Clasificación Semisupervisada en un Conjunto de Datos de Texto

En este ejemplo, los clasificadores semisupervisados se entrenan con el conjunto de datos de 20 grupos de noticias (que se descargarán automáticamente).

Puedes ajustar el número de categorías dando sus nombres al cargador de conjuntos de datos o estableciéndolas como None para obtener las 20 de ellas.

Out:

11314 documents
20 categories

Supervised SGDClassifier on 100% of the data:
Number of training samples: 8485
Unlabeled samples in training set: 0
Micro-averaged F1 score on test set: 0.909
----------

Supervised SGDClassifier on 20% of the training data:
Number of training samples: 1688
Unlabeled samples in training set: 0
Micro-averaged F1 score on test set: 0.791
----------

SelfTrainingClassifier on 20% of the training data (rest is unlabeled):
Number of training samples: 8485
Unlabeled samples in training set: 6797
End of iteration 1, added 2852 new labels.
End of iteration 2, added 694 new labels.
End of iteration 3, added 183 new labels.
End of iteration 4, added 68 new labels.
End of iteration 5, added 37 new labels.
End of iteration 6, added 31 new labels.
End of iteration 7, added 11 new labels.
End of iteration 8, added 8 new labels.
End of iteration 9, added 4 new labels.
End of iteration 10, added 2 new labels.
Micro-averaged F1 score on test set: 0.835
----------

LabelSpreading on 20% of the data (rest is unlabeled):
Number of training samples: 8485
Unlabeled samples in training set: 6797
Micro-averaged F1 score on test set: 0.640
----------

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)