Python linear_model.ARDRegression方法代码示例

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def getModels():
    result = []
    result.append("LinearRegression")
    result.append("BayesianRidge")
    result.append("ARDRegression")
    result.append("ElasticNet")
    result.append("HuberRegressor")
    result.append("Lasso")
    result.append("LassoLars")
    result.append("Rigid")
    result.append("SGDRegressor")
    result.append("SVR")
    result.append("MLPClassifier")
    result.append("KNeighborsClassifier")
    result.append("SVC")
    result.append("GaussianProcessClassifier")
    result.append("DecisionTreeClassifier")
    result.append("RandomForestClassifier")
    result.append("AdaBoostClassifier")
    result.append("GaussianNB")
    result.append("LogisticRegression")
    result.append("QuadraticDiscriminantAnalysis")
    return result 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def get_model(PARAMS):
    '''Get model according to parameters'''
    model_dict = {
        'LinearRegression': LinearRegression(),
        'Ridge': Ridge(),
        'Lars': Lars(),
        'ARDRegression': ARDRegression()

    }
    if not model_dict.get(PARAMS['model_name']):
        LOG.exception('Not supported model!')
        exit(1)

    model = model_dict[PARAMS['model_name']]
    model.normalize = bool(PARAMS['normalize'])

    return model 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def predict_features(self, df_features, df_target, idx=0, **kwargs):
        """For one variable, predict its neighbouring nodes.

        Args:
            df_features (pandas.DataFrame):
            df_target (pandas.Series):
            idx (int): (optional) for printing purposes
            kwargs (dict): additional options for algorithms

        Returns:
            list: scores of each feature relatively to the target
        """
        X = df_features.values
        y = df_target.values
        clf = ard(compute_score=True)
        clf.fit(X, y.ravel())

        return np.abs(clf.coef_) 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def test_model_ard_regression(self):
        model, X = fit_regression_model(
            linear_model.ARDRegression(), factor=0.001)
        model_onnx = convert_sklearn(
            model, "ard regression",
            [("input", FloatTensorType([None, X.shape[1]]))])
        self.assertIsNotNone(model_onnx)
        dump_data_and_model(
            X,
            model,
            model_onnx,
            basename="SklearnARDRegression-Dec4",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.2.1')",
        ) 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def test_check_is_fitted():
    # Check is ValueError raised when non estimator instance passed
    assert_raises(ValueError, check_is_fitted, ARDRegression, "coef_")
    assert_raises(TypeError, check_is_fitted, "SVR", "support_")

    ard = ARDRegression()
    svr = SVR(gamma='scale')

    try:
        assert_raises(NotFittedError, check_is_fitted, ard, "coef_")
        assert_raises(NotFittedError, check_is_fitted, svr, "support_")
    except ValueError:
        assert False, "check_is_fitted failed with ValueError"

    # NotFittedError is a subclass of both ValueError and AttributeError
    try:
        check_is_fitted(ard, "coef_", "Random message %(name)s, %(name)s")
    except ValueError as e:
        assert_equal(str(e), "Random message ARDRegression, ARDRegression")

    try:
        check_is_fitted(svr, "support_", "Another message %(name)s, %(name)s")
    except AttributeError as e:
        assert_equal(str(e), "Another message SVR, SVR")

    ard.fit(*make_blobs())
    svr.fit(*make_blobs())

    assert_equal(None, check_is_fitted(ard, "coef_"))
    assert_equal(None, check_is_fitted(svr, "support_")) 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def ARDRegression_on_fold(feature_sets, train, test, y, y_all, X, dim, dimsum, learn_options):
    '''
    '''
    clf = ARDRegression()
    clf.fit(X[train], y[train][:, 0])
    y_pred = clf.predict(X[test])[:, None]
    return y_pred, clf 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.linear_model.ARDRegression, lm.ARDRegression)
        self.assertIs(df.linear_model.BayesianRidge, lm.BayesianRidge)
        self.assertIs(df.linear_model.ElasticNet, lm.ElasticNet)
        self.assertIs(df.linear_model.ElasticNetCV, lm.ElasticNetCV)

        self.assertIs(df.linear_model.HuberRegressor, lm.HuberRegressor)

        self.assertIs(df.linear_model.Lars, lm.Lars)
        self.assertIs(df.linear_model.LarsCV, lm.LarsCV)
        self.assertIs(df.linear_model.Lasso, lm.Lasso)
        self.assertIs(df.linear_model.LassoCV, lm.LassoCV)
        self.assertIs(df.linear_model.LassoLars, lm.LassoLars)
        self.assertIs(df.linear_model.LassoLarsCV, lm.LassoLarsCV)
        self.assertIs(df.linear_model.LassoLarsIC, lm.LassoLarsIC)

