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python - 如何在 python 和 NLTK 中计算预测概率？

问题描述

我正在尝试通过使用LinearSVC并OneVsRestClassifier得到错误来计算 SVM 模型中的每个预测概率

AttributeError: 'LinearSVC' object has no attribute 'predict_proba'

试过的代码：

model = Pipeline([('vectorizer', CountVectorizer(ngram_range=(1,2))),
    ('tfidf', TfidfTransformer(use_idf=True)),
    ('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])

model.fit(X_train, y_train)
y_train.shape
pred = model.predict(X_test)

probas = model.predict_proba(X_test)

也试过：

from nltk.classify.scikitlearn import SklearnClassifier
from sklearn.svm import SVC

LinearSVC_classifier = SklearnClassifier(SVC(kernel='linear',probability=True))
prob_1 = LinearSVC_classifier.predict_proba(X_test)

但仍然出现错误AttributeError: 'SklearnClassifier' object has no attribute 'predict_proba'

请提出相同的建议。

标签： pythonpython-3.xmachine-learningnltk

解决方案

使用您的线性 SVM：

from sklearn.calibration import CalibratedClassifierCV
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import FeatureUnion, make_pipeline
from sklearn.svm import LinearSVC

word_vectorizer = TfidfVectorizer(ngram_range=(1, 2))
features = FeatureUnion([('words', word_vectorizer), ])
calibrated_svc = CalibratedClassifierCV(LinearSVC(), method='sigmoid', cv=3)
pipeline = make_pipeline(features, calibrated_svc)
pipeline.fit(train_x, train_y)
predicted = pipeline.predict_proba(test_x)

或使用逻辑回归：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import FeatureUnion, make_pipeline
from sklearn.linear_model import LogisticRegression

word_vectorizer = TfidfVectorizer(ngram_range=(1, 2))
features = FeatureUnion([('words', word_vectorizer), ])
pipeline = make_pipeline(features, LogisticRegression())
pipeline.fit(train_x, train_y)
predicted = pipeline.predict_proba(test_x)

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