diff --git a/.github/workflows/regenerate_types.yml b/.github/workflows/regenerate_types.yml index 5081159f2..0ca34a4dc 100644 --- a/.github/workflows/regenerate_types.yml +++ b/.github/workflows/regenerate_types.yml @@ -5,6 +5,8 @@ on: push: branches: [main] paths: + - "every_eval_ever/schemas/eval.schema.json" + - "every_eval_ever/schemas/instance_level_eval.schema.json" - "eval.schema.json" - "instance_level_eval.schema.json" @@ -25,15 +27,15 @@ jobs: - name: Regenerate types and run checks run: | uv run --project .github/ci-types datamodel-codegen \ - --input eval.schema.json \ - --output eval_types.py \ + --input every_eval_ever/schemas/eval.schema.json \ + --output every_eval_ever/eval_types.py \ --class-name EvaluationLog \ --output-model-type pydantic_v2.BaseModel \ --input-file-type jsonschema \ --formatters ruff-format ruff-check uv run --project .github/ci-types datamodel-codegen \ - --input instance_level_eval.schema.json \ - --output instance_level_types.py \ + --input every_eval_ever/schemas/instance_level_eval.schema.json \ + --output every_eval_ever/instance_level_types.py \ --class-name InstanceLevelEvaluationLog \ --output-model-type pydantic_v2.BaseModel \ --input-file-type jsonschema \ @@ -47,12 +49,12 @@ jobs: uses: peter-evans/create-pull-request@v7 with: add-paths: | - eval_types.py - instance_level_types.py + every_eval_ever/eval_types.py + every_eval_ever/instance_level_types.py base: main branch: auto/regenerate-types commit-message: "chore: regenerate Pydantic types from updated schema" title: "chore: regenerate Pydantic types" - body: "Auto-generated from schema changes in `eval.schema.json` and/or `instance_level_eval.schema.json`." + body: "Auto-generated from schema changes in `every_eval_ever/schemas/eval.schema.json` and/or `every_eval_ever/schemas/instance_level_eval.schema.json`." author: github-actions[bot] - committer: github-actions[bot] \ No newline at end of file + committer: github-actions[bot] diff --git a/.github/workflows/validate-data.yml b/.github/workflows/validate-data.yml new file mode 100644 index 000000000..336a84472 --- /dev/null +++ b/.github/workflows/validate-data.yml @@ -0,0 +1,44 @@ +name: Validate data + +on: + push: + branches: [ main ] + paths: + - 'every_eval_ever/validate.py' + - 'every_eval_ever/check_duplicate_entries.py' + - 'every_eval_ever/schemas/eval.schema.json' + - 'every_eval_ever/schemas/instance_level_eval.schema.json' + - 'data/**' + pull_request: + paths: + - 'every_eval_ever/validate.py' + - 'every_eval_ever/check_duplicate_entries.py' + - 'every_eval_ever/schemas/eval.schema.json' + - 'every_eval_ever/schemas/instance_level_eval.schema.json' + - 'data/**' + +jobs: + validate-json: + name: Validate data + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v5 + + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version: 3.12 + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.9.4" + # Disable cache because there are no requirements lockfiles + enable-cache: false + + - name: Check for duplicate entries + run: uv run python -m every_eval_ever check-duplicates data + + - name: Validate data + run: uv run python -m every_eval_ever validate data diff --git a/README.md b/README.md index 3d391079d..81154b949 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,21 @@ - 📋 **A metadata schema** ([`eval.schema.json`](eval.schema.json)) that defines the information needed for meaningful comparison of evaluation results, including [instance-level data](instance_level_eval.schema.json) - 🔧 **Validation** that checks data against the schema before it enters the repository -- 🔌 **Converters** for [Inspect AI](eval_converters/inspect/), [HELM](eval_converters/helm/), and [lm-eval-harness](eval_converters/lm_eval/), so you can transform your existing evaluation logs into the standard format +- 🔌 **Converters** for [Inspect AI](every_eval_ever/converters/inspect/), [HELM](every_eval_ever/converters/helm/), and [lm-eval-harness](every_eval_ever/converters/lm_eval/), so you can transform your existing evaluation logs into the standard format + +Install the package: + +```bash +pip install every-eval-ever +``` + +Optional converter dependencies: + +```bash +pip install 'every-eval-ever[inspect]' +pip install 'every-eval-ever[helm]' +pip install 'every-eval-ever[all]' +``` ### Terminology @@ -23,8 +37,8 @@ Leaderboard/evaluation data is split-up into files by individual model, and data ### TL;DR How to successfully submit -1. Data must conform to [`eval.schema.json`](eval.schema.json) (current version: `0.2.0`) -2. Validation runs automatically on every PR via [`validate_data.py`](utils/validate_data.py) +1. Data must conform to [`eval.schema.json`](eval.schema.json) (current version: `0.2.2`) +2. Validation runs automatically on every PR via [`.github/workflows/validate-data.yml`](.github/workflows/validate-data.yml) 3. An EvalEval member will review and merge your submission ### PR Naming Convention @@ -57,7 +71,7 @@ Note: Each file can contain multiple individual results related to one model. Se 2. For each model, use the Hugging Face (`developer_name/model_name`) naming convention to create a 2-tier folder structure. 3. Add a JSON file with results for each model and name it `{uuid}.json`. 4. [Optional] Include a [`utils/`](utils/) folder in your benchmark name folder with any scripts used to generate the data (see e.g. [`utils/global-mmlu-lite/adapter.py`](utils/global-mmlu-lite/adapter.py)). -5. [Validate] Validation runs automatically via [`validate-data.yml`](.github/workflows/validate-data.yml) using [`validate_data.py`](utils/validate_data.py) to check JSON files against the schema before merging. +5. [Validate] Validation runs automatically via [`validate-data.yml`](.github/workflows/validate-data.yml) using the package-local validator to check JSON files against the schema before merging. 6. [Submit] Two ways to submit your evaluation data: - **Option A: Drag & drop via Hugging Face** — Go to [evaleval/EEE_datastore](https://huggingface.co/datasets/evaleval/EEE_datastore) → click "Files and versions" → "Contribute" → "Upload files" → drag and drop your data → select "Open as a pull request to the main branch". See [step-by-step screenshots](https://docs.google.com/document/d/1dxTQF8ncGCzaAOIj0RX7E9Hg4THmUBzezDOYUp_XdCY/edit?usp=sharing). - **Option B: Clone & PR** — Clone the [Hugging Face repository](https://huggingface.co/datasets/evaleval/EEE_datastore), add your data under `data/`, and open a pull request @@ -104,13 +118,13 @@ For evaluations that include per-sample results, the individual results should b - **`multi_turn`**: Conversational evaluations with multiple exchanges — uses `messages` array - **`agentic`**: Tool-using evaluations with function calls and sandbox execution — uses `messages` array with `tool_calls` -Each instance captures: `input` (raw question + reference answer), `answer_attribution` (how the answer was extracted), `evaluation` (score, is_correct), and optional `token_usage` and `performance` metrics. Instance-level JSONL files are produced automatically by the [eval converters](eval_converters/README.md). +Each instance captures: `input` (raw question + reference answer), `answer_attribution` (how the answer was extracted), `evaluation` (score, is_correct), and optional `token_usage` and `performance` metrics. Instance-level JSONL files are produced automatically by the [eval converters](every_eval_ever/converters/README.md). Example `single_turn` instance: ```json { - "schema_version": "instance_level_eval_0.2.0", + "schema_version": "instance_level_eval_0.2.2", "evaluation_id": "math_eval/meta-llama/Llama-2-7b-chat/1706000000", "model_id": "meta-llama/Llama-2-7b-chat", "evaluation_name": "math_eval", @@ -144,24 +158,22 @@ At the instance level, agentic evaluations use `interaction_type: "agentic"` wit ## ✅ Data Validation -Validation uses Pydantic models generated from the JSON schemas. This enforces both structural constraints and custom validators (e.g. `score_type: "levels"` requires `level_names`). Requires [uv](https://docs.astral.sh/uv/). +Validation uses Pydantic models generated from the JSON schemas. This validates aggregate `.json` files against `EvaluationLog` and instance-level `_samples.jsonl` files line-by-line against `InstanceLevelEvaluationLog`. Requires [uv](https://docs.astral.sh/uv/). -### Validate files with `validate.py` - -Validate aggregate `.json` and instance-level `_samples.jsonl` files: +### Validate files with the package CLI ```sh -# Single file -uv run python validate.py data/benchmark/dev/model/uuid.json +# Single aggregate file +uv run python -m every_eval_ever validate data/benchmark/dev/model/uuid.json # Instance-level JSONL -uv run python validate.py data/benchmark/dev/model/uuid_samples.jsonl +uv run python -m every_eval_ever validate data/benchmark/dev/model/uuid_samples.jsonl # Entire directory (recurses into subdirectories) -uv run python validate.py data/benchmark/dev/model/ +uv run python -m every_eval_ever validate data/benchmark/dev/model/ # Multiple paths -uv run python validate.py file1.json file2_samples.jsonl data/ +uv run python -m every_eval_ever validate file1.json file2_samples.jsonl data/ ``` File type is determined by extension: `.json` validates against `EvaluationLog`, `.jsonl` validates each line against `InstanceLevelEvaluationLog`. @@ -169,14 +181,14 @@ File type is determined by extension: `.json` validates against `EvaluationLog`, #### Output formats ```sh -# Rich terminal output (default) — colored panels with field paths -uv run python validate.py data/ +# Rich terminal output (default) +uv run python -m every_eval_ever validate data/ # Machine-readable JSON -uv run python validate.py --format json data/ +uv run python -m every_eval_ever validate --format json data/ -# GitHub Actions annotations (::error file=...) -uv run python validate.py --format github data/ +# GitHub Actions annotations +uv run python -m every_eval_ever validate --format github data/ ``` #### Options @@ -186,7 +198,7 @@ uv run python validate.py --format github data/ | `--format {rich,json,github}` | `rich` | Output format | | `--max-errors N` | `50` | Maximum errors reported per JSONL file | -Exit code is `0` if all files pass, `1` if any fail. +Exit code is `0` if all files pass and `1` if any fail. ### Pre-commit hooks @@ -219,7 +231,7 @@ data/ └── {developer_name}/ └── {model_name}/ ├── {uuid}.json # aggregate results - └── {uuid}_samples.jsonl # instance-level results (optional) + └── {uuid}_samples.jsonl # instance-level results (optional) ``` Example evaluations included in the schema v0.2 release: @@ -291,11 +303,12 @@ The schema also supports **level-based metrics** (e.g. Low/Medium/High) and **un ## 🔧 Auto-generation of Pydantic Classes for Schema -Run following bash commands to generate pydantic classes for `eval.schema.json` and `instance_level_eval.schema.json` (to easier use in data converter scripts): +Run the following commands to generate the package-local Pydantic classes from the canonical package-local schemas: ```bash -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 --formatters ruff-format ruff-check -uv run datamodel-codegen --input instance_level_eval.schema.json --output instance_level_types.py --class-name InstanceLevelEvaluationLog --output-model-type pydantic_v2.BaseModel --input-file-type jsonschema --formatters ruff-format ruff-check +uv run datamodel-codegen --input every_eval_ever/schemas/eval.schema.json --output every_eval_ever/eval_types.py --class-name EvaluationLog --output-model-type pydantic_v2.BaseModel --input-file-type jsonschema --formatters ruff-format ruff-check +uv run datamodel-codegen --input every_eval_ever/schemas/instance_level_eval.schema.json --output every_eval_ever/instance_level_types.py --class-name InstanceLevelEvaluationLog --output-model-type pydantic_v2.BaseModel --input-file-type jsonschema --formatters ruff-format ruff-check +uv run python post_codegen.py ``` ## 🔌 Eval Converters @@ -304,11 +317,11 @@ We have prepared converters to make adapting to our schema as easy as possible. | Framework | Command | Instance-Level JSONL | |---|---|---| -| [Inspect AI](eval_converters/inspect/) | `uv run python3 -m eval_converters.inspect --log_path ` | Yes, if samples in log | -| [HELM](eval_converters/helm/) | `uv