ValueError：当 n_samples=0、test_size=0.2 和 train_size=None 时，生成的训练集将为空。调整上述任何参数

Question

我写了一个文本分类程序。当我运行该程序时，它会崩溃并显示错误，如下面的屏幕截图所示：

ValueError：当 n_samples=0、test_size=0.2 和 train_size=None 时，生成的训练集将为空。调整上述任何参数。

这是我的代码：

from sklearn.model_selection import train_test_split
from gensim.models.word2vec import Word2Vec
from sklearn.preprocessing import scale
from sklearn.linear_model import SGDClassifier
import nltk, string, json
import numpy as np

def cleanText(corpus):
    reviews = []
    for dd in corpus:
        #for d in dd:
        try:
            words = nltk.word_tokenize(dd['description'])
            words = [w.lower() for w in words]
            reviews.append(words)
            #break
        except:
            pass
    return reviews

with open('C:\\NLP\\bad.json') as fin:
    text = json.load(fin)
    neg_rev = cleanText(text)

with open('C:\\NLP\\good.json') as fin:
    text = json.load(fin)
    pos_rev = cleanText(text)

#1 for positive sentiment, 0 for negative
y = np.concatenate((np.ones(len(pos_rev)), np.zeros(len(neg_rev))))

x_train, x_test, y_train, y_test = train_test_split(np.concatenate((pos_rev, neg_rev)), y, test_size=0.2)

n_dim = 1104
#Initialize model and build vocab
imdb_w2v = Word2Vec(size=n_dim, min_count=10)
imdb_w2v.build_vocab(x_train)

#Train the model over train_reviews
imdb_w2v.train(x_train, total_examples=imdb_w2v.corpus_count, epochs=imdb_w2v.iter)

#Build word vector for training set
def buildWordVector(text, size):
    vec = np.zeros(size).reshape((1, size))
    count = 0.
    for word in text:
        try:
            vec += imdb_w2v[word].reshape((1, size))
            count += 1.
        except KeyError:
            continue
    if count != 0:
        vec /= count
    return vec

train_vecs = np.concatenate([buildWordVector(z, n_dim) for z in x_train])
train_vecs = scale(train_vecs)

#Train word2vec on test reviews
imdb_w2v.train(x_test, total_examples=imdb_w2v.corpus_count, epochs=imdb_w2v.iter)

#Build test vectors then scale
test_vecs = np.concatenate([buildWordVector(z, n_dim) for z in x_test])
test_vecs = scale(test_vecs)

#Use classification algorithm 
lr = SGDClassifier(loss='log', penalty='l1')
lr.fit(train_vecs, y_train)

print('Test Accuracy: %.2f'%lr.score(test_vecs, y_test))

我正在使用的数据可在此处获得：

1. 坏;

2. 好的

我将如何解决此错误？

Answer 1

得到同样的错误：ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters. 在我的情况下，数据的路径无效。检查加载文件的路径是否存在，或者读取文件的变量是否包含任何数据。