Mercurial > repos > bgruening > sklearn_nn_classifier

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/association_rules.py	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,116 @@
+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))
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_input_int_w.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,6 @@
+Transactions
+10	11	12	13	14	15
+16	11	12	13	14	15
+10	17	13	14
+10	18	19	13	15
+19	11	11	13	20	14
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_input_int_wo.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,5 @@
+10	11	12	13	14	15
+16	11	12	13	14	15
+10	17	13	14
+10	18	19	13	15
+19	11	11	13	20	14
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_input_str_w.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,6 @@
+Transactions
+Milk	Onion	Nutmeg	Kidney Beans	Eggs	Yogurt
+Dill	Onion	Nutmeg	Kidney Beans	Eggs	Yogurt
+Milk	Apple	Kidney Beans	Eggs
+Milk	Unicorn	Corn	Kidney Beans	Yogurt
+Corn	Onion	Onion	Kidney Beans	Ice cream	Eggs
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_input_str_wo.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,5 @@
+Milk	Onion	Nutmeg	Kidney Beans	Eggs	Yogurt
+Dill	Onion	Nutmeg	Kidney Beans	Eggs	Yogurt
+Milk	Apple	Kidney Beans	Eggs
+Milk	Unicorn	Corn	Kidney Beans	Yogurt
+Corn	Onion	Onion	Kidney Beans	Ice cream	Eggs
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_out_str.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,7 @@
+antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction
+['Eggs']	['Kidney Beans', 'Onion']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['Eggs']	['Onion']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['Eggs', 'Kidney Beans']	['Onion']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['Kidney Beans', 'Onion']	['Eggs']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['Onion']	['Eggs']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['Onion']	['Eggs', 'Kidney Beans']	0.6	0.8	0.6	1.0	1.25	0.12	inf
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_output_int.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,7 @@
+antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction
+['11']	['13', '14']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['11']	['14']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['11', '13']	['14']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['13', '14']	['11']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['14']	['11']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['14']	['11', '13']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mba_output_str.tabular	Sat May 01 00:40:45 2021 +0000
@@ -0,0 +1,7 @@
+antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction
+['Eggs']	['Kidney Beans', 'Onion']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['Eggs']	['Onion']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['Eggs', 'Kidney Beans']	['Onion']	0.8	0.6	0.6	0.7499999999999999	1.2499999999999998	0.12	1.5999999999999994
+['Kidney Beans', 'Onion']	['Eggs']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['Onion']	['Eggs']	0.6	0.8	0.6	1.0	1.25	0.12	inf
+['Onion']	['Eggs', 'Kidney Beans']	0.6	0.8	0.6	1.0	1.25	0.12	inf