def check_classifier_ratio(clf, method, cv):
# Passing distributions directly
p0 = Normal(mu=0.0)
p1 = Normal(mu=0.1)
ratio = ClassifierRatio(CalibratedClassifierCV(base_estimator=clf,
method=method,
cv=cv))
ratio.fit(numerator=p0, denominator=p1, n_samples=10000)
reals = np.linspace(-1, 1, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.1
assert np.mean(np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
# Passing X, y only
X = np.vstack((p0.rvs(5000), p1.rvs(5000)))
y = np.zeros(10000, dtype=np.int)
y[5000:] = 1
ratio = ClassifierRatio(CalibratedClassifierCV(base_estimator=clf,
method=method,
cv=cv))
ratio.fit(X=X, y=y)
reals = np.linspace(-1, 1, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.1
assert np.mean(np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
def check_classifier_ratio(clf, method, cv):
# Passing distributions directly
p0 = Normal(mu=0.0)
p1 = Normal(mu=0.1)
ratio = ClassifierRatio(
CalibratedClassifierCV(base_estimator=clf, method=method, cv=cv))
ratio.fit(numerator=p0, denominator=p1, n_samples=10000)
reals = np.linspace(-1, 1, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.1
assert np.mean(
np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
# Passing X, y only
X = np.vstack((p0.rvs(5000), p1.rvs(5000)))
y = np.zeros(10000, dtype=np.int)
y[5000:] = 1
ratio = ClassifierRatio(
CalibratedClassifierCV(base_estimator=clf, method=method, cv=cv))
ratio.fit(X=X, y=y)
reals = np.linspace(-1, 1, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.1
assert np.mean(
np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
def test_classifier_ratio_identity():
p = Normal(mu=0.0)
ratio = ClassifierRatio(
CalibratedClassifierCV(base_estimator=ElasticNetCV()))
ratio.fit(numerator=p, denominator=p, n_samples=10000)
reals = np.linspace(-0.5, 1.0, num=100).reshape(-1, 1)
assert ratio.score(reals, p.pdf(reals) / p.pdf(reals)) == 0.0
assert_array_almost_equal(ratio.predict(reals), np.ones(len(reals)))
assert_array_almost_equal(ratio.predict(reals, log=True),
np.zeros(len(reals)))
def test_classifier_ratio_identity():
p = Normal(mu=0.0)
ratio = ClassifierRatio(
CalibratedClassifierCV(base_estimator=ElasticNetCV()))
ratio.fit(numerator=p, denominator=p, n_samples=10000)
reals = np.linspace(-0.5, 1.0, num=100).reshape(-1, 1)
assert ratio.score(reals, p.pdf(reals) / p.pdf(reals)) == 0.0
assert_array_almost_equal(ratio.predict(reals), np.ones(len(reals)))
assert_array_almost_equal(ratio.predict(reals, log=True),
np.zeros(len(reals)))
def reconstruct_ratio_using_estimated_pdfs(classifier,
classifier_parameters,
cv_val=3,
with_linear_transformation=False,
add_variation=False,
n_samples=50000,
verbose=True,
inverse_weights=False,
test_by_ML_GB=False):
"""
Reconstruct weights by discriinative classifiers (calibrated and non-calibrated)
from `carl` on the generated samples defined by function `generate_samples`
"""
original, target, exact_weights, original_test, target_test, exact_weights_test = \
generate_samples(with_linear_transformation=with_linear_transformation,
add_variation=add_variation, n_samples=n_samples, verbose=verbose)
predicted_weights = []
for params in classifier_parameters:
if verbose:
print "Used parameters ", params
classifier_clone = clone(classifier)
classifier_clone.set_params(**params)
ratio = ClassifierRatio(base_estimator=classifier_clone,
random_state=42)
#reformat X0 and X1 into training data
X = numpy.vstack((original, target))
y = numpy.array([1] * original.shape[0] + [0] * target.shape[0])
# fit the ration
ratio.fit(X, y)
carl_weights_test = ratio.predict(original_test, log=False)
carl_weights_test[numpy.isinf(carl_weights_test)] = 0.
predicted_weights.append(carl_weights_test)
# plot 1d distribution for test sample
if verbose:
run_verbose_info(original_test,
target_test,
carl_weights_test,
exact_weights_test,
cv_val=cv_val)
regime = [False]
if inverse_weights:
regime = [True, False]
for inverse in regime:
plt.figure(figsize=(len(classifier_parameters) * 5, 4))
m = len(predicted_weights)
for n, (weights, params) in enumerate(
zip(predicted_weights, classifier_parameters)):
plt.subplot(1, m, n + 1)
if inverse:
plot_scatter_weights(1. / exact_weights_test,
1. / weights,
title="Inverse weights for\n" +
str(params))
else:
plot_scatter_weights(exact_weights_test,
weights,
title="Weights for\n" + str(params))
