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removed deprecated attributes and adjusted tests
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@mmaelicke
mmaelicke committed Dec 2, 2019
1 parent b97c563 commit 2b96612
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Showing 6 changed files with 108 additions and 135 deletions.
11 changes: 3 additions & 8 deletions skgstat/DirectionalVariogram.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def __init__(self,
model='spherical',
dist_func='euclidean',
bin_func='even',
normalize=True,
normalize=False,
fit_method='trf',
fit_sigma=None,
directional_model='triangle',
Expand All @@ -38,8 +38,7 @@ def __init__(self,
use_nugget=False,
maxlag=None,
n_lags=10,
verbose=False,
harmonize=False
verbose=False
):
r"""Variogram Class
Expand Down Expand Up @@ -193,8 +192,7 @@ def __init__(self,
function.
verbose : bool
Set the Verbosity of the class. Not Implemented yet.
harmonize : bool
this kind of works so far, but will be rewritten (and documented)
"""
# Set coordinates
self._X = np.asarray(coordinates)
Expand Down Expand Up @@ -254,9 +252,6 @@ def __init__(self,
# specify if the lag should be given absolute or relative to the maxlag
self._normalized = normalize

# specify if the experimental variogram shall be harmonized
self.harmonize = harmonize

# set the fitting method and sigma array
self.fit_method = fit_method
self._fit_sigma = None
Expand Down
1 change: 0 additions & 1 deletion skgstat/SpaceTimeVariogram.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,6 @@ def __init__(self,
estimator='matheron',
use_nugget=False,
model='product-sum',

verbose=False
):
# set coordinates array
Expand Down
198 changes: 82 additions & 116 deletions skgstat/Variogram.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -3,13 +3,14 @@
"""
import copy
import os
import warnings

import numpy as np
from pandas import DataFrame
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.spatial.distance import pdist, squareform
from numba import jit
from sklearn.isotonic import IsotonicRegression

from skgstat import estimators, models, binning

Expand All @@ -29,14 +30,13 @@ def __init__(self,
model='spherical',
dist_func='euclidean',
bin_func='even',
normalize=True,
normalize=False,
fit_method='trf',
fit_sigma=None,
use_nugget=False,
maxlag=None,
n_lags=10,
verbose=False,
harmonize=False
verbose=False
):
r"""Variogram Class
Expand Down Expand Up @@ -149,19 +149,8 @@ def __init__(self,
function.
verbose : bool
Set the Verbosity of the class. Not Implemented yet.
harmonize : bool
this kind of works so far, but will be rewritten (and documented)
"""
# deprecation warnings
if normalize and 'SKG_SUPPRESS' not in os.environ.keys(): # pragma: no cover
print('Warning: normalize will change the default value \
to False. You can add a SKG_SUPPRESS environment variable to suppress this warning.')
if 'SKG_SUPPRESS' not in os.environ.keys():
print("Warning: 'harmonize' is deprecated and will be removed\
with the next release. You can add a 'SKG_SUPPRESS' environment variable to \
suppress this warning.")

# Set coordinates
self._X = np.asarray(coordinates)

Expand All @@ -188,6 +177,9 @@ def __init__(self,
self._maxlag = None
self.maxlag = maxlag

# harmonize model placeholder
self._harmonize = False

# estimator can be a function or a string
self._estimator = None
self.set_estimator(estimator_name=estimator)
Expand All @@ -205,12 +197,6 @@ def __init__(self,
# specify if the lag should be given absolute or relative to the maxlag
self._normalized = normalize

# specify if the experimental variogram shall be harmonized
self.harmonize = harmonize
if harmonize:
raise DeprecationWarning('Argument will be removed with 0.3')
self._harmonize = None

# set if nugget effect shall be used
self._use_nugget = None
self.use_nugget = use_nugget
Expand Down Expand Up @@ -431,8 +417,8 @@ def bins(self, bins):
def n_lags(self):
"""Number of lag bins
Pass the number of lag bins to be used on
this Variogram instance. This will reset
Pass the number of lag bins to be used on
this Variogram instance. This will reset
the grouping index and fitting parameters
"""
Expand All @@ -444,12 +430,12 @@ def n_lags(self, n):
# string are not implemented yet
if isinstance(n, str):
raise NotImplementedError('n_lags string values not implemented')

# n_lags is int
elif isinstance(n, int):
if n < 1:
raise ValueError('n_lags has to be a positive integer')

# set parameter
self._n_lags = n

Expand All @@ -459,7 +445,7 @@ def n_lags(self, n):
# else
else:
raise ValueError('n_lags has to be a positive integer')

# reset the fitting
self.cof = None
self.cov = None
Expand Down Expand Up @@ -518,6 +504,8 @@ def set_model(self, model_name):
self.cof, self.cov = None, None

if isinstance(model_name, str):
# at first reset harmonize
self._harmonize = False
if model_name.lower() == 'spherical':
self._model = models.spherical
elif model_name.lower() == 'exponential':
Expand All @@ -530,12 +518,55 @@ def set_model(self, model_name):
self._model = models.stable
elif model_name.lower() == 'matern':
self._model = models.matern
elif model_name.lower() == 'harmonize':
self._harmonize = True
self._model = self._build_harmonized_model()
else:
raise ValueError(
'The theoretical Variogram function %s is not understood, please provide the function' % model_name)
else:
self._model = model_name

def _build_harmonized_model(self):
x = self.bins
y = self.experimental

_x = x[~np.isnan(y)]
_y = y[~np.isnan(y)]
regr = IsotonicRegression(increasing=True).fit(_x, _y)