run python3 -m eval_converters.helm --log_path ` | Always | -| [lm-evaluation-harness](eval_converters/lm_eval/) | `uv run python -m eval_converters.lm_eval --log_path ` | With `--include_samples` | +| [Inspect AI](every_eval_ever/converters/inspect/) | `every_eval_ever convert inspect --log_path ` | Yes, if samples in log | +| [HELM](every_eval_ever/converters/helm/) | `every_eval_ever convert helm --log_path ` | Always | +| [lm-evaluation-harness](every_eval_ever/converters/lm_eval/) | `every_eval_ever convert lm_eval --log_path --include_samples` | With `--include_samples` | -For full CLI usage and required input files, see the [Eval Converters README](eval_converters/README.md). +For full CLI usage and required input files, see the [Eval Converters README](every_eval_ever/converters/README.md). ## 🏆 ACL 2026 Shared Task @@ -337,4 +350,3 @@ Qualifying contributors will be invited as co-authors on the shared task paper. note = {Schema Release} } ``` - diff --git a/eval.schema.json b/eval.schema.json deleted file mode 100644 index 1c7f17271..000000000 --- a/eval.schema.json +++ /dev/null @@ -1,760 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema#", - "version": "0.2.2", - "type": "object", - "description": "Schema for storing and validating LLMs evaluation data, including model configuration, prompts, instances, Output, and evaluation metrics", - "required": [ - "schema_version", - "evaluation_id", - "retrieved_timestamp", - "source_metadata", - "model_info", - "eval_library", - "evaluation_results" - ], - "additionalProperties": false, - "properties": { - "schema_version": { - "type": "string", - "description": "Version of the schema used for this evaluation data" - }, - "evaluation_id": { - "type": "string", - "description": "Unique identifier for this specific evaluation run. Use eval_name/model_id/retrieved_timestamp format" - }, - "evaluation_timestamp": { - "type": "string", - "description": "Timestamp for when the evaluation was run" - }, - "retrieved_timestamp": { - "type": "string", - "description": "Timestamp for when this record was created - using Unix Epoch time format" - }, - "source_metadata": { - "type": "object", - "description": "Metadata about the source of the leaderboard data", - "required": [ - "source_type", - "source_organization_name", - "evaluator_relationship" - ], - "properties": { - "source_name": { - "type": "string", - "description": "Name of the source (e.g. title of the source leaderboard or name of the platform used for the evaluation)." - }, - "source_type": { - "type": "string", - "enum": [ - "documentation", - "evaluation_run" - ], - "description": "Whether the data comes from a direct evaluation run or from documentation" - }, - "source_organization_name": { - "type": "string", - "description": "Name of the organization that provides the data" - }, - "source_organization_url": { - "type": "string", - "description": "URL for the organization that provides the data" - }, - "source_organization_logo_url": { - "type": "string", - "description": "URL for the Logo for the organization that provides the data" - }, - "evaluator_relationship": { - "type": "string", - "description": "Relationship between the evaluator and the model", - "enum": [ - "first_party", - "third_party", - "collaborative", - "other" - ] - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "eval_library": { - "type": "object", - "description": "Evaluation library/framework used to run the evaluation", - "properties": { - "name": { - "type": "string", - "description": "Name of the evaluation library (e.g. inspect_ai, lm_eval, helm)" - }, - "version": { - "type": "string", - "description": "Version of the evaluation library. Use 'unknown' if the version is not available." - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - }, - "required": [ - "name", - "version" - ] - }, - "model_info": { - "$ref": "#/$defs/model_info" - }, - "evaluation_results": { - "type": "array", - "description": "Array of evaluation results", - "items": { - "type": "object", - "required": [ - "evaluation_name", - "source_data", - "metric_config", - "score_details" - ], - "properties": { - "evaluation_result_id": { - "type": "string", - "description": "Stable identifier for this metric result inside an evaluation run. Recommended deterministic join key for instance-level records." - }, - "evaluation_name": { - "type": "string", - "description": "Name of the evaluation" - }, - "source_data": { - "description": "Source of dataset for this evaluation: URL, HuggingFace dataset, or private/custom dataset.", - "oneOf": [ - { - "$ref": "#/$defs/source_data_url" - }, - { - "$ref": "#/$defs/source_data_hf" - }, - { - "$ref": "#/$defs/source_data_private" - } - ] - }, - "evaluation_timestamp": { - "type": "string", - "description": "Timestamp for when the evaluations were run" - }, - "metric_config": { - "type": "object", - "description": "Details about the metric", - "required": [ - "lower_is_better" - ], - "properties": { - "evaluation_description": { - "type": "string", - "description": "Description of the evaluation" - }, - "metric_id": { - "type": "string", - "description": "Stable metric identifier for joining/deduping/querying. Use a canonical global id when applicable (e.g. accuracy, f1_macro, auroc, rmse, pass_at_k). For benchmark/leaderboard-specific metrics, use a namespaced id (e.g. rewardbench.overall, lmarena.elo)." - }, - "metric_name": { - "type": "string", - "description": "Display name for the metric (e.g. Accuracy, F1-macro, pass@1)." - }, - "metric_kind": { - "type": "string", - "description": "Normalized metric family/type used for safe aggregation (e.g. accuracy, f1, auroc, rmse, mae, pass_rate, elo)." - }, - "metric_unit": { - "type": "string", - "description": "Unit of the metric values (e.g. proportion, percent, points, ms, tokens)." - }, - "metric_parameters": { - "type": "object", - "description": "Metric-specific parameters (e.g. {\"k\": 1} for pass@k).", - "additionalProperties": { - "type": [ - "string", - "number", - "boolean", - "null" - ] - } - }, - "lower_is_better": { - "type": "boolean", - "description": "Whether a lower score is better" - }, - "score_type": { - "type": "string", - "description": "Type of score", - "enum": [ - "binary", - "continuous", - "levels" - ] - }, - "level_names": { - "type": "array", - "description": "Names of the score levels", - "items": { - "type": "string" - } - }, - "level_metadata": { - "type": "array", - "description": "Additional Description for each Score Level", - "items": { - "type": "string" - } - }, - "has_unknown_level": { - "type": "boolean", - "description": "Indicates whether there is an Unknown Level - if True, then a score of -1 will be treated as Unknown" - }, - "min_score": { - "type": "number", - - "description": "Minimum possible score for continuous metric" - }, - "max_score": { - "type": "number", - "description": "Maximum possible score for continuous metric" - }, - "llm_scoring": { - "type": "object", - "description": "Configuration when LLM is used as scorer/judge", - "required": [ - "judges", - "input_prompt" - ], - "properties": { - "judges": { - "type": "array", - "description": "LLM judge(s) - single item for judge, multiple for jury", - "items": { - "$ref": "#/$defs/judge_config" - }, - "minItems": 1 - }, - "input_prompt": { - "type": "string", - "description": "Prompt template used for judging" - }, - "aggregation_method": { - "type": "string", - "enum": [ - "majority_vote", - "average", - "weighted_average", - "median" - ], - "description": "How to aggregate scores when multiple judges" - }, - "expert_baseline": { - "type": "number", - "description": "Expert/human baseline score for comparison" - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - }, - "if": { - "properties": { - "score_type": { - "const": "levels" - } - } - }, - "then": { - "required": [ - "level_names", - "has_unknown_level" - ] - }, - "else": { - "if": { - "properties": { - "score_type": { - "const": "continuous" - } - } - }, - "then": { - "required": [ - "min_score", - "max_score" - ] - } - } - }, - "score_details": { - "type": "object", - "description": "The score for the evaluation and related