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: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import itertools
import re
import sys
import tarfile
import time
from hashlib import sha256
from html.parser import HTMLParser
from pathlib import Path
from urllib.request import urlretrieve

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rcParams

from sklearn.datasets import get_data_home
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier, Perceptron, SGDClassifier
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.

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 = "https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz"
    ARCHIVE_SHA256 = "3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30"
    ARCHIVE_FILENAME = "reuters21578.tar.gz"

    if data_path is None:
        data_path = Path(get_data_home()) / "reuters"
    else:
        data_path = Path(data_path)
    if not data_path.exists():
        """Download the dataset."""
        print("downloading dataset (once and for all) into %s" % data_path)
        data_path.mkdir(parents=True, exist_ok=True)

        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 = data_path / ARCHIVE_FILENAME

        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)
        if _not_in_sphinx():
            sys.stdout.write("\r")

        # Check that the archive was not tampered:
        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256

        print("untarring Reuters dataset...")
        with tarfile.open(archive_path, "r:gz") as fp:
            fp.extractall(data_path, filter="data")
        print("done.")

    parser = ReutersParser()
    for filename in data_path.glob("*.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

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")

downloading dataset (once and for all) into /home/runner/scikit_learn_data/reuters
untarring Reuters dataset...
done.
Test set is 982 documents (90 positive)
                 SGD classifier :          973 train docs (   125 positive)    982 test docs (    90 positive) accuracy: 0.946 in 0.61s ( 1607 docs/s)
          Perceptron classifier :          973 train docs (   125 positive)    982 test docs (    90 positive) accuracy: 0.912 in 0.61s ( 1600 docs/s)
      NB Multinomial classifier :          973 train docs (   125 positive)    982 test docs (    90 positive) accuracy: 0.910 in 0.62s ( 1572 docs/s)
  Passive-Aggressive classifier :          973 train docs (   125 positive)    982 test docs (    90 positive) accuracy: 0.933 in 0.62s ( 1565 docs/s)


                 SGD classifier :         3862 train docs (   465 positive)    982 test docs (    90 positive) accuracy: 0.894 in 1.75s ( 2206 docs/s)
          Perceptron classifier :         3862 train docs (   465 positive)    982 test docs (    90 positive) accuracy: 0.934 in 1.75s ( 2204 docs/s)
      NB Multinomial classifier :         3862 train docs (   465 positive)    982 test docs (    90 positive) accuracy: 0.919 in 1.76s ( 2191 docs/s)
  Passive-Aggressive classifier :         3862 train docs (   465 positive)    982 test docs (    90 positive) accuracy: 0.955 in 1.76s ( 2188 docs/s)


                 SGD classifier :         6292 train docs (   783 positive)    982 test docs (    90 positive) accuracy: 0.961 in 2.88s ( 2187 docs/s)
          Perceptron classifier :         6292 train docs (   783 positive)    982 test docs (    90 positive) accuracy: 0.854 in 2.88s ( 2185 docs/s)
      NB Multinomial classifier :         6292 train docs (   783 positive)    982 test docs (    90 positive) accuracy: 0.930 in 2.89s ( 2177 docs/s)
  Passive-Aggressive classifier :         6292 train docs (   783 positive)    982 test docs (    90 positive) accuracy: 0.943 in 2.89s ( 2175 docs/s)


                 SGD classifier :         9248 train docs (  1210 positive)    982 test docs (    90 positive) accuracy: 0.949 in 4.02s ( 2298 docs/s)
          Perceptron classifier :         9248 train docs (  1210 positive)    982 test docs (    90 positive) accuracy: 0.953 in 4.03s ( 2297 docs/s)
      NB Multinomial classifier :         9248 train docs (  1210 positive)    982 test docs (    90 positive) accuracy: 0.936 in 4.04s ( 2291 docs/s)
  Passive-Aggressive classifier :         9248 train docs (  1210 positive)    982 test docs (    90 positive) accuracy: 0.968 in 4.04s ( 2290 docs/s)


                 SGD classifier :        12173 train docs (  1580 positive)    982 test docs (    90 positive) accuracy: 0.971 in 5.15s ( 2361 docs/s)
          Perceptron classifier :        12173 train docs (  1580 positive)    982 test docs (    90 positive) accuracy: 0.959 in 5.16s ( 2360 docs/s)
      NB Multinomial classifier :        12173 train docs (  1580 positive)    982 test docs (    90 positive) accuracy: 0.939 in 5.17s ( 2356 docs/s)
  Passive-Aggressive classifier :        12173 train docs (  1580 positive)    982 test docs (    90 positive) accuracy: 0.960 in 5.17s ( 2355 docs/s)


                 SGD classifier :        14432 train docs (  1801 positive)    982 test docs (    90 positive) accuracy: 0.964 in 6.17s ( 2337 docs/s)
          Perceptron classifier :        14432 train docs (  1801 positive)    982 test docs (    90 positive) accuracy: 0.960 in 6.18s ( 2336 docs/s)
      NB Multinomial classifier :        14432 train docs (  1801 positive)    982 test docs (    90 positive) accuracy: 0.935 in 6.19s ( 2332 docs/s)
  Passive-Aggressive classifier :        14432 train docs (  1801 positive)    982 test docs (    90 positive) accuracy: 0.966 in 6.19s ( 2332 docs/s)


                 SGD classifier :        17311 train docs (  2203 positive)    982 test docs (    90 positive) accuracy: 0.969 in 7.31s ( 2366 docs/s)
          Perceptron classifier :        17311 train docs (  2203 positive)    982 test docs (    90 positive) accuracy: 0.961 in 7.32s ( 2365 docs/s)
      NB Multinomial classifier :        17311 train docs (  2203 positive)    982 test docs (    90 positive) accuracy: 0.942 in 7.33s ( 2362 docs/s)
  Passive-Aggressive classifier :        17311 train docs (  2203 positive)    982 test docs (    90 positive) accuracy: 0.973 in 7.33s ( 2361 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() / 2.0,
            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()

• 
• 
• 
• 

Related examples

Classification of text documents using sparse features

Classification of text documents using sparse features

Prediction Latency

Prediction Latency

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison