.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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_applications_plot_out_of_core_classification.py: ====================================================== Out-of-core classification of text documents ====================================================== This is an example showing how scikit-learn can be used for classification using an out-of-core approach: learning from data that doesn't fit into main memory. We make use of an online classifier, i.e., one that supports the partial_fit method, that will be fed with batches of examples. To guarantee that the features space remains the same over time we leverage a HashingVectorizer that will project each example into the same feature space. This is especially useful in the case of text classification where new features (words) may appear in each batch. .. code-block:: default # Authors: Eustache Diemert # @FedericoV # License: BSD 3 clause from glob import glob import itertools import os.path import re import tarfile import time import sys import numpy as np import matplotlib.pyplot as plt from matplotlib import rcParams from html.parser import HTMLParser from urllib.request import urlretrieve from sklearn.datasets import get_data_home from sklearn.feature_extraction.text import HashingVectorizer from sklearn.linear_model import SGDClassifier from sklearn.linear_model import PassiveAggressiveClassifier from sklearn.linear_model import Perceptron from sklearn.naive_bayes import MultinomialNB def _not_in_sphinx(): # Hack to detect whether we are running by the sphinx builder return '__file__' in globals() Reuters Dataset related routines -------------------------------- The dataset used in this example is Reuters-21578 as provided by the UCI ML repository. It will be automatically downloaded and uncompressed on first run. .. code-block:: default class ReutersParser(HTMLParser): """Utility class to parse a SGML file and yield documents one at a time.""" def __init__(self, encoding='latin-1'): HTMLParser.__init__(self) self._reset() self.encoding = encoding def handle_starttag(self, tag, attrs): method = 'start_' + tag getattr(self, method, lambda x: None)(attrs) def handle_endtag(self, tag): method = 'end_' + tag getattr(self, method, lambda: None)() def _reset(self): self.in_title = 0 self.in_body = 0 self.in_topics = 0 self.in_topic_d = 0 self.title = "" self.body = "" self.topics = [] self.topic_d = "" def parse(self, fd): self.docs = [] for chunk in fd: self.feed(chunk.decode(self.encoding)) for doc in self.docs: yield doc self.docs = [] self.close() def handle_data(self, data): if self.in_body: self.body += data elif self.in_title: self.title += data elif self.in_topic_d: self.topic_d += data def start_reuters(self, attributes): pass def end_reuters(self): self.body = re.sub(r'\s+', r' ', self.body) self.docs.append({'title': self.title, 'body': self.body, 'topics': self.topics}) self._reset() def start_title(self, attributes): self.in_title = 1 def end_title(self): self.in_title = 0 def start_body(self, attributes): self.in_body = 1 def end_body(self): self.in_body = 0 def start_topics(self, attributes): self.in_topics = 1 def end_topics(self): self.in_topics = 0 def start_d(self, attributes): self.in_topic_d = 1 def end_d(self): self.in_topic_d = 0 self.topics.append(self.topic_d) self.topic_d = "" def stream_reuters_documents(data_path=None): """Iterate over documents of the Reuters dataset. The Reuters archive will automatically be downloaded and uncompressed if the `data_path` directory does not exist. Documents are represented as dictionaries with 'body' (str), 'title' (str), 'topics' (list(str)) keys. """ DOWNLOAD_URL = ('http://archive.ics.uci.edu/ml/machine-learning-databases/' 'reuters21578-mld/reuters21578.tar.gz') ARCHIVE_FILENAME = 'reuters21578.tar.gz' if data_path is None: data_path = os.path.join(get_data_home(), "reuters") if not os.path.exists(data_path): """Download the dataset.""" print("downloading dataset (once and for all) into %s" % data_path) os.mkdir(data_path) def progress(blocknum, bs, size): total_sz_mb = '%.2f MB' % (size / 1e6) current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6) if _not_in_sphinx(): sys.stdout.write( '\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb)) archive_path = os.path.join(data_path, ARCHIVE_FILENAME) urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress) if _not_in_sphinx(): sys.stdout.write('\r') print("untarring Reuters dataset...") tarfile.open(archive_path, 'r:gz').extractall(data_path) print("done.") parser = ReutersParser() for filename in glob(os.path.join(data_path, "*.sgm")): for doc in parser.parse(open(filename, 'rb')): yield doc Main ---- Create the vectorizer and limit the number of features to a reasonable maximum .. code-block:: default vectorizer = HashingVectorizer(decode_error='ignore', n_features=2 ** 18, alternate_sign=False) # Iterator over parsed Reuters SGML files. data_stream = stream_reuters_documents() # We learn a binary classification between the "acq" class and all the others. # "acq" was chosen as it is more or less evenly distributed in the Reuters # files. For other datasets, one should take care of creating a test set with # a realistic portion of positive instances. all_classes = np.array([0, 1]) positive_class = 'acq' # Here are some classifiers that support the `partial_fit` method partial_fit_classifiers = { 'SGD': SGDClassifier(max_iter=5), 'Perceptron': Perceptron(), 'NB Multinomial': MultinomialNB(alpha=0.01), 'Passive-Aggressive': PassiveAggressiveClassifier(), } def get_minibatch(doc_iter, size, pos_class=positive_class): """Extract a minibatch of examples, return a tuple X_text, y. Note: size is before excluding invalid docs with no topics assigned. """ data = [('{title}\n\n{body}'.format(**doc), pos_class in doc['topics']) for doc in itertools.islice(doc_iter, size) if doc['topics']] if not len(data): return np.asarray([], dtype=int), np.asarray([], dtype=int) X_text, y = zip(*data) return X_text, np.asarray(y, dtype=int) def iter_minibatches(doc_iter, minibatch_size): """Generator of minibatches.""" X_text, y = get_minibatch(doc_iter, minibatch_size) while len(X_text): yield X_text, y X_text, y = get_minibatch(doc_iter, minibatch_size) # test data statistics test_stats = {'n_test': 0, 'n_test_pos': 0} # First we hold out a number of examples to estimate accuracy n_test_documents = 1000 tick = time.time() X_test_text, y_test = get_minibatch(data_stream, 1000) parsing_time = time.time() - tick tick = time.time() X_test = vectorizer.transform(X_test_text) vectorizing_time = time.time() - tick test_stats['n_test'] += len(y_test) test_stats['n_test_pos'] += sum(y_test) print("Test set is %d documents (%d positive)" % (len(y_test), sum(y_test))) def progress(cls_name, stats): """Report progress information, return a string.""" duration = time.time() - stats['t0'] s = "%20s classifier : \t" % cls_name s += "%(n_train)6d train docs (%(n_train_pos)6d positive) " % stats s += "%(n_test)6d test docs (%(n_test_pos)6d positive) " % test_stats s += "accuracy: %(accuracy).3f " % stats s += "in %.2fs (%5d docs/s)" % (duration, stats['n_train'] / duration) return s cls_stats = {} for cls_name in partial_fit_classifiers: stats = {'n_train': 0, 'n_train_pos': 0, 'accuracy': 0.0, 'accuracy_history': [(0, 0)], 't0': time.time(), 'runtime_history': [(0, 0)], 'total_fit_time': 0.0} cls_stats[cls_name] = stats get_minibatch(data_stream, n_test_documents) # Discard test set # We will feed the classifier with mini-batches of 1000 documents; this means # we have at most 1000 docs in memory at any time. The smaller the document # batch, the bigger the relative overhead of the partial fit methods. minibatch_size = 1000 # Create the data_stream that parses Reuters SGML files and iterates on # documents as a stream. minibatch_iterators = iter_minibatches(data_stream, minibatch_size) total_vect_time = 0.0 # Main loop : iterate on mini-batches of examples for i, (X_train_text, y_train) in enumerate(minibatch_iterators): tick = time.time() X_train = vectorizer.transform(X_train_text) total_vect_time += time.time() - tick for cls_name, cls in