8000 DOC updated to notebook style for grid_search_text_feature_extraction.py by brendo-k · Pull Request #22558 · scikit-learn/scikit-learn · GitHub
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DOC updated to notebook style for grid_search_text_feature_extraction.py #22558

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Merged
merged 7 commits into from
Mar 12, 2022
Merged

DOC updated to notebook style for grid_search_text_feature_extraction.py #22558

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aa8f15c
updated to notebook style for grid_search_text_feature_extraction.py
brendo-k Feb 20, 2022
ac2cf8c
removed empty lines
brendo-k Feb 20, 2022
b82cbae
Update examples/model_selection/grid_search_text_feature_extraction.py
brendo-k Feb 27, 2022
0f20954
removed grid search block and combined it with tuning a model block
brendo-k Feb 27, 2022
2e4b81e
removed main
brendo-k Mar 12, 2022
d9f5f4d
tweaks
lesteve Mar 12, 2022
9f881e0
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
lesteve Mar 12, 2022
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61 changes: 32 additions & 29 deletions examples/model_selection/grid_search_text_feature_extraction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,11 @@
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

# %%
# Data loading
# ------------

from pprint import pprint
from time import time
import logging
Expand All @@ -59,13 +64,12 @@
# 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

Expand All @@ -77,9 +81,11 @@
print("%d categories" % len(data.target_names))
print()

# #############################################################################
# Define a pipeline combining a text feature extractor with a simple
# classifier
# %%
# Pipeline with hyperparameter tuning
# -----------------------------------

# Define a pipeline combining a text feature extractor with a simple classifier
pipeline = Pipeline(
[
("vect", CountVectorizer()),
Expand All @@ -88,8 +94,9 @@
]
)

# uncommenting more parameters will give better exploring power but will
# increase processing time in a combinatorial way
# 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),
Expand All @@ -102,25 +109,21 @@
# '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]))
# 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]))
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