Datasets:
File size: 11,243 Bytes
aadeb6f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 |
import json
import random
from pathlib import Path
from typing import Sequence, Callable
import fire
import h5py
import numpy as np
from tqdm import tqdm
from infer_eval_utils import (
read_time_series_data,
GENERATION_TASK_IDS,
CLASSIFICATION_TASK_IDS,
EVENT_DETECTION_TASK_IDS,
ANOMALY_DETECTION_TASK_IDS,
MCQ_TASK_IDS,
IMPUTATION_TASK_IDS
)
def read_raw_data(path: str | None) -> Sequence:
if path is None:
return []
return read_time_series_data(path)
def transform_raw_data_single_channel(raw_data: list | np.ndarray,
dataset_id: str) -> str:
if dataset_id in GENERATION_TASK_IDS:
data_str_list = []
for x in raw_data:
if x != "X":
data_str_list.append(f"{float(x):.3f}")
else:
data_str_list.append(x)
data_text = " ".join(data_str_list)
else:
data_text = " ".join([f"{float(x):.3f}" for x in raw_data])
return data_text
def transform_raw_data_to_text(raw_data: list | np.ndarray, dataset_id: str,
channel_detail: list[str]) -> str:
if isinstance(raw_data, np.ndarray):
if raw_data.ndim > 1 and raw_data.shape[1] > 1 and len(
channel_detail) == 0:
channel_detail = [f"channel {i}" for i in range(raw_data.shape[1])]
if len(channel_detail) <= 1:
data_text = transform_raw_data_single_channel(raw_data, dataset_id)
else:
data_text = ""
for channel_idx, channel_name in enumerate(channel_detail):
channel_data = raw_data[:, channel_idx]
channel_data_text = transform_raw_data_single_channel(
channel_data, dataset_id)
data_text += f"{channel_name}: {channel_data_text} "
else:
data_text = transform_raw_data_single_channel(raw_data, dataset_id)
return data_text
def transform_gt_data_to_text(gt_data: list | np.ndarray,
dataset_id: str) -> str:
gt_data = np.array(gt_data)
if gt_data.ndim == 1:
data_text = transform_raw_data_single_channel(gt_data, dataset_id)
else:
data_text = ""
for channel_idx in range(gt_data.shape[1]):
channel_data = gt_data[:, channel_idx]
channel_data_text = transform_raw_data_single_channel(
channel_data, dataset_id)
data_text += f"{channel_data_text}\n"
return data_text
def get_extra_instruction(dataset_id: str, ) -> str:
extra_instruction = ""
if dataset_id == "ASU01_ASG02":
extra_instruction = "Answer yes or no in the first line. If the Gravitational Wave is detected, answer the index of the starting time point in the second line."
elif dataset_id == "EAU01_EAG02":
extra_instruction = "Answer yes or no in the first line. If an Earthquake event is detected, answer the starting time point index of the P-wave in the second line, " \
"answer the starting time point index of the S-wave in the third line."
elif dataset_id == "MFU01_MFU02":
extra_instruction = "Output the diameter in the first line, and the position in the second line."
elif dataset_id == "PHU01":
extra_instruction = "Give each answer in a line. For example, if the answer is ['NORM', 'MI'], you should output: NORM\nMI."
elif dataset_id == "MAG01":
extra_instruction = "Give answer of each channel in a line so the number of predicted time points in each line should match the given one. For example, if " \
"it is required to predict the next 5 time points, and the predicted x0, x1, x2 are [[0.1, 0.2, 0.3, 0.4, 0.5], [0.4, 0.5, 0.6, 0.7, 0.8], [0.7, 0.8, 0.9, 0.1, 0.2]], " \
"you should output: 0.1 0.2 0.3 0.4 0.5\n0.4 0.5 0.6 0.7 0.8\n0.7 0.8 0.9 0.1 0.2."
elif dataset_id in ANOMALY_DETECTION_TASK_IDS:
extra_instruction = "Answer yes if anomaly points are detected, and no if there are only normal points."
elif dataset_id in GENERATION_TASK_IDS:
extra_instruction = "Output the values separated by spaces."
