Merge pull request #1000 from tkellogg/master

Fix fpmax for sparse matrices
rasbt · Dec 3, 2022 · 0a4013e · 0a4013e
2 parents f248eb6 + 29c97c0
commit 0a4013e
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diff --git a/docs/sources/CHANGELOG.md b/docs/sources/CHANGELOG.md
@@ -25,7 +25,7 @@ Version 0.22.0dev (TBD)
 ##### New Features and Enhancements
 
 - The `mlxtend.frequent_patterns.association_rules` function has a new metric - Zhangs Metric, which measures both association and dissociation. ([#980](https://github.com/rasbt/mlxtend/pull/980))
-
+- Internal `fpmax` code improvement that avoids casting a sparse DataFrame into a dense NumPy array. ([#1000](https://github.com/rasbt/mlxtend/pull/1000) via [Tim Kellogg](https://github.com/tkellogg))
 
 ### Version 0.21.0 (09/17/2022)
 

diff --git a/mlxtend/frequent_patterns/fpmax.py b/mlxtend/frequent_patterns/fpmax.py
@@ -87,10 +87,10 @@ def fpmax(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0):
 
     tree, rank = fpc.setup_fptree(df, min_support)
 
-    minsup = math.ceil(min_support * len(df.values))  # min support as count
+    minsup = math.ceil(min_support * len(df))  # min support as count
     generator = fpmax_step(tree, minsup, MFITree(rank), colname_map, max_len, verbose)
 
-    return fpc.generate_itemsets(generator, len(df.values), colname_map)
+    return fpc.generate_itemsets(generator, len(df), colname_map)
 
 
 def fpmax_step(tree, minsup, mfit, colnames, max_len, verbose):