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

import itertools
from pathlib import Path
from hashlib import sha256
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")
downloading dataset (once and for all) into /home/runner/scikit_learn_data/reuters
untarring Reuters dataset...
done.
Test set is 870 documents (58 positive)
                 SGD classifier :          955 train docs (    93 positive)    870 test docs (    58 positive) accuracy: 0.946 in 0.67s ( 1416 docs/s)
          Perceptron classifier :          955 train docs (    93 positive)    870 test docs (    58 positive) accuracy: 0.932 in 0.68s ( 1408 docs/s)
      NB Multinomial classifier :          955 train docs (    93 positive)    870 test docs (    58 positive) accuracy: 0.933 in 0.69s ( 1388 docs/s)
  Passive-Aggressive classifier :          955 train docs (    93 positive)    870 test docs (    58 positive) accuracy: 0.939 in 0.69s ( 1382 docs/s)


                 SGD classifier :         3836 train docs (   486 positive)    870 test docs (    58 positive) accuracy: 0.961 in 1.84s ( 2082 docs/s)
          Perceptron classifier :         3836 train docs (   486 positive)    870 test docs (    58 positive) accuracy: 0.894 in 1.85s ( 2078 docs/s)
      NB Multinomial classifier :         3836 train docs (   486 positive)    870 test docs (    58 positive) accuracy: 0.939 in 1.85s ( 2068 docs/s)
  Passive-Aggressive classifier :         3836 train docs (   486 positive)    870 test docs (    58 positive) accuracy: 0.975 in 1.86s ( 2065 docs/s)


                 SGD classifier :         6596 train docs (   924 positive)    870 test docs (    58 positive) accuracy: 0.976 in 2.98s ( 2209 docs/s)
          Perceptron classifier :         6596 train docs (   924 positive)    870 test docs (    58 positive) accuracy: 0.962 in 2.99s ( 2207 docs/s)
      NB Multinomial classifier :         6596 train docs (   924 positive)    870 test docs (    58 positive) accuracy: 0.948 in 3.00s ( 2200 docs/s)
  Passive-Aggressive classifier :         6596 train docs (   924 positive)    870 test docs (    58 positive) accuracy: 0.982 in 3.00s ( 2198 docs/s)


                 SGD classifier :         9120 train docs (  1214 positive)    870 test docs (    58 positive) accuracy: 0.976 in 4.10s ( 2223 docs/s)
          Perceptron classifier :         9120 train docs (  1214 positive)    870 test docs (    58 positive) accuracy: 0.929 in 4.11s ( 2221 docs/s)
      NB Multinomial classifier :         9120 train docs (  1214 positive)    870 test docs (    58 positive) accuracy: 0.955 in 4.11s ( 2216 docs/s)
  Passive-Aggressive classifier :         9120 train docs (  1214 positive)    870 test docs (    58 positive) accuracy: 0.983 in 4.12s ( 2214 docs/s)


                 SGD classifier :        11548 train docs (  1469 positive)    870 test docs (    58 positive) accuracy: 0.978 in 5.21s ( 2214 docs/s)
          Perceptron classifier :        11548 train docs (  1469 positive)    870 test docs (    58 positive) accuracy: 0.918 in 5.22s ( 2212 docs/s)
      NB Multinomial classifier :        11548 train docs (  1469 positive)    870 test docs (    58 positive) accuracy: 0.956 in 5.23s ( 2209 docs/s)
  Passive-Aggressive classifier :        11548 train docs (  1469 positive)    870 test docs (    58 positive) accuracy: 0.977 in 5.23s ( 2207 docs/s)


                 SGD classifier :        14468 train docs (  1778 positive)    870 test docs (    58 positive) accuracy: 0.975 in 6.37s ( 2270 docs/s)
          Perceptron classifier :        14468 train docs (  1778 positive)    870 test docs (    58 positive) accuracy: 0.944 in 6.38s ( 2269 docs/s)
      NB Multinomial classifier :        14468 train docs (  1778 positive)    870 test docs (    58 positive) accuracy: 0.961 in 6.39s ( 2265 docs/s)
  Passive-Aggressive classifier :        14468 train docs (  1778 positive)    870 test docs (    58 positive) accuracy: 0.963 in 6.39s ( 2264 docs/s)


                 SGD classifier :        17293 train docs (  2196 positive)    870 test docs (    58 positive) accuracy: 0.979 in 7.52s ( 2300 docs/s)
          Perceptron classifier :        17293 train docs (  2196 positive)    870 test docs (    58 positive) accuracy: 0.929 in 7.52s ( 2300 docs/s)
      NB Multinomial classifier :        17293 train docs (  2196 positive)    870 test docs (    58 positive) accuracy: 0.964 in 7.53s ( 2297 docs/s)
  Passive-Aggressive classifier :        17293 train docs (  2196 positive)    870 test docs (    58 positive) accuracy: 0.983 in 7.53s ( 2296 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()
  • Classification accuracy as a function of training examples (#)
  • Classification accuracy as a function of runtime (s)
  • Training Times
  • Prediction Times (1000 instances)

Total running time of the script: ( 0 minutes 10.316 seconds)

Gallery generated by Sphinx-Gallery