"# Author: Matt Terry <
[email protected]>\n#\n# License: BSD 3 clause\n\nimport numpy as np\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.datasets.twenty_newsgroups import strip_newsgroup_footer\nfrom sklearn.datasets.twenty_newsgroups import strip_newsgroup_quoting\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics import classification_report\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.svm import LinearSVC\n\n\nclass TextStats(TransformerMixin, BaseEstimator):\n \"\"\"Extract features from each document for DictVectorizer\"\"\"\n\n def fit(self, x, y=None):\n return self\n\n def transform(self, posts):\n return [{'length': len(text),\n 'num_sentences': text.count('.')}\n for text in posts]\n\n\nclass SubjectBodyExtractor(TransformerMixin, BaseEstimator):\n \"\"\"Extract the subject & body from a usenet post in a single pass.\n\n Takes a sequence of strings and produces a dict of sequences. Keys are\n `subject` and `body`.\n \"\"\"\n def fit(self, x, y=None):\n return self\n\n def transform(self, posts):\n # construct object dtype array with two columns\n # first column = 'subject' and second column = 'body'\n features = np.empty(shape=(len(posts), 2), dtype=object)\n for i, text in enumerate(posts):\n headers, _, bod = text.partition('\\n\\n')\n bod = strip_newsgroup_footer(bod)\n bod = strip_newsgroup_quoting(bod)\n features[i, 1] = bod\n\n prefix = 'Subject:'\n sub = ''\n for line in headers.split('\\n'):\n if line.startswith(prefix):\n sub = line[len(prefix):]\n break\n features[i, 0] = sub\n\n return features\n\n\npipeline = Pipeline([\n # Extract the subject & body\n ('subjectbody', SubjectBodyExtractor()),\n\n # Use ColumnTransformer to combine the features from subject and body\n ('union', ColumnTransformer(\n [\n # Pulling features from the post's subject line (first column)\n ('subject', TfidfVectorizer(min_df=50), 0),\n\n # Pipeline for standard bag-of-words model for body (second column)\n ('body_bow', Pipeline([\n ('tfidf', TfidfVectorizer()),\n ('best', TruncatedSVD(n_components=50)),\n ]), 1),\n\n # Pipeline for pulling ad hoc features from post's body\n ('body_stats', Pipeline([\n ('stats', TextStats()), # returns a list of dicts\n ('vect', DictVectorizer()), # list of dicts -> feature matrix\n ]), 1),\n ],\n\n # weight components in ColumnTransformer\n transformer_weights={\n 'subject': 0.8,\n 'body_bow': 0.5,\n 'body_stats': 1.0,\n }\n )),\n\n # Use a SVC classifier on the combined features\n ('svc', LinearSVC(dual=False)),\n], verbose=True)\n\n# limit the list of categories to make running this example faster.\ncategories = ['alt.atheism', 'talk.religion.misc']\nX_train, y_train = fetch_20newsgroups(random_state=1,\n subset='train',\n categories=categories,\n return_X_y=True)\nX_test, y_test = fetch_20newsgroups(random_state=1,\n subset='test',\n categories=categories,\n return_X_y=True)\n\npipeline.fit(X_train, y_train)\ny_pred = pipeline.predict(X_test)\nprint(classification_report(y_pred, y_test))"
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