Python Ridge.score方法代码示例

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def test_ridge():
    """Ridge regression convergence test using score

    TODO: for this test to be robust, we should use a dataset instead
    of np.random.
    """
    alpha = 1.0

    for solver in ("sparse_cg", "dense_cholesky", "lsqr"):
        # With more samples than features
        n_samples, n_features = 6, 5
        y = rng.randn(n_samples)
        X = rng.randn(n_samples, n_features)

        ridge = Ridge(alpha=alpha, solver=solver)
        ridge.fit(X, y)
        assert_equal(ridge.coef_.shape, (X.shape[1], ))
        assert_greater(ridge.score(X, y), 0.47)

        ridge.fit(X, y, sample_weight=np.ones(n_samples))
        assert_greater(ridge.score(X, y), 0.47)

        # With more features than samples
        n_samples, n_features = 5, 10
        y = rng.randn(n_samples)
        X = rng.randn(n_samples, n_features)
        ridge = Ridge(alpha=alpha, solver=solver)
        ridge.fit(X, y)
        assert_greater(ridge.score(X, y), .9)

        ridge.fit(X, y, sample_weight=np.ones(n_samples))
        assert_greater(ridge.score(X, y), 0.9)

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def test_ridge():
    # Ridge regression convergence test using score
    # TODO: for this test to be robust, we should use a dataset instead
    # of np.random.
    rng = np.random.RandomState(0)
    alpha = 1.0

    for solver in ("svd", "sparse_cg", "cholesky", "lsqr"):
        # With more samples than features
        n_samples, n_features = 6, 5
        y = rng.randn(n_samples)
        X = rng.randn(n_samples, n_features)

        ridge = Ridge(alpha=alpha, solver=solver)
        ridge.fit(X, y)
        assert_equal(ridge.coef_.shape, (X.shape[1], ))
        assert_greater(ridge.score(X, y), 0.47)

        if solver == "cholesky":
            # Currently the only solver to support sample_weight.
            ridge.fit(X, y, sample_weight=np.ones(n_samples))
            assert_greater(ridge.score(X, y), 0.47)

        # With more features than samples
        n_samples, n_features = 5, 10
        y = rng.randn(n_samples)
        X = rng.randn(n_samples, n_features)
        ridge = Ridge(alpha=alpha, solver=solver)
        ridge.fit(X, y)
        assert_greater(ridge.score(X, y), .9)

        if solver == "cholesky":
            # Currently the only solver to support sample_weight.
            ridge.fit(X, y, sample_weight=np.ones(n_samples))
            assert_greater(ridge.score(X, y), 0.9)

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def test_ridge():
    """Ridge regression convergence test using score

    TODO: for this test to be robust, we should use a dataset instead
    of np.random.
    """
    alpha = 1.0

    # With more samples than features
    n_samples, n_features = 6, 5
    y = np.random.randn(n_samples)
    X = np.random.randn(n_samples, n_features)

    ridge = Ridge(alpha=alpha)
    ridge.fit(X, y)
    assert_equal(ridge.coef_.shape, (X.shape[1],))
    assert ridge.score(X, y) > 0.5

    ridge.fit(X, y, sample_weight=np.ones(n_samples))
    assert ridge.score(X, y) > 0.5

    # With more features than samples
    n_samples, n_features = 5, 10
    y = np.random.randn(n_samples)
    X = np.random.randn(n_samples, n_features)
    ridge = Ridge(alpha=alpha)
    ridge.fit(X, y)
    assert ridge.score(X, y) > 0.9

    ridge.fit(X, y, sample_weight=np.ones(n_samples))
    assert ridge.score(X, y) > 0.9

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def _test_tolerance(filter_):
    ridge = Ridge(tol=1e-5)
    ridge.fit(filter_(X_diabetes), y_diabetes)
    score = ridge.score(filter_(X_diabetes), y_diabetes)

    ridge2 = Ridge(tol=1e-3)
    ridge2.fit(filter_(X_diabetes), y_diabetes)
    score2 = ridge2.score(filter_(X_diabetes), y_diabetes)

    assert_true(score >= score2)

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def _test_tolerance(filter_):
    ridge = Ridge(tol=1e-5, fit_intercept=False)
    ridge.fit(filter_(X_diabetes), y_diabetes)
    score = ridge.score(filter_(X_diabetes), y_diabetes)

    ridge2 = Ridge(tol=1e-3, fit_intercept=False)
    ridge2.fit(filter_(X_diabetes), y_diabetes)
    score2 = ridge2.score(filter_(X_diabetes), y_diabetes)

    assert score >= score2

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def test_ridge_singular():
    # test on a singular matrix
    rng = np.random.RandomState(0)
    n_samples, n_features = 6, 6
    y = rng.randn(n_samples // 2)
    y = np.concatenate((y, y))
    X = rng.randn(n_samples // 2, n_features)
    X = np.concatenate((X, X), axis=0)

    ridge = Ridge(alpha=0)
    ridge.fit(X, y)
    assert_greater(ridge.score(X, y), 0.9)

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def test_fit_simple_backupsklearn():
    df = pd.read_csv("./open_data/simple.txt", delim_whitespace=True)
    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.Ridge

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

    enet_wrapper = Solver(normalize=True, random_state=1234)
    print("h2o4gpu scikit wrapper fit()")
    enet_wrapper.fit(X, y)
    print("h2o4gpu scikit wrapper predict()")
    print(enet_wrapper.predict(X))
    print("h2o4gpu scikit wrapper score()")
    print(enet_wrapper.score(X, y))

    from sklearn.linear_model.ridge import Ridge
    enet_sk = Ridge(normalize=True, random_state=1234)
    print("Scikit fit()")
    enet_sk.fit(X, y)
    print("Scikit predict()")
    print(enet_sk.predict(X))
    print("Scikit score()")
    print(enet_sk.score(X, y))

    enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray()

    print(enet_sk.coef_)

    print(enet_sk_coef)

    print(enet_wrapper.coef_)

    print(enet_sk.intercept_)
    print(enet_wrapper.intercept_)

    print(enet_sk.n_iter_)
    print(enet_wrapper.n_iter_)

    print("Coeffs, intercept, and n_iters should match")
    assert np.allclose(enet_wrapper.coef_, enet_sk_coef)
    assert np.allclose(enet_wrapper.intercept_, enet_sk.intercept_)

# 需要导入模块: from sklearn.linear_model.ridge import Ridge [as 别名]
# 或者: from sklearn.linear_model.ridge.Ridge import score [as 别名]
def _test_ridge_diabetes(filter_):
    ridge = Ridge(fit_intercept=False)
    ridge.fit(filter_(X_diabetes), y_diabetes)
    return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)