.. _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. 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. 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:: python # Authors: Eustache Diemert # @FedericoV # License: BSD 3 clause from __future__ import print_function from glob import glob import itertools import os.path import re import tarfile import time import numpy as np import matplotlib.pyplot as plt from matplotlib import rcParams from sklearn.externals.six.moves import html_parser from sklearn.externals.six.moves import urllib 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 -------------------------------- .. code-block:: python class ReutersParser(html_parser.HTMLParser): """Utility class to parse a SGML file and yield documents one at a time.""" def __init__(self, encoding='latin-1'): html_parser.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(): print('\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb), end='') archive_path = os.path.join(data_path, ARCHIVE_FILENAME) urllib.request.urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress) if _not_in_sphinx(): print('\r', end='') 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:: python vectorizer = HashingVectorizer(decode_error='ignore', n_features=2 ** 18, non_negative=True) # 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(), '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 = [(u'{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:: Test set is 878 documents (108 positive) Passive-Aggressive classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.913 in 1.78s ( 539 docs/s) Perceptron classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.921 in 1.79s ( 538 docs/s) SGD classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.925 in 1.79s ( 536 docs/s) NB Multinomial classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.877 in 1.83s ( 526 docs/s) Passive-Aggressive classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.946 in 7.29s ( 536 docs/s) Perceptron classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.926 in 7.30s ( 536 docs/s) SGD classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.945 in 7.30s ( 535 docs/s) NB Multinomial classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.885 in 7.33s ( 533 docs/s) Passive-Aggressive classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.951 in 10.85s ( 628 docs/s) Perceptron classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.949 in 10.85s ( 628 docs/s) SGD classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.938 in 10.85s ( 628 docs/s) NB Multinomial classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.899 in 10.89s ( 626 docs/s) Passive-Aggressive classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.964 in 14.26s ( 684 docs/s) Perceptron classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.950 in 14.27s ( 684 docs/s) SGD classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.958 in 14.27s ( 683 docs/s) NB Multinomial classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.909 in 14.30s ( 682 docs/s) Passive-Aggressive classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.951 in 17.31s ( 674 docs/s) Perceptron classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.951 in 17.31s ( 674 docs/s) SGD classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.951 in 17.32s ( 674 docs/s) NB Multinomial classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.916 in 17.35s ( 673 docs/s) Passive-Aggressive classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.966 in 20.80s ( 703 docs/s) Perceptron classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.956 in 20.80s ( 703 docs/s) SGD classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.954 in 20.81s ( 702 docs/s) NB Multinomial classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.926 in 20.84s ( 701 docs/s) Passive-Aggressive classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.954 in 23.94s ( 725 docs/s) Perceptron classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 23.95s ( 724 docs/s) SGD classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.949 in 23.95s ( 724 docs/s) NB Multinomial classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.932 in 23.98s ( 723 docs/s) Plot results ------------ .. code-block:: python 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 = [] for cls_name, stats in sorted(cls_stats.items()): cls_runtime.append(stats['total_fit_time']) 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(0.25, len(cls_names) - 0.75, 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') autolabel(rectangles) 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(0.25, len(cls_names) - 0.75, 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.show() .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_001.png :scale: 47 * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_002.png :scale: 47 * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png :scale: 47 * .. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png :scale: 47 **Total running time of the script:** (0 minutes 25.914 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_out_of_core_classification.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_out_of_core_classification.ipynb `