# create the model function
def harmonize(x):
"""Monotonized Variogram
Return the isotonic harmonized experimental variogram.
This means, the experimental variogram is monotonic after harmonization.
The harmonization is done using following Hinterding (2003) using
the PAVA algorithm (Barlow and Bartholomew, 1972).
Returns
-------
gamma : numpy.ndarray
monotonized experimental variogram
References
----------
Barlow, R., D. Bartholomew, et al. (1972): Statistical Interference Under Order Restrictions.
John Wiley and Sons, New York.
Hiterding, A. (2003): Entwicklung hybrider Interpolationsverfahren für den automatisierten Betrieb am
Beispiel meteorologischer Größen. Dissertation, Institut für Geoinformatik, Westphälische
Wilhelms-Universität Münster, IfGIprints, Münster. ISBN: 3-936616-12-4
"""

if isinstance(x, (list, tuple, np.ndarray)):
return regr.transform(x)
else:
return regr.transform([x])

return harmonize

@property
def use_nugget(self):
return self._use_nugget
Expand Down Expand Up @@ -668,7 +699,6 @@ def fit_sigma(self):
-------
void
Notes
-----
The cost function is defined as:
Expand Down Expand Up @@ -855,9 +885,22 @@ def fit(self, force=False, method=None, sigma=None, **kwargs):
_x = x[~np.isnan(y)]
_y = y[~np.isnan(y)]

# the model function is re-defined. otherwise scipy cannot determine
# the number of parameters
# TODO: def f(n of par)
# handle harmonized models
if self._harmonize:
_x = np.linspace(0, np.max(_x), 100)
_y = self._model(_x)

# get the params
s = 0.95 * np.nanmax(_y)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
r = np.argwhere(_y >= s)[0][0]
n = _y[0] if self.use_nugget else 0.0

# set the params
self.cof = [r, s, n]
return


# Switch the method
# Trust Region Reflective
Expand All @@ -883,11 +926,6 @@ def fit(self, force=False, method=None, sigma=None, **kwargs):
**kwargs
)

# monotonization
elif self.fit_method == 'harmonize':
# TODO, make the harmonization and infer the cof from that
raise NotImplementedError

else:
raise ValueError("fit method has to be one of ['trf', 'lm']")

Expand All @@ -914,51 +952,9 @@ def transform(self, x):
# if instance is not fitted, fit it
if self.cof is None:
self.fit(force=True)

return np.fromiter(map(self.compiled_model, x), dtype=float)

@property
def compiled_model(self):
"""Compiled theoretical variogram model
Compile the model using the actual fitting parameters to return a
function implementing them.
The compiled_model will be removed in version 0.3. Use the
`Variogram.fitted_model` property instead. It is works in the same
way, but is significantly faster
Returns
-------
callable
"""
if not 'SKG_SUPPRESS' in os.environ:
print('Warning: compiled_model is deprecated and will be removed. \
Use Variogram.fitted_model instead. You can add an SKG_SUPPRESS environment variable to supress this warning.')
if self.cof is None:
self.fit(force=True)

# get the function
func = self._model

# get the pars
params = self.describe()
ming = params['nugget']
maxg = params['sill']

# define the wrapper
def model(x):
gamma = func(x, *self.cof)
if int(x) == 0 and not np.isfinite(gamma):
return ming
elif int(x) > 0 and not np.isfinite(gamma):
return maxg
else:
return gamma

# return
return model

# return the result
return self.fitted_model(x)

@property
def fitted_model(self):
Expand All @@ -967,8 +963,7 @@ def fitted_model(self):
Returns a callable that takes a distance value and returns a
semivariance. This model is fitted to the current Variogram
parameters. The function will be interpreted at return time with the
parameters hard-coded into the function code. This makes it way
faster than`Variogram.compiled_model`.
parameters hard-coded into the function code.
Returns
-------
Expand All @@ -985,7 +980,10 @@ def fitted_model(self):
# get the function
func = self._model

code = """model = lambda x: func(x, %s)""" % \
if self._harmonize:
code = """model = lambda x: func(x)"""
else:
code = """model = lambda x: func(x, %s)""" % \
(', '.join([str(_) for _ in cof]))

# run the code
Expand Down Expand Up @@ -1101,10 +1099,7 @@ def experimental(self):
Variogram.isotonic
"""
if self.harmonize:
return self.isotonic
else:
return self._experimental
return self._experimental

@property
# @jit
Expand Down Expand Up @@ -1138,35 +1133,6 @@ def _experimental(self):
# apply
return y.copy()

@property
def isotonic(self):
"""
Return the isotonic harmonized experimental variogram.
This means, the experimental variogram is monotonic after harmonization.
The harmonization is done using PAVA algorithm:
Barlow, R., D. Bartholomew, et al. (1972): Statistical Interference Under Order Restrictions.
John Wiley and Sons, New York.
Hiterding, A. (2003): Entwicklung hybrider Interpolationsverfahren für den automatisierten Betrieb am
Beispiel meteorologischer Größen. Dissertation, Institut für Geoinformatik, Westphälische
Wilhelms-Universität Münster, IfGIprints, Münster. ISBN: 3-936616-12-4
TODO: solve the import
:return: np.ndarray, monotonized experimental variogram
"""
# TODO this is imported in the function as sklearn is not a dependency (and should not be for now)
raise NotImplementedError

try:
from sklearn.isotonic import IsotonicRegression
y = self._experimental
x = self.bins
return IsotonicRegression().fit_transform(x,y)
except ImportError:
raise NotImplementedError('scikit-learn is not installed, but the isotonic function without sklear dependency is not installed yet.')

def __get_fit_bounds(self, x, y):
"""
Return the bounds for parameter space in fitting a variogram model.
Expand Down
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