Mercurial > repos > recetox > matchms_formatter

comparison matchms_similarity_wrapper.py @ 10:1b09315a3f87 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit f79a5b51599254817727bc9028b9797ea994cb4e
author recetox
date Tue, 27 Jun 2023 14:25:59 +0000
parents
children
comparison
equal deleted inserted replaced
9:715fe77be601 10:1b09315a3f87
1 import argparse
2 import json
3 import sys
4
5 from matchms import calculate_scores
6 from matchms.importing import load_from_mgf, load_from_msp
7 from matchms.similarity import (CosineGreedy, CosineHungarian, MetadataMatch,
8 ModifiedCosine, NeutralLossesCosine)
9 from spec2vec import Spec2Vec
10 from spec2vec.serialization.model_importing import load_weights, Word2VecLight
11
12
13 def convert_precursor_mz(spectrum):
14 """
15 Check the presence of precursor m/z since it is needed for ModifiedCosine similarity metric. Convert to float if
16 needed, raise error if missing.
17 """
18
19 if "precursor_mz" in spectrum.metadata:
20 metadata = spectrum.metadata
21 metadata["precursor_mz"] = float(metadata["precursor_mz"])
22 spectrum.metadata = metadata
23 return spectrum
24 else:
25 raise ValueError("Precursor_mz missing. Apply 'add_precursor_mz' filter first.")
26
27
28 def load_model(model_file, weights_file) -> Word2VecLight:
29 """
30 Read a lightweight version of a :class:`~gensim.models.Word2Vec` model from disk.
31
32 Parameters
33 ----------
34 model_file:
35 A path of json file to load the model.
36 weights_file:
37 A path of `.npy` file to load the model's weights.
38
39 Returns
40 -------
41 :class:`~spec2vec.serialization.model_importing.Word2VecLight` – a lightweight version of a
42 :class:`~gensim.models.Word2Vec`
43 """
44 with open(model_file, "r", encoding="utf-8") as f:
45 model: dict = json.load(f)
46 del (model["mapfile_path"])
47
48 weights = load_weights(weights_file, model["__weights_format"])
49 return Word2VecLight(model, weights)
50
51
52 def main(argv):
53 parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
54 parser.add_argument("-r", dest="ri_tolerance", type=float, help="Use RI filtering with given tolerance.")
55 parser.add_argument("-s", dest="symmetric", action='store_true', help="Computation is symmetric.")
56 parser.add_argument("--array_type", type=str, help="Type of array to use for storing scores (numpy or sparse).")
57 parser.add_argument("--ref", dest="references_filename", type=str, help="Path to reference spectra library.")
58 parser.add_argument("--ref_format", dest="references_format", type=str, help="Reference spectra library file format.")
59 parser.add_argument("--spec2vec_model", dest="spec2vec_model", type=str, help="Path to spec2vec model.")
60 parser.add_argument("--spec2vec_weights", dest="spec2vec_weights", type=str, help="Path to spec2vec weights.")
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.")
62 parser.add_argument("queries_filename", type=str, help="Path to query spectra.")
63 parser.add_argument("queries_format", type=str, help="Query spectra file format.")
64 parser.add_argument("similarity_metric", type=str, help='Metric to use for matching.')
65 parser.add_argument("tolerance", type=float, help="Tolerance to use for peak matching.")
66 parser.add_argument("mz_power", type=float, help="The power to raise mz to in the cosine function.")
67 parser.add_argument("intensity_power", type=float, help="The power to raise intensity to in the cosine function.")
68 parser.add_argument("output_filename_scores", type=str, help="Path where to store the output .json scores.")
69 args = parser.parse_args()
70
71 if args.queries_format == 'msp':
72 queries_spectra = list(load_from_msp(args.queries_filename))
73 elif args.queries_format == 'mgf':
74 queries_spectra = list(load_from_mgf(args.queries_filename))
75 else:
76 raise ValueError(f'File format {args.queries_format} not supported for query spectra.')
77
78 if args.symmetric:
79 reference_spectra = queries_spectra.copy()
80 else:
81 if args.references_format == 'msp':
82 reference_spectra = list(load_from_msp(args.references_filename))
83 elif args.references_format == 'mgf':
84 reference_spectra = list(load_from_mgf(args.references_filename))
85 else:
86 raise ValueError(f'File format {args.references_format} not supported for reference spectra library.')
87
88 if args.similarity_metric == 'CosineGreedy':
89 similarity_metric = CosineGreedy(args.tolerance, args.mz_power, args.intensity_power)
90 elif args.similarity_metric == 'CosineHungarian':
91 similarity_metric = CosineHungarian(args.tolerance, args.mz_power, args.intensity_power)
92 elif args.similarity_metric == 'ModifiedCosine':
93 similarity_metric = ModifiedCosine(args.tolerance, args.mz_power, args.intensity_power)
94 reference_spectra = list(map(convert_precursor_mz, reference_spectra))
95 queries_spectra = list(map(convert_precursor_mz, queries_spectra))
96 elif args.similarity_metric == 'NeutralLossesCosine':
97 similarity_metric = NeutralLossesCosine(args.tolerance, args.mz_power, args.intensity_power)
98 reference_spectra = list(map(convert_precursor_mz, reference_spectra))
99 queries_spectra = list(map(convert_precursor_mz, queries_spectra))
100 elif args.similarity_metric == 'Spec2Vec':
101 model = load_model(args.spec2vec_model, args.spec2vec_weights)
102 similarity_metric = Spec2Vec(model, intensity_weighting_power=args.intensity_power, allowed_missing_percentage=args.allowed_missing_percentage)
103 else:
104 return -1
105
106 print("Calculating scores...")
107 scores = calculate_scores(
108 references=reference_spectra,
109 queries=queries_spectra,
110 array_type=args.array_type,
111 similarity_function=similarity_metric,
112 is_symmetric=args.symmetric
113 )
114
115 if args.ri_tolerance is not None:
116 print("RI filtering with tolerance ", args.ri_tolerance)
117 ri_matches = calculate_scores(references=reference_spectra,
118 queries=queries_spectra,
119 similarity_function=MetadataMatch("retention_index", "difference", args.ri_tolerance),
120 array_type="numpy",
121 is_symmetric=args.symmetric).scores
122 scores.scores.add_coo_matrix(ri_matches, "MetadataMatch", join_type="inner")
123
124 write_outputs(args, scores)
125 return 0
126
127
128 def write_outputs(args, scores):
129 """Write Scores to json file."""
130 print("Storing outputs...")
131 scores.to_json(args.output_filename_scores)
132
133
134 if __name__ == "__main__":
135 main(argv=sys.argv[1:])
136 pass