From c85d99799a78bf7c5fbb4c08c7a7849185ab6e43 Mon Sep 17 00:00:00 2001 From: Arash Afkanpour Date: Mon, 15 Sep 2025 17:10:36 -0400 Subject: [PATCH 1/4] Added a script to evaluate different embeddings --- src/cfg/run_cfg.yaml | 14 ++-- src/run_embedding_eval.py | 136 +++++++++++++++++++++++++++++++++++ src/utils/constants.py | 2 +- src/utils/embedding_utils.py | 18 ++++- 4 files changed, 161 insertions(+), 9 deletions(-) create mode 100644 src/run_embedding_eval.py diff --git a/src/cfg/run_cfg.yaml b/src/cfg/run_cfg.yaml index 1048b7d..ac1fcb1 100644 --- a/src/cfg/run_cfg.yaml +++ b/src/cfg/run_cfg.yaml @@ -1,5 +1,5 @@ scientist_llm: - name: o3-mini + name: o4-mini provider: openai generation_cfg: capability_generation: @@ -62,14 +62,14 @@ capabilities_cfg: capabilities_dir: /fs01/projects/aieng/public/ace/artifacts results_dir: gs://ace-artifacts inspect_evals_dir: /fs01/projects/aieng/public/ace/inspect_evals/src/ace_evals - domain: personal_finance + domain: math method: "hierarchical" num_seed_capabilities: 1 - num_gen_capabilities: 20 + num_gen_capabilities: 100 num_gen_capabilities_buffer: 0.0 - num_capability_areas: 5 - num_gen_capabilities_per_run: 1 - num_gen_tasks_per_capability: 1 + num_capability_areas: 10 + num_gen_capabilities_per_run: 5 + num_gen_tasks_per_capability: 100 num_gen_tasks_buffer: 0.0 task_gen_few_shot: false task_gen_prompt_version: "v1" @@ -102,7 +102,7 @@ dimensionality_reduction_cfg: exp_cfg: seed: 37 trial_run: false - exp_id: "personal_finance_o3-mini_r1" + exp_id: "" defaults: - _self_ diff --git a/src/run_embedding_eval.py b/src/run_embedding_eval.py new file mode 100644 index 0000000..92347e0 --- /dev/null +++ b/src/run_embedding_eval.py @@ -0,0 +1,136 @@ +"""Script to discover new capabilities using LBO.""" + +import json +import logging +import os +import shutil + +import hydra +from omegaconf import DictConfig +from tqdm import tqdm + +from src.generate_tasks import ( + generate_tasks_using_llm, +) +from src.lbo import calculate_lbo_error, fit_lbo, select_capabilities_using_lbo +from src.model import Model +from src.utils import constants, prompts +from src.utils.capability_discovery_utils import ( + capability_satisfies_criterion, + knn_based_capability_discovery, + score_based_capability_discovery, + select_complete_capabilities, +) +from src.utils.capability_management_utils import ( + get_previous_capabilities, +) +from src.utils.data_utils import check_cfg, get_run_id +from src.utils.embedding_utils import ( + apply_dimensionality_reduction, + apply_dimensionality_reduction_to_test_capabilities, + generate_and_set_capabilities_embeddings, +) +from src.utils.lbo_utils import get_lbo_train_set + + +@hydra.main(version_base=None, config_path="cfg", config_name="run_cfg") +def main(cfg: DictConfig) -> None: + """ + Discover new capabilities using LBO. + + Args: + cfg (DictConfig): Configuration for the model. + """ + check_cfg(cfg, logger) + run_id = get_run_id(cfg) + + # Set the base capability directory + base_capability_dir = os.path.join( + constants.BASE_ARTIFACTS_DIR, + f"capabilities_{run_id}", + cfg.capabilities_cfg.domain, + ) + logger.info(f"base_capability_dir: {base_capability_dir}") + # Read the capabilities from the base directory + capabilities = get_previous_capabilities( + capability_dir=base_capability_dir, + score_dir_suffix=run_id, + ) + capabilities = sorted(capabilities, key=lambda x: x.name) + logger.info(f"All capability names:\n{capabilities}") + + # Select complete capabilities (same set of