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:

Test set is 982 documents (90 positive)
                 SGD classifier :          469 train docs (    49 positive)    982 test docs (    90 positive) accuracy: 0.914 in 0.71s (  660 docs/s)
          Perceptron classifier :          469 train docs (    49 positive)    982 test docs (    90 positive) accuracy: 0.909 in 0.71s (  658 docs/s)
      NB Multinomial classifier :          469 train docs (    49 positive)    982 test docs (    90 positive) accuracy: 0.910 in 0.72s (  652 docs/s)
  Passive-Aggressive classifier :          469 train docs (    49 positive)    982 test docs (    90 positive) accuracy: 0.925 in 0.72s (  650 docs/s)


                 SGD classifier :         3292 train docs (   397 positive)    982 test docs (    90 positive) accuracy: 0.955 in 2.34s ( 1408 docs/s)
          Perceptron classifier :         3292 train docs (   397 positive)    982 test docs (    90 positive) accuracy: 0.945 in 2.34s ( 1406 docs/s)
      NB Multinomial classifier :         3292 train docs (   397 positive)    982 test docs (    90 positive) accuracy: 0.918 in 2.35s ( 1402 docs/s)
  Passive-Aggressive classifier :         3292 train docs (   397 positive)    982 test docs (    90 positive) accuracy: 0.951 in 2.35s ( 1400 docs/s)


                 SGD classifier :         6221 train docs (   756 positive)    982 test docs (    90 positive) accuracy: 0.955 in 3.82s ( 1628 docs/s)
          Perceptron classifier :         6221 train docs (   756 positive)    982 test docs (    90 positive) accuracy: 0.955 in 3.82s ( 1627 docs/s)
      NB Multinomial classifier :         6221 train docs (   756 positive)    982 test docs (    90 positive) accuracy: 0.922 in 3.83s ( 1625 docs/s)
  Passive-Aggressive classifier :         6221 train docs (   756 positive)    982 test docs (    90 positive) accuracy: 0.968 in 3.83s ( 1624 docs/s)


                 SGD classifier :         9087 train docs (  1172 positive)    982 test docs (    90 positive) accuracy: 0.970 in 5.33s ( 1703 docs/s)
          Perceptron classifier :         9087 train docs (  1172 positive)    982 test docs (    90 positive) accuracy: 0.954 in 5.34s ( 1702 docs/s)
      NB Multinomial classifier :         9087 train docs (  1172 positive)    982 test docs (    90 positive) accuracy: 0.934 in 5.34s ( 1701 docs/s)
  Passive-Aggressive classifier :         9087 train docs (  1172 positive)    982 test docs (    90 positive) accuracy: 0.970 in 5.34s ( 1700 docs/s)


                 SGD classifier :        11564 train docs (  1419 positive)    982 test docs (    90 positive) accuracy: 0.970 in 6.74s ( 1715 docs/s)
          Perceptron classifier :        11564 train docs (  1419 positive)    982 test docs (    90 positive) accuracy: 0.956 in 6.74s ( 1714 docs/s)
      NB Multinomial classifier :        11564 train docs (  1419 positive)    982 test docs (    90 positive) accuracy: 0.936 in 6.75s ( 1713 docs/s)
  Passive-Aggressive classifier :        11564 train docs (  1419 positive)    982 test docs (    90 positive) accuracy: 0.973 in 6.75s ( 1712 docs/s)


                 SGD classifier :        14303 train docs (  1787 positive)    982 test docs (    90 positive) accuracy: 0.963 in 8.38s ( 1706 docs/s)
          Perceptron classifier :        14303 train docs (  1787 positive)    982 test docs (    90 positive) accuracy: 0.958 in 8.39s ( 1705 docs/s)
      NB Multinomial classifier :        14303 train docs (  1787 positive)    982 test docs (    90 positive) accuracy: 0.938 in 8.39s ( 1704 docs/s)
  Passive-Aggressive classifier :        14303 train docs (  1787 positive)    982 test docs (    90 positive) accuracy: 0.969 in 8.40s ( 1703 docs/s)


                 SGD classifier :        17198 train docs (  2130 positive)    982 test docs (    90 positive) accuracy: 0.970 in 10.01s ( 1717 docs/s)
          Perceptron classifier :        17198 train docs (  2130 positive)    982 test docs (    90 positive) accuracy: 0.968 in 10.01s ( 1717 docs/s)
      NB Multinomial classifier :        17198 train docs (  2130 positive)    982 test docs (    90 positive) accuracy: 0.941 in 10.02s ( 1716 docs/s)
  Passive-Aggressive classifier :        17198 train docs (  2130 positive)    982 test docs (    90 positive) accuracy: 0.975 in 10.02s ( 1715 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 11.026 seconds)

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