.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/text/plot_hashing_vs_dict_vectorizer.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_text_plot_hashing_vs_dict_vectorizer.py: =========================================== FeatureHasher and DictVectorizer Comparison =========================================== In this example we illustrate text vectorization, which is the process of representing non-numerical input data (such as dictionaries or text documents) as vectors of real numbers. We first compare :func:`~sklearn.feature_extraction.FeatureHasher` and :func:`~sklearn.feature_extraction.DictVectorizer` by using both methods to vectorize text documents that are preprocessed (tokenized) with the help of a custom Python function. Later we introduce and analyze the text-specific vectorizers :func:`~sklearn.feature_extraction.text.HashingVectorizer`, :func:`~sklearn.feature_extraction.text.CountVectorizer` and :func:`~sklearn.feature_extraction.text.TfidfVectorizer` that handle both the tokenization and the assembling of the feature matrix within a single class. The objective of the example is to demonstrate the usage of text vectorization API and to compare their processing time. See the example scripts :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` and :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` for actual learning on text documents. .. GENERATED FROM PYTHON SOURCE LINES 28-32 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 33-40 Load Data --------- We load data from :ref:`20newsgroups_dataset`, which comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training and one for testing. For the sake of simplicity and reducing the computational cost, we select a subset of 7 topics and use the training set only. .. GENERATED FROM PYTHON SOURCE LINES 40-58 .. code-block:: Python from sklearn.datasets import fetch_20newsgroups categories = [ "alt.atheism", "comp.graphics", "comp.sys.ibm.pc.hardware", "misc.forsale", "rec.autos", "sci.space", "talk.religion.misc", ] print("Loading 20 newsgroups training data") raw_data, _ = fetch_20newsgroups(subset="train", categories=categories, return_X_y=True) data_size_mb = sum(len(s.encode("utf-8")) for s in raw_data) / 1e6 print(f"{len(raw_data)} documents - {data_size_mb:.3f}MB") .. rst-class:: sphx-glr-script-out .. code-block:: none Loading 20 newsgroups training data 3803 documents - 6.245MB .. GENERATED FROM PYTHON SOURCE LINES 59-67 Define preprocessing functions ------------------------------ A token may be a word, part of a word or anything comprised between spaces or symbols in a string. Here we define a function that extracts the tokens using a simple regular expression (regex) that matches Unicode word characters. This includes most characters that can be part of a word in any language, as well as numbers and the underscore: .. GENERATED FROM PYTHON SOURCE LINES 67-83 .. code-block:: Python import re def tokenize(doc): """Extract tokens from doc. This uses a simple regex that matches word characters to break strings into tokens. For a more principled approach, see CountVectorizer or TfidfVectorizer. """ return (tok.lower() for tok in re.findall(r"\w+", doc)) list(tokenize("This is a simple example, isn't it?")) .. rst-class:: sphx-glr-script-out .. code-block:: none ['this', 'is', 'a', 'simple', 'example', 'isn', 't', 'it'] .. GENERATED FROM PYTHON SOURCE LINES 84-87 We define an additional function that counts the (frequency of) occurrence of each token in a given document. It returns a frequency dictionary to be used by the vectorizers. .. GENERATED FROM PYTHON SOURCE LINES 87-102 .. code-block:: Python from collections import defaultdict def token_freqs(doc): """Extract a dict mapping tokens from doc to their occurrences.""" freq = defaultdict(int) for tok in tokenize(doc): freq[tok] += 1 return freq token_freqs("That is one example, but this is another one") .. rst-class:: sphx-glr-script-out .. code-block:: none defaultdict(, {'that': 1, 'is': 2, 'one': 2, 'example': 1, 'but': 1, 'this': 1, 'another': 1}) .. GENERATED FROM PYTHON SOURCE LINES 103-109 Observe in particular that the repeated token `"is"` is counted twice for instance. Breaking a text document into word tokens, potentially losing the order information between the words in a sentence is often called a `Bag of Words representation `_. .. GENERATED FROM PYTHON SOURCE LINES 111-117 DictVectorizer -------------- First we benchmark the :func:`~sklearn.feature_extraction.DictVectorizer`, then we compare it to :func:`~sklearn.feature_extraction.FeatureHasher` as both of them receive dictionaries as input. .. GENERATED FROM PYTHON SOURCE LINES 117-135 .. code-block:: Python from time import time from sklearn.feature_extraction import DictVectorizer dict_count_vectorizers = defaultdict(list) t0 = time() vectorizer = DictVectorizer() vectorizer.fit_transform(token_freqs(d) for d in raw_data) duration = time() - t0 dict_count_vectorizers["vectorizer"].append( vectorizer.