.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_applications_plot_out_of_core_classification.py:
======================================================
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.
.. code-block:: default
# Authors: Eustache Diemert
# @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()
Reuters Dataset related routines
--------------------------------
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.
.. code-block:: default
class ReutersParser(HTMLParser):
"""Utility class to parse a SGML file and yield documents one at a time."""
def __init__(self, encoding='latin-1'):
HTMLParser.__init__(self)
self._reset()
self.encoding = encoding
def handle_starttag(self, tag, attrs):
method = 'start_' + tag
getattr(self, method, lambda x: None)(attrs)
def handle_endtag(self, tag):
method = 'end_' + tag
getattr(self, method, lambda: None)()
def _reset(self):
self.in_title = 0
self.in_body = 0
self.in_topics = 0
self.in_topic_d = 0
self.title = ""
self.body = ""
self.topics = []
self.topic_d = ""
def parse(self, fd):
self.docs = []
for chunk in fd:
self.feed(chunk.decode(self.encoding))
for doc in self.docs:
yield doc
self.docs = []
self.close()
def handle_data(self, data):
if self.in_body:
self.body += data
elif self.in_title:
self.title += data
elif self.in_topic_d:
self.topic_d += data
def start_reuters(self, attributes):
pass
def end_reuters(self):
self.body = re.sub(r'\s+', r' ', self.body)
self.docs.append({'title': self.title,
'body': self.body,
'topics': self.topics})
self._reset()
def start_title(self, attributes):
self.in_title = 1
def end_title(self):
self.in_title = 0
def start_body(self, attributes):
self.in_body = 1
def end_body(self):
self.in_body = 0
def start_topics(self, attributes):
self.in_topics = 1
def end_topics(self):
self.in_topics = 0
def start_d(self, attributes):
self.in_topic_d = 1
def end_d(self):
self.in_topic_d = 0
self.topics.append(self.topic_d)
self.topic_d = ""
def stream_reuters_documents(data_path=None):
"""Iterate over documents of the Reuters dataset.
The Reuters archive will automatically be downloaded and uncompressed if
the `data_path` directory does not exist.
Documents are represented as dictionaries with 'body' (str),
'title' (str), 'topics' (list(str)) keys.
"""
DOWNLOAD_URL = ('http://archive.ics.uci.edu/ml/machine-learning-databases/'
'reuters21578-mld/reuters21578.tar.gz')
ARCHIVE_FILENAME = 'reuters21578.tar.gz'
if data_path is None:
data_path = os.path.join(get_data_home(), "reuters")
if not os.path.exists(data_path):
"""Download the dataset."""
print("downloading dataset (once and for all) into %s" %
data_path)
os.mkdir(data_path)
def progress(blocknum, bs, size):
total_sz_mb = '%.2f MB' % (size / 1e6)
current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6)
if _not_in_sphinx():
sys.stdout.write(
'\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb))
archive_path = os.path.join(data_path, ARCHIVE_FILENAME)
urlretrieve(DOWNLOAD_URL, filename=archive_path,
reporthook=progress)
if _not_in_sphinx():
sys.stdout.write('\r')
print("untarring Reuters dataset...")
tarfile.open(archive_path, 'r:gz').extractall(data_path)
print("done.")
