATCH 47/73] DDS: Refactoring variable names and use BestValue classs --- spotpy/algorithms/dds.py | 267 ++++++++++++++++++++------------ spotpy/examples/tutorial_dds.py | 2 +- spotpy/unittests/test_dds.py | 12 +- 3 files changed, 172 insertions(+), 109 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 0f79db8e..eb584910 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -1,6 +1,33 @@ import numpy as np from spotpy.tools.fixedrandom import * from . import _algorithm +import spotpy.parameter + +class BestValue(object): + def __init__(self, parameters, obj_value): + # self.parameters = spotpy.parameter.Gamma() + # self.best_obj_val = spotpy.parameter.Gamma() + self.parameters = parameters + self.best_obj_val = obj_value + self.best_rep = 0 + + # TODO Docstring + def update(self,curr_parameters, curr_obj_val, curr_rep): + """ + Update on maximize + :param curr_parameters: + :param curr_obj_val: + :return: + """ + # TODO MAXIMAIZE + if self.best_obj_val is None or self.best_obj_val >= curr_obj_val: + self.best_obj_val = curr_obj_val + self.parameters = curr_parameters + self.best_rep = curr_rep + + def init(self,parameters,obj_value): # fake init # todo change that + self.parameters = parameters + self.best_obj_val = obj_value class DDS(_algorithm): @@ -57,13 +84,24 @@ def __init__(self, *args, **kwargs): save_sim: boolean * True: Simulation results will be saved * False: Simulation results will not be saved + :param r: neighborhood size perturbation parameter (r) that defines the random perturbation size standard + deviation as a fraction of the decision variable range. Default is 0.2. + :type r: float + """ + try: + self.r = kwargs.pop("r") + except KeyError: + self.r = 0.2 # default value + super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.best_value = BestValue(self.parameter(), 0) # TODO set obj_value in a better way + if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: @@ -87,157 +125,188 @@ def get_next_s_test(self): for rep in range(self.generator_repetitions - 1): yield rep + 1, self.next_s_test - def sample(self, repetitions, fraction1=0.2, trials=1, s_initial=[], to_max=1): + def sample(self, repetitions, trials=1, s_initial=[]): """ - Samples from the DDS Algorithm. User can define an own 's_initial' parameter configuration set. If not `s_initial` - is set, the algorithm defines an own + Samples from the DDS Algorithm. + + DDS is a greedy type of algorithm since the current solution, also the best solution identified so far, + is never updated with a solution that has an inferior value of the objective function. + + That means in detail: + The DDS Algorithm starts with an initial phase: + If the user does not defines an own initial configuration The DDS algorithm start with searching a parameter + configuration in between the given parameter bounds. + + The next phase is the dds algorithm itself which runs in a loop `repetion` times: + Based on the parameter configuration x_new the algorithm run the model and simulation with the current parameter set + and calculates the objective function value called F_curr. + + If F_curr > F_best, where F_best is the current max value objective function value, we set x_best = x_curr and + F_best = F_curr. + + Select k of all parameters to include them in the neighborhood calculation. This is performed by calcualating a + threshold Pn (probability in neighbourhood). + + The neighbourhood calculation perturb x_best on standard normal distribution and reflect the result if it + breaks the parameter boundary. + The updated parameter configuration is called x_curr :param repetitions: Maximum number of runs. :type repetitions: int - :param fraction1: value between 0 and 1 - :type fraction1: float :param trials: amount of runs DDS algorithm will be performed - :param s_initial: set an initial trial set - :param to_max: 1 to minimize objective function, -1 maximized objective function + :param s_initial: set an initial trial set as a first parameter configuration. If the set is empty the algorithm + select an own initial parameter configuration :return: a key-value set of all parameter combination which has been used. May changed in future. """ - self.fraction1 = fraction1 - - # Check if `to_max` is correct - if to_max != 1 and to_max != -1: - raise ValueError("please specify `to_max` as 1 or -1") - - result_list = [] + debug_results = [] # every iteration a map of all relevant values is stored, only for debug purpose. + # Spotpy will not need this values. self.set_repetiton(repetitions) - num_dec = len(self.min_bound) # num_dec is the number of decision variables + number_of_parameters = len(self.min_bound) # number_of_parameters is the amount of parameters if len(s_initial) == 0: - its = np.int(np.max([5, round(0.005 * repetitions)])) - elif len(s_initial) != num_dec: + initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(s_initial) != number_of_parameters: raise ValueError("User specified 's_initial' has not the same length as available parameters") else: - its = 1 + initial_iterations = 1 s_initial = np.array(s_initial) if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): - s_best = [] - j_best = [] + # repitionno_best saves on which iteration the best parameter configuration has been found + repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations + repetions_left, f_best, trial_initial = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, s_initial) + + # TODO use ParameterSet Class + + #x_best = list(trial_initial) + self.best_value.init(trial_initial, f_best) + + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters + self.generator_repetitions = repetions_left - s_range = self.max_bound - self.min_bound + self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - # Calculate the initial Solution, if `its` > 1 otherwise the user defined a own one. - # If we need to find an initial solution we iterating its times to warm um the algorithm by trying which - # randomized generated input matches best (has minimal / maximum likelihood) + for rep, x_curr, simulations in self.repeat(self.get_next_s_test()): + f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( - its) + ' random samples.') - i_left = repetitions - its # use this to reduce number of fevals in DDS loop - if i_left <= 0: - raise ValueError('# Initialization samples >= Max # function evaluations.') + self.best_value.update(x_curr,f_curr,rep + initial_iterations) - starting_generator = ( - (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if - self.discrete_flag[j] else self.min_bound[j] + s_range[j] * self.np_random.rand() for j in - range(int(num_dec))]) for rep in range(int(its))) + # TODO MAXIMIZE! + # if f_curr <= f_best: + # f_best = f_curr + # x_best = list(x_curr) + # repitionno_best = rep # iteration number best solution found - for rep, s_test, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep, s_test, simulations) # get obj function value + # prepare next x_curr parameter based on x_best + self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) - j_test = to_max * like + print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + + str(repitionno_best) + '\n\n') + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_initial, "objfunc_val": self.best_value.best_obj_val}) - if rep == 0: - j_best = j_test - s_best = list(s_test) + return debug_results - if j_test <= j_best: - j_best = j_test - s_best = list(s_test) + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, s_initial): + parameter_bound_range = self.max_bound - self.min_bound + number_of_parameters = len(parameter_bound_range) - else: # now its=1, using a user supplied initial solution. Calculate obj func value. - i_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - s_test = list(s_initial) # get from the inputs + # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm + # by trying which randomized generated input matches best + x_best = [] + f_best = [] + if initial_iterations > 1: # initial_iterations is the number of function evaluations to initialize the DDS algorithm solution + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + initial_iterations) + ' random samples.') + repetions_left = repetitions - initial_iterations # use this to reduce number of fevals in DDS loop + if repetions_left <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') - single_generator = ((i, s_test) for i in range(1)) - rep, s_test_param, simulations = next(self.repeat(single_generator)) + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + self.discrete_flag[j] else self.min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + for j in + range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) - j_test = self.postprocessing(rep, s_test, simulations) + for rep, x_curr, simulations in self.repeat(starting_generator): + like = self.postprocessing(rep, x_curr, simulations) # get obj function value - j_best = j_test - s_best = list(s_test) + f_curr = like - it_sbest = its # needed to initialize variable and avoid code failure when small # iterations - trial_initial = list(s_best) # extra variable here to simplify code for tracking initial DDS solution + if rep == 0: + f_best = f_curr + x_best = list(x_curr) - # important to set this field `generator_repetitions` so that - # method `get_next_s_test` can generate exact parameters - self.generator_repetitions = i_left + if f_curr <= f_best: + f_best = f_curr + x_best = list(x_curr) - self.next_s_test = self.calculate_next_s_test(s_best, 0) + else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. + repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + x_curr = list(s_initial) # get from the inputs - for rep, s_test, simulations in self.repeat(self.get_next_s_test()): + rep, s_test_param, simulations = self.simulate((0, x_curr)) - like = self.postprocessing(rep, s_test, simulations, chains=trial) + f_curr = self.postprocessing(rep, x_curr, simulations) - j_test = to_max * like + f_best = f_curr + x_best = list(x_curr) - if j_test <= j_best: - j_best = j_test - s_best = list(s_test) - it_sbest = rep + its # iteration number best solution found + return repetions_left, f_best, x_best - # prepare next s_test parameter based on s_best - self.next_s_test = self.calculate_next_s_test(s_best, rep) + def calculate_next_s_test(self, previous_x_curr, rep, r): + """ + Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. - print('Best solution found has obj function value of ' + str(to_max * j_best) + ' at ' - + str(it_sbest) + '\n\n') - result_list.append({"sbest": s_best, "trial_initial": trial_initial, "objfunc_val": to_max * j_best}) + `probability_neighborhood` is a threshold at which level a parameter is added to neighbourhood calculation. - return result_list + Using a normal distribution + The decision variable - def calculate_next_s_test(self, previous_s_tests, rep): - """ - Needs to run in side `sample` method. Calculate the next set of parameters based on a given set. - This is greedy algorithm belonging to the DDS algorithm. + `dvn_count` counts how many parameter configuration has been exchanged with neighbourhood values. + If no parameters has been exchanged just one will select and exchanged with it's neighbourhood value. - :param previous_s_tests: A set of parameters + :param previous_x_curr: A set of parameters :param rep: Position in DDS loop + :param r: neighbourhood size perturbation parameter :return: next parameter set """ - amount_params = len(previous_s_tests) + amount_params = len(previous_x_curr) - new_s_test = list(previous_s_tests) # define new_s_test initially as current (previous_s_tests for greedy) + new_x_curr = list(previous_x_curr) # define new_x_curr initially as current (previous_x_curr for greedy) randompar = self.np_random.rand(amount_params) - Pn = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) dvn_count = 0 # counter for how many decision variables vary in neighbour + # TODO simplify this with np.arrays for j in range(amount_params): - if randompar[j] < Pn: # then j th DV selected to vary in neighbour + if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(previous_s_tests[j], self.min_bound[j], self.max_bound[j], - self.fraction1, j) - new_s_test[j] = new_value # change relevant dec var value in stest + new_value = self.neigh_value_mixed(previous_x_curr[j], self.min_bound[j], self.max_bound[j], r, j) + new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_s_tests[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], self.fraction1, + new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], self.min_bound[dec_var - 1], + self.max_bound[dec_var - 1], r, dec_var - 1) - new_s_test[dec_var - 1] = new_value # change relevant dec var value in s_test + new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test - return new_s_test + return new_x_curr - def neigh_value_continuous(self, s, s_min, s_max, fraction1): + def neigh_value_continuous(self, s, s_min, s_max, r): """ select a RANDOM neighbouring real value of a SINGLE decision variable CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT @@ -247,17 +316,17 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): :param s: is a current SINGLE decision variable VALUE :param s_min: is the min of variable s :param s_max: is the max of variable s - :param fraction1: is the neighbourhood parameter (replaces V parameter~see not + :param r: is the neighbourhood parameter (replaces V parameter~see not It is defined as the ratio of the std deviation of the desired normal random number/s_range. Eg: - std dev desired = fraction1 * s_range - for comparison: variance (V) = (fraction1 * s_range)^2 + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min - s_new = s + self.np_random.normal(0, 1) * fraction1 * s_range + s_new = s + self.np_random.normal(0, 1) * r * s_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -292,7 +361,7 @@ def neigh_value_continuous(self, s, s_min, s_max, fraction1): return s_new - def neigh_value_discrete(self, s, s_min, s_max, fraction1): + def neigh_value_discrete(self, s, s_min, s_max, r): """ Created by B.Tolson and B.Yung, June 2006 Modified by B. Tolson & M. Asadzadeh, Sept 2008 @@ -305,16 +374,16 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): :param s: is a current SINGLE decision variable VALUE :param s_min: is the min of variable s :param s_max: is the max of variable s - :param fraction1: fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + :param r: r is the neighbourhood parameter (replaces V parameter~see notes) It is defined as the ratio of the std deviation of the desired normal random number/s_range. Eg: - std dev desired = fraction1 * s_range - for comparison: variance (V) = (fraction1 * s_range)^2 + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 :return: s_new, a new sample of values in beetween a given range """ s_range = s_max - s_min - delta = self.np_random.normal(0, 1) * fraction1 * s_range + delta = self.np_random.normal(0, 1) * r * s_range s_new = s + delta p_abs_or_ref = self.np_random.rand() @@ -350,8 +419,8 @@ def neigh_value_discrete(self, s, s_min, s_max, fraction1): s_new = sample + 1 return s_new - def neigh_value_mixed(self, s, s_min, s_max, fraction1, j): + def neigh_value_mixed(self, s, s_min, s_max, r, j): if not self.discrete_flag[j]: - return self.neigh_value_continuous(s, s_min, s_max, fraction1) + return self.neigh_value_continuous(s, s_min, s_max, r) else: - return self.neigh_value_discrete(s, s_min, s_max, fraction1) + return self.neigh_value_discrete(s, s_min, s_max, r) diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index 4ab21f3f..6b2d69e3 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -27,7 +27,7 @@ dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) print(describe(sampler)) -sampler.sample(rep, trials=1,fraction1=0.1) +sampler.sample(rep, trials=1, r=0.1) results.append(sampler.getdata()) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 583ad7a5..06a0142d 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -61,15 +61,15 @@ def run_a_dds(self, run): self.spot_setup._objfunc_switcher(original_result['objfunc']) sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) + sim_timeout=self.timeout,r=original_result["r_val"]) sampler._set_np_random(self.f_random) if original_result.get("s_initial") is not None: # if a parameter initialisation is given, test this: - results = sampler.sample(original_result["evatrials"], original_result["r_val"], + results = sampler.sample(original_result["evatrials"], original_result["trial_runs"], s_initial=original_result["s_initial"]) else: - results = sampler.sample(original_result["evatrials"], original_result["r_val"], + results = sampler.sample(original_result["evatrials"], original_result["trial_runs"]) for t in range(original_result["trial_runs"]): @@ -106,12 +106,6 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) - def test_wrong_min_max_input(self): - self.spot_setup._objfunc_switcher("ackley") - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) - if __name__ == '__main__': unittest.main() From 0d9ad00736750c0ff3a298263d40547e699de7ea Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 17:47:17 +0100 Subject: [PATCH 48/73] DDS: refctor generator --- spotpy/algorithms/dds.