__class__.__name__ + "\non freq dicts" ) dict_count_vectorizers["speed"].append(data_size_mb / duration) print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") print(f"Found {len(vectorizer.get_feature_names_out())} unique terms") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 1.001 s at 6.2 MB/s Found 47928 unique terms .. GENERATED FROM PYTHON SOURCE LINES 136-139 The actual mapping from text token to column index is explicitly stored in the `.vocabulary_` attribute which is a potentially very large Python dictionary: .. GENERATED FROM PYTHON SOURCE LINES 139-141 .. code-block:: Python type(vectorizer.vocabulary_) .. GENERATED FROM PYTHON SOURCE LINES 142-144 .. code-block:: Python len(vectorizer.vocabulary_) .. rst-class:: sphx-glr-script-out .. code-block:: none 47928 .. GENERATED FROM PYTHON SOURCE LINES 145-147 .. code-block:: Python vectorizer.vocabulary_["example"] .. rst-class:: sphx-glr-script-out .. code-block:: none 19145 .. GENERATED FROM PYTHON SOURCE LINES 148-164 FeatureHasher ------------- Dictionaries take up a large amount of storage space and grow in size as the training set grows. Instead of growing the vectors along with a dictionary, feature hashing builds a vector of pre-defined length by applying a hash function `h` to the features (e.g., tokens), then using the hash values directly as feature indices and updating the resulting vector at those indices. When the feature space is not large enough, hashing functions tend to map distinct values to the same hash code (hash collisions). As a result, it is impossible to determine what object generated any particular hash code. Because of the above it is impossible to recover the original tokens from the feature matrix and the best approach to estimate the number of unique terms in the original dictionary is to count the number of active columns in the encoded feature matrix. For such a purpose we define the following function: .. GENERATED FROM PYTHON SOURCE LINES 164-177 .. code-block:: Python import numpy as np def n_nonzero_columns(X): """Number of columns with at least one non-zero value in a CSR matrix. This is useful to count the number of features columns that are effectively active when using the FeatureHasher. """ return len(np.unique(X.nonzero()[1])) .. GENERATED FROM PYTHON SOURCE LINES 178-183 The default number of features for the :func:`~sklearn.feature_extraction.FeatureHasher` is 2**20. Here we set `n_features = 2**18` to illustrate hash collisions. **FeatureHasher on frequency dictionaries** .. GENERATED FROM PYTHON SOURCE LINES 183-197 .. code-block:: Python from sklearn.feature_extraction import FeatureHasher t0 = time() hasher = FeatureHasher(n_features=2**18) X = hasher.transform(token_freqs(d) for d in raw_data) duration = time() - t0 dict_count_vectorizers["vectorizer"].append( hasher.__class__.__name__ + "\non freq dicts" ) dict_count_vectorizers["speed"].append(data_size_mb / duration) print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") print(f"Found {n_nonzero_columns(X)} unique tokens") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 0.562 s at 11.1 MB/s Found 43873 unique tokens .. GENERATED FROM PYTHON SOURCE LINES 198-208 The number of unique tokens when using the :func:`~sklearn.feature_extraction.FeatureHasher` is lower than those obtained using the :func:`~sklearn.feature_extraction.DictVectorizer`. This is due to hash collisions. The number of collisions can be reduced by increasing the feature space. Notice that the speed of the vectorizer does not change significantly when setting a large number of features, though it causes larger coefficient dimensions and then requires more memory usage to store them, even if a majority of them is inactive. .. GENERATED FROM PYTHON SOURCE LINES 208-217 .. code-block:: Python t0 = time() hasher = FeatureHasher(n_features=2**22) X = hasher.transform(token_freqs(d) for d in raw_data) duration = time() - t0 print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") print(f"Found {n_nonzero_columns(X)} unique tokens") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 