626 lines
20 KiB
Python
626 lines
20 KiB
Python
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"""Caching loader for the 20 newsgroups text classification dataset.
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The description of the dataset is available on the official website at:
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http://people.csail.mit.edu/jrennie/20Newsgroups/
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Quoting the introduction:
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The 20 Newsgroups data set is a collection of approximately 20,000
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newsgroup documents, partitioned (nearly) evenly across 20 different
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newsgroups. To the best of my knowledge, it was originally collected
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by Ken Lang, probably for his Newsweeder: Learning to filter netnews
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paper, though he does not explicitly mention this collection. The 20
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newsgroups collection has become a popular data set for experiments
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in text applications of machine learning techniques, such as text
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classification and text clustering.
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This dataset loader will download the recommended "by date" variant of the
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dataset and which features a point in time split between the train and
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test sets. The compressed dataset size is around 14 Mb compressed. Once
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uncompressed the train set is 52 MB and the test set is 34 MB.
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"""
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# Copyright (c) 2011 Olivier Grisel <olivier.grisel@ensta.org>
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# License: BSD 3 clause
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import codecs
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import logging
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import os
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import pickle
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import re
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import shutil
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import tarfile
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from contextlib import suppress
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from numbers import Integral, Real
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import joblib
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import numpy as np
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import scipy.sparse as sp
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from .. import preprocessing
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from ..feature_extraction.text import CountVectorizer
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from ..utils import Bunch, check_random_state
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from ..utils._param_validation import Interval, StrOptions, validate_params
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from ..utils.fixes import tarfile_extractall
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from . import get_data_home, load_files
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from ._base import (
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RemoteFileMetadata,
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_convert_data_dataframe,
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_fetch_remote,
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_pkl_filepath,
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load_descr,
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)
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logger = logging.getLogger(__name__)
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# The original data can be found at:
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# https://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz
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ARCHIVE = RemoteFileMetadata(
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filename="20news-bydate.tar.gz",
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url="https://ndownloader.figshare.com/files/5975967",
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checksum="8f1b2514ca22a5ade8fbb9cfa5727df95fa587f4c87b786e15c759fa66d95610",
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)
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CACHE_NAME = "20news-bydate.pkz"
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TRAIN_FOLDER = "20news-bydate-train"
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TEST_FOLDER = "20news-bydate-test"
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def _download_20newsgroups(target_dir, cache_path, n_retries, delay):
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"""Download the 20 newsgroups data and stored it as a zipped pickle."""
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train_path = os.path.join(target_dir, TRAIN_FOLDER)
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test_path = os.path.join(target_dir, TEST_FOLDER)
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os.makedirs(target_dir, exist_ok=True)
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logger.info("Downloading dataset from %s (14 MB)", ARCHIVE.url)
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archive_path = _fetch_remote(
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ARCHIVE, dirname=target_dir, n_retries=n_retries, delay=delay
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)
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logger.debug("Decompressing %s", archive_path)
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with tarfile.open(archive_path, "r:gz") as fp:
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tarfile_extractall(fp, path=target_dir)
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with suppress(FileNotFoundError):
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os.remove(archive_path)
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# Store a zipped pickle
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cache = dict(
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train=load_files(train_path, encoding="latin1"),
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test=load_files(test_path, encoding="latin1"),
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)
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compressed_content = codecs.encode(pickle.dumps(cache), "zlib_codec")
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with open(cache_path, "wb") as f:
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f.write(compressed_content)
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shutil.rmtree(target_dir)
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return cache
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def strip_newsgroup_header(text):
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"""
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Given text in "news" format, strip the headers, by removing everything
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before the first blank line.
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Parameters
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----------
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text : str
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The text from which to remove the signature block.
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"""
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_before, _blankline, after = text.partition("\n\n")
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return after
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_QUOTE_RE = re.compile(
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r"(writes in|writes:|wrote:|says:|said:" r"|^In article|^Quoted from|^\||^>)"
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)
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def strip_newsgroup_quoting(text):
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"""
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Given text in "news" format, strip lines beginning with the quote
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characters > or |, plus lines that often introduce a quoted section
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(for example, because they contain the string 'writes:'.)
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Parameters
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----------
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text : str
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The text from which to remove the signature block.
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"""
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good_lines = [line for line in text.split("\n") if not _QUOTE_RE.search(line)]
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return "\n".join(good_lines)
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def strip_newsgroup_footer(text):
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"""
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Given text in "news" format, attempt to remove a signature block.