        self.assertIs(df.linear_model.LinearRegression, lm.LinearRegression)
        self.assertIs(df.linear_model.LogisticRegression, lm.LogisticRegression)
        self.assertIs(df.linear_model.LogisticRegressionCV, lm.LogisticRegressionCV)
        self.assertIs(df.linear_model.MultiTaskLasso, lm.MultiTaskLasso)
        self.assertIs(df.linear_model.MultiTaskElasticNet, lm.MultiTaskElasticNet)
        self.assertIs(df.linear_model.MultiTaskLassoCV, lm.MultiTaskLassoCV)
        self.assertIs(df.linear_model.MultiTaskElasticNetCV, lm.MultiTaskElasticNetCV)

        self.assertIs(df.linear_model.OrthogonalMatchingPursuit, lm.OrthogonalMatchingPursuit)
        self.assertIs(df.linear_model.OrthogonalMatchingPursuitCV, lm.OrthogonalMatchingPursuitCV)
        self.assertIs(df.linear_model.PassiveAggressiveClassifier, lm.PassiveAggressiveClassifier)
        self.assertIs(df.linear_model.PassiveAggressiveRegressor, lm.PassiveAggressiveRegressor)

        self.assertIs(df.linear_model.Perceptron, lm.Perceptron)
        self.assertIs(df.linear_model.RandomizedLasso, lm.RandomizedLasso)
        self.assertIs(df.linear_model.RandomizedLogisticRegression, lm.RandomizedLogisticRegression)
        self.assertIs(df.linear_model.RANSACRegressor, lm.RANSACRegressor)
        self.assertIs(df.linear_model.Ridge, lm.Ridge)
        self.assertIs(df.linear_model.RidgeClassifier, lm.RidgeClassifier)
        self.assertIs(df.linear_model.RidgeClassifierCV, lm.RidgeClassifierCV)
        self.assertIs(df.linear_model.RidgeCV, lm.RidgeCV)
        self.assertIs(df.linear_model.SGDClassifier, lm.SGDClassifier)
        self.assertIs(df.linear_model.SGDRegressor, lm.SGDRegressor)
        self.assertIs(df.linear_model.TheilSenRegressor, lm.TheilSenRegressor) 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def test_check_is_fitted():
    # Check is ValueError raised when non estimator instance passed
    assert_raises(ValueError, check_is_fitted, ARDRegression, "coef_")
    assert_raises(TypeError, check_is_fitted, "SVR", "support_")

    ard = ARDRegression()
    svr = SVR()

    try:
        assert_raises(NotFittedError, check_is_fitted, ard, "coef_")
        assert_raises(NotFittedError, check_is_fitted, svr, "support_")
    except ValueError:
        assert False, "check_is_fitted failed with ValueError"

    # NotFittedError is a subclass of both ValueError and AttributeError
    try:
        check_is_fitted(ard, "coef_", "Random message %(name)s, %(name)s")
    except ValueError as e:
        assert_equal(str(e), "Random message ARDRegression, ARDRegression")

    try:
        check_is_fitted(svr, "support_", "Another message %(name)s, %(name)s")
    except AttributeError as e:
        assert_equal(str(e), "Another message SVR, SVR")

    ard.fit(*make_blobs())
    svr.fit(*make_blobs())

    assert_equal(None, check_is_fitted(ard, "coef_"))
    assert_equal(None, check_is_fitted(svr, "support_")) 

# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import ARDRegression [as 别名]
def getSKLearnModel(modelName):
    if modelName == 'LinearRegression':
        model = linear_model.LinearRegression()
    elif modelName == 'BayesianRidge':
        model = linear_model.BayesianRidge()
    elif modelName == 'ARDRegression':
        model = linear_model.ARDRegression()
    elif modelName == 'ElasticNet':
        model = linear_model.ElasticNet()
    elif modelName == 'HuberRegressor':
        model = linear_model.HuberRegressor()
    elif modelName == 'Lasso':
        model = linear_model.Lasso()
    elif modelName == 'LassoLars':
        model = linear_model.LassoLars()
    elif modelName == 'Rigid':
        model = linear_model.Ridge()
    elif modelName == 'SGDRegressor':
        model = linear_model.SGDRegressor()
    elif modelName == 'SVR':
        model = SVR()
    elif modelName=='MLPClassifier':
        model = MLPClassifier()
    elif modelName=='KNeighborsClassifier':
        model = KNeighborsClassifier()
    elif modelName=='SVC':
        model = SVC()
    elif modelName=='GaussianProcessClassifier':
        model = GaussianProcessClassifier()
    elif modelName=='DecisionTreeClassifier':
        model = DecisionTreeClassifier()
    elif modelName=='RandomForestClassifier':
        model = RandomForestClassifier()
    elif modelName=='AdaBoostClassifier':
        model = AdaBoostClassifier()
    elif modelName=='GaussianNB':
        model = GaussianNB()
    elif modelName=='LogisticRegression':
        model = linear_model.LogisticRegression()
    elif modelName=='QuadraticDiscriminantAnalysis':
        model = QuadraticDiscriminantAnalysis()

    return model