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import numpy as np
from numpy.testing import assert_array_equal
from nose.tools import assert_equal
from nose.tools import assert_almost_equal
from nose.tools import assert_true
from nose.tools import assert_raises
from sklearn.utils.testing import assert_greater
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OutputCodeClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet, Ridge
from sklearn.tree import DecisionTreeClassifier
from sklearn.grid_search import GridSearchCV
from sklearn import svm
from sklearn import datasets
iris = datasets.load_iris()
rng = np.random.RandomState(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
n_classes = 3
# FIXME: - should use sets
# - should move to metrics module
def multilabel_precision(Y_true, Y_pred):
n_predictions = 0
n_correct = 0
for i in range(len(Y_true)):
n_predictions += len(Y_pred[i])
for label in Y_pred[i]:
if label in Y_true[i]:
n_correct += 1
return float(n_correct) / n_predictions
def multilabel_recall(Y_true, Y_pred):
n_labels = 0
n_correct = 0
for i in range(len(Y_true)):
n_labels += len(Y_true[i])
for label in Y_pred[i]:
if label in Y_true[i]:
n_correct += 1
return float(n_correct) / n_labels
def test_ovr_exceptions():
ovr = OneVsRestClassifier(LinearSVC())
assert_raises(ValueError, ovr.predict, [])
def test_ovr_fit_predict():
# A classifier which implements decision_function.
ovr = OneVsRestClassifier(LinearSVC())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovr.estimators_), n_classes)
pred2 = LinearSVC().fit(iris.data, iris.target).predict(iris.data)
assert_equal(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_true(np.mean(iris.target == pred) >= 0.65)
def test_ovr_multilabel():
# Toy dataset where features correspond directly to labels.
X = np.array([[0, 4, 5], [0, 5, 0], [3, 3, 3], [4, 0, 6], [6, 0, 0]])
y = [[1, 2], [1], [0, 1, 2], [0, 2], [0]]
Y = np.array([[0, 1, 1],
[0, 1, 0],
[1, 1, 1],
[1, 0, 1],
[1, 0, 0]])
for base_clf in (MultinomialNB(), LinearSVC(),
LinearRegression(), Ridge(),
ElasticNet(), Lasso(alpha=0.5)):
# test input as lists of tuples
clf = OneVsRestClassifier(base_clf).fit(X, y)
y_pred = clf.predict([[0, 4, 4]])[0]
assert_equal(set(y_pred), set([1, 2]))
assert_true(clf.multilabel_)
# test input as label indicator matrix
clf = OneVsRestClassifier(base_clf).fit(X, Y)
y_pred = clf.predict([[0, 4, 4]])[0]
assert_array_equal(y_pred, [0, 1, 1])
assert_true(clf.multilabel_)
def test_ovr_fit_predict_svc():
ovr = OneVsRestClassifier(svm.SVC())
ovr.fit(iris.data, iris.target)
assert_equal(len(ovr.estimators_), 3)
assert_greater(ovr.score(iris.data, iris.target), .9)
def test_ovr_multilabel_dataset():
base_clf = MultinomialNB(alpha=1)
for au, prec, recall in zip((True, False), (0.65, 0.74), (0.72, 0.84)):
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=2,
length=50,
allow_unlabeled=au,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test, Y_test = X[80:], Y[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
Y_pred = clf.predict(X_test)
assert_true(clf.multilabel_)
assert_almost_equal(multilabel_precision(Y_test, Y_pred), prec,
places=2)
assert_almost_equal(multilabel_recall(Y_test, Y_pred), recall,
places=2)
def test_ovr_gridsearch():
ovr = OneVsRestClassifier(LinearSVC())
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ovr, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
def test_ovr_coef_():
ovr = OneVsRestClassifier(LinearSVC())
ovr.fit(iris.data, iris.target)
shape = ovr.coef_.shape
assert_equal(shape[0], n_classes)
assert_equal(shape[1], iris.data.shape[1])
def test_ovr_coef_exceptions():
# Not fitted exception!
ovr = OneVsRestClassifier(LinearSVC())
# 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 test_ovo_exceptions():
ovo = OneVsOneClassifier(LinearSVC())
assert_raises(ValueError, ovo.predict, [])
def test_ovo_fit_predict():
# A classifier which implements decision_function.
ovo = OneVsOneClassifier(LinearSVC())
ovo.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovo.estimators_), n_classes * (n_classes - 1) / 2)
# A classifier which implements predict_proba.
ovo = OneVsOneClassifier(MultinomialNB())
ovo.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovo.estimators_), n_classes * (n_classes - 1) / 2)
def test_ovo_gridsearch():
ovo = OneVsOneClassifier(LinearSVC())
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ovo, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
def test_ecoc_exceptions():
ecoc = OutputCodeClassifier(LinearSVC())
assert_raises(ValueError, ecoc.predict, [])
def test_ecoc_fit_predict():
# A classifier which implements decision_function.
ecoc = OutputCodeClassifier(LinearSVC(), code_size=2)
ecoc.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ecoc.estimators_), n_classes * 2)
# A classifier which implements predict_proba.
ecoc = OutputCodeClassifier(MultinomialNB(), code_size=2)
ecoc.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ecoc.estimators_), n_classes * 2)
def test_ecoc_gridsearch():
ecoc = OutputCodeClassifier(LinearSVC())
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ecoc, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
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