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OpenCompass/opencompass/datasets/calm/evaluation/errors.py
Peng Bo 07c96ac659
Calm dataset (#1385) 
* Add CALM Dataset
2024-08-01 10:03:21 +08:00

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# flake8: noqa: E501
import importlib
import json
import os
from pathlib import Path
from ..evaluation.core_metrics import \
initialize_core_metric_evaluation_components
def initialize_error_identification_components(task, prompt_style):
"""Initialize error identification components.
Args:
task (str): The task for which error identification components are being initialized.
prompt_style (str): The style of prompt for error identification.
Returns:
Module: The error identification module corresponding to the provided task and prompt style.
"""
prompt_style_to_error_module_map = {
'basic': 'basic_adversarial',
'basic-CN': 'basic_adversarial',
'adversarial-ignore': 'basic_adversarial',
'adversarial-ignore-CN': 'basic_adversarial',
'adversarial-doubt': 'basic_adversarial',
'adversarial-doubt-CN': 'basic_adversarial',
'zero-shot-IcL': 'icl',
'zero-shot-IcL-CN': 'icl',
'one-shot-IcL': 'icl',
'one-shot-IcL-CN': 'icl',
'three-shot-IcL': 'icl',
'three-shot-IcL-CN': 'icl',
'zero-shot-CoT': 'cot',
'zero-shot-CoT-CN': 'cot',
'manual-CoT': 'cot',
'manual-CoT-CN': 'cot'
}
task_to_error_module_map = {
# association/
# correlation/
'CORR-B_correlation_CN': 'CLADDER',
'CORR-B_correlation_EN': 'CLADDER',
# explaining_away_effect/
'EAE-B_exp-away_CN': 'CLADDER',
'EAE-B_exp-away_EN': 'CLADDER',
# causal_discovery/
# abstract_reasoning/
'AR-B_CaLM-AR_CN': 'AR-B_CaLM-AR',
'AR-B_CaLM-AR_EN': 'AR-B_CaLM-AR',
# causal_attribution/
'CA-B_FA_CN': 'CA-B',
'CA-B_FA_EN': 'CA-B',
'CA-B_FP_CN': 'CA-B',
'CA-B_FP_EN': 'CA-B',
# event_causality_identification/
'ECI-B_CTB_CN': 'ECI',
'ECI-B_CTB_EN': 'ECI',
'ECI-B_ESC_CN': 'ECI',
'ECI-B_ESC_EN': 'ECI',
'ECI-B_MAVEN-ERE_CN': 'ECI',
'ECI-B_MAVEN-ERE_EN': 'ECI',
# pairwise_causal_discovery/
'PCD-B_COPA_CN': 'PCD-B',
'PCD-B_COPA_EN': 'PCD-B',
'PCD-B_E-CARE_CN': 'PCD-B',
'PCD-B_E-CARE_EN': 'PCD-B',
'PCD-C_COPA_CN': 'PCD-C',
'PCD-C_COPA_EN': 'PCD-C',
'PCD-C_E-CARE_CN': 'PCD-C',
'PCD-C_E-CARE_EN': 'PCD-C',
# counterfactual/
# actual_causality/
'AC-B_causal_judgement_CN': 'AC-B_causal_judgement',
'AC-B_causal_judgement_EN': 'AC-B_causal_judgement',
# counterfactual_reasoning/
'CR-B_det-counterfactual_CN': 'CLADDER',
'CR-B_det-counterfactual_EN': 'CLADDER',
'CR-C_CRASS_CN': 'CR-C_CRASS',
'CR-C_CRASS_EN': 'CR-C_CRASS',
# effect_of_the_treatment_on_the_treated/
'ETT-B_ETT-natural_CN': 'Natural',
'ETT-B_ETT-natural_EN': 'Natural',
'ETT-P_ETT-basic_CN': 'Probability',
'ETT-P_ETT-basic_EN': 'Probability',
'ETT-P_ETT-hard_CN': 'Probability',
'ETT-P_ETT-hard_EN': 'Probability',
# natural_direct_effect/
'NDE-B_NDE-natural_CN': 'Natural',
'NDE-B_NDE-natural_EN': 'Natural',
'NDE-P_NDE-basic_CN': 'Probability',
'NDE-P_NDE-basic_EN': 'Probability',
'NDE-P_NDE-hard_CN': 'Probability',
'NDE-P_NDE-hard_EN': 'Probability',
# natural_indirect_effect/
'NIE-B_NIE-natural_CN': 'Natural',
'NIE-B_NIE-natural_EN': 'Natural',
'NIE-P_NIE-basic_CN': 'Probability',
'NIE-P_NIE-basic_EN': 'Probability',
'NIE-P_NIE-hard_CN': 'Probability',
'NIE-P_NIE-hard_EN': 'Probability',
# probability_of_necessity/
'PN-P_PN-basic_CN': 'Probability',
'PN-P_PN-basic_EN': 'Probability',
'PN-P_PN-hard_CN': 'Probability',
'PN-P_PN-hard_EN': 'Probability',
# probability_of_sufficiency/
'PS-P_PS-basic_CN': 'Probability',
'PS-P_PS-basic_EN': 'Probability',
'PS-P_PS-hard_CN': 'Probability',
'PS-P_PS-hard_EN': 'Probability',
# intervention/
# average_treatment_effect/
'ATE-B_ATE-natural_CN': 'Natural',
'ATE-B_ATE-natural_EN': 'Natural',
'ATE-P_ATE-basic_CN': 'Probability',
'ATE-P_ATE-basic_EN': 'Probability',
'ATE-P_ATE-hard_CN': 'Probability',
'ATE-P_ATE-hard_EN': 'Probability',
# backdoor_adjustment_set/
'BAS-B_backadj_CN': 'CLADDER',
'BAS-B_backadj_EN': 'CLADDER',
'BAS-C_max-BAS_CN': 'AS',
'BAS-C_max-BAS_EN': 'AS',
'BAS-C_min-BAS_CN': 'AS',
'BAS-C_min-BAS_EN': 'AS',
'BAS-C_mix-BAS_CN': 'AS',
'BAS-C_mix-BAS_EN': 'AS',
