0

我正在使用以下代码将文档分为体育、政治和金钱三类。我可以看到这段代码计算了精确召回和 F1。但是我无法找到一种方法来使用此代码来测试自定义文档以预测其标签。

from nltk.corpus import stopwords, reuters
from nltk import word_tokenize
from nltk.stem.porter import PorterStemmer
import re

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.svm import LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import f1_score, precision_score, recall_score

cachedStopWords = stopwords.words("english")
def tokenize(text):
    min_length = 3
    words = map(lambda word: word.lower(), word_tokenize(text))
    words = [word for word in words if word not in cachedStopWords]
    tokens = (list(map(lambda token: PorterStemmer().stem(token),words)))
    p = re.compile('[a-zA-Z]+');
    filtered_tokens = list(filter (lambda token: p.match(token) and len(token) >= min_length,tokens))
    return filtered_tokens

def represent(documents, representer):
    train_docs_id = list(filter(lambda doc: doc.startswith("train"), documents))
    test_docs_id = list(filter(lambda doc: doc.startswith("test"), documents))

    train_docs = [reuters.raw(doc_id) for doc_id in train_docs_id]
    test_docs = [reuters.raw(doc_id) for doc_id in test_docs_id]

    # Learn and transform train documents
    vectorised_train_documents = representer.fit_transform(train_docs)
    vectorised_test_documents = representer.transform(test_docs)

    # Transform multilabel labels
    mlb = MultiLabelBinarizer()
    train_labels = mlb.fit_transform([reuters.categories(doc_id) for doc_id in train_docs_id]) 
    test_labels = mlb.transform([reuters.categories(doc_id) for doc_id in test_docs_id])

    return (vectorised_train_documents, train_labels, vectorised_test_documents, test_labels)

def evaluate(test_labels, predictions):
    precision = precision_score(test_labels, predictions, average='micro')
    recall = recall_score(test_labels, predictions, average='micro')
    f1 = f1_score(test_labels, predictions, average='micro')
    print("Micro-average quality numbers")
    print("Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}".format(precision, recall, f1))

    precision = precision_score(test_labels, predictions, average='macro')
    recall = recall_score(test_labels, predictions, average='macro')
    f1 = f1_score(test_labels, predictions, average='macro')

    print("Macro-average quality numbers")
    print("Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}".format(precision, recall, f1))

documents = reuters.fileids()
candidate = {'representer': TfidfVectorizer(tokenizer=tokenize),
             'estimator': OneVsRestClassifier(LinearSVC(random_state=42))}
train_docs, train_labels, test_docs, test_labels = represent(documents, candidate['representer'])
candidate['estimator'].fit(train_docs, train_labels)
predictions = candidate['estimator'].predict(test_docs)
evaluate(test_labels, predictions)

致谢： https ://github.com/miguelmalvarez/reuters-tc/blob/master/notebook/Classification_Reuters.ipynb

4

1 回答 1

0

您可以将自定义文档作为文本文件存储在文件夹中，比如说yourfolder. 之后，您可以使用以下代码对路透社数据进行训练并预测文本文档的标签。all_labels将包含每个文档的预测标签列表（作为元组）

import os


classifier=OneVsRestClassifier(LinearSVC(random_state=42))
vectorizer=TfidfVectorizer(tokenizer=tokenize)

#LOAD AND TRANSFORM TRAINING DOCS
documents = reuters.fileids()
train_docs_id = list(filter(lambda doc: doc.startswith("train"), documents))

train_docs = [reuters.raw(doc_id) for doc_id in train_docs_id]
vectorised_train_documents = vectorizer.fit_transform(train_docs)
mlb = MultiLabelBinarizer()
train_labels = mlb.fit_transform([reuters.categories(doc_id) for doc_id in train_docs_id]) 

#LEARN CLASSIFICATION MODEL
classifier=classifier.fit(vectorised_train_documents, train_labels)


#LOAD AND TRANSFORM TEST DOCS
documents_yours=os.listdir('yourfoldername')
test_docs_yours = [open('yourfoldername/'+doc_id).read() for doc_id in documents_yours]
vectorised_test_documents_yours = vectorizer.transform(test_docs_yours)


#MAKEPREIDCTIONS
predictions_yours=classifier.predict(vectorised_test_documents_yours)
all_labels = mlb.inverse_transform(predictions_yours)
all_labels
于 2017-11-26T07:50:38.357 回答