sklearn中训练模型长时间无响应

Question

import numpy as np
import random
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
from sklearn import svm
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression


def getTokens(inputString):  # custom tokenizer. ours tokens are characters rather than full words
    tokens = []
    for i in inputString:
        tokens.append(i)
    return tokens


filepath = '/data.csv' #path for password file
data = pd.read_csv(filepath, ',', error_bad_lines=False)

data = pd.DataFrame(data)
passwords = np.array(data)

random.shuffle(passwords)  # shuffling randomly for robustness
y = [d[1] for d in passwords]  # labels
allpasswords = [d[0] for d in passwords]  # actual passwords

vectorizer = TfidfVectorizer(tokenizer=getTokens)  # vectorizing
X = vectorizer.fit_transform(allpasswords)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)  #splitting


lgs = LogisticRegression(penalty='l2',multi_class='ovr')
lgs.fit(X_train, y_train)                                         #线性回归
print(lgs.score(X_test, y_test))

# svr = svm.SVR()
# svr.fit(X_train, y_train)                                     #SVM
# print(svr.score(X_test, y_test))

# from sklearn import neighbors
# knn = neighbors.KNeighborsRegressor()
# knn.fit(X_train,y_train)                                      #KNN
# print(knn.score(X_test,y_test))

# from sklearn import ensemble
# rf =ensemble.RandomForestRegressor(n_estimators=20)             #决策树
# rf.fit(X_train,y_train)
# print(rf.score(X_test,y_test))

#more testing
X_predict = ['faizanahmad','faizanahmad123','faizanahmad##','ajd1348#28t**','ffffffffff','kuiqwasdi','uiquiuiiuiuiuiuiuiuiuiuiui','mynameisfaizan','mynameis123faizan#','faizan','123456','abcdef']
X_predict = vectorizer.transform(X_predict)
y_Predict = lgs.predict(X_predict)                               #在这里更改模型名
print (y_Predict)

所用项目及数据来源，8M csv

仿照github上一个项目多试几个训练方法，但是除了原本的线性回归别的都会很慢，长时间无响应。
代码一直到向量化数据都是一样且正确的。具体代码如上，就是不知道哪里有问题，该如何解决，求指教...

Answer 1

1、你要清楚这个是个什么任务。

这是分类学习，而不是回归

2、清楚之后，看你写的代码就明白了为什么会出错

代码全是回归

# svr = svm.SVR()
# svr.fit(X_train, y_train)                                     #SVM
# print(svr.score(X_test, y_test))

# from sklearn import neighbors
# knn = neighbors.KNeighborsRegressor()
# knn.fit(X_train,y_train)                                      #KNN
# print(knn.score(X_test,y_test))

# from sklearn import ensemble
# rf =ensemble.RandomForestRegressor(n_estimators=20)             #决策树
# rf.fit(X_train,y_train)
# print(rf.score(X_test,y_test))

如下所示（其中KNN因为是训练集是稀疏的，超出了内存，所以会出错）：

3、加油