.. 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 ` 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 ---------- 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 ---------- | .. 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:** ( 4 minutes 1.957 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 ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_semi_supervised_newsgroups.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_