.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/model_selection/plot_grid_search_text_feature_extraction.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py>`
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_plot_grid_search_text_feature_extraction.py:
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is :ref:`20newsgroups_dataset` which will be
automatically downloaded, cached and reused for the document classification
example.
In this example, we tune the hyperparameters of a particular classifier using a
:class:`~sklearn.model_selection.RandomizedSearchCV`. For a demo on the
performance of some other classifiers, see the
:ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
notebook.
.. GENERATED FROM PYTHON SOURCE LINES 16-23
.. code-block:: default
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Mathieu Blondel <mathieu@mblondel.org>
# Arturo Amor <david-arturo.amor-quiroz@inria.fr>
# License: BSD 3 clause
.. GENERATED FROM PYTHON SOURCE LINES 24-30
Data loading
------------
We load two categories from the training set. You can adjust the number of
categories by adding their names to the list or setting `categories=None` when
calling the dataset loader :func:`~sklearn.datasets.fetch20newsgroups` to get
the 20 of them.
.. GENERATED FROM PYTHON SOURCE LINES 30-58
.. code-block:: default
from sklearn.datasets import fetch_20newsgroups
categories = [
"alt.atheism",
"talk.religion.misc",
]
data_train = fetch_20newsgroups(
subset="train",
categories=categories,
shuffle=True,
random_state=42,
remove=("headers", "footers", "quotes"),
)
data_test = fetch_20newsgroups(
subset="test",
categories=categories,
shuffle=True,
random_state=42,
remove=("headers", "footers", "quotes"),
)
print(f"Loading 20 newsgroups dataset for {len(data_train.target_names)} categories:")
print(data_train.target_names)
print(f"{len(data_train.data)} documents")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Loading 20 newsgroups dataset for 2 categories:
['alt.atheism', 'talk.religion.misc']
857 documents
.. GENERATED FROM PYTHON SOURCE LINES 59-64
Pipeline with hyperparameter tuning
-----------------------------------
We define a pipeline combining a text feature vectorizer with a simple
classifier yet effective for text classification.
.. GENERATED FROM PYTHON SOURCE LINES 64-77
.. code-block:: default
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import ComplementNB
from sklearn.pipeline import Pipeline
pipeline = Pipeline(
[
("vect", TfidfVectorizer()),
("clf", ComplementNB()),
]
)
pipeline
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-52 {color: black;background-color: white;}#sk-container-id-52 pre{padding: 0;}#sk-container-id-52 div.sk-toggleable {background-color: white;}#sk-container-id-52 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-52 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-52 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-52 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-52 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-52 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-52 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-52 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-52 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-52 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-52 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-52 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-52 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-52 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-52 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-52 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-52 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-52 div.sk-item {position: relative;z-index: 1;}#sk-container-id-52 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-52 div.sk-item::before, #sk-container-id-52 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-52 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-52 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-52 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-52 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-52 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-52 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-52 div.sk-label-container {text-align: center;}#sk-container-id-52 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-52 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-52" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('vect', TfidfVectorizer()), ('clf', ComplementNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-226" type="checkbox" ><label for="sk-estimator-id-226" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('vect', TfidfVectorizer()), ('clf', ComplementNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-227" type="checkbox" ><label for="sk-estimator-id-227" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfVectorizer</label><div class="sk-toggleable__content"><pre>TfidfVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-228" type="checkbox" ><label for="sk-estimator-id-228" class="sk-toggleable__label sk-toggleable__label-arrow">ComplementNB</label><div class="sk-toggleable__content"><pre>ComplementNB()</pre></div></div></div></div></div></div></div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 78-88
We define a grid of hyperparameters to be explored by the
:class:`~sklearn.model_selection.RandomizedSearchCV`. Using a
:class:`~sklearn.model_selection.GridSearchCV` instead would explore all the
possible combinations on the grid, which can be costly to compute, whereas the
parameter `n_iter` of the :class:`~sklearn.model_selection.RandomizedSearchCV`
controls the number of different random combination that are evaluated. Notice
that setting `n_iter` larger than the number of possible combinations in a
grid would lead to repeating already-explored combinations. We search for the
best parameter combination for both the feature extraction (`vect__`) and the
classifier (`clf__`).
