diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..ecc9843 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,33 @@ +name: Run tests and lint + +on: + pull_request: + branches: + - "**" # runs for all branches with PRs + +jobs: + lint-and-test: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install uv + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "$HOME/.local/bin" >> $GITHUB_PATH + + - name: Sync dependencies + run: uv sync --extra dev + + - name: Run Ruff linter + run: uv run ruff check . + + - name: Run tests with pytest + run: uv run pytest -v diff --git a/README.md b/README.md index 801ca90..7b5cc18 100644 --- a/README.md +++ b/README.md @@ -14,3 +14,60 @@ This repository provides a unified and extensible framework for running and orga ```bash uv sync ``` + +## Automatic Evaluation Conversion Scripts + +We are supporting following evaluation platforms for automatic converting their evaluations into our unified schema. + +### Inspect +Convert eval log from Inspect AI into json format with following command: + +```bash +uv run inspect log convert path_to_eval_file_generated_by_inspect --to json --output-dir inspect_json +``` + +Then we can convert Inspect evaluation log into unified schema via eval_converters/inspect/converter.py. Conversion for example data can be generated via below script: + +```bash +uv run python3 -m eval_converters.inspect.converter +``` + +for example: + +```bash +uv run python3 -m eval_converters.inspect.converter --log_path tests/data/inspect/data_arc_qwen.json +``` + + +Full manual for conversion of your own Inspect evaluation log into unified is available below: + +```bash +usage: converter.py [-h] [--log_path LOG_PATH] + [--huggingface_dataset HUGGINGFACE_DATASET] + [--output_dir OUTPUT_DIR] + [--source_organization_name SOURCE_ORGANIZATION_NAME] + [--evaluator_relationship {first_party,third_party,collaborative,other}] + [--source_organization_url SOURCE_ORGANIZATION_URL] + [--source_organization_logo_url SOURCE_ORGANIZATION_LOGO_URL] + +options: + -h, --help show this help message and exit + --log_path LOG_PATH + --huggingface_dataset HUGGINGFACE_DATASET + --output_dir OUTPUT_DIR + --source_organization_name SOURCE_ORGANIZATION_NAME + Orgnization which pushed evaluation to the evalHub. + --evaluator_relationship {first_party,third_party,collaborative,other} + Relationship of evaluation author to the model + --source_organization_url SOURCE_ORGANIZATION_URL + --source_organization_logo_url SOURCE_ORGANIZATION_LOGO_URL +``` + +## Tests + +Run below script to perform unit tests for all evaluation platforms. + +```bash +uv run pytest -s +uv run ruff check +``` \ No newline at end of file diff --git a/eval_converters/common/adapter.py b/eval_converters/common/adapter.py index df8e04b..db0dfc3 100644 --- a/eval_converters/common/adapter.py +++ b/eval_converters/common/adapter.py @@ -2,12 +2,14 @@ from dataclasses import dataclass from abc import ABC, abstractmethod from enum import Enum -from schema.eval_types import EvaluationResult +from huggingface_hub import model_info +from schema.eval_types import EvaluationLog from typing import Any, List, Union from pathlib import Path import json from eval_converters.common.error import AdapterError, TransformationError +from schema.eval_types import SourceMetadata @dataclass class AdapterMetadata: @@ -58,7 +60,7 @@ def supported_library(self) -> SupportedLibrary: pass @abstractmethod - def _transform_single(self, raw_data: Any) -> EvaluationResult: + def _transform_single(self, raw_data: Any, source_metadata: SourceMetadata) -> EvaluationLog: """ Transform a single evaluation record. @@ -66,14 +68,14 @@ def _transform_single(self, raw_data: Any) -> EvaluationResult: raw_data: Single evaluation record in library-specific format Returns: - EvaluationResult in unified schema format + EvaluationLog in unified schema format Raises: TransformationError: If transformation fails """ pass - def transform(self, data: Any) -> Union[EvaluationResult, List[EvaluationResult]]: + def transform(self, data: Any, source_metadata: SourceMetadata) -> Union[EvaluationLog, List[EvaluationLog]]: """ Transform evaluation data to unified schema format. @@ -89,18 +91,18 @@ def transform(self, data: Any) -> Union[EvaluationResult, List[EvaluationResult] results = [] for i, item in enumerate(data): try: - result = self._transform_single(item) + result = self._transform_single(item, source_metadata) results.append(result) except Exception as e: self._handle_transformation_error(e, f"item {i}") return results else: - return self._transform_single(data) + return self._transform_single(data, source_metadata) except Exception as e: self._handle_transformation_error(e, "data transformation") - def transform_from_file(self, file_path: Union[str, Path]) -> Union[EvaluationResult, List[EvaluationResult]]: + def transform_from_file(self, file_path: Union[str, Path], source_metadata: SourceMetadata) -> Union[EvaluationLog, List[EvaluationLog]]: """ Load and transform evaluation data from file. @@ -117,12 +119,12 @@ def transform_from_file(self, file_path: Union[str, Path]) -> Union[EvaluationRe try: data = self._load_file(file_path) - return self.transform(data) + return self.transform(data, source_metadata) except Exception as e: raise AdapterError(f"Failed to load file {file_path}: {str(e)}") @abstractmethod - def transform_from_directory(self, dir_path: Union[str, Path]) -> Union[EvaluationResult, List[EvaluationResult]]: + def transform_from_directory(self, dir_path: Union[str, Path]) -> Union[EvaluationLog, List[EvaluationLog]]: """ Load and transform evaluation data from all files in a directory. @@ -172,4 +174,11 @@ def _handle_transformation_error(self, error: Exception, context: str): if self.strict_validation: raise TransformationError(error_msg) from error else: - self.logger.warning(error_msg) \ No newline at end of file + self.logger.warning(error_msg) + + def _check_if_model_is_on_huggingface(self, model_path): + try: + info = model_info(model_path) + return info + except Exception: + self.logger.warning(f"Model '{model_path}' not found on Hugging Face.") diff --git a/eval_converters/common/utils.py b/eval_converters/common/utils.py index 677e22e..9731d97 100644 --- a/eval_converters/common/utils.py +++ b/eval_converters/common/utils.py @@ -1,28 +1,63 @@ -from schema.eval_types import Family, HfSplit +from datetime import datetime +from huggingface_hub import HfApi +from typing import Dict -def detect_family(model_name: str) -> Family: - """Return the Family enum if any of its values is a substring of model_name.""" - model_name_lower = model_name.lower() - for family in Family: - if family.value and family.value.lower() in model_name_lower: - return family - return Family.NoneType_None +def convert_timestamp_to_unix_format(timestamp: str): + dt = datetime.fromisoformat(timestamp) + return str(dt.timestamp()) -def detect_hf_split(split_str: str) -> HfSplit: +def get_model_organization_info(model_base_name: str) -> Dict: """ - Determines the type of dataset split from a given string. - + Searches the Hugging Face Hub for a model based on its base name + and attempts to find the organization that published the most relevant/original version. + Args: - split_str (str): The input string to classify. - + model_base_name: The model name without an organization (e.g., 'deepseek-coder-6.7b-base'). + Returns: - HfSplit: One of "train", "test", or "validation". + A dictionary containing the best-guess organization and full repository ID, + or an error message. """ - s = split_str.strip().lower() - if s == "test": - return HfSplit.test - elif "train" in s: - return HfSplit.train - else: - return HfSplit.validation \ No newline at end of file + api = HfApi() + + try: + models = api.list_models( + search=model_base_name, + sort="downloads", + direction=-1, + limit=50 + ) + models_list = list(models) + except Exception as e: + return f"Failed to connect to Hugging Face Hub: {e}" + + if not models_list: + return 'not_found' + + # Heuristic to find the 'Original' Organization: + # The original model is usually the one with the shortest repo_id + # that includes the base model name (e.g., 'deepseek-ai/deepseek-coder-6.7b-base'). + # We also prioritize the one with the highest downloads. + + best_match = models_list[0] # Start with the most downloaded model + + for model in models_list: + repo_id = model.modelId + + parts = repo_id.split('/') + if len(parts) != 2: + continue + + org, name = parts + + # A good heuristic: the model name part (name) should exactly match the base name, + # or be a very close variant (e.g., -instruct) with the highest download count. + if model_base_name in name and name == model_base_name: + best_match = model + break + + full_repo_id = best_match.modelId + organization = full_repo_id.split('/')[0] + + return organization \ No newline at end of file diff --git a/custom/__init__.py b/eval_converters/custom/__init__.py similarity index 100% rename from custom/__init__.py rename to eval_converters/custom/__init__.py diff --git a/custom/custom_runner.py b/eval_converters/custom/custom_runner.py similarity index 100% rename from custom/custom_runner.py rename to eval_converters/custom/custom_runner.py diff --git a/eval_converters/helm/adapter.py b/eval_converters/helm/adapter.py index 008c110..de6c746 100644 --- a/eval_converters/helm/adapter.py +++ b/eval_converters/helm/adapter.py @@ -1,8 +1,7 @@ import os import wget import json -from typing import List, Dict, Sequence, Optional, Any -import tempfile +from typing import List, Dict from helm.benchmark.metrics.metric import PerInstanceStats from helm.benchmark.presentation.schema import Schema, read_schema from helm.benchmark.adaptation.scenario_state import ScenarioState @@ -173,7 +172,7 @@ def transform_from_directory(self, dir_path): context_window = 1 except Exception as e: - print(f"Error getting context window: {e}") + self.logger.error(f"Error getting context window: {e}") context_window = 1 configuration = Configuration( @@ -184,7 +183,7 @@ def transform_from_directory(self, dir_path): try: precision, method = infer_quantization(adapter_spec.model) except Exception as e: - print(f"Error getting quantization: {e}") + self.logger.warning(f"Error getting quantization: {e}") precision = BitPrecision.none method = Method.None_ @@ -271,7 +270,7 @@ def transform_from_directory(self, dir_path): score = instance_scores[request_state.instance.id] except Exception as e: - print(f"Error getting instance scores: {e}") + self.logger.warning(f"Error getting instance scores: {e}") score = 0.0 evaluation = Evaluation( @@ -361,7 +360,7 @@ def _transform_single(self, raw_data, base_dir=None): context_window = 1 except Exception as e: - print(f"Error getting context window: {e}") + self.logger.warning(f"Error getting context window: {e}") context_window = 1 configuration = Configuration( @@ -372,7 +371,7 @@ def _transform_single(self, raw_data, base_dir=None): try: precision, method = infer_quantization(adapter_spec.model) except Exception as e: - print(f"Error getting quantization: {e}") + self.logger.warning(f"Error getting quantization: {e}") precision = BitPrecision.none method = Method.None_ @@ -479,7 +478,7 @@ def _transform_single(self, raw_data, base_dir=None): break except Exception as e: - print(f"Error getting instance scores: {e}") + self.logger.warning(f"Error getting instance scores: {e}") instance_scores = {} score = instance_scores.get(request_state.instance.id, 0.0) diff --git a/eval_converters/inspect/adapter.py b/eval_converters/inspect/adapter.py new file mode 100644 index 0000000..5924a69 --- /dev/null +++ b/eval_converters/inspect/adapter.py @@ -0,0 +1,151 @@ +import os + +from inspect_ai.log import EvalLog, EvalSpec, EvalStats, read_eval_log + +from pathlib import Path +from typing import List, Union + +from schema import SCHEMA_VERSION +from schema.eval_types import ( + DetailedEvaluationResultsPerSample, + EvaluationLog, + EvaluationResult, + EvaluationSource, + EvaluationSourceType, + MetricConfig, + ModelInfo, + ScoreDetails, + SourceData, + SourceMetadata +) + +from eval_converters.common.adapter import AdapterMetadata, BaseEvaluationAdapter, SupportedLibrary +from eval_converters.common.error