.. 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 :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_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:: Python # Author: Olivier Grisel # Peter Prettenhofer # Mathieu Blondel # Arturo Amor # 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.fetch_20newsgroups` to get the 20 of them. .. GENERATED FROM PYTHON SOURCE LINES 30-58 .. code-block:: Python 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:: Python 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

Pipeline(steps=[('vect', TfidfVectorizer()), ('clf', ComplementNB())])

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.. 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:: Python 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 106-124 .. code-block:: Python 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 125-131 .. code-block:: Python 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 26.991s .. GENERATED FROM PYTHON SOURCE LINES 132-137 .. code-block:: Python 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 138-145 .. code-block:: Python 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 146-150 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 150-164 .. code-block:: Python 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 165-171 We can use a `plotly.express.scatter `_ 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 171-199 .. code-block:: Python 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

.. GENERATED FROM PYTHON SOURCE LINES 200-219 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 `_ 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 219-249 .. code-block:: Python 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 .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 250-267 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`. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 28.922 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_text_feature_extraction.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/model_selection/plot_grid_search_text_feature_extraction.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_grid_search_text_feature_extraction.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_grid_search_text_feature_extraction.py ` .. include:: plot_grid_search_text_feature_extraction.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_