Sample pipeline for text feature extraction and evaluation

The dataset used in this example is The 20 newsgroups text 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 RandomizedSearchCV. For a demo on the performance of some other classifiers, see the Classification of text documents using sparse features notebook.

Traceback (most recent call last):
  File "/build/scikit-learn-cNihnX/scikit-learn-1.2.1/examples/model_selection/plot_grid_search_text_feature_extraction.py", line 38, in <module>
    data_train = fetch_20newsgroups(
  File "/build/scikit-learn-cNihnX/scikit-learn-1.2.1/.pybuild/cpython3_3.8/build/sklearn/datasets/_twenty_newsgroups.py", line 269, in fetch_20newsgroups
    cache = _download_20newsgroups(
  File "/build/scikit-learn-cNihnX/scikit-learn-1.2.1/.pybuild/cpython3_3.8/build/sklearn/datasets/_twenty_newsgroups.py", line 74, in _download_20newsgroups
    archive_path = _fetch_remote(ARCHIVE, dirname=target_dir)
  File "/build/scikit-learn-cNihnX/scikit-learn-1.2.1/.pybuild/cpython3_3.8/build/sklearn/datasets/_base.py", line 1323, in _fetch_remote
    raise IOError('Debian Policy Section 4.9 prohibits network access during build')
OSError: Debian Policy Section 4.9 prohibits network access during build
# 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

# %%
# 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.

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")

# %%
# Pipeline with hyperparameter tuning
# -----------------------------------
#
# We define a pipeline combining a text feature vectorizer with a simple
# classifier yet effective for text classification.

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

# %%
# 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__`).

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),
}

# %%
# 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`.

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)

# %%
from time import time

t0 = time()
random_search.fit(data_train.data, data_train.target)
print(f"Done in {time() - t0:.3f}s")

# %%
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]}")

# %%
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}")

# %%
# 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.

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)

# %%
# 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.

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

# %%
# 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`.

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

# %%
# 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`.

Total running time of the script: ( 0 minutes 0.000 seconds)

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