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As a rough heuristic, we assume that signatures are set apart by either
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a blank line or a line made of hyphens, and that it is the last such line
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in the file (disregarding blank lines at the end).
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Parameters
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----------
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text : str
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The text from which to remove the signature block.
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"""
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lines = text.strip().split("\n")
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for line_num in range(len(lines) - 1, -1, -1):
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line = lines[line_num]
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if line.strip().strip("-") == "":
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break
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if line_num > 0:
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return "\n".join(lines[:line_num])
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else:
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return text
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@validate_params(
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{
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"data_home": [str, os.PathLike, None],
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"subset": [StrOptions({"train", "test", "all"})],
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"categories": ["array-like", None],
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"shuffle": ["boolean"],
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"random_state": ["random_state"],
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"remove": [tuple],
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"download_if_missing": ["boolean"],
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"return_X_y": ["boolean"],
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"n_retries": [Interval(Integral, 1, None, closed="left")],
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"delay": [Interval(Real, 0.0, None, closed="neither")],
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},
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prefer_skip_nested_validation=True,
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)
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def fetch_20newsgroups(
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*,
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data_home=None,
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subset="train",
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categories=None,
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shuffle=True,
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random_state=42,
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remove=(),
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download_if_missing=True,
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return_X_y=False,
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n_retries=3,
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delay=1.0,
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):
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"""Load the filenames and data from the 20 newsgroups dataset \
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(classification).
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Download it if necessary.
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================= ==========
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Classes 20
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Samples total 18846
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Dimensionality 1
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Features text
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================= ==========
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Read more in the :ref:`User Guide <20newsgroups_dataset>`.
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Parameters
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----------
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data_home : str or path-like, default=None
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Specify a download and cache folder for the datasets. If None,
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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subset : {'train', 'test', 'all'}, default='train'
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Select the dataset to load: 'train' for the training set, 'test'
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for the test set, 'all' for both, with shuffled ordering.
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categories : array-like, dtype=str, default=None
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If None (default), load all the categories.
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If not None, list of category names to load (other categories
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ignored).
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shuffle : bool, default=True
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Whether or not to shuffle the data: might be important for models that
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make the assumption that the samples are independent and identically
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distributed (i.i.d.), such as stochastic gradient descent.
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random_state : int, RandomState instance or None, default=42
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Determines random number generation for dataset shuffling. Pass an int
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for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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remove : tuple, default=()
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May contain any subset of ('headers', 'footers', 'quotes'). Each of
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these are kinds of text that will be detected and removed from the
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newsgroup posts, preventing classifiers from overfitting on
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metadata.
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'headers' removes newsgroup headers, 'footers' removes blocks at the
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ends of posts that look like signatures, and 'quotes' removes lines
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that appear to be quoting another post.
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'headers' follows an exact standard; the other filters are not always
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correct.
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download_if_missing : bool, default=True
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If False, raise an OSError if the data is not locally available
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instead of trying to download the data from the source site.
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return_X_y : bool, default=False
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If True, returns `(data.data, data.target)` instead of a Bunch
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object.
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.. versionadded:: 0.22
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n_retries : int, default=3
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Number of retries when HTTP errors are encountered.
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.. versionadded:: 1.5
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delay : float, default=1.0
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Number of seconds between retries.
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.. versionadded:: 1.5
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Returns
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-------
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bunch : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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data : list of shape (n_samples,)
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The data list to learn.
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target: ndarray of shape (n_samples,)
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The target labels.
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filenames: list of shape (n_samples,)
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The path to the location of the data.
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DESCR: str
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The full description of the dataset.
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target_names: list of shape (n_classes,)
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The names of target classes.
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(data, target) : tuple if `return_X_y=True`
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A tuple of two ndarrays. The first contains a 2D array of shape
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(n_samples, n_classes) with each row representing one sample and each
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column representing the features. The second array of shape
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(n_samples,) contains the target samples.
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.. versionadded:: 0.22
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Examples
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--------
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>>> from sklearn.datasets import fetch_20newsgroups
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>>> cats = ['alt.atheism', 'sci.space']
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>>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)
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>>> list(newsgroups_train.target_names)
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['alt.atheism', 'sci.space']
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>>> newsgroups_train.filenames.shape
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(1073,)
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>>> newsgroups_train.target.shape
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(1073,)
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>>> newsgroups_train.target[:10]
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array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])
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"""
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data_home = get_data_home(data_home=data_home)
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cache_path = _pkl_filepath(data_home, CACHE_NAME)
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twenty_home = os.path.join(data_home, "20news_home")
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cache = None
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if os.path.exists(cache_path):
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try:
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with open(cache_path, "rb") as f:
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compressed_content = f.read()
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uncompressed_content = codecs.decode(compressed_content, "zlib_codec")
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cache = pickle.loads(uncompressed_content)
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except Exception as e:
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print(80 * "_")
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print("Cache loading failed")
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print(80 * "_")
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print(e)
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if cache is None:
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if download_if_missing:
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logger.info("Downloading 20news dataset. This may take a few minutes.")