capabilities were evaluated) + capabilities = select_complete_capabilities( + capabilities=capabilities, + strict=False, + num_tasks_lower_bound=int( + cfg.capabilities_cfg.num_gen_tasks_per_capability + * (1 - cfg.capabilities_cfg.num_gen_tasks_buffer) + ), + ) + capabilities = sorted(capabilities, key=lambda x: x.name) + logger.info(f"Selected capability names:\n{capabilities}") + + # Pre-load capability scores + for capability in tqdm(capabilities, desc="Loading capability scores"): + capability.load_scores( + subject_llm_name=cfg.subject_llm.name, + ) + logger.info(f"Capability: {capability.name}, scores: {capability.scores}") + + rmse_dict: Dict[str, float] = {} + avg_std_dict: Dict[str, float] = {} + for dim_reduction_method in ["t-sne", "pca"]: + for rep_string_order in ["n", "nd", "and"]: + # Embed capabilities using openai embedding model + generate_and_set_capabilities_embeddings( + capabilities=capabilities, + embedding_model_name=cfg.embedding_cfg.embedding_model, + embed_dimensions=cfg.embedding_cfg.embedding_size, + rep_string_order=rep_string_order, + ) + # Train-test split. + train_capabilities, test_capabilities = get_lbo_train_set( + input_data=capabilities, + train_frac=0.8, + input_categories=[capability.area for capability in capabilities], + seed=cfg.exp_cfg.seed, + ) + logger.info(f"num train_capabilities: {len(train_capabilities)}") + logger.info(f"num test_capabilities: {len(test_capabilities)}") + # Fit the dimensionality reduction model and transform all capabilities + _ = apply_dimensionality_reduction( + capabilities=capabilities, + dim_reduction_method_name=dim_reduction_method, + output_dimension_size=cfg.dimensionality_reduction_cfg.no_discovery_reduced_dimensionality_size, + embedding_model_name=cfg.embedding_cfg.embedding_model, + random_seed=cfg.exp_cfg.seed, + ) + # Fit model to training capabilities + # acquisition_function is unused. + lbo = fit_lbo( + capabilities=train_capabilities, + embedding_name=dim_reduction_method, + subject_llm_name=cfg.subject_llm.name, + acquisition_function="expected_variance_reduction", + ) + # Measure test set error. + rmse, avg_std = calculate_lbo_error( + lbo_model=lbo, + capabilities=test_capabilities, + embedding_name=dim_reduction_method, + subject_llm_name=cfg.subject_llm.name, + ) + logger.info(f"RMSE: {rmse}, avg_std: {avg_std}") + key = dim_reduction_method + "," + rep_string_order + rmse_dict[key] = rmse + avg_std_dict[key] = avg_std + + logger.info(f"rmse_dict: {rmse_dict}") + logger.info(f"avg_std_dict: {avg_std_dict}") + + +if __name__ == "__main__": + logger = logging.getLogger(__name__) + + main() diff --git a/src/utils/constants.py b/src/utils/constants.py index 7e8f312..9d2c6ec 100644 --- a/src/utils/constants.py +++ b/src/utils/constants.py @@ -4,7 +4,7 @@ from enum import Enum -BASE_ARTIFACTS_DIR = "/h/fkohankh/automated_capability_evaluation_logs" +BASE_ARTIFACTS_DIR = "/fs01/projects/aieng/public/ace/artifacts" GCP_BASE_ARTIFACTS_DIR = "gs://ace-artifacts" BASE_INSPECT_EVALS_DIR = "/h/fkohankh/automated_capability_evaluation_logs" diff --git a/src/utils/embedding_utils.py b/src/utils/embedding_utils.py index f94267e..92ba0e1 100644 --- a/src/utils/embedding_utils.py +++ b/src/utils/embedding_utils.py @@ -115,6 +115,7 @@ def generate_and_set_capabilities_embeddings( capabilities: List[Capability], embedding_model_name: str, embed_dimensions: int, + rep_string_order = "and" ) -> None: """Generate the capabilities embeddings using the OpenAI embedding model. @@ -125,6 +126,8 @@ def generate_and_set_capabilities_embeddings( capabilities (List[Capability]): The list of capabilities. embedding_model_name (str): The name of the embedding model to use. embed_dimensions (int): The number of dimensions for the embeddings. + rep_string_order (str): the order of fields that create the representation string. + Here, "a": Area, "n": Name, "d": Description. """ # Convert the embedding model name to `EmbeddingModelName` to ensure # that the provided model name is valid and supported. @@ -139,7 +142,20 @@ def generate_and_set_capabilities_embeddings( texts = [] for capability in capabilities: capability_dict = capability.to_dict(attribute_names=["name", "description"]) - rep_string = f"{capability_dict['name']} - {capability.area}: {capability_dict['description']}" + rep_string = "" + for char in rep_string_order: + if char == "a": + rep_string += capability.area + ", " + elif char == "n": + rep_string += capability_dict['name'] + ", " + elif char == "d": + rep_string += capability_dict['description'] + ", " + else: + raise ValueError(f"Invalid field code: {char}") + rep_string = rep_string.rstrip(", ") + if not rep_string: + raise ValueError("Representation string cannot be empty.") + print(f"Representation string: {rep_string}") texts.append(rep_string) embeddings = embedding_generator.generate_embeddings(texts) # Set embeddings for capabilities. From 02ce32d016599eb4f36dae3c066f5eddf23e959d Mon Sep 17 00:00:00 2001 From: Arash Afkanpour Date: Mon, 15 Sep 2025 17:42:34 -0400 Subject: [PATCH 2/4] reformatting files --- poetry.lock | 6752 ++++++++++++++++++---------------- src/run_embedding_eval.py | 2 +- src/utils/embedding_utils.py | 6 +- 3 files changed, 3656 insertions(+), 3104 deletions(-) diff --git a/poetry.lock b/poetry.lock index 38ae078..dceb53a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -86,28 +86,29 @@ wikipedia = ["wikipedia-api (>=0.8.1,<1.0)"] [[package]] name = "aiobotocore" -version = "2.21.1" +version = "2.24.2" description = "Async client for aws services using botocore and aiohttp" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" groups = ["main"] files = [ - {file = "aiobotocore-2.21.1-py3-none-any.whl", hash = "sha256:bd7c49a6d6f8a3d9444b0a94417c8da13813b5c7eec1c4f0ec2db7e8ce8f23e7"}, - {file = "aiobotocore-2.21.1.tar.gz", hash = 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+++ b/src/run_embedding_eval.py @@ -106,7 +106,7 @@ def main(cfg: DictConfig) -> None: embedding_model_name=cfg.embedding_cfg.embedding_model, random_seed=cfg.exp_cfg.seed, ) - # Fit model to training capabilities + # Fit model to training capabilities # acquisition_function is unused. lbo = fit_lbo( capabilities=train_capabilities, diff --git a/src/utils/embedding_utils.py b/src/utils/embedding_utils.py index 92ba0e1..56def4f 100644 --- a/src/utils/embedding_utils.py +++ b/src/utils/embedding_utils.py @@ -115,7 +115,7 @@ def generate_and_set_capabilities_embeddings( capabilities: List[Capability], embedding_model_name: str, embed_dimensions: int, - rep_string_order = "and" + rep_string_order="and", ) -> None: """Generate the capabilities embeddings using the OpenAI embedding model. @@ -147,9 +147,9 @@ def generate_and_set_capabilities_embeddings( if char == "a": rep_string += capability.area + ", " elif char == "n": - rep_string += capability_dict['name'] + ", " + rep_string += capability_dict["name"] + ", " elif char == "d": - rep_string += capability_dict['description'] + ", " + rep_string += capability_dict["description"] + ", " else: raise ValueError(f"Invalid field code: {char}") rep_string = rep_string.rstrip(", ") From 2d84150b38cf6afb30be13b167dc71b77a567d5f Mon Sep 17 00:00:00 2001 From: Arash Afkanpour Date: Wed, 17 Sep 2025 16:50:44 -0400 Subject: [PATCH 3/4] Add the calculation of confidence intervals --- src/run_embedding_eval.py | 106 ++++++++++++++++++++--------------- src/utils/embedding_utils.py | 4 +- 2 files changed, 64 insertions(+), 46 deletions(-) diff --git a/src/run_embedding_eval.py b/src/run_embedding_eval.py index 06bb943..299ad3a 100644 --- a/src/run_embedding_eval.py +++ b/src/run_embedding_eval.py @@ -1,24 +1,18 @@ """Script to discover new capabilities using LBO.""" -import json import logging import os -import shutil +from collections import defaultdict import hydra +import numpy as np from omegaconf import DictConfig +from scipy import stats from tqdm import tqdm -from src.generate_tasks import ( - generate_tasks_using_llm, -) -from src.lbo import calculate_lbo_error, fit_lbo, select_capabilities_using_lbo -from src.model import Model -from src.utils import constants, prompts +from src.lbo import calculate_lbo_error, fit_lbo +from src.utils import constants from src.utils.capability_discovery_utils import ( - capability_satisfies_criterion, - knn_based_capability_discovery, - score_based_capability_discovery, select_complete_capabilities, ) from src.utils.capability_management_utils import ( @@ -27,7 +21,6 @@ from src.utils.data_utils import check_cfg, get_run_id from src.utils.embedding_utils import ( apply_dimensionality_reduction, - apply_dimensionality_reduction_to_test_capabilities, generate_and_set_capabilities_embeddings, ) from src.utils.lbo_utils import get_lbo_train_set @@ -78,8 +71,11 @@ def main(cfg: DictConfig) -> None: ) logger.info(f"Capability: {capability.name}, scores: {capability.scores}") - rmse_dict: Dict[str, float] = {} - avg_std_dict: Dict[str, float] = {} + # Number of runs to calculate confidence intervals. + num_runs = 20 + + rmse_dict = defaultdict(list) + avg_std_dict = defaultdict(list) for dim_reduction_method in ["t-sne", "pca"]: for rep_string_order in ["n", "nd", "and"]: # Embed capabilities using openai embedding model @@ -89,15 +85,6 @@ def main(cfg: DictConfig) -> None: embed_dimensions=cfg.embedding_cfg.embedding_size, rep_string_order=rep_string_order, ) - # Train-test split. - train_capabilities, test_capabilities = get_lbo_train_set( - input_data=capabilities, - train_frac=0.8, - input_categories=[capability.area for capability in capabilities], - seed=cfg.exp_cfg.seed, - ) - logger.info(f"num train_capabilities: {len(train_capabilities)}") - logger.info(f"num test_capabilities: {len(test_capabilities)}") # Fit the dimensionality reduction model and transform all capabilities _ = apply_dimensionality_reduction( capabilities=capabilities, @@ -106,28 +93,59 @@ def main(cfg: DictConfig) -> None: embedding_model_name=cfg.embedding_cfg.embedding_model, random_seed=cfg.exp_cfg.seed, ) - # Fit model to training capabilities - # acquisition_function is unused. - lbo = fit_lbo( - capabilities=train_capabilities, - embedding_name=dim_reduction_method, - subject_llm_name=cfg.subject_llm.name, - acquisition_function="expected_variance_reduction", - ) - # Measure test set error. - rmse, avg_std = calculate_lbo_error( - lbo_model=lbo, - capabilities=test_capabilities, - embedding_name=dim_reduction_method, - subject_llm_name=cfg.subject_llm.name, - ) - logger.info(f"RMSE: {rmse}, avg_std: {avg_std}") - key = dim_reduction_method + "," + rep_string_order - rmse_dict[key] = rmse - avg_std_dict[key] = avg_std + for i in range(num_runs): + # Train-test split. + train_capabilities, test_capabilities = get_lbo_train_set( + input_data=capabilities, + train_frac=0.8, + input_categories=[capability.area for capability in capabilities], + seed=i, + ) + # Fit model to training capabilities + # acquisition_function is unused. + lbo = fit_lbo( + capabilities=train_capabilities, + embedding_name=dim_reduction_method, + subject_llm_name=cfg.subject_llm.name, + acquisition_function="expected_variance_reduction", + ) + # Measure test set error. + rmse, avg_std = calculate_lbo_error( + lbo_model=lbo, + capabilities=test_capabilities, + embedding_name=dim_reduction_method, + subject_llm_name=cfg.subject_llm.name, + ) + str_key = dim_reduction_method + "," + rep_string_order + rmse_dict[str_key].append(rmse) + avg_std_dict[str_key].append(avg_std) + + # Compute mean and 95% confidence interval + mean_values = {} + for key, values in rmse_dict.items(): + arr = np.array(values) + mean = arr.mean() + sem = stats.sem(arr) + ci = stats.t.interval(0.95, len(arr) - 1, loc=mean, scale=sem) + mean_values[key] = {"mean": mean, "95p_ci": ci} + for key, stats_dict in mean_values.items(): + ci_low, ci_high = stats_dict["95p_ci"] + logger.info( + f"{key}: RMSE={stats_dict['mean']:.3f}, 95% Confidence Interval=({ci_low:.3f}, {ci_high:.3f})" + ) - logger.info(f"rmse_dict: {rmse_dict}") - logger.info(f"avg_std_dict: {avg_std_dict}") + std_values = {} + for key, values in avg_std_dict.items(): + arr = np.array(values) + mean = arr.mean() + sem = stats.sem(arr) + ci = stats.t.interval(0.95, len(arr) - 1, loc=mean, scale=sem) + std_values[key] = {"mean": mean, "95p_ci": ci} + for key, stats_dict in std_values.items(): + ci_low, ci_high = stats_dict["95p_ci"] + logger.info( + f"{key}: Standard Deviation (Uncertainty)={stats_dict['mean']:.3f}, 95% Confidence Interval=({ci_low:.3f}, {ci_high:.3f})" + ) if __name__ == "__main__": diff --git a/src/utils/embedding_utils.py b/src/utils/embedding_utils.py index 56def4f..bd8406c 100644 --- a/src/utils/embedding_utils.py +++ b/src/utils/embedding_utils.py @@ -126,8 +126,8 @@ def generate_and_set_capabilities_embeddings( capabilities (List[Capability]): The list of capabilities. embedding_model_name (str): The name of the embedding model to use. embed_dimensions (int): The number of dimensions for the embeddings. - rep_string_order (str): the order of fields that create the representation string. - Here, "a": Area, "n": Name, "d": Description. + rep_string_order (str): the order of fields that create the representation + string. Here, "a": Area, "n": Name, "d": Description. """ # Convert the embedding model name to `EmbeddingModelName` to ensure # that the provided model name is valid and supported. From d5e450b7e88782a2e489c834e32a2a833c7833d1 Mon Sep 17 00:00:00 2001 From: Arash Afkanpour Date: Thu, 18 Sep 2025 11:10:43 -0400 Subject: [PATCH 4/4] Increased the number of runs to 100. --- src/run_embedding_eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/run_embedding_eval.py b/src/run_embedding_eval.py index 299ad3a..10cb9d9 100644 --- a/src/run_embedding_eval.py +++ b/src/run_embedding_eval.py @@ -72,7 +72,7 @@ def main(cfg: DictConfig) -> None: logger.info(f"Capability: {capability.name}, scores: {capability.scores}") # Number of runs to calculate confidence intervals. - num_runs = 20 + num_runs = 100 rmse_dict = defaultdict(list) avg_std_dict = defaultdict(list)