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