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.

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

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(),
    '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')

Out:

Test set is 975 documents (104 positive)
                 SGD classifier :          986 train docs (   135 positive)    975 test docs (   104 positive) accuracy: 0.801 in 0.93s ( 1056 docs/s)
          Perceptron classifier :          986 train docs (   135 positive)    975 test docs (   104 positive) accuracy: 0.905 in 0.94s ( 1053 docs/s)
      NB Multinomial classifier :          986 train docs (   135 positive)    975 test docs (   104 positive) accuracy: 0.899 in 0.95s ( 1032 docs/s)
  Passive-Aggressive classifier :          986 train docs (   135 positive)    975 test docs (   104 positive) accuracy: 0.929 in 0.96s ( 1029 docs/s)


                 SGD classifier :         3433 train docs (   409 positive)    975 test docs (   104 positive) accuracy: 0.954 in 2.16s ( 1592 docs/s)
          Perceptron classifier :         3433 train docs (   409 positive)    975 test docs (   104 positive) accuracy: 0.944 in 2.16s ( 1590 docs/s)
      NB Multinomial classifier :         3433 train docs (   409 positive)    975 test docs (   104 positive) accuracy: 0.908 in 2.18s ( 1577 docs/s)
  Passive-Aggressive classifier :         3433 train docs (   409 positive)    975 test docs (   104 positive) accuracy: 0.941 in 2.18s ( 1576 docs/s)


                 SGD classifier :         6288 train docs (   828 positive)    975 test docs (   104 positive) accuracy: 0.960 in 3.41s ( 1845 docs/s)
          Perceptron classifier :         6288 train docs (   828 positive)    975 test docs (   104 positive) accuracy: 0.950 in 3.41s ( 1844 docs/s)
      NB Multinomial classifier :         6288 train docs (   828 positive)    975 test docs (   104 positive) accuracy: 0.926 in 3.43s ( 1834 docs/s)
  Passive-Aggressive classifier :         6288 train docs (   828 positive)    975 test docs (   104 positive) accuracy: 0.966 in 3.43s ( 1833 docs/s)


                 SGD classifier :         9211 train docs (  1186 positive)    975 test docs (   104 positive) accuracy: 0.957 in 4.65s ( 1979 docs/s)
          Perceptron classifier :         9211 train docs (  1186 positive)    975 test docs (   104 positive) accuracy: 0.942 in 4.66s ( 1978 docs/s)
      NB Multinomial classifier :         9211 train docs (  1186 positive)    975 test docs (   104 positive) accuracy: 0.928 in 4.67s ( 1971 docs/s)
  Passive-Aggressive classifier :         9211 train docs (  1186 positive)    975 test docs (   104 positive) accuracy: 0.961 in 4.67s ( 1970 docs/s)


                 SGD classifier :        12065 train docs (  1550 positive)    975 test docs (   104 positive) accuracy: 0.960 in 5.84s ( 2066 docs/s)
          Perceptron classifier :        12065 train docs (  1550 positive)    975 test docs (   104 positive) accuracy: 0.935 in 5.84s ( 2065 docs/s)
      NB Multinomial classifier :        12065 train docs (  1550 positive)    975 test docs (   104 positive) accuracy: 0.936 in 5.86s ( 2058 docs/s)
  Passive-Aggressive classifier :        12065 train docs (  1550 positive)    975 test docs (   104 positive) accuracy: 0.961 in 5.86s ( 2058 docs/s)


                 SGD classifier :        14450 train docs (  1827 positive)    975 test docs (   104 positive) accuracy: 0.967 in 7.11s ( 2030 docs/s)
          Perceptron classifier :        14450 train docs (  1827 positive)    975 test docs (   104 positive) accuracy: 0.954 in 7.12s ( 2030 docs/s)
      NB Multinomial classifier :        14450 train docs (  1827 positive)    975 test docs (   104 positive) accuracy: 0.939 in 7.14s ( 2024 docs/s)
  Passive-Aggressive classifier :        14450 train docs (  1827 positive)    975 test docs (   104 positive) accuracy: 0.966 in 7.14s ( 2023 docs/s)


                 SGD classifier :        17277 train docs (  2151 positive)    975 test docs (   104 positive) accuracy: 0.951 in 8.40s ( 2056 docs/s)
          Perceptron classifier :        17277 train docs (  2151 positive)    975 test docs (   104 positive) accuracy: 0.961 in 8.40s ( 2056 docs/s)
      NB Multinomial classifier :        17277 train docs (  2151 positive)    975 test docs (   104 positive) accuracy: 0.941 in 8.42s ( 2051 docs/s)
  Passive-Aggressive classifier :        17277 train docs (  2151 positive)    975 test docs (   104 positive) accuracy: 0.964 in 8.42s ( 2050 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.

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()
  • Classification accuracy as a function of training examples (#)
  • Classification accuracy as a function of runtime (s)
  • Training Times
  • Prediction Times (1000 instances)

Total running time of the script: ( 0 minutes 9.819 seconds)

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