495 lines
18 KiB
Python
495 lines
18 KiB
Python
|
from __future__ import division
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import scipy.sparse as sp
|
||
|
|
|
||
|
|
from sklearn.utils.testing import assert_almost_equal
|
||
|
|
from sklearn.utils.testing import assert_raises
|
||
|
|
from sklearn.utils.testing import assert_false
|
||
|
|
from sklearn.utils.testing import assert_raises_regex
|
||
|
|
from sklearn.utils.testing import assert_raise_message
|
||
|
|
from sklearn.utils.testing import assert_array_equal
|
||
|
|
from sklearn.utils.testing import assert_equal
|
||
|
|
from sklearn.utils.testing import assert_greater
|
||
|
|
from sklearn.utils.testing import assert_not_equal
|
||
|
|
from sklearn.utils.testing import assert_array_almost_equal
|
||
|
|
from sklearn import datasets
|
||
|
|
from sklearn.base import clone
|
||
|
|
from sklearn.datasets import fetch_mldata
|
||
|
|
from sklearn.datasets import make_classification
|
||
|
|
from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier
|
||
|
|
from sklearn.exceptions import NotFittedError
|
||
|
|
from sklearn.externals.joblib import cpu_count
|
||
|
|
from sklearn.linear_model import Lasso
|
||
|
|
from sklearn.linear_model import LogisticRegression
|
||
|
|
from sklearn.linear_model import SGDClassifier
|
||
|
|
from sklearn.linear_model import SGDRegressor
|
||
|
|
from sklearn.metrics import jaccard_similarity_score
|
||
|
|
from sklearn.multiclass import OneVsRestClassifier
|
||
|
|
from sklearn.multioutput import ClassifierChain
|
||
|
|
from sklearn.multioutput import MultiOutputClassifier
|
||
|
|
from sklearn.multioutput import MultiOutputRegressor
|
||
|
|
from sklearn.svm import LinearSVC
|
||
|
|
from sklearn.base import ClassifierMixin
|
||
|
|
from sklearn.utils import shuffle
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_regression():
|
||
|
|
X, y = datasets.make_regression(n_targets=3)
|
||
|
|
X_train, y_train = X[:50], y[:50]
|
||
|
|
X_test, y_test = X[50:], y[50:]
|
||
|
|
|
||
|
|
references = np.zeros_like(y_test)
|
||
|
|
for n in range(3):
|
||
|
|
rgr = GradientBoostingRegressor(random_state=0)
|
||
|
|
rgr.fit(X_train, y_train[:, n])
|
||
|
|
references[:, n] = rgr.predict(X_test)
|
||
|
|
|
||
|
|
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
|
||
|
|
rgr.fit(X_train, y_train)
|
||
|
|
y_pred = rgr.predict(X_test)
|
||
|
|
|
||
|
|
assert_almost_equal(references, y_pred)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_regression_partial_fit():
|
||
|
|
X, y = datasets.make_regression(n_targets=3)
|
||
|
|
X_train, y_train = X[:50], y[:50]
|
||
|
|
X_test, y_test = X[50:], y[50:]
|
||
|
|
|
||
|
|
references = np.zeros_like(y_test)
|
||
|
|
half_index = 25
|
||
|
|
for n in range(3):
|
||
|
|
sgr = SGDRegressor(random_state=0, max_iter=5)
|
||
|
|
sgr.partial_fit(X_train[:half_index], y_train[:half_index, n])
|
||
|
|
sgr.partial_fit(X_train[half_index:], y_train[half_index:, n])
|
||
|
|
references[:, n] = sgr.predict(X_test)
|
||
|
|
|
||
|
|
sgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
|
||
|
|
|
||
|
|
