Mercurial > repos > recetox > matchms_formatter

annotate formatter.py @ 10:5c0e5344edf3 draft

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit 5661cf2406e0616d7b2f4bee1b57ec43716088de
author recetox
date Tue, 18 Oct 2022 10:59:57 +0000
parents 4ca9807c56e6
children 2f0545b02020
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1 import click
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2 from matchms.importing import scores_from_json
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3 from pandas import DataFrame
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6 def create_long_table(data: DataFrame, value_id: str) -> DataFrame:
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7 """Convert the table from compact into long format.
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8 See DataFrame.melt(...).
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10 Args:
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11 data (DataFrame): The data table to convert.
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12 value_id (str): The name to assign to the added column through conversion to long format.
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14 Returns:
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15 DataFrame: Table in long format.
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16 """
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17 return data.transpose().melt(ignore_index=False, var_name='compound', value_name=value_id)
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20 def join_df(x: DataFrame, y: DataFrame, on=[], how="inner") -> DataFrame:
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21 """Shortcut functions to join to dataframes on columns and index
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23 Args:
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24 x (DataFrame): Table X
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25 y (DataFrame): Table Y
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26 on (list, optional): Columns on which to join. Defaults to [].
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27 how (str, optional): Join method, see DataFrame.join(...). Defaults to "inner".
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29 Returns:
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30 DataFrame: Joined dataframe.
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31 """
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32 df_x = x.set_index([x.index] + on)
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33 df_y = y.set_index([y.index] + on)
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34 combined = df_x.join(df_y, how=how)
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35 return combined
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38 def get_top_k_matches(data: DataFrame, k: int) -> DataFrame:
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39 """Function to get top k matches from dataframe with scores.
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41 Args:
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42 data (DataFrame): A table with score column.
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43 k (int): Number of top scores to retrieve.
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45 Returns:
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46 DataFrame: Table containing only the top k best matches for each compound.
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47 """
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48 return data.groupby(level=0, group_keys=False).apply(DataFrame.nlargest, n=k, columns=['score'])
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51 def filter_thresholds(data: DataFrame, t_score: float, t_matches: float) -> DataFrame:
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52 """Filter a dataframe with scores and matches to only contain values above specified thresholds.
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54 Args:
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55 data (DataFrame): Table to filter.
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56 t_score (float): Score threshold.
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57 t_matches (float): Matches threshold.
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59 Returns:
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60 DataFrame: Filtered dataframe.
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61 """
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62 filtered = data[data['score'] > t_score]
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63 filtered = filtered[filtered['matches'] > t_matches]
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64 return filtered
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66
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67 def scores_to_dataframes(scores):
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68 """Unpack scores from matchms.scores into two dataframes of scores and matches.
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70 Args:
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71 scores (matchms.scores): matchms.scores object.
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73 Returns:
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74 DataFrame: Scores
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75 DataFrame: Matches
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76 """
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77 query_names = [spectra.metadata['compound_name'] for spectra in scores.queries]
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78 reference_names = [spectra.metadata['compound_name'] for spectra in scores.references]
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80 dataframe_scores = DataFrame(data=[entry["score"] for entry in scores.scores], index=reference_names, columns=query_names)
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81 dataframe_matches = DataFrame(data=[entry["matches"] for entry in scores.scores], index=reference_names, columns=query_names)
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83 return dataframe_scores, dataframe_matches
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86 def load_data(scores_filename: str) -> DataFrame:
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87 """Load data from filenames and join on compound id.
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89 Args:
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90 scores_filename (str): Path to json file with serialized scores.
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92 Returns:
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93 DataFrame: Joined dataframe on compounds containing scores and matches in long format.
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94 """
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95 scores = scores_from_json(scores_filename)
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96 scores, matches = scores_to_dataframes(scores)
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98 scores_long = create_long_table(scores, 'score')
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99 matches_long = create_long_table(matches, 'matches')
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101 combined = join_df(matches_long, scores_long, on=['compound'], how='inner')
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102 return combined
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103
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105 @click.group()
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106 @click.option('--sf', 'scores_filename', type=click.Path(exists=True), required=True)
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107 @click.option('--o', 'output_filename', type=click.Path(writable=True), required=True)
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108 @click.pass_context
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109 def cli(ctx, scores_filename, output_filename):
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110 ctx.ensure_object(dict)
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111 ctx.obj['data'] = load_data(scores_filename)
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112 pass
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114
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115 @cli.command()
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116 @click.option('--st', 'scores_threshold', type=float, required=True)
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117 @click.option('--mt', 'matches_threshold', type=float, required=True)
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118 @click.pass_context
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119 def get_thresholded_data(ctx, scores_threshold, matches_threshold):
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120 result = filter_thresholds(ctx.obj['data'], scores_threshold, matches_threshold)
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121 return result
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123
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124 @cli.command()
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125 @click.option('--k', 'k', type=int, required=True)
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126 @click.pass_context
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127 def get_top_k_data(ctx, k):
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128 result = get_top_k_matches(ctx.obj['data'], k)
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129 return result
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131
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132 @cli.result_callback()
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133 def write_output(result: DataFrame, scores_filename, output_filename):
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134 result = result.reset_index().rename(columns={'level_0': 'query', 'compound': 'reference'})
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135 result.to_csv(output_filename, sep="\t", index=False)
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138 if __name__ == '__main__':
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139 cli(obj={})