def test_ovr_partial_fit():
# Test if partial_fit is working as intended
X, y = shuffle(iris.data, iris.target, random_state=0)
ovr = OneVsRestClassifier(MultinomialNB())
ovr.partial_fit(X[:100], y[:100], np.unique(y))
ovr.partial_fit(X[100:], y[100:])
pred = ovr.predict(X)
ovr2 = OneVsRestClassifier(MultinomialNB())
pred2 = ovr2.fit(X, y).predict(X)
assert_almost_equal(pred, pred2)
assert len(ovr.estimators_) == len(np.unique(y))
assert np.mean(y == pred) > 0.65
# Test when mini batches doesn't have all classes
# with SGDClassifier
X = np.abs(np.random.randn(14, 2))
y = [1, 1, 1, 1, 2, 3, 3, 0, 0, 2, 3, 1, 2, 3]
ovr = OneVsRestClassifier(
SGDClassifier(max_iter=1, tol=None, shuffle=False, random_state=0))
ovr.partial_fit(X[:7], y[:7], np.unique(y))
ovr.partial_fit(X[7:], y[7:])
pred = ovr.predict(X)
ovr1 = OneVsRestClassifier(
SGDClassifier(max_iter=1, tol=None, shuffle=False, random_state=0))
pred1 = ovr1.fit(X, y).predict(X)
assert np.mean(pred == y) == np.mean(pred1 == y)
# test partial_fit only exists if estimator has it:
ovr = OneVsRestClassifier(SVC())
assert not hasattr(ovr, "partial_fit")
def test_ovr_pipeline():
# Test with pipeline of length one
# This test is needed because the multiclass estimators may fail to detect
# the presence of predict_proba or decision_function.
clf = Pipeline([("tree", DecisionTreeClassifier())])
ovr_pipe = OneVsRestClassifier(clf)
ovr_pipe.fit(iris.data, iris.target)
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_array_equal(ovr.predict(iris.data), ovr_pipe.predict(iris.data))
def test_ovr_coef_exceptions():
# Not fitted exception!
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
# lambda is needed because we don't want coef_ to be evaluated right away
assert_raises(ValueError, lambda x: ovr.coef_, None)
# Doesn't have coef_ exception!
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_raises(AttributeError, lambda x: ovr.coef_, None)
def plot_subfigure(X, Y, subplot, title, transform):
if transform == "pca":
X = PCA(n_components=2).fit_transform(X)
elif transform == "cca":
X = CCA(n_components=2).fit(X, Y).transform(X)
else:
raise ValueError
min_x = np.min(X[:, 0])
max_x = np.max(X[:, 0])
min_y = np.min(X[:, 1])
max_y = np.max(X[:, 1])
classif = OneVsRestClassifier(SVC(kernel='linear'))
classif.fit(X, Y)
plt.subplot(2, 2, subplot)
plt.title(title)
zero_class = np.where(Y[:, 0])
one_class = np.where(Y[:, 1])
plt.scatter(X[:, 0], X[:, 1], s=40, c='gray', edgecolors=(0, 0, 0))
plt.scatter(X[zero_class, 0],
X[zero_class, 1],
s=160,
edgecolors='b',
facecolors='none',
linewidths=2,
label='Class 1')
plt.scatter(X[one_class, 0],
X[one_class, 1],
s=80,
edgecolors='orange',
facecolors='none',
linewidths=2,
label='Class 2')
plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
'Boundary\nfor class 1')
plot_hyperplane(classif.estimators_[1], min_x, max_x, 'k-.',
'Boundary\nfor class 2')
plt.xticks(())
plt.yticks(())
plt.xlim(min_x - .5 * max_x, max_x + .5 * max_x)
plt.ylim(min_y - .5 * max_y, max_y + .5 * max_y)
if subplot == 2:
plt.xlabel('First principal component')
plt.ylabel('Second principal component')
plt.legend(loc="upper left")
def test_ovr_fit_predict():
# A classifier which implements decision_function.
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert len(ovr.estimators_) == n_classes
clf = LinearSVC(random_state=0)
pred2 = clf.fit(iris.data, iris.target).predict(iris.data)
assert np.mean(iris.target == pred) == np.mean(iris.target == pred2)