sgr.partial_fit(X_train[:half_index], y_train[:half_index])
|
||
|
|
sgr.partial_fit(X_train[half_index:], y_train[half_index:])
|
||
|
|
|
||
|
|
y_pred = sgr.predict(X_test)
|
||
|
|
assert_almost_equal(references, y_pred)
|
||
|
|
assert_false(hasattr(MultiOutputRegressor(Lasso), 'partial_fit'))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_regression_one_target():
|
||
|
|
# Test multi target regression raises
|
||
|
|
X, y = datasets.make_regression(n_targets=1)
|
||
|
|
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
|
||
|
|
assert_raises(ValueError, rgr.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_sparse_regression():
|
||
|
|
X, y = datasets.make_regression(n_targets=3)
|
||
|
|
X_train, y_train = X[:50], y[:50]
|
||
|
|
X_test = X[50:]
|
||
|
|
|
||
|
|
for sparse in [sp.csr_matrix, sp.csc_matrix, sp.coo_matrix, sp.dok_matrix,
|
||
|
|
sp.lil_matrix]:
|
||
|
|
rgr = MultiOutputRegressor(Lasso(random_state=0))
|
||
|
|
rgr_sparse = MultiOutputRegressor(Lasso(random_state=0))
|
||
|
|
|
||
|
|
rgr.fit(X_train, y_train)
|
||
|
|
rgr_sparse.fit(sparse(X_train), y_train)
|
||
|
|
|
||
|
|
assert_almost_equal(rgr.predict(X_test),
|
||
|
|
rgr_sparse.predict(sparse(X_test)))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_sample_weights_api():
|
||
|
|
X = [[1, 2, 3], [4, 5, 6]]
|
||
|
|
y = [[3.141, 2.718], [2.718, 3.141]]
|
||
|
|
w = [0.8, 0.6]
|
||
|
|
|
||
|
|
rgr = MultiOutputRegressor(Lasso())
|
||
|
|
assert_raises_regex(ValueError, "does not support sample weights",
|
||
|
|
rgr.fit, X, y, w)
|
||
|
|
|
||
|
|
# no exception should be raised if the base estimator supports weights
|
||
|
|
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
|
||
|
|
rgr.fit(X, y, w)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_sample_weight_partial_fit():
|
||
|
|
# weighted regressor
|
||
|
|
X = [[1, 2, 3], [4, 5, 6]]
|
||
|
|
y = [[3.141, 2.718], [2.718, 3.141]]
|
||
|
|
w = [2., 1.]
|
||
|
|
rgr_w = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
|
||
|
|
rgr_w.partial_fit(X, y, w)
|
||
|
|
|
||
|
|
# weighted with different weights
|
||
|
|
w = [2., 2.]
|
||
|
|
rgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
|
||
|
|
rgr.partial_fit(X, y, w)
|
||
|
|
|
||
|
|
assert_not_equal(rgr.predict(X)[0][0], rgr_w.predict(X)[0][0])
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_target_sample_weights():
|
||
|
|
# weighted regressor
|
||
|
|
Xw = [[1, 2, 3], [4, 5, 6]]
|
||
|
|
yw = [[3.141, 2.718], [2.718, 3.141]]
|
||
|
|
w = [2., 1.]
|
||
|
|
rgr_w = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
|
||
|
|
rgr_w.fit(Xw, yw, w)
|
||
|
|
|
||
|
|
# unweighted, but with repeated samples
|
||
|
|
X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
|
||
|
|
y = [[3.141, 2.718], [3.141, 2.718], [2.718, 3.141]]
|
||
|
|
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
|
||
|
|
rgr.fit(X, y)
|
||
|
|
|
||
|
|
X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
|
||
|
|
assert_almost_equal(rgr.predict(X_test), rgr_w.predict(X_test))
|
||
|
|
|
||
|
|
|
||
|
|
# Import the data
|
||
|
|
iris = datasets.load_iris()
