自定义函数转换器未按预期执行 - sklearn 管道

Question

我正在尝试将自定义转换器定义为管道的一部分，但它没有被识别为这样，并且不会继承 fit_transform 方法。每个 fit 和 transform 都可以自己正常工作。

class feature_union(TransformerMixin, BaseEstimator):

    def __init__(self):
        self.Xt = None
        self.PI2_categories = ['D3', 'D4', 'A6', 'A5', 'D1', 'D2', 'A8', 'B2', 'E1', 
                               'A1', 'A2', 'C1', 'C4', 'A7', 'C2', 'C3', 'A4', 'A3', 'B1']     

    def fit(self, X, y=None):

        product_columns = ['Product_Info_1', 'Product_Info_3', 'Product_Info_5', 'Product_Info_6', 'Product_Info_7'] + self.PI2_categories
        product_idx = [col for col in range(X.shape[1]) if X.columns[col] in product_columns]

        personal_columns = ['Ins_Age', 'Ht', 'Wt', 'BMI']
        personal_idx = [col for col in range(X.shape[1]) if X.columns[col] in personal_columns]

        medical_hist_columns = ["Medical_History_{}".format(x) for x in range(1, 42, 1)]
        medical_hist_idx = [col for col in range(X.shape[1]) if X.columns[col] in medical_hist_columns]

        family_hist_columns = ["Family_Hist_{}".format(x) for x in range(1, 6, 1)]
        family_hist_idx = [col for col in range(X.shape[1]) if X.columns[col] in family_hist_columns]

        insured_info_columns = ["InsuredInfo_{}".format(x) for x in range(1, 8, 1)]
        insured_info_idx = [col for col in range(X.shape[1]) if X.columns[col] in insured_info_columns]

        insurance_hist_columns = ["Insurance_History_{}".format(x) for x in range(1, 10, 1)]
        insurance_hist_idx = [col for col in range(X.shape[1]) if X.columns[col] in insurance_hist_columns]

        employment_info_columns = ["Employment_Info_{}".format(x) for x in range(1, 7, 1)]
        employment_info_idx = [col for col in range(X.shape[1]) if X.columns[col] in employment_info_columns]

        medical_keyword_columns = ["Medical_Keyword_{}".format(x) for x in range(1, 49, 1)]
        medical_keyword_idx = [col for col in range(X.shape[1]) if X.columns[col] in medical_keyword_columns]

        medical_keyword_columns = ["Medical_Keyword_{}".format(x) for x in range(1, 49, 1)]
        medical_keyword_idx = [col for col in range(X.shape[1]) if X.columns[col] in medical_keyword_columns]

        get_original_features = lambda X: X
        get_product_columns  = lambda X: X[:, product_idx]
        get_personal_columns = lambda X: X[:, personal_idx]
        get_medical_hist_columns = lambda X: X[:, medical_hist_idx]
        get_family_hist_columns = lambda X: X[:, family_hist_idx]
        get_insured_info_columns = lambda X: X[:, insured_info_idx]
        get_insurance_hist_columns = lambda X: X[:, insurance_hist_idx]
        get_employment_info_columns = lambda X: X[:, employment_info_idx]
        get_medical_keyword_columns = lambda X: X[:, medical_keyword_idx]


        get_medical_and_family = lambda X: X[:, medical_keyword_idx + medical_hist_idx + family_hist_idx]


        union = FeatureUnion([
            ("original_features", FunctionTransformer(get_original_features)),

            ("product_interaction", Pipeline([('select_product', FunctionTransformer(get_product_columns)),
                                              ('product_interaction', PolynomialFeatures(2, include_bias=False, interaction_only=True))
                                            ])),

            ("personal_interaction", Pipeline([('select_personal', FunctionTransformer(get_personal_columns)),
                                              ('personal_interaction', PolynomialFeatures(4, include_bias=False, interaction_only=True))
                                            ])),

            ("medical_hist_interaction", Pipeline([('select_medical', FunctionTransformer(get_medical_hist_columns)),
                                                   ('medical_interaction', PolynomialFeatures(2, include_bias=False, interaction_only=True))
                                                 ])),

            ("family_hist_interaction", Pipeline([('select_family_hist', FunctionTransformer(get_family_hist_columns)),
                                                  ('family_hist_interaction', PolynomialFeatures(5, include_bias=False, interaction_only=True))
                                                ])),

            ("insured_info_interaction", Pipeline([('select_insured_info', FunctionTransformer(get_insured_info_columns)),
                                                   ('insured_info_interaction', PolynomialFeatures(2, include_bias=False, interaction_only=True))
                                                 ])),

            ("insurance_hist_interaction", Pipeline([('select_insurance_hist', FunctionTransformer(get_insurance_hist_columns)),
                                                   ('insurance_hist_interaction', PolynomialFeatures(2, include_bias=False, interaction_only=True))
                                                   ])),

            ("employment_info_interaction", Pipeline([('select_employment_info', FunctionTransformer(get_employment_info_columns)),
                                                    ('employment_info_interaction', PolynomialFeatures(2, include_bias=False, interaction_only=True))
                                                    ])),

            ("medical_keyword_interaction", Pipeline([('select_medical_keyword', FunctionTransformer(get_medical_keyword_columns)),
                                                    ('medical_keyword_interaction', PolynomialFeatures(2, include_bias=False, interaction_only=True))
                                                    ])),

            ])

        Xt = union.fit_transform(X)

        return self.Xt

    def transform(self, X, y=None):
        Xt = self.Xt
        return Xt

fit 函数中的所有内容基本上是无关紧要的，但我不想通过砍掉它来引起混乱。我只是看不出我定义它的方式有什么问题。

查看我的管道：

pipeline_feat_union = Pipeline([('preprocess', preprocess()),
                                ('feat_union', feature_union()),
                                ('classifier', GaussianNB())])

当我调用 fit 方法时出现错误：

AttributeError: 'NoneType' object has no attribute 'transform'

Answer 1

我遇到了同样的问题。 GuassianNB() 类没有定义transform 方法。

但是，如果您将分类器包含在管道中，则根本不需要使用 transform 方法。您唯一需要的两个方法是fit 方法和predict 方法。

pipeline_feat_union.fit(X_train, y_train)
pipeline_feat_union.predict(X_train)