Mercurial > repos > bgruening > keras_model_builder
annotate association_rules.py @ 10:9b6faa256f15 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author | bgruening |
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date | Sat, 01 May 2021 01:12:10 +0000 |
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children | 624e2afa1313 |
rev | line source |
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10
9b6faa256f15
"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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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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9 |
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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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16 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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17 infile : str |
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18 File paths of input vector |
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19 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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20 outfile : str |
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21 File path to output matrix |
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22 |
9b6faa256f15
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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23 min_support: float |
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24 Minimum support |
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25 |
9b6faa256f15
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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26 min_confidence: float |
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27 Minimum confidence |
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28 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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29 min_lift: float |
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30 Minimum lift |
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31 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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32 min_conviction: float |
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33 Minimum conviction |
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34 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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35 max_length: int |
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36 Maximum length |
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37 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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38 """ |
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39 warnings.simplefilter('ignore') |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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40 |
9b6faa256f15
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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41 with open(inputs, 'r') as param_handler: |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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42 params = json.load(param_handler) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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43 |
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44 input_header = params['header0'] |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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45 header = 'infer' if input_header else None |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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52 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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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57 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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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62 |
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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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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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65 |
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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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69 |
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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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72 |
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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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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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75 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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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79 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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80 # The next 3 steps are intended to fix the order of the association |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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)) |