|
||
|
|
# create a multiple targets by randomized shuffling and concatenating y.
|
||
|
|
X = iris.data
|
||
|
|
y1 = iris.target
|
||
|
|
y2 = shuffle(y1, random_state=1)
|
||
|
|
y3 = shuffle(y1, random_state=2)
|
||
|
|
y = np.column_stack((y1, y2, y3))
|
||
|
|
n_samples, n_features = X.shape
|
||
|
|
n_outputs = y.shape[1]
|
||
|
|
n_classes = len(np.unique(y1))
|
||
|
|
classes = list(map(np.unique, (y1, y2, y3)))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification_partial_fit_parallelism():
|
||
|
|
sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
|
||
|
|
mor = MultiOutputClassifier(sgd_linear_clf, n_jobs=-1)
|
||
|
|
mor.partial_fit(X, y, classes)
|
||
|
|
est1 = mor.estimators_[0]
|
||
|
|
mor.partial_fit(X, y)
|
||
|
|
est2 = mor.estimators_[0]
|
||
|
|
if cpu_count() > 1:
|
||
|
|
# parallelism requires this to be the case for a sane implementation
|
||
|
|
assert_false(est1 is est2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification_partial_fit():
|
||
|
|
# test if multi_target initializes correctly with base estimator and fit
|
||
|
|
# assert predictions work as expected for predict
|
||
|
|
|
||
|
|
sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
|
||
|
|
multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
|
||
|
|
|
||
|
|
# train the multi_target_linear and also get the predictions.
|
||
|
|
half_index = X.shape[0] // 2
|
||
|
|
multi_target_linear.partial_fit(
|
||
|
|
X[:half_index], y[:half_index], classes=classes)
|
||
|
|
|
||
|
|
first_predictions = multi_target_linear.predict(X)
|
||
|
|
assert_equal((n_samples, n_outputs), first_predictions.shape)
|
||
|
|
|
||
|
|
multi_target_linear.partial_fit(X[half_index:], y[half_index:])
|
||
|
|
second_predictions = multi_target_linear.predict(X)
|
||
|
|
assert_equal((n_samples, n_outputs), second_predictions.shape)
|
||
|
|
|
||
|
|
# train the linear classification with each column and assert that
|
||
|
|
# predictions are equal after first partial_fit and second partial_fit
|
||
|
|
for i in range(3):
|
||
|
|
# create a clone with the same state
|
||
|
|
sgd_linear_clf = clone(sgd_linear_clf)
|
||
|
|
sgd_linear_clf.partial_fit(
|
||
|
|
X[:half_index], y[:half_index, i], classes=classes[i])
|
||
|
|
assert_array_equal(sgd_linear_clf.predict(X), first_predictions[:, i])
|
||
|
|
sgd_linear_clf.partial_fit(X[half_index:], y[half_index:, i])
|
||
|
|
assert_array_equal(sgd_linear_clf.predict(X), second_predictions[:, i])
|
||
|
|
|
||
|
|
|
||
|
|
def test_mutli_output_classifiation_partial_fit_no_first_classes_exception():
|
||
|
|
sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
|
||
|
|
multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
|
||
|
|
assert_raises_regex(ValueError, "classes must be passed on the first call "
|
||
|
|
"to partial_fit.",
|
||
|
|
multi_target_linear.partial_fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification():
|
||
|
|
# test if multi_target initializes correctly with base estimator and fit
|
||
|
|
# assert predictions work as expected for predict, prodict_proba and score
|
||
|
|
|
||
|
|
forest = RandomForestClassifier(n_estimators=10, random_state=1)
|
||
|
|
multi_target_forest = MultiOutputClassifier(forest)
