Python3: Multi-Label-Text Klassifizierung mit Reuters 21578 Datensatz

Question

Ich verwende den folgenden Code, um ein Dokument in drei Kategorien Sport, Politik und Geld zu klassifizieren. Ich kann sehen, dass dieser Code Precision Recall und F1 berechnet. Aber ich bin nicht in der Lage, eine Möglichkeit zu finden, diesen Code zu verwenden, um gegen ein benutzerdefiniertes Dokument zu testen und dessen Label vorherzusagen.

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) 

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

Answer 1

Sie Ihre individuelle Dokumente als Textdateien in einem Ordner können yourfolder speichern können, sagen. Danach können Sie den folgenden Code verwenden, um auf Reuters-Daten zu trainieren und Etiketten für Ihre Textdokumente vorherzusagen. all_labels wird die Liste der vorhergesagten Etiketten (als Tupel) für jedes Dokument enthalten

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