0.571 s at 10.9 MB/s Found 47668 unique tokens .. GENERATED FROM PYTHON SOURCE LINES 218-227 We confirm that the number of unique tokens gets closer to the number of unique terms found by the :func:`~sklearn.feature_extraction.DictVectorizer`. **FeatureHasher on raw tokens** Alternatively, one can set `input_type="string"` in the :func:`~sklearn.feature_extraction.FeatureHasher` to vectorize the strings output directly from the customized `tokenize` function. This is equivalent to passing a dictionary with an implied frequency of 1 for each feature name. .. GENERATED FROM PYTHON SOURCE LINES 227-239 .. code-block:: Python t0 = time() hasher = FeatureHasher(n_features=2**18, input_type="string") X = hasher.transform(tokenize(d) for d in raw_data) duration = time() - t0 dict_count_vectorizers["vectorizer"].append( hasher.__class__.__name__ + "\non raw tokens" ) dict_count_vectorizers["speed"].append(data_size_mb / duration) print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") print(f"Found {n_nonzero_columns(X)} unique tokens") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 0.534 s at 11.7 MB/s Found 43873 unique tokens .. GENERATED FROM PYTHON SOURCE LINES 240-241 We now plot the speed of the above methods for vectorizing. .. GENERATED FROM PYTHON SOURCE LINES 241-253 .. code-block:: Python import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(12, 6)) y_pos = np.arange(len(dict_count_vectorizers["vectorizer"])) ax.barh(y_pos, dict_count_vectorizers["speed"], align="center") ax.set_yticks(y_pos) ax.set_yticklabels(dict_count_vectorizers["vectorizer"]) ax.invert_yaxis() _ = ax.set_xlabel("speed (MB/s)") .. image-sg:: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_001.png :alt: plot hashing vs dict vectorizer :srcset: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 254-277 In both cases :func:`~sklearn.feature_extraction.FeatureHasher` is approximately twice as fast as :func:`~sklearn.feature_extraction.DictVectorizer`. This is handy when dealing with large amounts of data, with the downside of losing the invertibility of the transformation, which in turn makes the interpretation of a model a more complex task. The `FeatureHeasher` with `input_type="string"` is slightly faster than the variant that works on frequency dict because it does not count repeated tokens: each token is implicitly counted once, even if it was repeated. Depending on the downstream machine learning task, it can be a limitation or not. Comparison with special purpose text vectorizers ------------------------------------------------ :func:`~sklearn.feature_extraction.text.CountVectorizer` accepts raw data as it internally implements tokenization and occurrence counting. It is similar to the :func:`~sklearn.feature_extraction.DictVectorizer` when used along with the customized function `token_freqs` as done in the previous section. The difference being that :func:`~sklearn.feature_extraction.text.CountVectorizer` is more flexible. In particular it accepts various regex patterns through the `token_pattern` parameter. .. GENERATED FROM PYTHON SOURCE LINES 277-289 .. code-block:: Python from sklearn.feature_extraction.text import CountVectorizer t0 = time() vectorizer = CountVectorizer() vectorizer.fit_transform(raw_data) duration = time() - t0 dict_count_vectorizers["vectorizer"].append(vectorizer.__class__.__name__) dict_count_vectorizers["speed"].append(data_size_mb / duration) print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") print(f"Found {len(vectorizer.get_feature_names_out())} unique terms") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 0.666 s at 9.4 MB/s Found 47885 unique terms .. GENERATED FROM PYTHON SOURCE LINES 290-304 We see that using the :func:`~sklearn.feature_extraction.text.CountVectorizer` implementation is approximately twice as fast as using the :func:`~sklearn.feature_extraction.DictVectorizer` along with the simple function we defined for mapping the tokens. The reason is that :func:`~sklearn.feature_extraction.text.CountVectorizer` is optimized by reusing a compiled regular expression for the full training set instead of creating one per document as done in our naive tokenize function. Now we make a similar experiment with the :func:`~sklearn.feature_extraction.text.HashingVectorizer`, which is equivalent to combining the "hashing trick" implemented by the :func:`~sklearn.feature_extraction.FeatureHasher` class and the text preprocessing and tokenization of the :func:`~sklearn.feature_extraction.text.CountVectorizer`. .. GENERATED FROM PYTHON SOURCE LINES 304-315 .. code-block:: Python from sklearn.feature_extraction.text import HashingVectorizer t0 = time() vectorizer = HashingVectorizer(n_features=2**18) vectorizer.fit_transform(raw_data) duration = time() - t0 dict_count_vectorizers["vectorizer"].append(vectorizer.