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 986 documents (159 positive)
                 SGD classifier :          972 train docs (   115 positive)    986 test docs (   159 positive) accuracy: 0.766 in 0.71s ( 1374 docs/s)
          Perceptron classifier :          972 train docs (   115 positive)    986 test docs (   159 positive) accuracy: 0.858 in 0.71s ( 1365 docs/s)
      NB Multinomial classifier :          972 train docs (   115 positive)    986 test docs (   159 positive) accuracy: 0.839 in 0.74s ( 1322 docs/s)
  Passive-Aggressive classifier :          972 train docs (   115 positive)    986 test docs (   159 positive) accuracy: 0.901 in 0.74s ( 1314 docs/s)


                 SGD classifier :         3279 train docs (   397 positive)    986 test docs (   159 positive) accuracy: 0.920 in 1.94s ( 1690 docs/s)
          Perceptron classifier :         3279 train docs (   397 positive)    986 test docs (   159 positive) accuracy: 0.925 in 1.94s ( 1688 docs/s)
      NB Multinomial classifier :         3279 train docs (   397 positive)    986 test docs (   159 positive) accuracy: 0.850 in 1.95s ( 1682 docs/s)
  Passive-Aggressive classifier :         3279 train docs (   397 positive)    986 test docs (   159 positive) accuracy: 0.927 in 1.95s ( 1680 docs/s)


                 SGD classifier :         6133 train docs (   819 positive)    986 test docs (   159 positive) accuracy: 0.950 in 3.15s ( 1944 docs/s)
          Perceptron classifier :         6133 train docs (   819 positive)    986 test docs (   159 positive) accuracy: 0.729 in 3.16s ( 1943 docs/s)
      NB Multinomial classifier :         6133 train docs (   819 positive)    986 test docs (   159 positive) accuracy: 0.871 in 3.16s ( 1939 docs/s)
  Passive-Aggressive classifier :         6133 train docs (   819 positive)    986 test docs (   159 positive) accuracy: 0.954 in 3.16s ( 1938 docs/s)


                 SGD classifier :         9027 train docs (  1209 positive)    986 test docs (   159 positive) accuracy: 0.941 in 4.32s ( 2089 docs/s)
          Perceptron classifier :         9027 train docs (  1209 positive)    986 test docs (   159 positive) accuracy: 0.931 in 4.32s ( 2088 docs/s)
      NB Multinomial classifier :         9027 train docs (  1209 positive)    986 test docs (   159 positive) accuracy: 0.888 in 4.33s ( 2085 docs/s)
  Passive-Aggressive classifier :         9027 train docs (  1209 positive)    986 test docs (   159 positive) accuracy: 0.954 in 4.33s ( 2084 docs/s)


                 SGD classifier :        11929 train docs (  1554 positive)    986 test docs (   159 positive) accuracy: 0.944 in 5.48s ( 2177 docs/s)
          Perceptron classifier :        11929 train docs (  1554 positive)    986 test docs (   159 positive) accuracy: 0.885 in 5.48s ( 2176 docs/s)
      NB Multinomial classifier :        11929 train docs (  1554 positive)    986 test docs (   159 positive) accuracy: 0.906 in 5.49s ( 2174 docs/s)
  Passive-Aggressive classifier :        11929 train docs (  1554 positive)    986 test docs (   159 positive) accuracy: 0.963 in 5.49s ( 2173 docs/s)


                 SGD classifier :        14875 train docs (  1886 positive)    986 test docs (   159 positive) accuracy: 0.942 in 6.68s ( 2227 docs/s)
          Perceptron classifier :        14875 train docs (  1886 positive)    986 test docs (   159 positive) accuracy: 0.952 in 6.68s ( 2227 docs/s)
      NB Multinomial classifier :        14875 train docs (  1886 positive)    986 test docs (   159 positive) accuracy: 0.909 in 6.68s ( 2225 docs/s)
  Passive-Aggressive classifier :        14875 train docs (  1886 positive)    986 test docs (   159 positive) accuracy: 0.957 in 6.69s ( 2224 docs/s)


                 SGD classifier :        17605 train docs (  2145 positive)    986 test docs (   159 positive) accuracy: 0.946 in 7.81s ( 2254 docs/s)
          Perceptron classifier :        17605 train docs (  2145 positive)    986 test docs (   159 positive) accuracy: 0.931 in 7.81s ( 2253 docs/s)
      NB Multinomial classifier :        17605 train docs (  2145 positive)    986 test docs (   159 positive) accuracy: 0.910 in 7.82s ( 2252 docs/s)
  Passive-Aggressive classifier :        17605 train docs (  2145 positive)    986 test docs (   159 positive) accuracy: 0.962 in 7.82s ( 2251 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()
  • ../../_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 9.332 seconds)

Estimated memory usage: 8 MB

Gallery generated by Sphinx-Gallery