Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate association_rules.py @ 46:e10e776f2109 draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author bgruening
date Sun, 05 Nov 2023 15:25:59 +0000
parents 06d772036a62
children
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73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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1 import argparse
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2 import json
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3 import warnings
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5 import pandas as pd
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6 from mlxtend.frequent_patterns import association_rules, fpgrowth
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7 from mlxtend.preprocessing import TransactionEncoder
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10 def main(
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11 inputs,
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12 infile,
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13 outfile,
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14 min_support=0.5,
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15 min_confidence=0.5,
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16 min_lift=1.0,
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17 min_conviction=1.0,
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18 max_length=None,
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19 ):
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20 """
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21 Parameter
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22 ---------
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23 input : str
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24 File path to galaxy tool parameter
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26 infile : str
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27 File paths of input vector
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29 outfile : str
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30 File path to output matrix
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32 min_support: float
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33 Minimum support
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35 min_confidence: float
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36 Minimum confidence
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38 min_lift: float
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39 Minimum lift
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41 min_conviction: float
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42 Minimum conviction
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44 max_length: int
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45 Maximum length
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47 """
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48 warnings.simplefilter("ignore")
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49
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50 with open(inputs, "r") as param_handler:
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51 params = json.load(param_handler)
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53 input_header = params["header0"]
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54 header = "infer" if input_header else None
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56 with open(infile) as fp:
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57 lines = fp.read().splitlines()
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59 if header is not None:
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60 lines = lines[1:]
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62 dataset = []
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63 for line in lines:
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64 line_items = line.split("\t")
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65 dataset.append(line_items)
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67 # TransactionEncoder learns the unique labels in the dataset and transforms the
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68 # input dataset (a Python list of lists) into a one-hot encoded NumPy boolean array
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69 te = TransactionEncoder()
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70 te_ary = te.fit_transform(dataset)
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72 # Turn the encoded NumPy array into a DataFrame
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73 df = pd.DataFrame(te_ary, columns=te.columns_)
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75 # Extract frequent itemsets for association rule mining
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76 # use_colnames: Use DataFrames' column names in the returned DataFrame instead of column indices
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77 frequent_itemsets = fpgrowth(
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78 df, min_support=min_support, use_colnames=True, max_len=max_length
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79 )
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81 # Get association rules, with confidence larger than min_confidence
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82 rules = association_rules(
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83 frequent_itemsets, metric="confidence", min_threshold=min_confidence
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84 )
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86 # Filter association rules, keeping rules with lift and conviction larger than min_liftand and min_conviction
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87 rules = rules[(rules["lift"] >= min_lift) & (rules["conviction"] >= min_conviction)]
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89 # Convert columns from frozenset to list (more readable)
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90 rules["antecedents"] = rules["antecedents"].apply(list)
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91 rules["consequents"] = rules["consequents"].apply(list)
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92
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93 # The next 3 steps are intended to fix the order of the association
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94 # rules generated, so tests that rely on diff'ing a desired output
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95 # with an expected output can pass
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96
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97 # 1) Sort entry in every row/column for columns 'antecedents' and 'consequents'
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98 rules["antecedents"] = rules["antecedents"].apply(lambda row: sorted(row))
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99 rules["consequents"] = rules["consequents"].apply(lambda row: sorted(row))
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100
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101 # 2) Create two temporary string columns to sort on
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102 rules["ant_str"] = rules["antecedents"].apply(lambda row: " ".join(row))
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103 rules["con_str"] = rules["consequents"].apply(lambda row: " ".join(row))
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104
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105 # 3) Sort results so they are re-producable
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106 rules.sort_values(by=["ant_str", "con_str"], inplace=True)
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107 del rules["ant_str"]
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108 del rules["con_str"]
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109 rules.reset_index(drop=True, inplace=True)
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110
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111 # Write association rules and metrics to file
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112 rules.to_csv(outfile, sep="\t", index=False)
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113
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114
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115 if __name__ == "__main__":
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116 aparser = argparse.ArgumentParser()
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117 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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118 aparser.add_argument("-y", "--infile", dest="infile", required=True)
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119 aparser.add_argument("-o", "--outfile", dest="outfile", required=True)
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120 aparser.add_argument("-s", "--support", dest="support", default=0.5)
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121 aparser.add_argument("-c", "--confidence", dest="confidence", default=0.5)
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122 aparser.add_argument("-l", "--lift", dest="lift", default=1.0)
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123 aparser.add_argument("-v", "--conviction", dest="conviction", default=1.0)
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124 aparser.add_argument("-t", "--length", dest="length", default=5)
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125 args = aparser.parse_args()
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126
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127 main(
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128 args.inputs,
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129 args.infile,
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130 args.outfile,
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131 min_support=float(args.support),
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132 min_confidence=float(args.confidence),
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133 min_lift=float(args.lift),
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134 min_conviction=float(args.conviction),
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135 max_length=int(args.length),
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136 )