details", - "required": [ - "score" - ], - "properties": { - "score": { - "type": "number", - "description": "The score for the evaluation" - }, - "details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - }, - "uncertainty": { - "type": "object", - "description": "Quantification of uncertainty around the reported score", - "properties": { - "standard_error": { - "type": "object", - "description": "Standard error of the score estimate (SE_mean = standard_deviation / sqrt(num_samples))", - "properties": { - "value": { - "type": "number", - "description": "The standard error value" - }, - "method": { - "type": "string", - "description": "How the standard error was computed (e.g. 'analytic', 'bootstrap', 'jackknife')" - } - }, - "required": [ - "value" - ] - }, - "confidence_interval": { - "type": "object", - "description": "Lower and upper bounds for the metric at a given confidence level.", - "properties": { - "lower": { - "type": "number", - "description": "Lower bound of the confidence interval" - }, - "upper": { - "type": "number", - "description": "Upper bound of the confidence interval" - }, - "confidence_level": { - "type": "number", - "description": "Confidence level (e.g. 0.95 for a 95% confidence interval)", - "minimum": 0, - "maximum": 1 - }, - "method": { - "type": "string", - "description": "How the confidence interval was computed" - } - }, - "required": [ - "lower", - "upper" - ] - }, - "standard_deviation": { - "type": "number", - "description": "Standard deviation of the per-sample scores" - }, - "num_samples": { - "type": "integer", - "description": "Number of samples used to compute the uncertainty estimates" - }, - "num_bootstrap_samples": { - "type": "integer", - "description": "Number of bootstrap resamples used, if bootstrap method was applied" - } - } - } - } - }, - "generation_config": { - "type": "object", - "properties": { - "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" - }, - "execution_command": { - "type": "string", - "description": "Command used to run the model to generate results" - }, - "reasoning": { - "type": "boolean", - "description": "Whether reasoning orchain-of-thought was used to generate results" - }, - "prompt_template": { - "type": "string", - "description": "Input prompt template for task (should contain agentic info if needed)." - }, - "agentic_eval_config": { - "type": "object", - "description": "General configuration for agentic evaluations.", - "properties": { - "available_tools": { - "type": "array", - "description": "List of all available tools with their configurations", - "items": { - "type": "object", - "properties": { - "name": { - "type": "string", - "description": "e.g. bash, calculator, ..." - }, - "description": { - "type": "string" - }, - "parameters": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - } - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "eval_plan": { - "type": "object", - "description": "Plan (solvers) used in evaluation. Solvers are crucial parts of Inspect evaluations which can serve a wide variety of purposes like providing system prompts, prompt engineering, model generation or multi-turn dialog.", - "properties": { - "name": { - "type": "string" - }, - "steps": { - "type": "array", - "description": "Array of evaluation plan steps (each step is a JSON-serialized string)", - "items": { - "type": "string" - } - }, - "config": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "eval_limits": { - "type": "object", - "description": "Listed evaluation limits like time limit, message limit, token limit.", - "properties": { - "time_limit": { - "type": "integer", - "description": "Time limit for evaluation." - }, - "message_limit": { - "type": "integer", - "description": "Message limit for evaluation." - }, - "token_limit": { - "type": "integer", - "description": "Token limit for evaluation." - } - } - }, - "sandbox": { - "type": "object", - "properties": { - "type": { - "type": "string", - "description": "Type of sandbox e.g. docker" - }, - "config": { - "type": "string", - "description": "Config file name/path e.g. compose.yaml. TODO or full