.. GENERATED FROM PYTHON SOURCE LINES 88-99
.. code-block:: default
import numpy as np
parameter_grid = {
"vect__max_df": (0.2, 0.4, 0.6, 0.8, 1.0),
"vect__min_df": (1, 3, 5, 10),
"vect__ngram_range": ((1, 1), (1, 2)), # unigrams or bigrams
"vect__norm": ("l1", "l2"),
"clf__alpha": np.logspace(-6, 6, 13),
}
.. GENERATED FROM PYTHON SOURCE LINES 100-106
In this case `n_iter=40` is not an exhaustive search of the hyperparameters'
grid. In practice it would be interesting to increase the parameter `n_iter`
to get a more informative analysis. As a consequence, the computional time
increases. We can reduce it by taking advantage of the parallelisation over
the parameter combinations evaluation by increasing the number of CPUs used
via the parameter `n_jobs`.
.. GENERATED FROM PYTHON SOURCE LINES 106-123
.. code-block:: default
from pprint import pprint
from sklearn.model_selection import RandomizedSearchCV
random_search = RandomizedSearchCV(
estimator=pipeline,
param_distributions=parameter_grid,
n_iter=40,
random_state=0,
n_jobs=2,
verbose=1,
)
print("Performing grid search...")
print("Hyperparameters to be evaluated:")
pprint(parameter_grid)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Performing grid search...
Hyperparameters to be evaluated:
{'clf__alpha': array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,
1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06]),
'vect__max_df': (0.2, 0.4, 0.6, 0.8, 1.0),
'vect__min_df': (1, 3, 5, 10),
'vect__ngram_range': ((1, 1), (1, 2)),
'vect__norm': ('l1', 'l2')}
.. GENERATED FROM PYTHON SOURCE LINES 124-130
.. code-block:: default
from time import time
t0 = time()
random_search.fit(data_train.data, data_train.target)
print(f"Done in {time() - t0:.3f}s")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Fitting 5 folds for each of 40 candidates, totalling 200 fits
Done in 25.142s
.. GENERATED FROM PYTHON SOURCE LINES 131-136
.. code-block:: default
print("Best parameters combination found:")
best_parameters = random_search.best_estimator_.get_params()
for param_name in sorted(parameter_grid.keys()):
print(f"{param_name}: {best_parameters[param_name]}")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Best parameters combination found:
clf__alpha: 0.01
vect__max_df: 0.2
vect__min_df: 1
vect__ngram_range: (1, 1)
vect__norm: l1
.. GENERATED FROM PYTHON SOURCE LINES 137-144
.. code-block:: default
test_accuracy = random_search.score(data_test.data, data_test.target)
print(
"Accuracy of the best parameters using the inner CV of "
f"the random search: {random_search.best_score_:.3f}"
)
print(f"Accuracy on test set: {test_accuracy:.3f}")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Accuracy of the best parameters using the inner CV of the random search: 0.816
Accuracy on test set: 0.709
.. GENERATED FROM PYTHON SOURCE LINES 145-149
The prefixes `vect` and `clf` are required to avoid possible ambiguities in
the pipeline, but are not necessary for visualizing the results. Because of
this, we define a function that will rename the tuned hyperparameters and
improve the readability.
.. GENERATED FROM PYTHON SOURCE LINES 149-163
.. code-block:: default
import pandas as pd
def shorten_param(param_name):
"""Remove components' prefixes in param_name."""
if "__" in param_name:
return param_name.rsplit("__", 1)[1]
return param_name
cv_results = pd.DataFrame(random_search.cv_results_)
cv_results = cv_results.rename(shorten_param, axis=1)
.. GENERATED FROM PYTHON SOURCE LINES 164-170
We can use a `plotly.express.scatter
<https://plotly.com/python-api-reference/generated/plotly.express.scatter.html>`_
to visualize the trade-off between scoring time and mean test score (i.e. "CV
score"). Passing the cursor over a given point displays the corresponding
parameters. Error bars correspond to one standard deviation as computed in the
different folds of the cross-validation.
