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 982 documents (90 positive)
                 SGD classifier :          983 train docs (   118 positive)    982 test docs (    90 positive) accuracy: 0.941 in 0.69s ( 1417 docs/s)
          Perceptron classifier :          983 train docs (   118 positive)    982 test docs (    90 positive) accuracy: 0.838 in 0.70s ( 1413 docs/s)
      NB Multinomial classifier :          983 train docs (   118 positive)    982 test docs (    90 positive) accuracy: 0.908 in 0.71s ( 1377 docs/s)
  Passive-Aggressive classifier :          983 train docs (   118 positive)    982 test docs (    90 positive) accuracy: 0.936 in 0.72s ( 1373 docs/s)


                 SGD classifier :         3905 train docs (   495 positive)    982 test docs (    90 positive) accuracy: 0.959 in 1.98s ( 1975 docs/s)
          Perceptron classifier :         3905 train docs (   495 positive)    982 test docs (    90 positive) accuracy: 0.958 in 1.98s ( 1973 docs/s)
      NB Multinomial classifier :         3905 train docs (   495 positive)    982 test docs (    90 positive) accuracy: 0.918 in 2.00s ( 1956 docs/s)
  Passive-Aggressive classifier :         3905 train docs (   495 positive)    982 test docs (    90 positive) accuracy: 0.956 in 2.00s ( 1954 docs/s)


                 SGD classifier :         6846 train docs (   839 positive)    982 test docs (    90 positive) accuracy: 0.964 in 3.18s ( 2152 docs/s)
          Perceptron classifier :         6846 train docs (   839 positive)    982 test docs (    90 positive) accuracy: 0.949 in 3.18s ( 2151 docs/s)
      NB Multinomial classifier :         6846 train docs (   839 positive)    982 test docs (    90 positive) accuracy: 0.924 in 3.20s ( 2139 docs/s)
  Passive-Aggressive classifier :         6846 train docs (   839 positive)    982 test docs (    90 positive) accuracy: 0.970 in 3.20s ( 2138 docs/s)


                 SGD classifier :         9664 train docs (  1119 positive)    982 test docs (    90 positive) accuracy: 0.959 in 4.38s ( 2204 docs/s)
          Perceptron classifier :         9664 train docs (  1119 positive)    982 test docs (    90 positive) accuracy: 0.969 in 4.39s ( 2203 docs/s)
      NB Multinomial classifier :         9664 train docs (  1119 positive)    982 test docs (    90 positive) accuracy: 0.932 in 4.40s ( 2195 docs/s)
  Passive-Aggressive classifier :         9664 train docs (  1119 positive)    982 test docs (    90 positive) accuracy: 0.962 in 4.40s ( 2194 docs/s)


                 SGD classifier :        12094 train docs (  1413 positive)    982 test docs (    90 positive) accuracy: 0.968 in 5.55s ( 2178 docs/s)
          Perceptron classifier :        12094 train docs (  1413 positive)    982 test docs (    90 positive) accuracy: 0.964 in 5.55s ( 2177 docs/s)
      NB Multinomial classifier :        12094 train docs (  1413 positive)    982 test docs (    90 positive) accuracy: 0.934 in 5.57s ( 2171 docs/s)
  Passive-Aggressive classifier :        12094 train docs (  1413 positive)    982 test docs (    90 positive) accuracy: 0.970 in 5.57s ( 2170 docs/s)


                 SGD classifier :        14954 train docs (  1824 positive)    982 test docs (    90 positive) accuracy: 0.950 in 6.77s ( 2209 docs/s)
          Perceptron classifier :        14954 train docs (  1824 positive)    982 test docs (    90 positive) accuracy: 0.899 in 6.77s ( 2208 docs/s)
      NB Multinomial classifier :        14954 train docs (  1824 positive)    982 test docs (    90 positive) accuracy: 0.940 in 6.79s ( 2203 docs/s)
  Passive-Aggressive classifier :        14954 train docs (  1824 positive)    982 test docs (    90 positive) accuracy: 0.961 in 6.79s ( 2202 docs/s)


                 SGD classifier :        17257 train docs (  2073 positive)    982 test docs (    90 positive) accuracy: 0.963 in 7.92s ( 2180 docs/s)
          Perceptron classifier :        17257 train docs (  2073 positive)    982 test docs (    90 positive) accuracy: 0.966 in 7.92s ( 2179 docs/s)
      NB Multinomial classifier :        17257 train docs (  2073 positive)    982 test docs (    90 positive) accuracy: 0.939 in 7.93s ( 2175 docs/s)
  Passive-Aggressive classifier :        17257 train docs (  2073 positive)    982 test docs (    90 positive) accuracy: 0.970 in 7.94s ( 2174 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 8.880 seconds)

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