return extra_instruction
def extract_gt(data: dict, dataset_id: str) -> str | dict | Path:
if dataset_id in CLASSIFICATION_TASK_IDS:
gt = data["gt_result"]["gt_class"]
if isinstance(gt, dict) and len(gt) == 1:
gt = gt["default"]
if isinstance(gt, list) and len(gt) == 1:
gt = gt[0]
elif dataset_id in GENERATION_TASK_IDS:
gt = data["gt_ts"]["path"].strip("/")
elif dataset_id in EVENT_DETECTION_TASK_IDS:
gt = data["gt_result"]
elif dataset_id in ANOMALY_DETECTION_TASK_IDS:
gt = data["gt_result"]["contain"]
elif dataset_id in MCQ_TASK_IDS:
gt = data["gt_result"]["answer"]
else:
raise ValueError(f"Unsupported dataset id: {dataset_id}")
return gt
def initialize_model() -> Callable:
"""
Initialize the model here. The model can be called by:
```python
response = model(prompt)
# or
response = model(prompt, max_tokens=max_tokens) # to limit the response length
```
"""
pass
def infer_dataset(model: Callable, dataset_data: list, scits_dir: Path,
dataset_id: str, output_path: Path):
print(f"Inferring {dataset_id}")
if dataset_id in GENERATION_TASK_IDS:
ext = "h5"
else:
ext = "jsonl"
output_path = Path(output_path) / f"{dataset_id}.{ext}"
output_path.parent.mkdir(parents=True, exist_ok=True)
completed_ids = []
if str(output_path).endswith(".jsonl"):
has_metadata = False
if output_path.exists():
if str(output_path).endswith(".jsonl"):
with open(output_path, 'r') as f:
for line in f.readlines():
data = json.loads(line)
if "id" in data:
completed_ids.append(data["id"])
else:
has_metadata = True
elif str(output_path).endswith(".h5"):
with h5py.File(output_path, 'r') as f:
completed_ids = list(f.keys())
completed_ids = set(completed_ids)
random.shuffle(dataset_data)
dataset_data = dataset_data[:10]
try:
seq_length = dataset_data[0]["input_ts"]["length"]
except:
seq_length = None
for sample in tqdm(dataset_data):
id = sample["id"].replace(
"/", "%2F") # to avoid errors related to "/" in hdf5
if id in completed_ids:
continue
# Load raw data
if sample["input_ts"] is None:
raw_data_path = None
channel_detail = None
else:
raw_data_path = scits_dir / sample["input_ts"]["path"].strip("/")
channel_detail = sample["input_ts"]["channel_detail"]
raw_data = read_raw_data(raw_data_path)
raw_data_text = transform_raw_data_to_text(raw_data, dataset_id,
channel_detail)
gt = extract_gt(sample, dataset_id)
extra_instruction = get_extra_instruction(dataset_id)
if dataset_id in GENERATION_TASK_IDS:
# give max_tokens to save cost for generation tasks
gt_data = read_time_series_data(scits_dir / gt)
gt_data_text = transform_gt_data_to_text(
gt_data, dataset_id)
max_tokens = len(gt_data_text)
else:
max_tokens = None
prompt_text = f'{sample["input_text"]} {extra_instruction} Give me the answer directly, ' \
f'without any other extra content (including punctuation). ' \
f'{raw_data_text}'
output_text = model(text=prompt_text, max_tokens=max_tokens)
# print(f"output_text: {output_text}")
if dataset_id not in GENERATION_TASK_IDS:
with open(output_path, 'a') as writer:
if not has_metadata:
metadata = {}
if dataset_id in EVENT_DETECTION_TASK_IDS:
metadata["seq_length"] = seq_length
writer.write(json.dumps(metadata) + "\n")
has_metadata = True
pred_results = output_text
if "class_list" in sample["gt_result"] and isinstance(
sample["gt_result"]["class_list"], dict) and len(
sample["gt_result"]["class_list"]) > 1:
if len(output_text.split("\n")) != len(
sample["gt_result"]["class_list"]):
pred_results = "NA"
else:
pred_results = {}
for class_name, pred_result in zip(
sample["gt_result"]["class_list"],
output_text.split("\n")):
pred_results[class_name] = pred_result
writer.write(
json.dumps({
"id": id,
"output": pred_results,
"ground_truth": gt
}) + "\n")
else:
if "\n" not in output_text:
pred_result = np.fromstring(output_text.strip(),
dtype=np.float32,
sep=' ')
else:
try:
pred_result = np.vstack([
np.fromstring(x.strip(), dtype=np.float32, sep=' ')
for x in output_text.split("\n")
]).transpose()
except ValueError:
pred_result = np.array([])
with h5py.File(output_path, 'a') as writer:
writer[f"{id}/pred_result"] = pred_result
writer[f"{id}/gt_path"] = gt.__str__().encode("utf-8")
if dataset_id in IMPUTATION_TASK_IDS:
writer[f"{id}/input_ts_path"] = sample["input_ts"][
"path"].strip("/").encode("utf-8")
if "base_path" not in writer:
writer["base_path"] = scits_dir.__str__().encode("utf-8")
def infer(
scits_dir: str,
output_dir: str,
):
# Initialize caller
model: Callable = initialize_model()
scits_dir = Path(scits_dir)
output_dir = Path(output_dir)
dataset_data = []
prev_dataset_id = None
with open(scits_dir / "meta_data.jsonl", 'r') as f:
for line in f.readlines():
sample = json.loads(line)
dataset_id = "_".join(sample["task_id"])
if dataset_id != prev_dataset_id:
if prev_dataset_id is not None:
infer_dataset(model, dataset_data, scits_dir, prev_dataset_id,
output_dir)
dataset_data = []
prev_dataset_id = dataset_id
dataset_data.append(sample)
infer_dataset(model, dataset_data, scits_dir, prev_dataset_id, output_dir)
if __name__ == '__main__':
fire.Fire(infer)
|