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cache = _download_20newsgroups(
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target_dir=twenty_home,
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cache_path=cache_path,
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n_retries=n_retries,
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delay=delay,
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)
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else:
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raise OSError("20Newsgroups dataset not found")
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if subset in ("train", "test"):
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data = cache[subset]
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elif subset == "all":
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data_lst = list()
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target = list()
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filenames = list()
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for subset in ("train", "test"):
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data = cache[subset]
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data_lst.extend(data.data)
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target.extend(data.target)
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filenames.extend(data.filenames)
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data.data = data_lst
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data.target = np.array(target)
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data.filenames = np.array(filenames)
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fdescr = load_descr("twenty_newsgroups.rst")
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data.DESCR = fdescr
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if "headers" in remove:
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data.data = [strip_newsgroup_header(text) for text in data.data]
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if "footers" in remove:
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data.data = [strip_newsgroup_footer(text) for text in data.data]
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if "quotes" in remove:
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data.data = [strip_newsgroup_quoting(text) for text in data.data]
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if categories is not None:
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labels = [(data.target_names.index(cat), cat) for cat in categories]
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# Sort the categories to have the ordering of the labels
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labels.sort()
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labels, categories = zip(*labels)
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mask = np.isin(data.target, labels)
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data.filenames = data.filenames[mask]
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data.target = data.target[mask]
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# searchsorted to have continuous labels
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data.target = np.searchsorted(labels, data.target)
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data.target_names = list(categories)
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# Use an object array to shuffle: avoids memory copy
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data_lst = np.array(data.data, dtype=object)
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data_lst = data_lst[mask]
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data.data = data_lst.tolist()
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if shuffle:
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random_state = check_random_state(random_state)
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indices = np.arange(data.target.shape[0])
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random_state.shuffle(indices)
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data.filenames = data.filenames[indices]
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data.target = data.target[indices]
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# Use an object array to shuffle: avoids memory copy
|
||
|
|
data_lst = np.array(data.data, dtype=object)
|
||
|
|
data_lst = data_lst[indices]
|
||
|
|
data.data = data_lst.tolist()
|
||
|
|
|
||
|
|
if return_X_y:
|
||
|
|
return data.data, data.target
|
||
|
|
|
||
|
|
return data
|
||
|
|
|
||
|
|
|
||
|
|
@validate_params(
|
||
|
|
{
|
||
|
|
"subset": [StrOptions({"train", "test", "all"})],
|
||
|
|
"remove": [tuple],
|
||
|
|
"data_home": [str, os.PathLike, None],
|
||
|
|
"download_if_missing": ["boolean"],
|
||
|
|
"return_X_y": ["boolean"],
|
||
|
|
"normalize": ["boolean"],
|
||
|
|
"as_frame": ["boolean"],
|
||
|
|
"n_retries": [Interval(Integral, 1, None, closed="left")],
|
||
|
|
"delay": [Interval(Real, 0.0, None, closed="neither")],
|
||
|
|
},
|
||
|
|
prefer_skip_nested_validation=True,
|
||
|
|
)
|
||
|
|
def fetch_20newsgroups_vectorized(
|
||
|
|
*,
|
||
|
|
subset="train",
|
||
|
|
remove=(),
|
||
|
|
data_home=None,
|
||
|
|
download_if_missing=True,
|
||
|
|
return_X_y=False,
|
||
|
|
normalize=True,
|
||
|
|
as_frame=False,
|
||
|
|
n_retries=3,
|
||
|
|
delay=1.0,
|
||
|
|
):
|
||
|
|
"""Load and vectorize the 20 newsgroups dataset (classification).
|
||
|
|
|
||
|
|
Download it if necessary.
