下面是代码：
from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer from sklearn import decomposition, ensemble import pandas, xgboost, numpy, textblob, string from keras.preprocessing import text, sequence from keras import layers, models, optimizers # load the dataset data = open('data/corpus').read() labels, texts = [], [] for i, line in enumerate(data.split("\n")): content = line.split() labels.append(content[0]) texts.append(" ".join(content[1:])) # create a dataframe using texts and lables trainDF = pandas.DataFrame() trainDF['text'] = texts trainDF['label'] = labels # split the dataset into training and validation datasets train_x, valid_x, train_y, valid_y = model_selection.train_test_split(trainDF['text'], trainDF['label']) # label encode the ta讯享网
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