.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_out_of_core_classification.py" .. LINE NUMBERS ARE GIVEN BELOW. .. 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. .. GENERATED FROM PYTHON SOURCE LINES 16-48 .. 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() .. GENERATED FROM PYTHON SOURCE LINES 49-55 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. .. GENERATED FROM PYTHON SOURCE LINES 55-181 .. 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 .. GENERATED FROM PYTHON SOURCE LINES 182-187 Main ---- Create the vectorizer and limit the number of features to a reasonable maximum .. GENERATED FROM PYTHON SOURCE LINES 187-324 .. 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 .. code-block:: none downloading dataset (once and for all) into /home/runner/scikit_learn_data/reuters untarring Reuters dataset... done. Test set is 467 documents (42 positive) SGD classifier : 966 train docs ( 88 positive) 467 test docs ( 42 positive) accuracy: 0.916 in 0.68s ( 1421 docs/s) Perceptron classifier : 966 train docs ( 88 positive) 467 test docs ( 42 positive) accuracy: 0.921 in 0.68s ( 1416 docs/s) NB Multinomial classifier : 966 train docs ( 88 positive) 467 test docs ( 42 positive) accuracy: 0.910 in 0.69s ( 1399 docs/s) Passive-Aggressive classifier : 966 train docs ( 88 positive) 467 test docs ( 42 positive) accuracy: 0.916 in 0.69s ( 1394 docs/s) SGD classifier : 3776 train docs ( 498 positive) 467 test docs ( 42 positive) accuracy: 0.946 in 1.79s ( 2113 docs/s) Perceptron classifier : 3776 train docs ( 498 positive) 467 test docs ( 42 positive) accuracy: 0.940 in 1.79s ( 2110 docs/s) NB Multinomial classifier : 3776 train docs ( 498 positive) 467 test docs ( 42 positive) accuracy: 0.914 in 1.80s ( 2101 docs/s) Passive-Aggressive classifier : 3776 train docs ( 498 positive) 467 test docs ( 42 positive) accuracy: 0.953 in 1.80s ( 2098 docs/s) SGD classifier : 6617 train docs ( 826 positive) 467 test docs ( 42 positive) accuracy: 0.957 in 2.95s ( 2239 docs/s) Perceptron classifier : 6617 train docs ( 826 positive) 467 test docs ( 42 positive) accuracy: 0.959 in 2.96s ( 2237 docs/s) NB Multinomial classifier : 6617 train docs ( 826 positive) 467 test docs ( 42 positive) accuracy: 0.927 in 2.96s ( 2232 docs/s) Passive-Aggressive classifier : 6617 train docs ( 826 positive) 467 test docs ( 42 positive) accuracy: 0.953 in 2.97s ( 2230 docs/s) SGD classifier : 9403 train docs ( 1187 positive) 467 test docs ( 42 positive) accuracy: 0.955 in 4.08s ( 2305 docs/s) Perceptron classifier : 9403 train docs ( 1187 positive) 467 test docs ( 42 positive) accuracy: 0.949 in 4.08s ( 2304 docs/s) NB Multinomial classifier : 9403 train docs ( 1187 positive) 467 test docs ( 42 positive) accuracy: 0.940 in 4.09s ( 2299 docs/s) Passive-Aggressive classifier : 9403 train docs ( 1187 positive) 467 test docs ( 42 positive) accuracy: 0.953 in 4.09s ( 2298 docs/s) SGD classifier : 11939 train docs ( 1518 positive) 467 test docs ( 42 positive) accuracy: 0.953 in 5.17s ( 2310 docs/s) Perceptron classifier : 11939 train docs ( 1518 positive) 467 test docs ( 42 positive) accuracy: 0.912 in 5.17s ( 2309 docs/s) NB Multinomial classifier : 11939 train docs ( 1518 positive) 467 test docs ( 42 positive) accuracy: 0.944 in 5.18s ( 2305 docs/s) Passive-Aggressive classifier : 11939 train docs ( 1518 positive) 467 test docs ( 42 positive) accuracy: 0.959 in 5.18s ( 2304 docs/s) SGD classifier : 14827 train docs ( 1823 positive) 467 test docs ( 42 positive) accuracy: 0.949 in 6.28s ( 2361 docs/s) Perceptron classifier : 14827 train docs ( 1823 positive) 467 test docs ( 42 positive) accuracy: 0.955 in 6.28s ( 2360 docs/s) NB Multinomial classifier : 14827 train docs ( 1823 positive) 467 test docs ( 42 positive) accuracy: 0.944 in 6.29s ( 2358 docs/s) Passive-Aggressive classifier : 14827 train docs ( 1823 positive) 467 test docs ( 42 positive) accuracy: 0.966 in 6.29s ( 2357 docs/s) SGD classifier : 17698 train docs ( 2188 positive) 467 test docs ( 42 positive) accuracy: 0.957 in 7.37s ( 2401 docs/s) Perceptron classifier : 17698 train docs ( 2188 positive) 467 test docs ( 42 positive) accuracy: 0.955 in 7.37s ( 2401 docs/s) NB Multinomial classifier : 17698 train docs ( 2188 positive) 467 test docs ( 42 positive) accuracy: 0.946 in 7.38s ( 2398 docs/s) Passive-Aggressive classifier : 17698 train docs ( 2188 positive) 467 test docs ( 42 positive) accuracy: 0.961 in 7.38s ( 2398 docs/s) .. GENERATED FROM PYTHON SOURCE LINES 325-334 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. .. GENERATED FROM PYTHON SOURCE LINES 334-431 .. 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() / 2.0, 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() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_001.png :alt: Classification accuracy as a function of training examples (#) :srcset: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_002.png :alt: Classification accuracy as a function of runtime (s) :srcset: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png :alt: Training Times :srcset: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png :alt: Prediction Times (1000 instances) :srcset: /auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 10.175 seconds) .. _sphx_glr_download_auto_examples_applications_plot_out_of_core_classification.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_out_of_core_classification.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_out_of_core_classification.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_