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from pathlib import Path
import pandas as pd
from rex.utils.initialization import set_seed_and_log_path
from rex.utils.io import load_json
from rich.console import Console
from rich.table import Table
from src.task import SchemaGuidedInstructBertTask
set_seed_and_log_path(log_path="tmp_eval.log")
if __name__ == "__main__":
task_dir = "mirror_outputs/Mirror_Pretrain_AllExcluded_2"
# task_dir = "mirror_outputs/Mirror_SingleTask_wPTAllExcluded_Event_ACE05"
task: SchemaGuidedInstructBertTask = SchemaGuidedInstructBertTask.from_taskdir(
task_dir,
load_best_model=True,
initialize=False,
dump_configfile=False,
update_config={
"regenerate_cache": True,
"eval_on_data": ["dev"],
"select_best_on_data": "dev",
"select_best_by_key": "metric",
"best_metric_field": "general_spans.micro.f1",
"eval_batch_size": 32,
},
)
table = Table(title=task_dir)
data_pairs = [
# fmt: off
# UIE eval data
# ["ent_ace04_test", "resources/Mirror/uie/ent/ace04/test.jsonl"],
# ["ent_ace05_test", "resources/Mirror/uie/ent/ace05/test.jsonl"],
["ent_conll03_test", "resources/Mirror/uie/ent/conll03/test.jsonl"],
# ["rel_ace05_test", "resources/Mirror/uie/rel/ace05-rel/test.jsonl"],
["rel_conll04_test", "resources/Mirror/uie/rel/conll04/test.jsonl"],
# ["rel_nyt_test", "resources/Mirror/uie/rel/nyt/test.jsonl"],
# ["rel_scierc_test", "resources/Mirror/uie/rel/scierc/test.jsonl"],
["event_ace05_test", "resources/Mirror/uie/event/ace05-evt/test.jsonl"],
# ["event_casie_test", "resources/Mirror/uie/event/casie/test.jsonl"],
# ["absa_14res_test", "resources/Mirror/uie/absa/14res/test.jsonl"],
# ["absa_14lap_test", "resources/Mirror/uie/absa/14lap/test.jsonl"],
# ["absa_15res_test", "resources/Mirror/uie/absa/15res/test.jsonl"],
# ["absa_16res_test", "resources/Mirror/uie/absa/16res/test.jsonl"],
# # discontinuous NER
# ["discontinuous_ent", "resources/Mirror/new_abilities_v2/cadec/new/test.jsonl"],
# # hyper-RE
# ["hyper_rel", "resources/Mirror/new_abilities_v2/HyperRED/new/test.jsonl"],
# # zero-shot NER
# ["ent_movie", "resources/Mirror/v1.3/ent/en/MIT_MOVIE_Review/instructed/test.jsonl"],
# ["ent_restaurant", "resources/Mirror/v1.3/ent/en/MIT_Restaurant_Review/instructed/test.jsonl"],
# ["ent_ai", "resources/Mirror/v1.3/ent/en/CrossNER_AI/instructed/test.jsonl"],
# ["ent_literature", "resources/Mirror/v1.3/ent/en/CrossNER_literature/instructed/test.jsonl"],
# ["ent_music", "resources/Mirror/v1.3/ent/en/CrossNER_music/instructed/test.jsonl"],
# ["ent_politics", "resources/Mirror/v1.3/ent/en/CrossNER_politics/instructed/test.jsonl"],
# ["ent_science", "resources/Mirror/v1.3/ent/en/CrossNER_science/instructed/test.jsonl"],
# # mrc
# ["span_squad2", "resources/Mirror/v1.3/span/en/squad_v2/dev.jsonl"],
# # glue
# ["cls_glue_cola", "resources/Mirror/v1.3/cls/en/CoLA/formated/dev.jsonl"],
# ["cls_glue_qqp", "resources/Mirror/v1.3/cls/en/QQP/new/dev.jsonl"],
# ["cls_glue_mnli", "resources/Mirror/v1.3/cls/en/MNLI/formated/MNLI_dev.jsonl"],
# ["cls_glue_sst2", "resources/Mirror/v1.3/cls/en/SST-2/instructed/SST-2_dev.jsonl"],
# ["cls_glue_qnli", "resources/Mirror/v1.3/cls/en/QNLI/processed/QNLI_dev.jsonl"],
# ["cls_glue_rte", "resources/Mirror/v1.3/cls/en/RTE/formated/RTE_dev.jsonl"],
# ["cls_glue_mrpc", "resources/Mirror/v1.3/cls/en/MRPC/formated/dev.jsonl"],
# fmt: on
]
eval_res = {"task": [], "dataset": [], "metric_val": []}
table.add_column("Task", justify="left", style="cyan")
table.add_column("Dataset", justify="left", style="magenta")
table.add_column("Metric (%)", justify="right", style="green")
for dname, fpath in data_pairs:
dname = dname.lower()
task.data_manager.update_datapath(dname, fpath)
_, res = task.eval(dname, verbose=True, dump=True, dump_middle=True)
# res = load_json(Path(task_dir) / "measures" / f"{dname}.json")["metrics"]
if dname.startswith("ent_"):
eval_res["task"].append("ent")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["ent"]["micro"]["f1"])
elif dname.startswith("rel_"):
eval_res["task"].append("rel")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["rel"]["rel"]["micro"]["f1"])
elif dname.startswith("event_"):
eval_res["task"].append("event")
eval_res["dataset"].append(dname + "_tgg")
eval_res["metric_val"].append(res["event"]["trigger_cls"]["f1"])
eval_res["task"].append("event")
eval_res["dataset"].append(dname + "_arg")
eval_res["metric_val"].append(res["event"]["arg_cls"]["f1"])
elif dname.startswith("absa_"):
eval_res["task"].append("absa")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["rel"]["rel"]["micro"]["f1"])
elif dname.startswith("cls_"):
eval_res["task"].append("cls")
eval_res["dataset"].append(dname)
if "_glue_" in dname:
if "_cola" in dname:
eval_res["metric_val"].append(res["cls"]["mcc"])
else:
eval_res["metric_val"].append(res["cls"]["acc"])
else:
eval_res["metric_val"].append(res["cls"]["mf1"]["micro"]["f1"])
elif dname.startswith("span"):
eval_res["task"].append("span_em")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["span"]["em"])
eval_res["task"].append("span_f1")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["span"]["f1"]["f1"])
elif dname.startswith("discontinuous_ent"):
eval_res["task"].append("discontinuous_ent")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["discontinuous_ent"]["micro"]["f1"])
elif dname.startswith("hyper_rel"):
eval_res["task"].append("hyper_rel")
eval_res["dataset"].append(dname)
eval_res["metric_val"].append(res["hyper_rel"]["micro"]["f1"])
else:
raise ValueError
for i in range(len(eval_res["task"])):
table.add_row(
eval_res["task"][i],
eval_res["dataset"][i],
f"{100*eval_res['metric_val'][i]:.3f}",
)
console = Console()
console.print(table)
df = pd.DataFrame(eval_res)
df.to_excel(task.measures_path.joinpath("data_eval_res.xlsx"))
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