File size: 10,422 Bytes
3232d64 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
import glob
import json
import math
import os
from dataclasses import dataclass
import dateutil
import numpy as np
from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
from src.submission.check_validity import is_model_on_hub
@dataclass
class EvalResult:
eval_name: str # org_model_precision (uid)
full_model: str # org/model (path on hub)
org: str
model: str
revision: str # commit hash, "" if main
results: dict
precision: Precision = Precision.Unknown
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
weight_type: WeightType = WeightType.Original # Original or Adapter
architecture: str = "Unknown"
license: str = "?"
likes: int = 0
num_params: int = 0
date: str = "" # submission date of request file
still_on_hub: bool = False
@classmethod
def init_from_json_file(self, json_filepath):
"""Inits the result from the specific model result file"""
with open(json_filepath) as fp:
data = json.load(fp)
config = data.get("config")
# Eğer config None ise, boş bir sözlük ile devam et
if config is None:
print(f"Warning: Config is None in {json_filepath}")
config = {}
# Precision
precision = Precision.from_str(config.get("model_dtype"))
# Get model and org
org_and_model = config.get("model_name", config.get("model_args", None))
# Eğer org_and_model None ise, varsayılan bir değer kullan
if org_and_model is None:
print(f"Warning: org_and_model is None in {json_filepath}")
org_and_model = "unknown/unknown"
org_and_model = org_and_model.split("/", 1)
if len(org_and_model) == 1:
org = None
model = org_and_model[0]
result_key = f"{model}_{precision.value.name}"
else:
org = org_and_model[0]
model = org_and_model[1]
result_key = f"{org}_{model}_{precision.value.name}"
full_model = "/".join(org_and_model)
still_on_hub, _, model_config = is_model_on_hub(
full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
)
architecture = "?"
if model_config is not None:
architectures = getattr(model_config, "architectures", None)
if architectures:
architecture = ";".join(architectures)
# Extract results available in this file (some results are split in several files)
results = {}
# model_evaluation_mlflow_result.json dosyasındaki metrikleri oku
if "results" in data:
# OpenAI Scores
if "openai_scores" in data["results"]:
openai_scores = data["results"]["openai_scores"]
results["OpenAI-Scores"] = {
"accuracy": openai_scores.get("accuracy", 0),
"relevance": openai_scores.get("relevance", 0),
"coherence": openai_scores.get("coherence", 0)
}
# BLEU Score
if "bleu" in data["results"]:
results["BLEU"] = {
"mean": data["results"]["bleu"].get("mean", 0)
}
# ROUGE Scores
if "rouge" in data["results"]:
rouge = data["results"]["rouge"]
results["ROUGE"] = {
"rouge1_mean": rouge.get("rouge1", {}).get("mean", 0),
"rouge2_mean": rouge.get("rouge2", {}).get("mean", 0),
"rougeL_mean": rouge.get("rougeL", {}).get("mean", 0)
}
# BERT Score
if "bert_score" in data["results"]:
bert_score = data["results"]["bert_score"]
results["BERT-Score"] = {
"precision_mean": bert_score.get("precision", {}).get("mean", 0),
"recall_mean": bert_score.get("recall", {}).get("mean", 0),
"f1_mean": bert_score.get("f1", {}).get("mean", 0)
}
# Cosine Similarity
if "cosine_similarity_turkish" in data["results"]:
results["Cosine-Similarity"] = {
"turkish_mean": data["results"]["cosine_similarity_turkish"].get("mean", 0),
"multilingual_mean": data["results"].get("cosine_similarity_multilingual", {}).get("mean", 0)
}
# Evaluation Metrics
if "evaluation_metrics" in data["results"]:
eval_metrics = data["results"]["evaluation_metrics"]
results["Evaluation-Metrics"] = {
"total_samples": eval_metrics.get("total_samples", 0),
"avg_input_length": eval_metrics.get("avg_input_length", 0),
"avg_prediction_length": eval_metrics.get("avg_prediction_length", 0),
"avg_reference_length": eval_metrics.get("avg_reference_length", 0)
}
# Eski format için geriye dönük uyumluluk
task_results = {}
for task in Tasks:
task = task.value
benchmark = task.benchmark
metric = task.metric
if benchmark in results and metric in results[benchmark]:
task_results[benchmark] = results[benchmark][metric] * 100.0
return self(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=task_results,
precision=precision,
revision= config.get("model_sha", ""),
still_on_hub=still_on_hub,
architecture=architecture
)
def update_with_request_file(self, requests_path):
"""Finds the relevant request file for the current model and updates info with it"""
request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
try:
with open(request_file, "r") as f:
request = json.load(f)
self.model_type = ModelType.from_str(request.get("model_type", ""))
self.weight_type = WeightType[request.get("weight_type", "Original")]
self.license = request.get("license", "?")
self.likes = request.get("likes", 0)
self.num_params = request.get("params", 0)
self.date = request.get("submitted_time", "")
except Exception:
print(f"Could not find request file for {self.org}/{self.model}")
def to_dict(self):
"""Converts the Eval Result to a dict compatible with our dataframe display"""
average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
data_dict = {
"eval_name": self.eval_name, # not a column, just a save name,
AutoEvalColumn.precision.name: self.precision.value.name,
AutoEvalColumn.model_type.name: self.model_type.value.name,
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
AutoEvalColumn.weight_type.name: self.weight_type.value.name,
AutoEvalColumn.architecture.name: self.architecture,
AutoEvalColumn.model.name: make_clickable_model(self.full_model),
AutoEvalColumn.dummy.name: self.full_model,
AutoEvalColumn.revision.name: self.revision,
AutoEvalColumn.average.name: average,
AutoEvalColumn.license.name: self.license,
AutoEvalColumn.likes.name: self.likes,
AutoEvalColumn.params.name: self.num_params,
AutoEvalColumn.still_on_hub.name: self.still_on_hub,
}
for task in Tasks:
data_dict[task.value.col_name] = self.results[task.value.benchmark]
return data_dict
def get_request_file_for_model(requests_path, model_name, precision):
"""Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
request_files = os.path.join(
requests_path,
f"{model_name}_eval_request_*.json",
)
request_files = glob.glob(request_files)
# Select correct request file (precision)
request_file = ""
request_files = sorted(request_files, reverse=True)
for tmp_request_file in request_files:
with open(tmp_request_file, "r") as f:
req_content = json.load(f)
if (
req_content["status"] in ["FINISHED"]
and req_content["precision"] == precision.split(".")[-1]
):
request_file = tmp_request_file
return request_file
def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
"""From the path of the results folder root, extract all needed info for results"""
model_result_filepaths = []
for root, _, files in os.walk(results_path):
# We should only have json files in model results
if len(files) == 0 or any([not f.endswith(".json") for f in files]):
continue
# Sort the files by date
try:
files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
except dateutil.parser._parser.ParserError:
files = [files[-1]]
for file in files:
model_result_filepaths.append(os.path.join(root, file))
eval_results = {}
for model_result_filepath in model_result_filepaths:
# Creation of result
eval_result = EvalResult.init_from_json_file(model_result_filepath)
eval_result.update_with_request_file(requests_path)
# Store results of same eval together
eval_name = eval_result.eval_name
if eval_name in eval_results.keys():
eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
else:
eval_results[eval_name] = eval_result
results = []
for v in eval_results.values():
try:
v.to_dict() # we test if the dict version is complete
results.append(v)
except KeyError: # not all eval values present
continue
return results
|