Python pipeline.make_pipeline方法代码示例

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def main():
    raw_data = load_iris()
    data = pd.DataFrame(raw_data["data"], columns=raw_data["feature_names"])

    pipeline = FeatureUnion([
        ("1", make_pipeline(
            FunctionTransformer(lambda X: X.loc[:, ["sepal length (cm)"]]),
            # other transformations
        )),
        ("2", make_pipeline(
            FunctionTransformer(lambda X: X.loc[:, ["sepal width (cm)"]]),
            # other transformations
        ))
    ])

    X = pipeline.fit_transform(data)
    print(X["sepal length (cm)"].mean())
    print(X["sepal width (cm)"].mean()) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def main():
    raw_data = load_iris()
    data = pd.DataFrame(raw_data["data"], columns=raw_data["feature_names"])
    data.loc[:, "class"] = raw_data["target"]

    pipeline = PandasFeatureUnion([
        ("1", make_pipeline(
            PandasTransform(lambda X: X.loc[:, ["sepal length (cm)"]]),
            # other transformations
        )),
        ("2", make_pipeline(
            PandasTransform(lambda X: X.loc[:, ["sepal width (cm)"]]),
            # other transformations
        ))
    ])

    X = pipeline.fit_transform(data)
    print(X["sepal length (cm)"].mean())
    print(X["sepal width (cm)"].mean()) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def main():
    raw_data = load_iris()
    data = pd.DataFrame(raw_data["data"], columns=raw_data["feature_names"])
    data.loc[:, "class"] = raw_data["target"]

    pipeline = FeatureUnion([
        ("1", make_pipeline(
            PandasTransform(lambda X: X.loc[:, ["sepal length (cm)"]]),
            # other transformations
        )),
        ("2", make_pipeline(
            PandasTransform(lambda X: X.loc[:, ["sepal width (cm)"]]),
            # other transformations
        ))
    ])

    X = pipeline.fit_transform(data)
    print(X["sepal length (cm)"].mean())
    print(X["sepal width (cm)"].mean()) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_gradient_boosting_with_init_pipeline():
    # Check that the init estimator can be a pipeline (see issue #13466)

    X, y = make_regression(random_state=0)
    init = make_pipeline(LinearRegression())
    gb = GradientBoostingRegressor(init=init)
    gb.fit(X, y)  # pipeline without sample_weight works fine

    with pytest.raises(
            ValueError,
            match='The initial estimator Pipeline does not support sample '
                  'weights'):
        gb.fit(X, y, sample_weight=np.ones(X.shape[0]))

    # Passing sample_weight to a pipeline raises a ValueError. This test makes
    # sure we make the distinction between ValueError raised by a pipeline that
    # was passed sample_weight, and a ValueError raised by a regular estimator
    # whose input checking failed.
    with pytest.raises(
            ValueError,
            match='nu <= 0 or nu > 1'):
        # Note that NuSVR properly supports sample_weight
        init = NuSVR(gamma='auto', nu=1.5)
        gb = GradientBoostingRegressor(init=init)
        gb.fit(X, y, sample_weight=np.ones(X.shape[0])) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_pipeline():
    # Render a pipeline object
    pipeline = make_pipeline(StandardScaler(), LogisticRegression(C=999))
    expected = """
Pipeline(memory=None,
         steps=[('standardscaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('logisticregression',
                 LogisticRegression(C=999, class_weight=None, dual=False,
                                    fit_intercept=True, intercept_scaling=1,
                                    l1_ratio=None, max_iter=100,
                                    multi_class='warn', n_jobs=None,
                                    penalty='l2', random_state=None,
                                    solver='warn', tol=0.0001, verbose=0,
                                    warm_start=False))],
         verbose=False)"""

    expected = expected[1:]  # remove first \n
    assert pipeline.__repr__() == expected 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_make_pipeline():
    t1 = Transf()
    t2 = Transf()
    pipe = make_pipeline(t1, t2)
    assert isinstance(pipe, Pipeline)
    assert_equal(pipe.steps[0][0], "transf-1")
    assert_equal(pipe.steps[1][0], "transf-2")

