.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/semi_supervised/plot_semi_supervised_newsgroups.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py>`
to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py:
================================================
Semi-supervised Classification on a Text Dataset
================================================
In this example, semi-supervised classifiers are trained on the 20 newsgroups
dataset (which will be automatically downloaded).
You can adjust the number of categories by giving their names to the dataset
loader or setting them to `None` to get all 20 of them.
.. GENERATED FROM PYTHON SOURCE LINES 12-96
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
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
----------
|
.. code-block:: default
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)
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 40.905 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_semi_supervised_newsgroups.py <plot_semi_supervised_newsgroups.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_semi_supervised_newsgroups.ipynb <plot_semi_supervised_newsgroups.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_