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Python LogisticRegression.decision_function方法代码示例

本文整理汇总了Python中sklearn.linear_model.logistic.LogisticRegression.decision_function方法的典型用法代码示例。如果您正苦于以下问题:Python LogisticRegression.decision_function方法的具体用法?Python LogisticRegression.decision_function怎么用?Python LogisticRegression.decision_function使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在sklearn.linear_model.logistic.LogisticRegression的用法示例。


在下文中一共展示了LogisticRegression.decision_function方法的4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_write_parameters

# 需要导入模块: from sklearn.linear_model.logistic import LogisticRegression [as 别名]
# 或者: from sklearn.linear_model.logistic.LogisticRegression import decision_function [as 别名]
def test_write_parameters():
    # Test that we can write to coef_ and intercept_
    clf = LogisticRegression(random_state=0)
    clf.fit(X, Y1)
    clf.coef_[:] = 0
    clf.intercept_[:] = 0
    assert_array_almost_equal(clf.decision_function(X), 0)
开发者ID:huafengw,项目名称:scikit-learn,代码行数:9,代码来源:test_logistic.py

示例2: test_sparsify

# 需要导入模块: from sklearn.linear_model.logistic import LogisticRegression [as 别名]
# 或者: from sklearn.linear_model.logistic.LogisticRegression import decision_function [as 别名]
def test_sparsify():
    # Test sparsify and densify members.
    n_samples, n_features = iris.data.shape
    target = iris.target_names[iris.target]
    clf = LogisticRegression(random_state=0).fit(iris.data, target)

    pred_d_d = clf.decision_function(iris.data)

    clf.sparsify()
    assert_true(sp.issparse(clf.coef_))
    pred_s_d = clf.decision_function(iris.data)

    sp_data = sp.coo_matrix(iris.data)
    pred_s_s = clf.decision_function(sp_data)

    clf.densify()
    pred_d_s = clf.decision_function(sp_data)

    assert_array_almost_equal(pred_d_d, pred_s_d)
    assert_array_almost_equal(pred_d_d, pred_s_s)
    assert_array_almost_equal(pred_d_d, pred_d_s)
开发者ID:huafengw,项目名称:scikit-learn,代码行数:23,代码来源:test_logistic.py

示例3: test_multinomial_binary_probabilities

# 需要导入模块: from sklearn.linear_model.logistic import LogisticRegression [as 别名]
# 或者: from sklearn.linear_model.logistic.LogisticRegression import decision_function [as 别名]
def test_multinomial_binary_probabilities():
    # Test multinomial LR gives expected probabilities based on the
    # decision function, for a binary problem.
    X, y = make_classification()
    clf = LogisticRegression(multi_class='multinomial', solver='saga')
    clf.fit(X, y)

    decision = clf.decision_function(X)
    proba = clf.predict_proba(X)

    expected_proba_class_1 = (np.exp(decision) /
                              (np.exp(decision) + np.exp(-decision)))
    expected_proba = np.c_[1-expected_proba_class_1, expected_proba_class_1]

    assert_almost_equal(proba, expected_proba)
开发者ID:huafengw,项目名称:scikit-learn,代码行数:17,代码来源:test_logistic.py

示例4: test_fit_credit_backupsklearn

# 需要导入模块: from sklearn.linear_model.logistic import LogisticRegression [as 别名]
# 或者: from sklearn.linear_model.logistic.LogisticRegression import decision_function [as 别名]
def test_fit_credit_backupsklearn():
    df = pd.read_csv("./open_data/creditcard.csv")
    X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
    y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
    Solver = h2o4gpu.LogisticRegression

    enet_h2o4gpu = Solver(glm_stop_early=False)
    print("h2o4gpu fit()")
    enet_h2o4gpu.fit(X, y)
    print("h2o4gpu predict()")
    print(enet_h2o4gpu.predict(X))
    print("h2o4gpu score()")
    print(enet_h2o4gpu.score(X,y))

    enet = Solver(dual=True, max_iter=100, tol=1E-4, intercept_scaling=0.99, random_state=1234)
    print("h2o4gpu scikit wrapper fit()")
    enet.fit(X, y)
    print("h2o4gpu scikit wrapper predict()")
    print(enet.predict(X))
    print("h2o4gpu scikit wrapper predict_proba()")
    print(enet.predict_proba(X))
    print("h2o4gpu scikit wrapper predict_log_proba()")
    print(enet.predict_log_proba(X))
    print("h2o4gpu scikit wrapper score()")
    print(enet.score(X,y))
    print("h2o4gpu scikit wrapper decision_function()")
    print(enet.decision_function(X))
    print("h2o4gpu scikit wrapper densify()")
    print(enet.densify())
    print("h2o4gpu scikit wrapper sparsify")
    print(enet.sparsify())
    
    from sklearn.linear_model.logistic import  LogisticRegression
    enet_sk = LogisticRegression(dual=True, max_iter=100, tol=1E-4, intercept_scaling=0.99, random_state=1234)
    print("Scikit fit()")
    enet_sk.fit(X, y)
    print("Scikit predict()")
    print(enet_sk.predict(X))
    print("Scikit predict_proba()")
    print(enet_sk.predict_proba(X))
    print("Scikit predict_log_proba()")
    print(enet_sk.predict_log_proba(X))
    print("Scikit score()")
    print(enet_sk.score(X,y))
    print("Scikit decision_function()")
    print(enet_sk.decision_function(X))
    print("Scikit densify()")
    print(enet_sk.densify())
    print("Sciki sparsify")
    print(enet_sk.sparsify())

    enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray()
    print(enet_sk.coef_)
    print(enet_sk_coef)
    print(enet.coef_)
    print(enet_sk.intercept_)
    print("Coeffs, intercept, and n_iters should match")
    assert np.allclose(enet.coef_, enet_sk_coef)
    assert np.allclose(enet.intercept_, enet_sk.intercept_)
    assert np.allclose(enet.n_iter_, enet_sk.n_iter_)
    print("Preds should match")
    assert np.allclose(enet.predict_proba(X), enet_sk.predict_proba(X))
    assert np.allclose(enet.predict(X), enet_sk.predict(X))
    assert np.allclose(enet.predict_log_proba(X), enet_sk.predict_log_proba(X))
开发者ID:wamsiv,项目名称:h2o4gpu,代码行数:66,代码来源:test_logistic_sklearn_wrapper.py


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