config? Not sure based on the Inspect docs" - } - } - }, - "max_attempts": { - "type": "integer", - "description": "Maximum number of submission attempts (default 1).", - "default": 1 - }, - "incorrect_attempt_feedback": { - "type": "string", - "description": "Feedback from the model after incorrect attempt." - } - }, - "additionalProperties": false - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - } - } - } - }, - "detailed_evaluation_results": { - "type": "object", - "description": "Reference to the evaluation results for all individual samples in the evaluation", - "properties": { - "format": { - "type": "string", - "description": "Format of the detailed evaluation results", - "enum": [ - "jsonl", - "json" - ] - }, - "file_path": { - "type": "string", - "description": "Path to the detailed evaluation results file" - }, - "hash_algorithm": { - "type": "string", - "description": "Hash algorithm used for checksum and sample_hash in instance-level data", - "enum": [ - "sha256", - "md5" - ] - }, - "checksum": { - "type": "string", - "description": "Checksum value of the file" - }, - "total_rows": { - "type": "integer", - "description": "Total number of rows in the detailed evaluation results file" - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - } - }, - "$defs": { - "judge_config": { - "type": "object", - "description": "Configuration for a single LLM judge/juror", - "required": [ - "model_info" - ], - "properties": { - "model_info": { - "$ref": "#/$defs/model_info" - }, - "temperature": { - "type": "number" - }, - "weight": { - "type": "number", - "description": "Weight of this judge's score in aggregation (used in jury)" - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "model_info": { - "type": "object", - "description": "Complete model specification including basic information, technical configuration and inference settings", - "required": [ - "name", - "id" - ], - "properties": { - "name": { - "type": "string", - "description": "Model name provided by evaluation source" - }, - "id": { - "type": "string", - "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", - "description": "Name of organization that provides the model (e.g. 'OpenAI')" - }, - "inference_platform": { - "type": "string", - "description": "Name of inference platform which provides an access to models by API to run the evaluations or provides models weights to run them locally (e.g. HuggingFace, Bedrock, Together AI)" - }, - "inference_engine": { - "type": "object", - "description": "Name of inference engine which provides an access to optimized models to use them for local evaluations (e.g. vLLM, Ollama).", - "properties": { - "name": { - "type": "string", - "description": "Name of the inference engine" - }, - "version": { - "type": "string", - "description": "Version of the inference engine" - } - } - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "source_data_url": { - "type": "object", - "description": "URL source for the evaluation data", - "required": [ - "dataset_name", - "source_type", - "url" - ], - "properties": { - "dataset_name": { - "type": "string", - "description": "Name of the source dataset" - }, - "source_type": { - "const": "url" - }, - "url": { - "type": "array", - "items": { - "type": "string" - }, - "minItems": 1, - "description": "URL(s) for the source of the evaluation data" - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "source_data_hf": { - "type": "object", - "description": "Details about HuggingFace dataset used for evaluation", - "required": [ - "dataset_name", - "source_type" - ], - "properties": { - "dataset_name": { - "type": "string", - "description": "Name of the source dataset" - }, - "source_type": { - "const": "hf_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": "string" - } - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "source_data_private": { - "type": "object", - "description": "Generic source data when neither URL array nor HuggingFace dataset applies", - "required": [ - "dataset_name", - "source_type" - ], - "properties": { - "dataset_name": { - "type": "string", - "description": "Name