Question

我正在研究评分系统（毕业设计）。我已经预处理了数据，然后在数据上使用了TfidfVectorizer，并使用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，它们总结为（265,8581）所以当我尝试输入一些新的随机句子进行预测时，我收到了这条消息

如果你愿意的话，你可以查看使用的代码（Full＆amp; long）;

使用的代码;

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：我在

之后得到了提到的错误

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

您永远不会在测试时调用fit_transform()，只调用transform()并使用与训练数据相同的矢量图。

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)
...

在你的代码中，你正在使用一个新的TfidfVectorizer，它显然不会知道训练数据，也不知道训练数据有8581个功能。

测试数据的制备方法应与您准备列车数据的方式一致。否则，即使您没有收到错误，结果也是错误的，并且模型将不会像在实际情况中那样执行。

请参阅我的其他答案，解释不同特征预处理技术的类似情况：

我会将这个问题标记为其中一个的副本，但是看到你完全使用新的矢量化器并且有一种不同的方法来转换列车数据，我回答了这个问题。从下次开始，请先搜索问题，然后在发布问题之前尝试了解类似情况下发生的事情。

评分系统 - 输入功能

1 个答案: