问题
I'm trying to use tpot with my inputs in pandas dataframes. I keep getting the error:
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
I believe this error is from isnan not being able to handle my data structure, but I'm unsure how to format it differently. I have a combination of categorical and continuous inputs and continuous outputs. Here's an example of code with similar data:
train_x=[[1,2,3],['test1','test2','test3'],[56.2,4.5,3.4]]
train_y=[[3,6,7]]
from tpot import TPOTRegressor
tpot=TPOTRegressor()
Do I have to convert my categorical data somehow? dataframe.values and dataframe.as_matrix give me objects that also give me an error.
回答1:
That's right - you need to convert your categorical values. TPOT assumes that all data will come in a scikit-learn compatible format, which entails that all of the data is numeric. We only recently added support for missing values, though most scikit-learn algorithms do not accept data with missing values either.
I reworked your example below to show how pandas can be used to convert the categorical values to numerical values.
import pandas as pd
from tpot import TPOTRegressor
train_x = pd.DataFrame()
train_x['a'] = [1,2,3,4]
train_x['b'] = ['test1','test2','test3','test4']
train_x['c'] = [56.2,4.5,3.4,6.7]
# This line one-hot encodes the categorical variables
train_x = pd.get_dummies(train_x).values
# Print train_x out to understand what one-hot encoding entails
print(train_x)
train_y = [3,6,7,9]
my_tpot = TPOTRegressor(cv=2)
my_tpot.fit(train_x, train_y)
来源：https://stackoverflow.com/questions/49822035/categorical-data-with-tpot