訓練朴素貝葉斯文本分類算法后，如何預測單個文本文件的主題

Question

我已經使用文本和訓練數據訓練並測試了朴素貝葉斯算法。 現在我想預測單個文本文件的主題。

這是我的代碼，

#importing test, train data
import sklearn.datasets as skd

categories = ['business', 'entertainment','local', 'sports', 'world']
sinhala_train = skd.load_files('Cleant data\stemmed_filtered_sinhala-set1', categories= categories, encoding= 'utf-8')
sinhala_test = skd.load_files('Cleant data\stemmed_filtered_sinhala-set2',categories= categories, encoding= 'utf-8')
name_file = "adaderana_67571.txt"
A = open(name_file, encoding='utf-8')
new_file = A.read()

from sklearn.feature_extraction.text import CountVectorizer
count_vectorization = CountVectorizer()
train_data_tf = count_vectorization.fit_transform(sinhala_train.data)
train_data_tf.shape

from sklearn.feature_extraction.text import TfidfTransformer
tfidf_trans = TfidfTransformer()
train_data_tfidf = tfidf_trans.fit_transform(train_data_tf)
train_data_tfidf.shape

from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB().fit(train_data_tfidf, sinhala_train.target)

test_data_tf = count_vectorization.transform(sinhala_test.data)
test_data_tfidf = tfidf_trans.fit_transform(test_data_tf)
predicted = clf.predict(test_data_tfidf)

from sklearn import metrics
from sklearn.metrics import accuracy_score
print("Accuracy of the model:", accuracy_score(sinhala_test.target, predicted))
print(metrics.classification_report(sinhala_test.target, predicted, target_names=sinhala_test.target_names)),
metrics.confusion_matrix(sinhala_test.target, predicted)

這是我的 output，

Accuracy of the model: 0.864
               precision    recall  f1-score   support

     business       0.78      0.94      0.85       100
entertainment       0.95      0.86      0.90       100
        local       0.89      0.65      0.75       100
       sports       0.91      0.93      0.92       100
        world       0.83      0.94      0.88       100

    micro avg       0.86      0.86      0.86       500
    macro avg       0.87      0.86      0.86       500
 weighted avg       0.87      0.86      0.86       500

array([[94,  2,  4,  0,  0],
       [ 2, 86,  2,  4,  6],
       [19,  0, 65,  5, 11],
       [ 1,  3,  1, 93,  2],
       [ 5,  0,  1,  0, 94]], dtype=int64)

現在我想預測文本文件new_file的主題。

有人可以幫我編寫代碼來預測這個文本文件的主題。

Answer 1

我解決了我的問題。 這是我用來預測主題的代碼。

docs_new1 = sinhala_test_1
docs_new = [docs_new1]
X_new_counts = count_vectorization.transform(docs_new)
X_new_tfidf = tfidf_trans.transform(X_new_counts)

predicted_topic = clf.predict(X_new_tfidf)

for doc, category in zip(docs_new, predicted_topic):
    topic = ( sinhala_train.target_names[category])
return topic