PYTHON

complete pipeline sample

# Import the hashing vectorizer
from sklearn.feature_extraction.text import HashingVectorizer

# Instantiate the winning model pipeline: pl
pl = Pipeline([
        ('union', FeatureUnion(
            transformer_list = [
                ('numeric_features', Pipeline([
                    ('selector', get_numeric_data),
                    ('imputer', Imputer())
                ])),
                ('text_features', Pipeline([
                    ('selector', get_text_data),
                    ('vectorizer', HashingVectorizer(token_pattern=TOKENS_ALPHANUMERIC,
                                                     non_negative=True, norm=None, binary=False,
                                                     ngram_range=(1, 2))),
                    ('dim_red', SelectKBest(chi2, chi_k))
                ]))
             ]
        )),
        ('int', SparseInteractions(degree=2)),
        ('scale', MaxAbsScaler()),
        ('clf', OneVsRestClassifier(LogisticRegression()))
    ])
# Fit to the training data
pl.fit(X_train, y_train)

# Compute and print accuracy
accuracy = pl.score(X_test, y_test)
print("
Accuracy on budget dataset: ", accuracy)