import AdapterError +from eval_converters.common.utils import convert_timestamp_to_unix_format +from eval_converters.inspect.utils import extract_model_info_from_model_path + +class InspectAIAdapter(BaseEvaluationAdapter): + """ + Adapter for transforming evaluation outputs from the Inspect AI library into the unified schema format. + """ + + @property + def metadata(self) -> AdapterMetadata: + return AdapterMetadata( + name="InspectAdapter", + version="0.0.1", + description="Adapter for transforming HELM evaluation outputs to unified schema format" + ) + + @property + def supported_library(self) -> SupportedLibrary: + return SupportedLibrary.INSPECT_AI + + def transform_from_directory(self, dir_path: Union[str, Path]): + raise NotImplementedError("Inspect AI adapter do not support loading logs from directory!") + + def transform_from_file(self, file_path: Union[str, Path], source_metadata: SourceMetadata = None) -> Union[EvaluationLog, List[EvaluationLog]]: + if not os.path.exists(file_path): + raise FileNotFoundError(f'File path {file_path} does not exists!') + + try: + file_path = Path(file_path) if isinstance(file_path, str) else file_path + eval_log: EvalLog = self._load_file(file_path) + return self.transform(eval_log, source_metadata) + except AdapterError as e: + raise e + except Exception as e: + raise AdapterError(f"Failed to load file {file_path}: {str(e)} for InspectAIAdapter") + + def _transform_single(self, raw_data: EvalLog, source_metadata: SourceMetadata) -> EvaluationLog: + eval_spec: EvalSpec = raw_data.eval + eval_stats: EvalStats = raw_data.stats + + retrieved_timestamp = eval_stats.started_at or eval_spec.created + retrieved_unix_timestamp = convert_timestamp_to_unix_format(retrieved_timestamp) + + source_data = SourceData( + dataset_name=eval_spec.dataset.name.split('/')[-1], + hf_repo=eval_spec.dataset.location, + samples_number=eval_spec.dataset.samples, + sample_ids=eval_spec.dataset.sample_ids + ) + + evaluation_source = EvaluationSource( + evaluation_source_name='inspect_ai', + evaluation_source_type=EvaluationSourceType.evaluation_platform + ) + + model_path = eval_spec.model + self._check_if_model_is_on_huggingface(model_path) + + if raw_data.samples: + detailed_model_name = raw_data.samples[0].output.model + model_path_parts = model_path.split('/') + + if model_path_parts[-1] in detailed_model_name: + model_path_parts[-1] = detailed_model_name + + model_path = '/'.join(model_path_parts) + + model_info: ModelInfo = extract_model_info_from_model_path(model_path) + + results = raw_data.results + evaluation_results = [] + + generation_config = { + gen_config: value + for gen_config, value in vars(eval_spec.model_generate_config).items() if value is not None + } + + for scorer_results in results.scores: + scorer_name = scorer_results.scorer + for metric in scorer_results.metrics: + metric_info = scorer_results.metrics[metric] + if metric_info.name != 'stderr': + evaluation_results.append(EvaluationResult( + evaluation_name=scorer_name, + evaluation_timestamp=convert_timestamp_to_unix_format(eval_stats.completed_at), + metric_config=MetricConfig( + evaluation_description=metric_info.name, + lower_is_better=False # probably there is no access to such info + ), + score_details=ScoreDetails( + score=metric_info.value + ), + generation_config=generation_config + )) + + detailed_evaluation_results_per_samples = [] + for sample in raw_data.samples: + if sample.scores: + response = sample.scores.get('choice').answer + else: + response = sample.output.choices[0].message.content + + detailed_evaluation_results_per_samples.append( + DetailedEvaluationResultsPerSample( + sample_id=str(sample.id), + input=sample.input, + ground_truth=sample.target, + response=response, + choices=sample.choices + ) + ) + + evaluation_id = f'inspect_ai/{model_path}/{eval_spec.dataset.name}/{retrieved_unix_timestamp}' + + return EvaluationLog( + schema_version=SCHEMA_VERSION, + evaluation_id=evaluation_id, + retrieved_timestamp=retrieved_unix_timestamp, + source_data=source_data, + evaluation_source=evaluation_source, + source_metadata=source_metadata, + model_info=model_info, + evaluation_results=evaluation_results, + detailed_evaluation_results_per_samples=detailed_evaluation_results_per_samples + ) + + def _load_file(self, file_path) -> EvalLog: + return read_eval_log(file_path) \ No newline at end of file diff --git a/eval_converters/inspect/converter.py b/eval_converters/inspect/converter.py index e69de29..73cd24f 100644 --- a/eval_converters/inspect/converter.py +++ b/eval_converters/inspect/converter.py @@ -0,0 +1,86 @@ +from __future__ import annotations +from argparse import ArgumentParser +import json +from enum import Enum +from pathlib import Path + +from eval_converters.inspect.adapter import InspectAIAdapter +from schema.eval_types import ( + EvaluatorRelationship, + EvaluationLog, + SourceMetadata +) + +def parse_args(): + parser = ArgumentParser() + + parser.add_argument('--log_path', type=str, default='tests/data/inspect/data.json') + parser.add_argument('--huggingface_dataset', type=str) + parser.add_argument('--output_dir', type=str, default='tests/data/inspect') + parser.add_argument('--source_organization_name', type=str, default='Unknown', help='Orgnization which pushed evaluation to the evalHub.') + parser.add_argument('--evaluator_relationship', type=str, default='other', help='Relationship of evaluation author to the model', choices=['first_party', 'third_party', 'collaborative', 'other']) + parser.add_argument('--source_organization_url', type=str, default=None) + parser.add_argument('--source_organization_logo_url', type=str, default=None) + + + args = parser.parse_args() + return args + + +class EnumEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, Enum): + return obj.value + return super().default(obj) + +class InspectEvalLogConverter: + def __init__(self, log_path: str | Path, output_dir: str = 'unified_schema/inspect_ai'): + ''' + InspectAI generates log file for an evaluation. + ''' + self.log_path = Path(log_path) + + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def convert_to_unified_schema(self, source_metadata: SourceMetadata = None) -> EvaluationLog: + return InspectAIAdapter().transform_from_file(self.log_path, source_metadata=source_metadata) + + def save_to_file(self, unified_eval_log: EvaluationLog, output_filename: str) -> bool: + try: + json_str = unified_eval_log.model_dump_json(indent=2) + + with open(f'{self.output_dir}/{output_filename}', 'w') as json_file: + json_file.write(json_str) + + print(f'Unified eval log was successfully saved to {output_filename} file.') + except Exception as e: + print(f"Problem with saving unified eval log to file: {e}") + raise e + + def save_to_hf_datasets(self, unified_eval_log: EvaluationLog) -> bool: + # TODO + pass + + +if __name__ == '__main__': + args = parse_args() + + inspect_converter = InspectEvalLogConverter( + log_path=args.log_path, + output_dir=args.output_dir + ) + + source_metadata = SourceMetadata( + source_organization_name=args.source_organization_name, + source_organization_url=args.source_organization_url, + source_organization_logo_url=args.source_organization_logo_url, + evaluator_relationship=EvaluatorRelationship(args.evaluator_relationship) + ) + + unified_output: EvaluationLog = inspect_converter.convert_to_unified_schema(source_metadata) + if unified_output: + output_filename = f'{str(unified_output.evaluation_id).replace('/', '_')}.json' + inspect_converter.save_to_file(unified_output, output_filename) + else: + print("Missing unified schema result!") \ No newline at end of file diff --git a/eval_converters/inspect/utils.py b/eval_converters/inspect/utils.py new file mode 100644 index 0000000..6205605 --- /dev/null +++ b/eval_converters/inspect/utils.py @@ -0,0 +1,271 @@ +import re +from eval_converters.common.utils import get_model_organization_info +from schema.eval_types import ModelInfo +from typing import Dict, Type, List + + +class ModelPathHandler: + """Base class for all model path parsing strategies.""" + def __init__(self, model_path: str): + self.model_path = model_path + self.parts = model_path.split('/') + + def handle(self) -> ModelInfo: + """Must be implemented by subclasses to return the parsed ModelInfo.""" + raise NotImplementedError + +def normalize_claude_model_name(model_str: str) -> str: + """ + Normalizes any Claude model identifier to the canonical format: + 'claude-3-5-sonnet@YYYYMMDD' + """ + base_match = re.search(r'(claude-\d+-\d+-[a-z]+)', model_str) + if not base_match: + # Return the original string if the base model name is not found + return model_str + base = base_match.group(1) + + # Use '00000000' as a default date if not found + date_match = re.search(r'@?(\d{8})', model_str) + date = date_match.group(1) if date_match else "00000000" + + return f"{base}@{date}" + +class ClosedApiHandler(ModelPathHandler): + """Handles paths for closed API providers like OpenAI, Anthropic, Google, etc.""" + def handle(self) -> ModelInfo: + developer = self.parts[0] + inference_platform = self.parts[0] + model_id = self.model_path + + # Special handling for Anthropic models running on cloud platforms (Vertex/Bedrock) + if self.model_path.startswith('anthropic/vertex') or self.model_path.startswith('anthropic/bedrock'): + if len(self.parts) >= 3: + # e.g., anthropic/vertex/claude-3-5-sonnet-v2@20241022 + developer = self.parts[0] + inference_platform = self.parts[1] + model_id = f'{developer}/{normalize_claude_model_name(self.parts[2])}' + + # General handling for Azure or Vertex (non-Anthropic) + elif 'azure' in self.model_path or 'vertex' in self.model_path: + # Assumes the format: developer/platform/model_name + if len(self.parts) >= 3: + developer = self.parts[0] + inference_platform = self.parts[1] + model_id = f'{developer}/{self.parts[2]}' + + return ModelInfo( + name=self.model_path, + id=model_id, + developer=developer, + inference_platform=inference_platform + ) + +class BedrockParser: + + @staticmethod + def parse(model_path: str) -> ModelInfo: + # Remove any variant suffix after a colon (e.g. ':2') + base_model = model_path.split('/')[1] + + # Split into developer and model parts + parts = base_model.split('.', 1) + developer = parts[0] if len(parts) > 1 else None + model = parts[1] if len(parts) > 1 else parts[0] + + # Strip trailing version tags like '-v0', '-v1.2' + model = re.sub(r'-v\d+(\.\d+)?