|
||
|
|
|
||
|
|
# train the multi_target_forest and also get the predictions.
|
||
|
|
multi_target_forest.fit(X, y)
|
||
|
|
|
||
|
|
predictions = multi_target_forest.predict(X)
|
||
|
|
assert_equal((n_samples, n_outputs), predictions.shape)
|
||
|
|
|
||
|
|
predict_proba = multi_target_forest.predict_proba(X)
|
||
|
|
|
||
|
|
assert len(predict_proba) == n_outputs
|
||
|
|
for class_probabilities in predict_proba:
|
||
|
|
assert_equal((n_samples, n_classes), class_probabilities.shape)
|
||
|
|
|
||
|
|
assert_array_equal(np.argmax(np.dstack(predict_proba), axis=1),
|
||
|
|
predictions)
|
||
|
|
|
||
|
|
# train the forest with each column and assert that predictions are equal
|
||
|
|
for i in range(3):
|
||
|
|
forest_ = clone(forest) # create a clone with the same state
|
||
|
|
forest_.fit(X, y[:, i])
|
||
|
|
assert_equal(list(forest_.predict(X)), list(predictions[:, i]))
|
||
|
|
assert_array_equal(list(forest_.predict_proba(X)),
|
||
|
|
list(predict_proba[i]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multiclass_multioutput_estimator():
|
||
|
|
# test to check meta of meta estimators
|
||
|
|
svc = LinearSVC(random_state=0)
|
||
|
|
multi_class_svc = OneVsRestClassifier(svc)
|
||
|
|
multi_target_svc = MultiOutputClassifier(multi_class_svc)
|
||
|
|
|
||
|
|
multi_target_svc.fit(X, y)
|
||
|
|
|
||
|
|
predictions = multi_target_svc.predict(X)
|
||
|
|
assert_equal((n_samples, n_outputs), predictions.shape)
|
||
|
|
|
||
|
|
# train the forest with each column and assert that predictions are equal
|
||
|
|
for i in range(3):
|
||
|
|
multi_class_svc_ = clone(multi_class_svc) # create a clone
|
||
|
|
multi_class_svc_.fit(X, y[:, i])
|
||
|
|
assert_equal(list(multi_class_svc_.predict(X)),
|
||
|
|
list(predictions[:, i]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multiclass_multioutput_estimator_predict_proba():
|
||
|
|
seed = 542
|
||
|
|
|
||
|
|
# make test deterministic
|
||
|
|
rng = np.random.RandomState(seed)
|
||
|
|
|
||
|
|
# random features
|
||
|
|
X = rng.normal(size=(5, 5))
|
||
|
|
|
||
|
|
# random labels
|
||
|
|
y1 = np.array(['b', 'a', 'a', 'b', 'a']).reshape(5, 1) # 2 classes
|
||
|
|
y2 = np.array(['d', 'e', 'f', 'e', 'd']).reshape(5, 1) # 3 classes
|
||
|
|
|
||
|
|
Y = np.concatenate([y1, y2], axis=1)
|
||
|
|
|
||
|
|
clf = MultiOutputClassifier(LogisticRegression(random_state=seed))
|
||
|
|
|
||
|
|
clf.fit(X, Y)
|
||
|
|
|
||
|
|
y_result = clf.predict_proba(X)
|
||
|
|
y_actual = [np.array([[0.23481764, 0.76518236],
|
||
|
|
[0.67196072, 0.32803928],
|
||
|
|
[0.54681448, 0.45318552],
|
||
|
|
[0.34883923, 0.65116077],
|
||
|
|
[0.73687069, 0.26312931]]),
|
||
|
|
np.array([[0.5171785, 0.23878628, 0.24403522],
|
||
|
|
[0.22141451, 0.64102704, 0.13755846],
|
||
|
|
[0.16751315, 0.18256843, 0.64991843],
|
||
|
|
[0.27357372, 0.55201592, 0.17441036],
|
||
|
|
[0.65745193, 0.26062899, 0.08191907]])]
|
||
|
|
|
||
|
|
for i in range(len(y_actual)):
|
||
|
|
assert_almost_equal(y_result[i], y_actual[i])
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification_sample_weights():
|
||
|
|
# weighted classifier
|
||
|
|
Xw = [[1, 2, 3], [4, 5, 6]]
|
||
|
|
yw = [[3, 2], [2, 3]]
|
||
|
|
w = np.asarray([2., 1.])
|
||
|
|
forest = RandomForestClassifier(n_estimators=10, random_state=1)
|
||
|
|
clf_w = MultiOutputClassifier(forest)
|
||
|
|
clf_w.fit(Xw, yw, w)
|
||
|
|
|
||
|
|
# unweighted, but with repeated samples
|
||
|
|
X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
|
||
|
|
y = [[3, 2], [3, 2], [2, 3]]
|
||
|
|
forest = RandomForestClassifier(n_estimators=10, random_state=1)
|
||
|
|
clf = MultiOutputClassifier(forest)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
|
||
|
|
assert_almost_equal(clf.predict(X_test), clf_w.predict(X_test))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_classification_partial_fit_sample_weights():
|
||
|
|
# weighted classifier
|
||
|
|
Xw = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
|
||
|
|
yw = [[3, 2], [2, 3], [3, 2]]
|
||
|
|
w = np.asarray([2., 1., 1.])
