Padds v05

.travis.yml

@@ -14,8 +14,9 @@ install:
   - pip install pandas
   - pip install scipy
   - pip install click
+  - pip install deap
   # Need to force pytest-cov to v2.6 as current version (2.6.1) is deprecated
-  - pip install pytest pytest-pep8 pytest-cov==2.6
+  - pip install pytest==3.10.1 pytest-pep8 pytest-cov==2.6
   # Use docutils to generate html describe
   - pip install docutils
   - pip install numba

spotpy/__init__.py

@@ -38,5 +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 unittests
 __version__ = '1.5.2'

spotpy/algorithms/__init__.py

@@ -31,4 +31,5 @@
 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.
+from .dds import dds         # Dynamically Dimensioned Search algorithm by Bryan Tolson.
+from .padds import padds

spotpy/algorithms/_algorithm.py

@@ -15,6 +15,8 @@
 import numpy as np
 import time
 import threading
+import inspect
+import sys
 
 
 try:
@@ -49,7 +51,7 @@ def __init__(self, repetitions, algorithm_name, optimization_direction, parnames
             self.compare = self.maximizer
             print('The objective function will be minimized')
         if optimization_direction == 'grid':
-            self.compare = self.grid    
+            self.compare = self.grid
 
         self.rep = 0
         self.parnames = parnames
@@ -66,9 +68,9 @@ def __init__(self, repetitions, algorithm_name, optimization_direction, parnames
 
     def minimizer(self, objval, params):
         if objval < self.objectivefunction_min:
-            self.objectivefunction_min = objval 
+            self.objectivefunction_min = objval
             self.params_min = list(params)
-        
+
     def maximizer(self, objval, params):
         if objval > self.objectivefunction_max:
             self.objectivefunction_max = objval
@@ -87,14 +89,15 @@ def __call__(self, objectivefunction, params, block_print=False):
         self.rep+=1
         if type(objectivefunction) == type([]): #TODO: change to iterable
             self.compare(objectivefunction[0], params)
-
+        elif type(objectivefunction) == type(np.array([])):
+            pass
         else:
             self.compare(objectivefunction, params)
 
-        
+
         if self.rep == self.repetitions:
             self.stop = True
-            
+
         if not block_print:
             self.print_status()
 
@@ -107,38 +110,38 @@ def print_status(self):
             timestr = time.strftime("%H:%M:%S", time.gmtime(round(avg_time_per_run * (self.repetitions - (self.rep + 1)))))
             if self.optimization_direction == 'minimize':
                 text = '%i of %i, minimal objective function=%g, time remaining: %s' % (
-                        self.rep, self.repetitions, self.objectivefunction_min, timestr)           
-                
+                        self.rep, self.repetitions, self.objectivefunction_min, timestr)
+
             if self.optimization_direction == 'maximize':
                 text = '%i of %i, maximal objective function=%g, time remaining: %s' % (
-                        self.rep, self.repetitions, self.objectivefunction_max, timestr)  
-                
+                        self.rep, self.repetitions, self.objectivefunction_max, timestr)
+
             if self.optimization_direction == 'grid':
                 text = '%i of %i, min objf=%g, max objf=%g, time remaining: %s' % (
                         self.rep, self.repetitions, self.objectivefunction_min, self.objectivefunction_max, timestr)
-            
+
             print(text)
             self.last_print = time.time()
-    
+
     def print_status_final(self):
         print('\n*** Final SPOTPY summary ***')
         print('Total Duration: ' + str(round((time.time() - self.starttime), 2)) + ' seconds')
         print('Total Repetitions:', self.rep)
 
-        if self.optimization_direction == 'minimize':    
+        if self.optimization_direction == 'minimize':
             print('Minimal objective value: %g' % (self.objectivefunction_min))
             print('Corresponding parameter setting:')
             for i in range(self.parameters):
                 text = '%s: %g' % (self.parnames[i], self.params_min[i])
                 print(text)
-            
+
         if self.optimization_direction == 'maximize':
             print('Maximal objective value: %g' % (self.objectivefunction_max))
             print('Corresponding parameter setting:')
             for i in range(self.parameters):
                 text = '%s: %g' % (self.parnames[i], self.params_max[i])
                 print(text)
-    
+
         if self.optimization_direction == 'grid':
             print('Minimal objective value: %g' % (self.objectivefunction_min))
             print('Corresponding parameter setting:')
@@ -151,10 +154,10 @@ def print_status_final(self):
             for i in range(self.parameters):
                 text = '%s: %g' % (self.parnames[i], self.params_max[i])
                 print(text)
-         
+
         print('******************************\n')
-        
-        
+
+
     def __repr__(self):
         return 'Min objectivefunction: %g \n Max objectivefunction: %g' % (
                 self.objectivefunction_min, self.objectivefunction_max)
@@ -206,10 +209,10 @@ class _algorithm(object):
     _unaccepted_parameter_types = (parameter.List, )
 