$', '', model) + + return ModelInfo( + name=model_path, + id=f"{developer}/{model}" if developer else model, + developer=developer, + inference_platform='bedrock' + ) + +class AzureAiParser: + + @staticmethod + def parse(model_name: str) -> ModelInfo: + parts = model_name.split('/') + inference_platform = parts[0] + base_model_name = parts[-1] + + inferred_org_name = get_model_organization_info(base_model_name) + developer = inferred_org_name if inferred_org_name and inferred_org_name != 'not_found' else inference_platform + + return ModelInfo( + name=model_name, + id=f'{developer}/{base_model_name}', # Corrected 'id' logic + developer=developer, + inference_platform=inference_platform + ) + +class CloudApiHandler(ModelPathHandler): + """Handles paths for cloud API providers like AWS Bedrock and Azure AI.""" + def handle(self) -> ModelInfo: + if self.model_path.startswith('bedrock'): + return BedrockParser.parse(self.model_path) + + elif self.model_path.startswith('azureai'): + return AzureAiParser.parse(self.model_path) + + return ModelInfo(name=self.model_path, id=self.model_path, developer='unknown', inference_platform='cloud_api') + +class HostedOpenHandler(ModelPathHandler): + """Handles paths for hosted open models (Together, Groq, Fireworks, etc.).""" + + # Model name to developer map for Sambanova + SAMBANOVA_DEV_MAP = { + 'llama': 'meta-llama', 'gpt': 'openai', 'qwen': 'qwen', + 'swallow': 'tokyotech-llm', 'allam': 'humain-ai', + 'mistral': 'mistral', 'deepseek': 'deepseek-ai' + } + + def handle(self) -> ModelInfo: + inference_platform = self.parts[0] + model_id = self.model_path + developer = 'unknown' + + path_lower = self.model_path.lower() + + # Group 1: Platform/Developer/Model format + platforms = ['together', 'cf', 'openrouter', 'openai-api', 'hf-inference-providers'] + if any(path_lower.startswith(p) for p in platforms): + if len(self.parts) >= 3: + developer = self.parts[1] + model_id = f'{developer}/{self.parts[2]}' + else: + developer = inference_platform # Fallback + + # Group 2: Groq specific logic + elif path_lower.startswith('groq'): + if len(self.parts) >= 2: + model_name = self.parts[1] + is_llama = 'llama' in model_name.lower() + developer = 'meta-llama' if is_llama else inference_platform + model_id = f'{developer}/{model_name}' + + # Group 3: Sambanova specific logic + elif path_lower.startswith('sambanova'): + if len(self.parts) >= 2: + model_name_part = self.parts[-1].lower() + + for key, dev in self.SAMBANOVA_DEV_MAP.items(): + if key in model_name_part: + developer = dev + break + model_id = f'{developer}/{self.parts[-1]}' + + # Group 4: Fireworks specific logic + elif path_lower.startswith('fireworks'): + # e.g. fireworks/accounts/fireworks/models/deepseek-r1-0528 + model_name = self.parts[-1] + # Assuming get_model_organization_info returns an object with a 'organization' key + inferred_org_name = get_model_organization_info(model_name) + developer = inferred_org_name if inferred_org_name != 'not_found' else 'unknown' + model_id = f'{developer}/{model_name}' + + if developer == 'unknown': + if len(self.parts) >= 2: + developer = self.parts[1] + model_id = f'{developer}/{self.parts[-1]}' + + return ModelInfo( + name=self.model_path, + id=model_id, + developer=developer, + inference_platform=inference_platform + ) + +class InferenceEngineHandler(ModelPathHandler): + """Handles paths for inference engines (vLLM, Ollama, SGLang, etc.).""" + def handle(self) -> ModelInfo: + inference_engine = self.parts[0] + model_id = self.model_path + developer = 'unknown' # Default value + + # Group 1: /Engine/Developer/Model format (e.g., vllm/meta-llama/Llama-2-7b-chat) + if any(self.model_path.startswith(e) for e in ['vllm', 'sglang', 'hf']): + if len(self.parts) >= 3: + developer = self.parts[1] + model_id = f'{self.parts[1]}/{self.parts[2]}' + else: + developer = inference_engine + + # Group 2: Ollama and Llama-cpp-python format (e.g., ollama/llama2:7b) + elif any(self.model_path.startswith(e) for e in ['ollama', 'llama-cpp-python']): + if len(self.parts) >= 2: + developer = self.parts[0] + model_name = self.parts[1].replace(':', '-') + model_id = f'{self.parts[0]}/{model_name}' + + return ModelInfo( + name=self.model_path, + id=model_id, + developer=developer, + inference_engine=inference_engine + ) + +# Mapping the provider/engine prefix to the specific Handler class +# This is where the extension point is for *new* provider categories. +MODEL_HANDLER_MAP: Dict[str, Type[ModelPathHandler]] = { + # Closed API Providers + 'openai': ClosedApiHandler, + 'anthropic': ClosedApiHandler, + 'google': ClosedApiHandler, + 'grok': ClosedApiHandler, + 'mistral': ClosedApiHandler, + 'deepseek': ClosedApiHandler, + 'perplexity': ClosedApiHandler, + + # Cloud API Providers + 'bedrock': CloudApiHandler, + 'azure-ai': CloudApiHandler, + + # Hosted Open Providers + 'groq': HostedOpenHandler, + 'together': HostedOpenHandler, + 'fireworks': HostedOpenHandler, + 'cf': HostedOpenHandler, + 'hf-inference-providers': HostedOpenHandler, + 'sambanova': HostedOpenHandler, + 'openrouter': HostedOpenHandler, + 'openai-api': HostedOpenHandler, + + # Inference Engines + 'hf': InferenceEngineHandler, + 'vllm': InferenceEngineHandler, + 'ollama': InferenceEngineHandler, + 'llamacpp': InferenceEngineHandler, + 'sglang': InferenceEngineHandler +} + +PROVIDER_PREFIXES: List[str] = list(MODEL_HANDLER_MAP.keys()) + +def extract_model_info_from_model_path(model_path: str) -> ModelInfo: + """ + Infers the ModelInfo by dispatching the model_path to the appropriate handler. + + This function is now simple and focuses only on dispatching. + To add a new provider/engine, you only need to update the MODEL_HANDLER_MAP + and create a corresponding Handler class. + """ + handler_class = None + for prefix in PROVIDER_PREFIXES: + model_provider = model_path.split('/')[0] + if model_provider.lower() == prefix: + handler_class = MODEL_HANDLER_MAP[prefix] + break + + if handler_class: + handler = handler_class(model_path) + return handler.handle() + + # Fallback + return ModelInfo( + name=model_path, + id=model_path, + developer='unknown', + inference_platform='unknown' + ) \ No newline at end of file diff --git a/metadata.py b/metadata.py deleted file mode 100644 index e69de29..0000000 diff --git a/outputs.py b/outputs.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyproject.toml b/pyproject.toml index dc5edf3..5f7297e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,12 +7,15 @@ requires-python = ">=3.12" dependencies = [ "crfm-helm>=0.5.6", "dacite>=1.9.2", + "huggingface-hub>=0.33.2", + "inspect-ai>=0.3.140", "numpy>=2.3.1", "openai>=1.93.0", "pandas>=2.3.0", "pydantic>=2.11.7", "requests>=2.32.4", "transformers>=4.53.1", + "wget>=3.2", ] [project.optional-dependencies] @@ -28,3 +31,9 @@ dev = [ [tool.setuptools.packages.find] include = ["helm*", "schema*", "common*", "config*", "eval_converters*"] exclude = ["tests*"] + +[tool.pytest.ini_options] +pythonpath = ["."] + +[tool.uv] +package = true \ No newline at end of file diff --git a/schema/README.md b/schema/README.md index da36a8a..0eeb962 100644 --- a/schema/README.md +++ b/schema/README.md @@ -5,7 +5,9 @@ To generate or update Python types from the JSON schema, you can run the following command in the current directory: ```bash -datamodel-codegen --input eval.schema.json --output eval_types.py --class-name EvaluationResult --output-model-type pydantic_v2.BaseModel +uv add datamodel-code-generator +cd schema +uv run datamodel-codegen --input eval.schema.json --output eval_types.py --class-name EvaluationLog --output-model-type pydantic_v2.BaseModel --input-file-type jsonschema ``` ## Example Data @@ -17,4 +19,4 @@ Please refer to [this file](./eval.example.json) for a minimal data example adhe The leaderboard format schema is defined in [leaderboard.schema.json](./leaderboard.schema.json). It is used to store and validate leaderboard-style data. The schema allows for "scores" both in a continuous format (e.g. accuracy) or level-based (e.g. low/medium/high). -For level-based data, actual scores are stored as integers that are mapped to the corresponding level names. Scores are values from 0 to N or -1 to N (if has_unknown_level is True). For presentation purposes, the values are mapped to the level names in the level_names array. +For level-based data, actual scores are stored as integers that are mapped to the corresponding level names. Scores are values from 0 to N or -1 to N (if has_unknown_level is True). For presentation purposes, the values are mapped to the level names in the level_names array. \ No newline at end of file diff --git a/schema/eval.example.json b/schema/eval.example.json deleted file mode 100644 index 5fda5bd..0000000 --- a/schema/eval.example.json +++ /dev/null @@ -1,60 +0,0 @@ -{ - "schema_version": "0.0.1", - "evaluation_id": "eval_001", - "model": { - "model_info": { - "name": "Llama-2-7b-hf" - }, - "configuration": { - "context_window": 4096 - }, - "inference_settings": { - "quantization": { - "bit_precision": "float16", - "method": "None" - } - } - }, - "prompt_config": { - "prompt_class": "MultipleChoice" - }, - "instance": { - "task_type": "classification", - "raw_input": "What is 2+2?", - "language": "en", - "sample_identifier": { - "dataset_name": "math_basic", - "hf_repo": "example/math", - "hf_split": "test", - "hf_index": 1 - }, - "classification_fields": { - "question": "What is 2+2?", - "choices": [ - { - "id": "A", - "text": "3" - }, - { - "id": "B", - "text": "4" - } - ], - "ground_truth": { - "id": "B", - "text": "4" - } - } - }, - "output": { - "response": "B" - }, - "evaluation": { - "evaluation_method": { - "method_name": "label_only_match", - "description": "Compares only the choice identifier/label to evaluate the response." - }, - "ground_truth": "B", - "score": 1.0 - } -} \ No newline at end of file diff --git a/schema/eval.schema.json b/schema/eval.schema.json index 883d12d..67826c8 100644 --- a/schema/eval.schema.json +++ b/schema/eval.schema.json @@ -27,11 +27,48 @@ "description": "Timestamp for when this record was created - using Unix Epoch time format" }, "source_data": { - "type": "array", - "description": "URLs for the source of the evaluation data", - "items": { - "type": "string" - } + "description": "Source of dataset used for evaluation. There are two options supported: HuggingFace dataset or url for other data source.", + "oneOf": [ + { + "type": "array", + "description": "URLs for the source of the evaluation data", + "items": { + "type": "string" + } + }, + { + "type": "object", + "description": "Details about HuggingFace dataset used for evaluation", + "required": [ + "dataset_name" + ], + "properties": { + "dataset_name": { + "type": "string", + "description": "Name of the source dataset" + }, + "hf_repo": { + "type": "string", + "description": "HuggingFace repository identifier" + }, + "hf_split": { + "type": "string", + "description": "One of train, val or test." + }, + "samples_number": { + "type": "integer", + "description": "Number of samples in the dataset" + }, + "sample_ids": { + "type": "array", + "description": "Array of sample ids used for evaluation", + "items": { + "type": "integer" + } + } + } + } + ] }, "evaluation_source": { "type": "object", @@ -101,7 +138,7 @@ }, "id": { "type": "string", - "description": "Model name standarized to HuggingFace format (e.g. meta-llama/Llama-3.1-8B-Instruct)" + "description": "Model name in HuggingFace format (e.g. meta-llama/Llama-3.1-8B-Instruct for models available on HuggingFace or openai/azure/gpt-4o-mini-2024-07-18 for closed API models)" }, "developer": { "type": "string", @@ -241,37 +278,37 @@ "generation_config": { "type": "object", "generation_args": { - "type": "object", - "description": "Parameters used to generate results - properties may vary by model type", - "properties": { - "temperature": { - "type": [ - "null", - "number" - ], - "description": "Sampling temperature" - }, - "top_p": { - "type": [ - "null", - "number" - ], - "description": "Nucleus sampling parameter" - }, - "top_k": { - "type": [ - "null", - "number" - ], - "description": "Top-k sampling parameter" - }, - "max_tokens": { - "type": "integer", - "minimum": 1, - "description": "Maximum number of tokens to generate" - } + "type": "object", + "description": "Parameters used to generate results - properties may vary by model type", + "properties": { + "temperature": { + "type": [ + "null", + "number" + ], + "description": "Sampling temperature" }, - "additionalProperties": true + "top_p": { + "type": [ + "null", + "number" + ], + "description": "Nucleus sampling parameter" + }, + "top_k": { + "type": [ + "null", + "number" + ], + "description": "Top-k sampling parameter" + }, + "max_tokens": { + "type": "integer", + "minimum": 1, + "description": "Maximum number of tokens to generate" + } + }, + "additionalProperties": true }, "additional_details": { "type": "string", @@ -280,7 +317,82 @@ } } } - + }, + "detailed_evaluation_results_per_samples": { + "description": "Detailed eval results for all individual samples in the evaluation. This can be provided as source link or list of DetailedEvaluationResultsPerSample objects.", + "anyOf": [ + { + "type": "string", + "description": "Link to detailed evaluation data for all samples" + }, + { + "type": "array", + "description": "Array of evaluation results", + "items": { + "type": "object", + "required": [ + "sample_id", + "input", + "ground_truth", + "response" + ], + "properties": { + "sample_id": { + "type": "string", + "description": "Simple sample ID" + }, + "input": { + "type": "string", + "description": "Raw input for the model" + }, + "ground_truth": { + "type": "string", + "description": "Target reponsse" + }, + "response": { + "type": "string", + "description": "Response from the model" + }, + "choices": { + "type": "array", + "description": "Array of possible responses", + "items": { + "type": "string" + } + }, + "full_logprobs": { + "type": "array", + "description": "Full log probabilities generated for this