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pythonでテキスト分類の復習

機械学習-DeepLearning

scikitlearnでテキスト分類を復習です。

KerasでのDeep Learning結果との精度比較を行うためにもベース知識として、個人的な備忘録です。

内容

scikit-learnにもともと付属している 20 news groupデータセットを読み込み、各種手法で分類するサンプルです。

ソースコードの大部分は、Classification of text documents using sparse features — scikit-learn 0.18.1 documentationがほとんどで、
一部、scikit-learn v18 に合わせてパラメタの名前を調整しています。

実行するとき、"--all_categories"を指定すると、KNeighborsClassifierが落ちてしまう(私のメモリ1Gという心もとない環境のためと思います)ので、ひとまずコメントアウトしています。

grid searchがまだ仕上がっていないのでちょっと待ってね。

全体

#!/usr/local/bin/python2.7
# encoding: utf-8
'''
Created on 2017/02/03

based on http://scikit-learn.org/stable/auto_examples/text/document_classification_20newsgroups.html

@author: mzi
'''
from __future__ import print_function

import sys
import os

import logging
import numpy as np
from optparse import OptionParser
from time import time
import matplotlib.pyplot as plt
import gc

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.linear_model import RidgeClassifier
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import NearestCentroid
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LassoCV
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import GridSearchCV
from sklearn.utils.extmath import density
from sklearn import metrics

def size_mb(docs):
    return sum(len(s.encode('utf-8')) for s in docs) / 1e6

def trim(s):
    """Trim string to fit on terminal (assuming 80-column display)"""
    return s if len(s) <= 80 else s[:77] + "..."

def benchmark(clf):
    print('_' * 80)
    print("Training: ")
    print(clf)
    t0 = time()
    clf.fit(X_train, y_train)
    train_time = time() - t0
    print("train time: %0.3fs" % train_time)

    t0 = time()
    pred = clf.predict(X_test)
    test_time = time() - t0
    print("test time:  %0.3fs" % test_time)

    score = metrics.accuracy_score(y_test, pred)
    print("accuracy:   %0.3f" % score)

    if hasattr(clf, 'coef_'):
        print("dimensionality: %d" % clf.coef_.shape[1])
        print("density: %f" % density(clf.coef_))

        if opts.print_top10 and feature_names is not None:
            print("top 10 keywords per class:")
            for i, label in enumerate(target_names):
                top10 = np.argsort(clf.coef_[i])[-10:]
                print(trim("%s: %s" % (label, " ".join(feature_names[top10]))))
        print()

    if opts.print_report:
        print("classification report:")
        print(metrics.classification_report(y_test, pred,
                                            target_names=target_names))

    if opts.print_cm:
        print("confusion matrix:")
        print(metrics.confusion_matrix(y_test, pred))

    print()
    clf_descr = str(clf).split('(')[0]
    return clf_descr, score, train_time, test_time

if __name__ == "__main__":

    # Display progress logs on stdout
    logging.basicConfig(level=logging.INFO,
                        format='%(asctime)s %(levelname)s %(message)s')

    # parse commandline arguments
    op = OptionParser()
    op.add_option("--report",
                  action="store_true", dest="print_report",
                  help="Print a detailed classification report.")
    op.add_option("--chi2_select",
                  action="store", type="int", dest="select_chi2",
                  help="Select some number of features using a chi-squared test")
    op.add_option("--confusion_matrix",
                  action="store_true", dest="print_cm",
                  help="Print the confusion matrix.")
    op.add_option("--top10",
                  action="store_true", dest="print_top10",
                  help="Print ten most discriminative terms per class"
                       " for every classifier.")
    op.add_option("--all_categories",
                  action="store_true", dest="all_categories",
                  help="Whether to use all categories or not.")
    op.add_option("--use_hashing",
                  action="store_true",
                  help="Use a hashing vectorizer.")
    op.add_option("--n_features",
                  action="store", type=int, default=2 ** 16,
                  help="n_features when using the hashing vectorizer.")
    op.add_option("--filtered",
                  action="store_true",
                  help="Remove newsgroup information that is easily overfit: "
                       "headers, signatures, and quoting.")

    (opts, args) = op.parse_args()
    if len(args) > 0:
        op.error("this script takes no arguments.")
        sys.exit(1)
    
    print(__doc__)
    op.print_help()
    print()

    #Categories
    if opts.all_categories:
        categories = None
    else:
        categories = [
            'alt.atheism',
            'talk.religion.misc',
            'comp.graphics',
            'sci.space',
        ]
    
    #Remove headers
    if opts.filtered:
        remove = ('headers', 'footers', 'quotes')
    else:
        remove = ()
    
    print("Loading 20 newsgroups dataset for categories:")
    print(categories if categories else "all")
    
    data_train = fetch_20newsgroups(subset='train', categories=categories,
                                    shuffle=True, random_state=42,
                                    remove=remove)
    
    data_test = fetch_20newsgroups(subset='test', categories=categories,
                                   shuffle=True, random_state=42,
                                   remove=remove)
    print('data loaded')

