Plot Out Of Core Classification¶
====================================================== 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.
Imports for Out-of-Core Text Classification with Incremental Learning¶
HashingVectorizer combined with partial_fit-capable classifiers enables text classification on datasets too large to fit in memory: Unlike CountVectorizer which builds a vocabulary from the entire corpus (requiring all documents in memory), HashingVectorizer maps each token to a fixed feature index via a hash function, producing a stateless transformation that can be applied to any document independently. The n_features=2**18 parameter sets the hash table size (262,144 buckets), trading off collision risk against memory usage. This fixed-dimension output is essential for incremental learning because the feature space must remain consistent across mini-batches.
Four classifiers supporting partial_fit – SGDClassifier, Perceptron, MultinomialNB, and Passive-Aggressive (via SGDClassifier with hinge loss) – are trained on streaming mini-batches from the Reuters-21578 corpus: Each mini-batch of 1000 documents is vectorized, then each classifier updates its parameters incrementally without revisiting previous batches. MultinomialNB accumulates sufficient statistics (word counts per class), while the linear models perform stochastic gradient updates. The classes=all_classes argument in partial_fit is required on the first call to inform classifiers about all possible labels, since early batches may not contain all classes. The learning curves track accuracy over the number of processed documents, revealing how quickly each algorithm converges and whether it suffers from catastrophic forgetting of patterns seen in earlier batches.
# 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 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 = ""
Stream Reuters Documents¶
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.
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": SGDClassifier(
loss="hinge", penalty=None, learning_rate="pa1", eta0=1.0
),
}
Get Minibatch¶
Extract a minibatch of examples, return a tuple X_text, y.
Note: size is before excluding invalid docs with no topics assigned.
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)
Iter Minibatches¶
Generator of minibatches.
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)))
Progress¶
Report progress information, return a string.
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")
# %%
# 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.
Plot Accuracy¶
Plot accuracy as a function of x.
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")
Autolabel¶
attach some text vi autolabel on rectangles.
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()