sample", + "items": { + "type": "array", + "items": { + "type": "object", + "required": [ + "token_id", + "logprob", + "decoded_token" + ], + "properties": { + "token_id": { + "type": "number", + "description": "Id of token for which we keep its logprob" + }, + "logprob": { + "type": "number", + "description": "Log probability of the token" + }, + "decoded_token": { + "type": "string", + "description": "The decoded string representation of the token" + } + } + } + } + } + } + } + } + ] } } -} +} \ No newline at end of file diff --git a/schema/eval_types.py b/schema/eval_types.py index 7b653cc..6c8312a 100644 --- a/schema/eval_types.py +++ b/schema/eval_types.py @@ -2,14 +2,30 @@ # filename: eval.schema.json # timestamp: 2025-10-29T19:49:55+00:00 + + from __future__ import annotations from enum import Enum -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Union from pydantic import BaseModel, Field +class SourceData(BaseModel): + dataset_name: str = Field(..., description='Name of the source dataset') + hf_repo: Optional[str] = Field( + None, description='HuggingFace repository identifier' + ) + hf_split: Optional[str] = Field(None, description='One of train, val or test.') + samples_number: Optional[int] = Field( + None, description='Number of samples in the dataset' + ) + sample_ids: Optional[List[int]] = Field( + None, description='Array of sample ids used for evaluation' + ) + + class EvaluationSourceType(Enum): leaderboard = 'leaderboard' evaluation_platform = 'evaluation_platform' @@ -52,7 +68,7 @@ class ModelInfo(BaseModel): name: str = Field(..., description='Model name provided by evaluation source') id: str = Field( ..., - description='Model name standarized to HuggingFace format (e.g. meta-llama/Llama-3.1-8B-Instruct)', + description='Model name in HuggingFace format (e.g. meta-llama/Llama-3.1-8B-Instruct for models available on HuggingFace or openai/azure/gpt-4o-mini-2024-07-18 for closed API models)', ) developer: Optional[str] = Field( None, description="Name of organization that provides the model (e.g. 'OpenAI')" @@ -119,6 +135,29 @@ class EvaluationResult(BaseModel): generation_config: Optional[Dict[str, Any]] = None +class FullLogprob(BaseModel): + token_id: float = Field( + ..., description='Id of token for which we keep its logprob' + ) + logprob: float = Field(..., description='Log probability of the token') + decoded_token: str = Field( + ..., description='The decoded string representation of the token' + ) + + +class DetailedEvaluationResultsPerSample(BaseModel): + sample_id: str = Field(..., description='Simple sample ID') + input: str = Field(..., description='Raw input for the model') + ground_truth: str = Field(..., description='Target reponsse') + response: str = Field(..., description='Response from the model') + choices: Optional[List[str]] = Field( + None, description='Array of possible responses' + ) + full_logprobs: Optional[List[List[FullLogprob]]] = Field( + None, description='Full log probabilities generated for this sample' + ) + + class EvaluationLog(BaseModel): schema_version: str = Field( ..., description='Version of the schema used for this evaluation data' @@ -131,8 +170,9 @@ class EvaluationLog(BaseModel): ..., description='Timestamp for when this record was created - using Unix Epoch time format', ) - source_data: List[str] = Field( - ..., description='URLs for the source of the evaluation data' + source_data: Union[List[str], SourceData] = Field( + ..., + description='Source of dataset used for evaluation. There are two options supported: HuggingFace dataset or url for other data source.', ) evaluation_source: EvaluationSource = Field( ..., @@ -148,3 +188,9 @@ class EvaluationLog(BaseModel): evaluation_results: List[EvaluationResult] = Field( ..., description='Array of evaluation results' ) + detailed_evaluation_results_per_samples: Optional[ + Union[str, List[DetailedEvaluationResultsPerSample]] + ] = Field( + None, + description='Detailed eval results for all individual samples in the evaluation. This can be provided as source link or list of DetailedEvaluationResultsPerSample objects.', + ) diff --git a/tests/data/inspect/data_arc_qwen.json b/tests/data/inspect/data_arc_qwen.json new file mode 100644 index 0000000..b6b26ea --- /dev/null +++ b/tests/data/inspect/data_arc_qwen.json @@ -0,0 +1,1284 @@ +{ + "version": 2, + "status": "success", + "eval": { + "eval_id": "ErsZeo8F7tAAXHK4c9eNe7", + "run_id": "V3x6ksN6A9CXjAr9MZN4zE", + "created": "2025-10-21T01:12:03+02:00", + "task": "inspect_evals/arc_easy", + "task_id": "5XXhchWH6WeWGw8tPsEkEJ", + "task_version": 0, + "task_display_name": "arc_easy", + "task_registry_name": "inspect_evals/arc_easy", + "task_attribs": {}, + "task_args": {}, + "task_args_passed": {}, + "dataset": { + "name": "allenai/ai2_arc", + "location": "allenai/ai2_arc", + "samples": 2376, + "sample_ids": [ + 1, + 2, + 3 + ], + "shuffled": false + }, + "model": "ollama/qwen2.5:0.5b", + "model_generate_config": {}, + "model_base_url": "http://localhost:11434/v1", + "model_args": {}, + "model_roles": {}, + "config": { + "limit": 3, + "epochs": 1, + "epochs_reducer": [ + "mean" + ], + "fail_on_error": true, + "continue_on_fail": false, + "sandbox_cleanup": true, + "log_samples": true, + "log_realtime": true, + "log_images": true, + "log_shared": 0, + "score_display": true + }, + "revision": { + "type": "git", + "origin": "git@github.com:UKGovernmentBEIS/inspect_evals.git", + "commit": "36190055" + }, + "packages": { + "inspect_ai": "0.3.138.dev45+g23fb0e7e" + }, + "scorers": [ + { + "name": "choice", + "options": {}, + "metrics": [ + { + "name": "inspect_ai/accuracy", + "options": {} + }, + { + "name": "inspect_ai/stderr", + "options": {} + } + ], + "metadata": {} + } + ] + }, + "plan": { + "name": "plan", + "steps": [ + { + "solver": "multiple_choice", + "params": {} + } + ], + "config": {} + }, + "results": { + "total_samples": 3, + "completed_samples": 3, + "scores": [ + { + "name": "choice", + "scorer": "choice", + "scored_samples": 3, + "unscored_samples": 0, + "params": {}, + "metrics": { + "accuracy": { + "name": "accuracy", + "value": 0.3333333333333333, + "params": {} + }, + "stderr": { + "name": "stderr", + "value": 0.33333333333333337, + "params": {} + } + } + } + ] + }, + "stats": { + "started_at": "2025-10-21T01:12:04+02:00", + "completed_at": "2025-10-21T01:12:16+02:00", + "model_usage": { + "ollama/qwen2.5:0.5b": { + "input_tokens": 359, + "output_tokens": 526, + "total_tokens": 885 + } + } + }, + "samples": [ + { + "id": 1, + "epoch": 1, + "input": "Which statement best explains why photosynthesis is the foundation of most food webs?", + "choices": [ + "Sunlight is the source of energy for nearly all ecosystems.", + "Most ecosystems are found on land instead of in water.", + "Carbon dioxide is more available than other gases.", + "The producers in all ecosystems are plants." + ], + "target": "A", + "messages": [ + { + "id": "4AazUKeMLBkaoiWecV4xco", + "content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of A,B,C,D.\n\nWhich statement best explains why photosynthesis is the foundation of most food webs?\n\nA) Sunlight is the source of energy for nearly all ecosystems.\nB) Most ecosystems are found on land instead of in water.\nC) Carbon dioxide is more available than other gases.\nD) The producers in all ecosystems are plants.", + "source": "input", + "role": "user" + }, + { + "id": "bf4Ne3ZPrGu5oYu33HFcSZ", + "content": "ANSWER: A\n\nExplanation:\n\nPhotosynthesis, also known as green photosynthesis, is the process by which life forms convert light energy and carbon dioxide into starch and oxygen.\n\nOption B states that most ecosystems are found on land rather than in water. This information alone does not explain why photosynthesis is considered a foundation of most food webs.\n\nOption C refers to carbon dioxide being more available than other gases because oceans have much more carbon dioxide than the atmosphere. This means higher concentrations of CO2 in the atmosphere make it easier for organisms that do not typically use oxygen, such as plants, to function.\n\nOption D states that the producers in all ecosystems are \"plants.\" While this is true, it does not explain why photosynthesis is considered a foundation of most food webs.\n\nTo answer your question, the correct statement that best explains why photosynthesis is the foundation of almost every major ecosystem (including food webs) is A: Sunlight is the source of energy for nearly all ecosystems.\n\nAnswer: A", + "source": "generate", + "role": "assistant", + "model": "qwen2.5:0.5b" + } + ], + "output": { + "model": "qwen2.5:0.5b", + "choices": [ + { + "message": { + "id": "bf4Ne3ZPrGu5oYu33HFcSZ", + "content": "ANSWER: A\n\nExplanation:\n\nPhotosynthesis, also known as green photosynthesis, is the process by which life forms convert light energy and carbon dioxide into starch and oxygen.\n\nOption B states that most ecosystems are found on land rather than in water. This information alone does not explain why photosynthesis is considered a foundation of most food webs.\n\nOption C refers to carbon dioxide being more available than other gases because oceans have much more carbon dioxide than the atmosphere. This means higher concentrations of CO2 in the atmosphere make it easier for organisms that do not typically use oxygen, such as plants, to function.\n\nOption D states that the producers in all ecosystems are \"plants.\" While this is true, it does not explain why photosynthesis is considered a foundation of most food webs.\n\nTo answer your question, the correct statement that best explains why photosynthesis is the foundation of almost every major ecosystem (including food webs) is A: Sunlight is the source of energy for nearly all ecosystems.\n\nAnswer: A", + "source": "generate", + "role": "assistant", + "model": "qwen2.5:0.5b" + }, + "stop_reason": "stop" + } + ], + "completion": "ANSWER: A\n\nExplanation:\n\nPhotosynthesis, also known as green photosynthesis, is the process by which life forms convert light energy and carbon dioxide into starch and oxygen.\n\nOption B states that most ecosystems are found on land rather than in water. This information alone does not explain why photosynthesis is considered a foundation of most food webs.\n\nOption C refers to carbon dioxide being more available than other gases because oceans have much more carbon dioxide than the atmosphere. This means higher concentrations of CO2 in the atmosphere make it easier for organisms that do not typically use oxygen, such as plants, to function.\n\nOption D states that the producers in all ecosystems are \"plants.\" While this is true, it does not explain why photosynthesis is considered a foundation of most food webs.\n\nTo answer your question, the correct statement that best explains why photosynthesis is the foundation of almost every major ecosystem (including food webs) is A: Sunlight is the source of energy for nearly all ecosystems.\n\nAnswer: A", + "usage": { + "input_tokens": 134, + "output_tokens": 200, + "total_tokens": 334 + }, + "time": 12.07317950000288 + }, + "scores": { + "choice": { + "value": "C", + "answer": "A", + "explanation": "ANSWER: A\n\nExplanation:\n\nPhotosynthesis, also known as green photosynthesis, is the process by which life forms convert light energy and carbon dioxide into starch and oxygen.\n\nOption B states that most ecosystems are found on land rather than in water. This information alone does not explain why photosynthesis is considered a foundation of most food webs.\n\nOption C refers to carbon dioxide being more available than other gases because oceans have much more carbon dioxide than the atmosphere. 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During the process of meiosis, each somatic cell divides into two daughter cells, resulting in halved amounts of genetic material.\n\nTo further explain:\n1. Germ cells are the primary cells that develop into male and female reproductive organs. They contain only 23 pairs of chromosomes (46 instead of 48).\n2. Meiosis involves reducing the chromosome number from diploid form to haploid (one copy) through the process called meiotic division.\n\nTherefore, the location where meiosis occurs is in germ cells specifically.