307 lines
13 KiB
Python
307 lines
13 KiB
Python
|
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
from __future__ import division
|
||
|
|
import numpy as np
|
||
|
|
from scipy import sparse
|
||
|
|
from sklearn.model_selection import LeaveOneOut
|
||
|
|
|
||
|
|
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
|
||
|
|
assert_greater, assert_almost_equal,
|
||
|
|
assert_greater_equal,
|
||
|
|
assert_array_equal,
|
||
|
|
assert_raises,
|
||
|
|
ignore_warnings)
|
||
|
|
from sklearn.datasets import make_classification, make_blobs
|
||
|
|
from sklearn.naive_bayes import MultinomialNB
|
||
|
|
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
|
||
|
|
from sklearn.svm import LinearSVC
|
||
|
|
from sklearn.pipeline import Pipeline
|
||
|
|
from sklearn.preprocessing import Imputer
|
||
|
|
from sklearn.metrics import brier_score_loss, log_loss
|
||
|
|
from sklearn.calibration import CalibratedClassifierCV
|
||
|
|
from sklearn.calibration import _sigmoid_calibration, _SigmoidCalibration
|
||
|
|
from sklearn.calibration import calibration_curve
|
||
|
|
|
||
|
|
|
||
|
|
@ignore_warnings
|
||
|
|
def test_calibration():
|
||
|
|
"""Test calibration objects with isotonic and sigmoid"""
|
||
|
|
n_samples = 100
|
||
|
|
X, y = make_classification(n_samples=2 * n_samples, n_features=6,
|
||
|
|
random_state=42)
|
||
|
|
sample_weight = np.random.RandomState(seed=42).uniform(size=y.size)
|
||
|
|
|
||
|
|
X -= X.min() # MultinomialNB only allows positive X
|
||
|
|
|
||
|
|
# split train and test
|
||
|
|
X_train, y_train, sw_train = \
|
||
|
|
X[:n_samples], y[:n_samples], sample_weight[:n_samples]
|
||
|
|
X_test, y_test = X[n_samples:], y[n_samples:]
|
||
|
|
|
||
|
|
# Naive-Bayes
|
||
|
|
clf = MultinomialNB().fit(X_train, y_train, sample_weight=sw_train)
|
||
|
|
prob_pos_clf = clf.predict_proba(X_test)[:, 1]
|
||
|
|
|
||
|
|
pc_clf = CalibratedClassifierCV(clf, cv=y.size + 1)
|
||
|
|
assert_raises(ValueError, pc_clf.fit, X, y)
|
||
|
|
|
||
|
|
# Naive Bayes with calibration
|
||
|
|
for this_X_train, this_X_test in [(X_train, X_test),
|
||
|
|
(sparse.csr_matrix(X_train),
|
||
|
|
sparse.csr_matrix(X_test))]:
|
||
|
|
for method in ['isotonic', 'sigmoid']:
|
||
|
|
pc_clf = CalibratedClassifierCV(clf, method=method, cv=2)
|
||
|
|
# Note that this fit overwrites the fit on the entire training
|
||
|
|
# set
|
||
|
|
pc_clf.fit(this_X_train, y_train, sample_weight=sw_train)
|
||
|
|
prob_pos_pc_clf = pc_clf.predict_proba(this_X_test)[:, 1]
|
||
|
|
|
||
|
|
# Check that brier score has improved after calibration
|
||
|
|
assert_greater(brier_score_loss(y_test, prob_pos_clf),
|
||
|
|
brier_score_loss(y_test, prob_pos_pc_clf))
|
||
|
|
|
||
|
|
# Check invariance against relabeling [0, 1] -> [1, 2]
|
||
|
|
pc_clf.fit(this_X_train, y_train + 1, sample_weight=sw_train)
|
||
|
|
prob_pos_pc_clf_relabeled = pc_clf.predict_proba(this_X_test)[:, 1]
|
||
|
|
assert_array_almost_equal(prob_pos_pc_clf,
|
||
|
|
prob_pos_pc_clf_relabeled)
|
||
|
|
|
||
|
|
# Check invariance against relabeling [0, 1] -> [-1, 1]
|
||
|
|
pc_clf.fit(this_X_train, 2 * y_train - 1, sample_weight=sw_train)
|
||
|
|
prob_pos_pc_clf_relabeled = pc_clf.predict_proba(this_X_test)[:, 1]
|
||
|
|
assert_array_almost_equal(prob_pos_pc_clf,
|
||
|
|
prob_pos_pc_clf_relabeled)
|
||
|
|
|
||
|
|
# Check invariance against relabeling [0, 1] -> [1, 0]
|
||
|
|
pc_clf.fit(this_X_train, (y_train + 1) % 2,
|
||
|
|
sample_weight=sw_train)
|
||
|
|
prob_pos_pc_clf_relabeled = \
|
||
|
|
pc_clf.predict_proba(this_X_test)[:, 1]
|
||