    # order of labels in `target_names` can be different from `categories`
    target_names = data_train.target_names

    data_train_size_mb = size_mb(data_train.data)
    data_test_size_mb = size_mb(data_test.data)
    print("%d documents - %0.3fMB (training set)" % (
        len(data_train.data), data_train_size_mb))
    print("%d documents - %0.3fMB (test set)" % (
        len(data_test.data), data_test_size_mb))
    print()

    # split a training set and a test set
    y_train, y_test = data_train.target, data_test.target
    
    print("Extracting features from the training data using a sparse vectorizer")
    t0 = time()
    if opts.use_hashing:
        vectorizer = HashingVectorizer(stop_words='english', non_negative=True,
                                       n_features=opts.n_features)
        X_train = vectorizer.transform(data_train.data)
    else:
        vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                     stop_words='english')
        X_train = vectorizer.fit_transform(data_train.data)
    duration = time() - t0
    print("done in %fs at %0.3fMB/s" % (duration, data_train_size_mb / duration))
    print("n_samples: %d, n_features: %d" % X_train.shape)
    print()

    print("Extracting features from the test data using the same vectorizer")
    t0 = time()
    X_test = vectorizer.transform(data_test.data)
    duration = time() - t0
    print("done in %fs at %0.3fMB/s" % (duration, data_test_size_mb / duration))
    print("n_samples: %d, n_features: %d" % X_test.shape)
    print()

    # mapping from integer feature name to original token string
    if opts.use_hashing:
        feature_names = None
    else:
        feature_names = vectorizer.get_feature_names()

    # Extract features by chi2
    if opts.select_chi2:
        print("Extracting %d best features by a chi-squared test" %
              opts.select_chi2)
        t0 = time()
        ch2 = SelectKBest(chi2, k=opts.select_chi2)
        X_train = ch2.fit_transform(X_train, y_train)
        X_test = ch2.transform(X_test)
        if feature_names:
            # keep selected feature names
            feature_names = [feature_names[i] for i
                             in ch2.get_support(indices=True)]
        print("done in %fs" % (time() - t0))
        print()
    
    if feature_names:
        feature_names = np.asarray(feature_names)
    
    # Classification
    results = []
    for clf, name in (
            (RidgeClassifier(tol=1e-2, solver="sag"), "Ridge Classifier"),
            (Perceptron(n_iter=50), "Perceptron"),
            (PassiveAggressiveClassifier(n_iter=50), "Passive-Aggressive"),
            #(KNeighborsClassifier(n_neighbors=10), "kNN"),
            (RandomForestClassifier(n_estimators=100), "Random forest")):
        print('=' * 80)
        print(name)
        results.append(benchmark(clf))
    
    for penalty in ["l2", "l1"]:
        print('=' * 80)
        print("%s penalty" % penalty.upper())
        # Train Liblinear model
        results.append(benchmark(LinearSVC(loss='squared_hinge', penalty=penalty,
                                                dual=False, tol=1e-3)))
    
        # Train SGD model
        results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
                                               penalty=penalty)))
    
    # Train SGD with Elastic Net penalty
    print('=' * 80)
    print("Elastic-Net penalty")
    results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
                                           penalty="elasticnet")))
    
    # Train NearestCentroid without threshold
    print('=' * 80)
    print("NearestCentroid (aka Rocchio classifier)")
    results.append(benchmark(NearestCentroid()))
    
    # Train sparse Naive Bayes classifiers
    print('=' * 80)
    print("Naive Bayes")
    results.append(benchmark(MultinomialNB(alpha=.01)))
    results.append(benchmark(BernoulliNB(alpha=.01)))
    
    print('=' * 80)
    print("LinearSVC with L1-based feature selection")
    # The smaller C, the stronger the regularization.
    # The more regularization, the more sparsity.
    clf = LinearSVC(penalty="l1", dual=False, tol=1e-3)
    results.append(benchmark(Pipeline([
      ('feature_selection', SelectFromModel(clf)),
      ('classification', LinearSVC())
    ])))
    
    # make some plots
    indices = np.arange(len(results))
    
    results = [[x[i] for x in results] for i in range(4)]
    
    clf_names, score, training_time, test_time = results
    training_time = np.array(training_time) / np.max(training_time)
    test_time = np.array(test_time) / np.max(test_time)
    
    plt.figure(figsize=(12, 8))
    plt.title("Score")
    plt.barh(indices, score, .2, label="score", color='navy')
    plt.barh(indices + .3, training_time, .2, label="training time",
             color='c')
    plt.barh(indices + .6, test_time, .2, label="test time", color='darkorange')
    plt.yticks(())
    plt.legend(loc='best')
    plt.subplots_adjust(left=.25)
    plt.subplots_adjust(top=.95)
    plt.subplots_adjust(bottom=.05)
    
    for i, c in zip(indices, clf_names):
        plt.text(-.3, i, c)
    
    plt.show()