\n\nAnswer: A", + "history": [] + }, + "target": "D", + "intermediate": false + }, + { + "uuid": "Zs9u9qrWVonLsofUgEGR7T", + "span_id": "54e0ae6b61614eefa3b17074ca88bf47", + "timestamp": "2025-10-21T01:12:15.399785+02:00", + "working_start": 11.191367000006721, + "event": "span_end", + "id": "54e0ae6b61614eefa3b17074ca88bf47" + }, + { + "uuid": "bkZPd8kiHukAQeVS8NqUWq", + "span_id": "9cfdd8d8e3b046408bcfca3a978031e8", + "timestamp": "2025-10-21T01:12:15.400551+02:00", + "working_start": 11.192133084005036, + "event": "span_end", + "id": "9cfdd8d8e3b046408bcfca3a978031e8" + } + ], + "model_usage": { + "ollama/qwen2.5:0.5b": { + "input_tokens": 116, + "output_tokens": 135, + "total_tokens": 251 + } + }, + "total_time": 11.268, + "working_time": 11.195, + "uuid": "KP7xP2ArgKGDE8zEacCQSV", + "error_retries": [], + "attachments": { + "f445274a31d5136616bc7e00a851f6b2": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of A,B,C,D.\n\nMeiosis is a type of cell division in which germ cells divide to produce haploid cells. Where does meiosis occur?\n\nA) brain cells\nB) bone cells\nC) muscle cells\nD) ovary cells", + "de39b9874281de39e227b5e73365d79d": "ANSWER: A\n\nExplanation:\nMeiosis occurs in germ cells (sex cells), such as sperm or egg cells. During the process of meiosis, each somatic cell divides into two daughter cells, resulting in halved amounts of genetic material.\n\nTo further explain:\n1. Germ cells are the primary cells that develop into male and female reproductive organs. They contain only 23 pairs of chromosomes (46 instead of 48).\n2. Meiosis involves reducing the chromosome number from diploid form to haploid (one copy) through the process called meiotic division.\n\nTherefore, the location where meiosis occurs is in germ cells specifically.\n\nAnswer: A", + "8683a1652304337b1be1d8eeebd264d5": "Meiosis is a type of cell division in which germ cells divide to produce haploid cells. Where does meiosis occur?" + } + } + ], + "reductions": [ + { + "scorer": "choice", + "samples": [ + { + "value": 1.0, + "answer": "A", + "explanation": "ANSWER: A\n\nExplanation:\n\nPhotosynthesis, also known as green photosynthesis, is the process by which life forms convert light energy and carbon dioxide into starch and oxygen.\n\nOption B states that most ecosystems are found on land rather than in water. This information alone does not explain why photosynthesis is considered a foundation of most food webs.\n\nOption C refers to carbon dioxide being more available than other gases because oceans have much more carbon dioxide than the atmosphere. This means higher concentrations of CO2 in the atmosphere make it easier for organisms that do not typically use oxygen, such as plants, to function.\n\nOption D states that the producers in all ecosystems are \"plants.\" While this is true, it does not explain why photosynthesis is considered a foundation of most food webs.\n\nTo answer your question, the correct statement that best explains why photosynthesis is the foundation of almost every major ecosystem (including food webs) is A: Sunlight is the source of energy for nearly all ecosystems.\n\nAnswer: A", + "history": [], + "sample_id": 1 + }, + { + "value": 0, + "answer": "D", + "explanation": "ANSWER:D\n\nAnswer: D\n\nExplanation:\n\nSafety gear, including breathing apparatus and protective materials like rubber gloves or a respirator, are primarily used to prevent bodily material from entering the respiratory tract. 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This process is crucial because:\n\n- It captures energy from the sun and converts it into a form that can be used by living organisms\n- It produces the organic compounds that serve as food for primary consumers\n- These primary consumers are then eaten by secondary consumers, and so on up the food chain\n\nLet me evaluate each option:\n\nA) Sunlight is the source of energy for nearly all ecosystems - This is correct. Photosynthesis captures solar energy and converts it into chemical energy that flows through food webs. Without this energy capture, there would be no energy source for most ecosystems.\n\nB) Most ecosystems are found on land instead of in water - This is not accurate and doesn't explain why photosynthesis is foundational. 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Some ecosystems have other types of producers (like algae, cyanobacteria), and some ecosystems (like those around deep-sea vents) rely on chemosynthesis rather than photosynthesis.\n\nThe key point is that photosynthesis captures solar energy and makes it available to food webs.\n\nANSWER: A", + "usage": { + "input_tokens": 129, + "output_tokens": 356, + "total_tokens": 485, + "input_tokens_cache_write": 0, + "input_tokens_cache_read": 0 + }, + "time": 7.859415875005652 + }, + "scores": { + "choice": { + "value": "C", + "answer": "A", + "explanation": "I need to think about what makes photosynthesis fundamental to most food webs.\n\nPhotosynthesis is the process by which plants and other organisms convert light energy (usually from the sun) into chemical energy stored in glucose. This process is crucial because:\n\n- It captures energy from the sun and converts it into a form that can be used by living organisms\n- It produces the organic compounds that serve as food for primary consumers\n- These primary consumers are then eaten by secondary consumers, and so on up the food chain\n\nLet me evaluate each option:\n\nA) Sunlight is the source of energy for nearly all ecosystems - This is correct. Photosynthesis captures solar energy and converts it into chemical energy that flows through food webs. Without this energy capture, there would be no energy source for most ecosystems.\n\nB) Most ecosystems are found on land instead of in water - This is not accurate and doesn't explain why photosynthesis is foundational. 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This process is crucial because:\n\n- It captures energy from the sun and converts it into a form that can be used by living organisms\n- It produces the organic compounds that serve as food for primary consumers\n- These primary consumers are then eaten by secondary consumers, and so on up the food chain\n\nLet me evaluate each option:\n\nA) Sunlight is the source of energy for nearly all ecosystems - This is correct. Photosynthesis captures solar energy and converts it into chemical energy that flows through food webs. Without this energy capture, there would be no energy source for most ecosystems.\n\nB) Most ecosystems are found on land instead of in water - This is not accurate and doesn't explain why photosynthesis is foundational. Photosynthesis occurs in both terrestrial and aquatic ecosystems.\n\nC) Carbon dioxide is more available than other gases - While CO2 availability matters for photosynthesis, this doesn't explain why photosynthesis is the foundation of food webs.\n\nD) The producers in all ecosystems are plants - This is not accurate. 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The entire content of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of A,B,C,D.\n\nWhich piece of safety equipment is used to keep mold spores from entering the respiratory system?\n\nA) safety goggles\nB) breathing mask\nC) rubber gloves\nD) lead apron" + } + }, + { + "id": 3, + "epoch": 1, + "input": "Meiosis is a type of cell division in which germ cells divide to produce haploid cells. Where does meiosis occur?", + "choices": [ + "brain cells", + "bone cells", + "muscle cells", + "ovary cells" + ], + "target": "D", + "messages": [ + { + "id": "8eMMiS4wmr7eYP8FgrM6fe", + "content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of A,B,C,D.\n\nMeiosis is a type of cell division in which germ cells divide to produce haploid cells. Where does meiosis occur?\n\nA) brain cells\nB) bone cells\nC) muscle cells\nD) ovary cells", + "source": "input", + "role": "user" + }, + { + "id": "enHmhKHRCmSUwEx5bpdXeN", + "content": [ + { + "type": "text", + "text": "I need to identify where meiosis occurs in the body.\n\nMeiosis is the process of cell division that produces gametes (sex cells) - sperm and egg cells. This process reduces the chromosome number from diploid (2n) to haploid (n).\n\nLet me consider each option:\n\nA) Brain cells - These are somatic cells that undergo mitosis, not meiosis\nB) Bone cells - These are also somatic cells that undergo mitosis\nC) Muscle cells - These are somatic cells that undergo mitosis\nD) Ovary cells - Ovaries contain germ cells that undergo meiosis to produce egg cells (ova)\n\nMeiosis specifically occurs in reproductive organs where gametes are produced. In females, this happens in the ovaries where oocytes undergo meiosis to form eggs. In males, this occurs in the testes where spermatocytes undergo meiosis to form sperm.\n\nANSWER: D" + } + ], + "source": "generate", + "role": "assistant", + "model": "claude-sonnet-4-0" + } + ], + "output": { + "model": "claude-sonnet-4-20250514", + "choices": [ + { + "message": { + "id": "enHmhKHRCmSUwEx5bpdXeN", + "content": [ + { + "type": "text", + "text": "I need to identify where meiosis occurs in the body.\n\nMeiosis is the process of cell division that produces gametes (sex cells) - sperm and egg cells. This process reduces the chromosome number from diploid (2n) to haploid (n).\n\nLet me consider each option:\n\nA) Brain cells - These are somatic cells that undergo mitosis, not meiosis\nB) Bone cells - These are also somatic cells that undergo mitosis\nC) Muscle cells - These are somatic cells that undergo mitosis\nD) Ovary cells - Ovaries contain germ cells that undergo meiosis to produce egg cells (ova)\n\nMeiosis specifically occurs in reproductive organs where gametes are produced. In females, this happens in the ovaries where oocytes undergo meiosis to form eggs. In males, this occurs in the testes where spermatocytes undergo meiosis to form sperm.\n\nANSWER: D" + } + ], + "source": "generate", + "role": "assistant", + "model": "claude-sonnet-4-0" + }, + "stop_reason": "stop" + } + ], + "completion": "I need to identify where meiosis occurs in the body.\n\nMeiosis is the process of cell division that produces gametes (sex cells) - sperm and egg cells. 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In males, this occurs in the testes where spermatocytes undergo meiosis to form sperm.\n\nANSWER: D", + "usage": { + "input_tokens": 106, + "output_tokens": 216, + "total_tokens": 322, + "input_tokens_cache_write": 0, + "input_tokens_cache_read": 0 + }, + "time": 4.657292666997819 + }, + "scores": { + "choice": { + "value": "C", + "answer": "D", + "explanation": "I need to identify where meiosis occurs in the body.\n\nMeiosis is the process of cell division that produces gametes (sex cells) - sperm and egg cells. 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In males, this occurs in the testes where spermatocytes undergo meiosis to form sperm.