File size: 8,781 Bytes
3824ea0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
import os
import json
class Evaluator():
def __init__(self, dataset, result_dir, raw_dir=""):
self.dataset = dataset
self.result_dir = result_dir
if self.dataset == "openmolinst":
self.raw_dir = raw_dir
def _load_json(self, file_path):
with open(file_path, 'r') as f:
contents = json.load(f)
return contents
def _dump_json(self, content, file_path):
with open(file_path, 'w') as f:
json.dump(content, f, indent=4, ensure_ascii=False)
def _get_pred_gold_smolinstruct(self, json_file):
logs = self._load_json(json_file)
preds, gts = list(), list()
for log in logs:
preds.append([log["extracted_answer"]])
gts.append([log["extracted_gt"]])
return preds, gts
def _get_pred_gold_openmolinst(self, csv_file, json_file):
gts = pd.read_csv(csv_file)
preds = list()
logs = self._load_json(json_file)
for log in logs:
preds.append(log["extracted_answer"])
return preds, gts
def _get_pred_gold_mumoinstruct(self, json_file):
logs = self._load_json(json_file)
input_smiles, input_props, output_smiles, output_props = dict(), dict(), dict(), dict()
for log in logs:
task = log['metadata']['subtask']
pred_smi = [log["extracted_answer"]] if isinstance(log["extracted_answer"], str) \
else log["extracted_answer"]
if task in list(input_smiles.keys()):
input_smiles[task].append(log['metadata']['source_smiles'])
input_props[task].append(log['metadata']['source_props'])
output_smiles[task].append(pred_smi)
else:
input_smiles[task] = [(log['metadata']['source_smiles'])]
input_props[task] = [(log['metadata']['source_props'])]
output_smiles[task] = [pred_smi]
for task in input_smiles.keys():
output_props[task] = generate_props(output_smiles[task])
return input_smiles, input_props, output_smiles, output_props
def eval(self, json_file, task=None):
if self.dataset == "smolinstruct":
pred_list, gold_list = self._get_pred_gold_smolinstruct(json_file=json_file)
if task in ('forward_synthesis', 'description_guided_generation', 'name_conversion-i2s'):
r = calculate_smiles_metrics(pred_list, gold_list)
elif task in ('retrosynthesis',):
r = calculate_smiles_metrics(pred_list, gold_list, metrics=('exact_match', 'fingerprint', 'multiple_match'))
elif task in ('molecule_captioning',):
r = calculate_text_metrics(pred_list, gold_list)
elif task in ('name_conversion-i2f', 'name_conversion-s2f'):
r = calculate_formula_metrics(pred_list, gold_list, metrics=('element_match',))
elif task in ('name_conversion-s2i',):
r = calculate_formula_metrics(pred_list, gold_list, metrics=('split_match',))
elif task in ('property_prediction-esol', 'property_prediction-lipo'):
r = calculate_number_metrics(pred_list, gold_list)
elif task in ('property_prediction-bbbp', 'property_prediction-clintox', 'property_prediction-hiv', 'property_prediction-sider'):
r = calculate_boolean_metrics(pred_list, gold_list)
else:
raise ValueError(task)
print (r)
return r
elif self.dataset == "openmolinst":
preds, golds = self._get_pred_gold_openmolinst(csv_file=os.path.join(self.raw_dir, "openmolinst_"+task+".csv"),
json_file=json_file)
if task == "moledit_add_component":
r = eval_moledit_add_component(data=golds, target=preds)
elif task == "moledit_delete_component":
r = eval_moledit_delete_component(data=golds, target=preds)
elif task == "moledit_sub_component":
r = eval_moledit_sub_component(data=golds, target=preds)
elif task == "molopt_logP":
r = eval_molopt_logP(data=golds, target=preds)
elif task == "molopt_MR":
r = eval_molopt_MR(data=golds, target=preds)
elif task == "molopt_QED":
r = eval_molopt_QED(data=golds, target=preds)
else:
raise ValueError(task)
print (r)
return r
elif self.dataset == "mumoinstruct":
input_smiles, input_props, output_smiles, output_props = self._get_pred_gold_mumoinstruct(json_file)
results = dict()
for task in input_smiles.keys():
print (f"\n###### {task} ######")
r, _ = compute_metrics(input_smiles=input_smiles[task], \
input_props=input_props[task], \
output_smiles=output_smiles[task], \
output_props_df=output_props[task], \
task=task, \
normalize=None)
print (r)
results[task] = r
return results
def evaluate_smolinstruct(self):
result_files = os.listdir(self.result_dir)
results = dict()
for file_name in result_files:
if "smolinstruct" not in file_name:
continue
task = file_name[:-5].replace("smolinstruct_", "")
print (f"\n###### {task} ######")
results[task] = self.eval(json_file=os.path.join(self.result_dir, file_name), task=task)
self._dump_json(results, os.path.join(self.result_dir, "metrics.json"))
def evaluate_openmolinst(self):
result_files = os.listdir(self.result_dir)
results = dict()
for file_name in result_files:
if "openmolinst" not in file_name:
continue
task = file_name[:-5].replace("openmolinst_", "")
print (f"\n###### {task} ######")
results[task] = self.eval(json_file=os.path.join(self.result_dir, file_name), task=task)
self._dump_json(results, os.path.join(self.result_dir, "metrics.json"))
def evaluate_mumoinstruct(self):
result_files = os.listdir(self.result_dir)
for file_name in result_files:
if "mumoinstruct" not in file_name:
continue
results = self.eval(json_file=os.path.join(self.result_dir, file_name))
self._dump_json(results, os.path.join(self.result_dir, "metrics.json"))
def run(self):
if self.dataset == "smolinstruct":
self.evaluate_smolinstruct()
elif self.dataset == "openmolinst":
self.evaluate_openmolinst()
elif self.dataset == "mumoinstruct":
self.evaluate_mumoinstruct()
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="")
parser.add_argument("--dataset_name", type=str, help="name of the dataset")
parser.add_argument("--result_dir", type=str, help="path to result files")
parser.add_argument("--raw_dir", type=str, default="evaluation/datasets/openmolinst_raw", help="path to raw files (for OpenMolInst)")
args = parser.parse_args()
if args.dataset_name == "smolinstruct":
from utils.smolinstruct_metrics import calculate_smiles_metrics, calculate_formula_metrics, calculate_text_metrics, calculate_number_metrics, calculate_boolean_metrics
workflow = Evaluator(dataset=args.dataset_name,
result_dir=args.result_dir)
workflow.run()
elif args.dataset_name == "openmolinst":
from utils.openmolinst_metrics import eval_moledit_add_component, eval_moledit_delete_component, eval_moledit_sub_component, eval_molopt_logP, eval_molopt_MR, eval_molopt_QED
import pandas as pd
workflow = Evaluator(dataset=args.dataset_name,
result_dir=args.result_dir,
raw_dir=args.raw_dir)
workflow.run()
elif args.dataset_name == "mumoinstruct":
from utils.mumoinstruct_metrics import generate_props, compute_metrics
workflow = Evaluator(dataset=args.dataset_name,
result_dir=args.result_dir)
workflow.run()
|