Note
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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 969 documents (155 positive)
SGD classifier : 910 train docs ( 163 positive) 969 test docs ( 155 positive) accuracy: 0.878 in 2.40s ( 379 docs/s)
Perceptron classifier : 910 train docs ( 163 positive) 969 test docs ( 155 positive) accuracy: 0.898 in 2.41s ( 377 docs/s)
NB Multinomial classifier : 910 train docs ( 163 positive) 969 test docs ( 155 positive) accuracy: 0.840 in 2.45s ( 370 docs/s)
Passive-Aggressive classifier : 910 train docs ( 163 positive) 969 test docs ( 155 positive) accuracy: 0.911 in 2.46s ( 370 docs/s)
SGD classifier : 3726 train docs ( 530 positive) 969 test docs ( 155 positive) accuracy: 0.940 in 7.12s ( 523 docs/s)
Perceptron classifier : 3726 train docs ( 530 positive) 969 test docs ( 155 positive) accuracy: 0.933 in 7.12s ( 523 docs/s)
NB Multinomial classifier : 3726 train docs ( 530 positive) 969 test docs ( 155 positive) accuracy: 0.848 in 7.17s ( 519 docs/s)
Passive-Aggressive classifier : 3726 train docs ( 530 positive) 969 test docs ( 155 positive) accuracy: 0.945 in 7.18s ( 518 docs/s)
SGD classifier : 6543 train docs ( 801 positive) 969 test docs ( 155 positive) accuracy: 0.945 in 11.55s ( 566 docs/s)
Perceptron classifier : 6543 train docs ( 801 positive) 969 test docs ( 155 positive) accuracy: 0.955 in 11.56s ( 566 docs/s)
NB Multinomial classifier : 6543 train docs ( 801 positive) 969 test docs ( 155 positive) accuracy: 0.862 in 11.61s ( 563 docs/s)
Passive-Aggressive classifier : 6543 train docs ( 801 positive) 969 test docs ( 155 positive) accuracy: 0.947 in 11.61s ( 563 docs/s)
SGD classifier : 9486 train docs ( 1220 positive) 969 test docs ( 155 positive) accuracy: 0.953 in 16.53s ( 574 docs/s)
Perceptron classifier : 9486 train docs ( 1220 positive) 969 test docs ( 155 positive) accuracy: 0.940 in 16.53s ( 573 docs/s)
NB Multinomial classifier : 9486 train docs ( 1220 positive) 969 test docs ( 155 positive) accuracy: 0.878 in 16.58s ( 572 docs/s)
Passive-Aggressive classifier : 9486 train docs ( 1220 positive) 969 test docs ( 155 positive) accuracy: 0.955 in 16.59s ( 571 docs/s)
SGD classifier : 12404 train docs ( 1539 positive) 969 test docs ( 155 positive) accuracy: 0.953 in 21.11s ( 587 docs/s)
Perceptron classifier : 12404 train docs ( 1539 positive) 969 test docs ( 155 positive) accuracy: 0.959 in 21.12s ( 587 docs/s)
NB Multinomial classifier : 12404 train docs ( 1539 positive) 969 test docs ( 155 positive) accuracy: 0.891 in 21.17s ( 585 docs/s)
Passive-Aggressive classifier : 12404 train docs ( 1539 positive) 969 test docs ( 155 positive) accuracy: 0.960 in 21.17s ( 585 docs/s)
SGD classifier : 14786 train docs ( 1833 positive) 969 test docs ( 155 positive) accuracy: 0.961 in 26.15s ( 565 docs/s)
Perceptron classifier : 14786 train docs ( 1833 positive) 969 test docs ( 155 positive) accuracy: 0.960 in 26.15s ( 565 docs/s)
NB Multinomial classifier : 14786 train docs ( 1833 positive) 969 test docs ( 155 positive) accuracy: 0.903 in 26.20s ( 564 docs/s)
Passive-Aggressive classifier : 14786 train docs ( 1833 positive) 969 test docs ( 155 positive) accuracy: 0.969 in 26.21s ( 564 docs/s)
SGD classifier : 17211 train docs ( 2112 positive) 969 test docs ( 155 positive) accuracy: 0.956 in 30.59s ( 562 docs/s)
Perceptron classifier : 17211 train docs ( 2112 positive) 969 test docs ( 155 positive) accuracy: 0.961 in 30.61s ( 562 docs/s)
NB Multinomial classifier : 17211 train docs ( 2112 positive) 969 test docs ( 155 positive) accuracy: 0.914 in 30.74s ( 559 docs/s)
Passive-Aggressive classifier : 17211 train docs ( 2112 positive) 969 test docs ( 155 positive) accuracy: 0.966 in 30.75s ( 559 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()
Total running time of the script: ( 0 minutes 34.506 seconds)