.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_out_of_core_classification.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. 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. .. GENERATED FROM PYTHON SOURCE LINES 15-46 .. 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() .. GENERATED FROM PYTHON SOURCE LINES 47-53 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. .. GENERATED FROM PYTHON SOURCE LINES 53-181 .. 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 .. GENERATED FROM PYTHON SOURCE LINES 182-187 Main ---- Create the vectorizer and limit the number of features to a reasonable maximum .. GENERATED FROM PYTHON SOURCE LINES 187-314 .. 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 downloading dataset (once and for all) into /home/mapologo/scikit_learn_data/reuters untarring Reuters dataset... done. Test set is 986 documents (159 positive) SGD classifier : 955 train docs ( 93 positive) 986 test docs ( 159 positive) accuracy: 0.896 in 0.75s ( 1269 docs/s) Perceptron classifier : 955 train docs ( 93 positive) 986 test docs ( 159 positive) accuracy: 0.860 in 0.76s ( 1261 docs/s) NB Multinomial classifier : 955 train docs ( 93 positive) 986 test docs ( 159 positive) accuracy: 0.839 in 0.79s ( 1216 docs/s) Passive-Aggressive classifier : 955 train docs ( 93 positive) 986 test docs ( 159 positive) accuracy: 0.914 in 0.79s ( 1209 docs/s) SGD classifier : 3370 train docs ( 401 positive) 986 test docs ( 159 positive) accuracy: 0.933 in 2.19s ( 1537 docs/s) Perceptron classifier : 3370 train docs ( 401 positive) 986 test docs ( 159 positive) accuracy: 0.919 in 2.20s ( 1534 docs/s) NB Multinomial classifier : 3370 train docs ( 401 positive) 986 test docs ( 159 positive) accuracy: 0.847 in 2.22s ( 1519 docs/s) Passive-Aggressive classifier : 3370 train docs ( 401 positive) 986 test docs ( 159 positive) accuracy: 0.929 in 2.22s ( 1516 docs/s) SGD classifier : 6243 train docs ( 840 positive) 986 test docs ( 159 positive) accuracy: 0.955 in 3.78s ( 1653 docs/s) Perceptron classifier : 6243 train docs ( 840 positive) 986 test docs ( 159 positive) accuracy: 0.945 in 3.78s ( 1651 docs/s) NB Multinomial classifier : 6243 train docs ( 840 positive) 986 test docs ( 159 positive) accuracy: 0.867 in 3.81s ( 1639 docs/s) Passive-Aggressive classifier : 6243 train docs ( 840 positive) 986 test docs ( 159 positive) accuracy: 0.949 in 3.81s ( 1638 docs/s) SGD classifier : 9060 train docs ( 1161 positive) 986 test docs ( 159 positive) accuracy: 0.951 in 5.31s ( 1707 docs/s) Perceptron classifier : 9060 train docs ( 1161 positive) 986 test docs ( 159 positive) accuracy: 0.947 in 5.31s ( 1705 docs/s) NB Multinomial classifier : 9060 train docs ( 1161 positive) 986 test docs ( 159 positive) accuracy: 0.896 in 5.33s ( 1698 docs/s) Passive-Aggressive classifier : 9060 train docs ( 1161 positive) 986 test docs ( 159 positive) accuracy: 0.944 in 5.34s ( 1696 docs/s) SGD classifier : 11937 train docs ( 1525 positive) 986 test docs ( 159 positive) accuracy: 0.928 in 6.80s ( 1756 docs/s) Perceptron classifier : 11937 train docs ( 1525 positive) 986 test docs ( 159 positive) accuracy: 0.946 in 6.80s ( 1754 docs/s) NB Multinomial classifier : 11937 train docs ( 1525 positive) 986 test docs ( 159 positive) accuracy: 0.910 in 6.83s ( 1748 docs/s) Passive-Aggressive classifier : 11937 train docs ( 1525 positive) 986 test docs ( 159 positive) accuracy: 0.960 in 6.83s ( 1747 docs/s) SGD classifier : 14352 train docs ( 1822 positive) 986 test docs ( 159 positive) accuracy: 0.951 in 8.29s ( 1730 docs/s) Perceptron classifier : 14352 train docs ( 1822 positive) 986 test docs ( 159 positive) accuracy: 0.958 in 8.30s ( 1729 docs/s) NB Multinomial classifier : 14352 train docs ( 1822 positive) 986 test docs ( 159 positive) accuracy: 0.912 in 8.32s ( 1724 docs/s) Passive-Aggressive classifier : 14352 train docs ( 1822 positive) 986 test docs ( 159 positive) accuracy: 0.957 in 8.33s ( 1723 docs/s) SGD classifier : 17184 train docs ( 2125 positive) 986 test docs ( 159 positive) accuracy: 0.959 in 9.80s ( 1754 docs/s) Perceptron classifier : 17184 train docs ( 2125 positive) 986 test docs ( 159 positive) accuracy: 0.934 in 9.80s ( 1753 docs/s) NB Multinomial classifier : 17184 train docs ( 2125 positive) 986 test docs ( 159 positive) accuracy: 0.912 in 9.82s ( 1749 docs/s) Passive-Aggressive classifier : 17184 train docs ( 2125 positive) 986 test docs ( 159 positive) accuracy: 0.965 in 9.83s ( 1748 docs/s) .. GENERATED FROM PYTHON SOURCE LINES 315-324 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. .. GENERATED FROM PYTHON SOURCE LINES 324-420 .. 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 :alt: Classification accuracy as a function of training examples (#) :class: sphx-glr-multi-img * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_002.png :alt: Classification accuracy as a function of runtime (s) :class: sphx-glr-multi-img * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png :alt: Training Times :class: sphx-glr-multi-img * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png :alt: Prediction Times (1000 instances) :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 55.885 seconds) .. _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:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_out_of_core_classification.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_out_of_core_classification.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_