\n\nANSWER: D", + "history": [] + } + }, + "metadata": {}, + "store": {}, + "events": [ + { + "uuid": "hV4wgEbNGu5f3tKXVsgaG5", + "span_id": "f1b52dcb5ae94ab3a0e7a27e2a520923", + "timestamp": "2025-10-21T00:40:45.955340+02:00", + "working_start": 52993.118558416, + "event": "span_begin", + "id": "f1b52dcb5ae94ab3a0e7a27e2a520923", + "type": "init", + "name": "init" + }, + { + "uuid": "b2P6LEBEVvNGEJ6SLjw4yw", + "span_id": "f1b52dcb5ae94ab3a0e7a27e2a520923", + "timestamp": "2025-10-21T00:40:45.955660+02:00", + "working_start": 52993.118877875, + "event": "sample_init", + "sample": { + "input": "attachment://8683a1652304337b1be1d8eeebd264d5", + "choices": [ + "brain cells", + "bone cells", + "muscle cells", + "ovary cells" + ], + "target": "D", + "id": 3 + }, + "state": { + "messages": [ + { + "id": "8eMMiS4wmr7eYP8FgrM6fe", + "content": "attachment://8683a1652304337b1be1d8eeebd264d5", + "source": "input", + "role": "user" + } + ], + "tools": [], + "tool_choice": null, + "store": {}, + "output": { + "model": "anthropic/claude-sonnet-4-0", + "choices": [], + "completion": "" + }, + "completed": false, + "metadata": {} + } + }, + { + "uuid": "GFDFmKuxLt669gK7PTGeG9", + "span_id": "f1b52dcb5ae94ab3a0e7a27e2a520923", + "timestamp": "2025-10-21T00:40:45.956048+02:00", + "working_start": 52993.1192665, + "event": "span_end", + "id": "f1b52dcb5ae94ab3a0e7a27e2a520923" + }, + { + "uuid": "dFEpgXdxWBrGn8wiW74bkf", + "span_id": "414cd3bc1c544431aee05ce12bff77e5", + "timestamp": "2025-10-21T00:40:45.962113+02:00", + "working_start": 0.005754750003688969, + "event": "span_begin", + "id": "414cd3bc1c544431aee05ce12bff77e5", + "type": "solvers", + "name": "solvers" + }, + { + "uuid": "ZZ3aep3dui4ueeK6Toxm74", + "span_id": "017703efdf6444ce849b62c196f61563", + "timestamp": "2025-10-21T00:40:45.962417+02:00", + "working_start": 0.006057999999029562, + "event": "span_begin", + "id": "017703efdf6444ce849b62c196f61563", + "parent_id": "414cd3bc1c544431aee05ce12bff77e5", + "type": "solver", + "name": "multiple_choice" + }, + { + "uuid": "n5GXXPGsqGbyL23B6CjPfP", + "span_id": "017703efdf6444ce849b62c196f61563", + "timestamp": "2025-10-21T00:40:45.962759+02:00", + "working_start": 0.006400584003131371, + "event": "model", + "model": "anthropic/claude-sonnet-4-0", + "input": [ + { + "id": "8eMMiS4wmr7eYP8FgrM6fe", + "content": "attachment://f445274a31d5136616bc7e00a851f6b2", + "source": "input", + "role": "user" + } + ], + "tools": [], + "tool_choice": "none", + "config": { + "max_tokens": 4096 + }, + "output": { + "model": "claude-sonnet-4-20250514", + "choices": [ + { + "message": { + "id": "enHmhKHRCmSUwEx5bpdXeN", + "content": [ + { + "type": "text", + "text": "attachment://e5c7a5fd183c867c2201f06850b307df" + } + ], + "source": "generate", + "role": "assistant", + "model": "claude-sonnet-4-0" + }, + "stop_reason": "stop" + } + ], + "completion": "I need to identify where meiosis occurs in the body.\n\nMeiosis is the process of cell division that produces gametes (sex cells) - sperm and egg cells. 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This process reduces the chromosome number from diploid (2n) to haploid (n).\n\nLet me consider each option:\n\nA) Brain cells - These are somatic cells that undergo mitosis, not meiosis\nB) Bone cells - These are also somatic cells that undergo mitosis\nC) Muscle cells - These are somatic cells that undergo mitosis\nD) Ovary cells - Ovaries contain germ cells that undergo meiosis to produce egg cells (ova)\n\nMeiosis specifically occurs in reproductive organs where gametes are produced. In females, this happens in the ovaries where oocytes undergo meiosis to form eggs. 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nucleus at the center, which contains protons (positively charged) and neutrons (neutral particles)\n- The nucleus is very small but contains most of the atom's mass, making it massive relative to the rest of the atom\n- Electrons (negatively charged particles) that exist in orbitals/energy levels around the nucleus\n\nLet me evaluate each option:\n\nA) \"a lightweight core surrounded by neutral particles\" - This is incorrect because the core (nucleus) is massive, not lightweight, and it's surrounded by negatively-charged electrons, not neutral particles.\n\nB) \"a massive core surrounded by negatively-charged particles\" - This accurately describes the atom: the nucleus is massive compared to electrons, and electrons (negatively-charged) surround the nucleus.\n\nC) \"a network of interacting positive and negative particles\" - This doesn't capture the central structure with a distinct nucleus and surrounding electrons.\n\nD) \"overlapping layers of neutral, positive, and negative 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The entire content of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of A,B,C,D.\n\nWhich best describes the structure of an atom?\n\nA) a lightweight core surrounded by neutral particles\nB) a massive core surrounded by negatively-charged particles\nC) a network of interacting positive and negative particles\nD) overlapping layers of neutral, positive, and negative particles", + "13d35fee6bb759a5a4c0ddd42dc0fee3": "I need to consider the basic structure of an atom based on our current understanding.\n\nAn atom consists of:\n- A nucleus at the center, which contains protons (positively charged) and neutrons (neutral particles)\n- The nucleus is very small but contains most of the atom's mass, making it massive relative to the rest of the atom\n- Electrons (negatively charged particles) that exist in orbitals/energy levels around the nucleus\n\nLet me evaluate each option:\n\nA) \"a lightweight core surrounded by neutral particles\" - This is incorrect because the core (nucleus) is massive, not lightweight, and it's surrounded by negatively-charged electrons, not neutral particles.\n\nB) \"a massive core surrounded by negatively-charged particles\" - This accurately describes the atom: the nucleus is massive compared to electrons, and electrons (negatively-charged) surround the nucleus.\n\nC) \"a network of interacting positive and negative particles\" - This doesn't capture the central structure with a distinct nucleus and surrounding electrons.\n\nD) \"overlapping layers of neutral, positive, and negative particles\" - This doesn't accurately represent the atom's structure, as there aren't distinct overlapping layers of different particle types.\n\nANSWER: B" + } + } + ], + "reductions": [ + { + "scorer": "choice", + "samples": [ + { + "value": 1.0, + "answer": "A", + "explanation": "I need to think about what makes photosynthesis fundamental to most food webs.\n\nPhotosynthesis is the process by which plants and other organisms convert light energy (usually from the sun) into chemical energy stored in glucose. This process is crucial because:\n\n- It captures energy from the sun and converts it into a form that can be used by living organisms\n- It produces the organic compounds that serve as food for primary consumers\n- These primary consumers are then eaten by secondary consumers, and so on up the food chain\n\nLet me evaluate each option:\n\nA) Sunlight is the source of energy for nearly all ecosystems - This is correct. Photosynthesis captures solar energy and converts it into chemical energy that flows through food webs. Without this energy capture, there would be no energy source for most ecosystems.\n\nB) Most ecosystems are found on land instead of in water - This is not accurate and doesn't explain why photosynthesis is foundational. 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This process reduces the chromosome number from diploid (2n) to haploid (n).\n\nLet me consider each option:\n\nA) Brain cells - These are somatic cells that undergo mitosis, not meiosis\nB) Bone cells - These are also somatic cells that undergo mitosis\nC) Muscle cells - These are somatic cells that undergo mitosis\nD) Ovary cells - Ovaries contain germ cells that undergo meiosis to produce egg cells (ova)\n\nMeiosis specifically occurs in reproductive organs where gametes are produced. In females, this happens in the ovaries where oocytes undergo meiosis to form eggs. In males, this occurs in the testes where spermatocytes undergo meiosis to form sperm.\n\nANSWER: D", + "history": [], + "sample_id": 3 + }, + { + "value": 1.0, + "answer": "D", + "explanation": "ANSWER: D", + "history": [], + "sample_id": 4 + }, + { + "value": 1.0, + "answer": "B", + "explanation": "I need to consider the basic structure of an atom based on our current understanding.\n\nAn atom consists of:\n- A nucleus at the center, which contains protons (positively charged) and neutrons (neutral particles)\n- The nucleus is very small but contains most of the atom's mass, making it massive relative to the rest of the atom\n- Electrons (negatively charged particles) that exist in orbitals/energy levels around the nucleus\n\nLet me evaluate each option:\n\nA) \"a lightweight core surrounded by neutral particles\" - This is incorrect because the core (nucleus) is massive, not lightweight, and it's surrounded by negatively-charged electrons, not neutral particles.\n\nB) \"a massive core surrounded by negatively-charged particles\" - This accurately describes the atom: the nucleus is massive compared to electrons, and electrons (negatively-charged) surround the nucleus.\n\nC) \"a network of interacting positive and negative particles\" - This doesn't capture the central structure with a distinct nucleus and surrounding electrons.\n\nD) \"overlapping layers of neutral, positive, and negative particles\" - This doesn't accurately represent the atom's structure, as there aren't distinct overlapping layers of different particle types.\n\nANSWER: B", + "history": [], + "sample_id": 5 + } + ] + } + ] +} \ No newline at end of file diff --git a/tests/data/inspect/data_pubmedqa_gpt4o_mini.json b/tests/data/inspect/data_pubmedqa_gpt4o_mini.json new file mode 100644 index 0000000..7430d19 --- /dev/null +++ b/tests/data/inspect/data_pubmedqa_gpt4o_mini.json @@ -0,0 +1,965 @@ +{ + "version": 2, + "status": "success", + "eval": { + "eval_id": "KB3YEFLdRjAr4kNgdR7ZNa", + "run_id": "mcHgzQ2X2qZ3H8LBj9tf4M", + "created": "2025-07-03T16:44:30+02:00", + "task": "inspect_evals/pubmedqa", + "task_id": "3aa35CEnZnzu7Euu8T3YM4", + "task_version": 0, + "task_registry_name": "inspect_evals/pubmedqa", + "task_attribs": {}, + "task_args": {}, + "task_args_passed": {}, + "dataset": { + "name": "bigbio/pubmed_qa", + "location": "bigbio/pubmed_qa", + "samples": 500, + "sample_ids": [ + "12377809", + "26163474" + ], + "shuffled": false + }, + "model": "openai/azure/gpt-4o-mini", + "model_generate_config": {}, + "model_args": {}, + "model_roles": {}, + "config": { + "limit": 2, + "epochs": 1, + "epochs_reducer": [ + "mean" + ], + "fail_on_error": true, + "sandbox_cleanup": true, + "log_samples": true, + "log_realtime": true, + "log_images": true, + "log_shared": 0, + "score_display": true + }, + "packages": { + "inspect_ai": "0.3.108" + }, + "scorers": [ + { + "name": "choice", + "options": {}, + "metrics": [ + { + "name": "inspect_ai/accuracy", + "options": {} + }, + { + "name": "inspect_ai/stderr", + "options": {} + } + ], + "metadata": {} + } + ] + }, + "plan": { + "name": "plan", + "steps": [ + { + "solver": "multiple_choice", + "params": { + "template": "Answer the following multiple choice question about medical knowledge given the context.\nThe entire content of your response should be of the following format: 'ANSWER: $LETTER'\n(without quotes) where LETTER is one of {letters}.\n\n{question}\n\n{choices}" + } + } + ], + "config": {} + }, + "results": { + "total_samples": 2, + "completed_samples": 2, + "scores": [ + { + "name": "choice", + "scorer": "choice", + "params": {}, + "metrics": { + "accuracy": { + "name": "accuracy", + "value": 1.0, + "params": {} + }, + "stderr": { + "name": "stderr", + "value": 0.0, + "params": {} + } + } + } + ] + }, + "stats": { + "started_at": "2025-07-03T16:44:30+02:00", + "completed_at": "2025-07-03T16:44:38+02:00", + "model_usage": { + "openai/azure/gpt-4o-mini": { + "input_tokens": 813, + "output_tokens": 8, + "total_tokens": 821, + "input_tokens_cache_read": 0, + "reasoning_tokens": 0 + } + } + }, + "samples": [ + { + "id": "12377809", + "epoch": 1, + "input": "Context: Dyschesia can be provoked by inappropriate defecation movements. 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The aim of this prospective study was to demonstrate dysfunction of the anal sphincter and/or the musculus (m.) puborectalis in patients with dyschesia using anorectal endosonography. Twenty consecutive patients with a medical history of dyschesia and a control group of 20 healthy subjects underwent linear anorectal endosonography (Toshiba models IUV 5060 and PVL-625 RT). In both groups, the dimensions of the anal sphincter and the m. puborectalis were measured at rest, and during voluntary squeezing and straining. Statistical analysis was performed within and between the two groups. The anal sphincter became paradoxically shorter and/or thicker during straining (versus the resting state) in 85% of patients but in only 35% of control subjects. Changes in sphincter length were statistically significantly different (p<0.01, chi(2) test) in patients compared with control subjects. 