apriori.py line 224: ValueError: negative dimensions are not allowed

https://github.com/rasbt/mlxtend/blob/115278bac14d7fc278885c0722da03f1c3b91604/mlxtend/frequent_patterns/apriori.py#L224

Processing 24785850 combinations | Sampling itemset size 6
Traceback (most recent call last):
File "***.py", line 116, in
frequent_itemsets = apriori(df, min_support=0.8, use_colnames=True, verbose=1)

File "C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\frequent_patterns\apriori.py", line 219, in apriori
_bools = X[:, combin[:, 0]] == all_ones

File "C:\ProgramData\Anaconda3\lib\site-packages\scipy\sparse_index.py", line 53, in getitem
return self._get_sliceXarray(row, col)

File "C:\ProgramData\Anaconda3\lib\site-packages\scipy\sparse\csc.py", line 222, in _get_sliceXarray
return self._major_index_fancy(col)._minor_slice(row)

File "C:\ProgramData\Anaconda3\lib\site-packages\scipy\sparse\compressed.py", line 693, in _major_index_fancy
res_indices = np.empty(nnz, dtype=idx_dtype)

ValueError: negative dimensions are not allowed

In my apriori.py, variable "combin" is a (4130975, 6) dataframe comprise of indices (dtype = int32).

In compressed.py, numpy cumsum takes the dtype from indices of "combin".

Negative values appeared after the numpy cumsum reached maximum of int32.

Not sure if it is an exception for numpy cumsum or mlxtend apriori.

Start with mlxtend/frequent_patterns/apriori.py at the linked line and trace the reported scipy sparse indexing failure back through the int32 combin array and NumPy cumsum behavior. Reproduce the reported apriori call, then determine whether the failure belongs in mlxtend or a dependency; done means the large-combination case no longer raises this error or the limitation is clearly documented with coverage.