partial_fit_classifiers.items(): tick = time.time() # update estimator with examples in the current mini-batch cls.partial_fit(X_train, y_train, classes=all_classes) # accumulate test accuracy stats cls_stats[cls_name]['total_fit_time'] += time.time() - tick cls_stats[cls_name]['n_train'] += X_train.shape[0] cls_stats[cls_name]['n_train_pos'] += sum(y_train) tick = time.time() cls_stats[cls_name]['accuracy'] = cls.score(X_test, y_test) cls_stats[cls_name]['prediction_time'] = time.time() - tick acc_history = (cls_stats[cls_name]['accuracy'], cls_stats[cls_name]['n_train']) cls_stats[cls_name]['accuracy_history'].append(acc_history) run_history = (cls_stats[cls_name]['accuracy'], total_vect_time + cls_stats[cls_name]['total_fit_time']) cls_stats[cls_name]['runtime_history'].append(run_history) if i % 3 == 0: print(progress(cls_name, cls_stats[cls_name])) if i % 3 == 0: print('\n') .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Test set is 973 documents (125 positive) SGD classifier : 965 train docs ( 134 positive) 973 test docs ( 125 positive) accuracy: 0.921 in 0.68s ( 1419 docs/s) Perceptron classifier : 965 train docs ( 134 positive) 973 test docs ( 125 positive) accuracy: 0.872 in 0.69s ( 1407 docs/s) NB Multinomial classifier : 965 train docs ( 134 positive) 973 test docs ( 125 positive) accuracy: 0.874 in 0.71s ( 1355 docs/s) Passive-Aggressive classifier : 965 train docs ( 134 positive) 973 test docs ( 125 positive) accuracy: 0.920 in 0.71s ( 1350 docs/s) SGD classifier : 3790 train docs ( 506 positive) 973 test docs ( 125 positive) accuracy: 0.963 in 1.96s ( 1932 docs/s) Perceptron classifier : 3790 train docs ( 506 positive) 973 test docs ( 125 positive) accuracy: 0.949 in 1.96s ( 1929 docs/s) NB Multinomial classifier : 3790 train docs ( 506 positive) 973 test docs ( 125 positive) accuracy: 0.884 in 1.98s ( 1912 docs/s) Passive-Aggressive classifier : 3790 train docs ( 506 positive) 973 test docs ( 125 positive) accuracy: 0.947 in 1.98s ( 1910 docs/s) SGD classifier : 6523 train docs ( 916 positive) 973 test docs ( 125 positive) accuracy: 0.950 in 3.21s ( 2031 docs/s) Perceptron classifier : 6523 train docs ( 916 positive) 973 test docs ( 125 positive) accuracy: 0.923 in 3.21s ( 2030 docs/s) NB Multinomial classifier : 6523 train docs ( 916 positive) 973 test docs ( 125 positive) accuracy: 0.909 in 3.23s ( 2019 docs/s) Passive-Aggressive classifier : 6523 train docs ( 916 positive) 973 test docs ( 125 positive) accuracy: 0.953 in 3.23s ( 2017 docs/s) SGD classifier : 9434 train docs ( 1242 positive) 973 test docs ( 125 positive) accuracy: 0.927 in 4.48s ( 2107 docs/s) Perceptron classifier : 9434 train docs ( 1242 positive) 973 test docs ( 125 positive) accuracy: 0.947 in 4.48s ( 2106 docs/s) NB Multinomial classifier : 9434 train docs ( 1242 positive) 973 test docs ( 125 positive) accuracy: 0.918 in 4.50s ( 2098 docs/s) Passive-Aggressive classifier : 9434 train docs ( 1242 positive) 973 test docs ( 125 positive) accuracy: 0.956 in 4.50s ( 2096 docs/s) SGD classifier : 11845 train docs ( 1468 positive) 973 test docs ( 125 positive) accuracy: 0.949 in 5.75s ( 2061 docs/s) Perceptron classifier : 11845 train docs ( 1468 positive) 973 test docs ( 125 positive) accuracy: 0.942 in 5.75s ( 2060 docs/s) NB Multinomial classifier : 11845 train docs ( 1468 positive) 973 test docs ( 125 positive) accuracy: 0.922 in 5.77s ( 2054 docs/s) Passive-Aggressive classifier : 11845 train docs ( 1468 positive) 973 test docs ( 125 positive) accuracy: 0.942 in 5.77s ( 2053 docs/s) SGD classifier : 14770 train docs ( 1856 positive) 973 test docs ( 125 positive) accuracy: 0.959 in 7.10s ( 2079 docs/s) Perceptron classifier : 14770 train docs ( 1856 positive) 973 test docs ( 125 positive) accuracy: 0.956 in 7.11s ( 2078 docs/s) NB Multinomial classifier : 14770 train docs ( 1856 positive) 973 test docs ( 125 positive) accuracy: 0.925 in 7.12s ( 2073 docs/s) Passive-Aggressive classifier : 14770 train docs ( 1856 positive) 973 test docs ( 125 positive) accuracy: 0.957 in 7.13s ( 2072 docs/s) SGD classifier : 17723 train docs ( 2218 positive) 973 test