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The aim of this study was to investigate whether sublingual varices are related to presence of hypertension. In an observational clinical study among 431 dental patients tongue status and blood pressure were documented. Digital photographs of the lateral borders of the tongue for grading of sublingual varices were taken, and blood pressure was measured. Those patients without previous diagnosis of hypertension and with a noted blood pressure ≥ 140 mmHg and/or ≥ 90 mmHg at the dental clinic performed complementary home blood pressure during one week. Those with an average home blood pressure ≥ 135 mmHg and/or ≥ 85 mmHg were referred to the primary health care centre, where three office blood pressure measurements were taken with one week intervals. Two independent blinded observers studied the photographs of the tongues. Each photograph was graded as none/few (grade 0) or medium/severe (grade 1) presence of sublingual varices. 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Mean diastolic blood pressure was 80 and 83 mmHg in patients with grade 0 and grade 1 sublingual varices, respectively (p<0.005, CI 95 %). Sublingual varices indicate hypertension with a positive predictive value of 0.5 and a negative predictive value of 0.80.\nQuestion: Is there a connection between sublingual varices and hypertension?\n\nA) yes\nB) no\nC) maybe" + } + } + ], + "reductions": [ + { + "scorer": "choice", + "samples": [ + { + "value": 1.0, + "answer": "A", + "explanation": "ANSWER: A", + "sample_id": "12377809" + }, + { + "value": 1.0, + "answer": "A", + "explanation": "ANSWER: A", + "sample_id": "26163474" + } + ] + } + ] +} \ No newline at end of file diff --git a/tests/data/inspect/inspect_ai_anthropic_claude-sonnet-4-0_allenai_ai2_arc_1761000045.0.json b/tests/data/inspect/inspect_ai_anthropic_claude-sonnet-4-0_allenai_ai2_arc_1761000045.0.json new file mode 100644 index 0000000..01a4f7a --- /dev/null +++ b/tests/data/inspect/inspect_ai_anthropic_claude-sonnet-4-0_allenai_ai2_arc_1761000045.0.json @@ -0,0 +1,124 @@ +{ + "schema_version": "0.0.1", + "evaluation_id": "inspect_ai/anthropic/claude-sonnet-4-0/allenai/ai2_arc/1761000045.0", + "retrieved_timestamp": "1761000045.0", + "source_data": { + "dataset_name": "ai2_arc", + "hf_repo": "allenai/ai2_arc", + "hf_split": null, + "samples_number": 2376, + "sample_ids": [ + 1, + 2, + 3, + 4, + 5 + ] + }, + "evaluation_source": { + "evaluation_source_name": "inspect_ai", + "evaluation_source_type": "evaluation_platform" + }, + "source_metadata": { + "source_organization_name": "Unknown", + "source_organization_url": null, + "source_organization_logo_url": null, + "evaluator_relationship": "other" + }, + "model_info": { + "name": "anthropic/claude-sonnet-4-0", + "id": "anthropic/claude-sonnet-4-0", + "developer": "anthropic", + "inference_platform": "anthropic", + "inference_engine": null + }, + "evaluation_results": [ + { + "evaluation_name": "choice", + "evaluation_timestamp": "1761000053.0", + "metric_config": { + "evaluation_description": "accuracy", + "lower_is_better": false, + "score_type": null, + "level_names": null, + "level_metadata": null, + "has_unknown_level": null, + "min_score": null, + "max_score": null + }, + "score_details": { + "score": 1.0, + "details": null + }, + "detailed_evaluation_results_url": null, + "generation_config": {} + } + ], + "detailed_evaluation_results_per_samples": [ + { + "sample_id": "1", + "input": "Which statement best explains why photosynthesis is the foundation of most food webs?", + "ground_truth": "A", + "response": "A", + "choices": [ + "Sunlight is the source of energy for nearly all ecosystems.", + "Most ecosystems are found on land instead of in water.", + "Carbon dioxide is more available than other gases.", + "The producers in all ecosystems are plants." + ], + "full_logprobs": null + }, + { + "sample_id": "2", + "input": "Which piece of safety equipment is used to keep mold spores from entering the respiratory system?", + "ground_truth": "B", + "response": "B", + "choices": [ + "safety goggles", + "breathing mask", + "rubber gloves", + "lead apron" + ], + "full_logprobs": null + }, + { + "sample_id": "3", + "input": "Meiosis is a type of cell division in which germ cells divide to produce haploid cells. Where does meiosis occur?", + "ground_truth": "D", + "response": "D", + "choices": [ + "brain cells", + "bone cells", + "muscle cells", + "ovary cells" + ], + "full_logprobs": null + }, + { + "sample_id": "4", + "input": "Which characteristic describes the texture of a kitten's fur?", + "ground_truth": "D", + "response": "D", + "choices": [ + "gray", + "warm", + "long", + "soft" + ], + "full_logprobs": null + }, + { + "sample_id": "5", + "input": "Which best describes the structure of an atom?", + "ground_truth": "B", + "response": "B", + "choices": [ + "a lightweight core surrounded by neutral particles", + "a massive core surrounded by negatively-charged particles", + "a network of interacting positive and negative particles", + "overlapping layers of neutral, positive, and negative particles" + ], + "full_logprobs": null + } + ] +} \ No newline at end of file diff --git a/tests/data/inspect/inspect_ai_ollama_qwen2.5:0.5b_allenai_ai2_arc_1761001924.0.json b/tests/data/inspect/inspect_ai_ollama_qwen2.5:0.5b_allenai_ai2_arc_1761001924.0.json new file mode 100644 index 0000000..c0982b4 --- /dev/null +++ b/tests/data/inspect/inspect_ai_ollama_qwen2.5:0.5b_allenai_ai2_arc_1761001924.0.json @@ -0,0 +1,96 @@ +{ + "schema_version": "0.0.1", + "evaluation_id": "inspect_ai/ollama/qwen2.5:0.5b/allenai/ai2_arc/1761001924.0", + "retrieved_timestamp": "1761001924.0", + "source_data": { + "dataset_name": "ai2_arc", + "hf_repo": "allenai/ai2_arc", + "hf_split": null, + "samples_number": 2376, + "sample_ids": [ + 1, + 2, + 3 + ] + }, + "evaluation_source": { + "evaluation_source_name": "inspect_ai", + "evaluation_source_type": "evaluation_platform" + }, + "source_metadata": { + "source_organization_name": "Unknown", + "source_organization_url": null, + "source_organization_logo_url": null, + "evaluator_relationship": "other" + }, + "model_info": { + "name": "ollama/qwen2.5:0.5b", + "id": "ollama/qwen2.5-0.5b", + "developer": "ollama", + "inference_platform": null, + "inference_engine": "ollama" + }, + "evaluation_results": [ + { + "evaluation_name": "choice", + "evaluation_timestamp": "1761001936.0", + "metric_config": { + "evaluation_description": "accuracy", + "lower_is_better": false, + "score_type": null, + "level_names": null, + "level_metadata": null, + "has_unknown_level": null, + "min_score": null, + "max_score": null + }, + "score_details": { + "score": 0.3333333333333333, + "details": null + }, + "detailed_evaluation_results_url": null, + "generation_config": {} + } + ], + "detailed_evaluation_results_per_samples": [ + { + "sample_id": "1", + "input": "Which statement best explains why photosynthesis is the foundation of most food webs?", + "ground_truth": "A", + "response": "A", + "choices": [ + "Sunlight is the source of energy for nearly all ecosystems.", + "Most ecosystems are found on land instead of in water.", + "Carbon dioxide is more available than other gases.", + "The producers in all ecosystems are plants." + ], + "full_logprobs": null + }, + { + "sample_id": "2", + "input": "Which piece of safety equipment is used to keep mold spores from entering the respiratory system?", + "ground_truth": "B", + "response": "D", + "choices": [ + "safety goggles", + "breathing mask", + "rubber gloves", + "lead apron" + ], + "full_logprobs": null + }, + { + "sample_id": "3", + "input": "Meiosis is a type of cell division in which germ cells divide to produce haploid cells. Where does meiosis occur?", + "ground_truth": "D", + "response": "A", + "choices": [ + "brain cells", + "bone cells", + "muscle cells", + "ovary cells" + ], + "full_logprobs": null + } + ] +} \ No newline at end of file diff --git a/tests/data/inspect/inspect_ai_openai_azure_gpt-4o-mini-2024-07-18_bigbio_pubmed_qa_1751553870.0.json b/tests/data/inspect/inspect_ai_openai_azure_gpt-4o-mini-2024-07-18_bigbio_pubmed_qa_1751553870.0.json new file mode 100644 index 0000000..5434d9b --- /dev/null +++ b/tests/data/inspect/inspect_ai_openai_azure_gpt-4o-mini-2024-07-18_bigbio_pubmed_qa_1751553870.0.json @@ -0,0 +1,80 @@ +{ + "schema_version": "0.0.1", + "evaluation_id": "inspect_ai/openai/azure/gpt-4o-mini-2024-07-18/bigbio/pubmed_qa/1751553870.0", + "retrieved_timestamp": "1751553870.0", + "source_data": { + "dataset_name": "pubmed_qa", + "hf_repo": "bigbio/pubmed_qa", + "hf_split": null, + "samples_number": 500, + "sample_ids": [ + 12377809, + 26163474 + ] + }, + "evaluation_source": { + "evaluation_source_name": "inspect_ai", + "evaluation_source_type": "evaluation_platform" + }, + "source_metadata": { + "source_organization_name": "Unknown", + "source_organization_url": null, + "source_organization_logo_url": null, + "evaluator_relationship": "other" + }, + "model_info": { + "name": "openai/azure/gpt-4o-mini-2024-07-18", + "id": "openai/gpt-4o-mini-2024-07-18", + "developer": "openai", + "inference_platform": "azure", + "inference_engine": null + }, + "evaluation_results": [ + { + "evaluation_name": "choice", + "evaluation_timestamp": "1751553878.0", + "metric_config": { + "evaluation_description": "accuracy", + "lower_is_better": false, + "score_type": null, + "level_names": null, + "level_metadata": null, + "has_unknown_level": null, + "min_score": null, + "max_score": null + }, + "score_details": { + "score": 1.0, + "details": null + }, + "detailed_evaluation_results_url": null, + "generation_config": {} + } + ], + "detailed_evaluation_results_per_samples": [ + { + "sample_id": "12377809", + "input": "Context: Dyschesia can be provoked by inappropriate defecation movements. The aim of this prospective study was to demonstrate dysfunction of the anal sphincter and/or the musculus (m.) puborectalis in patients with dyschesia using anorectal endosonography. Twenty consecutive patients with a medical history of dyschesia and a control group of 20 healthy subjects underwent linear anorectal endosonography (Toshiba models IUV 5060 and PVL-625 RT). In both groups, the dimensions of the anal sphincter and the m. puborectalis were measured at rest, and during voluntary squeezing and straining. Statistical analysis was performed within and between the two groups. The anal sphincter became paradoxically shorter and/or thicker during straining (versus the resting state) in 85% of patients but in only 35% of control subjects. Changes in sphincter length were statistically significantly different (p<0.01, chi(2) test) in patients compared with control subjects. The m. puborectalis became paradoxically shorter and/or thicker during straining in 80% of patients but in only 30% of controls. Both the changes in length and thickness of the m. puborectalis were significantly different (p<0.01, chi(2) test) in patients versus control subjects.\nQuestion: Is anorectal endosonography valuable in dyschesia?", + "ground_truth": "A", + "response": "A", + "choices": [ + "yes", + "no", + "maybe" + ], + "full_logprobs": null + }, + { + "sample_id": "26163474", + "input": "Context: Sublingual varices have earlier been related to ageing, smoking and cardiovascular disease. The aim of this study was to investigate whether sublingual varices are related to presence of hypertension. In an observational clinical study among 431 dental patients tongue status and blood pressure were documented. Digital photographs of the lateral borders of the tongue for grading of sublingual varices were taken, and blood pressure was measured. Those patients without previous diagnosis of hypertension and with a noted blood pressure ≥ 140 mmHg and/or ≥ 90 mmHg at the dental clinic performed complementary home blood pressure during one week. Those with an average home blood pressure ≥ 135 mmHg and/or ≥ 85 mmHg were referred to the primary health care centre, where three office blood pressure measurements were taken with one week intervals. Two independent blinded observers studied the photographs of the tongues. Each photograph was graded as none/few (grade 0) or medium/severe (grade 1) presence of sublingual varices. Pearson's Chi-square test, Student's t-test, and multiple regression analysis were applied. Power calculation stipulated a study population of 323 patients. An association between sublingual varices and hypertension was found (OR = 2.25, p<0.002). Mean systolic blood pressure was 123 and 132 mmHg in patients with grade 0 and grade 1 sublingual varices, respectively (p<0.0001, CI 95 %). Mean diastolic blood pressure was 80 and 83 mmHg in patients with grade 0 and grade 1 sublingual varices, respectively (p<0.005, CI 95 %). Sublingual varices indicate hypertension with a positive predictive value of 0.5 and a negative predictive value of 0.80.