     def __init__(self, spot_setup, dbname=None, dbformat=None, dbinit=True,
-                 dbappend=False, parallel='seq', save_sim=True, breakpoint=None, 
-                 backup_every_rep=100, save_threshold=-np.inf, db_precision=np.float16, 
+                 dbappend=False, parallel='seq', save_sim=True, breakpoint=None,
+                 backup_every_rep=100, save_threshold=-np.inf, db_precision=np.float16,
                  sim_timeout=None, random_state=None, optimization_direction='grid', algorithm_name=''):
-        
+
         # Initialize the user defined setup class
         self.setup = spot_setup
         param_info = parameter.get_parameters_array(self.setup, unaccepted_parameter_types=self._unaccepted_parameter_types)
@@ -290,6 +293,12 @@ def __init__(self, spot_setup, dbname=None, dbformat=None, dbinit=True,
         self.repeat = ForEach(self.simulate)
 
 
+        # method "save" needs to know whether objective function result is list or float, default is float
+        self.like_struct_typ = type(1.1)
+
+
+
+
     def __str__(self):
         return '{type}({mtype}())->{dbname}'.format(
             type=type(self).__name__,
@@ -307,7 +316,7 @@ def get_parameters(self):
         return pars[self.non_constant_positions]
 
     def set_repetiton(self, repetitions):
-        self.status = _RunStatistic(repetitions, self.algorithm_name, 
+        self.status = _RunStatistic(repetitions, self.algorithm_name,
                                     self.optimization_direction, self.parnames)
         # In MPI, this command will do nothing on the master process
         # but the worker processes are going to wait for jobs.
@@ -336,22 +345,29 @@ def _init_database(self, like, randompar, simulations):
 
             self.dbinit = False
 
+
+    def __is_list_type(self, data):
+        if type(data) == type:
+            return data == list or data == type(np.array([]))
+        else:
+            return type(data) == list or type(data) == type(np.array([]))
+
     def save(self, like, randompar, simulations, chains=1):
         # Initialize the database if no run was performed so far
         self._init_database(like, randompar, simulations)
 
-        #try if like is a list of values compare it with save threshold setting
-        try:
+        if self.__is_list_type(self.like_struct_typ) and self.__is_list_type(self.save_threshold):
             if all(i > j for i, j in zip(like, self.save_threshold)): #Compares list/list
                 self.datawriter.save(like, randompar, simulations, chains=chains)
-        #If like value is not a iterable, it is assumed to be a float
-        except TypeError: # This is also used if not threshold was set
-            try:
-                if like>self.save_threshold: #Compares float/float
-                    self.datawriter.save(like, randompar, simulations, chains=chains)
-            except TypeError:# float/list would result in an error, because it does not make sense
-                if like[0]>self.save_threshold: #Compares list/float
-                    self.datawriter.save(like, randompar, simulations, chains=chains)
+        if not self.__is_list_type(self.like_struct_typ) and not self.__is_list_type(self.save_threshold):
+            if like>self.save_threshold: #Compares float/float
+                self.datawriter.save(like, randompar, simulations, chains=chains)
+        if self.__is_list_type(self.like_struct_typ) and not self.__is_list_type(self.save_threshold):
+            if like[0]>self.save_threshold: #Compares list/float
+                self.datawriter.save(like, randompar, simulations, chains=chains)
+        if not self.__is_list_type(self.like_struct_typ) and self.__is_list_type(self.save_threshold): #Compares float/list
+            if (like > self.save_threshold).all:
+                self.datawriter.save(like, randompar, simulations, chains=chains)
 
     def read_breakdata(self, dbname):
         ''' Read data from a pickle file if a breakpoint is set.
@@ -392,7 +408,7 @@ def postprocessing(self, rep, params, simulation, chains=1, save_run=True, negat
         # Save everything in the database, if save is True
         # This is needed as some algorithms just want to know the fitness,
         # before they actually save the run in a database (e.g. sce-ua)
-        
+
         self.status(like,params,block_print=block_print)
 
         if save_run is True and simulation is not None:

spotpy/algorithms/demcz.py

@@ -555,30 +555,6 @@ def _dream_proposals(currentVectors, history, dimensions, nChains, DEpairs, gamm
     return proposalVectors
 
 
-def _dream2_proposals(currentVectors, history, dimensions, nChains, DEpairs,
-
-                      gamma, jitter, eps):
-    """
-    generates and returns proposal vectors given the current states
-    NOT USED ATM
-    """
-
-    sampleRange = history.ncombined_history
-    currentIndex = np.arange(sampleRange - nChains, sampleRange)[:, np.newaxis]
-    combined_history = history.combined_history
-
-    # choose some chains without replacement to combine
-    chains = _random_no_replace(1, sampleRange - 1, nChains)
-
-    # makes sure we have already selected the current chain so it is not replaced
-    # this ensures that the the two chosen chains cannot be the same as the
-    # chain for which the jump is
-    chains += (chains >= currentIndex)
-
-    proposalVectors = combined_history[chains[:, 0], :]
-    return proposalVectors
-
-
 class _GRConvergence:
     """
     Gelman Rubin convergence diagnostic calculator class. It currently only calculates the naive