|
||
|
|
sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
|
||
|
|
clf_w = MultiOutputClassifier(sgd_linear_clf)
|
||
|
|
clf_w.fit(Xw, yw, w)
|
||
|
|
|
||
|
|
# unweighted, but with repeated samples
|
||
|
|
X = [[1, 2, 3], [1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
|
||
|
|
y = [[3, 2], [3, 2], [2, 3], [3, 2]]
|
||
|
|
sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
|
||
|
|
clf = MultiOutputClassifier(sgd_linear_clf)
|
||
|
|
clf.fit(X, y)
|
||
|
|
X_test = [[1.5, 2.5, 3.5]]
|
||
|
|
assert_array_almost_equal(clf.predict(X_test), clf_w.predict(X_test))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multi_output_exceptions():
|
||
|
|
# NotFittedError when fit is not done but score, predict and
|
||
|
|
# and predict_proba are called
|
||
|
|
moc = MultiOutputClassifier(LinearSVC(random_state=0))
|
||
|
|
assert_raises(NotFittedError, moc.predict, y)
|
||
|
|
assert_raises(NotFittedError, moc.predict_proba, y)
|
||
|
|
assert_raises(NotFittedError, moc.score, X, y)
|
||
|
|
# ValueError when number of outputs is different
|
||
|
|
# for fit and score
|
||
|
|
y_new = np.column_stack((y1, y2))
|
||
|
|
moc.fit(X, y)
|
||
|
|
assert_raises(ValueError, moc.score, X, y_new)
|
||
|
|
# ValueError when y is continuous
|
||
|
|
assert_raise_message(ValueError, "Unknown label type", moc.fit, X, X[:, 1])
|
||
|
|
|
||
|
|
|
||
|
|
def generate_multilabel_dataset_with_correlations():
|
||
|
|
# Generate a multilabel data set from a multiclass dataset as a way of
|
||
|
|
# by representing the integer number of the original class using a binary
|
||
|
|
# encoding.
|
||
|
|
X, y = make_classification(n_samples=1000,
|
||
|
|
n_features=100,
|
||
|
|
n_classes=16,
|
||
|
|
n_informative=10,
|
||
|
|
random_state=0)
|
||
|
|
|
||
|
|
Y_multi = np.array([[int(yyy) for yyy in format(yy, '#06b')[2:]]
|
||
|
|
for yy in y])
|
||
|
|
return X, Y_multi
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_fit_and_predict_with_logistic_regression():
|
||
|
|
# Fit classifier chain and verify predict performance
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression())
|
||
|
|
classifier_chain.fit(X, Y)
|
||
|
|
|
||
|
|
Y_pred = classifier_chain.predict(X)
|
||
|
|
assert_equal(Y_pred.shape, Y.shape)
|
||
|
|
|
||
|
|
Y_prob = classifier_chain.predict_proba(X)
|
||
|
|
Y_binary = (Y_prob >= .5)
|
||
|
|
assert_array_equal(Y_binary, Y_pred)
|
||
|
|
|
||
|
|
assert_equal([c.coef_.size for c in classifier_chain.estimators_],
|
||
|
|
list(range(X.shape[1], X.shape[1] + Y.shape[1])))
|
||
|
|
|
||
|
|
assert isinstance(classifier_chain, ClassifierMixin)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_fit_and_predict_with_linear_svc():
|
||
|
|
# Fit classifier chain and verify predict performance using LinearSVC
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
classifier_chain = ClassifierChain(LinearSVC())
|
||
|
|
classifier_chain.fit(X, Y)
|
||
|
|
|
||
|
|
Y_pred = classifier_chain.predict(X)
|
||
|
|
assert_equal(Y_pred.shape, Y.shape)
|
||
|
|
|
||
|
|
Y_decision = classifier_chain.decision_function(X)
|
||
|
|
|
||
|
|
Y_binary = (Y_decision >= 0)
|
||
|
|
assert_array_equal(Y_binary, Y_pred)
|
||
|
|
assert not hasattr(classifier_chain, 'predict_proba')
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_fit_and_predict_with_sparse_data():
|
||
|
|
# Fit classifier chain with sparse data
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
X_sparse = sp.csr_matrix(X)
|
||
|
|
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression())
