Mercurial > repos > recetox > matchms_convert

annotate matchms_similarity_wrapper.py @ 3:4216e42a5534 draft default tip

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit f79a5b51599254817727bc9028b9797ea994cb4e
author recetox
date Tue, 27 Jun 2023 14:21:13 +0000
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1 import argparse
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2 import json
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3 import sys
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5 from matchms import calculate_scores
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6 from matchms.importing import load_from_mgf, load_from_msp
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7 from matchms.similarity import (CosineGreedy, CosineHungarian, MetadataMatch,
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8 ModifiedCosine, NeutralLossesCosine)
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9 from spec2vec import Spec2Vec
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10 from spec2vec.serialization.model_importing import load_weights, Word2VecLight
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13 def convert_precursor_mz(spectrum):
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14 """
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15 Check the presence of precursor m/z since it is needed for ModifiedCosine similarity metric. Convert to float if
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16 needed, raise error if missing.
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17 """
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19 if "precursor_mz" in spectrum.metadata:
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20 metadata = spectrum.metadata
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21 metadata["precursor_mz"] = float(metadata["precursor_mz"])
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22 spectrum.metadata = metadata
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23 return spectrum
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24 else:
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25 raise ValueError("Precursor_mz missing. Apply 'add_precursor_mz' filter first.")
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28 def load_model(model_file, weights_file) -> Word2VecLight:
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29 """
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30 Read a lightweight version of a :class:`~gensim.models.Word2Vec` model from disk.
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32 Parameters
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33 ----------
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34 model_file:
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35 A path of json file to load the model.
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36 weights_file:
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37 A path of `.npy` file to load the model's weights.
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38
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39 Returns
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40 -------
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41 :class:`~spec2vec.serialization.model_importing.Word2VecLight` – a lightweight version of a
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42 :class:`~gensim.models.Word2Vec`
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43 """
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44 with open(model_file, "r", encoding="utf-8") as f:
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45 model: dict = json.load(f)
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46 del (model["mapfile_path"])
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48 weights = load_weights(weights_file, model["__weights_format"])
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49 return Word2VecLight(model, weights)
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52 def main(argv):
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53 parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
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54 parser.add_argument("-r", dest="ri_tolerance", type=float, help="Use RI filtering with given tolerance.")
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55 parser.add_argument("-s", dest="symmetric", action='store_true', help="Computation is symmetric.")
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56 parser.add_argument("--array_type", type=str, help="Type of array to use for storing scores (numpy or sparse).")
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57 parser.add_argument("--ref", dest="references_filename", type=str, help="Path to reference spectra library.")
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58 parser.add_argument("--ref_format", dest="references_format", type=str, help="Reference spectra library file format.")
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59 parser.add_argument("--spec2vec_model", dest="spec2vec_model", type=str, help="Path to spec2vec model.")
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60 parser.add_argument("--spec2vec_weights", dest="spec2vec_weights", type=str, help="Path to spec2vec weights.")
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61 parser.add_argument("--allow_missing_percentage", dest="allowed_missing_percentage", type=lambda x: float(x) * 100.0, help="Maximum percentage of missing peaks in model corpus.")
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62 parser.add_argument("queries_filename", type=str, help="Path to query spectra.")
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63 parser.add_argument("queries_format", type=str, help="Query spectra file format.")
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64 parser.add_argument("similarity_metric", type=str, help='Metric to use for matching.')
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65 parser.add_argument("tolerance", type=float, help="Tolerance to use for peak matching.")
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66 parser.add_argument("mz_power", type=float, help="The power to raise mz to in the cosine function.")
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67 parser.add_argument("intensity_power", type=float, help="The power to raise intensity to in the cosine function.")
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68 parser.add_argument("output_filename_scores", type=str, help="Path where to store the output .json scores.")
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69 args = parser.parse_args()
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70
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71 if args.queries_format == 'msp':
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72 queries_spectra = list(load_from_msp(args.queries_filename))
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73 elif args.queries_format == 'mgf':
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74 queries_spectra = list(load_from_mgf(args.queries_filename))
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75 else:
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76 raise ValueError(f'File format {args.queries_format} not supported for query spectra.')
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77
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78 if args.symmetric:
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79 reference_spectra = queries_spectra.copy()
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80 else:
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81 if args.references_format == 'msp':
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82 reference_spectra = list(load_from_msp(args.references_filename))
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83 elif args.references_format == 'mgf':
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84 reference_spectra = list(load_from_mgf(args.references_filename))
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85 else:
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86 raise ValueError(f'File format {args.references_format} not supported for reference spectra library.')
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87
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88 if args.similarity_metric == 'CosineGreedy':
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89 similarity_metric = CosineGreedy(args.tolerance, args.mz_power, args.intensity_power)
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90 elif args.similarity_metric == 'CosineHungarian':
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91 similarity_metric = CosineHungarian(args.tolerance, args.mz_power, args.intensity_power)
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92 elif args.similarity_metric == 'ModifiedCosine':
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93 similarity_metric = ModifiedCosine(args.tolerance, args.mz_power, args.intensity_power)
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94 reference_spectra = list(map(convert_precursor_mz, reference_spectra))
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95 queries_spectra = list(map(convert_precursor_mz, queries_spectra))
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96 elif args.similarity_metric == 'NeutralLossesCosine':
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97 similarity_metric = NeutralLossesCosine(args.tolerance, args.mz_power, args.intensity_power)
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98 reference_spectra = list(map(convert_precursor_mz, reference_spectra))
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99 queries_spectra = list(map(convert_precursor_mz, queries_spectra))
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100 elif args.similarity_metric == 'Spec2Vec':
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101 model = load_model(args.spec2vec_model, args.spec2vec_weights)
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102 similarity_metric = Spec2Vec(model, intensity_weighting_power=args.intensity_power, allowed_missing_percentage=args.allowed_missing_percentage)
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103 else:
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104 return -1
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105
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106 print("Calculating scores...")
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107 scores = calculate_scores(
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108 references=reference_spectra,
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109 queries=queries_spectra,
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110 array_type=args.array_type,
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111 similarity_function=similarity_metric,
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112 is_symmetric=args.symmetric
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113 )
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114
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115 if args.ri_tolerance is not None:
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116 print("RI filtering with tolerance ", args.ri_tolerance)
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117 ri_matches = calculate_scores(references=reference_spectra,
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118 queries=queries_spectra,
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119 similarity_function=MetadataMatch("retention_index", "difference", args.ri_tolerance),
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120 array_type="numpy",
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121 is_symmetric=args.symmetric).scores
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122 scores.scores.add_coo_matrix(ri_matches, "MetadataMatch", join_type="inner")
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123
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124 write_outputs(args, scores)
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125 return 0
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126
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127
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128 def write_outputs(args, scores):
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129 """Write Scores to json file."""
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130 print("Storing outputs...")
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131 scores.to_json(args.output_filename_scores)
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132
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133
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134 if __name__ == "__main__":
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135 main(argv=sys.argv[1:])
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136 pass