运行时异常.python回调函数评估中出现异常:

Question

我正在为Coursera的机器学习：回归课程做作业。我使用的是kc_house_data.gl/数据集和GraphLab Create。我正在向train_data和test_data添加新变量，它们是旧变量的组合。然后我取所有这些变量的平均值。这些是我要添加的变量：

bedrooms_squared = bedrooms * bedrooms
bed_bath_rooms = bedrooms*bathrooms
log_sqft_living = log(sqft_living)
lat_plus_long = lat + long 

这是我的代码：

train_data['bedrooms_squared'] = train_data['bedrooms'].apply(lambda x: x**2)
test_data['bedrooms_squared'] = test_data['bedrooms'].apply(lambda x: x**2)
# create the remaining 3 features in both TEST and TRAIN data
train_data['bed_bath_rooms'] = train_data.apply(lambda row: row['bedrooms'] * row['bathrooms'])
test_data['bed_bath_rooms'] = test_data.apply(lambda row: row['bedrooms'] * row['bathrooms'])
train_data['log_sqft_living'] = train_data['sqft_living'].apply(lambda x: log(x))
test_data['log_sqft_living'] = test_data['bedrooms'].apply(lambda x: log(x))
train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
test_data['bedrooms_squared'].mean()
test_data['bed_bath_rooms'].mean()
test_data['log_sqft_living'].mean()
test_data['lat_plus_long'].mean()

这是我得到的错误：

RuntimeError: Runtime Exception. Exception in python callback function evaluation: 
ValueError('math domain error',): 
Traceback (most recent call last):
  File "graphlabcythoncy_pylambda_workers.pyx", line 426, in graphlab.cython.cy_pylambda_workers._eval_lambda
  File "graphlabcythoncy_pylambda_workers.pyx", line 169, in graphlab.cython.cy_pylambda_workers.lambda_evaluator.eval_simple
  File "<ipython-input-13-1cdbcd5f5d9b>", line 5, in <lambda>
ValueError: math domain error

我不知道这意味着什么。你知道是什么原因造成的吗？我该怎么解决？谢谢

Answer 1

您的问题是log正在接收一个负数。

CCD_ 2仅为大于零的数字定义。

你需要检查你的价值观。

Answer 2

请添加/学习异常以使您的代码更加健壮：

try:
    train_data['log_sqft_living'] = train_data['sqft_living'].apply(lambda x: log(x))
    test_data['log_sqft_living'] = test_data['bedrooms'].apply(lambda x: log(x))
    train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
    train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
    test_data['bedrooms_squared'].mean()
    test_data['bed_bath_rooms'].mean()
    test_data['log_sqft_living'].mean()
    test_data['lat_plus_long'].mean()
except e as Exception:
    print "ERROR in function:", e