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 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, tol=1e-3),
    '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 = [('{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 955 documents (93 positive)
                 SGD classifier :          969 train docs (   155 positive)    955 test docs (    93 positive) accuracy: 0.918 in 0.85s ( 1134 docs/s)
          Perceptron classifier :          969 train docs (   155 positive)    955 test docs (    93 positive) accuracy: 0.854 in 0.86s ( 1130 docs/s)
      NB Multinomial classifier :          969 train docs (   155 positive)    955 test docs (    93 positive) accuracy: 0.904 in 0.86s ( 1120 docs/s)
  Passive-Aggressive classifier :          969 train docs (   155 positive)    955 test docs (    93 positive) accuracy: 0.857 in 0.87s ( 1116 docs/s)


                 SGD classifier :         3267 train docs (   410 positive)    955 test docs (    93 positive) accuracy: 0.947 in 2.35s ( 1392 docs/s)
          Perceptron classifier :         3267 train docs (   410 positive)    955 test docs (    93 positive) accuracy: 0.942 in 2.35s ( 1391 docs/s)
      NB Multinomial classifier :         3267 train docs (   410 positive)    955 test docs (    93 positive) accuracy: 0.910 in 2.35s ( 1388 docs/s)
  Passive-Aggressive classifier :         3267 train docs (   410 positive)    955 test docs (    93 positive) accuracy: 0.953 in 2.36s ( 1387 docs/s)


                 SGD classifier :         6150 train docs (   722 positive)    955 test docs (    93 positive) accuracy: 0.946 in 3.88s ( 1583 docs/s)
          Perceptron classifier :         6150 train docs (   722 positive)    955 test docs (    93 positive) accuracy: 0.942 in 3.89s ( 1582 docs/s)
      NB Multinomial classifier :         6150 train docs (   722 positive)    955 test docs (    93 positive) accuracy: 0.920 in 3.89s ( 1580 docs/s)
  Passive-Aggressive classifier :         6150 train docs (   722 positive)    955 test docs (    93 positive) accuracy: 0.951 in 3.89s ( 1579 docs/s)


                 SGD classifier :         8994 train docs (  1097 positive)    955 test docs (    93 positive) accuracy: 0.950 in 5.48s ( 1640 docs/s)
          Perceptron classifier :         8994 train docs (  1097 positive)    955 test docs (    93 positive) accuracy: 0.949 in 5.49s ( 1639 docs/s)
      NB Multinomial classifier :         8994 train docs (  1097 positive)    955 test docs (    93 positive) accuracy: 0.934 in 5.49s ( 1637 docs/s)
  Passive-Aggressive classifier :         8994 train docs (  1097 positive)    955 test docs (    93 positive) accuracy: 0.955 in 5.49s ( 1637 docs/s)


                 SGD classifier :        11853 train docs (  1482 positive)    955 test docs (    93 positive) accuracy: 0.953 in 7.09s ( 1672 docs/s)
          Perceptron classifier :        11853 train docs (  1482 positive)    955 test docs (    93 positive) accuracy: 0.925 in 7.09s ( 1671 docs/s)
      NB Multinomial classifier :        11853 train docs (  1482 positive)    955 test docs (    93 positive) accuracy: 0.939 in 7.10s ( 1670 docs/s)
  Passive-Aggressive classifier :        11853 train docs (  1482 positive)    955 test docs (    93 positive) accuracy: 0.961 in 7.10s ( 1670 docs/s)


                 SGD classifier :        14257 train docs (  1845 positive)    955 test docs (    93 positive) accuracy: 0.946 in 8.60s ( 1658 docs/s)
          Perceptron classifier :        14257 train docs (  1845 positive)    955 test docs (    93 positive) accuracy: 0.952 in 8.60s ( 1658 docs/s)
      NB Multinomial classifier :        14257 train docs (  1845 positive)    955 test docs (    93 positive) accuracy: 0.940 in 8.60s ( 1656 docs/s)
  Passive-Aggressive classifier :        14257 train docs (  1845 positive)    955 test docs (    93 positive) accuracy: 0.958 in 8.61s ( 1656 docs/s)


                 SGD classifier :        17210 train docs (  2161 positive)    955 test docs (    93 positive) accuracy: 0.956 in 10.22s ( 1683 docs/s)
          Perceptron classifier :        17210 train docs (  2161 positive)    955 test docs (    93 positive) accuracy: 0.951 in 10.22s ( 1683 docs/s)
      NB Multinomial classifier :        17210 train docs (  2161 positive)    955 test docs (    93 positive) accuracy: 0.942 in 10.23s ( 1682 docs/s)
  Passive-Aggressive classifier :        17210 train docs (  2161 positive)    955 test docs (    93 positive) accuracy: 0.960 in 10.23s ( 1681 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 = [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()
  • ../../_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 11.215 seconds)

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