分类特征稀疏的文本
这个例子展示了如何使用 scikit-learn 中的单词包方法，根据主题对文档进行分类。本例使用scipy.sparse中的矩阵来存储特征，并演示各种能够有效处理稀疏矩阵的分类器。
本例中使用的数据集是20条新闻组数据集。通过scikit-learn可以自动下载该数据集，并进行缓存。
下述条形图展示了各个不同分类器，其信息包括精度、训练时间(已归一化)和测试时间(已归一化)。
import logging
import numpy as np
from optparse import OptionParser
import sys
from time import time
import matplotlib.pyplot as plt
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 SelectFromModel
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, ComplementNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import NearestCentroid
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils.extmath import density
from sklearn import metrics
# 在stdout上显示进度日志
logging.basicConfig(level=logging.INFO,format='%(asctime)s %(levelname)s %(message)s')
# 解析命令行参数
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.")
<Option at 0x7febca4f9320: --filtered>
def is_interactive():
return not hasattr(sys.modules['__main__'], '__file__')
# Jupyter notebook上的运行方法
argv = [] if is_interactive() else sys.argv[1:]
(opts, args) = op.parse_args(argv)
if len(args) > 0:
op.error("this script takes no arguments.")
sys.exit(1)
print(__doc__)
op.print_help()
print()
Automatically created module for IPython interactive environment
Usage: ipykernel_launcher.py [options]
Options:
-h, --help show this help message and exit
--report Print a detailed classification report.
--chi2_select=SELECT_CHI2
Select some number of features using a chi-squared
test
--confusion_matrix Print the confusion matrix.
--top10 Print ten most discriminative terms per class for
every classifier.
--all_categories Whether to use all categories or not.
--use_hashing Use a hashing vectorizer.
--n_features=N_FEATURES
n_features when using the hashing vectorizer.
--filtered Remove newsgroup information that is easily overfit:
headers, signatures, and quoting.
# 从训练集中加载一些类别
if opts.all_categories:
categories = None
else:
categories = [
'alt.atheism',
'talk.religion.misc',
'comp.graphics',
'sci.space',
]
if opts.filtered:
remove = ('headers', 'footers', 'quotes')
else:
remove = ()
print("Loading 20 newsgroups dataset for categories:")
print(categories if categories else "all")
Loading 20 newsgroups dataset for categories:
['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space']
# 下载数据集
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)
# target_names中的标签顺序可以与categories中的不同
target_names = data_train.target_names
def size_mb(docs):
return sum(len(s.encode('utf-8')) for s in docs) / 1e6
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("%d categories" % len(target_names))
2034 documents - 3.980MB (training set)
1353 documents - 2.867MB (test set)
4 categories
# 划分测试,训练集
y_train, y_test = data_train.target, data_test.target
print("使用稀疏向量机从训练数据中提取特征")
t0 = time()
if opts.use_hashing:
vectorizer = HashingVectorizer(stop_words='english', alternate_sign=False,
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)
使用稀疏向量机从训练数据中提取特征
done in 0.476004s at 8.360MB/s
n_samples: 2034, n_features: 33809
print("使用相同的矢量化器从测试数据中提取特征")
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)
使用相同的矢量化器从测试数据中提取特征
done in 0.311447s at 9.207MB/s
n_samples: 1353, n_features: 33809
# 从整数的特征名称映射到原始的token字符串
if opts.use_hashing:
feature_names = None
else:
feature_names = vectorizer.get_feature_names()
if opts.select_chi2:
print("使用卡方检验提取 %d 个特征" %
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))
if feature_names:
feature_names = np.asarray(feature_names)
# 修剪字符串以适应终端(假设显示80列)
def trim(s):
return s if len(s) <= 80 else s[:77] + "..."
