.. 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 :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py: ================================================ التصنيف شبه المُشرف على مجموعة بيانات نصية ================================================ في هذا المثال، يتم تدريب المصنفات شبه المُشرفة على مجموعة بيانات 20 مجموعة إخبارية (والتي سيتم تنزيلها تلقائيًا). يمكنك ضبط عدد الفئات عن طريق إعطاء أسمائها إلى محمل مجموعة البيانات أو تعيينها إلى 'None' للحصول على جميع الفئات العشرين. .. GENERATED FROM PYTHON SOURCE LINES 13-110 .. rst-class:: sphx-glr-script-out .. code-block:: none 2823 documents 5 categories Supervised SGDClassifier on 100% of the data: Number of training samples: 2117 Unlabeled samples in training set: 0 Micro-averaged F1 score on test set: 0.880 ---------- Supervised SGDClassifier on 20% of the training data: Number of training samples: 422 Unlabeled samples in training set: 0 Micro-averaged F1 score on test set: 0.738 ---------- SelfTrainingClassifier on 20% of the training data (rest is unlabeled): Number of training samples: 2117 Unlabeled samples in training set: 1695 End of iteration 1, added 1115 new labels. End of iteration 2, added 193 new labels. End of iteration 3, added 50 new labels. End of iteration 4, added 25 new labels. End of iteration 5, added 9 new labels. End of iteration 6, added 8 new labels. End of iteration 7, added 5 new labels. End of iteration 8, added 3 new labels. End of iteration 9, added 3 new labels. End of iteration 10, added 4 new labels. Micro-averaged F1 score on test set: 0.790 ---------- LabelSpreading on 20% of the data (rest is unlabeled): Number of training samples: 2117 Unlabeled samples in training set: 1695 Micro-averaged F1 score on test set: 0.661 ---------- | .. code-block:: Python # المؤلفون: مطوري مكتبة ساي كيت ليرن # معرف الترخيص: BSD-3-Clause import numpy as np from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.linear_model import SGDClassifier from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.preprocessing import FunctionTransformer from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier # تحميل مجموعة البيانات التي تحتوي على أول خمس فئات data = fetch_20newsgroups( subset="train", categories=[ "alt.atheism", "comp.graphics", "comp.os.ms-windows.misc", "comp.sys.ibm.pc.hardware", "comp.sys.mac.hardware", ], ) print("%d documents" % len(data.filenames)) print("%d categories" % len(data.target_names)) print() # المعاملات sdg_params = dict(alpha=1e-5, penalty="l2", loss="log_loss") vectorizer_params = dict(ngram_range=(1, 2), min_df=5, max_df=0.8) # خط أنابيب مُشرف pipeline = Pipeline( [ ("vect", CountVectorizer(**vectorizer_params)), ("tfidf", TfidfTransformer()), ("clf", SGDClassifier(**sdg_params)), ] ) # خط أنابيب SelfTraining st_pipeline = Pipeline( [ ("vect", CountVectorizer(**vectorizer_params)), ("tfidf", TfidfTransformer()), ("clf", SelfTrainingClassifier(SGDClassifier(**sdg_params), verbose=True)), ] ) # خط أنابيب LabelSpreading ls_pipeline = Pipeline( [ ("vect", CountVectorizer(**vectorizer_params)), ("tfidf", TfidfTransformer()), # LabelSpreading لا يدعم المصفوفات الكثيفة ("toarray", FunctionTransformer(lambda x: x.toarray())), ("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) # تحديد قناع 20% من مجموعة البيانات التدريبية y_mask = np.random.rand(len(y_train)) < 0.2 # X_20 و y_20 هي مجموعة فرعية من مجموعة البيانات التدريبية المحددة بواسطة القناع 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) # تعيين المجموعة الفرعية غير المقنعة لتكون غير مصنفة 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) 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 10.723 seconds) .. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_semi_supervised_newsgroups.ipynb ` .. 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-zip :download:`Download zipped: plot_semi_supervised_newsgroups.zip ` .. include:: plot_semi_supervised_newsgroups.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_