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.

# Authors: Eustache Diemert <eustache@diemert.fr>
#          @FedericoV <https://github.com/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 sys

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.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()

Main

Create the vectorizer and limit the number of features to a reasonable maximum

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(tol=1e-3),
    'NB Multinomial': MultinomialNB(alpha=0.01),
    'Passive-Aggressive': PassiveAggressiveClassifier(tol=1e-3),
}


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')

Out:

downloading dataset (once and for all) into /home/circleci/scikit_learn_data/reuters
untarring Reuters dataset...
done.
Test set is 988 documents (122 positive)
                 SGD classifier :          928 train docs (   142 positive)    988 test docs (   122 positive) accuracy: 0.919 in 0.78s ( 1193 docs/s)
          Perceptron classifier :          928 train docs (   142 positive)    988 test docs (   122 positive) accuracy: 0.889 in 0.78s ( 1188 docs/s)
      NB Multinomial classifier :          928 train docs (   142 positive)    988 test docs (   122 positive) accuracy: 0.878 in 0.79s ( 1176 docs/s)
  Passive-Aggressive classifier :          928 train docs (   142 positive)    988 test docs (   122 positive) accuracy: 0.910 in 0.79s ( 1171 docs/s)


                 SGD classifier :         3435 train docs (   509 positive)    988 test docs (   122 positive) accuracy: 0.944 in 2.33s ( 1476 docs/s)
          Perceptron classifier :         3435 train docs (   509 positive)    988 test docs (   122 positive) accuracy: 0.914 in 2.33s ( 1474 docs/s)
      NB Multinomial classifier :         3435 train docs (   509 positive)    988 test docs (   122 positive) accuracy: 0.892 in 2.34s ( 1470 docs/s)
  Passive-Aggressive classifier :         3435 train docs (   509 positive)    988 test docs (   122 positive) accuracy: 0.947 in 2.34s ( 1468 docs/s)


                 SGD classifier :         6036 train docs (   742 positive)    988 test docs (   122 positive) accuracy: 0.950 in 3.87s ( 1559 docs/s)
          Perceptron classifier :         6036 train docs (   742 positive)    988 test docs (   122 positive) accuracy: 0.934 in 3.87s ( 1557 docs/s)
      NB Multinomial classifier :         6036 train docs (   742 positive)    988 test docs (   122 positive) accuracy: 0.900 in 3.88s ( 1554 docs/s)
  Passive-Aggressive classifier :         6036 train docs (   742 positive)    988 test docs (   122 positive) accuracy: 0.966 in 3.89s ( 1553 docs/s)


                 SGD classifier :         8976 train docs (  1071 positive)    988 test docs (   122 positive) accuracy: 0.946 in 5.50s ( 1632 docs/s)
          Perceptron classifier :         8976 train docs (  1071 positive)    988 test docs (   122 positive) accuracy: 0.948 in 5.50s ( 1631 docs/s)
      NB Multinomial classifier :         8976 train docs (  1071 positive)    988 test docs (   122 positive) accuracy: 0.918 in 5.51s ( 1629 docs/s)
  Passive-Aggressive classifier :         8976 train docs (  1071 positive)    988 test docs (   122 positive) accuracy: 0.953 in 5.51s ( 1628 docs/s)


                 SGD classifier :        11911 train docs (  1472 positive)    988 test docs (   122 positive) accuracy: 0.950 in 7.20s ( 1653 docs/s)
          Perceptron classifier :        11911 train docs (  1472 positive)    988 test docs (   122 positive) accuracy: 0.951 in 7.22s ( 1650 docs/s)
      NB Multinomial classifier :        11911 train docs (  1472 positive)    988 test docs (   122 positive) accuracy: 0.929 in 7.22s ( 1648 docs/s)
  Passive-Aggressive classifier :        11911 train docs (  1472 positive)    988 test docs (   122 positive) accuracy: 0.959 in 7.23s ( 1648 docs/s)


                 SGD classifier :        14337 train docs (  1772 positive)    988 test docs (   122 positive) accuracy: 0.962 in 8.76s ( 1635 docs/s)
          Perceptron classifier :        14337 train docs (  1772 positive)    988 test docs (   122 positive) accuracy: 0.928 in 8.77s ( 1635 docs/s)
      NB Multinomial classifier :        14337 train docs (  1772 positive)    988 test docs (   122 positive) accuracy: 0.928 in 8.77s ( 1634 docs/s)
  Passive-Aggressive classifier :        14337 train docs (  1772 positive)    988 test docs (   122 positive) accuracy: 0.950 in 8.78s ( 1633 docs/s)


                 SGD classifier :        17253 train docs (  2116 positive)    988 test docs (   122 positive) accuracy: 0.956 in 10.35s ( 1666 docs/s)
          Perceptron classifier :        17253 train docs (  2116 positive)    988 test docs (   122 positive) accuracy: 0.943 in 10.35s ( 1666 docs/s)
      NB Multinomial classifier :        17253 train docs (  2116 positive)    988 test docs (   122 positive) accuracy: 0.933 in 10.36s ( 1665 docs/s)
  Passive-Aggressive classifier :        17253 train docs (  2116 positive)    988 test docs (   122 positive) accuracy: 0.959 in 10.36s ( 1664 docs/s)

Plot results

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, 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()
  • ../../_images/sphx_glr_plot_out_of_core_classification_001.png
  • ../../_images/sphx_glr_plot_out_of_core_classification_002.png
  • ../../_images/sphx_glr_plot_out_of_core_classification_003.png
  • ../../_images/sphx_glr_plot_out_of_core_classification_004.png

Total running time of the script: ( 0 minutes 14.283 seconds)

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