.. 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_model_selection_grid_search_text_feature_extraction.py: ========================================================== Sample pipeline for text feature extraction and evaluation ========================================================== The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for the document classification example. You can adjust the number of categories by giving their names to the dataset loader or setting them to None to get the 20 of them. Here is a sample output of a run on a quad-core machine:: Loading 20 newsgroups dataset for categories: ['alt.atheism', 'talk.religion.misc'] 1427 documents 2 categories Performing grid search... pipeline: ['vect', 'tfidf', 'clf'] parameters: {'clf__alpha': (1.0000000000000001e-05, 9.9999999999999995e-07), 'clf__max_iter': (10, 50, 80), 'clf__penalty': ('l2', 'elasticnet'), 'tfidf__use_idf': (True, False), 'vect__max_n': (1, 2), 'vect__max_df': (0.5, 0.75, 1.0), 'vect__max_features': (None, 5000, 10000, 50000)} done in 1737.030s Best score: 0.940 Best parameters set: clf__alpha: 9.9999999999999995e-07 clf__max_iter: 50 clf__penalty: 'elasticnet' tfidf__use_idf: True vect__max_n: 2 vect__max_df: 0.75 vect__max_features: 50000 .. code-block:: default # Author: Olivier Grisel # Peter Prettenhofer # Mathieu Blondel # License: BSD 3 clause from pprint import pprint from time import time import logging from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.linear_model import SGDClassifier from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline print(__doc__) # Display progress logs on stdout logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') # ############################################################################# # Load some categories from the training set categories = [ 'alt.atheism', 'talk.religion.misc', ] # Uncomment the following to do the analysis on all the categories #categories = None print("Loading 20 newsgroups dataset for categories:") print(categories) data = fetch_20newsgroups(subset='train', categories=categories) print("%d documents" % len(data.filenames)) print("%d categories" % len(data.target_names)) print() # ############################################################################# # Define a pipeline combining a text feature extractor with a simple # classifier pipeline = Pipeline([ ('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', SGDClassifier()), ]) # uncommenting more parameters will give better exploring power but will # increase processing time in a combinatorial way parameters = { 'vect__max_df': (0.5, 0.75, 1.0), # 'vect__max_features': (None, 5000, 10000, 50000), 'vect__ngram_range': ((1, 1), (1, 2)), # unigrams or bigrams # 'tfidf__use_idf': (True, False), # 'tfidf__norm': ('l1', 'l2'), 'clf__max_iter': (20,), 'clf__alpha': (0.00001, 0.000001), 'clf__penalty': ('l2', 'elasticnet'), # 'clf__max_iter': (10, 50, 80), } if __name__ == "__main__": # multiprocessing requires the fork to happen in a __main__ protected # block # find the best parameters for both the feature extraction and the # classifier grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1) print("Performing grid search...") print("pipeline:", [name for name, _ in pipeline.steps]) print("parameters:") pprint(parameters) t0 = time() grid_search.fit(data.data, data.target) print("done in %0.3fs" % (time() - t0)) print() print("Best score: %0.3f" % grid_search.best_score_) print("Best parameters set:") best_parameters = grid_search.best_estimator_.get_params() for param_name in sorted(parameters.keys()): print("\t%s: %r" % (param_name, best_parameters[param_name])) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_auto_examples_model_selection_grid_search_text_feature_extraction.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/grid_search_text_feature_extraction.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: grid_search_text_feature_extraction.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: grid_search_text_feature_extraction.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_