|
||
|
|
|
||
|
|
This is a convenience function; the transformation is done using the
|
||
|
|
default settings for
|
||
|
|
:class:`~sklearn.feature_extraction.text.CountVectorizer`. For more
|
||
|
|
advanced usage (stopword filtering, n-gram extraction, etc.), combine
|
||
|
|
fetch_20newsgroups with a custom
|
||
|
|
:class:`~sklearn.feature_extraction.text.CountVectorizer`,
|
||
|
|
:class:`~sklearn.feature_extraction.text.HashingVectorizer`,
|
||
|
|
:class:`~sklearn.feature_extraction.text.TfidfTransformer` or
|
||
|
|
:class:`~sklearn.feature_extraction.text.TfidfVectorizer`.
|
||
|
|
|
||
|
|
The resulting counts are normalized using
|
||
|
|
:func:`sklearn.preprocessing.normalize` unless normalize is set to False.
|
||
|
|
|
||
|
|
================= ==========
|
||
|
|
Classes 20
|
||
|
|
Samples total 18846
|
||
|
|
Dimensionality 130107
|
||
|
|
Features real
|
||
|
|
================= ==========
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <20newsgroups_dataset>`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
subset : {'train', 'test', 'all'}, default='train'
|
||
|
|
Select the dataset to load: 'train' for the training set, 'test'
|
||
|
|
for the test set, 'all' for both, with shuffled ordering.
|
||
|
|
|
||
|
|
remove : tuple, default=()
|
||
|
|
May contain any subset of ('headers', 'footers', 'quotes'). Each of
|
||
|
|
these are kinds of text that will be detected and removed from the
|
||
|
|
newsgroup posts, preventing classifiers from overfitting on
|
||
|
|
metadata.
|
||
|
|
|
||
|
|
'headers' removes newsgroup headers, 'footers' removes blocks at the
|
||
|
|
ends of posts that look like signatures, and 'quotes' removes lines
|
||
|
|
that appear to be quoting another post.
|
||
|
|
|
||
|
|
data_home : str or path-like, default=None
|
||
|
|
Specify an download and cache folder for the datasets. If None,
|
||
|
|
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
|
||
|
|
|
||
|
|
download_if_missing : bool, default=True
|
||
|
|
If False, raise an OSError if the data is not locally available
|
||
|
|
instead of trying to download the data from the source site.
|
||
|
|
|
||
|
|
return_X_y : bool, default=False
|
||
|
|
If True, returns ``(data.data, data.target)`` instead of a Bunch
|
||
|
|
object.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
normalize : bool, default=True
|
||
|
|
If True, normalizes each document's feature vector to unit norm using
|
||
|
|
:func:`sklearn.preprocessing.normalize`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.22
|
||
|
|
|
||
|
|
as_frame : bool, default=False
|
||
|
|
If True, the data is a pandas DataFrame including columns with
|
||
|
|
appropriate dtypes (numeric, string, or categorical). The target is
|
||
|
|
a pandas DataFrame or Series depending on the number of
|
||
|
|
`target_columns`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24
|
||
|
|
|
||
|
|
n_retries : int, default=3
|
||
|
|
Number of retries when HTTP errors are encountered.
|
||
|
|
|
||
|
|
.. versionadded:: 1.5
|
||
|
|
|
||
|
|
delay : float, default=1.0
|
||
|
|
Number of seconds between retries.
|
||
|
|
|
||
|
|
.. versionadded:: 1.5
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
bunch : :class:`~sklearn.utils.Bunch`
|
||
|
|
Dictionary-like object, with the following attributes.
|
||
|
|
|
||
|
|
data: {sparse matrix, dataframe} of shape (n_samples, n_features)
|
||
|
|
The input data matrix. If ``as_frame`` is `True`, ``data`` is
|
||
|
|
a pandas DataFrame with sparse columns.
|
||
|
|
target: {ndarray, series} of shape (n_samples,)
|
||
|
|
The target labels. If ``as_frame`` is `True`, ``target`` is a
|
||
|
|
pandas Series.
|
||
|
|
target_names: list of shape (n_classes,)
|
||
|
|
The names of target classes.
|
||
|
|
DESCR: str
|
||
|
|
The full description of the dataset.
