Replace _support function

For unknbown reasons, np.sum is slow on a very large boolean array.

mlxtend/frequent_patterns/apriori.py

@@ -121,32 +121,6 @@ def apriori(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0,
 
     """
 
-    def _support(_x, _n_rows, _is_sparse):
-        """DRY private method to calculate support as the
-        row-wise sum of values / number of rows
-
-        Parameters
-        -----------
-
-        _x : matrix of bools or binary
-
-        _n_rows : numeric, number of rows in _x
-
-        _is_sparse : bool True if _x is sparse
-
-        Returns
-        -----------
-        np.array, shape = (n_rows, )
-
-        Examples
-        -----------
-        For usage examples, please see
-        http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/apriori/
-
-        """
-        out = (np.sum(_x, axis=0) / _n_rows)
-        return np.array(out).reshape(-1)
-
     if min_support <= 0.:
         raise ValueError('`min_support` must be a positive '
                          'number within the interval `(0, 1]`. '
@@ -180,7 +154,17 @@ def _support(_x, _n_rows, _is_sparse):
         # dense DataFrame
         X = df.values
         is_sparse = False
-    support = _support(X, X.shape[0], is_sparse)
+    if is_sparse:
+        # Count nonnull entries via direct access to X indices;
+        # this requires X to be stored in CSC format, and to call
+        # X.eliminate_zeros() to remove null entries from X.
+        support = np.array([X.indptr[idx+1] - X.indptr[idx]
+                            for idx in range(X.shape[1])], dtype=int)
+    else:
+        # Faster than np.count_nonzero(X, axis=0) or np.sum(X, axis=0), why?
+        support = np.array([np.count_nonzero(X[:, idx])
+                            for idx in range(X.shape[1])], dtype=int)
+    support = support / X.shape[0]
     support_dict = {1: support[support >= min_support]}
     itemset_dict = {1: [(idx,) for idx in np.where(support >= min_support)[0]]}
     max_itemset = 1
@@ -199,9 +183,6 @@ def _support(_x, _n_rows, _is_sparse):
                 processed += 1
                 count[:] = 0
                 for item in itemset:
-                    # Count nonnull entries via direct access to X indices;
-                    # this requires X to be stored in CSC format, and to call
-                    # X.eliminate_zeros() to remove null entries from X.
                     count[X.indices[X.indptr[item]:X.indptr[item+1]]] += 1
                 support = np.count_nonzero(count == len(itemset)) / X.shape[0]
                 if support >= min_support: