使用cv.fit_transform（corpus）.toarray（）的内存错误

如果有人能帮助cv.fit_transform（corpus）.toarray（）来处理大小约为732066 x <140（推文）的语料库，我将不胜感激 . 文本已被清理以减少功能和维度，但我不断收到以下错误

这是我开始的方式

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


# Importing the dataset
cols = ["text","geocoordinates0","geocoordinates1","grid"]
dataset = pd.read_csv('tweets.tsv', delimiter = '\t', usecols=cols, quoting = 3, error_bad_lines=False, low_memory=False)

# Removing Non-ASCII characters
def remove_non_ascii_1(dataset):
    return ''.join([i if ord(i) < 128 else ' ' for i in dataset])

# Cleaning the texts
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, 732066):
    review = re.sub('[^a-zA-Z]', ' ', str(dataset['text'][i]))
    review = review.lower()
    review = review.split()
    ps = PorterStemmer()
    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
    review = ' '.join(review)
    corpus.append(review)

# Creating the Bag of Words model
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 3].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

# Fitting Naive Bayes to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

# Applying k-Fold Cross Validation
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
accuracies.mean()
accuracies.std()

以下是输出错误：

X = cv.fit_transform（corpus）.toarray（）Traceback（最近一次调用最后一次）：文件“”，第1行，在X = cv.fit_transform（语料库）.toarray（）文件“C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ scipy \ sparse \ compressed.py“，第920行，在toarray中返回self.tocoo（copy = False）.toarray（order = order，out = out）文件”C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ scipy \ sparse \ coo.py“，第252行，在toarray中B = self._process_toarray_args（order，out）文件”C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ scipy \ sparse \ base.py“，第1009行，在_process_toarray_args中返回np.zeros（self.shape，dtype = self.dtype，order = order）MemoryError

非常感谢！

PS：在删除了arraylist并按照@Kumar的建议使用MultinomiaNB后，我现在有以下错误：

from sklearn.naive_bayes import MultinomialNB 
classifier = MultinomialNB()
classifier.fit(X_train, y_train)

回溯（最近一次调用最后一次）：文件“”，第1行，在classifier.fit（X_train，y_train）文件“C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ sklearn \ naive_bayes.py”，行566，in fit Y = labelbin.fit_transform（y）文件“C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ sklearn \ base.py”，第494行，在fit_transform中返回self.fit（X，** fit_params）.transform（X）文件“C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ sklearn \ preprocessing \ label.py”，第296行，in fit self.y_type_ = type_of_target（y）文件“C： \ anaconda3 \ envs \ py35 \ lib \ site-packages \ sklearn \ utils \ multiclass.py“，第275行，在type_of_target if（len（np.unique（y））> 2）或（y.ndim> = 2和len（y [0]）> 1）：文件“C：\ Anaconda3 \ envs \ py35 \ lib \ site-packages \ numpy \ lib \ arraysetops.py”，第198行，在唯一的ar.sort（）中TypeError：unorderable类型：str（）> float（）