parser = ReutersParser()
for filename in glob(os.path.join(data_path, "*.sgm")):
for doc in parser.parse(open(filename, 'rb')):
yield doc
Main
----
Create the vectorizer and limit the number of features to a reasonable
maximum
.. code-block:: default
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')
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Test set is 878 documents (108 positive)
SGD classifier : 973 train docs ( 125 positive) 878 test docs ( 108 positive) accuracy: 0.923 in 0.76s ( 1279 docs/s)
Perceptron classifier : 973 train docs ( 125 positive) 878 test docs ( 108 positive) accuracy: 0.904 in 0.76s ( 1275 docs/s)
NB Multinomial classifier : 973 train docs ( 125 positive) 878 test docs ( 108 positive) accuracy: 0.877 in 0.78s ( 1245 docs/s)
Passive-Aggressive classifier : 973 train docs ( 125 positive) 878 test docs ( 108 positive) accuracy: 0.904 in 0.78s ( 1241 docs/s)
SGD classifier : 3869 train docs ( 420 positive) 878 test docs ( 108 positive) accuracy: 0.956 in 2.09s ( 1852 docs/s)
Perceptron classifier : 3869 train docs ( 420 positive) 878 test docs ( 108 positive) accuracy: 0.902 in 2.09s ( 1850 docs/s)
NB Multinomial classifier : 3869 train docs ( 420 positive) 878 test docs ( 108 positive) accuracy: 0.882 in 2.11s ( 1836 docs/s)
Passive-Aggressive classifier : 3869 train docs ( 420 positive) 878 test docs ( 108 positive) accuracy: 0.919 in 2.11s ( 1834 docs/s)
SGD classifier : 6643 train docs ( 788 positive) 878 test docs ( 108 positive) accuracy: 0.924 in 3.69s ( 1799 docs/s)
Perceptron classifier : 6643 train docs ( 788 positive) 878 test docs ( 108 positive) accuracy: 0.935 in 3.69s ( 1797 docs/s)
NB Multinomial classifier : 6643 train docs ( 788 positive) 878 test docs ( 108 positive) accuracy: 0.902 in 3.72s ( 1785 docs/s)
Passive-Aggressive classifier : 6643 train docs ( 788 positive) 878 test docs ( 108 positive) accuracy: 0.958 in 3.72s ( 1784 docs/s)
SGD classifier : 9177 train docs ( 1124 positive) 878 test docs ( 108 positive) accuracy: 0.946 in 5.17s ( 1775 docs/s)
Perceptron classifier : 9177 train docs ( 1124 positive) 878 test docs ( 108 positive) accuracy: 0.951 in 5.17s ( 1774 docs/s)
NB Multinomial classifier : 9177 train docs ( 1124 positive) 878 test docs ( 108 positive) accuracy: 0.911 in 5.19s ( 1768 docs/s)
Passive-Aggressive classifier : 9177 train docs ( 1124 positive) 878 test docs ( 108 positive) accuracy: 0.965 in 5.19s ( 1768 docs/s)
SGD classifier : 12057 train docs ( 1511 positive) 878 test docs ( 108 positive) accuracy: 0.926 in 6.51s ( 1851 docs/s)
Perceptron classifier : 12057 train docs ( 1511 positive) 878 test docs ( 108 positive) accuracy: 0.943 in 6.51s ( 1850 docs/s)
NB Multinomial classifier : 12057 train docs ( 1511 positive) 878 test docs ( 108 positive) accuracy: 0.923 in 6.53s ( 1845 docs/s)
Passive-Aggressive classifier : 12057 train docs ( 1511 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 6.54s ( 1844 docs/s)
SGD classifier : 14901 train docs ( 1792 positive) 878 test docs ( 108 positive) accuracy: 0.959 in 7.85s ( 1898 docs/s)
Perceptron classifier : 14901 train docs ( 1792 positive) 878 test docs ( 108 positive) accuracy: 0.956 in 7.85s ( 1898 docs/s)
NB Multinomial classifier : 14901 train docs ( 1792 positive) 878 test docs ( 108 positive) accuracy: 0.924 in 7.87s ( 1892 docs/s)
Passive-Aggressive classifier : 14901 train docs ( 1792 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 7.88s ( 1891 docs/s)
SGD classifier : 17289 train docs ( 2109 positive) 878 test docs ( 108 positive) accuracy: 0.926 in 9.21s ( 1877 docs/s)
Perceptron classifier : 17289 train docs ( 2109 positive) 878 test docs ( 108 positive) accuracy: 0.913 in 9.21s ( 1877 docs/s)
NB Multinomial classifier : 17289 train docs ( 2109 positive) 878 test docs ( 108 positive) accuracy: 0.931 in 9.23s ( 1873 docs/s)
Passive-Aggressive classifier : 17289 train docs ( 2109 positive) 878 test docs ( 108 positive) accuracy: 0.962 in 9.23s ( 1873 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.
.. code-block:: default
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()
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_001.png
:alt: Classification accuracy as a function of training examples (#)
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_002.png
:alt: Classification accuracy as a function of runtime (s)
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png
:alt: Training Times
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png
:alt: Prediction Times (1000 instances)
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 10.228 seconds)
.. _sphx_glr_download_auto_examples_applications_plot_out_of_core_classification.py:
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