__class__.__name__) dict_count_vectorizers["speed"].append(data_size_mb / duration) print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 0.502 s at 12.5 MB/s .. GENERATED FROM PYTHON SOURCE LINES 316-339 We can observe that this is the fastest text tokenization strategy so far, assuming that the downstream machine learning task can tolerate a few collisions. TfidfVectorizer --------------- In a large text corpus, some words appear with higher frequency (e.g. "the", "a", "is" in English) and do not carry meaningful information about the actual contents of a document. If we were to feed the word count data directly to a classifier, those very common terms would shadow the frequencies of rarer yet more informative terms. In order to re-weight the count features into floating point values suitable for usage by a classifier it is very common to use the tf-idf transform as implemented by the :func:`~sklearn.feature_extraction.text.TfidfTransformer`. TF stands for "term-frequency" while "tf-idf" means term-frequency times inverse document-frequency. We now benchmark the :func:`~sklearn.feature_extraction.text.TfidfVectorizer`, which is equivalent to combining the tokenization and occurrence counting of the :func:`~sklearn.feature_extraction.text.CountVectorizer` along with the normalizing and weighting from a :func:`~sklearn.feature_extraction.text.TfidfTransformer`. .. GENERATED FROM PYTHON SOURCE LINES 339-351 .. code-block:: Python from sklearn.feature_extraction.text import TfidfVectorizer t0 = time() vectorizer = TfidfVectorizer() vectorizer.fit_transform(raw_data) duration = time() - t0 dict_count_vectorizers["vectorizer"].append(vectorizer.__class__.__name__) dict_count_vectorizers["speed"].append(data_size_mb / duration) print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s") print(f"Found {len(vectorizer.get_feature_names_out())} unique terms") .. rst-class:: sphx-glr-script-out .. code-block:: none done in 0.649 s at 9.6 MB/s Found 47885 unique terms .. GENERATED FROM PYTHON SOURCE LINES 352-356 Summary ------- Let's conclude this notebook by summarizing all the recorded processing speeds in a single plot: .. GENERATED FROM PYTHON SOURCE LINES 356-366 .. code-block:: Python fig, ax = plt.subplots(figsize=(12, 6)) y_pos = np.arange(len(dict_count_vectorizers["vectorizer"])) ax.barh(y_pos, dict_count_vectorizers["speed"], align="center") ax.set_yticks(y_pos) ax.set_yticklabels(dict_count_vectorizers["vectorizer"]) ax.invert_yaxis() _ = ax.set_xlabel("speed (MB/s)") .. image-sg:: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_002.png :alt: plot hashing vs dict vectorizer :srcset: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 367-384 Notice from the plot that :func:`~sklearn.feature_extraction.text.TfidfVectorizer` is slightly slower than :func:`~sklearn.feature_extraction.text.CountVectorizer` because of the extra operation induced by the :func:`~sklearn.feature_extraction.text.TfidfTransformer`. Also notice that, by setting the number of features `n_features = 2**18`, the :func:`~sklearn.feature_extraction.text.HashingVectorizer` performs better than the :func:`~sklearn.feature_extraction.text.CountVectorizer` at the expense of inversibility of the transformation due to hash collisions. We highlight that :func:`~sklearn.feature_extraction.text.CountVectorizer` and :func:`~sklearn.feature_extraction.text.HashingVectorizer` perform better than their equivalent :func:`~sklearn.feature_extraction.DictVectorizer` and :func:`~sklearn.feature_extraction.FeatureHasher` on manually tokenized documents since the internal tokenization step of the former vectorizers compiles a regular expression once and then reuses it for all the documents. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.933 seconds) .. _sphx_glr_download_auto_examples_text_plot_hashing_vs_dict_vectorizer.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_hashing_vs_dict_vectorizer.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_hashing_vs_dict_vectorizer.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_hashing_vs_dict_vectorizer.zip ` .. include:: plot_hashing_vs_dict_vectorizer.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_