mirror of
https://github.com/open-compass/opencompass.git
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170 lines
9.1 KiB
Python
170 lines
9.1 KiB
Python
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task_hiearchy_dict = {
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# association/
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# correlation/
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"CORR-B_correlation_CN":"association/correlation/",
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"CORR-B_correlation_EN":"association/correlation/",
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# explaining_away_effect/
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"EAE-B_exp-away_CN":"association/explaining_away_effect/",
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"EAE-B_exp-away_EN":"association/explaining_away_effect/",
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# causal_discovery/
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# abstract_reasoning/
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"AR-B_CaLM-AR_CN":"causal_discovery/abstract_reasoning/",
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"AR-B_CaLM-AR_EN":"causal_discovery/abstract_reasoning/",
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# causal_attribution/
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"CA-B_FA_CN":"causal_discovery/causal_attribution/",
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"CA-B_FA_EN":"causal_discovery/causal_attribution/",
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"CA-B_FP_CN":"causal_discovery/causal_attribution/",
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"CA-B_FP_EN":"causal_discovery/causal_attribution/",
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# event_causality_identification/
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"ECI-B_CTB_CN":"causal_discovery/event_causality_identification/",
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"ECI-B_CTB_EN":"causal_discovery/event_causality_identification/",
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"ECI-B_ESC_CN":"causal_discovery/event_causality_identification/",
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"ECI-B_ESC_EN":"causal_discovery/event_causality_identification/",
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"ECI-B_MAVEN-ERE_CN":"causal_discovery/event_causality_identification/",
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"ECI-B_MAVEN-ERE_EN":"causal_discovery/event_causality_identification/",
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# pairwise_causal_discovery/
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"PCD-B_COPA_CN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-B_COPA_EN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-B_E-CARE_CN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-B_E-CARE_EN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-C_COPA_CN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-C_COPA_EN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-C_E-CARE_CN":"causal_discovery/pairwise_causal_discovery/",
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"PCD-C_E-CARE_EN":"causal_discovery/pairwise_causal_discovery/",
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# counterfactual/
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# actual_causality/
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"AC-B_causal_judgement_CN":"counterfactual/actual_causality/",
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"AC-B_causal_judgement_EN":"counterfactual/actual_causality/",
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# causal_explanation_generation/
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"CEG-O_E-CARE_CN":"counterfactual/causal_explanation_generation/",
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"CEG-O_E-CARE_EN":"counterfactual/causal_explanation_generation/",
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# counterfactual_reasoning/
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"CR-B_det-counterfactual_CN":"counterfactual/counterfactual_reasoning/",
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"CR-B_det-counterfactual_EN":"counterfactual/counterfactual_reasoning/",
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"CR-C_CRASS_CN":"counterfactual/counterfactual_reasoning/",
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"CR-C_CRASS_EN":"counterfactual/counterfactual_reasoning/",
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# effect_of_the_treatment_on_the_treated/
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"ETT-B_ETT-natural_CN":"counterfactual/effect_of_the_treatment_on_the_treated/",
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"ETT-B_ETT-natural_EN":"counterfactual/effect_of_the_treatment_on_the_treated/",
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"ETT-P_ETT-basic_CN":"counterfactual/effect_of_the_treatment_on_the_treated/",
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"ETT-P_ETT-basic_EN":"counterfactual/effect_of_the_treatment_on_the_treated/",
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"ETT-P_ETT-hard_CN":"counterfactual/effect_of_the_treatment_on_the_treated/",
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"ETT-P_ETT-hard_EN":"counterfactual/effect_of_the_treatment_on_the_treated/",
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# natural_direct_effect/
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"NDE-B_NDE-natural_CN":"counterfactual/natural_direct_effect/",
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"NDE-B_NDE-natural_EN":"counterfactual/natural_direct_effect/",
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"NDE-P_NDE-basic_CN":"counterfactual/natural_direct_effect/",
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"NDE-P_NDE-basic_EN":"counterfactual/natural_direct_effect/",
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"NDE-P_NDE-hard_CN":"counterfactual/natural_direct_effect/",
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"NDE-P_NDE-hard_EN":"counterfactual/natural_direct_effect/",
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# natural_indirect_effect/
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"NIE-B_NIE-natural_CN":"counterfactual/natural_indirect_effect/",
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"NIE-B_NIE-natural_EN":"counterfactual/natural_indirect_effect/",
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"NIE-P_NIE-basic_CN":"counterfactual/natural_indirect_effect/",
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"NIE-P_NIE-basic_EN":"counterfactual/natural_indirect_effect/",
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"NIE-P_NIE-hard_CN":"counterfactual/natural_indirect_effect/",
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"NIE-P_NIE-hard_EN":"counterfactual/natural_indirect_effect/",
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# probability_of_necessity/
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"PN-P_PN-basic_CN":"counterfactual/probability_of_necessity/",
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"PN-P_PN-basic_EN":"counterfactual/probability_of_necessity/",
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"PN-P_PN-hard_CN":"counterfactual/probability_of_necessity/",
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"PN-P_PN-hard_EN":"counterfactual/probability_of_necessity/",
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# probability_of_sufficiency/
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"PS-P_PS-basic_CN":"counterfactual/probability_of_sufficiency/",
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"PS-P_PS-basic_EN":"counterfactual/probability_of_sufficiency/",
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"PS-P_PS-hard_CN":"counterfactual/probability_of_sufficiency/",
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"PS-P_PS-hard_EN":"counterfactual/probability_of_sufficiency/",
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# intervention/
