diff association_rules.py @ 3:0a1812986bc3 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/association_rules.py	Wed Aug 09 11:10:37 2023 +0000
@@ -0,0 +1,136 @@
+import argparse
+import json
+import warnings
+
+import pandas as pd
+from mlxtend.frequent_patterns import association_rules, fpgrowth
+from mlxtend.preprocessing import TransactionEncoder
+
+
+def main(
+    inputs,
+    infile,
+    outfile,
+    min_support=0.5,
+    min_confidence=0.5,
+    min_lift=1.0,
+    min_conviction=1.0,
+    max_length=None,
+):
+    """
+    Parameter
+    ---------
+    input : str
+        File path to galaxy tool parameter
+
+    infile : str
+        File paths of input vector
+
+    outfile : str
+        File path to output matrix
+
+    min_support: float
+        Minimum support
+
+    min_confidence: float
+        Minimum confidence
+
+    min_lift: float
+        Minimum lift
+
+    min_conviction: float
+        Minimum conviction
+
+    max_length: int
+        Maximum length
+
+    """
+    warnings.simplefilter("ignore")
+
+    with open(inputs, "r") as param_handler:
+        params = json.load(param_handler)
+
+    input_header = params["header0"]
+    header = "infer" if input_header else None
+
+    with open(infile) as fp:
+        lines = fp.read().splitlines()
+
+    if header is not None:
+        lines = lines[1:]
+
+    dataset = []
+    for line in lines:
+        line_items = line.split("\t")
+        dataset.append(line_items)
+
+    # TransactionEncoder learns the unique labels in the dataset and transforms the
+    # input dataset (a Python list of lists) into a one-hot encoded NumPy boolean array
+    te = TransactionEncoder()
+    te_ary = te.fit_transform(dataset)
+
+    # Turn the encoded NumPy array into a DataFrame
+    df = pd.DataFrame(te_ary, columns=te.columns_)
+
+    # Extract frequent itemsets for association rule mining
+    # use_colnames: Use DataFrames' column names in the returned DataFrame instead of column indices
+    frequent_itemsets = fpgrowth(
+        df, min_support=min_support, use_colnames=True, max_len=max_length
+    )
+
+    # Get association rules, with confidence larger than min_confidence
+    rules = association_rules(
+        frequent_itemsets, metric="confidence", min_threshold=min_confidence
+    )
+
+    # Filter association rules, keeping rules with lift and conviction larger than min_liftand and min_conviction
+    rules = rules[(rules["lift"] >= min_lift) & (rules["conviction"] >= min_conviction)]
+
+    # Convert columns from frozenset to list (more readable)
+    rules["antecedents"] = rules["antecedents"].apply(list)
+    rules["consequents"] = rules["consequents"].apply(list)
+
+    # The next 3 steps are intended to fix the order of the association
+    # rules generated, so tests that rely on diff'ing a desired output
+    # with an expected output can pass
+
+    # 1) Sort entry in every row/column for columns 'antecedents' and 'consequents'
+    rules["antecedents"] = rules["antecedents"].apply(lambda row: sorted(row))
+    rules["consequents"] = rules["consequents"].apply(lambda row: sorted(row))
+
+    # 2) Create two temporary string columns to sort on
+    rules["ant_str"] = rules["antecedents"].apply(lambda row: " ".join(row))
+    rules["con_str"] = rules["consequents"].apply(lambda row: " ".join(row))
+
+    # 3) Sort results so they are re-producable
+    rules.sort_values(by=["ant_str", "con_str"], inplace=True)
+    del rules["ant_str"]
+    del rules["con_str"]
+    rules.reset_index(drop=True, inplace=True)
+
+    # Write association rules and metrics to file
+    rules.to_csv(outfile, sep="\t", index=False)
+
+
+if __name__ == "__main__":
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-y", "--infile", dest="infile", required=True)
+    aparser.add_argument("-o", "--outfile", dest="outfile", required=True)
+    aparser.add_argument("-s", "--support", dest="support", default=0.5)
+    aparser.add_argument("-c", "--confidence", dest="confidence", default=0.5)
+    aparser.add_argument("-l", "--lift", dest="lift", default=1.0)
+    aparser.add_argument("-v", "--conviction", dest="conviction", default=1.0)
+    aparser.add_argument("-t", "--length", dest="length", default=5)
+    args = aparser.parse_args()
+
+    main(
+        args.inputs,
+        args.infile,
+        args.outfile,
+        min_support=float(args.support),
+        min_confidence=float(args.confidence),
+        min_lift=float(args.lift),
+        min_conviction=float(args.conviction),
+        max_length=int(args.length),
+    )