|
|
if method == "sigmoid":
|
||
|
|
assert_array_almost_equal(prob_pos_pc_clf,
|
||
|
|
1 - prob_pos_pc_clf_relabeled)
|
||
|
|
else:
|
||
|
|
# Isotonic calibration is not invariant against relabeling
|
||
|
|
# but should improve in both cases
|
||
|
|
assert_greater(brier_score_loss(y_test, prob_pos_clf),
|
||
|
|
brier_score_loss((y_test + 1) % 2,
|
||
|
|
prob_pos_pc_clf_relabeled))
|
||
|
|
|
||
|
|
# Check failure cases:
|
||
|
|
# only "isotonic" and "sigmoid" should be accepted as methods
|
||
|
|
clf_invalid_method = CalibratedClassifierCV(clf, method="foo")
|
||
|
|
assert_raises(ValueError, clf_invalid_method.fit, X_train, y_train)
|
||
|
|
|
||
|
|
# base-estimators should provide either decision_function or
|
||
|
|
# predict_proba (most regressors, for instance, should fail)
|
||
|
|
clf_base_regressor = \
|
||
|
|
CalibratedClassifierCV(RandomForestRegressor(), method="sigmoid")
|
||
|
|
assert_raises(RuntimeError, clf_base_regressor.fit, X_train, y_train)
|
||
|
|
|
||
|
|
|
||
|
|
def test_sample_weight():
|
||
|
|
n_samples = 100
|
||
|
|
X, y = make_classification(n_samples=2 * n_samples, n_features=6,
|
||
|
|
random_state=42)
|
||
|
|
|
||
|
|
sample_weight = np.random.RandomState(seed=42).uniform(size=len(y))
|
||
|
|
X_train, y_train, sw_train = \
|
||
|
|
X[:n_samples], y[:n_samples], sample_weight[:n_samples]
|
||
|
|
X_test = X[n_samples:]
|
||
|
|
|
||
|
|
for method in ['sigmoid', 'isotonic']:
|
||
|
|
base_estimator = LinearSVC(random_state=42)
|
||
|
|
calibrated_clf = CalibratedClassifierCV(base_estimator, method=method)
|
||
|
|
calibrated_clf.fit(X_train, y_train, sample_weight=sw_train)
|
||
|
|
probs_with_sw = calibrated_clf.predict_proba(X_test)
|
||
|
|
|
||
|
|
# As the weights are used for the calibration, they should still yield
|
||
|
|
# a different predictions
|
||
|
|
calibrated_clf.fit(X_train, y_train)
|
||
|
|
probs_without_sw = calibrated_clf.predict_proba(X_test)
|
||
|
|
|
||
|
|
diff = np.linalg.norm(probs_with_sw - probs_without_sw)
|
||
|
|
assert_greater(diff, 0.1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_calibration_multiclass():
|
||
|
|
"""Test calibration for multiclass """
|
||
|
|
# test multi-class setting with classifier that implements
|
||
|
|
# only decision function
|
||
|
|
clf = LinearSVC()
|
||
|
|
X, y_idx = make_blobs(n_samples=100, n_features=2, random_state=42,
|
||
|
|
centers=3, cluster_std=3.0)
|
||
|
|
|
||
|
|
# Use categorical labels to check that CalibratedClassifierCV supports
|
||
|
|
# them correctly
|
||
|
|
target_names = np.array(['a', 'b', 'c'])
|
||
|
|
y = target_names[y_idx]
|
||
|
|
|
||
|
|
X_train, y_train = X[::2], y[::2]
|
||
|
|
X_test, y_test = X[1::2], y[1::2]
|
||
|
|
|
||
|
|
clf.fit(X_train, y_train)
|
||
|
|
for method in ['isotonic', 'sigmoid']:
|
||
|
|
cal_clf = CalibratedClassifierCV(clf, method=method, cv=2)
|
||
|
|
cal_clf.fit(X_train, y_train)
|
||
|
|
probas = cal_clf.predict_proba(X_test)
|
||
|
|
assert_array_almost_equal(np.sum(probas, axis=1), np.ones(len(X_test)))
|
||
|
|
|
||
|
|
# Check that log-loss of calibrated classifier is smaller than
|
||
|
|
# log-loss of naively turned OvR decision function to probabilities
|
||
|
|
# via softmax
|
||
|
|
def softmax(y_pred):
|
||
|
|
e = np.exp(-y_pred)
|
||
|
|
return e / e.sum(axis=1).reshape(-1, 1)
|
||
|
|
|
||
|
|
uncalibrated_log_loss = \
|
||
|
|
log_loss(y_test, softmax(clf.decision_function(X_test)))
|
||
|
|
calibrated_log_loss = log_loss(y_test, probas)
|
||
|
|
assert_greater_equal(uncalibrated_log_loss, calibrated_log_loss)
