Add benchmark script

This is a work in progress.

mlxtend/frequent_patterns/tests/benchmark.py

@@ -0,0 +1,105 @@
+# Sebastian Raschka 2014-2019
+# myxtend Machine Learning Library Extensions
+# Author: Sebastian Raschka <sebastianraschka.com>
+#
+# License: BSD 3 clause
+
+from mlxtend.preprocessing import TransactionEncoder
+from mlxtend.frequent_patterns import apriori
+import pandas as pd
+import numpy as np
+import gzip
+import os
+import sys
+from time import time
+import signal
+from contextlib import contextmanager
+
+
+@contextmanager
+def timeout(time):
+    # Register a function to raise a TimeoutError on the signal.
+    signal.signal(signal.SIGALRM, raise_timeout)
+    # Schedule the signal to be sent after ``time``.
+    signal.alarm(time)
+
+    try:
+        yield
+    except TimeoutError:
+        pass
+    finally:
+        # Unregister the signal so it won't be triggered
+        # if the timeout is not reached.
+        signal.signal(signal.SIGALRM, signal.SIG_IGN)
+
+
+def raise_timeout(signum, frame):
+    raise TimeoutError
+
+
+files = [
+    # "chess.dat.gz",
+    # "connect.dat.gz",
+    "mushroom.dat.gz",
+    "pumsb.dat.gz",
+    "pumsb_star.dat.gz",
+    # "T10I4D100K.dat.gz",
+    # "T40I10D100K.dat.gz",
+    # "kosarak.dat.gz", # this file is too large in sparse format
+    # "kosarak-1k.dat.gz",
+    # "kosarak-10k.dat.gz",
+    # "kosarak-50k.dat.gz",
+    # "kosarak-100k.dat.gz",
+    # "kosarak-200k.dat.gz",
+]
+
+
+low_memory = True
+commit = "b731fd2"
+test_supports = [0.5, 0.3, 0.1, 0.05, 0.03, 0.01, 0.005, 0.003, 0.001]
+
+for sparse, col_major in [[False, True], [False, False], [True, True]]:
+    sys.stdout = open("Results/{}-sparse{}-col_major{}.out".format(
+                          commit, sparse, col_major), "w")
+    for filename in files:
+        with gzip.open(os.path.join("data", filename)) if filename.endswith(
+            ".gz"
+        ) else open(os.path.join("data", filename)) as f:
+            data = f.readlines()
+
+        dataset = [list(map(int, line.split())) for line in data]
+        items = np.unique([item for itemset in dataset for item in itemset])
+        print("{} contains {} transactions and {} items".format(
+                  filename, len(dataset), len(items)))
+
+        te = TransactionEncoder()
+        te_ary = te.fit(dataset).transform(dataset, sparse=sparse)
+        columns = ["c"+str(i) for i in te.columns_]
+        if sparse:
+            try:
+                df = pd.DataFrame.sparse.from_spmatrix(te_ary, columns=columns)
+            except AttributeError:
+                # pandas < 0.25
+                df = pd.SparseDataFrame(te_ary, columns=columns,
+                                        default_fill_value=False)
+        else:
+            df = pd.DataFrame(te_ary, columns=columns)
+            if col_major:
+                df = pd.DataFrame({col: df[col] for col in df.columns})
+        np.info(df.values)
+
+        kwds = {"use_colnames": False, "low_memory": low_memory}
+        for min_support in test_supports:
+            tick = time()
+            with timeout(120):
+                print(apriori(df, min_support=min_support, verbose=1, **kwds))
+            print("\nmin_support={} temps: {}\n".format(
+                      min_support, time() - tick))
+            if time() - tick < 10:
+                times = []
+                for _ in range(5):
+                    tick = time()
+                    apriori(df, min_support=min_support, verbose=0, **kwds)
+                    times.append(time() - tick)
+                print("Times:", times)
+    sys.stdout.close()