.. GENERATED FROM PYTHON SOURCE LINES 170-198
.. code-block:: default
import plotly.express as px
param_names = [shorten_param(name) for name in parameter_grid.keys()]
labels = {
"mean_score_time": "CV Score time (s)",
"mean_test_score": "CV score (accuracy)",
}
fig = px.scatter(
cv_results,
x="mean_score_time",
y="mean_test_score",
error_x="std_score_time",
error_y="std_test_score",
hover_data=param_names,
labels=labels,
)
fig.update_layout(
title={
"text": "trade-off between scoring time and mean test score",
"y": 0.95,
"x": 0.5,
"xanchor": "center",
"yanchor": "top",
}
)
fig
.. raw:: html
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</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 199-218
Notice that the cluster of models in the upper-left corner of the plot have
the best trade-off between accuracy and scoring time. In this case, using
bigrams increases the required scoring time without improving considerably the
accuracy of the pipeline.
.. note:: For more information on how to customize an automated tuning to
maximize score and minimize scoring time, see the example notebook
:ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
We can also use a `plotly.express.parallel_coordinates
<https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
to further visualize the mean test score as a function of the tuned
hyperparameters. This helps finding interactions between more than two
hyperparameters and provide intuition on their relevance for improving the
performance of a pipeline.
We apply a `math.log10` transformation on the `alpha` axis to spread the
active range and improve the readability of the plot. A value :math:`x` on
said axis is to be understood as :math:`10^x`.
.. GENERATED FROM PYTHON SOURCE LINES 218-248
.. code-block:: default
import math
column_results = param_names + ["mean_test_score", "mean_score_time"]
transform_funcs = dict.fromkeys(column_results, lambda x: x)
# Using a logarithmic scale for alpha
transform_funcs["alpha"] = math.log10
# L1 norms are mapped to index 1, and L2 norms to index 2
transform_funcs["norm"] = lambda x: 2 if x == "l2" else 1
# Unigrams are mapped to index 1 and bigrams to index 2
transform_funcs["ngram_range"] = lambda x: x[1]
fig = px.parallel_coordinates(
cv_results[column_results].apply(transform_funcs),
color="mean_test_score",
color_continuous_scale=px.colors.sequential.Viridis_r,
labels=labels,
)
fig.update_layout(
title={
"text": "Parallel coordinates plot of text classifier pipeline",
"y": 0.99,
"x": 0.5,
"xanchor": "center",
"yanchor": "top",
}
)
fig
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<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 249-266
The parallel coordinates plot displays the values of the hyperparameters on
different columns while the performance metric is color coded. It is possible
to select a range of results by clicking and holding on any axis of the
parallel coordinate plot. You can then slide (move) the range selection and
cross two selections to see the intersections. You can undo a selection by
clicking once again on the same axis.
In particular for this hyperparameter search, it is interesting to notice that
the top performing models do not seem to depend on the regularization `norm`,
but they do depend on a trade-off between `max_df`, `min_df` and the
regularization strength `alpha`. The reason is that including noisy features
(i.e. `max_df` close to :math:`1.0` or `min_df` close to :math:`0`) tend to
overfit and therefore require a stronger regularization to compensate. Having
less features require less regularization and less scoring time.
The best accuracy scores are obtained when `alpha` is between :math:`10^{-6}`
and :math:`10^0`, regardless of the hyperparameter `norm`.
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.. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py:
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:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.2.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
:alt: Launch binder
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:download:`Download Python source code: plot_grid_search_text_feature_extraction.py <plot_grid_search_text_feature_extraction.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_grid_search_text_feature_extraction.ipynb <plot_grid_search_text_feature_extraction.ipynb>`
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_