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index eb584910..9186a6f2 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -117,13 +117,13 @@ def __init__(self, *args, **kwargs): def _set_np_random(self, f_rand): self.np_random = f_rand - def get_next_s_test(self): + def get_next_x_curr(self): """ Fake a generator to run self.repeat to use multiprocessing """ # We need to shift position and length of the sampling process - for rep in range(self.generator_repetitions - 1): - yield rep + 1, self.next_s_test + for rep in range(self.generator_repetitions): + yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.r) def sample(self, repetitions, trials=1, s_initial=[]): """ @@ -191,21 +191,17 @@ def sample(self, repetitions, trials=1, s_initial=[]): # method `get_next_s_test` can generate exact parameters self.generator_repetitions = repetions_left - self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) + #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - for rep, x_curr, simulations in self.repeat(self.get_next_s_test()): + for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) self.best_value.update(x_curr,f_curr,rep + initial_iterations) # TODO MAXIMIZE! - # if f_curr <= f_best: - # f_best = f_curr - # x_best = list(x_curr) - # repitionno_best = rep # iteration number best solution found # prepare next x_curr parameter based on x_best - self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) + #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') @@ -261,7 +257,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions return repetions_left, f_best, x_best - def calculate_next_s_test(self, previous_x_curr, rep, r): + def calculate_next_s_test(self, previous_x_curr, rep, all_rep, r): """ Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. This is greedy algorithm belonging to the DDS algorithm. From 730cea58a48bea5bc3b7690713f1ff13c12cdd18 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:37:11 +0200 Subject: [PATCH 49/73] Start writing dds in python. Very raw version --- spotpy/examples/dds.py | 20361 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 20361 insertions(+) create mode 100644 spotpy/examples/dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py new file mode 100644 index 00000000..383a457e --- /dev/null +++ b/spotpy/examples/dds.py @@ -0,0 +1,20361 @@ +import numpy as np +import random + + +class FixedRandomizer(): + def __init__(self): + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=[ + 0.05584386259223517, + 0.2535410132532582, + 0.3555753282652794, + 0.9767060208907836, + 0.46324425193900864, + 0.3950042024070052, + 0.5889712334602943, + 0.274782124811474, + 0.46535661133090267, + 0.2384618204031883, + 0.9016519630874807, + 0.2764917577582132, + 0.3478041353510165, + 0.20491028154260693, + 0.6414137312302646, + 0.08968343622461994, + 0.39692730727476133, + 0.9967532542326776, + 0.841419416942886, + 0.6618842788308826, + 0.31882636417540466, + 0.8354743646674859, + 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0.07231989308449034, + -0.654969725627664, + -1.1885568435104552, + 0.745449087352912, + -0.11451494925492176, + -0.42053138348011143, + -2.180044685215999, + -0.5863400273711193, + 0.9733894116381485, + -1.5380051834695678, + -0.8934864797237319, + -1.8166433233884607, + -1.6753856720898266, + -0.7749818761500956, + 1.6550131388895926, + 0.010466942524873549, + 1.0105377516892615, + -0.051870128281102525, + -0.5243040829263796, + -0.20276337726918783, + 0.05811174019879224, + 0.6221996966541786, + 0.46242904765338133, + -1.2985231897722824, + -0.5162081560822431, + -0.7553807158854496, + 0.35823893652515626, + 2.370608856072925, + -0.30360437519425315, + 1.2139469312964672, + 0.5203344997331647, + 1.2583371304217381, + 0.20634917734795732, + -0.06438169964327194 + ] + + def my_rand(self,dim_x=1,dim_y=1): + #x = np.array(dim_y * [dim_x * [0]]) + x = dim_x * [0] + for i in range(dim_x): + #for j in range(dim_y): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + else: + raise Exception("ERROR END ind my_randN") + if len(x) == 1: + return x[0] + else: + return x + + def my_randi + + +f_rand = FixedRandomizer() +print(f_rand.my_rand(10)) +print(np.random.rand(10)) +exit() + +#todo read in users own initial soultion, no we just generate it self + +def ackley(vector): + firstSum = 0.0 + secondSum = 0.0 + for c in range(len(vector)): + firstSum += c ** 2.0 + secondSum += np.cos(2.0 * np.pi * vector[c]) + n = float(len(vector)) + return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + + +def get_objfunc(x): + return ackley(x)[0] + + +def dds(sinitial,its,to_max,fraction1,maxiter): + + # ! name,LowerB,UpperB,Integer? + # 1 -2.0 2.0 0 + # 2 -2.0 2.0 0 + # 3 -2.0 2.0 0 + # 4 -2.0 2.0 0 + # 5 -2.0 2.0 0 + # 6 -2.0 2.0 0 + # 7 -2.0 2.0 0 + # 8 -2.0 2.0 0 + # 9 -2.0 2.0 0 + # 10 -2.0 2.0 0 + + # TODO read in from somewhere + + + + S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting + S_max = np.array(10*[2.0]) # dec variable maximums - get from starting + + + + + Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) + + # zzz = [] # temporary flag + num_dec = len(S_min) # num_dec is the number of decision variables + solution = np.array((maxiter) * [(3+num_dec) * [0]]) + + + # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour + # + # dvn_count = 0 + # new_value = 0 + # dec_var = 0 + + stest = [] + sbest = [] + # Jtest = [] + Jbest = [] + + S_range = S_max - S_min + + + + # ================================================================================================= + # INITIAL SOLUTION + # ================================================================================================= + + + + if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution + ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); + ileft = maxiter - its # use this to reduce number of fevals in DDS loop + if ileft <= 0: + # raise error error('#Initialization samples >= Max # function evaluations.') + pass + + for i in range(its): + if Discrete_flag == 0: # continuous variable + stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + else: # discrete case + for j in range(num_dec): + stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + + Jtest = to_max * get_objfunc(stest) # get obj function value + + + + if i == 0: + Jbest = Jtest + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + + solution[i, 0] = i + solution[i, 1] = to_max * Jbest + solution[i, 2] = to_max * Jtest + solution[i, 3:3 + num_dec] = stest + + else: # know its=1, using a user supplied initial solution. Calculate obj func value. + ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop + stest = sinitial # get from the inputs + Jtest = get_objfunc(stest) # get obj function value + Jbest = Jtest + sbest = list(stest) + solution[0, 0] = 1 + solution[0, 1] = to_max * Jbest + solution[0, 2] = to_max * Jtest + solution[0, 3:3 + num_dec] = stest + + + + it_sbest=its # needed to initialize variable and avoid code failure when small # iterations + trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution + + # END First inititlisation + + + for i in range(ileft): # remaining F evals after initialization + # Determine variable selected as neighbour + Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + dvn_count = 0 # counter for how many decision variables vary in neighbour + stest = list(sbest) # define stest initially as current (sbest for greedy) + randnums = np.random.rand(num_dec) + for j in range(num_dec): + if randnums[j] < Pn: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + + # TODO make this method!! + # TODO more efficient!! + + stest[j] = new_value # change relevant dec var value in stest + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + # TODO more efficient! + stest[dec_var] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) + + #print(Jtest,Jbest) + + if Jtest <= Jbest: + Jbest = Jtest + sbest = list(stest) + it_sbest = i + its # iteration number best solution found + + + ### write new status file so that best sol'n not lost with long + ### runs (i.e. SWAT or other models called). June 05 - BT + # Comment this part of code out for fast problems!! + # filenam='status.out'; + # fid = fopen(filenam,'w'); % opens file and discards current contents + # zzz=to_max*Jbest; + # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); + # fprintf(fid,'under parameter set below: \n'); + # fprintf(fid,' %e ',sbest); + # fclose(fid); + ### + + # accumulate results + solution[i + its, 0] = i + its + solution[i + its, 1] = to_max * Jbest + solution[i + its, 2] = to_max * Jtest + solution[i + its, 3:3 + num_dec] = stest + + + # end DDS function loop + + print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') + + + print("trial_initial: ",trial_initial) + + print("sbest: ", sbest) + + + #print([solution, it_sbest, sbest, ]) + + + + +def neigh_value_continuous(s,s_min,s_max,fraction1): + # select a RANDOM neighbouring real value of a SINGLE decision variable + # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + # s_range is the range of the real variable (s_max-s_min) + + s_range = s_max - s_min + + snew = s + np.random.normal(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + P_Abs_or_Ref = np.random.rand() + + + if snew < s_min: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50%chance reflect + snew = s_min + (s_min - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past s_max then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max: + snew = s_min + + + elif snew > s_max: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = s_max - (snew - s_max) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past s_min then value should be s_max for same reasons as above + if snew < s_min: + snew = s_max + + return snew + + +def neigh_value_discrete(s,s_min,s_max,fraction1): + # Created by B.Tolson and B.Yung, June 2006 + # Modified by B. Tolson & M. Asadzadeh, Sept 2008 + # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + # 2- Round the new value at the end of generation. + # select a RANDOM neighbouring integer value of a SINGLE decision variable + # discrete distribution is approximately normal + # alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + # variables: + # s is a current SINGLE decision variable VALUE + # s_min is the min of variable s + # s_max is the max of variable s + # delta_s_min is the minimum perturbation size for each decision variable + # equals [] if continuous DV (blank) + # equals 1 if discrete integer valued DV + # snew is the neighboring VALUE of the decision variable + # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) + # It is defined as the ratio of the std deviation of the desired + # normal random number/s_range. Eg: + # std dev desired = fraction1 * s_range + # for comparison: variance (V) = (fraction1 * s_range)^2 + + # s_range is the range of the real variable (s_max-s_min) + s_range = s_max - s_min + delta = np.random.normal(0, 1) * fraction1 * s_range + snew = s + delta + + P_Abs_or_Ref = np.random.rand() + if snew < s_min - 0.5: # works for any pos or neg s_min + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_min - 0.5) + ((s_min - 0.5) - snew) + else: # with 50% chance absorb + snew = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if snew > s_max + 0.5: + snew = s_min + + elif snew > s_max + 0.5: # works for any pos or neg s_max + if P_Abs_or_Ref <= 0.5: # with 50% chance reflect + snew = (s_max + 0.5) - (snew - (s_max + 0.5)) + else: # with 50% chance absorb + snew = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if snew < s_min - 0.5: + snew = s_max + + snew = np.round(snew) # New value must be integer + if snew == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + if sample < s: + snew = sample + else: # must increment option number by one + snew = sample + 1 + return snew + +def neigh_value_mixed(s,s_min,s_max,fraction1,j): + # todo discrete flagg as a class field + Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + + if Discrete_flag: + return neigh_value_continuous(s, s_min, s_max, fraction1) + else: + return neigh_value_discrete(s,s_min,s_max,fraction1) + + +#1 for minimization +#farction is r_val +# maxiter is line 6 -> 1000 +# np.random.seed(150) +for runs in range(2): + maxiter = 1000 + num_samples=np.max([5,round(0.005*maxiter)]) + Initial_solution = [] # TODO if user had seom, read it in + + dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file From 3981aac63907107f68665fa3ef7795927cd2891d Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 14:56:22 +0200 Subject: [PATCH 50/73] short before substitution with own random handler --- spotpy/examples/dds.py | 49 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 45 insertions(+), 4 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index 383a457e..aa58aeac 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -20025,18 +20025,59 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END ind my_randN") + raise Exception("ERROR END in my_rand") if len(x) == 1: return x[0] else: return x - def my_randi + def my_randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + return vals[pos] + else: + raise Exception("ERROR END in my_randint") + + def my_randn(self,loc,scale): + + if loc != 0 or scale != 1: + raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! + + if self.normal_counter < self.max_normal_counter: + x = self.normal_list[self.normal_counter] + self.normal_counter += 1 + return x + + else: + raise Exception("ERROR END in my_randn") + + # TODO: better Exceptions + + + + +# TODO Convert this to unittest f_rand = FixedRandomizer() -print(f_rand.my_rand(10)) -print(np.random.rand(10)) +# print(f_rand.my_rand(10)) +# print(np.random.rand(10)) + +# for k in range(100): +# print(f_rand.my_randint(1,101010)) +# print(np.random.randint(1,101010)) +# print("----------------------") + +print(np.random.normal(0, 1)) +print(f_rand.my_randn(0,1)) + exit() #todo read in users own initial soultion, no we just generate it self From 0625f94c10a38fe60b87fe38eea1a9fcdcf71e65 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 13 Sep 2018 21:13:43 +0200 Subject: [PATCH 51/73] Working version, which is same as Matlab, extended with own random funcs --- spotpy/examples/dds.py | 106 +++++++++++++++++++++++++++++++---------- 1 file changed, 80 insertions(+), 26 deletions(-) diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py index aa58aeac..8ff63fd3 100644 --- a/spotpy/examples/dds.py +++ b/spotpy/examples/dds.py @@ -10009,9 +10009,9 @@ def __init__(self): 0.715363917574095 ] - + self.uniform_list*=3 self.max_normal_counter = 10000 - self.max_uniform_counter = 10000 + self.max_uniform_counter = 30000 self.normal_list = [ 0.9723088366617443, @@ -20025,7 +20025,7 @@ def my_rand(self,dim_x=1,dim_y=1): x[i] = self.uniform_list[self.uniform_counter] self.uniform_counter = self.uniform_counter + 1 else: - raise Exception("ERROR END in my_rand") + raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) if len(x) == 1: return x[0] else: @@ -20066,7 +20066,7 @@ def my_randn(self,loc,scale): # TODO Convert this to unittest -f_rand = FixedRandomizer() +# f_rand = FixedRandomizer() # print(f_rand.my_rand(10)) # print(np.random.rand(10)) @@ -20075,10 +20075,9 @@ def my_randn(self,loc,scale): # print(np.random.randint(1,101010)) # print("----------------------") -print(np.random.normal(0, 1)) -print(f_rand.my_randn(0,1)) +# print(np.random.normal(0, 1)) +# print(f_rand.my_randn(0,1)) -exit() #todo read in users own initial soultion, no we just generate it self @@ -20092,11 +20091,22 @@ def ackley(vector): return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] + +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1=sum1+vector[i]**2 + sum2=sum2+np.cos(2*np.pi*vector[i]) + return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) + + def get_objfunc(x): - return ackley(x)[0] + return ackley10(x) -def dds(sinitial,its,to_max,fraction1,maxiter): +def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): # ! name,LowerB,UpperB,Integer? # 1 -2.0 2.0 0 @@ -20157,10 +20167,13 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(its): if Discrete_flag == 0: # continuous variable - stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples + stest = S_min + S_range * f_rand.my_rand(num_dec) + else: # discrete case for j in range(num_dec): - stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); + stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) Jtest = to_max * get_objfunc(stest) # get obj function value @@ -20199,32 +20212,58 @@ def dds(sinitial,its,to_max,fraction1,maxiter): for i in range(ileft): # remaining F evals after initialization # Determine variable selected as neighbour - Pn = 1.0 - np.log(i) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour + Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour dvn_count = 0 # counter for how many decision variables vary in neighbour stest = list(sbest) # define stest initially as current (sbest for greedy) - randnums = np.random.rand(num_dec) + + + #TODO back: randnums = np.random.rand(num_dec) + randnums = f_rand.my_rand(num_dec) + + # print(stest) + # print(randnums); + # print(Pn); + # choosed_nums = [] for j in range(num_dec): if randnums[j] < Pn: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j) + new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) + # choosed_nums+=[j] # TODO make this method!! # TODO more efficient!! stest[j] = new_value # change relevant dec var value in stest + # print(choosed_nums) + # print(stest) + # print("--------------------") + + if dvn_count == 0: # no DVs selected at random, so select ONE - dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - new_value = neigh_value_mixed(sbest[dec_var], S_min[dec_var], S_max[dec_var], fraction1, dec_var) + #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour + dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) + + + new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) # TODO more efficient! - stest[dec_var] = new_value # change relevant dec var value in stest + + stest[dec_var-1] = new_value # change relevant dec var value in stest + + # get ojective function value + Jtest = to_max * get_objfunc(stest) - #print(Jtest,Jbest) + # if (i+1)%100 == 0: + # print(Jtest) + + #print([Jtest, Jbest]); + #print(stest) if Jtest <= Jbest: + Jbest = Jtest sbest = list(stest) it_sbest = i + its # iteration number best solution found @@ -20282,7 +20321,10 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): s_range = s_max - s_min - snew = s + np.random.normal(0, 1) * fraction1 * s_range + #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range + snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range + + # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -20291,7 +20333,8 @@ def neigh_value_continuous(s,s_min,s_max,fraction1): # boundaries reflective with 50# chance and absorptive with 50# chance. # M. Asadzadeh and B. Tolson Dec 2008 - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() if snew < s_min: # works for any pos or neg s_min @@ -20345,10 +20388,13 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): # s_range is the range of the real variable (s_max-s_min) s_range = s_max - s_min - delta = np.random.normal(0, 1) * fraction1 * s_range + #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range + delta = f_rand.my_randn(0, 1) * fraction1 * s_range snew = s + delta - P_Abs_or_Ref = np.random.rand() + #TODO back: P_Abs_or_Ref = np.random.rand() + P_Abs_or_Ref = f_rand.my_rand() + if snew < s_min - 0.5: # works for any pos or neg s_min if P_Abs_or_Ref <= 0.5: # with 50% chance reflect snew = (s_min - 0.5) + ((s_min - 0.5) - snew) @@ -20373,7 +20419,12 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): snew = np.round(snew) # New value must be integer if snew == s: # pick a number between s_max and s_min by a Uniform distribution - sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value + sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) + print(sample) + print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) + exit() + if sample < s: snew = sample else: # must increment option number by one @@ -20382,9 +20433,9 @@ def neigh_value_discrete(s,s_min,s_max,fraction1): def neigh_value_mixed(s,s_min,s_max,fraction1,j): # todo discrete flagg as a class field - Discrete_flag = 1 # TODO should be a vector, I did not understand yet form where this should come + Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - if Discrete_flag: + if Discrete_flag == 0: return neigh_value_continuous(s, s_min, s_max, fraction1) else: return neigh_value_discrete(s,s_min,s_max,fraction1) @@ -20394,9 +20445,12 @@ def neigh_value_mixed(s,s_min,s_max,fraction1,j): #farction is r_val # maxiter is line 6 -> 1000 # np.random.seed(150) + +f_rand = FixedRandomizer() + for runs in range(2): maxiter = 1000 num_samples=np.max([5,round(0.005*maxiter)]) Initial_solution = [] # TODO if user had seom, read it in - dds(Initial_solution,num_samples,1,0.2,1000) \ No newline at end of file + dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file From 96d5f981b11b4865477ac56760a57a3788472817 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:02:25 +0200 Subject: [PATCH 52/73] Add new class FixedrRandom to control randomized algorithms in Matlab vs Python --- spotpy/tools/fixedrandom.py | 1 + 1 file changed, 1 insertion(+) diff --git a/spotpy/tools/fixedrandom.py b/spotpy/tools/fixedrandom.py index 0b150cd8..96454da4 100644 --- a/spotpy/tools/fixedrandom.py +++ b/spotpy/tools/fixedrandom.py @@ -13,6 +13,7 @@ # replaces numpy.random module in a way + class FixedRandomizerEndOfDataException(Exception): pass From 10136d1824ff4a0178cd2c2a0ce83fde5b48a917 Mon Sep 17 00:00:00 2001 From: bees4ever Date: Mon, 17 Sep 2018 16:06:11 +0200 Subject: [PATCH 53/73] Add first unittest for DDS to compare Matlab vs Python output --- spotpy/examples/dds.py | 20456 --------------------------------------- 1 file changed, 20456 deletions(-) delete mode 100644 spotpy/examples/dds.py diff --git a/spotpy/examples/dds.py b/spotpy/examples/dds.py deleted file mode 100644 index 8ff63fd3..00000000 --- a/spotpy/examples/dds.py +++ /dev/null @@ -1,20456 +0,0 @@ -import numpy as np -import random - - -class FixedRandomizer(): - def __init__(self): - self.uniform_counter = 0 - self.normal_counter = 0 - self.uniform_list=[ - 0.05584386259223517, - 0.2535410132532582, - 0.3555753282652794, - 0.9767060208907836, - 0.46324425193900864, - 0.3950042024070052, - 0.5889712334602943, - 0.274782124811474, - 0.46535661133090267, - 0.2384618204031883, - 0.9016519630874807, - 0.2764917577582132, - 0.3478041353510165, - 0.20491028154260693, - 0.6414137312302646, - 0.08968343622461994, - 0.39692730727476133, - 0.9967532542326776, - 0.841419416942886, - 0.6618842788308826, - 0.31882636417540466, - 0.8354743646674859, - 0.028589608077657536, - 0.6079846845146457, - 0.6335957316411811, - 0.41559355020526556, - 0.30770270978552716, - 0.027064337038376518, - 0.7477269802923528, - 0.1502196251407184, - 0.8105389307758969, - 0.2184395961255544, - 0.8632392886169169, - 0.5468106182968853, - 0.6821964128103708, - 0.6281853256117305, - 0.8491284581827278, - 0.26182629209071273, - 0.6517490926530392, - 0.23627743562210546, - 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0.010466942524873549, - 1.0105377516892615, - -0.051870128281102525, - -0.5243040829263796, - -0.20276337726918783, - 0.05811174019879224, - 0.6221996966541786, - 0.46242904765338133, - -1.2985231897722824, - -0.5162081560822431, - -0.7553807158854496, - 0.35823893652515626, - 2.370608856072925, - -0.30360437519425315, - 1.2139469312964672, - 0.5203344997331647, - 1.2583371304217381, - 0.20634917734795732, - -0.06438169964327194 - ] - - def my_rand(self,dim_x=1,dim_y=1): - #x = np.array(dim_y * [dim_x * [0]]) - x = dim_x * [0] - for i in range(dim_x): - #for j in range(dim_y): - if self.uniform_counter < self.max_uniform_counter: - x[i] = self.uniform_list[self.uniform_counter] - self.uniform_counter = self.uniform_counter + 1 - else: - raise Exception("ERROR END in my_rand. Counter is: "+str(self.uniform_counter)) - if len(x) == 1: - return x[0] - else: - return x - - def my_randint(self,x_from,x_to): - vals = [j for j in range(x_from,x_to)] - vals_size = len(vals) - if vals_size == 0: - raise ValueError("x_to >= x_from") - fraq = 1 / vals_size - if self.uniform_counter < self.max_uniform_counter: - q_uni = self.uniform_list[self.uniform_counter] - pos = np.int(np.floor(q_uni / fraq)) - self.uniform_counter += 1 - return vals[pos] - else: - raise Exception("ERROR END in my_randint") - - def my_randn(self,loc,scale): - - if loc != 0 or scale != 1: - raise ValueError("Only my_randn(0,1) implemented yet!") # TODO! - - if self.normal_counter < self.max_normal_counter: - x = self.normal_list[self.normal_counter] - self.normal_counter += 1 - return x - - else: - raise Exception("ERROR END in my_randn") - - # TODO: better Exceptions - - - - - - -# TODO Convert this to unittest -# f_rand = FixedRandomizer() -# print(f_rand.my_rand(10)) -# print(np.random.rand(10)) - -# for k in range(100): -# print(f_rand.my_randint(1,101010)) -# print(np.random.randint(1,101010)) -# print("----------------------") - -# print(np.random.normal(0, 1)) -# print(f_rand.my_randn(0,1)) - - -#todo read in users own initial soultion, no we just generate it self - -def ackley(vector): - firstSum = 0.0 - secondSum = 0.0 - for c in range(len(vector)): - firstSum += c ** 2.0 - secondSum += np.cos(2.0 * np.pi * vector[c]) - n = float(len(vector)) - return [-20.0 * np.exp(-0.2 * np.sqrt(firstSum / n)) - np.exp(secondSum / n) + 20 + np.e] - - - -def ackley10(vector): - length = len(vector) - sum1 = 0 - sum2 = 0 - for i in range(length): - sum1=sum1+vector[i]**2 - sum2=sum2+np.cos(2*np.pi*vector[i]) - return -20*np.exp(-0.2*(sum1/length)**0.5)-np.exp(sum2/length) - - -def get_objfunc(x): - return ackley10(x) - - -def dds(f_rand,sinitial,its,to_max,fraction1,maxiter): - - # ! name,LowerB,UpperB,Integer? - # 1 -2.0 2.0 0 - # 2 -2.0 2.0 0 - # 3 -2.0 2.0 0 - # 4 -2.0 2.0 0 - # 5 -2.0 2.0 0 - # 6 -2.0 2.0 0 - # 7 -2.0 2.0 0 - # 8 -2.0 2.0 0 - # 9 -2.0 2.0 0 - # 10 -2.0 2.0 0 - - # TODO read in from somewhere - - - - S_min = np.array(10*[-2.0]) # dec variable minimums - get from starting - S_max = np.array(10*[2.0]) # dec variable maximums - get from starting - - - - - Discrete_flag = 0 # TODO determine if variable is type : continuous (0) or integer (1) - - # zzz = [] # temporary flag - num_dec = len(S_min) # num_dec is the number of decision variables - solution = np.array((maxiter) * [(3+num_dec) * [0]]) - - - # Lasttime=np.array(1*[num_dec*[0]]) # tracks iterations since last sample as a neighbour - # - # dvn_count = 0 - # new_value = 0 - # dec_var = 0 - - stest = [] - sbest = [] - # Jtest = [] - Jbest = [] - - S_range = S_max - S_min - - - - # ================================================================================================= - # INITIAL SOLUTION - # ================================================================================================= - - - - if its > 1: # its is the number of function evaluations to initialize the DDS algorithm solution - ## print(' Finding best starting point for trial % using % random samples.\n',trial_num,its); - ileft = maxiter - its # use this to reduce number of fevals in DDS loop - if ileft <= 0: - # raise error error('#Initialization samples >= Max # function evaluations.') - pass - - for i in range(its): - if Discrete_flag == 0: # continuous variable - #TODO back: stest = S_min + S_range * np.random.rand(num_dec) # uniform random samples - stest = S_min + S_range * f_rand.my_rand(num_dec) - - else: # discrete case - for j in range(num_dec): - # TODO back: stest[j] = np.random.randint(S_min[j], S_max[j] + 1) # randi([S_min(1,j), S_max(1,j)],1,1); - stest[j] = f_rand.my_randint(S_min[j], S_max[j] + 1) - - Jtest = to_max * get_objfunc(stest) # get obj function value - - - - if i == 0: - Jbest = Jtest - - if Jtest <= Jbest: - Jbest = Jtest - sbest = list(stest) - - solution[i, 0] = i - solution[i, 1] = to_max * Jbest - solution[i, 2] = to_max * Jtest - solution[i, 3:3 + num_dec] = stest - - else: # know its=1, using a user supplied initial solution. Calculate obj func value. - ileft = maxiter - 1 # use this to reduce number of fevals in DDS loop - stest = sinitial # get from the inputs - Jtest = get_objfunc(stest) # get obj function value - Jbest = Jtest - sbest = list(stest) - solution[0, 0] = 1 - solution[0, 1] = to_max * Jbest - solution[0, 2] = to_max * Jtest - solution[0, 3:3 + num_dec] = stest - - - - it_sbest=its # needed to initialize variable and avoid code failure when small # iterations - trial_initial=list(sbest)# extra variable here to simplify code for tracking initial DDS solution - - # END First inititlisation - - - for i in range(ileft): # remaining F evals after initialization - # Determine variable selected as neighbour - Pn = 1.0 - np.log(i+1) / np.log(ileft) # 1.0-i/ileft;# probability of being selected as neighbour - dvn_count = 0 # counter for how many decision variables vary in neighbour - stest = list(sbest) # define stest initially as current (sbest for greedy) - - - #TODO back: randnums = np.random.rand(num_dec) - randnums = f_rand.my_rand(num_dec) - - # print(stest) - # print(randnums); - # print(Pn); - # choosed_nums = [] - for j in range(num_dec): - if randnums[j] < Pn: # then j th DV selected to vary in neighbour - dvn_count = dvn_count + 1 - new_value = neigh_value_mixed(sbest[j], S_min[j], S_max[j], fraction1, j+1) - - # choosed_nums+=[j] - # TODO make this method!! - # TODO more efficient!! - - stest[j] = new_value # change relevant dec var value in stest - - # print(choosed_nums) - # print(stest) - # print("--------------------") - - - if dvn_count == 0: # no DVs selected at random, so select ONE - #TODO back: dec_var = np.int(np.ceil((num_dec-1) * np.random.rand())) # which dec var to modify for neighbour - dec_var = np.int(np.ceil((num_dec) * f_rand.my_rand())) - - - new_value = neigh_value_mixed(sbest[dec_var-1], S_min[dec_var-1], S_max[dec_var-1], fraction1, dec_var-1) - # TODO more efficient! - - stest[dec_var-1] = new_value # change relevant dec var value in stest - - - - # get ojective function value - - Jtest = to_max * get_objfunc(stest) - - - # if (i+1)%100 == 0: - # print(Jtest) - - #print([Jtest, Jbest]); - #print(stest) - if Jtest <= Jbest: - - Jbest = Jtest - sbest = list(stest) - it_sbest = i + its # iteration number best solution found - - - ### write new status file so that best sol'n not lost with long - ### runs (i.e. SWAT or other models called). June 05 - BT - # Comment this part of code out for fast problems!! - # filenam='status.out'; - # fid = fopen(filenam,'w'); % opens file and discards current contents - # zzz=to_max*Jbest; - # fprintf(fid,'Current best objective function value of %12.5f found at iteration %6.0f\n',zzz,i+its); - # fprintf(fid,'under parameter set below: \n'); - # fprintf(fid,' %e ',sbest); - # fclose(fid); - ### - - # accumulate results - solution[i + its, 0] = i + its - solution[i + its, 1] = to_max * Jbest - solution[i + its, 2] = to_max * Jtest - solution[i + its, 3:3 + num_dec] = stest - - - # end DDS function loop - - print('Best solution found has obj function value of ' + str(to_max * Jbest) + ' \n\n') - - - print("trial_initial: ",trial_initial) - - print("sbest: ", sbest) - - - #print([solution, it_sbest, sbest, ]) - - - - -def neigh_value_continuous(s,s_min,s_max,fraction1): - # select a RANDOM neighbouring real value of a SINGLE decision variable - # CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - # s_range is the range of the real variable (s_max-s_min) - - s_range = s_max - s_min - - #TODO back: snew = s + np.random.normal(0, 1) * fraction1 * s_range - snew = s + f_rand.my_randn(0, 1) * fraction1 * s_range - - - - # NEED to deal with variable upper and lower bounds: - # Originally bounds in DDS were 100# reflective - # But some times DVs are right on the boundary and with 100# reflective - # boundaries it is hard to detect them. Therefore, we decided to make the - # boundaries reflective with 50# chance and absorptive with 50# chance. - # M. Asadzadeh and B. Tolson Dec 2008 - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - - if snew < s_min: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50%chance reflect - snew = s_min + (s_min - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past s_max then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max: - snew = s_min - - - elif snew > s_max: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = s_max - (snew - s_max) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past s_min then value should be s_max for same reasons as above - if snew < s_min: - snew = s_max - - return snew - - -def neigh_value_discrete(s,s_min,s_max,fraction1): - # Created by B.Tolson and B.Yung, June 2006 - # Modified by B. Tolson & M. Asadzadeh, Sept 2008 - # Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - # 2- Round the new value at the end of generation. - # select a RANDOM neighbouring integer value of a SINGLE decision variable - # discrete distribution is approximately normal - # alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - # variables: - # s is a current SINGLE decision variable VALUE - # s_min is the min of variable s - # s_max is the max of variable s - # delta_s_min is the minimum perturbation size for each decision variable - # equals [] if continuous DV (blank) - # equals 1 if discrete integer valued DV - # snew is the neighboring VALUE of the decision variable - # fraction1 is the neighbourhood parameter (replaces V parameter~see notes) - # It is defined as the ratio of the std deviation of the desired - # normal random number/s_range. Eg: - # std dev desired = fraction1 * s_range - # for comparison: variance (V) = (fraction1 * s_range)^2 - - # s_range is the range of the real variable (s_max-s_min) - s_range = s_max - s_min - #TODO back: delta = np.random.normal(0, 1) * fraction1 * s_range - delta = f_rand.my_randn(0, 1) * fraction1 * s_range - snew = s + delta - - #TODO back: P_Abs_or_Ref = np.random.rand() - P_Abs_or_Ref = f_rand.my_rand() - - if snew < s_min - 0.5: # works for any pos or neg s_min - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_min - 0.5) + ((s_min - 0.5) - snew) - else: # with 50% chance absorb - snew = s_min - - # if reflection goes past (s_max+0.5) then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if snew > s_max + 0.5: - snew = s_min - - elif snew > s_max + 0.5: # works for any pos or neg s_max - if P_Abs_or_Ref <= 0.5: # with 50% chance reflect - snew = (s_max + 0.5) - (snew - (s_max + 0.5)) - else: # with 50% chance absorb - snew = s_max - - # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if snew < s_min - 0.5: - snew = s_max - - snew = np.round(snew) # New value must be integer - if snew == s: # pick a number between s_max and s_min by a Uniform distribution - #TODO back: sample = s_min - 1 + np.ceil((s_max - s_min) * np.random.rand()) # last term gives range = # options - 1. First terms shift to allow min value - sample = s_min - 1 + np.ceil((s_max - s_min) * f_rand.my_rand()) - print(sample) - print(s_min - 1 + np.ceil((s_max - s_min) * np.random.rand())) - exit() - - if sample < s: - snew = sample - else: # must increment option number by one - snew = sample + 1 - return snew - -def neigh_value_mixed(s,s_min,s_max,fraction1,j): - # todo discrete flagg as a class field - Discrete_flag = 0 # TODO should be a vector, I did not understand yet form where this should come - - if Discrete_flag == 0: - return neigh_value_continuous(s, s_min, s_max, fraction1) - else: - return neigh_value_discrete(s,s_min,s_max,fraction1) - - -#1 for minimization -#farction is r_val -# maxiter is line 6 -> 1000 -# np.random.seed(150) - -f_rand = FixedRandomizer() - -for runs in range(2): - maxiter = 1000 - num_samples=np.max([5,round(0.005*maxiter)]) - Initial_solution = [] # TODO if user had seom, read it in - - dds(f_rand,Initial_solution,num_samples,1,0.2,maxiter) \ No newline at end of file From c80b50a987e2dc9e9b8439e2ebec8306ca8502ec Mon Sep 17 00:00:00 2001 From: bees4ever Date: Thu, 20 Sep 2018 15:20:33 +0200 Subject: [PATCH 54/73] use get_fitness to calculate objective function the spotpy way --- spotpy/unittests/test_dds.py | 1 + 1 file changed, 1 insertion(+) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 06a0142d..616530d5 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -1,5 +1,6 @@ import unittest import sys + import numpy as np try: From b350f5589d942a45dd636b14779ad14a4d11ac8e Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Thu, 1 Nov 2018 14:26:37 +0100 Subject: [PATCH 55/73] Refactor sample method and clean up comments --- spotpy/unittests/test_dds.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 616530d5..624aed8b 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -107,6 +107,12 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) + def test_wrong_min_max_input(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) + if __name__ == '__main__': unittest.main() From 4027eba1f4d4ec34128e549cb88d9298c1481f70 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 16:42:12 +0100 Subject: [PATCH 56/73] DDS: Refactoring variable names and use BestValue classs --- spotpy/unittests/test_dds.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 624aed8b..616530d5 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -107,12 +107,6 @@ def test_own_initial_too_much(self): sim_timeout=self.timeout) self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) - def test_wrong_min_max_input(self): - self.spot_setup._objfunc_switcher("ackley") - sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", - sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000,to_max=1.1) - if __name__ == '__main__': unittest.main() From 2484f0b5f7b055a097085ac9e08cc5642bf4f935 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sun, 11 Nov 2018 20:02:23 +0100 Subject: [PATCH 57/73] DDS: fix rebase --- spotpy/algorithms/__init__.py | 2 +- spotpy/algorithms/dds.