|
||
|
|
|
||
|
|
# Test that calibration of a multiclass classifier decreases log-loss
|
||
|
|
# for RandomForestClassifier
|
||
|
|
X, y = make_blobs(n_samples=100, n_features=2, random_state=42,
|
||
|
|
cluster_std=3.0)
|
||
|
|
X_train, y_train = X[::2], y[::2]
|
||
|
|
X_test, y_test = X[1::2], y[1::2]
|
||
|
|
|
||
|
|
clf = RandomForestClassifier(n_estimators=10, random_state=42)
|
||
|
|
clf.fit(X_train, y_train)
|
||
|
|
clf_probs = clf.predict_proba(X_test)
|
||
|
|
loss = log_loss(y_test, clf_probs)
|
||
|
|
|
||
|
|
for method in ['isotonic', 'sigmoid']:
|
||
|
|
cal_clf = CalibratedClassifierCV(clf, method=method, cv=3)
|
||
|
|
cal_clf.fit(X_train, y_train)
|
||
|
|
cal_clf_probs = cal_clf.predict_proba(X_test)
|
||
|
|
cal_loss = log_loss(y_test, cal_clf_probs)
|
||
|
|
assert_greater(loss, cal_loss)
|
||
|
|
|
||
|
|
|
||
|
|
def test_calibration_prefit():
|
||
|
|
"""Test calibration for prefitted classifiers"""
|
||
|
|
n_samples = 50
|
||
|
|
X, y = make_classification(n_samples=3 * n_samples, n_features=6,
|
||
|
|
random_state=42)
|
||
|
|
sample_weight = np.random.RandomState(seed=42).uniform(size=y.size)
|
||
|
|
|
||
|
|
X -= X.min() # MultinomialNB only allows positive X
|
||
|
|
|
||
|
|
# split train and test
|
||
|
|
X_train, y_train, sw_train = \
|
||
|
|
X[:n_samples], y[:n_samples], sample_weight[:n_samples]
|
||
|
|
X_calib, y_calib, sw_calib = \
|
||
|
|
X[n_samples:2 * n_samples], y[n_samples:2 * n_samples], \
|
||
|
|
sample_weight[n_samples:2 * n_samples]
|
||
|
|
X_test, y_test = X[2 * n_samples:], y[2 * n_samples:]
|
||
|
|
|
||
|
|
# Naive-Bayes
|
||
|
|
clf = MultinomialNB()
|
||
|
|
clf.fit(X_train, y_train, sw_train)
|
||
|
|
prob_pos_clf = clf.predict_proba(X_test)[:, 1]
|
||
|
|
|
||
|
|
# Naive Bayes with calibration
|
||
|
|
for this_X_calib, this_X_test in [(X_calib, X_test),
|
||
|
|
(sparse.csr_matrix(X_calib),
|
||
|
|
sparse.csr_matrix(X_test))]:
|
||
|
|
for method in ['isotonic', 'sigmoid']:
|
||
|
|
pc_clf = CalibratedClassifierCV(clf, method=method, cv="prefit")
|
||
|
|
|
||
|
|
for sw in [sw_calib, None]:
|
||
|
|
pc_clf.fit(this_X_calib, y_calib, sample_weight=sw)
|
||
|
|
y_prob = pc_clf.predict_proba(this_X_test)
|
||
|
|
y_pred = pc_clf.predict(this_X_test)
|
||
|
|
prob_pos_pc_clf = y_prob[:, 1]
|
||
|
|
assert_array_equal(y_pred,
|
||
|
|
np.array([0, 1])[np.argmax(y_prob, axis=1)])
|
||
|
|
|
||
|
|
assert_greater(brier_score_loss(y_test, prob_pos_clf),
|
||
|
|
brier_score_loss(y_test, prob_pos_pc_clf))
|
||
|
|
|
||
|
|
|
||
|
|
def test_sigmoid_calibration():
|
||
|
|
"""Test calibration values with Platt sigmoid model"""
|
||
|
|
exF = np.array([5, -4, 1.0])
|
||
|
|
exY = np.array([1, -1, -1])
|
||
|
|
# computed from my python port of the C++ code in LibSVM
|
||
|
|
AB_lin_libsvm = np.array([-0.20261354391187855, 0.65236314980010512])
|
||
|
|
assert_array_almost_equal(AB_lin_libsvm,
|
||
|
|
_sigmoid_calibration(exF, exY), 3)
|
||
|
|
lin_prob = 1. / (1. + np.exp(AB_lin_libsvm[0] * exF + AB_lin_libsvm[1]))
|
||
|
|
sk_prob = _SigmoidCalibration().fit(exF, exY).predict(exF)
|
||
|
|
assert_array_almost_equal(lin_prob, sk_prob, 6)
|
||
|
|
|
||
|
|
# check that _SigmoidCalibration().fit only accepts 1d array or 2d column
|
||
|
|
# arrays
|
||
|
|
assert_raises(ValueError, _SigmoidCalibration().fit,
|
||
|
|
np.vstack((exF, exF)), exY)
|
||
|
|
|
||
|
|
|
||
|
|
def test_calibration_curve():
|
||
|
|
"""Check calibration_curve function"""
|
||
|
|
y_true = np.array([0, 0, 0, 1, 1, 1])
|
||
|
|
y_pred = np.array([0., 0.1, 0.2, 0.8, 0.9, 1.])