# causal_effect_identification/
'CEI-B_0.2-UC_CN': 'CEI-B',
'CEI-B_0.2-UC_EN': 'CEI-B',
'CEI-B_0.4-UC_CN': 'CEI-B',
'CEI-B_0.4-UC_EN': 'CEI-B',
'CEI-B_0.6-UC_CN': 'CEI-B',
'CEI-B_0.6-UC_EN': 'CEI-B',
'CEI-B_0.8-UC_CN': 'CEI-B',
'CEI-B_0.8-UC_EN': 'CEI-B',
# collider_bias/
'CB-B_collider-bias_CN': 'CLADDER',
'CB-B_collider-bias_EN': 'CLADDER',
# controlled_direct_effect/
'CDE-B_CDE-natural_CN': 'Natural',
'CDE-B_CDE-natural_EN': 'Natural',
'CDE-P_CDE-basic_CN': 'Probability',
'CDE-P_CDE-basic_EN': 'Probability',
'CDE-P_CDE-hard_CN': 'Probability',
'CDE-P_CDE-hard_EN': 'Probability',
# frontdoor_adjustment_set/
'FAS-C_FAS_CN': 'AS',
'FAS-C_FAS_EN': 'AS',
# instrumental_variable/
'IV-C_CaLM-IV_CN': 'AS',
'IV-C_CaLM-IV_EN': 'AS',
}
error_task_module_name = task_to_error_module_map.get(task)
error_prompt_module_name = prompt_style_to_error_module_map.get(
prompt_style)
if error_task_module_name and error_prompt_module_name:
error_module = importlib.import_module(
f'opencompass.datasets.calm.evaluation.error.{error_prompt_module_name}.{error_task_module_name}'
)
return error_module
else:
raise NotImplementedError(
f'No get_score function found for task {task} and prompt {prompt_style}.'
)
def identify_model_errors(items, task, prompt_style, gt_items):
"""Identify errors in model responses based on provided items, task, and
prompt style.
Args:
items (list): A list of items containing model responses.
task (str): The task type, note that CEG-O_E-CARE is not supported for error analysis.
prompt_style (str): The style of prompt used, note that explicit-function is not supported for error analysis.
gt_items (list): A list of ground truth items.
Returns:
dict: A dictionary containing error metrics for the model responses. (Same response to all questions, language inconsistency, limitation of instruction-following, repetition, empty response.)
"""
if task == 'CEG-O_E-CARE' or prompt_style in [
'explicit-function', 'explicit-function-CN'
]:
print(
'CEG-O_E-CARE and explicit-function prompts are not supported for error identification.'
)
return
language_error, nonstandrad, repetition, empty = 0., 0., 0., 0.
error_module = initialize_error_identification_components(
task, prompt_style)
get_gt_label, get_pred_label, compute_acc = initialize_core_metric_evaluation_components(
task)
pred_list = []
for item, gt_item in zip(items, gt_items):
pred_label = get_pred_label(item, gt_item, prompt_style,
task.split('-')[0])
pred_error = get_item_error(item, task, error_module, prompt_style)
pred_list.append(pred_label)
language_error += pred_error['language_error']
nonstandrad += pred_error['nonstandrad']
repetition += pred_error['repetition']
empty += pred_error['empty']
abnormalities = error_module.check_abnormality(pred_list)
return {
'Same response to all questions': 1 if abnormalities != 0 else 0,
'Language inconsistency': language_error / len(pred_list),
'Limitation of instruction-following': nonstandrad / len(pred_list),
'Repetition': repetition / len(pred_list),
'Empty response': empty / len(pred_list),
}
def get_item_error(model_response, task, error_module, prompt_style):
"""Analyze errors in a single model response for a given task and prompt
style.
Args:
model_response (str): The model's response to analyze.
task (str): The task type.
error_module: The error module containing error identification methods.
prompt_style (str): The style of prompt used.
Returns:
dict: A dictionary containing error metrics for the model response. (Language inconsistency, nonstandardization, repetition, empty response.)
"""
model_response = model_response.strip().lower()
if 'CN' in task:
language_error = error_module.contains_english(model_response)
elif 'CN' not in task:
language_error = error_module.contains_chinese(model_response)
nonstandrad = error_module.check_standalization(model_response,
prompt_style,
type=task.split('-')[0])
repetition = error_module.check_repetition(model_response)
empty = error_module.check_empty(model_response)
return {
'language_error': language_error,
'nonstandrad': nonstandrad,
'repetition': repetition,
'empty': empty,
}
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