|
||
|
|
frame: dataframe of shape (n_samples, n_features + 1)
|
||
|
|
Only present when `as_frame=True`. Pandas DataFrame with ``data``
|
||
|
|
and ``target``.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24
|
||
|
|
|
||
|
|
(data, target) : tuple if ``return_X_y`` is True
|
||
|
|
`data` and `target` would be of the format defined in the `Bunch`
|
||
|
|
description above.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from sklearn.datasets import fetch_20newsgroups_vectorized
|
||
|
|
>>> newsgroups_vectorized = fetch_20newsgroups_vectorized(subset='test')
|
||
|
|
>>> newsgroups_vectorized.data.shape
|
||
|
|
(7532, 130107)
|
||
|
|
>>> newsgroups_vectorized.target.shape
|
||
|
|
(7532,)
|
||
|
|
"""
|
||
|
|
data_home = get_data_home(data_home=data_home)
|
||
|
|
filebase = "20newsgroup_vectorized"
|
||
|
|
if remove:
|
||
|
|
filebase += "remove-" + "-".join(remove)
|
||
|
|
target_file = _pkl_filepath(data_home, filebase + ".pkl")
|
||
|
|
|
||
|
|
# we shuffle but use a fixed seed for the memoization
|
||
|
|
data_train = fetch_20newsgroups(
|
||
|
|
data_home=data_home,
|
||
|
|
subset="train",
|
||
|
|
categories=None,
|
||
|
|
shuffle=True,
|
||
|
|
random_state=12,
|
||
|
|
remove=remove,
|
||
|
|
download_if_missing=download_if_missing,
|
||
|
|
n_retries=n_retries,
|
||
|
|
delay=delay,
|
||
|
|
)
|
||
|
|
|
||
|
|
data_test = fetch_20newsgroups(
|
||
|
|
data_home=data_home,
|
||
|
|
subset="test",
|
||
|
|
categories=None,
|
||
|
|
shuffle=True,
|
||
|
|
random_state=12,
|
||
|
|
remove=remove,
|
||
|
|
download_if_missing=download_if_missing,
|
||
|
|
n_retries=n_retries,
|
||
|
|
delay=delay,
|
||
|
|
)
|
||
|
|
|
||
|
|
if os.path.exists(target_file):
|
||
|
|
try:
|
||
|
|
X_train, X_test, feature_names = joblib.load(target_file)
|
||
|
|
except ValueError as e:
|
||
|
|
raise ValueError(
|
||
|
|
f"The cached dataset located in {target_file} was fetched "
|
||
|
|
"with an older scikit-learn version and it is not compatible "
|
||
|
|
"with the scikit-learn version imported. You need to "
|
||
|
|
f"manually delete the file: {target_file}."
|
||
|
|
) from e
|
||
|
|
else:
|
||
|
|
vectorizer = CountVectorizer(dtype=np.int16)
|
||
|
|
X_train = vectorizer.fit_transform(data_train.data).tocsr()
|
||
|
|
X_test = vectorizer.transform(data_test.data).tocsr()
|
||
|
|
feature_names = vectorizer.get_feature_names_out()
|
||
|
|
|
||
|
|
joblib.dump((X_train, X_test, feature_names), target_file, compress=9)
|
||
|
|
|
||
|
|
# the data is stored as int16 for compactness
|
||
|
|
# but normalize needs floats
|
||
|
|
if normalize:
|
||
|
|
X_train = X_train.astype(np.float64)
|
||
|
|
X_test = X_test.astype(np.float64)
|
||
|
|
preprocessing.normalize(X_train, copy=False)
|
||
|
|
preprocessing.normalize(X_test, copy=False)
|
||
|
|
|
||
|
|
target_names = data_train.target_names
|
||
|
|
|
||
|
|
if subset == "train":
|
||
|
|
data = X_train
|
||
|
|
target = data_train.target
|
||
|
|
elif subset == "test":
|
||
|
|
data = X_test
|
||
|
|
target = data_test.target
|
||
|
|
elif subset == "all":
|
||
|
|
data = sp.vstack((X_train, X_test)).tocsr()
|
||
|
|
target = np.concatenate((data_train.target, data_test.target))
|
||
|
|
|
||
|
|
fdescr = load_descr("twenty_newsgroups.rst")
|
||
|
|
|
||
|
|
frame = None
|
||
|
|
target_name = ["category_class"]
|
||
|
|
|
||
|
|
if as_frame:
|
||
|
|
frame, data, target = _convert_data_dataframe(
|
||
|
|
"fetch_20newsgroups_vectorized",
|
||
|
|
data,
|
||
|
|
target,
|
||
|
|
feature_names,
|
||
|
|
target_names=target_name,
|
||
|
|
sparse_data=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
if return_X_y:
|
||
|
|
return data, target
|
||
|
|
|
||
|
|
return Bunch(
|
||
|
|
data=data,
|
||
|
|
target=target,
|
||
|
|
frame=frame,
|
||
|
|
target_names=target_names,
|
||
|
|
feature_names=feature_names,
|
||
|
|
DESCR=fdescr,
|
||
|
|
)
|