|
||
|
|
prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=2)
|
||
|
|
prob_true_unnormalized, prob_pred_unnormalized = \
|
||
|
|
calibration_curve(y_true, y_pred * 2, n_bins=2, normalize=True)
|
||
|
|
assert_equal(len(prob_true), len(prob_pred))
|
||
|
|
assert_equal(len(prob_true), 2)
|
||
|
|
assert_almost_equal(prob_true, [0, 1])
|
||
|
|
assert_almost_equal(prob_pred, [0.1, 0.9])
|
||
|
|
assert_almost_equal(prob_true, prob_true_unnormalized)
|
||
|
|
assert_almost_equal(prob_pred, prob_pred_unnormalized)
|
||
|
|
|
||
|
|
# probabilities outside [0, 1] should not be accepted when normalize
|
||
|
|
# is set to False
|
||
|
|
assert_raises(ValueError, calibration_curve, [1.1], [-0.1],
|
||
|
|
normalize=False)
|
||
|
|
|
||
|
|
|
||
|
|
def test_calibration_nan_imputer():
|
||
|
|
"""Test that calibration can accept nan"""
|
||
|
|
X, y = make_classification(n_samples=10, n_features=2,
|
||
|
|
n_informative=2, n_redundant=0,
|
||
|
|
random_state=42)
|
||
|
|
X[0, 0] = np.nan
|
||
|
|
clf = Pipeline(
|
||
|
|
[('imputer', Imputer()),
|
||
|
|
('rf', RandomForestClassifier(n_estimators=1))])
|
||
|
|
clf_c = CalibratedClassifierCV(clf, cv=2, method='isotonic')
|
||
|
|
clf_c.fit(X, y)
|
||
|
|
clf_c.predict(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_calibration_prob_sum():
|
||
|
|
# Test that sum of probabilities is 1. A non-regression test for
|
||
|
|
# issue #7796
|
||
|
|
num_classes = 2
|
||
|
|
X, y = make_classification(n_samples=10, n_features=5,
|
||
|
|
n_classes=num_classes)
|
||
|
|
clf = LinearSVC(C=1.0)
|
||
|
|
clf_prob = CalibratedClassifierCV(clf, method="sigmoid", cv=LeaveOneOut())
|
||
|
|
clf_prob.fit(X, y)
|
||
|
|
|
||
|
|
probs = clf_prob.predict_proba(X)
|
||
|
|
assert_array_almost_equal(probs.sum(axis=1), np.ones(probs.shape[0]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_calibration_less_classes():
|
||
|
|
# Test to check calibration works fine when train set in a test-train
|
||
|
|
# split does not contain all classes
|
||
|
|
# Since this test uses LOO, at each iteration train set will not contain a
|
||
|
|
# class label
|
||
|
|
X = np.random.randn(10, 5)
|
||
|
|
y = np.arange(10)
|
||
|
|
clf = LinearSVC(C=1.0)
|
||
|
|
cal_clf = CalibratedClassifierCV(clf, method="sigmoid", cv=LeaveOneOut())
|
||
|
|
cal_clf.fit(X, y)
|
||
|
|
|
||
|
|
for i, calibrated_classifier in \
|
||
|
|
enumerate(cal_clf.calibrated_classifiers_):
|
||
|
|
proba = calibrated_classifier.predict_proba(X)
|
||
|
|
assert_array_equal(proba[:, i], np.zeros(len(y)))
|
||
|
|
assert_equal(np.all(np.hstack([proba[:, :i],
|
||
|
|
proba[:, i + 1:]])), True)
|