本文整理汇总了Python中sklearn.multioutput.ClassifierChain类的典型用法代码示例。如果您正苦于以下问题：Python ClassifierChain类的具体用法？Python ClassifierChain怎么用？Python ClassifierChain使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了ClassifierChain类的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_classifier_chain_vs_independent_models
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
yeast = fetch_mldata('yeast')
X = yeast['data']
Y = yeast['target'].transpose().toarray()
X_train = X[:2000, :]
X_test = X[2000:, :]
Y_train = Y[:2000, :]
Y_test = Y[2000:, :]
ovr = OneVsRestClassifier(LogisticRegression())
ovr.fit(X_train, Y_train)
Y_pred_ovr = ovr.predict(X_test)
chain = ClassifierChain(LogisticRegression(),
order=np.array([0, 2, 4, 6, 8, 10,
12, 1, 3, 5, 7, 9,
11, 13]))
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))示例2: test_classifier_chain_fit_and_predict_with_sparse_data_and_cv
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)示例3: test_classifier_chain_fit_and_predict_with_linear_svc
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')示例4: test_classifier_chain_fit_and_predict_with_logistic_regression
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])))示例5: test_classifier_chain_vs_independent_models
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))示例6: test_classifier_chain_fit_and_predict_with_sparse_data
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)示例7: test_classifier_chain_crossval_fit_and_predict
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))示例8: test_classifier_chain_random_order
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)