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index 8109af2e..2ba4f545 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -30,5 +30,5 @@ from .abc import abc # Artificial Bee Colony from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis -from .list import list # Samples from given spotpy database +from .list_sampler import list_sampler # Samples from given spotpy database from .dds import DDS # Dynamically Dimensioned Search algorithm by Bryan Tolson. \ No newline at end of file diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 9186a6f2..60fc5583 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -257,7 +257,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions return repetions_left, f_best, x_best - def calculate_next_s_test(self, previous_x_curr, rep, all_rep, r): + def calculate_next_s_test(self, previous_x_curr, rep, r): """ Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. This is greedy algorithm belonging to the DDS algorithm. From 637af65a17829c91cac70882aa2f46944c6f7b1a Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Tue, 20 Nov 2018 18:53:29 +0100 Subject: [PATCH 58/73] Introduce BestValue in combination with ParameterSet. Propose a copy method to Parameteret --- spotpy/algorithms/dds.py | 133 +++++++++++++++++++-------------------- spotpy/parameter.py | 7 +++ 2 files changed, 72 insertions(+), 68 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 60fc5583..429daa48 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -2,8 +2,12 @@ from spotpy.tools.fixedrandom import * from . import _algorithm import spotpy.parameter +from spotpy.parameter import ParameterSet class BestValue(object): + # print(ps) + # print([ps.maxbound, ps.minbound, ps.optguess, ps.step, ps.random]) + def __init__(self, parameters, obj_value): # self.parameters = spotpy.parameter.Gamma() # self.best_obj_val = spotpy.parameter.Gamma() @@ -29,6 +33,15 @@ def init(self,parameters,obj_value): # fake init # todo change that self.parameters = parameters self.best_obj_val = obj_value + def copy(self): + to_copy = BestValue(self.parameters.copy(),self.best_obj_val) + to_copy.best_rep = self.best_rep + return to_copy + + def __str__(self): + return "BestValue(best_obj_val = " + str(self.best_obj_val) + ", best_rep = " + str(self.best_rep) + ", " \ + + str(self.parameters) + class DDS(_algorithm): """ @@ -98,9 +111,9 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random - self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.best_value = BestValue(self.parameter(), 0) # TODO set obj_value in a better way + + self.best_value = BestValue(ParameterSet(self.parameter()), 0) # TODO set obj_value in a better way if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] @@ -123,7 +136,7 @@ def get_next_x_curr(self): """ # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions): - yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.r) + yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.generator_repetitions,self.r) def sample(self, repetitions, trials=1, s_initial=[]): """ @@ -164,7 +177,7 @@ def sample(self, repetitions, trials=1, s_initial=[]): self.set_repetiton(repetitions) - number_of_parameters = len(self.min_bound) # number_of_parameters is the amount of parameters + number_of_parameters = len(self.best_value.parameters) # number_of_parameters is the amount of parameters if len(s_initial) == 0: initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) @@ -173,44 +186,33 @@ def sample(self, repetitions, trials=1, s_initial=[]): else: initial_iterations = 1 s_initial = np.array(s_initial) - if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): + if not (np.all(s_initial <= self.best_value.parameters.maxbound) and np.all(s_initial >= self.best_value.parameters.minbound)): raise ValueError("User specified 's_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): # repitionno_best saves on which iteration the best parameter configuration has been found repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations - repetions_left, f_best, trial_initial = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, s_initial) + repetions_left, trial_best_value = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, s_initial) + self.best_value = trial_best_value.copy() - # TODO use ParameterSet Class - - #x_best = list(trial_initial) - self.best_value.init(trial_initial, f_best) - # important to set this field `generator_repetitions` so that # method `get_next_s_test` can generate exact parameters self.generator_repetitions = repetions_left - #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - self.best_value.update(x_curr,f_curr,rep + initial_iterations) - - # TODO MAXIMIZE! - - # prepare next x_curr parameter based on x_best - #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) - print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') - debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_initial, "objfunc_val": self.best_value.best_obj_val}) + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters, "objfunc_val": self.best_value.best_obj_val}) return debug_results - def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, s_initial): - parameter_bound_range = self.max_bound - self.min_bound + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, x_initial): + best_value = BestValue(ParameterSet(self.parameter()), None) + max_bound, min_bound = best_value.parameters.maxbound, best_value.parameters.minbound + parameter_bound_range = max_bound - min_bound number_of_parameters = len(parameter_bound_range) # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. @@ -226,16 +228,13 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions raise ValueError('# Initialization samples >= Max # function evaluations.') starting_generator = ( - (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if - self.discrete_flag[j] else self.min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + (rep, [self.np_random.randint(np.int(min_bound[j]), np.int(max_bound[j]) + 1) if + self.discrete_flag[j] else min_bound[j] + parameter_bound_range[j] * self.np_random.rand() for j in range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) for rep, x_curr, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep, x_curr, simulations) # get obj function value - - f_curr = like - + f_curr = self.postprocessing(rep, x_curr, simulations) # get obj function value if rep == 0: f_best = f_curr x_best = list(x_curr) @@ -246,18 +245,15 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - x_curr = list(s_initial) # get from the inputs - - rep, s_test_param, simulations = self.simulate((0, x_curr)) - - f_curr = self.postprocessing(rep, x_curr, simulations) + x_best = x_initial.copy() # get from the inputs, must be a ParameterSet + rep, s_test_param, simulations = self.simulate((0, x_best)) + f_best = self.postprocessing(rep, x_best, simulations) - f_best = f_curr - x_best = list(x_curr) + best_value.parameters.set_by_array(np.array(x_best)) + best_value.best_obj_val = f_best + return repetions_left, best_value - return repetions_left, f_best, x_best - - def calculate_next_s_test(self, previous_x_curr, rep, r): + def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): """ Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. This is greedy algorithm belonging to the DDS algorithm. @@ -276,53 +272,54 @@ def calculate_next_s_test(self, previous_x_curr, rep, r): :return: next parameter set """ amount_params = len(previous_x_curr) - - new_x_curr = list(previous_x_curr) # define new_x_curr initially as current (previous_x_curr for greedy) + new_x_curr = previous_x_curr.copy() # define new_x_curr initially as current (previous_x_curr for greedy) randompar = self.np_random.rand(amount_params) - probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(rep_limit) dvn_count = 0 # counter for how many decision variables vary in neighbour # TODO simplify this with np.arrays + min_bound = previous_x_curr.minbound + max_bound = previous_x_curr.maxbound + for j in range(amount_params): if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(previous_x_curr[j], self.min_bound[j], self.max_bound[j], r, j) + new_value = self.neigh_value_mixed(previous_x_curr[j], min_bound[j], max_bound[j], r, j) new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], r, + new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], min_bound[dec_var - 1], max_bound[dec_var - 1], r, dec_var - 1) new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test return new_x_curr - def neigh_value_continuous(self, s, s_min, s_max, r): + def neigh_value_continuous(self, s, x_min, x_max, r): """ select a RANDOM neighbouring real value of a SINGLE decision variable CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT variables: - s_range is the range of the real variable (s_max-s_min) + x_range is the range of the real variable (s_max-s_min) :param s: is a current SINGLE decision variable VALUE - :param s_min: is the min of variable s - :param s_max: is the max of variable s + :param x_min: is the min of variable s + :param x_max: is the max of variable s :param r: is the neighbourhood parameter (replaces V parameter~see not It is defined as the ratio of the std deviation of the desired - normal random number/s_range. Eg: - std dev desired = r * s_range - for comparison: variance (V) = (r * s_range)^2 - :return: s_new, a new sample of values in beetween a given range + normal random number/x_range. Eg: + std dev desired = r * x_range + for comparison: variance (V) = (r * x_range)^2 + :return: x_new, a new sample of values in beetween a given range """ - s_range = s_max - s_min + x_range = x_max - x_min - s_new = s + self.np_random.normal(0, 1) * r * s_range + x_new = s + self.np_random.normal(0, 1) * r * x_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -333,29 +330,29 @@ def neigh_value_continuous(self, s, s_min, s_max, r): p_abs_or_ref = self.np_random.rand() - if s_new < s_min: # works for any pos or neg s_min + if x_new < x_min: # works for any pos or neg x_min if p_abs_or_ref <= 0.5: # with 50%chance reflect - s_new = s_min + (s_min - s_new) + x_new = x_min + (x_min - x_new) else: # with 50% chance absorb - s_new = s_min + x_new = x_min - # if reflection goes past s_max then value should be s_min since without reflection + # if reflection goes past x_max then value should be x_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if s_new > s_max: - s_new = s_min + if x_new > x_max: + x_new = x_min - elif s_new > s_max: # works for any pos or neg s_max + elif x_new > x_max: # works for any pos or neg x_max if p_abs_or_ref <= 0.5: # with 50% chance reflect - s_new = s_max - (s_new - s_max) + x_new = x_max - (x_new - x_max) else: # with 50% chance absorb - s_new = s_max + x_new = x_max - # if reflection goes past s_min then value should be s_max for same reasons as above - if s_new < s_min: - s_new = s_max + # if reflection goes past x_min then value should be x_max for same reasons as above + if x_new < x_min: + x_new = x_max - return s_new + return x_new def neigh_value_discrete(self, s, s_min, s_max, r): """ diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 819eddce..759b4b32 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -9,6 +9,7 @@ import numpy.random as rnd import numpy as np import sys +import copy if sys.version_info[0] >= 3: unicode = str @@ -656,6 +657,12 @@ def __dir__(self): attrs = [attr for attr in vars(type(self)) if not attr.startswith('_')] return attrs + list(self.__info['name']) + list(self.__info.dtype.names) + def set_by_array(self,array): + for i, a in enumerate(array): + self.__setitem__(i,a) + + def copy(self): + return ParameterSet(copy.deepcopy(self.__info)) def get_classes(): keys = [] From c21fcda80e17dec9e97fd15e7f1d647cadb7e9b3 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Tue, 20 Nov 2018 18:53:29 +0100 Subject: [PATCH 59/73] Introduce BestValue in combination with ParameterSet. Propose a copy method to ParameterSet --- spotpy/algorithms/dds.py | 147 +++++++++++++++++------------------ spotpy/parameter.py | 7 ++ spotpy/unittests/test_dds.py | 8 +- 3 files changed, 83 insertions(+), 79 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 60fc5583..8571742e 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -2,8 +2,12 @@ from spotpy.tools.fixedrandom import * from . import _algorithm import spotpy.parameter +from spotpy.parameter import ParameterSet class BestValue(object): + # print(ps) + # print([ps.maxbound, ps.minbound, ps.optguess, ps.step, ps.random]) + def __init__(self, parameters, obj_value): # self.parameters = spotpy.parameter.Gamma() # self.best_obj_val = spotpy.parameter.Gamma() @@ -29,6 +33,15 @@ def init(self,parameters,obj_value): # fake init # todo change that self.parameters = parameters self.best_obj_val = obj_value + def copy(self): + to_copy = BestValue(self.parameters.copy(),self.best_obj_val) + to_copy.best_rep = self.best_rep + return to_copy + + def __str__(self): + return "BestValue(best_obj_val = " + str(self.best_obj_val) + ", best_rep = " + str(self.best_rep) + ", " \ + + str(self.parameters) + class DDS(_algorithm): """ @@ -98,9 +111,9 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random - self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] - self.best_value = BestValue(self.parameter(), 0) # TODO set obj_value in a better way + + self.best_value = BestValue(ParameterSet(self.parameter()), 0) # TODO set obj_value in a better way if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] @@ -123,9 +136,9 @@ def get_next_x_curr(self): """ # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions): - yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.r) + yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.generator_repetitions,self.r) - def sample(self, repetitions, trials=1, s_initial=[]): + def sample(self, repetitions, trials=1, x_initial=[]): """ Samples from the DDS Algorithm. @@ -154,7 +167,7 @@ def sample(self, repetitions, trials=1, s_initial=[]): :param repetitions: Maximum number of runs. :type repetitions: int :param trials: amount of runs DDS algorithm will be performed - :param s_initial: set an initial trial set as a first parameter configuration. If the set is empty the algorithm + :param x_initial: set an initial trial set as a first parameter configuration. If the set is empty the algorithm select an own initial parameter configuration :return: a key-value set of all parameter combination which has been used. May changed in future. """ @@ -164,53 +177,42 @@ def sample(self, repetitions, trials=1, s_initial=[]): self.set_repetiton(repetitions) - number_of_parameters = len(self.min_bound) # number_of_parameters is the amount of parameters + number_of_parameters = len(self.best_value.parameters) # number_of_parameters is the amount of parameters - if len(s_initial) == 0: + if len(x_initial) == 0: initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) - elif len(s_initial) != number_of_parameters: - raise ValueError("User specified 's_initial' has not the same length as available parameters") + elif len(x_initial) != number_of_parameters: + raise ValueError("User specified 'x_initial' has not the same length as available parameters") else: initial_iterations = 1 - s_initial = np.array(s_initial) - if not (np.all(s_initial <= self.max_bound) and np.all(s_initial >= self.min_bound)): - raise ValueError("User specified 's_initial' but the values are not within the parameter range") + x_initial = np.array(x_initial) + if not (np.all(x_initial <= self.best_value.parameters.maxbound) and np.all(x_initial >= self.best_value.parameters.minbound)): + raise ValueError("User specified 'x_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): # repitionno_best saves on which iteration the best parameter configuration has been found repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations - repetions_left, f_best, trial_initial = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, s_initial) + repetions_left, trial_best_value = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, x_initial) + self.best_value = trial_best_value.copy() - # TODO use ParameterSet Class - - #x_best = list(trial_initial) - self.best_value.init(trial_initial, f_best) - # important to set this field `generator_repetitions` so that # method `get_next_s_test` can generate exact parameters self.generator_repetitions = repetions_left - #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters, 0, self.r) - for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - self.best_value.update(x_curr,f_curr,rep + initial_iterations) - - # TODO MAXIMIZE! - - # prepare next x_curr parameter based on x_best - #self.next_s_test = self.calculate_next_s_test(self.best_value.parameters,rep,self.r) - print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') - debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_initial, "objfunc_val": self.best_value.best_obj_val}) + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters, "objfunc_val": self.best_value.best_obj_val}) return debug_results - def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, s_initial): - parameter_bound_range = self.max_bound - self.min_bound + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, x_initial): + best_value = BestValue(ParameterSet(self.parameter()), None) + max_bound, min_bound = best_value.parameters.maxbound, best_value.parameters.minbound + parameter_bound_range = max_bound - min_bound number_of_parameters = len(parameter_bound_range) # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. @@ -226,16 +228,13 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions raise ValueError('# Initialization samples >= Max # function evaluations.') starting_generator = ( - (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if - self.discrete_flag[j] else self.min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + (rep, [self.np_random.randint(np.int(min_bound[j]), np.int(max_bound[j]) + 1) if + self.discrete_flag[j] else min_bound[j] + parameter_bound_range[j] * self.np_random.rand() for j in range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) for rep, x_curr, simulations in self.repeat(starting_generator): - like = self.postprocessing(rep, x_curr, simulations) # get obj function value - - f_curr = like - + f_curr = self.postprocessing(rep, x_curr, simulations) # get obj function value if rep == 0: f_best = f_curr x_best = list(x_curr) @@ -246,18 +245,15 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - x_curr = list(s_initial) # get