GausianNB：无法将字符串转换为浮点数：'Thu Apr 16 23:58:58 2015'

Question

我正在尝试使用 GaussianNB 解决机器学习问题。我的某些字段的日期格式不正确，因此我将它们转换为 UNIX 格式。例如，state_changed_at 列在 csv 中的值为1449619185。我正在将其转换为正确的日期格式。

当我选择这些日期特征来训练我的模型时，我收到了这个错误：

无法将字符串转换为浮点数：'Thu Apr 16 23:58:58 2015'

import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.naive_bayes import BernoulliNB
from sklearn.naive_bayes import MultinomialNB
import time
from sklearn.naive_bayes import GaussianNB

train = pd.read_csv("datasets/train2.csv")
test = pd.read_csv("datasets/test.csv")
train.head()

import time

#   state_changed_at,deadline,created_at,launched_at are date time fields
# and I'm converting it into unix format
unix_cols = ['deadline','state_changed_at','launched_at','created_at']

for x in unix_cols:
    train[x] = train[x].apply(lambda k: time.ctime(k))
    test[x] = test[x].apply(lambda k: time.ctime(k))


#   state_changed_at,deadline,created_at,launched_at are date time fields.
cols_to_use = ['keywords_len' ,'keywords_count','state_changed_at','deadline','created_at','launched_at']

target = train['final_status']

# data for modeling
k_train = train[cols_to_use]
k_test = test[cols_to_use]


gnb = GaussianNB()

model = MultinomialNB()
model.fit(k_train, target)  # this lines gives me error saying: could not convert string to float: 'Thu Apr 16 23:58:58 2015'

expected = target
predicted = model.predict(k_test)
print(model.score(k_test, predicted, sample_weight=None))

Answer 1

你的困惑是什么？你给了它 DateTime 列。 SKLearn.fit 不接受该类型。具体来说：

Parameters: 
X : array-like, dtype=float64, size=[n_samples, n_features]
Y : array, dtype=float64, size=[n_samples]

如果您想按时间训练，您需要将它们转换（或保留）为可摄取类型，即基本数字。在执行拟合时，您能否将它们保留为原始的、基于纪元的整数形式？