# A classifier which implements predict_proba.
ovr = OneVsRestClassifier(MultinomialNB())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert np.mean(iris.target == pred) > 0.65
def test_ovr_ovo_regressor():
# test that ovr and ovo work on regressors which don't have a decision_
# function
ovr = OneVsRestClassifier(DecisionTreeRegressor())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert len(ovr.estimators_) == n_classes
assert_array_equal(np.unique(pred), [0, 1, 2])
# we are doing something sensible
assert np.mean(pred == iris.target) > .9
ovr = OneVsOneClassifier(DecisionTreeRegressor())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert len(ovr.estimators_) == n_classes * (n_classes - 1) / 2
assert_array_equal(np.unique(pred), [0, 1, 2])
# we are doing something sensible
assert np.mean(pred == iris.target) > .9
def test_ovr_coef_():
for base_classifier in [
SVC(kernel='linear', random_state=0),
LinearSVC(random_state=0)
]:
# SVC has sparse coef with sparse input data
ovr = OneVsRestClassifier(base_classifier)
for X in [iris.data, sp.csr_matrix(iris.data)]:
# test with dense and sparse coef
ovr.fit(X, iris.target)
shape = ovr.coef_.shape
assert shape[0] == n_classes
assert shape[1] == iris.data.shape[1]
# don't densify sparse coefficients
assert (sp.issparse(ovr.estimators_[0].coef_) == sp.issparse(
ovr.coef_))
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 (jaccard_score(Y_test, Y_pred_chain, average='samples') >
jaccard_score(Y_test, Y_pred_ovr, average='samples'))
def test_ovr_multilabel_predict_proba():
base_clf = MultinomialNB(alpha=1)
for au in (False, True):
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=3,
length=50,
allow_unlabeled=au,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
# Decision function only estimator.
decision_only = OneVsRestClassifier(svm.SVR()).fit(X_train, Y_train)
assert not hasattr(decision_only, 'predict_proba')
# Estimator with predict_proba disabled, depending on parameters.
decision_only = OneVsRestClassifier(svm.SVC(probability=False))
assert not hasattr(decision_only, 'predict_proba')
decision_only.fit(X_train, Y_train)
assert not hasattr(decision_only, 'predict_proba')
assert hasattr(decision_only, 'decision_function')
# Estimator which can get predict_proba enabled after fitting
gs = GridSearchCV(svm.SVC(probability=False),
param_grid={'probability': [True]})
proba_after_fit = OneVsRestClassifier(gs)
assert not hasattr(proba_after_fit, 'predict_proba')
proba_after_fit.fit(X_train, Y_train)
assert hasattr(proba_after_fit, 'predict_proba')
Y_pred = clf.predict(X_test)
Y_proba = clf.predict_proba(X_test)
# predict assigns a label if the probability that the
# sample has the label is greater than 0.5.
pred = Y_proba > .5
assert_array_equal(pred, Y_pred)
def test_ovr_fit_predict_svc():
ovr = OneVsRestClassifier(svm.SVC())
ovr.fit(iris.data, iris.target)
assert len(ovr.estimators_) == 3
assert ovr.score(iris.data, iris.target) > .9
print(__doc__)
# Load a multi-label dataset from https://www.openml.org/d/40597
X, Y = fetch_openml('yeast', version=4, return_X_y=True)
Y = Y == 'TRUE'
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=.2,
random_state=0)
# Fit an independent logistic regression model for each class using the
# OneVsRestClassifier wrapper.
base_lr = LogisticRegression()
ovr = OneVsRestClassifier(base_lr)
ovr.fit(X_train, Y_train)
Y_pred_ovr = ovr.predict(X_test)
ovr_jaccard_score = jaccard_score(Y_test, Y_pred_ovr, average='samples')
# Fit an ensemble of logistic regression classifier chains and take the
# take the average prediction of all the chains.
chains = [
ClassifierChain(base_lr, order='random', random_state=i) for i in range(10)
]
for chain in chains:
chain.fit(X_train, Y_train)
Y_pred_chains = np.array([chain.predict(X_test) for chain in chains])
chain_jaccard_scores = [
jaccard_score(Y_test, Y_pred_chain >= .5, average='samples')
for Y_pred_chain in Y_pred_chains
# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.5,
random_state=0)
# Learn to predict each class against the other
classifier = OneVsRestClassifier(
svm.SVC(kernel='linear', probability=True, random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
##############################################################################
# Plot of a ROC curve for a specific class
# Use label_binarize to be multi-label like settings
Y = label_binarize(y, classes=[0, 1, 2])
n_classes = Y.shape[1]
# Split into training and test
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=.5,
random_state=random_state)
# We use OneVsRestClassifier for multi-label prediction
from mrex.multiclass import OneVsRestClassifier
# Run classifier
classifier = OneVsRestClassifier(svm.LinearSVC(random_state=random_state))
classifier.fit(X_train, Y_train)
y_score = classifier.decision_function(X_test)
###############################################################################
# The average precision score in multi-label settings
# ....................................................
from mrex.metrics import precision_recall_curve
from mrex.metrics import average_precision_score
# For each class
precision = dict()
recall = dict()
average_precision = dict()
for i in range(n_classes):
precision[i], recall[i], _ = precision_recall_curve(
Y_test[:, i], y_score[:, i])