基准分类器
def benchmark(clf):
print('_' * 80)
print("训练: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("训练时间: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("最佳时间: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("准确率: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("维数: %d" % clf.coef_.shape[1])
print("密度: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("每个类的前十个词:")
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("分类报告:")
print(metrics.classification_report(y_test, pred,
target_names=target_names))
if opts.print_cm:
print("混淆矩阵:")
print(metrics.confusion_matrix(y_test, pred))
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
results = []
for clf, name in (
(RidgeClassifier(tol=1e-2, solver="sag"), "岭分类器"),
(Perceptron(max_iter=50, tol=1e-3), "感知器"),
(PassiveAggressiveClassifier(max_iter=50, tol=1e-3),
"PAC分类器"),
(KNeighborsClassifier(n_neighbors=10), "K近邻"),
(RandomForestClassifier(n_estimators=100), "随机森林")):
print('=' * 80)
print(name)
results.append(benchmark(clf))
================================================================================
岭分类器
________________________________________________________________________________
训练:
RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,
max_iter=None, normalize=False, random_state=None, solver='sag',
tol=0.01)
训练时间: 0.202s
最佳时间: 0.002s
准确率: 0.897
维数: 33809
密度: 1.000000
================================================================================
感知器
________________________________________________________________________________
训练:
Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,
fit_intercept=True, max_iter=50, n_iter_no_change=5, n_jobs=None,
penalty=None, random_state=0, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)
训练时间: 0.030s
最佳时间: 0.003s
准确率: 0.888
维数: 33809
密度: 0.255302
================================================================================
PAC分类器
________________________________________________________________________________
训练:
PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,
early_stopping=False, fit_intercept=True,
loss='hinge', max_iter=50, n_iter_no_change=5,
n_jobs=None, random_state=None, shuffle=True,
tol=0.001, validation_fraction=0.1, verbose=0,
warm_start=False)
训练时间: 0.063s
最佳时间: 0.003s
准确率: 0.902
维数: 33809
密度: 0.700487
================================================================================
K近邻
________________________________________________________________________________
训练:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=10, p=2,
weights='uniform')
训练时间: 0.002s
最佳时间: 0.235s
准确率: 0.858
================================================================================
随机森林
________________________________________________________________________________
训练:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
训练时间: 1.752s
最佳时间: 0.084s
准确率: 0.822
for penalty in ["l2", "l1"]:
print('=' * 80)
print("%s 罚项" % penalty.upper())
# 训练Liblinear模型
results.append(benchmark(LinearSVC(penalty=penalty, dual=False,
tol=1e-3)))
# 训练SGD model模型
results.append(benchmark(SGDClassifier(alpha=.0001, max_iter=50,
penalty=penalty)))
================================================================================
L2 罚项
________________________________________________________________________________
训练:
LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=None, tol=0.001,
verbose=0)
训练时间: 0.274s
最佳时间: 0.003s
准确率: 0.900
维数: 33809
密度: 1.000000
________________________________________________________________________________
训练:
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=50,
n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,
random_state=None, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)
训练时间: 0.050s
最佳时间: 0.003s
准确率: 0.899
维数: 33809
密度: 0.573353
================================================================================
L1 罚项
________________________________________________________________________________
训练:
LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l1', random_state=None, tol=0.001,
verbose=0)
训练时间: 0.257s
最佳时间: 0.002s
准确率: 0.873
维数: 33809