|
||
|
|
classifier_chain.fit(X_sparse, Y)
|
||
|
|
Y_pred_sparse = classifier_chain.predict(X_sparse)
|
||
|
|
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression())
|
||
|
|
classifier_chain.fit(X, Y)
|
||
|
|
Y_pred_dense = classifier_chain.predict(X)
|
||
|
|
|
||
|
|
assert_array_equal(Y_pred_sparse, Y_pred_dense)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_fit_and_predict_with_sparse_data_and_cv():
|
||
|
|
# Fit classifier chain with sparse data cross_val_predict
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
X_sparse = sp.csr_matrix(X)
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression(), cv=3)
|
||
|
|
classifier_chain.fit(X_sparse, Y)
|
||
|
|
Y_pred = classifier_chain.predict(X_sparse)
|
||
|
|
assert_equal(Y_pred.shape, Y.shape)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_random_order():
|
||
|
|
# Fit classifier chain with random order
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
classifier_chain_random = ClassifierChain(LogisticRegression(),
|
||
|
|
order='random',
|
||
|
|
random_state=42)
|
||
|
|
classifier_chain_random.fit(X, Y)
|
||
|
|
Y_pred_random = classifier_chain_random.predict(X)
|
||
|
|
|
||
|
|
assert_not_equal(list(classifier_chain_random.order), list(range(4)))
|
||
|
|
assert_equal(len(classifier_chain_random.order_), 4)
|
||
|
|
assert_equal(len(set(classifier_chain_random.order_)), 4)
|
||
|
|
|
||
|
|
classifier_chain_fixed = \
|
||
|
|
ClassifierChain(LogisticRegression(),
|
||
|
|
order=classifier_chain_random.order_)
|
||
|
|
classifier_chain_fixed.fit(X, Y)
|
||
|
|
Y_pred_fixed = classifier_chain_fixed.predict(X)
|
||
|
|
|
||
|
|
# Randomly ordered chain should behave identically to a fixed order chain
|
||
|
|
# with the same order.
|
||
|
|
assert_array_equal(Y_pred_random, Y_pred_fixed)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_crossval_fit_and_predict():
|
||
|
|
# Fit classifier chain with cross_val_predict and verify predict
|
||
|
|
# performance
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
classifier_chain_cv = ClassifierChain(LogisticRegression(), cv=3)
|
||
|
|
classifier_chain_cv.fit(X, Y)
|
||
|
|
|
||
|
|
classifier_chain = ClassifierChain(LogisticRegression())
|
||
|
|
classifier_chain.fit(X, Y)
|
||
|
|
|
||
|
|
Y_pred_cv = classifier_chain_cv.predict(X)
|
||
|
|
Y_pred = classifier_chain.predict(X)
|
||
|
|
|
||
|
|
assert_equal(Y_pred_cv.shape, Y.shape)
|
||
|
|
assert_greater(jaccard_similarity_score(Y, Y_pred_cv), 0.4)
|
||
|
|
|
||
|
|
assert_not_equal(jaccard_similarity_score(Y, Y_pred_cv),
|
||
|
|
jaccard_similarity_score(Y, Y_pred))
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_chain_vs_independent_models():
|
||
|
|
# Verify that an ensemble of classifier chains (each of length
|
||
|
|
# N) can achieve a higher Jaccard similarity score than N independent
|
||
|
|
# models
|
||
|
|
X, Y = generate_multilabel_dataset_with_correlations()
|
||
|
|
X_train = X[:600, :]
|
||
|
|
X_test = X[600:, :]
|
||
|
|
Y_train = Y[:600, :]
|
||
|
|
Y_test = Y[600:, :]
|
||
|
|
|
||
|
|
ovr = OneVsRestClassifier(LogisticRegression())
|
||
|
|
ovr.fit(X_train, Y_train)
|
||
|
|
Y_pred_ovr = ovr.predict(X_test)
|
||
|
|
|
||
|
|
chain = ClassifierChain(LogisticRegression())
|
||
|
|
chain.fit(X_train, Y_train)
|
||
|
|
Y_pred_chain = chain.predict(X_test)
|
||
|
|
|
||
|
|
assert_greater(jaccard_similarity_score(Y_test, Y_pred_chain),
|
||
|
|
jaccard_similarity_score(Y_test, Y_pred_ovr))
|