评分系统 - 输入特征

Question

我正在研究评分系统（毕业设计）。我对数据进行了预处理，然后对数据使用了 TfidfVectorizer 并使用了 LinearSVC 来拟合模型。

系统如下，它有265个任意长度的定义；但总的来说，它们的形状为 (265, 8581) 所以当我尝试输入一些新的随机句子来预测它时，我收到这条消息

Error Message

如果你愿意的话，你可以看看使用的代码（Full & long）；

使用代码；

def normalize(df):
    lst = []
    for x in range(len(df)):
        text = re.sub(r"[,.'!?]",'', df[x])
        lst.append(text)
    filtered_sentence = ' '.join(lst)
    return filtered_sentence


def stopWordRemove(df):
    stop = stopwords.words("english")
    needed_words = []
    for x in range(len(df)):

        words = word_tokenize(df)
        for word in words:
            if word not in stop:
                needed_words.append(word)
    return needed_words


def prepareDataSets(df):
    sentences = []
    for index, d in df.iterrows():
        Definitions = stopWordRemove(d['Definitions'].lower())
        Definitions_normalized = normalize(Definitions)
        if d['Results'] == 'F':
            sentences.append([Definitions, 'false'])
        else:
            sentences.append([Definitions, 'true'])
    df_sentences = DataFrame(sentences, columns=['Definitions', 'Results'])
    for x in range(len(df_sentences)):
        df_sentences['Definitions'][x] = ' '.join(df_sentences['Definitions'][x])
    return df_sentences

def featureExtraction(data):
    vectorizer = TfidfVectorizer(min_df=10, max_df=0.75, ngram_range=(1,3))
    tfidf_data = vectorizer.fit_transform(data)
    return tfidf_data

def learning(clf, X, Y):
    X_train, X_test,  Y_train, Y_test = \
    cross_validation.train_test_split(X,Y, test_size=.2,random_state=43)
    classifier = clf()
    classifier.fit(X_train, Y_train)
    predict = cross_validation.cross_val_predict(classifier, X_test, Y_test, cv=5)
    scores = cross_validation.cross_val_score(classifier, X_test, Y_test, cv=5)
    print(scores)
    print ("Accuracy of %s: %0.2f(+/- %0.2f)" % (classifier, scores.mean(), scores.std() *2))
    print (classification_report(Y_test, predict))

然后我运行这些脚本 : 在

之后我得到了提到的错误

test = LinearSVC()
data, target = preprocessed_df['Definitions'], preprocessed_df['Results']
tfidf_data = featureExtraction(data)
X_train, X_test,  Y_train, Y_test = \
cross_validation.train_test_split(tfidf_data,target, test_size=.2,random_state=43)
test.fit(tfidf_data, target)
predict = cross_validation.cross_val_predict(test, X_test, Y_test, cv=10)
scores = cross_validation.cross_val_score(test, X_test, Y_test, cv=10)
print(scores)
print ("Accuracy of %s: %0.2f(+/- %0.2f)" % (test, scores.mean(), scores.std() *2))
print (classification_report(Y_test, predict))
Xnew = ["machine learning is playing games in home"]
tvect = TfidfVectorizer(min_df=1, max_df=1.0, ngram_range=(1,3))
X_test= tvect.fit_transform(Xnew)
ynew = test.predict(X_test)

Answer 1

您从不在测试中调用 fit_transform()，仅调用 transform() 并使用与训练数据相同的向量化器。

这样做：

def featureExtraction(data):
    vectorizer = TfidfVectorizer(min_df=10, max_df=0.75, ngram_range=(1,3))
    tfidf_data = vectorizer.fit_transform(data)

    # Here I am returning the vectorizer as well, which was used to generate the training data
    return vectorizer, tfidf_data
...
...
tfidf_vectorizer, tfidf_data = featureExtraction(data)
...
...

# Now using the same vectorizer on test data
X_test= tfidf_vectorizer.transform(Xnew)
...

在您的代码中，您使用的是新的 TfidfVectorizer，它显然不知道训练数据，也不知道训练数据具有 8581 个特征。

应始终以与准备训练数据相同的方式准备测试数据。否则，即使您没有出错，结果也是错误的，并且模型不会像实际情况中那样执行。

查看我的其他回答，解释不同特征预处理技术的类似情况：

我会把这个问题标记为其中一个的重复问题，但看到你正在使用一个新的矢量化器并且有不同的方法来转换火车数据，我回答了这个问题。从下次开始，请先搜索问题并尝试了解类似场景中发生的情况，然后再发帖。

评分系统 - 输入特征

Grading System - Input Features

python

machine-learning

svm

scikit-learn

data-science