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# average_treatment_effect/
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"ATE-B_ATE-natural_CN":"intervention/average_treatment_effect/",
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"ATE-B_ATE-natural_EN":"intervention/average_treatment_effect/",
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"ATE-P_ATE-basic_CN":"intervention/average_treatment_effect/",
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"ATE-P_ATE-basic_EN":"intervention/average_treatment_effect/",
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"ATE-P_ATE-hard_CN":"intervention/average_treatment_effect/",
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"ATE-P_ATE-hard_EN":"intervention/average_treatment_effect/",
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# backdoor_adjustment_set/
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"BAS-B_backadj_CN":"intervention/backdoor_adjustment_set/",
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"BAS-B_backadj_EN":"intervention/backdoor_adjustment_set/",
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"BAS-C_max-BAS_CN":"intervention/backdoor_adjustment_set/",
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"BAS-C_max-BAS_EN":"intervention/backdoor_adjustment_set/",
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"BAS-C_min-BAS_CN":"intervention/backdoor_adjustment_set/",
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"BAS-C_min-BAS_EN":"intervention/backdoor_adjustment_set/",
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"BAS-C_mix-BAS_CN":"intervention/backdoor_adjustment_set/",
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"BAS-C_mix-BAS_EN":"intervention/backdoor_adjustment_set/",
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# causal_effect_identification/
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"CEI-B_0.2-UC_CN":"intervention/causal_effect_identification/",
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"CEI-B_0.2-UC_EN":"intervention/causal_effect_identification/",
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"CEI-B_0.4-UC_CN":"intervention/causal_effect_identification/",
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"CEI-B_0.4-UC_EN":"intervention/causal_effect_identification/",
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"CEI-B_0.6-UC_CN":"intervention/causal_effect_identification/",
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"CEI-B_0.6-UC_EN":"intervention/causal_effect_identification/",
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"CEI-B_0.8-UC_CN":"intervention/causal_effect_identification/",
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"CEI-B_0.8-UC_EN":"intervention/causal_effect_identification/",
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# collider_bias/
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"CB-B_collider-bias_CN":"intervention/collider_bias/",
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"CB-B_collider-bias_EN":"intervention/collider_bias/",
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# controlled_direct_effect/
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"CDE-B_CDE-natural_CN":"intervention/controlled_direct_effect/",
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"CDE-B_CDE-natural_EN":"intervention/controlled_direct_effect/",
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"CDE-P_CDE-basic_CN":"intervention/controlled_direct_effect/",
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"CDE-P_CDE-basic_EN":"intervention/controlled_direct_effect/",
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"CDE-P_CDE-hard_CN":"intervention/controlled_direct_effect/",
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"CDE-P_CDE-hard_EN":"intervention/controlled_direct_effect/",
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# frontdoor_adjustment_set/
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"FAS-C_FAS_CN":"intervention/frontdoor_adjustment_set/",
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"FAS-C_FAS_EN":"intervention/frontdoor_adjustment_set/",
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# instrumental_variable/
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"IV-C_CaLM-IV_CN":"intervention/instrumental_variable/",
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"IV-C_CaLM-IV_EN":"intervention/instrumental_variable/",}
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dict_keys = list(task_hiearchy_dict.keys())
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error_dict = {"Same response to all questions":[],
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"Language inconsistency":[],
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"Limitation of instruction-following":[],
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"Repetition":[],
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"Empty response":[],}
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for error in error_dict:
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for key in dict_keys:
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if "CEG-O_E-CARE" in key:
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continue
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error_dict[error].append([f"calm_{key}", error])
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English_avg = []
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Chinese_avg = []
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for key in dict_keys:
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if key.endswith("EN"):
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English_avg.append([f"calm_{key}", "Accuracy"])
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else:
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assert key.endswith("CN")
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Chinese_avg.append([f"calm_{key}", "Accuracy"])
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calm_summary_groups = [
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# English Average
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{'name': 'English Average', 'subsets': English_avg},
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# Chinese Average
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{'name': 'Chinese Average', 'subsets': Chinese_avg},
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# Accuracy Average
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{'name': 'Accuracy Average', 'subsets': ['English Average', 'Chinese Average']},
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]
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for error in error_dict:
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calm_summary_groups.append({'name': error+" Average", 'subsets': error_dict[error]})
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summarizer = dict(
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dataset_abbrs = [
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'###### CALM-Lite Accuracy ######',
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'Accuracy Average',
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'English Average',
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'Chinese Average',
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'###### CALM-Lite Errors ######',
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'Same response to all questions Average',
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'Language inconsistency Average',
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'Limitation of instruction-following Average',
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'Repetition Average',
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'Empty response Average',
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],
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summary_groups=calm_summary_groups,
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)
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