    pipe = make_pipeline(t1, t2, FitParamT())
    assert isinstance(pipe, Pipeline)
    assert_equal(pipe.steps[0][0], "transf-1")
    assert_equal(pipe.steps[1][0], "transf-2")
    assert_equal(pipe.steps[2][0], "fitparamt")

    assert_raise_message(
        TypeError,
        'Unknown keyword arguments: "random_parameter"',
        make_pipeline, t1, t2, random_parameter='rnd'
    ) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_lasso_cv_with_some_model_selection():
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.model_selection import StratifiedKFold
    from sklearn import datasets
    from sklearn.linear_model import LassoCV

    diabetes = datasets.load_diabetes()
    X = diabetes.data
    y = diabetes.target

    pipe = make_pipeline(
        StandardScaler(),
        LassoCV(cv=StratifiedKFold(n_splits=5))
    )
    pipe.fit(X, y) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def build_language_classifier(texts, labels, verbose=False, random_state=None):
    """Train a text classifier with scikit-learn

    The text classifier is composed of two elements assembled in a pipeline:

    - A text feature extractor (`TfidfVectorizer`) that extract the relative
      frequencies of unigrams, bigrams and trigrams of characters in the text.

    - An instance of `SGDClassifier` for the classification it-self. To speed
      up training it is recommended to enable early stopping.

    `random_state` is passed to the underlying `SGDClassifier` instance.
    """
    language_classifier = make_pipeline(
        TfidfVectorizer(analyzer="char", ngram_range=(1, 3),
                        min_df=2, max_df=0.9, norm="l2", dtype=np.float32),
        SGDClassifier(early_stopping=True, validation_fraction=0.2,
                      n_iter_no_change=3, max_iter=1000, tol=1e-3,
                      alpha=1e-5, penalty="l2", verbose=verbose,
                      random_state=random_state)
    )
    return language_classifier.fit(texts, labels) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_time(pipeline_name, name, path):
    if pipeline_name == "LR":
        pipeline = make_pipeline(LogisticRegression())

    if pipeline_name == "FGS":
        pipeline = make_pipeline(FeatureGradientSelector(), LogisticRegression())

    if pipeline_name == "Tree":
        pipeline = make_pipeline(SelectFromModel(ExtraTreesClassifier(n_estimators=50)), LogisticRegression())
    
    test_benchmark = Benchmark()
    print("Dataset:\t", name)
    print("Pipeline:\t", pipeline_name)
    starttime = datetime.datetime.now()
    test_benchmark.run_test(pipeline, name, path)
    endtime = datetime.datetime.now()
    print("Used time: ", (endtime - starttime).microseconds/1000)
    print("") 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test():
    url_zip_train = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_train.binary.bz2'
    urllib.request.urlretrieve(url_zip_train, filename='train.bz2')

    f_svm = open('train.svm', 'wt')
    with bz2.open('train.bz2', 'rb') as f_zip:
        data = f_zip.read()
        f_svm.write(data.decode('utf-8'))
    f_svm.close()


    X, y = load_svmlight_file('train.svm')
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)


    pipeline = make_pipeline(FeatureGradientSelector(n_epochs=1, n_features=10), LogisticRegression())
    # pipeline = make_pipeline(SelectFromModel(ExtraTreesClassifier(n_estimators=50)), LogisticRegression())

    pipeline.fit(X_train, y_train)

    print("Pipeline Score: ", pipeline.score(X_train, y_train)) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_mdr_sklearn_pipeline():
    """Ensure that MDR can be used as a transformer in a scikit-learn pipeline"""
    features = np.array([[2,    0],
                         [0,    0],
                         [0,    1],
                         [0,    0],
                         [0,    0],
                         [0,    0],
                         [0,    1],
                         [0,    0],
                         [0,    0],
                         [0,    1],
                         [0,    0],
                         [0,    0],
                         [0,    0],
                         [1,    1],
                         [1,    1]])