密度: 0.005568
________________________________________________________________________________
训练:
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=50,
n_iter_no_change=5, n_jobs=None, penalty='l1', power_t=0.5,
random_state=None, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)
训练时间: 0.187s
最佳时间: 0.003s
准确率: 0.882
维数: 33809
密度: 0.023049
# 训练带弹性网络(Elastic Net)罚项的SGD模型
print('=' * 80)
print("弹性网络(Elastic Net)罚项")
results.append(benchmark(SGDClassifier(alpha=.0001, max_iter=50,
penalty="elasticnet")))
================================================================================
弹性网络(Elastic Net)罚项
________________________________________________________________________________
训练:
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=50,
n_iter_no_change=5, n_jobs=None, penalty='elasticnet',
power_t=0.5, random_state=None, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)
训练时间: 0.295s
最佳时间: 0.003s
准确率: 0.897
维数: 33809
密度: 0.185956
# 训练不带阈值的Rocchio分类器
print('=' * 80)
print("不带阈值的Rocchio分类器")
results.append(benchmark(NearestCentroid()))
================================================================================
不带阈值的Rocchio分类器
________________________________________________________________________________
训练:
NearestCentroid(metric='euclidean', shrink_threshold=None)
训练时间: 0.007s
最佳时间: 0.002s
准确率: 0.855
# 训练稀疏朴素贝叶斯分类器
print('=' * 80)
print("稀疏朴素贝叶斯分类器")
results.append(benchmark(MultinomialNB(alpha=.01)))
results.append(benchmark(BernoulliNB(alpha=.01)))
results.append(benchmark(ComplementNB(alpha=.1)))
================================================================================
稀疏朴素贝叶斯分类器
________________________________________________________________________________
训练:
MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
训练时间: 0.007s
最佳时间: 0.003s
准确率: 0.899
维数: 33809
密度: 1.000000
________________________________________________________________________________
训练:
BernoulliNB(alpha=0.01, binarize=0.0, class_prior=None, fit_prior=True)
训练时间: 0.010s
最佳时间: 0.008s
准确率: 0.884
维数: 33809
密度: 1.000000
________________________________________________________________________________
训练:
ComplementNB(alpha=0.1, class_prior=None, fit_prior=True, norm=False)
训练时间: 0.007s
最佳时间: 0.002s
准确率: 0.911
维数: 33809
密度: 1.000000
print('=' * 80)
print("基于l1的特征选择的LinearSVC")
# The smaller C, the stronger the regularization.
# The more regularization, the more sparsity.
results.append(benchmark(Pipeline([
('feature_selection', SelectFromModel(LinearSVC(penalty="l1", dual=False,
tol=1e-3))),
('classification', LinearSVC(penalty="l2"))])))
================================================================================
基于l1的特征选择的LinearSVC
________________________________________________________________________________
训练:
Pipeline(memory=None,
steps=[('feature_selection',
SelectFromModel(estimator=LinearSVC(C=1.0, class_weight=None,
dual=False,
fit_intercept=True,
intercept_scaling=1,
loss='squared_hinge',
max_iter=1000,
multi_class='ovr',
penalty='l1',
random_state=None,
tol=0.001, verbose=0),
max_features=None, norm_order=1, prefit=False,
threshold=None)),
('classification',
LinearSVC(C=1.0, class_weight=None, dual=True,
fit_intercept=True, intercept_scaling=1,
loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=None,
tol=0.0001, verbose=0))],
verbose=False)
训练时间: 0.277s
最佳时间: 0.002s
准确率: 0.880
# 参考翻译
classifier_dic={
'RidgeClassifier':'岭分类器(Ridge)',
'Perceptron':'感知器(Perceptron)',
'PassiveAggressiveClassifier':'PAC分类器',
'KNeighborsClassifier':'K近邻(KNN)',
'RandomForestClassifier':'随机森林',
'LinearSVC':'线性SVC',
'SGDClassifier':'SGD分类器',
'NearestCentroid':'线性SVC',
'MultinomialNB':'(多项式)稀疏朴素贝叶斯分类器',
'BernoulliNB':'(伯努利)稀疏朴素贝叶斯分类器',
'ComplementNB':'(补偿)稀疏朴素贝叶斯分类器',
'Pipeline':'基于l1的特征选择的LinearSVC',
}
# 绘图
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("模型对比")
plt.barh(indices, score, .2, label="得分(score)", color='navy')
plt.barh(indices + .3, training_time, .2, label="训练时间",
color='c')
plt.barh(indices + .6, test_time, .2, label="最佳时间", 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, classifier_dic[c])
plt.show()
