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.. 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
.. GENERATED FROM PYTHON SOURCE LINES 43-49
.. code-block:: default
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
.. GENERATED FROM PYTHON SOURCE LINES 50-52
Data loading
------------
.. GENERATED FROM PYTHON SOURCE LINES 52-84
.. code-block:: default
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
# 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()
.. GENERATED FROM PYTHON SOURCE LINES 85-87
Pipeline with hyperparameter tuning
-----------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 87-130
.. code-block:: default
# Define a pipeline combining a text feature extractor with a simple classifier
pipeline = Pipeline(
[
("vect", CountVectorizer()),
("tfidf", TfidfTransformer()),
("clf", SGDClassifier()),
]
)
# Parameters to use for grid search. 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),
}
# 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:
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