    classes = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
    clf = make_pipeline(MDR(), LogisticRegression())
    cv_scores = cross_val_score(clf, features, classes, cv=StratifiedKFold(n_splits=5, shuffle=True))
    assert np.mean(cv_scores) > 0. 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_mdr_sklearn_pipeline_parallel():
    """Ensure that MDR can be used as a transformer in a parallelized scikit-learn pipeline"""
    features = np.array([[2,    0],
                         [0,    0],
                         [0,    1],
                         [0,    0],
                         [0,    0],
                         [0,    0],
                         [0,    1],
                         [0,    0],
                         [0,    0],
                         [0,    1],
                         [0,    0],
                         [0,    0],
                         [0,    0],
                         [1,    1],
                         [1,    1]])

    classes = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
    clf = make_pipeline(MDR(), LogisticRegression())
    cv_scores = cross_val_score(clf, features, classes, cv=StratifiedKFold(n_splits=5, shuffle=True), n_jobs=-1)
    assert np.mean(cv_scores) > 0. 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_compare_with_sklearn(self):
        from lale.operators import make_pipeline
        tfm = PCA()
        clf = LogisticRegression(LogisticRegression.solver.lbfgs, LogisticRegression.multi_class.auto)
        trainable = make_pipeline(tfm, clf)
        digits = sklearn.datasets.load_digits()
        trained = trainable.fit(digits.data, digits.target)
        predicted = trained.predict(digits.data)
        from sklearn.pipeline import make_pipeline as scikit_make_pipeline
        from sklearn.decomposition import PCA as SklearnPCA
        from sklearn.linear_model import LogisticRegression as SklearnLR
        sklearn_pipeline = scikit_make_pipeline(SklearnPCA(), SklearnLR(solver="lbfgs", multi_class="auto"))
        sklearn_pipeline.fit(digits.data, digits.target)
        predicted_sklearn = sklearn_pipeline.predict(digits.data)

        from sklearn.metrics import accuracy_score
        lale_score = accuracy_score(digits.target, predicted)
        scikit_score = accuracy_score(digits.target, predicted_sklearn)
        self.assertEqual(lale_score, scikit_score) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_import_from_sklearn_pipeline_feature_union(self):
        from sklearn.pipeline import FeatureUnion        
        from sklearn.decomposition import PCA
        from sklearn.kernel_approximation import Nystroem
        from sklearn.neighbors import KNeighborsClassifier
        from sklearn.pipeline import make_pipeline
        union = FeatureUnion([("pca", PCA(n_components=1)), ("nys", Nystroem(n_components=2, random_state=42))])        
        sklearn_pipeline = make_pipeline(union, KNeighborsClassifier())
        lale_pipeline = import_from_sklearn_pipeline(sklearn_pipeline)
        self.assertEqual(len(lale_pipeline.edges()), 3)
        from lale.lib.sklearn.pca import PCAImpl
        from lale.lib.sklearn.nystroem import NystroemImpl
        from lale.lib.lale.concat_features import ConcatFeaturesImpl
        from lale.lib.sklearn.k_neighbors_classifier import KNeighborsClassifierImpl
        self.assertEqual(lale_pipeline.edges()[0][0]._impl_class(), PCAImpl)
        self.assertEqual(lale_pipeline.edges()[0][1]._impl_class(), ConcatFeaturesImpl)
        self.assertEqual(lale_pipeline.edges()[1][0]._impl_class(), NystroemImpl)
        self.assertEqual(lale_pipeline.edges()[1][1]._impl_class(), ConcatFeaturesImpl)
        self.assertEqual(lale_pipeline.edges()[2][0]._impl_class(), ConcatFeaturesImpl)
        self.assertEqual(lale_pipeline.edges()[2][1]._impl_class(), KNeighborsClassifierImpl)
        self.assert_equal_predictions(sklearn_pipeline, lale_pipeline) 

# 需要导入模块: from sklearn import pipeline [as 别名]
# 或者: from sklearn.pipeline import make_pipeline [as 别名]
def test_bagging_with_pipeline():
    estimator = BaggingClassifier(make_pipeline(SelectKBest(k=1),
                                                DecisionTreeClassifier()),
                                  max_features=2)
    estimator.fit(iris.data, iris.target)
    assert isinstance(estimator[0].steps[-1][1].random_state, int)