from the inputs - - rep, s_test_param, simulations = self.simulate((0, x_curr)) - - f_curr = self.postprocessing(rep, x_curr, simulations) + x_best = x_initial.copy() # get from the inputs, must be a ParameterSet + rep, s_test_param, simulations = self.simulate((0, x_best)) + f_best = self.postprocessing(rep, x_best, simulations) - f_best = f_curr - x_best = list(x_curr) + best_value.parameters.set_by_array(np.array(x_best)) + best_value.best_obj_val = f_best + return repetions_left, best_value - return repetions_left, f_best, x_best - - def calculate_next_s_test(self, previous_x_curr, rep, r): + def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): """ Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. This is greedy algorithm belonging to the DDS algorithm. @@ -276,53 +272,54 @@ def calculate_next_s_test(self, previous_x_curr, rep, r): :return: next parameter set """ amount_params = len(previous_x_curr) - - new_x_curr = list(previous_x_curr) # define new_x_curr initially as current (previous_x_curr for greedy) + new_x_curr = previous_x_curr.copy() # define new_x_curr initially as current (previous_x_curr for greedy) randompar = self.np_random.rand(amount_params) - probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(self.generator_repetitions) + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(rep_limit) dvn_count = 0 # counter for how many decision variables vary in neighbour # TODO simplify this with np.arrays + min_bound = previous_x_curr.minbound + max_bound = previous_x_curr.maxbound + for j in range(amount_params): if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(previous_x_curr[j], self.min_bound[j], self.max_bound[j], r, j) + new_value = self.neigh_value_mixed(previous_x_curr[j], min_bound[j], max_bound[j], r, j) new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], self.min_bound[dec_var - 1], - self.max_bound[dec_var - 1], r, + new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], min_bound[dec_var - 1], max_bound[dec_var - 1], r, dec_var - 1) new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test return new_x_curr - def neigh_value_continuous(self, s, s_min, s_max, r): + def neigh_value_continuous(self, s, x_min, x_max, r): """ select a RANDOM neighbouring real value of a SINGLE decision variable CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT variables: - s_range is the range of the real variable (s_max-s_min) + x_range is the range of the real variable (s_max-s_min) :param s: is a current SINGLE decision variable VALUE - :param s_min: is the min of variable s - :param s_max: is the max of variable s + :param x_min: is the min of variable s + :param x_max: is the max of variable s :param r: is the neighbourhood parameter (replaces V parameter~see not It is defined as the ratio of the std deviation of the desired - normal random number/s_range. Eg: - std dev desired = r * s_range - for comparison: variance (V) = (r * s_range)^2 - :return: s_new, a new sample of values in beetween a given range + normal random number/x_range. Eg: + std dev desired = r * x_range + for comparison: variance (V) = (r * x_range)^2 + :return: x_new, a new sample of values in beetween a given range """ - s_range = s_max - s_min + x_range = x_max - x_min - s_new = s + self.np_random.normal(0, 1) * r * s_range + x_new = s + self.np_random.normal(0, 1) * r * x_range # NEED to deal with variable upper and lower bounds: # Originally bounds in DDS were 100# reflective @@ -333,29 +330,29 @@ def neigh_value_continuous(self, s, s_min, s_max, r): p_abs_or_ref = self.np_random.rand() - if s_new < s_min: # works for any pos or neg s_min + if x_new < x_min: # works for any pos or neg x_min if p_abs_or_ref <= 0.5: # with 50%chance reflect - s_new = s_min + (s_min - s_new) + x_new = x_min + (x_min - x_new) else: # with 50% chance absorb - s_new = s_min + x_new = x_min - # if reflection goes past s_max then value should be s_min since without reflection + # if reflection goes past x_max then value should be x_min since without reflection # the approach goes way past lower bound. This keeps X close to lower bound when X current # is close to lower bound: - if s_new > s_max: - s_new = s_min + if x_new > x_max: + x_new = x_min - elif s_new > s_max: # works for any pos or neg s_max + elif x_new > x_max: # works for any pos or neg x_max if p_abs_or_ref <= 0.5: # with 50% chance reflect - s_new = s_max - (s_new - s_max) + x_new = x_max - (x_new - x_max) else: # with 50% chance absorb - s_new = s_max + x_new = x_max - # if reflection goes past s_min then value should be s_max for same reasons as above - if s_new < s_min: - s_new = s_max + # if reflection goes past x_min then value should be x_max for same reasons as above + if x_new < x_min: + x_new = x_max - return s_new + return x_new def neigh_value_discrete(self, s, s_min, s_max, r): """ diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 819eddce..759b4b32 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -9,6 +9,7 @@ import numpy.random as rnd import numpy as np import sys +import copy if sys.version_info[0] >= 3: unicode = str @@ -656,6 +657,12 @@ def __dir__(self): attrs = [attr for attr in vars(type(self)) if not attr.startswith('_')] return attrs + list(self.__info['name']) + list(self.__info.dtype.names) + def set_by_array(self,array): + for i, a in enumerate(array): + self.__setitem__(i,a) + + def copy(self): + return ParameterSet(copy.deepcopy(self.__info)) def get_classes(): keys = [] diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index 06a0142d..fa60be24 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -67,7 +67,7 @@ def run_a_dds(self, run): if original_result.get("s_initial") is not None: # if a parameter initialisation is given, test this: results = sampler.sample(original_result["evatrials"], - original_result["trial_runs"], s_initial=original_result["s_initial"]) + original_result["trial_runs"], x_initial=original_result["s_initial"]) else: results = sampler.sample(original_result["evatrials"], original_result["trial_runs"]) @@ -92,19 +92,19 @@ def test_own_initial_out_of_borders_ackley_1(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) - self.assertRaises(ValueError,sampler.sample,1000, s_initial=list(np.random.uniform(-2, 2, 9)) + [3]) + self.assertRaises(ValueError,sampler.sample,1000, x_initial=list(np.random.uniform(-2, 2, 9)) + [3]) def test_own_initial_too_lees(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 9))) + self.assertRaises(ValueError, sampler.sample, 1000, x_initial=list(np.random.uniform(-2, 2, 9))) def test_own_initial_too_much(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000, s_initial=list(np.random.uniform(-2, 2, 11))) + self.assertRaises(ValueError, sampler.sample, 1000, x_initial=list(np.random.uniform(-2, 2, 11))) if __name__ == '__main__': From a54dd5398c1ca9526ce8119b916561903408230d Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Tue, 20 Nov 2018 19:17:02 +0100 Subject: [PATCH 60/73] Always maximize in BestValue and DDS. Tidy up BestValue. --- spotpy/algorithms/dds.py | 37 +++++++++++++------------------ spotpy/examples/spot_setup_dds.py | 6 ++--- spotpy/unittests/test_dds.py | 4 ++-- 3 files changed, 21 insertions(+), 26 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 8571742e..dc951a15 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -1,38 +1,35 @@ import numpy as np from spotpy.tools.fixedrandom import * from . import _algorithm -import spotpy.parameter from spotpy.parameter import ParameterSet + class BestValue(object): - # print(ps) - # print([ps.maxbound, ps.minbound, ps.optguess, ps.step, ps.random]) + """ + BestValue holds a parameter set and a best objective value, which is used by the DDS Algorithm. + Every time a new parameter set is sampled the `update` method is run and saves the new parameters, if the the + new calculated objective value is greater equals the current best one + """ def __init__(self, parameters, obj_value): - # self.parameters = spotpy.parameter.Gamma() - # self.best_obj_val = spotpy.parameter.Gamma() self.parameters = parameters self.best_obj_val = obj_value self.best_rep = 0 - # TODO Docstring - def update(self,curr_parameters, curr_obj_val, curr_rep): + def update(self, curr_parameters, curr_obj_val, curr_rep): """ - Update on maximize - :param curr_parameters: - :param curr_obj_val: + Update on parameter set and the corresponding objective value on maximize + :param curr_parameters: a ParameterSet object + :type curr_parameters: ParameterSet + :param curr_obj_val: calculated new objective value + :type curr_obj_val: float :return: """ - # TODO MAXIMAIZE - if self.best_obj_val is None or self.best_obj_val >= curr_obj_val: + if self.best_obj_val is None or self.best_obj_val <= curr_obj_val: self.best_obj_val = curr_obj_val self.parameters = curr_parameters self.best_rep = curr_rep - def init(self,parameters,obj_value): # fake init # todo change that - self.parameters = parameters - self.best_obj_val = obj_value - def copy(self): to_copy = BestValue(self.parameters.copy(),self.best_obj_val) to_copy.best_rep = self.best_rep @@ -111,14 +108,12 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random - - - self.best_value = BestValue(ParameterSet(self.parameter()), 0) # TODO set obj_value in a better way + self.best_value = BestValue(ParameterSet(self.parameter()), None) if hasattr(self.setup, "params"): self.discrete_flag = [u.is_distinct for u in self.setup.params] else: - self.discrete_flag = [False] * len(self.max_bound) + self.discrete_flag = [False] * len(self.best_value.parameters) # self.generator_repetitions will be set in `sample` and is needed to generate a # generator which sends back actual parameter s_test @@ -239,7 +234,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions f_best = f_curr x_best = list(x_curr) - if f_curr <= f_best: + if f_curr >= f_best: f_best = f_curr x_best = list(x_curr) diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index e6577b4c..a6f50968 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -11,7 +11,7 @@ def ackley10(vector): for i in range(length): sum1 = sum1 + vector[i] ** 2 sum2 = sum2 + np.cos(2 * np.pi * vector[i]) - return -20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length) + return -1*(-20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length)) def griewank10(vector): @@ -23,7 +23,7 @@ def griewank10(vector): sum1 = sum1 + (vector[i] ** 2) / 4000 term2 = term2 * np.cos(vector[i] / (i + 1) ** 0.5) - return sum1 - term2 + term3 + return -1*(sum1 - term2 + term3) class spot_setup(object): @@ -77,7 +77,7 @@ def evaluation(self): def objectivefunction(self, simulation, evaluation, params): if self.objfunc is None: - return -rmse(evaluation, simulation) + return -1*rmse(evaluation, simulation) else: pars, names = params return self.objfunc(pars) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index fa60be24..efc9cd10 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -73,8 +73,8 @@ def run_a_dds(self, run): original_result["trial_runs"]) for t in range(original_result["trial_runs"]): - print(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"]) - self.assertAlmostEqual(results[t]["objfunc_val"], original_result["results"][t]["objfunc_val"], + print(results[t]["objfunc_val"], -1*original_result["results"][t]["objfunc_val"]) + self.assertAlmostEqual(results[t]["objfunc_val"], -1*original_result["results"][t]["objfunc_val"], delta=0.000001) py_sbest = results[t]["sbest"] matlb_sbest = original_result["results"][t]["sbest"] From 858a5ce554cd8c2b9620ea0dca1459fd6f496026 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Wed, 21 Nov 2018 16:51:19 +0100 Subject: [PATCH 61/73] use distinct flag in parameter class --- spotpy/algorithms/dds.py | 22 ++++++++++------------ spotpy/parameter.py | 6 +++--- 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index dc951a15..5710d69a 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -108,12 +108,8 @@ def __init__(self, *args, **kwargs): super(DDS, self).__init__(*args, **kwargs) self.np_random = np.random - self.best_value = BestValue(ParameterSet(self.parameter()), None) - if hasattr(self.setup, "params"): - self.discrete_flag = [u.is_distinct for u in self.setup.params] - else: - self.discrete_flag = [False] * len(self.best_value.parameters) + self.best_value = BestValue(ParameterSet(self.parameter()), None) # self.generator_repetitions will be set in `sample` and is needed to generate a # generator which sends back actual parameter s_test @@ -209,6 +205,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions max_bound, min_bound = best_value.parameters.maxbound, best_value.parameters.minbound parameter_bound_range = max_bound - min_bound number_of_parameters = len(parameter_bound_range) + discrete_flag = best_value.parameters.distinct # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm @@ -224,7 +221,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions starting_generator = ( (rep, [self.np_random.randint(np.int(min_bound[j]), np.int(max_bound[j]) + 1) if - self.discrete_flag[j] else min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + discrete_flag[j] else min_bound[j] + parameter_bound_range[j] * self.np_random.rand() for j in range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) @@ -282,12 +279,12 @@ def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - new_value = self.neigh_value_mixed(previous_x_curr[j], min_bound[j], max_bound[j], r, j) + new_value = self.neigh_value_mixed(previous_x_curr, min_bound[j], max_bound[j], r, j) new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_x_curr[dec_var - 1], min_bound[dec_var - 1], max_bound[dec_var - 1], r, + new_value = self.neigh_value_mixed(previous_x_curr, min_bound[dec_var - 1], max_bound[dec_var - 1], r, dec_var - 1) new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test @@ -407,8 +404,9 @@ def neigh_value_discrete(self, s, s_min, s_max, r): s_new = sample + 1 return s_new - def neigh_value_mixed(self, s, s_min, s_max, r, j): - if not self.discrete_flag[j]: - return self.neigh_value_continuous(s, s_min, s_max, r) + def neigh_value_mixed(self, x_curr, x_min, x_max, r, j): + s = x_curr[j] + if not x_curr.distinct[j]: + return self.neigh_value_continuous(s, x_min, x_max, r) else: - return self.neigh_value_discrete(s, s_min, s_max, r) + return self.neigh_value_discrete(s, x_min, x_max, r) diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 759b4b32..ed94991e 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -212,7 +212,7 @@ def __init__(self, rndfunc, rndfuncname, *args, **kwargs): self.description = arghelper.get('doc') - self.is_distinct = not not arghelper.get("distinct") + self.distinct = not not arghelper.get("distinct") arghelper.check_complete() def __call__(self, **kwargs): @@ -225,7 +225,7 @@ def astuple(self): """ Returns a tuple of a realization and the other parameter properties """ - return self(), self.name, self.step, self.optguess, self.minbound, self.maxbound + return self(), self.name, self.step, self.optguess, self.minbound, self.maxbound, self.distinct def __repr__(self): """ @@ -681,7 +681,7 @@ def generate(parameters): """ dtype = [('random', ' Date: Wed, 21 Nov 2018 17:08:27 +0100 Subject: [PATCH 62/73] fix `astuple` method in parameter class --- spotpy/parameter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spotpy/parameter.py b/spotpy/parameter.py index ed94991e..7a9b1af8 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -327,7 +327,7 @@ def __call__(self, size=None): raise IndexError(text) def astuple(self): - return self(), self.name, 0, 0, 0, 0 + return self(), self.name, 0, 0, 0, 0, self.distinct class Constant(Base): @@ -353,7 +353,7 @@ def __call__(self, size=None): return self.value def astuple(self): - return self(), self.name, 0, self.value, self.value, self.value + return self(), self.name, 0, self.value, self.value, self.value, self.distinct class Normal(Base): From 1532f1b60ef61757f3e0d7905ad0f765ff3aacdb Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Wed, 21 Nov 2018 18:57:25 +0100 Subject: [PATCH 63/73] refactor DDS: use a DDSGenerator --- spotpy/algorithms/dds.py | 336 +++++++++++++++++++---------------- spotpy/unittests/test_dds.py | 6 +- 2 files changed, 181 insertions(+), 161 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 5710d69a..c17b569e 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -11,6 +11,7 @@ class BestValue(object): new calculated objective value is greater equals the current best one """ + def __init__(self, parameters, obj_value): self.parameters = parameters self.best_obj_val = obj_value @@ -30,8 +31,14 @@ def update(self, curr_parameters, curr_obj_val, curr_rep): self.parameters = curr_parameters self.best_rep = curr_rep + def update_param(self, curr_parameters, curr_obj_val, curr_rep): + if self.best_obj_val is None or self.best_obj_val <= curr_obj_val: + self.best_obj_val = curr_obj_val + self.parameters.set_by_array(curr_parameters) + self.best_rep = curr_rep + def copy(self): - to_copy = BestValue(self.parameters.copy(),self.best_obj_val) + to_copy = BestValue(self.parameters.copy(), self.best_obj_val) to_copy.best_rep = self.best_rep return to_copy @@ -39,6 +46,149 @@ def __str__(self): return "BestValue(best_obj_val = " + str(self.best_obj_val) + ", best_rep = " + str(self.best_rep) + ", " \ + str(self.parameters) + def reset_rep(self): + self.best_rep = 0 + + +class DDSGenerator: + """ + This class is used by the DDS algorithm to generate a new sample of parameters based on the current one. + Current parameter are exchanged in `ParameterSet` objects. + """ + + def __init__(self, np_random): + self.np_random = np_random + + def neigh_value_continuous(self, s, x_min, x_max, r): + """ + select a RANDOM neighbouring real value of a SINGLE decision variable + CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + variables: + x_range is the range of the real variable (s_max-s_min) + + :param s: is a current SINGLE decision variable VALUE + :param x_min: is the min of variable s + :param x_max: is the max of variable s + :param r: is the neighbourhood parameter (replaces V parameter~see not + It is defined as the ratio of the std deviation of the desired + normal random number/x_range. Eg: + std dev desired = r * x_range + for comparison: variance (V) = (r * x_range)^2 + :return: x_new, a new sample of values in beetween a given range + """ + + x_range = x_max - x_min + + x_new = s + self.np_random.normal(0, 1) * r * x_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + p_abs_or_ref = self.np_random.rand() + + if x_new < x_min: # works for any pos or neg x_min + if p_abs_or_ref <= 0.5: # with 50%chance reflect + x_new = x_min + (x_min - x_new) + else: # with 50% chance absorb + x_new = x_min + + # if reflection goes past x_max then value should be x_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if x_new > x_max: + x_new = x_min + + elif x_new > x_max: # works for any pos or neg x_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + x_new = x_max - (x_new - x_max) + else: # with 50% chance absorb + x_new = x_max + + # if reflection goes past x_min then value should be x_max for same reasons as above + if x_new < x_min: + x_new = x_max + + return x_new + + def neigh_value_discrete(self, s, s_min, s_max, r): + """ + Created by B.Tolson and B.Yung, June 2006 + Modified by B. Tolson & M. Asadzadeh, Sept 2008 + Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + 2- Round the new value at the end of generation. + select a RANDOM neighbouring integer value of a SINGLE decision variable + discrete distribution is approximately normal + alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param r: r is the neighbourhood parameter (replaces V parameter~see notes) + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 + :return: s_new, a new sample of values in beetween a given range + """ + + s_range = s_max - s_min + delta = self.np_random.normal(0, 1) * r * s_range + s_new = s + delta + + p_abs_or_ref = self.np_random.rand() + + if s_new < s_min - 0.5: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_min - 0.5) + ((s_min - 0.5) - s_new) + else: # with 50% chance absorb + s_new = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if s_new > s_max + 0.5: + s_new = s_min + + elif s_new > s_max + 0.5: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_max + 0.5) - (s_new - (s_max + 0.5)) + else: # with 50% chance absorb + s_new = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if s_new < s_min - 0.5: + s_new = s_max + + s_new = np.round(s_new) # New value must be integer + if s_new == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) + if sample < s: + s_new = sample + else: # must increment option number by one + s_new = sample + 1 + return s_new + + def neigh_value_mixed(self, x_curr, r, j): + """ + + :param x_curr: + :type x_curr: ParameterSet + :param r: + :param j: + :return: + """ + s = x_curr[j] + x_min = x_curr.minbound[j] + x_max = x_curr.maxbound[j] + if not x_curr.distinct[j]: + return self.neigh_value_continuous(s, x_min, x_max, r) + else: + return self.neigh_value_discrete(s, x_min, x_max, r) + class DDS(_algorithm): """ @@ -115,11 +265,11 @@ def __init__(self, *args, **kwargs): # generator which sends back actual parameter s_test self.generator_repetitions = -1 - # holds currents best parameter - self.next_s_test = [] + self.dds_generator = DDSGenerator(self.np_random) def _set_np_random(self, f_rand): self.np_random = f_rand + self.dds_generator.np_random = f_rand def get_next_x_curr(self): """ @@ -127,9 +277,9 @@ def get_next_x_curr(self): """ # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions): - yield rep, self.calculate_next_s_test(self.best_value.parameters,rep,self.generator_repetitions,self.r) + yield rep, self.calculate_next_s_test(self.best_value.parameters, rep, self.generator_repetitions, self.r) - def sample(self, repetitions, trials=1, x_initial=[]): + def sample(self, repetitions, trials=1, x_initial=np.array([])): """ Samples from the DDS Algorithm. @@ -148,8 +298,8 @@ def sample(self, repetitions, trials=1, x_initial=[]): If F_curr > F_best, where F_best is the current max value objective function value, we set x_best = x_curr and F_best = F_curr. - Select k of all parameters to include them in the neighborhood calculation. This is performed by calcualating a - threshold Pn (probability in neighbourhood). + Select k of all parameters to include them in the neighborhood calculation. This is performed by calculating a + threshold probability_neighborhood (probability in neighbourhood). The neighbourhood calculation perturb x_best on standard normal distribution and reflect the result if it breaks the parameter boundary. @@ -163,8 +313,9 @@ def sample(self, repetitions, trials=1, x_initial=[]): :return: a key-value set of all parameter combination which has been used. May changed in future. """ - debug_results = [] # every iteration a map of all relevant values is stored, only for debug purpose. - # Spotpy will not need this values. + # every iteration a map of all relevant values is stored, only for debug purpose. + # Spotpy will not need this values. + debug_results = [] self.set_repetiton(repetitions) @@ -177,15 +328,18 @@ def sample(self, repetitions, trials=1, x_initial=[]): else: initial_iterations = 1 x_initial = np.array(x_initial) - if not (np.all(x_initial <= self.best_value.parameters.maxbound) and np.all(x_initial >= self.best_value.parameters.minbound)): + if not (np.all(x_initial <= self.best_value.parameters.maxbound) and np.all( + x_initial >= self.best_value.parameters.minbound)): raise ValueError("User specified 'x_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): # repitionno_best saves on which iteration the best parameter configuration has been found repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations - repetions_left, trial_best_value = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, x_initial) + repetions_left, trial_best_value = self.calc_initial_para_configuration(initial_iterations, trial, + repetitions, x_initial) self.best_value = trial_best_value.copy() + self.best_value.reset_rep() # important to set this field `generator_repetitions` so that # method `get_next_s_test` can generate exact parameters @@ -193,10 +347,12 @@ def sample(self, repetitions, trials=1, x_initial=[]): for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - self.best_value.update(x_curr,f_curr,rep + initial_iterations) + self.best_value.update(x_curr, f_curr, rep + initial_iterations) + print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') - debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters, "objfunc_val": self.best_value.best_obj_val}) + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters, + "objfunc_val": self.best_value.best_obj_val}) return debug_results @@ -210,9 +366,8 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm # by trying which randomized generated input matches best - x_best = [] - f_best = [] - if initial_iterations > 1: # initial_iterations is the number of function evaluations to initialize the DDS algorithm solution + # initial_iterations is the number of function evaluations to initialize the DDS algorithm solution + if initial_iterations > 1: print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( initial_iterations) + ' random samples.') repetions_left = repetitions - initial_iterations # use this to reduce number of fevals in DDS loop @@ -223,26 +378,18 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions (rep, [self.np_random.randint(np.int(min_bound[j]), np.int(max_bound[j]) + 1) if discrete_flag[j] else min_bound[j] + parameter_bound_range[j] * self.np_random.rand() for j in - range(int(number_of_parameters))]) for rep in range(int(initial_iterations))) + range(number_of_parameters)]) for rep in range(int(initial_iterations))) for rep, x_curr, simulations in self.repeat(starting_generator): f_curr = self.postprocessing(rep, x_curr, simulations) # get obj function value - if rep == 0: - f_best = f_curr - x_best = list(x_curr) - - if f_curr >= f_best: - f_best = f_curr - x_best = list(x_curr) + best_value.update_param(x_curr,f_curr,rep) else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - x_best = x_initial.copy() # get from the inputs, must be a ParameterSet - rep, s_test_param, simulations = self.simulate((0, x_best)) - f_best = self.postprocessing(rep, x_best, simulations) + rep, x_test_param, simulations = self.simulate((0, x_initial)) # get from the inputs + f_best = self.postprocessing(rep, x_test_param, simulations) + best_value.update_param(x_test_param, f_best, rep) - best_value.parameters.set_by_array(np.array(x_best)) - best_value.best_obj_val = f_best return repetions_left, best_value def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): @@ -267,146 +414,19 @@ def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): new_x_curr = previous_x_curr.copy() # define new_x_curr initially as current (previous_x_curr for greedy) randompar = self.np_random.rand(amount_params) - probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(rep_limit) dvn_count = 0 # counter for how many decision variables vary in neighbour - # TODO simplify this with np.arrays - min_bound = previous_x_curr.minbound - max_bound = previous_x_curr.maxbound - for j in range(amount_params): if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour dvn_count = dvn_count + 1 - - new_value = self.neigh_value_mixed(previous_x_curr, min_bound[j], max_bound[j], r, j) + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, j) new_x_curr[j] = new_value # change relevant dec var value in x_curr if dvn_count == 0: # no DVs selected at random, so select ONE dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - new_value = self.neigh_value_mixed(previous_x_curr, min_bound[dec_var - 1], max_bound[dec_var - 1], r, - dec_var - 1) + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, dec_var - 1) new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test return new_x_curr - - def neigh_value_continuous(self, s, x_min, x_max, r): - """ - select a RANDOM neighbouring real value of a SINGLE decision variable - CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT - variables: - x_range is the range of the real variable (s_max-s_min) - - :param s: is a current SINGLE decision variable VALUE - :param x_min: is the min of variable s - :param x_max: is the max of variable s - :param r: is the neighbourhood parameter (replaces V parameter~see not - It is defined as the ratio of the std deviation of the desired - normal random number/x_range. Eg: - std dev desired = r * x_range - for comparison: variance (V) = (r * x_range)^2 - :return: x_new, a new sample of values in beetween a given range - """ - - x_range = x_max - x_min - - x_new = s + self.np_random.normal(0, 1) * r * x_range - - # NEED to deal with variable upper and lower bounds: - # Originally bounds in DDS were 100# reflective - # But some times DVs are right on the boundary and with 100# reflective - # boundaries it is hard to detect them. Therefore, we decided to make the - # boundaries reflective with 50# chance and absorptive with 50# chance. - # M. Asadzadeh and B. Tolson Dec 2008 - - p_abs_or_ref = self.np_random.rand() - - if x_new < x_min: # works for any pos or neg x_min - if p_abs_or_ref <= 0.5: # with 50%chance reflect - x_new = x_min + (x_min - x_new) - else: # with 50% chance absorb - x_new = x_min - - # if reflection goes past x_max then value should be x_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if x_new > x_max: - x_new = x_min - - elif x_new > x_max: # works for any pos or neg x_max - if p_abs_or_ref <= 0.5: # with 50% chance reflect - x_new = x_max - (x_new - x_max) - else: # with 50% chance absorb - x_new = x_max - - # if reflection goes past x_min then value should be x_max for same reasons as above - if x_new < x_min: - x_new = x_max - - return x_new - - def neigh_value_discrete(self, s, s_min, s_max, r): - """ - Created by B.Tolson and B.Yung, June 2006 - Modified by B. Tolson & M. Asadzadeh, Sept 2008 - Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) - 2- Round the new value at the end of generation. - select a RANDOM neighbouring integer value of a SINGLE decision variable - discrete distribution is approximately normal - alternative to this appoach is reflecting triangular distribution (see Azadeh work) - - :param s: is a current SINGLE decision variable VALUE - :param s_min: is the min of variable s - :param s_max: is the max of variable s - :param r: r is the neighbourhood parameter (replaces V parameter~see notes) - It is defined as the ratio of the std deviation of the desired - normal random number/s_range. Eg: - std dev desired = r * s_range - for comparison: variance (V) = (r * s_range)^2 - :return: s_new, a new sample of values in beetween a given range - """ - - s_range = s_max - s_min - delta = self.np_random.normal(0, 1) * r * s_range - s_new = s + delta - - p_abs_or_ref = self.np_random.rand() - - if s_new < s_min - 0.5: # works for any pos or neg s_min - if p_abs_or_ref <= 0.5: # with 50% chance reflect - s_new = (s_min - 0.5) + ((s_min - 0.5) - s_new) - else: # with 50% chance absorb - s_new = s_min - - # if reflection goes past (s_max+0.5) then value should be s_min since without reflection - # the approach goes way past lower bound. This keeps X close to lower bound when X current - # is close to lower bound: - if s_new > s_max + 0.5: - s_new = s_min - - elif s_new > s_max + 0.5: # works for any pos or neg s_max - if p_abs_or_ref <= 0.5: # with 50% chance reflect - s_new = (s_max + 0.5) - (s_new - (s_max + 0.5)) - else: # with 50% chance absorb - s_new = s_max - - # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above - if s_new < s_min - 0.5: - s_new = s_max - - s_new = np.round(s_new) # New value must be integer - if s_new == s: # pick a number between s_max and s_min by a Uniform distribution - sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) - if sample < s: - s_new = sample - else: # must increment option number by one - s_new = sample + 1 - return s_new - - def neigh_value_mixed(self, x_curr, x_min, x_max, r, j): - s = x_curr[j] - if not x_curr.distinct[j]: - return self.neigh_value_continuous(s, x_min, x_max, r) - else: - return self.neigh_value_discrete(s, x_min, x_max, r) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index efc9cd10..4e3043f8 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -92,19 +92,19 @@ def test_own_initial_out_of_borders_ackley_1(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) - self.assertRaises(ValueError,sampler.sample,1000, x_initial=list(np.random.uniform(-2, 2, 9)) + [3]) + self.assertRaises(ValueError,sampler.sample,1000, x_initial=np.random.uniform(-2, 2, 9) + [3]) def test_own_initial_too_lees(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000, x_initial=list(np.random.uniform(-2, 2, 9))) + self.assertRaises(ValueError, sampler.sample, 1000, x_initial=np.random.uniform(-2, 2, 9)) def test_own_initial_too_much(self): self.spot_setup._objfunc_switcher("ackley") sampler = spotpy.algorithms.DDS(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", sim_timeout=self.timeout) - self.assertRaises(ValueError, sampler.sample, 1000, x_initial=list(np.random.uniform(-2, 2, 11))) + self.assertRaises(ValueError, sampler.sample, 1000, x_initial=np.random.uniform(-2, 2, 11)) if __name__ == '__main__': From 0fe5a5a38d25f802058d891e2e4f939f334690f4 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Wed, 21 Nov 2018 18:57:42 +0100 Subject: [PATCH 64/73] fix parameter unittest --- spotpy/unittests/test_parameter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spotpy/unittests/test_parameter.py b/spotpy/unittests/test_parameter.py index aa76ebad..2d0bbd0b 100644 --- a/spotpy/unittests/test_parameter.py +++ b/spotpy/unittests/test_parameter.py @@ -65,11 +65,11 @@ def test_list_without_repeat_raises_index_error_on_invalid_extra_call(self): def test_astuple(self): _ = self.list_param() - v, name, step, optguess, minbound, maxbound = self.list_param.astuple() + v, name, step, optguess, minbound, maxbound, distinct = self.list_param.astuple() self.assertEqual(self.values[0], v) self.assertEqual("test", name) - # the values of step, optguess, minbound and maxbound don't matter + # the values of step, optguess, minbound, maxbound and distinct don't matter class TestUniformParameterDistribution(unittest.TestCase): From d79480c87b93d303fce0cce82612deadd12eb8cd Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Wed, 21 Nov 2018 18:59:21 +0100 Subject: [PATCH 65/73] tidy up py.test outputs --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 6bd30c69..2867b500 100644 --- a/.gitignore +++ b/.gitignore @@ -31,6 +31,9 @@ wheels/ .installed.cfg *.egg +# Python Unittest tool `py.test` +.pytest_cache/* + # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. From c561390f31848410694f8d0afd812f8703481eaa Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Fri, 23 Nov 2018 14:54:27 +0100 Subject: [PATCH 66/73] finalize DDS and update tutorial_dds.py --- spotpy/algorithms/dds.py | 2 +- spotpy/examples/tutorial_dds.py | 8 ++++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index c17b569e..ad70920d 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -353,7 +353,7 @@ def sample(self, repetitions, trials=1, x_initial=np.array([])): + str(repitionno_best) + '\n\n') debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters, "objfunc_val": self.best_value.best_obj_val}) - + self.final_call() return debug_results def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, x_initial): diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py index 6b2d69e3..33d73e48 100644 --- a/spotpy/examples/tutorial_dds.py +++ b/spotpy/examples/tutorial_dds.py @@ -16,6 +16,10 @@ from spotpy.examples.spot_setup_dds import spot_setup from spotpy.examples.spot_setup_dds import ackley10 +import matplotlib +matplotlib.use('agg') + + #Create samplers for every algorithm: results=[] spot_setup=spot_setup() @@ -23,11 +27,11 @@ timeout=10 #Given in Seconds -parallel = "seq" +parallel = "umpc" dbformat = "csv" sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) print(describe(sampler)) -sampler.sample(rep, trials=1, r=0.1) +sampler.sample(rep, trials=1) results.append(sampler.getdata()) From 5beb76ba47bccda512a56e7c19b8c42287bc0621 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Fri, 18 Jan 2019 17:15:52 +0100 Subject: [PATCH 67/73] Add first DDS Benchmarks --- spotpy/algorithms/dds.py | 5 +- spotpy/examples/dds/benchmark_dds.py | 117 ++++++++++++++++++ .../dds_sceua_durations.png.png | Bin 0 -> 11775 bytes .../dds_sceua_likes_30-1000.png | Bin 0 -> 26774 bytes .../dds_sceua_likes_300-10000.png | Bin 0 -> 25792 bytes spotpy/examples/dds/dds_parallel.py | 83 +++++++++++++ 6 files changed, 202 insertions(+), 3 deletions(-) create mode 100644 spotpy/examples/dds/benchmark_dds.py create mode 