Unable to transform test data after MCA fitting the training data

data = pd.read_csv("data/training set.csv")
X = data.loc[:, 'OS.1':'DSA.1']

df = pd.DataFrame(X)

mca = prince.MCA(
               n_components=2,
               n_iter=3,
               copy=True,
               check_input=True,
               engine='auto',
               random_state=42
                )

mca = mca.fit(df)

df_new = df.loc[0:5, :]
I = mca.transform(df_new)
print(I)

Output: File “C:/…/clustering/k means.py”, line 62, in <module> I = mca.transform(df_new) File “C:..\clustering\interpreter2\lib\site-packages\prince\mca.py”, line 47, in transform return self.row_coordinates(X) File “C:..\clustering\interpreter2\lib\site-packages\prince\mca.py”, line 37, in row_coordinates return super().row_coordinates(self.one_hot_.transform(X)) File “C:..\clustering\interpreter2\lib\site-packages\prince\ca.py”, line 111, in row_coordinates X = X / X.sum(axis=1) File “C:\python36\lib\site-packages\scipy\sparse\base.py”, line 1015, in sum np.ones((n, 1), dtype=res_dtype)) File “C:\python36\lib\site-packages\scipy\sparse\base.py”, line 499, in mul result = self._mul_vector(np.ravel(other)) File “C:\python36\lib\site-packages\scipy\sparse\coo.py”, line 571, in _mul_vector other.dtype.char)) File “C:\python36\lib\site-packages\scipy\sparse\sputils.py”, line 60, in upcast_char t = upcast(*map(np.dtype, args)) File “C:\python36\lib\site-packages\scipy\sparse\sputils.py”, line 52, in upcast raise TypeError(‘no supported conversion for types: %r’ % (args,)) TypeError: no supported conversion for types: (dtype(‘O’), dtype(‘O’))

This is how data looks like

print(df_new)

output:
    0  1   2   3
0  9  8   9   9
1  8  7   8   6
2  8  7   9   9
3  8  7   9   9
4  8  7   8   7
5  9  8  10  10

python 3.6.4 scikit 0.20.2 numpy 1.16.1 pandas 0.24.1

[MaxHalford]

@adeebabdulsalam can you see if the latest version fixes your issue (if you have time of course)?

[MaxHalford]

Great! Sorry this took so much time. Have a very nice day!