"""
==================================================
Column Transformer with Heterogeneous Data Sources
==================================================

Datasets can often contain components that require different feature
extraction and processing pipelines. This scenario might occur when:

1. your dataset consists of heterogeneous data types (e.g. raster images and
   text captions),
2. your dataset is stored in a :class:`pandas.DataFrame` and different columns
   require different processing pipelines.

This example demonstrates how to use
:class:`~sklearn.compose.ColumnTransformer` on a dataset containing
different types of features. The choice of features is not particularly
helpful, but serves to illustrate the technique.
"""

# Author: Matt Terry <matt.terry@gmail.com>
#
# License: BSD 3 clause

import numpy as np

from sklearn.preprocessing import FunctionTransformer
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.svm import LinearSVC

##############################################################################
# 20 newsgroups dataset
# ---------------------
#
# We will use the :ref:`20 newsgroups dataset <20newsgroups_dataset>`, which
# comprises posts from newsgroups on 20 topics. This dataset is split
# into train and test subsets based on messages posted before and after
# a specific date. We will only use posts from 2 categories to speed up running
# time.

categories = ['sci.med', 'sci.space']
X_train, y_train = fetch_20newsgroups(random_state=1,
                                      subset='train',
                                      categories=categories,
                                      remove=('footers', 'quotes'),
                                      return_X_y=True)
X_test, y_test = fetch_20newsgroups(random_state=1,
                                    subset='test',
                                    categories=categories,
                                    remove=('footers', 'quotes'),
                                    return_X_y=True)

##############################################################################
# Each feature comprises meta information about that post, such as the subject,
# and the body of the news post.

print(X_train[0])

##############################################################################
# Creating transformers
# ---------------------
#
# First, we would like a transformer that extracts the subject and
# body of each post. Since this is a stateless transformation (does not
# require state information from training data), we can define a function that
# performs the data transformation then use
# :class:`~sklearn.preprocessing.FunctionTransformer` to create a scikit-learn
# transformer.


def subject_body_extractor(posts):
    # construct object dtype array with two columns
    # first column = 'subject' and second column = 'body'
    features = np.empty(shape=(len(posts), 2), dtype=object)
    for i, text in enumerate(posts):
        # temporary variable `_` stores '\n\n'
        headers, _, body = text.partition('\n\n')
        # store body text in second column
        features[i, 1] = body

        prefix = 'Subject:'
        sub = ''
        # save text after 'Subject:' in first column
        for line in headers.split('\n'):
            if line.startswith(prefix):
                sub = line[len(prefix):]
                break
        features[i, 0] = sub

    return features


subject_body_transformer = FunctionTransformer(subject_body_extractor)

##############################################################################
# We will also create a transformer that extracts the
# length of the text and the number of sentences.


def text_stats(posts):
    return [{'length': len(text),
             'num_sentences': text.count('.')}
            for text in posts]


text_stats_transformer = FunctionTransformer(text_stats)

##############################################################################
# Classification pipeline
# -----------------------
#
# The pipeline below extracts the subject and body from each post using
# ``SubjectBodyExtractor``, producing a (n_samples, 2) array. This array is
# then used to compute standard bag-of-words features for the subject and body
# as well as text length and number of sentences on the body, using
# ``ColumnTransformer``. We combine them, with weights, then train a
# classifier on the combined set of features.

pipeline = Pipeline([
    # Extract subject & body
    ('subjectbody', subject_body_transformer),
    # Use ColumnTransformer to combine the subject and body features
    ('union', ColumnTransformer(
        [
            # bag-of-words for subject (col 0)
            ('subject', TfidfVectorizer(min_df=50), 0),
            # bag-of-words with decomposition for body (col 1)
            ('body_bow', Pipeline([
                ('tfidf', TfidfVectorizer()),
                ('best', TruncatedSVD(n_components=50)),
            ]), 1),
            # Pipeline for pulling text stats from post's body
            ('body_stats', Pipeline([
                ('stats', text_stats_transformer),  # returns a list of dicts
                ('vect', DictVectorizer()),  # list of dicts -> feature matrix
            ]), 1),
        ],
        # weight above ColumnTransformer features
        transformer_weights={
            'subject': 0.8,
            'body_bow': 0.5,
            'body_stats': 1.0,
        }
    )),
    # Use a SVC classifier on the combined features
    ('svc', LinearSVC(dual=False)),
], verbose=True)

##############################################################################
# Finally, we fit our pipeline on the training data and use it to predict
# topics for ``X_test``. Performance metrics of our pipeline are then printed.

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
print('Classification report:\n\n{}'.format(
    classification_report(y_test, y_pred))
)
