How featureUnion numeric and text functions in python sklearn correctly

Question

I am trying to use featureunion for the first time in the sklearn pipeline to combine numeric (2 columns) and text functions (1 column) to classify several classes.

from sklearn.preprocessing import FunctionTransformer
from sklearn.pipeline import Pipeline
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import FeatureUnion

get_text_data = FunctionTransformer(lambda x: x['text'], validate=False)
get_numeric_data = FunctionTransformer(lambda x: x[['num1','num2']], validate=False)

process_and_join_features = FeatureUnion(
         [
            ('numeric_features', Pipeline([
                ('selector', get_numeric_data),
                ('clf', OneVsRestClassifier(LogisticRegression()))
            ])),
             ('text_features', Pipeline([
                ('selector', get_text_data),
                ('vec', CountVectorizer()),
                ('clf', OneVsRestClassifier(LogisticRegression()))
            ]))
         ]
    )

In this code, "text" has text columns, and "num1", "num2" have 2 numeric columns.

Error message

TypeError: All estimators should implement fit and transform. 'Pipeline(memory=None,
 steps=[('selector', FunctionTransformer(accept_sparse=False,
      func=<function <lambda> at 0x7fefa8efd840>, inv_kw_args=None,
      inverse_func=None, kw_args=None, pass_y='deprecated',
      validate=False)), ('clf', OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weigh...=None, solver='liblinear', tol=0.0001,
      verbose=0, warm_start=False),
      n_jobs=1))])' (type <class 'sklearn.pipeline.Pipeline'>) doesn't

Any step I missed?

Answer 1

A FeatureUnion , . , , , - fit transform , transform.

, :

process_and_join_features = Pipeline([
    ('features', FeatureUnion([
            ('numeric_features', Pipeline([
                ('selector', get_numeric_data)
            ])),
             ('text_features', Pipeline([
                ('selector', get_text_data),
                ('vec', CountVectorizer())
            ]))
         ])),
    ('clf', OneVsRestClassifier(LogisticRegression()))
])

. - scikit-learn, .

Answer 2

, @Ken Syme , . , , .

ModelTransformer by Zac, :

class ModelTransformer(TransformerMixin):

    def __init__(self, model):
        self.model = model

    def fit(self, *args, **kwargs):
        self.model.fit(*args, **kwargs)
        return self

    def transform(self, X, **transform_params):
        return DataFrame(self.model.predict(X))


process_and_join_features = FeatureUnion(
         [
            ('numeric_features', Pipeline([
                ('selector', get_numeric_data),
                ('clf', ModelTransformer(OneVsRestClassifier(LogisticRegression())))
            ])),
             ('text_features', Pipeline([
                ('selector', get_text_data),
                ('vec', CountVectorizer()),
                ('clf', ModelTransformer(OneVsRestClassifier(LogisticRegression())))
            ]))
         ]
)

FeatureUnion (, make_pipeline).