Mercurial > repos > bgruening > sklearn_svm_classifier

annotate association_rules.py @ 21:14fa42b095c4 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Sat, 01 May 2021 01:53:41 +0000
parents
children b878e4cdd63a
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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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4
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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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8
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10 def main(inputs, infile, outfile, min_support=0.5, min_confidence=0.5, min_lift=1.0, min_conviction=1.0, max_length=None):
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11 """
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12 Parameter
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13 ---------
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14 input : str
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15 File path to galaxy tool parameter
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17 infile : str
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18 File paths of input vector
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20 outfile : str
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21 File path to output matrix
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23 min_support: float
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24 Minimum support
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26 min_confidence: float
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27 Minimum confidence
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29 min_lift: float
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30 Minimum lift
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32 min_conviction: float
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33 Minimum conviction
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35 max_length: int
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36 Maximum length
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37
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38 """
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39 warnings.simplefilter('ignore')
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40
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41 with open(inputs, 'r') as param_handler:
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42 params = json.load(param_handler)
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44 input_header = params['header0']
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45 header = 'infer' if input_header else None
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46
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47 with open(infile) as fp:
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48 lines = fp.read().splitlines()
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49
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50 if header is not None:
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51 lines = lines[1:]
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52
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53 dataset = []
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54 for line in lines:
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55 line_items = line.split("\t")
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56 dataset.append(line_items)
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58 # TransactionEncoder learns the unique labels in the dataset and transforms the
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59 # input dataset (a Python list of lists) into a one-hot encoded NumPy boolean array
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60 te = TransactionEncoder()
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61 te_ary = te.fit_transform(dataset)
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63 # Turn the encoded NumPy array into a DataFrame
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64 df = pd.DataFrame(te_ary, columns=te.columns_)
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66 # Extract frequent itemsets for association rule mining
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67 # use_colnames: Use DataFrames' column names in the returned DataFrame instead of column indices
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68 frequent_itemsets = fpgrowth(df, min_support=min_support, use_colnames=True, max_len=max_length)
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70 # Get association rules, with confidence larger than min_confidence
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71 rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=min_confidence)
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73 # Filter association rules, keeping rules with lift and conviction larger than min_liftand and min_conviction
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74 rules = rules[(rules['lift'] >= min_lift) & (rules['conviction'] >= min_conviction)]
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76 # Convert columns from frozenset to list (more readable)
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77 rules['antecedents'] = rules['antecedents'].apply(list)
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78 rules['consequents'] = rules['consequents'].apply(list)
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80 # The next 3 steps are intended to fix the order of the association
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81 # rules generated, so tests that rely on diff'ing a desired output
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82 # with an expected output can pass
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83
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84 # 1) Sort entry in every row/column for columns 'antecedents' and 'consequents'
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85 rules['antecedents'] = rules['antecedents'].apply(lambda row: sorted(row))
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86 rules['consequents'] = rules['consequents'].apply(lambda row: sorted(row))
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87
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88 # 2) Create two temporary string columns to sort on
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89 rules['ant_str'] = rules['antecedents'].apply(lambda row: " ".join(row))
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90 rules['con_str'] = rules['consequents'].apply(lambda row: " ".join(row))
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91
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92 # 3) Sort results so they are re-producable
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93 rules.sort_values(by=['ant_str', 'con_str'], inplace=True)
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94 del rules['ant_str']
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95 del rules['con_str']
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96 rules.reset_index(drop=True, inplace=True)
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97
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98 # Write association rules and metrics to file
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99 rules.to_csv(outfile, sep="\t", index=False)
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100
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101
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102 if __name__ == '__main__':
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103 aparser = argparse.ArgumentParser()
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104 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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105 aparser.add_argument("-y", "--infile", dest="infile", required=True)
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106 aparser.add_argument("-o", "--outfile", dest="outfile", required=True)
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107 aparser.add_argument("-s", "--support", dest="support", default=0.5)
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108 aparser.add_argument("-c", "--confidence", dest="confidence", default=0.5)
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109 aparser.add_argument("-l", "--lift", dest="lift", default=1.0)
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110 aparser.add_argument("-v", "--conviction", dest="conviction", default=1.0)
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111 aparser.add_argument("-t", "--length", dest="length", default=5)
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112 args = aparser.parse_args()
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113
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114 main(args.inputs, args.infile, args.outfile,
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115 min_support=float(args.support), min_confidence=float(args.confidence),
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116 min_lift=float(args.lift), min_conviction=float(args.conviction), max_length=int(args.length))