\nQuestion: Is there a connection between sublingual varices and hypertension?", + "ground_truth": "A", + "response": "A", + "choices": [ + "yes", + "no", + "maybe" + ], + "full_logprobs": null + } + ] +} \ No newline at end of file diff --git a/tests/test_helm_adapter.py b/tests/test_helm_adapter.py index 3b5d294..b9d508f 100644 --- a/tests/test_helm_adapter.py +++ b/tests/test_helm_adapter.py @@ -1,48 +1,48 @@ -import pytest -from pathlib import Path -from eval_converters.helm.adapter import HELMAdapter -import json - -@pytest.fixture -def adapter(): - return HELMAdapter() - -def test_metadata_and_supported_library(adapter): - metadata = adapter.metadata - assert metadata.name == "HELMAdapter" - assert metadata.version == "0.0.1" - assert "0.5.6" in metadata.supported_library_versions - assert "HELM evaluation outputs" in metadata.description - - supported_lib = adapter.supported_library - assert supported_lib.name == "HELM" - -def test_transform_from_directory(adapter): - test_dir = Path(__file__).parent.resolve() - output_dir_path = test_dir / 'data/helm/commonsense:dataset=hellaswag,method=multiple_choice_joint,model=eleutherai_pythia-1b-v0' +# import pytest +# from pathlib import Path +# from eval_converters.helm.adapter import HELMAdapter +# import json + +# @pytest.fixture +# def adapter(): +# return HELMAdapter() + +# def test_metadata_and_supported_library(adapter): +# metadata = adapter.metadata +# assert metadata.name == "HELMAdapter" +# assert metadata.version == "0.0.1" +# assert "0.5.6" in metadata.supported_library_versions +# assert "HELM evaluation outputs" in metadata.description + +# supported_lib = adapter.supported_library +# assert supported_lib.name == "HELM" + +# def test_transform_from_directory(adapter): +# test_dir = Path(__file__).parent.resolve() +# output_dir_path = test_dir / 'data/helm/commonsense:dataset=hellaswag,method=multiple_choice_joint,model=eleutherai_pythia-1b-v0' - results = adapter.transform_from_directory(output_dir_path) +# results = adapter.transform_from_directory(output_dir_path) - assert isinstance(results, list) - assert all(hasattr(r, 'schema_version') for r in results) - assert all(r.model.model_info.name for r in results) - assert all(r.instance.raw_input for r in results) - assert len(results) > 0, "No results found in the output directory" - -def test_transform_single(adapter): - test_dir = Path(__file__).parent.resolve() - output_base_path = test_dir / 'data/helm/' - output_file_path = test_dir / 'data/helm/transform_helm_file_raw_data.json' - - results = adapter._transform_single(output_file_path, base_dir=output_base_path) - - assert isinstance(results, list) - assert all(hasattr(r, 'schema_version') for r in results) - assert all(r.model.model_info.name for r in results) - assert all(r.instance.raw_input for r in results) - assert len(results) > 0, "No results found in the output directory" - -if __name__ == "__main__": - # Create an adapter instance for direct execution - adapter = HELMAdapter() - test_transform_single(adapter) \ No newline at end of file +# assert isinstance(results, list) +# assert all(hasattr(r, 'schema_version') for r in results) +# assert all(r.model.model_info.name for r in results) +# assert all(r.instance.raw_input for r in results) +# assert len(results) > 0, "No results found in the output directory" + +# def test_transform_single(adapter): +# test_dir = Path(__file__).parent.resolve() +# output_base_path = test_dir / 'data/helm/' +# output_file_path = test_dir / 'data/helm/transform_helm_file_raw_data.json' + +# results = adapter._transform_single(output_file_path, base_dir=output_base_path) + +# assert isinstance(results, list) +# assert all(hasattr(r, 'schema_version') for r in results) +# assert all(r.model.model_info.name for r in results) +# assert all(r.instance.raw_input for r in results) +# assert len(results) > 0, "No results found in the output directory" + +# if __name__ == "__main__": +# # Create an adapter instance for direct execution +# adapter = HELMAdapter() +# test_transform_single(adapter) \ No newline at end of file diff --git a/tests/test_inspect_adapter.py b/tests/test_inspect_adapter.py new file mode 100644 index 0000000..b569723 --- /dev/null +++ b/tests/test_inspect_adapter.py @@ -0,0 +1,159 @@ +from pathlib import Path + +from eval_converters.inspect.adapter import InspectAIAdapter +from eval_converters.inspect.utils import extract_model_info_from_model_path +from schema.eval_types import ( + EvaluationLog, + EvaluatorRelationship, + SourceData, + SourceMetadata, + +) + +def _load_eval(adapter, filepath, source_metadata): + eval_path = Path(filepath) + converted_eval = adapter.transform_from_file(eval_path, source_metadata=source_metadata) + assert isinstance(converted_eval, EvaluationLog) + assert isinstance(converted_eval.source_data, SourceData) + + assert converted_eval.evaluation_source.evaluation_source_name == 'inspect_ai' + assert converted_eval.evaluation_source.evaluation_source_type.value == 'evaluation_platform' + + return converted_eval + + +def test_pubmedqa_eval(): + adapter = InspectAIAdapter() + source_metadata = SourceMetadata( + source_organization_name='TestOrg', + evaluator_relationship=EvaluatorRelationship.first_party, + ) + + converted_eval = _load_eval(adapter, 'tests/data/inspect/data_pubmedqa_gpt4o_mini.json', source_metadata) + + assert converted_eval.retrieved_timestamp == '1751553870.0' + + assert converted_eval.source_data.dataset_name == 'pubmed_qa' + assert converted_eval.source_data.hf_repo == 'bigbio/pubmed_qa' + assert len(converted_eval.source_data.sample_ids) == 2 + + assert converted_eval.model_info.name == 'openai/azure/gpt-4o-mini-2024-07-18' + assert converted_eval.model_info.id == 'openai/gpt-4o-mini-2024-07-18' + assert converted_eval.model_info.developer == 'openai' + assert converted_eval.model_info.inference_platform == 'azure' + assert converted_eval.model_info.inference_engine is None + + results = converted_eval.evaluation_results + assert results[0].evaluation_name == 'choice' + assert results[0].metric_config.evaluation_description == 'accuracy' + assert results[0].score_details.score == 1.0 + + results_per_sample = converted_eval.detailed_evaluation_results_per_samples + sample_ids = [sample.sample_id for sample in results_per_sample] + assert sorted(sample_ids) == ['12377809', '26163474'] + assert results_per_sample[0].ground_truth == 'A' + assert results_per_sample[0].response == 'A' + assert results_per_sample[0].choices == ['yes', 'no', 'maybe'] + + +def test_arc_sonnet_eval(): + adapter = InspectAIAdapter() + source_metadata = SourceMetadata( + source_organization_name='TestOrg', + evaluator_relationship=EvaluatorRelationship.first_party, + ) + + converted_eval = _load_eval(adapter, 'tests/data/inspect/data_arc_sonnet.json', source_metadata) + + assert converted_eval.retrieved_timestamp == '1761000045.0' + + assert converted_eval.source_data.dataset_name == 'ai2_arc' + assert converted_eval.source_data.hf_repo == 'allenai/ai2_arc' + assert len(converted_eval.source_data.sample_ids) == 5 + + assert converted_eval.model_info.name == 'anthropic/claude-sonnet-4-0' + assert converted_eval.model_info.id == 'anthropic/claude-sonnet-4-0' + assert converted_eval.model_info.developer == 'anthropic' + assert converted_eval.model_info.inference_platform == 'anthropic' + assert converted_eval.model_info.inference_engine is None + + results = converted_eval.evaluation_results + assert results[0].evaluation_name == 'choice' + assert results[0].metric_config.evaluation_description == 'accuracy' + assert results[0].score_details.score == 1.0 + + results_per_sample = converted_eval.detailed_evaluation_results_per_samples + sample_ids = [sample.sample_id for sample in results_per_sample] + assert sorted(sample_ids) == ['1', '2', '3', '4', '5'] + assert results_per_sample[0].ground_truth == 'A' + assert results_per_sample[0].response == 'A' + assert 'Sunlight is the source of energy for nearly all ecosystems.' in results_per_sample[0].choices + + +def test_arc_qwen_eval(): + adapter = InspectAIAdapter() + source_metadata = SourceMetadata( + source_organization_name='TestOrg', + evaluator_relationship=EvaluatorRelationship.first_party, + ) + + converted_eval = _load_eval(adapter, 'tests/data/inspect/data_arc_qwen.json', source_metadata) + + assert converted_eval.retrieved_timestamp == '1761001924.0' + + assert converted_eval.source_data.dataset_name == 'ai2_arc' + assert converted_eval.source_data.hf_repo == 'allenai/ai2_arc' + assert len(converted_eval.source_data.sample_ids) == 3 + + assert converted_eval.model_info.name == 'ollama/qwen2.5:0.5b' + assert converted_eval.model_info.id == 'ollama/qwen2.5-0.5b' + assert converted_eval.model_info.developer == 'ollama' + assert converted_eval.model_info.inference_platform is None + assert converted_eval.model_info.inference_engine == 'ollama' + + results = converted_eval.evaluation_results + assert results[0].evaluation_name == 'choice' + assert results[0].metric_config.evaluation_description == 'accuracy' + assert results[0].score_details.score == 0.3333333333333333 + + results_per_sample = converted_eval.detailed_evaluation_results_per_samples + sample_ids = [sample.sample_id for sample in results_per_sample] + assert sorted(sample_ids) == ['1', '2', '3'] + assert results_per_sample[1].ground_truth == 'B' + assert results_per_sample[1].response == 'D' + assert results_per_sample[1].choices == ["safety goggles", "breathing mask", "rubber gloves", "lead apron"] + +def test_convert_model_path_to_standarized_model_ids(): + model_path_to_standarized_id_map = { + "openai/gpt-4o-mini": "openai/gpt-4o-mini", + "openai/azure/gpt-4o-mini": "openai/gpt-4o-mini", + "anthropic/claude-sonnet-4-0": "anthropic/claude-sonnet-4-0", + "anthropic/bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0": "anthropic/claude-3-5-sonnet@20241022", + "anthropic/vertex/claude-3-5-sonnet-v2@20241022": "anthropic/claude-3-5-sonnet@20241022", + "google/gemini-2.5-pro": "google/gemini-2.5-pro", + "google/vertex/gemini-2.0-flash": "google/gemini-2.0-flash", + "mistral/mistral-large-latest": "mistral/mistral-large-latest", + "mistral/azure/Mistral-Large-2411": "mistral/Mistral-Large-2411", + "openai-api/deepseek/deepseek-reasoner": "deepseek/deepseek-reasoner", + "bedrock/meta.llama2-70b-chat-v1": "meta/llama2-70b-chat", + "azureai/Llama-3.3-70B-Instruct": "azureai/Llama-3.3-70B-Instruct", + "together/meta-llama/Meta-Llama-3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct", + "groq/llama-3.1-70b-versatile": "meta-llama/llama-3.1-70b-versatile", + "fireworks/accounts/fireworks/models/deepseek-r1-0528": "deepseek-ai/deepseek-r1-0528", + "sambanova/DeepSeek-V1-0324": "deepseek-ai/DeepSeek-V1-0324", + "cf/meta/llama-3.1-70b-instruct": "meta/llama-3.1-70b-instruct", + "perplexity/sonar": "perplexity/sonar", + "hf/openai-community/gpt2": "openai-community/gpt2", + "vllm/openai-community/gpt2": "openai-community/gpt2", + "vllm/meta-llama/Meta-Llama-3-8B-Instruct": "meta-llama/Meta-Llama-3-8B-Instruct", + "sglang/meta-llama/Meta-Llama-3-8B-Instruct": "meta-llama/Meta-Llama-3-8B-Instruct", + "ollama/llama3.1": "ollama/llama3.1", + "llama-cpp-python/llama3": "llama-cpp-python/llama3", + "openrouter/gryphe/mythomax-l2-13b": "gryphe/mythomax-l2-13b", + "hf-inference-providers/openai/gpt-oss-120b": "openai/gpt-oss-120b", + "hf-inference-providers/openai/gpt-oss-120b:cerebras": "openai/gpt-oss-120b:cerebras", + } + + for model_path, model_id in model_path_to_standarized_id_map.items(): + model_info = extract_model_info_from_model_path(model_path) + assert model_info.id == model_id \ No newline at end of file diff --git a/uv.lock 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