Spaces:
Runtime error
Runtime error
File size: 5,123 Bytes
50320ea 47c3e08 50320ea af1e58a 50320ea af1e58a a2f69c9 50320ea a578d96 50320ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import evaluate
import datasets
# import moses
from moses import metrics
import pandas as pd
from tdc import Evaluator
from tdc import Oracle
_DESCRIPTION = """
Comprehensive suite of metrics designed to assess the performance of molecular generation models, for understanding how well a model can produce novel, chemically valid molecules that are relevant to specific research objectives.
"""
_KWARGS_DESCRIPTION = """
Args:
generated_smiles (`list` of `string`): A collection of SMILES (Simplified Molecular Input Line Entry System) strings generated by the model, ideally encompassing more than 30,000 samples.
train_smiles (`list` of `string`): The dataset of SMILES strings used to train the model, serving as a reference to evaluate the novelty and diversity of the generated molecules.
Returns:
Dectionary item containing various metrics to evaluate model performance
"""
_CITATION = """
@article{DBLP:journals/corr/abs-1811-12823,
author = {Daniil Polykovskiy and
Alexander Zhebrak and
Benjam{\'{\i}}n S{\'{a}}nchez{-}Lengeling and
Sergey Golovanov and
Oktai Tatanov and
Stanislav Belyaev and
Rauf Kurbanov and
Aleksey Artamonov and
Vladimir Aladinskiy and
Mark Veselov and
Artur Kadurin and
Sergey I. Nikolenko and
Al{\'{a}}n Aspuru{-}Guzik and
Alex Zhavoronkov},
title = {Molecular Sets {(MOSES):} {A} Benchmarking Platform for Molecular
Generation Models},
journal = {CoRR},
volume = {abs/1811.12823},
year = {2018},
url = {http://arxiv.org/abs/1811.12823},
eprinttype = {arXiv},
eprint = {1811.12823},
timestamp = {Fri, 26 Nov 2021 15:34:30 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1811-12823.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class molgen_metric(evaluate.Measurement):
"""TODO: Short description of my evaluation module."""
def _info(self):
return evaluate.MeasurementInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"generated_smiles": datasets.Sequence(datasets.Value("string")),
"train_smiles": datasets.Sequence(datasets.Value("string")),
}
if self.config_name == "multilabel"
else {
"generated_smiles": datasets.Value("string"),
"train_smiles": datasets.Value("string"),
}
),
reference_urls=["https://github.com/molecularsets/moses", "https://tdcommons.ai/functions/oracles/"],
)
def _compute(self, generated_smiles, train_smiles = None):
Results = metrics.get_all_metrics(gen = generated_smiles, train= train_smiles)
generated_smiles = [s for s in generated_smiles if s != '']
evaluator = Evaluator(name = 'KL_Divergence')
KL_Divergence = evaluator(generated_smiles, train_smiles)
Results.update({
"KL_Divergence": KL_Divergence,
})
oracle_list = [
'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
'DRD2', 'LogP', 'Rediscovery', 'Similarity',
'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
]
for oracle_name in oracle_list:
oracle = Oracle(name=oracle_name)
if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
score = oracle(generated_smiles)
if isinstance(score, dict):
score = {key: sum(values)/len(values) for key, values in score.items()}
else:
score = oracle(generated_smiles)
if isinstance(score, list):
score = sum(score) / len(score)
Results.update({f"{oracle_name}": score})
keys_to_remove = ["FCD/TestSF", "SNN/TestSF", "Frag/TestSF", "Scaf/TestSF"]
for key in keys_to_remove:
Results.pop(key, None)
return {"results": Results}
|