docs ( 125 positive) accuracy: 0.959 in 8.47s ( 2093 docs/s) Perceptron classifier : 17723 train docs ( 2218 positive) 973 test docs ( 125 positive) accuracy: 0.938 in 8.47s ( 2092 docs/s) NB Multinomial classifier : 17723 train docs ( 2218 positive) 973 test docs ( 125 positive) accuracy: 0.925 in 8.49s ( 2087 docs/s) Passive-Aggressive classifier : 17723 train docs ( 2218 positive) 973 test docs ( 125 positive) accuracy: 0.962 in 8.49s ( 2087 docs/s) Plot results ------------ The plot represents the learning curve of the classifier: the evolution of classification accuracy over the course of the mini-batches. Accuracy is measured on the first 1000 samples, held out as a validation set. To limit the memory consumption, we queue examples up to a fixed amount before feeding them to the learner. .. code-block:: default def plot_accuracy(x, y, x_legend): """Plot accuracy as a function of x.""" x = np.array(x) y = np.array(y) plt.title('Classification accuracy as a function of %s' % x_legend) plt.xlabel('%s' % x_legend) plt.ylabel('Accuracy') plt.grid(True) plt.plot(x, y) rcParams['legend.fontsize'] = 10 cls_names = list(sorted(cls_stats.keys())) # Plot accuracy evolution plt.figure() for _, stats in sorted(cls_stats.items()): # Plot accuracy evolution with #examples accuracy, n_examples = zip(*stats['accuracy_history']) plot_accuracy(n_examples, accuracy, "training examples (#)") ax = plt.gca() ax.set_ylim((0.8, 1)) plt.legend(cls_names, loc='best') plt.figure() for _, stats in sorted(cls_stats.items()): # Plot accuracy evolution with runtime accuracy, runtime = zip(*stats['runtime_history']) plot_accuracy(runtime, accuracy, 'runtime (s)') ax = plt.gca() ax.set_ylim((0.8, 1)) plt.legend(cls_names, loc='best') # Plot fitting times plt.figure() fig = plt.gcf() cls_runtime = [stats['total_fit_time'] for cls_name, stats in sorted(cls_stats.items())] cls_runtime.append(total_vect_time) cls_names.append('Vectorization') bar_colors = ['b', 'g', 'r', 'c', 'm', 'y'] ax = plt.subplot(111) rectangles = plt.bar(range(len(cls_names)), cls_runtime, width=0.5, color=bar_colors) ax.set_xticks(np.linspace(0, len(cls_names) - 1, len(cls_names))) ax.set_xticklabels(cls_names, fontsize=10) ymax = max(cls_runtime) * 1.2 ax.set_ylim((0, ymax)) ax.set_ylabel('runtime (s)') ax.set_title('Training Times') def autolabel(rectangles): """attach some text vi autolabel on rectangles.""" for rect in rectangles: height = rect.get_height() ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, '%.4f' % height, ha='center', va='bottom') plt.setp(plt.xticks()[1], rotation=30) autolabel(rectangles) plt.tight_layout() plt.show() # Plot prediction times plt.figure() cls_runtime = [] cls_names = list(sorted(cls_stats.keys())) for cls_name, stats in sorted(cls_stats.items()): cls_runtime.append(stats['prediction_time']) cls_runtime.append(parsing_time) cls_names.append('Read/Parse\n+Feat.Extr.') cls_runtime.append(vectorizing_time) cls_names.append('Hashing\n+Vect.') ax = plt.subplot(111) rectangles = plt.bar(range(len(cls_names)), cls_runtime, width=0.5, color=bar_colors) ax.set_xticks(np.linspace(0, len(cls_names) - 1, len(cls_names))) ax.set_xticklabels(cls_names, fontsize=8) plt.setp(plt.xticks()[1], rotation=30) ymax = max(cls_runtime) * 1.2 ax.set_ylim((0, ymax)) ax.set_ylabel('runtime (s)') ax.set_title('Prediction Times (%d instances)' % n_test_documents) autolabel(rectangles) plt.tight_layout() plt.show() .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_001.png :class: sphx-glr-multi-img * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_002.png :class: sphx-glr-multi-img * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png :class: sphx-glr-multi-img * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 10.253 seconds) **Estimated memory usage:** 8 MB .. _sphx_glr_download_auto_examples_applications_plot_out_of_core_classification.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_out_of_core_classification.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_out_of_core_classification.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_