Dimension mismatch- Not able to apply the transformation on new data

[NaSed]

Hi,
I am using MCA and apply it on training data and then after fitting the model on training data I would like to apply the model on my validation data that has smaller samples than training data and the below error is produced:

trInput.shape: (4393, 13405) # Size of training data
valInput.shape: (1883, 13405)# Size of validation data
MCA(benzecri=False, check_input=True, copy=True, engine='sklearn',
    n_components=2, n_iter='auto', random_state=0)
  File "/Modular_helpers_v3.py", line 88, in Aggregate_RareSNPs
    Feat_val = mcaObj.transform(valInput)
  File "/mca.py", line 272, in transform
    return self.row_coordinates(X)
  File "/mca.py", line 260, in row_coordinates
    return super().row_coordinates(pd.get_dummies(X))
  File "/Code/mca.py", line 197, in row_coordinates
    data=X @ sparse.diags(self.col_masses_.to_numpy() ** -0.5) @ self.V_.T,
  File "/lib/python3.6/site-packages/scipy/sparse/base.py", line 566, in __rmatmul__
    return self.__rmul__(other)
  File "/lib/python3.6/site-packages/scipy/sparse/base.py", line 550, in __rmul__
    return (self.transpose() * tr).transpose()
  File "/lib/python3.6/site-packages/scipy/sparse/base.py", line 516, in __mul__
    raise ValueError('dimension mismatch')
ValueError: dimension mismatch

BTW, since I am using skopt package and it cannot work with the latest version of sklearn, I am using sklearn = 0.22.0 and copy the MCA code from prince's source code in a file called mca.py and made it working.

[NaSed]

I found the reason of causing this issue and hope that you guys resolve this issue in your package.
The thing is that the number of different categories in my training data is different from my validation data. For example, column one in training data includes values of ['a', 'b','c','a','b','b','c'] and column one in validation data includes values of ['b', 'a', 'a']. So the first column in training data creates 3 dummy variables (for a, b and c) using get_dummy() function while the first column in validation data creates only 2 dummy variables (for a and b). This difference makes the number of dummy columns different in training and validation data and ValueError: dimension mismatch occurs.

This is an example:

X=pd.DataFrame(data={'x':['d','b','b','a','s','s','c','c'],'y':['d','d','b','b','a','a','c','c']})
mca = prince.MCA(n_components=2, n_iter='auto', copy=True, check_input=True, engine='sklearn', random_state=0)
mca = mca.fit(X)
mca.transform(X.iloc[0:5,])

[MaxHalford]

Just fixed this in master! The fix will be part of version 0.7.2. Thanks for opening the issue