Categorical interactions in statsmodels?

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In R, I have a data frame with two categorical predictors, one of which has multiple levels, and a categorical response. I am running a multinomial logistic regression on each of the categorical predictors, plus the interaction of the two categorical predictors.

library(VGAM)
x1 <- as.factor(rep(c(1,2,3,3,2,1,1,2,3,3,2,1),5))
x2 <- as.factor(rep(c(1,1,1,1,1,1,2,2,2,2,2,2),5))
y <- as.factor(rep(c(1,2,3,1,2,3,1,2,3,1,2,3),5))

VGAM's vglm function has the ability to handle the categorical variables and their interactions.

M <- vglm(y ~ x1*x2, family=multinomial)

However, I now have to do this work in Python, and I am having a hard time getting the categorical variables to function as cleanly in statsmodels as they do in R. R does the categorical encoding from a factor variable just fine and then does the interactions. statsmodels has not done that for me (yet).

I have the Python function that fits multinomial logistic regressions, smf.mnlogit (smf coming from `import statsmodels.formulas.api as smf'). How can I use that with the factor variables to get the interactions that I get in R?

Here is the Python code I've tried:

# import packages
#
import pandas as pd
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf

# Define data
#
x1 = np.array([1,2,3,3,2,1,1,2,3,3,2,1] * 5)
x2 = np.array([1,1,1,1,1,1,2,2,2,2,2,2] * 5)
y = np.array([1,2,3,1,2,3,1,2,3,1,2,3] * 5)

# Make data frame
#
df = pd.DataFrame({'x1': x1, 'x2': x2, 'y': y})

# Make the columns categorical
#
df['x1'] = df['x1'].astype('category')
df['x2'] = df['x2'].astype('category')
df['y'] = df['y'].astype('category')

# fit the multinomial logistic regression
#
mlr = smf.mnlogit(formula='y ~ x1*x2', data=df).fit()

I get the following error:

ValueError: endog has evaluated to an array with multiple columns that has shape (60, 3). This occurs when the variable converted to endog is non-numeric (e.g., bool or str).

logistic-regression statsmodels categorical-data r python
1 Answers

I think the categorical columns get one hot encoded once they are used as a target variable due to which you are getting this error. A possible solution to this would be to encode various categories to numbers and then normalize to supply it to the logit() function (Although it is not right to encode string categories to integer values). Consider the following example:

df_log[target] = pd.Categorical(df_log[target])
df_log[target] = df_log[target].cat.codes
min_max_scaler = preprocessing.MinMaxScaler()
df_log[[target]] = min_max_scaler.fit_transform(df_log[[target]])
formula = "target ~ x1 + x2"
model = smf.logit(formula=formula, data=df_log).fit()
user_133176470
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