TypeError: SparseDataFrame() takes no arguments in Principal Component Analysis using Light_FAMD

When attempting to perform a FAMD according to the instructions on https://pypi.org/project/light-famd/#factor-analysis-of-mixed-data-famd, I keep getting the same error over and over again, namely: TypeError: SparseDataFrame() takes no arguments.

How to fix this problem? It occurs not only on my own data set but also whenever I try it on a basic randomly-generated data set such as created like this:

X_n = pd.DataFrame(data=np.random.randint(0,100,size=(10,2)),columns=list('AB'))
X_c = pd.DataFrame(np.random.choice(list('abcde'),size=(10,4),replace=True),columns =list('CDEF'))
X = pd.concat([X_n,X_c],axis=1)

The code is the following:

import pandas as pd
import numpy as np
import light_famd
from light_famd import FAMD
famd = FAMD(n_components=2)
famd.fit(X)
print(famd.explained_variance_)
print(famd.column_correlation(X))

It gives the error already at famd.fit(X).

It does this not only for Light_FAMD but also for sklearn and prince (which I have also tried).

Answer

The sparse dataframe in pandas is outdated. Check their git

SparseDataFrame changed by DataFrame in line 25 one_hot.py default_fill_value was removed one_hot.py index was removed one_hot.py SparseData False one_hot.py to_dense remove in line 105 mfa.py

You can try to install the latest version from git:

python -m pip install git+https://github.com/Cauchemare/Light_FAMD.git

Should run fine:

import pandas as pd
import numpy as np
import light_famd
from light_famd import FAMD

X_n = pd.DataFrame(data=np.random.randint(0,100,size=(10,2)),columns=list('AB'))
X_c = pd.DataFrame(np.random.choice(list('abcde'),size=(10,4),replace=True),columns =list('CDEF'))
X = pd.concat([X_n,X_c],axis=1)

famd = FAMD(n_components=2)
famd.fit(X)
print(famd.explained_variance_)
[17.77604109  9.92849978]

This last part still throws some warnings:

print(famd.column_correlation(X))