Semi-supervised Classification on a Text Dataset¶

import os

import numpy as np

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.semi_supervised import SelfTrainingClassifier
from sklearn.semi_supervised import LabelSpreading
from sklearn.metrics import f1_score

data = fetch_20newsgroups(subset='train', categories=None)
print("%d documents" % len(data.filenames))
print("%d categories" % len(data.target_names))
print()

# Parameters
sdg_params = dict(alpha=1e-5, penalty='l2', loss='log')
vectorizer_params = dict(ngram_range=(1, 2), min_df=5, max_df=0.8)

# Supervised Pipeline
pipeline = Pipeline([
    ('vect', CountVectorizer(**vectorizer_params)),
    ('tfidf', TfidfTransformer()),
    ('clf', SGDClassifier(**sdg_params)),
])
# SelfTraining Pipeline
st_pipeline = Pipeline([
    ('vect', CountVectorizer(**vectorizer_params)),
    ('tfidf', TfidfTransformer()),
    ('clf', SelfTrainingClassifier(SGDClassifier(**sdg_params), verbose=True)),
])
# LabelSpreading Pipeline
ls_pipeline = Pipeline([
    ('vect', CountVectorizer(**vectorizer_params)),
    ('tfidf', TfidfTransformer()),
    # LabelSpreading does not support dense matrices
    ('todense', FunctionTransformer(lambda x: x.todense())),
    ('clf', LabelSpreading()),
])


def eval_and_print_metrics(clf, X_train, y_train, X_test, y_test):
    print("Number of training samples:", len(X_train))
    print("Unlabeled samples in training set:",
          sum(1 for x in y_train if x == -1))
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print("Micro-averaged F1 score on test set: "
          "%0.3f" % f1_score(y_test, y_pred, average='micro'))
    print("-" * 10)
    print()


if __name__ == "__main__":
    X, y = data.data, data.target
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    print("Supervised SGDClassifier on 100% of the data:")
    eval_and_print_metrics(pipeline, X_train, y_train, X_test, y_test)

    # select a mask of 20% of the train dataset
    y_mask = np.random.rand(len(y_train)) < 0.2

    # X_20 and y_20 are the subset of the train dataset indicated by the mask
    X_20, y_20 = map(list, zip(*((x, y)
                     for x, y, m in zip(X_train, y_train, y_mask) if m)))
    print("Supervised SGDClassifier on 20% of the training data:")
    eval_and_print_metrics(pipeline, X_20, y_20, X_test, y_test)

    # set the non-masked subset to be unlabeled
    y_train[~y_mask] = -1
    print("SelfTrainingClassifier on 20% of the training data (rest "
          "is unlabeled):")
    eval_and_print_metrics(st_pipeline, X_train, y_train, X_test, y_test)

    if 'CI' not in os.environ:
        # LabelSpreading takes too long to run in the online documentation
        print("LabelSpreading on 20% of the data (rest is unlabeled):")
        eval_and_print_metrics(ls_pipeline, X_train, y_train, X_test, y_test)