100644 spotpy/examples/dds/benchmark_plots/dds_sceua_durations.png.png create mode 100644 spotpy/examples/dds/benchmark_plots/dds_sceua_likes_30-1000.png create mode 100644 spotpy/examples/dds/benchmark_plots/dds_sceua_likes_300-10000.png create mode 100644 spotpy/examples/dds/dds_parallel.py diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index ad70920d..d34f009a 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -44,7 +44,7 @@ def copy(self): def __str__(self): return "BestValue(best_obj_val = " + str(self.best_obj_val) + ", best_rep = " + str(self.best_rep) + ", " \ - + str(self.parameters) + + str(self.parameters) + ")" def reset_rep(self): self.best_rep = 0 @@ -351,8 +351,7 @@ def sample(self, repetitions, trials=1, x_initial=np.array([])): print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + str(repitionno_best) + '\n\n') - debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters, - "objfunc_val": self.best_value.best_obj_val}) + debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters,"objfunc_val": self.best_value.best_obj_val}) self.final_call() return debug_results diff --git a/spotpy/examples/dds/benchmark_dds.py b/spotpy/examples/dds/benchmark_dds.py new file mode 100644 index 00000000..16e928af --- /dev/null +++ b/spotpy/examples/dds/benchmark_dds.py @@ -0,0 +1,117 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from pprint import pprint +import numpy as np +import matplotlib.pylab as plt +import json + +import time + +try: + import spotpy +except ImportError: + import sys + + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup + + + +spot_setup = spot_setup() + +# Create samplers for every algorithm: +results = [] + +benchmarks_dict = [] +benchmarks_duration = {"dds":[], "sceua":[], "dds_like":[],"sceua_like":[]} +reps = [300, 1000, 3000, 4000, 5000, 10000] + + +for rep in reps: + + timeout = 10 # Given in Seconds + + parallel = "seq" + dbformat = "csv" + + start = time.time() + dds_sampler = spotpy.algorithms.DDS(spot_setup, parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) + dds_sampler.sample(rep, trials=1) + results.append(dds_sampler.getdata()) + dds_elapsed = time.time() - start + + start = time.time() + sceua_sampler = spotpy.algorithms.sceua(spot_setup, parallel=parallel, dbname='SCEUA', dbformat=dbformat, + sim_timeout=timeout, alt_objfun=None) + sceua_sampler.sample(rep) + results.append(sceua_sampler.getdata()) + sceua_elapsed = time.time() - start + + + print("#########################################") + + #print(dds_elapsed, dds_sampler.status.params) + + print(sceua_elapsed, sceua_sampler.status.params) + + benchmarks_dict.append({ + "rep": rep, + "dds_time": dds_elapsed, + "sceua_time": sceua_elapsed, + "dds_like": dds_sampler.status.objectivefunction, + "sceua_like": sceua_sampler.status.objectivefunction, + "dds_param": list(dds_sampler.status.params), + "sceua_param": list(sceua_sampler.status.params) + }) + benchmarks_duration["dds"].append(dds_elapsed) + benchmarks_duration["sceua"].append(sceua_elapsed) + benchmarks_duration["sceua_like"].append(sceua_sampler.status.objectivefunction) + benchmarks_duration["dds_like"].append(dds_sampler.status.objectivefunction) + +print(json.dumps(benchmarks_dict)) + + +def autolabel(rects): + """ + Attach a text label above each bar displaying its height + """ + for rect in rects: + height = rect.get_height() + ax.text(rect.get_x() + rect.get_width()/2., 1.05*height, + '%d' % int(height), + ha='center', va='bottom') + + + +fig = plt.figure(figsize=(10, 6)) +ax = plt.subplot(111) + +rep_labels = [str(j) for j in reps] +x_pos = [i for i, _ in enumerate(rep_labels)] + + +X = np.arange(len(benchmarks_duration["dds"])) +dds_plot = ax.bar(x_pos, benchmarks_duration["dds_like"], color = 'b', width = 0.45) +sceua_plot = ax.bar([j+0.45 for j in x_pos], benchmarks_duration["sceua_like"], color = 'g', width = 0.45) + +#dds_plot = ax.bar(x_pos, benchmarks_duration["dds"], color = 'b', width = 0.45) +#sceua_plot = ax.bar([j+0.45 for j in x_pos], benchmarks_duration["sceua"], color = 'g', width = 0.45) + + + +plt.xticks(x_pos, rep_labels) +plt.legend(("DDS", "SCEUA")) +plt.xlabel("Repetitions") +plt.ylabel("Best Objective Function Value") + +autolabel(dds_plot) +autolabel(sceua_plot) + +plt.show() +plt.savefig("MPI_TEST") +# + diff --git 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z@_yEnX43xnm~_d#LrTef#RL5r)4c;5;OzI(UGdaj?=D;wm}r0;BOY^5-aC%sQ-dAu3M z$3q!%GimF3O<%36es0HF?aG3bs{~8*;X`+4=jR|9?JtohNggeLLvhwE!W%XW^bOP$ zU_wDaR#qMd@z6E%HgG^*V;5??aQr-fp7Hs!vZ|_Y-^-W6&*aYi*p6-U{#f1X(z|!> zLWs;%#}t67f51kAXR{&eS#3^mi>1Zsr1vrc0#yi0TefU@^$~RRQeIwzKru1aaxZYU zS6BCW`_@)XP0ilk9!xWAAV_~NUYOwjDdVuyz~HuzuzzH(#B{W_o@3;)A0{Oe-huC#QN^Y mp9ov{8TIE-H0%i+oZPv1;MHTL3j)Lk_U=;I`EtiG=l=s^sw)}* literal 0 HcmV?d00001 diff --git a/spotpy/examples/dds/dds_parallel.py b/spotpy/examples/dds/dds_parallel.py new file mode 100644 index 00000000..8e3bb554 --- /dev/null +++ b/spotpy/examples/dds/dds_parallel.py @@ -0,0 +1,83 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from pprint import pprint +import numpy as np +import matplotlib.pylab as plt +import json + +import time + +try: + import spotpy +except ImportError: + import sys + + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup + + +spot_setup = spot_setup() + +# Create samplers for every algorithm: +results = [] +benchmarks_duration = {"dds":[], "sceua":[], "dds_like":[],"sceua_like":[]} +reps = [300, 1000, 3000, 4000, 5000, 10000] + + +for rep in reps: + + timeout = 10 # Given in Seconds + + parallel = "mpc" + dbformat = "csv" + + start = time.time() + dds_sampler = spotpy.algorithms.DDS(spot_setup, parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) + dds_sampler.sample(rep, trials=1) + results.append(dds_sampler.getdata()) + dds_elapsed = time.time() - start + + benchmarks_duration["dds"].append(dds_elapsed) + benchmarks_duration["dds_like"].append(dds_sampler.status.objectivefunction) + + + + +def autolabel(rects): + """ + Attach a text label above each bar displaying its height + """ + for rect in rects: + height = rect.get_height() + ax.text(rect.get_x() + rect.get_width()/2., 1.05*height, + '%d' % int(height), + ha='center', va='bottom') + + + + + +fig = plt.figure(figsize=(10, 6)) +ax = plt.subplot(111) +rep_labels = [str(j) for j in reps] +x_pos = [i for i, _ in enumerate(rep_labels)] +X = np.arange(len(benchmarks_duration["dds"])) +dds_time = ax.bar(x_pos, benchmarks_duration["dds"], color = 'b', width = 0.45) + +plt.xticks(x_pos, rep_labels) +plt.xlabel("Repetitions") +plt.ylabel("Best Objective Function Value") + +autolabel(dds_plot) +autolabel(sceua_plot) + +plt.savefig("MPI_TEST") + + +dds_like = ax.bar([j+0.45 for j in x_pos], benchmarks_duration["dds_like"], color = 'g', width = 0.45) + +plt.ylabel("Best Objective Function Value") \ No newline at end of file From fe4d8812bbebb9caee820a1c81ee7cfc4ee9adf2 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Fri, 25 Jan 2019 08:53:33 +0100 Subject: [PATCH 68/73] DDS parallel tests and results --- .../dds/DDS_PARALLEL_DURATION_all.png | Bin 0 -> 36964 bytes .../DDS_PARALLEL_OBJECTIVEFUNCTION_all.png | Bin 0 -> 33560 bytes spotpy/examples/dds/dds_parallel.py | 75 +++++------------- spotpy/examples/dds/dds_parallel_data.json | 1 + spotpy/examples/dds/dds_parallel_plot.py | 51 ++++++++++++ spotpy/examples/dds/dds_parallel_run.py | 8 ++ 6 files changed, 79 insertions(+), 56 deletions(-) create mode 100644 spotpy/examples/dds/DDS_PARALLEL_DURATION_all.png create mode 100644 spotpy/examples/dds/DDS_PARALLEL_OBJECTIVEFUNCTION_all.png create mode 100644 spotpy/examples/dds/dds_parallel_data.json create mode 100644 spotpy/examples/dds/dds_parallel_plot.py create mode 100644 spotpy/examples/dds/dds_parallel_run.py diff --git a/spotpy/examples/dds/DDS_PARALLEL_DURATION_all.png b/spotpy/examples/dds/DDS_PARALLEL_DURATION_all.png new file mode 100644 index 0000000000000000000000000000000000000000..e5c36be6517895702b32a7d56ab93b519ebcfc17 GIT binary patch literal 36964 zcmeFacU+X`_AQR_Bqk=&M2*IRBym&(1*HlISYjEFB1jP_Hb4PEx=7O)H5Nbz5Jj2_ zBE1Pn5sZjRS2{>h+DLEG%iOhxF*)ac&%NJ!fB)P+e#g%#I5WKSzE9bE?X}i^ysWOe 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b/.travis.yml @@ -11,10 +11,12 @@ install: - pip install pathos - pip install numpy - pip install matplotlib - - pip install pandas + - pip install pandas==0.23.4 - pip install scipy - pip install click - - pip install pytest pytest-pep8 pytest-cov + - pip install pytest==4.0.1 + - pip install pytest-pep8 + - pip install pytest-cov==2.6.0 # Use docutils to generate html describe - pip install docutils - pip install numba From c688607604c918ee53a5a1bcafff7fc513df0f71 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Fri, 1 Feb 2019 15:00:00 +0100 Subject: [PATCH 70/73] - refactor DDS Algorithm - new dds tutorial --- .travis.yml | 2 +- spotpy/__init__.py | 1 - spotpy/algorithms/__init__.py | 2 +- spotpy/algorithms/dds.py | 9 +- .../dds/DDS_PARALLEL_DURATION_all.png | Bin 36964 -> 0 bytes .../DDS_PARALLEL_OBJECTIVEFUNCTION_all.png | Bin 33560 -> 0 bytes .../dds_sceua_durations.png.png | Bin 11775 -> 0 bytes .../dds_sceua_likes_30-1000.png | Bin 26774 -> 0 bytes .../dds_sceua_likes_300-10000.png | Bin 25792 -> 0 bytes spotpy/examples/dds/dds_parallel_data.json | 1 - spotpy/examples/spot_setup_dds.py | 2 +- spotpy/examples/tutorial_dds.py | 51 ------ spotpy/examples/tutorial_dds_hymod.py | 155 ++++++++++++++++++ spotpy/parameter.py | 10 +- spotpy/tools/__init__.py | 1 - .../dds_tests}/fixedrandom.py | 2 +- .../dds_tests}/normal_list.txt | 0 .../{DDS_references => dds_tests}/run_1.json | 0 .../{DDS_references => dds_tests}/run_2.json | 0 .../{DDS_references => dds_tests}/run_3.json | 0 .../{DDS_references => dds_tests}/run_4.json | 0 .../{DDS_references => dds_tests}/run_5.json | 0 .../{DDS_references => dds_tests}/run_6.json | 0 .../{DDS_references => dds_tests}/run_7.json | 0 .../run_own_input_1.json | 0 .../run_own_input_2.json | 0 .../dds_tests}/uniform_list.txt | 0 spotpy/unittests/test_dds.py | 16 +- spotpy/unittests/test_parameter.py | 4 +- 29 files changed, 178 insertions(+), 78 deletions(-) delete mode 100644 spotpy/examples/dds/DDS_PARALLEL_DURATION_all.png delete mode 100644 spotpy/examples/dds/DDS_PARALLEL_OBJECTIVEFUNCTION_all.png delete mode 100644 spotpy/examples/dds/benchmark_plots/dds_sceua_durations.png.png delete mode 100644 spotpy/examples/dds/benchmark_plots/dds_sceua_likes_30-1000.png delete mode 100644 spotpy/examples/dds/benchmark_plots/dds_sceua_likes_300-10000.png delete mode 100644 spotpy/examples/dds/dds_parallel_data.json delete mode 100644 spotpy/examples/tutorial_dds.py create mode 100644 spotpy/examples/tutorial_dds_hymod.py delete mode 100644 spotpy/tools/__init__.py rename spotpy/{tools => unittests/dds_tests}/fixedrandom.py (100%) rename spotpy/{tools => unittests/dds_tests}/normal_list.txt (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_1.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_2.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_3.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_4.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_5.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_6.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_7.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_own_input_1.json (100%) rename spotpy/unittests/{DDS_references => dds_tests}/run_own_input_2.json (100%) rename spotpy/{tools => unittests/dds_tests}/uniform_list.txt (100%) diff --git a/.travis.yml b/.travis.yml index e6fc20d1..520f8aeb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,7 +11,7 @@ install: - pip install pathos - pip install numpy - pip install matplotlib - - pip install pandas==0.23.4 + - pip install pandas - pip install scipy - pip install click # Need to force pytest-cov to v2.6 as current version (2.6.1) is deprecated diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 13e6ceb7..4adacaf1 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -38,6 +38,5 @@ from . import examples # Contains tutorials how to use SPOTPY from . import describe # Contains some helper functions to describe smaplers and setups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures -from . import tools __version__ = '1.4.2' \ No newline at end of file diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index 2ba4f545..c448006c 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -31,4 +31,4 @@ from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis from .list_sampler import list_sampler # Samples from given spotpy database -from .dds import DDS # Dynamically Dimensioned Search algorithm by Bryan Tolson. \ No newline at end of file +from .dds import dds # Dynamically Dimensioned Search algorithm by Bryan Tolson. \ No newline at end of file diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index d34f009a..f9b31b3b 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -1,5 +1,4 @@ import numpy as np -from spotpy.tools.fixedrandom import * from . import _algorithm from spotpy.parameter import ParameterSet @@ -184,13 +183,13 @@ def neigh_value_mixed(self, x_curr, r, j): s = x_curr[j] x_min = x_curr.minbound[j] x_max = x_curr.maxbound[j] - if not x_curr.distinct[j]: + if not x_curr.as_int[j]: return self.neigh_value_continuous(s, x_min, x_max, r) else: return self.neigh_value_discrete(s, x_min, x_max, r) -class DDS(_algorithm): +class dds(_algorithm): """ Implements the Dynamically dimensioned search algorithm for computationally efficient watershed model calibration @@ -255,7 +254,7 @@ def __init__(self, *args, **kwargs): except KeyError: self.r = 0.2 # default value - super(DDS, self).__init__(*args, **kwargs) + super(dds, self).__init__(*args, **kwargs) self.np_random = np.random @@ -360,7 +359,7 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions max_bound, min_bound = best_value.parameters.maxbound, best_value.parameters.minbound parameter_bound_range = max_bound - min_bound number_of_parameters = len(parameter_bound_range) - discrete_flag = best_value.parameters.distinct + discrete_flag = best_value.parameters.as_int # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm diff --git a/spotpy/examples/dds/DDS_PARALLEL_DURATION_all.png b/spotpy/examples/dds/DDS_PARALLEL_DURATION_all.png deleted file mode 100644 index e5c36be6517895702b32a7d56ab93b519ebcfc17..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 36964 zcmeFacU+X`_AQR_Bqk=&M2*IRBym&(1*HlISYjEFB1jP_Hb4PEx=7O)H5Nbz5Jj2_ zBE1Pn5sZjRS2{>h+DLEG%iOhxF*)ac&%NJ!fB)P+e#g%#I5WKSzE9bE?X}i^ysWOe 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z@_yEnX43xnm~_d#LrTef#RL5r)4c;5;OzI(UGdaj?=D;wm}r0;BOY^5-aC%sQ-dAu3M z$3q!%GimF3O<%36es0HF?aG3bs{~8*;X`+4=jR|9?JtohNggeLLvhwE!W%XW^bOP$ zU_wDaR#qMd@z6E%HgG^*V;5??aQr-fp7Hs!vZ|_Y-^-W6&*aYi*p6-U{#f1X(z|!> zLWs;%#}t67f51kAXR{&eS#3^mi>1Zsr1vrc0#yi0TefU@^$~RRQeIwzKru1aaxZYU zS6BCW`_@)XP0ilk9!xWAAV_~NUYOwjDdVuyz~HuzuzzH(#B{W_o@3;)A0{Oe-huC#QN^Y mp9ov{8TIE-H0%i+oZPv1;MHTL3j)Lk_U=;I`EtiG=l=s^sw)}* diff --git a/spotpy/examples/dds/dds_parallel_data.json b/spotpy/examples/dds/dds_parallel_data.json deleted file mode 100644 index c82aef58..00000000 --- a/spotpy/examples/dds/dds_parallel_data.json +++ /dev/null @@ -1 +0,0 @@ -{"dds_duration": [1.1293659210205078, 3.254117250442505, 4.888171672821045, 18.719818592071533, 34.56907820701599, 169.47716689109802, 337.86882615089417], "rep": [30, 500, 1000, 5000, 10000, 50000, 100000], "dds_like": [-8384.884435178812, -8269.480874403698, -8268.453892284442, -8268.51195094138, -8269.65509041187, -8268.1421690868, -8267.791798085422]} \ No newline at end of file diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index a6f50968..67e93585 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -49,7 +49,7 @@ def _objfunc_switcher(self, name): elif name == "griewank": self.objfunc = griewank10 self.params = [Uniform('d' + str(j), -500, 700, 1.5, 3.0, -500, 700, - doc=str(j) + 'distinc parameter within a boundary', distinct=True) + doc=str(j) + 'distinc parameter within a boundary', as_int=True) for j in range(2)] + [Uniform('c' + str(j), -500, 700, 1.5, 3.0, -500, 700, doc=str(j) + 'continuous parameter within a boundary') for j in range(8)] diff --git a/spotpy/examples/tutorial_dds.py b/spotpy/examples/tutorial_dds.py deleted file mode 100644 index 33d73e48..00000000 --- a/spotpy/examples/tutorial_dds.