Mercurial > repos > recetox > matchms_formatter

annotate formatter.py @ 4:da32f57b4f88 draft

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"planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit 5b13d8883fe18bee3aa992c2e34aa1fa79419442"
author recetox
date Fri, 11 Feb 2022 10:33:48 +0000
parents 364976b9aba6
children 4ca9807c56e6
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0a08bed94964 "planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit 85f60c94ccb3cb7706694cbb7ff6d59dcb41c0c9"
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1 import click
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2 from pandas import DataFrame, read_csv
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5 def create_long_table(data: DataFrame, value_id: str) -> DataFrame:
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6 """Convert the table from compact into long format.
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7 See DataFrame.melt(...).
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9 Args:
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10 data (DataFrame): The data table to convert.
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11 value_id (str): The name to assign to the added column through conversion to long format.
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13 Returns:
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14 DataFrame: Table in long format.
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15 """
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16 return data.transpose().melt(ignore_index=False, var_name='compound', value_name=value_id)
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19 def join_df(x: DataFrame, y: DataFrame, on=[], how="inner") -> DataFrame:
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20 """Shortcut functions to join to dataframes on columns and index
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22 Args:
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23 x (DataFrame): Table X
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24 y (DataFrame): Table Y
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25 on (list, optional): Columns on which to join. Defaults to [].
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26 how (str, optional): Join method, see DataFrame.join(...). Defaults to "inner".
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28 Returns:
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29 DataFrame: Joined dataframe.
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30 """
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31 df_x = x.set_index([x.index] + on)
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32 df_y = y.set_index([y.index] + on)
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33 combined = df_x.join(df_y, how=how)
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34 return combined
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37 def get_top_k_matches(data: DataFrame, k: int) -> DataFrame:
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38 """Function to get top k matches from dataframe with scores.
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40 Args:
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41 data (DataFrame): A table with score column.
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42 k (int): Number of top scores to retrieve.
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44 Returns:
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45 DataFrame: Table containing only the top k best matches for each compound.
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46 """
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47 return data.groupby(level=0, group_keys=False).apply(DataFrame.nlargest, n=k, columns=['score'])
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50 def filter_thresholds(data: DataFrame, t_score: float, t_matches: float) -> DataFrame:
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51 """Filter a dataframe with scores and matches to only contain values above specified thresholds.
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53 Args:
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54 data (DataFrame): Table to filter.
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55 t_score (float): Score threshold.
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56 t_matches (float): Matches threshold.
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58 Returns:
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59 DataFrame: Filtered dataframe.
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60 """
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61 filtered = data[data['score'] > t_score]
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62 filtered = filtered[filtered['matches'] > t_matches]
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63 return filtered
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66 def load_data(scores_filename: str, matches_filename: str) -> DataFrame:
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67 """Load data from filenames and join on compound id.
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69 Args:
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70 scores_filename (str): Path to scores table.
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71 matches_filename (str): Path to matches table.
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73 Returns:
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74 DataFrame: Joined dataframe on compounds containing scores an matches in long format.
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75 """
1
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76 matches = read_csv(matches_filename, sep=None, index_col=0)
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77 scores = read_csv(scores_filename, sep=None, index_col=0)
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79 scores_long = create_long_table(scores, 'score')
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80 matches_long = create_long_table(matches, 'matches')
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82 combined = join_df(matches_long, scores_long, on=['compound'], how='inner')
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83 return combined
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86 @click.group()
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87 @click.option('--sf', 'scores_filename', type=click.Path(exists=True), required=True)
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88 @click.option('--mf', 'matches_filename', type=click.Path(exists=True), required=True)
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89 @click.option('--o', 'output_filename', type=click.Path(writable=True), required=True)
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90 @click.pass_context
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91 def cli(ctx, scores_filename, matches_filename, output_filename):
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92 ctx.ensure_object(dict)
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93 ctx.obj['data'] = load_data(scores_filename, matches_filename)
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94 pass
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95
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96
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97 @cli.command()
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98 @click.option('--st', 'scores_threshold', type=float, required=True)
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99 @click.option('--mt', 'matches_threshold', type=float, required=True)
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100 @click.pass_context
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101 def get_thresholded_data(ctx, scores_threshold, matches_threshold):
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102 result = filter_thresholds(ctx.obj['data'], scores_threshold, matches_threshold)
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103 return result
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104
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105
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106 @cli.command()
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107 @click.option('--k', 'k', type=int, required=True)
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108 @click.pass_context
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109 def get_top_k_data(ctx, k):
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110 result = get_top_k_matches(ctx.obj['data'], k)
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111 return result
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112
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113
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114 @cli.resultcallback()
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115 def write_output(result: DataFrame, scores_filename, matches_filename, output_filename):
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116 input_file = read_csv(scores_filename, sep=None, iterator=True)
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117 sep = input_file._engine.data.dialect.delimiter
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118
0
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119 result = result.reset_index().rename(columns={'level_0': 'query', 'compound': 'reference'})
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120 result.to_csv(output_filename, sep=sep, index=False)
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121
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122
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123 if __name__ == '__main__':
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124 cli(obj={})