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy - - -from spotpy.describe import describe - -from spotpy.examples.spot_setup_dds import spot_setup -from spotpy.examples.spot_setup_dds import ackley10 - -import matplotlib -matplotlib.use('agg') - - -#Create samplers for every algorithm: -results=[] -spot_setup=spot_setup() -rep=3000 -timeout=10 #Given in Seconds - - -parallel = "umpc" -dbformat = "csv" -sampler=spotpy.algorithms.DDS(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) -sampler.sample(rep, trials=1) -results.append(sampler.getdata()) - - - -sampler=spotpy.algorithms.dream(spot_setup,parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) -sampler.sample(rep) -results.append(sampler.getdata()) - - - -#algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream'] -spotpy.analyser.plot_parametertrace_algorithms(results,algorithmnames=["DDS","DREAM"],parameternames=['0','1']) - - -print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x -#evaluation = spot_setup.evaluation() diff --git a/spotpy/examples/tutorial_dds_hymod.py b/spotpy/examples/tutorial_dds_hymod.py new file mode 100644 index 00000000..305dc347 --- /dev/null +++ b/spotpy/examples/tutorial_dds_hymod.py @@ -0,0 +1,155 @@ +# -*- coding: utf-8 -*- +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This class holds example code how to use the dream algorithm +''' + +import numpy as np +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup +import pylab as plt + + +if __name__ == "__main__": + parallel ='seq' # Runs everthing in sequential mode + np.random.seed(2000) # Makes the results reproduceable + + # Initialize the Hymod example + # In this case, we tell the setup which algorithm we want to use, so + # we can use this exmaple for different algorithms + spot_setup=spot_setup() + + #Select number of maximum allowed repetitions + rep=5000 + + # Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY + # to jump into the def objectivefunction in the spot_setup class (default is + # spotpy.objectivefunctions.rmse) + sampler=spotpy.algorithms.dds(spot_setup, dbname='DDS_hymod', dbformat='csv', alt_objfun=None) + + #Start the sampler, one can specify ngs, kstop, peps and pcento id desired + sampler.sample(rep) + + # Load the results gained with the sceua sampler, stored in SCEUA_hymod.csv + results = spotpy.analyser.load_csv_results('DDS_hymod') + + print(results) + + fig= plt.figure(1,figsize=(9,5)) + plt.plot(results['like1']) + plt.show() + plt.ylabel('RMSE') + plt.xlabel('Iteration') + fig.savefig('hymod_objectivefunction.png',dpi=300) + + # Example plot to show the parameter distribution ###### + fig= plt.figure(2,figsize=(9,9)) + normed_value = 1 + + plt.subplot(5,2,1) + x = results['parcmax'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) #Ignores burn-in chain + plt.plot(x[index],'.') + plt.ylabel('cmax') + plt.ylim(spot_setup.cmax.minbound, spot_setup.cmax.maxbound) + + + plt.subplot(5,2,2) + x = x[int(len(results)*0.9):] #choose the last 10% of the sample + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('cmax') + plt.xlim(spot_setup.cmax.minbound, spot_setup.cmax.maxbound) + + + plt.subplot(5,2,3) + x = results['parbexp'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('bexp') + plt.ylim(spot_setup.bexp.minbound, spot_setup.bexp.maxbound) + + plt.subplot(5,2,4) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('bexp') + plt.xlim(spot_setup.bexp.minbound, spot_setup.bexp.maxbound) + + + + plt.subplot(5,2,5) + x = results['paralpha'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('alpha') + plt.ylim(spot_setup.alpha.minbound, spot_setup.alpha.maxbound) + + + plt.subplot(5,2,6) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('alpha') + plt.xlim(spot_setup.alpha.minbound, spot_setup.alpha.maxbound) + + + plt.subplot(5,2,7) + x = results['parKs'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('Ks') + plt.ylim(spot_setup.Ks.minbound, spot_setup.Ks.maxbound) + + + plt.subplot(5,2,8) + x = x[int(len(results)*0.9):] + + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('Ks') + plt.xlim(spot_setup.Ks.minbound, spot_setup.Ks.maxbound) + + + plt.subplot(5,2,9) + x = results['parKq'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('Kq') + plt.ylim(spot_setup.Kq.minbound, spot_setup.Kq.maxbound) + plt.xlabel('Iterations') + + plt.subplot(5,2,10) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('Kq') + plt.xlabel('Parameter range') + plt.xlim(spot_setup.Kq.minbound, spot_setup.Kq.maxbound) + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) \ No newline at end of file diff --git a/spotpy/parameter.py b/spotpy/parameter.py index 7a9b1af8..56c9e57c 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -212,7 +212,7 @@ def __init__(self, rndfunc, rndfuncname, *args, **kwargs): self.description = arghelper.get('doc') - self.distinct = not not arghelper.get("distinct") + self.as_int = not not arghelper.get("as_int") arghelper.check_complete() def __call__(self, **kwargs): @@ -225,7 +225,7 @@ def astuple(self): """ Returns a tuple of a realization and the other parameter properties """ - return self(), self.name, self.step, self.optguess, self.minbound, self.maxbound, self.distinct + return self(), self.name, self.step, self.optguess, self.minbound, self.maxbound, self.as_int def __repr__(self): """ @@ -327,7 +327,7 @@ def __call__(self, size=None): raise IndexError(text) def astuple(self): - return self(), self.name, 0, 0, 0, 0, self.distinct + return self(), self.name, 0, 0, 0, 0, self.as_int class Constant(Base): @@ -353,7 +353,7 @@ def __call__(self, size=None): return self.value def astuple(self): - return self(), self.name, 0, self.value, self.value, self.value, self.distinct + return self(), self.name, 0, self.value, self.value, self.value, self.as_int class Normal(Base): @@ -681,7 +681,7 @@ def generate(parameters): """ dtype = [('random', ' Date: Sat, 2 Feb 2019 11:08:44 +0100 Subject: [PATCH 71/73] DDS: use self.status and not BestValue class --- spotpy/algorithms/dds.py | 95 ++++++++------------------ spotpy/examples/dds/dds_parallel.py | 2 +- spotpy/unittests/__init__.py | 0 spotpy/unittests/dds_tests/__init__.py | 0 4 files changed, 28 insertions(+), 69 deletions(-) create mode 100644 spotpy/unittests/__init__.py create mode 100644 spotpy/unittests/dds_tests/__init__.py diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index f9b31b3b..1dd54057 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -3,52 +3,6 @@ from spotpy.parameter import ParameterSet -class BestValue(object): - """ - BestValue holds a parameter set and a best objective value, which is used by the DDS Algorithm. - Every time a new parameter set is sampled the `update` method is run and saves the new parameters, if the the - new calculated objective value is greater equals the current best one - - """ - - def __init__(self, parameters, obj_value): - self.parameters = parameters - self.best_obj_val = obj_value - self.best_rep = 0 - - def update(self, curr_parameters, curr_obj_val, curr_rep): - """ - Update on parameter set and the corresponding objective value on maximize - :param curr_parameters: a ParameterSet object - :type curr_parameters: ParameterSet - :param curr_obj_val: calculated new objective value - :type curr_obj_val: float - :return: - """ - if self.best_obj_val is None or self.best_obj_val <= curr_obj_val: - self.best_obj_val = curr_obj_val - self.parameters = curr_parameters - self.best_rep = curr_rep - - def update_param(self, curr_parameters, curr_obj_val, curr_rep): - if self.best_obj_val is None or self.best_obj_val <= curr_obj_val: - self.best_obj_val = curr_obj_val - self.parameters.set_by_array(curr_parameters) - self.best_rep = curr_rep - - def copy(self): - to_copy = BestValue(self.parameters.copy(), self.best_obj_val) - to_copy.best_rep = self.best_rep - return to_copy - - def __str__(self): - return "BestValue(best_obj_val = " + str(self.best_obj_val) + ", best_rep = " + str(self.best_rep) + ", " \ - + str(self.parameters) + ")" - - def reset_rep(self): - self.best_rep = 0 - - class DDSGenerator: """ This class is used by the DDS algorithm to generate a new sample of parameters based on the current one. @@ -258,7 +212,7 @@ def __init__(self, *args, **kwargs): self.np_random = np.random - self.best_value = BestValue(ParameterSet(self.parameter()), None) + self.status.params = ParameterSet(self.parameter()) # self.generator_repetitions will be set in `sample` and is needed to generate a # generator which sends back actual parameter s_test @@ -276,7 +230,7 @@ def get_next_x_curr(self): """ # We need to shift position and length of the sampling process for rep in range(self.generator_repetitions): - yield rep, self.calculate_next_s_test(self.best_value.parameters, rep, self.generator_repetitions, self.r) + yield rep, self.calculate_next_s_test(self.status.params, rep, self.generator_repetitions, self.r) def sample(self, repetitions, trials=1, x_initial=np.array([])): """ @@ -318,7 +272,7 @@ def sample(self, repetitions, trials=1, x_initial=np.array([])): self.set_repetiton(repetitions) - number_of_parameters = len(self.best_value.parameters) # number_of_parameters is the amount of parameters + number_of_parameters = len(self.status.params) # number_of_parameters is the amount of parameters if len(x_initial) == 0: initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) @@ -327,40 +281,44 @@ def sample(self, repetitions, trials=1, x_initial=np.array([])): else: initial_iterations = 1 x_initial = np.array(x_initial) - if not (np.all(x_initial <= self.best_value.parameters.maxbound) and np.all( - x_initial >= self.best_value.parameters.minbound)): + if not (np.all(x_initial <= self.status.params.maxbound) and np.all( + x_initial >= self.status.params.minbound)): raise ValueError("User specified 'x_initial' but the values are not within the parameter range") # Users can define trial runs in within "repetition" times the algorithm will be executed for trial in range(trials): + self.status.objectivefunction = -1e308 # repitionno_best saves on which iteration the best parameter configuration has been found repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations - repetions_left, trial_best_value = self.calc_initial_para_configuration(initial_iterations, trial, + repetions_left = self.calc_initial_para_configuration(initial_iterations, trial, repetitions, x_initial) - self.best_value = trial_best_value.copy() - self.best_value.reset_rep() + self.fix_status_params_format() + trial_best_value = self.status.params.copy() # important to set this field `generator_repetitions` so that # method `get_next_s_test` can generate exact parameters self.generator_repetitions = repetions_left for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): - f_curr = self.postprocessing(rep, x_curr, simulations, chains=trial) - self.best_value.update(x_curr, f_curr, rep + initial_iterations) + self.postprocessing(rep, x_curr, simulations, chains=trial) + self.fix_status_params_format() - print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + print('Best solution found has obj function value of ' + str(self.status.objectivefunction) + ' at ' + str(repitionno_best) + '\n\n') - debug_results.append({"sbest": self.best_value.parameters, "trial_initial": trial_best_value.parameters,"objfunc_val": self.best_value.best_obj_val}) + debug_results.append({"sbest": self.status.params, "trial_initial": trial_best_value,"objfunc_val": self.status.objectivefunction}) self.final_call() return debug_results + def fix_status_params_format(self): + start_params = ParameterSet(self.parameter()) + start_params.set_by_array([j for j in self.status.params]) + self.status.params = start_params + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, x_initial): - best_value = BestValue(ParameterSet(self.parameter()), None) - max_bound, min_bound = best_value.parameters.maxbound, best_value.parameters.minbound + max_bound, min_bound = self.status.params.maxbound, self.status.params.minbound parameter_bound_range = max_bound - min_bound number_of_parameters = len(parameter_bound_range) - discrete_flag = best_value.parameters.as_int - + discrete_flag = self.status.params.as_int # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm # by trying which randomized generated input matches best @@ -379,16 +337,17 @@ def calc_initial_para_configuration(self, initial_iterations, trial, repetitions range(number_of_parameters)]) for rep in range(int(initial_iterations))) for rep, x_curr, simulations in self.repeat(starting_generator): - f_curr = self.postprocessing(rep, x_curr, simulations) # get obj function value - best_value.update_param(x_curr,f_curr,rep) + self.postprocessing(rep, x_curr, simulations) # get obj function value + # status setting update + self.fix_status_params_format() else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop - rep, x_test_param, simulations = self.simulate((0, x_initial)) # get from the inputs - f_best = self.postprocessing(rep, x_test_param, simulations) - best_value.update_param(x_test_param, f_best, rep) + rep, x_test_param, simulations = self.simulate((0, x_initial)) # get from the inputs + self.postprocessing(rep, x_test_param, simulations) + self.fix_status_params_format() - return repetions_left, best_value + return repetions_left def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): """ diff --git a/spotpy/examples/dds/dds_parallel.py b/spotpy/examples/dds/dds_parallel.py index 667539c5..264bfa90 100644 --- a/spotpy/examples/dds/dds_parallel.py +++ b/spotpy/examples/dds/dds_parallel.py @@ -32,7 +32,7 @@ parallel = "mpi" dbformat = "csv" start = time.time() -dds_sampler = spotpy.algorithms.DDS(spot_setup, parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +dds_sampler = spotpy.algorithms.dds(spot_setup, parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) dds_sampler.sample(rep, trials=1) dds_elapsed = time.time() - start print(dds_elapsed) diff --git a/spotpy/unittests/__init__.py b/spotpy/unittests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/spotpy/unittests/dds_tests/__init__.py b/spotpy/unittests/dds_tests/__init__.py new file mode 100644 index 00000000..e69de29b From 733fc01014549363f70d753e3e4d838b52fa1aaf Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sat, 2 Feb 2019 11:37:07 +0100 Subject: [PATCH 72/73] try to fix travis --- spotpy/unittests/__init__.py | 0 spotpy/unittests/dds_tests/__init__.py | 0 spotpy/unittests/dds_tests/fixedrandom.py | 107 ---------------------- spotpy/unittests/test_dds.py | 68 +++++++++++++- 4 files changed, 66 insertions(+), 109 deletions(-) delete mode 100644 spotpy/unittests/__init__.py delete mode 100644 spotpy/unittests/dds_tests/__init__.py delete mode 100644 spotpy/unittests/dds_tests/fixedrandom.py diff --git a/spotpy/unittests/__init__.py b/spotpy/unittests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/spotpy/unittests/dds_tests/__init__.py b/spotpy/unittests/dds_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/spotpy/unittests/dds_tests/fixedrandom.py b/spotpy/unittests/dds_tests/fixedrandom.py deleted file mode 100644 index e68df300..00000000 --- a/spotpy/unittests/dds_tests/fixedrandom.py +++ /dev/null @@ -1,107 +0,0 @@ -''' -Copyright (c) 2018 by Benjamin Manns -This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). -:author: Benjamin Manns -''' - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals -import numpy as np -import os - - -# replaces numpy.random module in a way -class FixedRandomizerEndOfDataException(Exception): - pass - - -class FixedRandomizer(): - def __init__(self): - self.debug = False - self.uniform_counter = 0 - self.normal_counter = 0 - self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/uniform_list.txt")) - - self.uniform_list*=3 - self.max_normal_counter = 10000 - self.max_uniform_counter = 30000 - - self.normal_list = list(np.loadtxt(os.path.dirname(__file__)+"/normal_list.txt")) - - def rand(self,dim_x=1,dim_y=1): - #x = np.array(dim_y * [dim_x * [0]]) - x = dim_x * [0] - for i in range(dim_x): - #for j in range(dim_y): - if self.uniform_counter < self.max_uniform_counter: - x[i] = self.uniform_list[self.uniform_counter] - self.uniform_counter = self.uniform_counter + 1 - if self.debug: - print("fixrand::rand() counter = "+str(self.uniform_counter)) - else: - raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) - if len(x) == 1: - return x[0] - else: - return x - - def randint(self,x_from,x_to): - vals = [j for j in range(x_from,x_to)] - vals_size = len(vals) - if vals_size == 0: - raise ValueError("x_to >= x_from") - fraq = 1 / vals_size - if self.uniform_counter < self.max_uniform_counter: - q_uni = self.uniform_list[self.uniform_counter] - pos = np.int(np.floor(q_uni / fraq)) - self.uniform_counter += 1 - if self.debug: - print("fixrand::randint() counter = " + str(self.uniform_counter)) - return vals[pos] - else: - raise FixedRandomizerEndOfDataException("No more data left.") - - def normal(self,loc,scale,size=1): - x = [] - for j in range(size): - if self.normal_counter < self.max_normal_counter: - x.append(self.normal_list[self.normal_counter]*scale + loc) - self.normal_counter += 1 - if self.debug: - print("fixrand::normal() counter = " + str(self.normal_counter)) - - else: - raise FixedRandomizerEndOfDataException("No more data left.") - if len(x) == 1: - return x[0] - else: - return x - - -# TODO UNITEST irgendwie - - #print(f_rand.normal(12,1,12)) - #print(np.random.normal(12,1,12)) - - #f_rand.normal(12,1,12)- - # print(np.var(np.random.normal(12,1,12)-np.random.normal(12,1,12))) - # print("-------------------------") - - - - -# TODO Convert this to unittest -# f_rand = FixedRandomizer() -# print(f_rand.my_rand(10)) -# print(np.random.rand(10)) - -# for k in range(100): -# print(f_rand.my_randint(1,101010)) -# print(np.random.randint(1,101010)) -# print("----------------------") - -# print(np.random.normal(0, 1)) -# print(f_rand.my_randn(0,1)) - diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index a0da69d2..b6013bdd 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -8,13 +8,77 @@ sys.path.append(".") import spotpy -from spotpy.unittests.dds_tests.fixedrandom import FixedRandomizer - import os from spotpy.examples.spot_setup_dds import spot_setup import json +# replaces numpy.random module in a way +class FixedRandomizerEndOfDataException(Exception): + pass + + +class FixedRandomizer(): + def __init__(self): + self.debug = False + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/dds_tests/uniform_list.txt")) + + self.uniform_list*=3 + self.max_normal_counter = 10000 + self.max_uniform_counter = 30000 + + self.normal_list = list(np.loadtxt(os.path.dirname(__file__)+"/dds_tests/normal_list.txt")) + + def rand(self, dim_x=1, dim_y=1): + x = dim_x * [0] + for i in range(dim_x): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + if self.debug: + print("fixrand::rand() counter = "+str(self.uniform_counter)) + else: + raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) + if len(x) == 1: + return x[0] + else: + return x + + def randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1 / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + if self.debug: + print("fixrand::randint() counter = " + str(self.uniform_counter)) + return vals[pos] + else: + raise FixedRandomizerEndOfDataException("No more data left.") + + def normal(self,loc,scale,size=1): + x = [] + for j in range(size): + if self.normal_counter < self.max_normal_counter: + x.append(self.normal_list[self.normal_counter]*scale + loc) + self.normal_counter += 1 + if self.debug: + print("fixrand::normal() counter = " + str(self.normal_counter)) + + else: + raise FixedRandomizerEndOfDataException("No more data left.") + if len(x) == 1: + return x[0] + else: + return x + + class TestDDS(unittest.TestCase): def setUp(self): self.spot_setup = spot_setup() From 2a81c06e70e775a3a3c6e717c6ab1c8cf5dcdd06 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Sat, 2 Feb 2019 12:03:49 +0100 Subject: [PATCH 73/73] some python 2 type errors --- spotpy/unittests/test_dds.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spotpy/unittests/test_dds.py b/spotpy/unittests/test_dds.py index b6013bdd..c98760b5 100644 --- a/spotpy/unittests/test_dds.py +++ b/spotpy/unittests/test_dds.py @@ -51,7 +51,7 @@ def randint(self,x_from,x_to): vals_size = len(vals) if vals_size == 0: raise ValueError("x_to >= x_from") - fraq = 1 / vals_size + fraq = 1. / vals_size if self.uniform_counter < self.max_uniform_counter: q_uni = self.uniform_list[self.uniform_counter] pos = np.int(np.floor(q_uni / fraq))