of the source dataset" - }, - "source_type": { - "const": "other" - }, - "additional_details": { - "type": "object", - "description": "Additional parameters (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - } - } -} diff --git a/eval.schema.json b/eval.schema.json new file mode 120000 index 000000000..c1943c02f --- /dev/null +++ b/eval.schema.json @@ -0,0 +1 @@ +every_eval_ever/schemas/eval.schema.json \ No newline at end of file diff --git a/eval_converters/__init__.py b/eval_converters/__init__.py deleted file mode 100644 index 872025a51..000000000 --- a/eval_converters/__init__.py +++ /dev/null @@ -1 +0,0 @@ -SCHEMA_VERSION = '0.2.1' \ No newline at end of file diff --git a/every_eval_ever/__init__.py b/every_eval_ever/__init__.py new file mode 100644 index 000000000..5e49a619a --- /dev/null +++ b/every_eval_ever/__init__.py @@ -0,0 +1,16 @@ +"""every_eval_ever public package API.""" + +from __future__ import annotations + +import importlib +from typing import Any + +__all__ = ["eval_types", "instance_level_types"] + + +def __getattr__(name: str) -> Any: + if name in {"eval_types", "instance_level_types"}: + module = importlib.import_module(f".{name}", __name__) + globals()[name] = module + return module + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/every_eval_ever/__main__.py b/every_eval_ever/__main__.py new file mode 100644 index 000000000..bfdcd0c11 --- /dev/null +++ b/every_eval_ever/__main__.py @@ -0,0 +1,4 @@ +from .cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/utils/check_duplicate_entries.py b/every_eval_ever/check_duplicate_entries.py similarity index 96% rename from utils/check_duplicate_entries.py rename to every_eval_ever/check_duplicate_entries.py index cbf2b4fb5..a9bb862eb 100644 --- a/utils/check_duplicate_entries.py +++ b/every_eval_ever/check_duplicate_entries.py @@ -4,7 +4,6 @@ import os from typing import Any, Dict, List - IGNORE_KEYS = {"retrieved_timestamp", "evaluation_id"} @@ -65,7 +64,7 @@ def normalized_hash(payload: Dict[str, Any]) -> str: return hashlib.sha256(encoded.encode("utf-8")).hexdigest() -def main() -> None: +def main(argv: List[str] | None = None) -> int: parser = argparse.ArgumentParser( prog="check_duplicate_entries", description="Detects duplicate evaluation entries ignoring scrape timestamp fields.", @@ -73,7 +72,7 @@ def main() -> None: parser.add_argument( "paths", nargs="+", type=str, help="File or folder paths to JSON data" ) - args = parser.parse_args() + args = parser.parse_args(argv) file_paths = expand_paths(args.paths) print() @@ -112,7 +111,7 @@ def main() -> None: if not duplicate_groups: print("No duplicates found.") print() - return + return 0 ignore_label = ", ".join(f"`{key}`" for key in sorted(IGNORE_KEYS)) print(f"Found duplicate entries (ignoring keys: {ignore_label}).") @@ -131,8 +130,8 @@ def main() -> None: ) print() - raise SystemExit(1) + return 1 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/every_eval_ever/cli.py b/every_eval_ever/cli.py new file mode 100644 index 000000000..104e8ec9a --- /dev/null +++ b/every_eval_ever/cli.py @@ -0,0 +1,326 @@ +"""Top-level CLI for conversion and validation.""" + +from __future__ import annotations + +import argparse +import json +import sys +import uuid +from pathlib import Path +from typing import Any + +EVALUATOR_RELATIONSHIP_CHOICES = [ + "first_party", + "third_party", + "collaborative", + "other", +] + + +def _common_metadata(args: argparse.Namespace) -> dict[str, Any]: + return { + "source_organization_name": args.source_organization_name, + "evaluator_relationship": args.evaluator_relationship, + "source_organization_url": args.source_organization_url, + "source_organization_logo_url": args.source_organization_logo_url, + "eval_library_name": args.eval_library_name, + "eval_library_version": args.eval_library_version, + "parent_eval_output_dir": args.output_dir, + } + + +def _output_dir_for_log(base_output: Path, log: Any) -> Path: + dataset = "unknown" + if log.evaluation_results and log.evaluation_results[0].source_data: + dataset = log.evaluation_results[0].source_data.dataset_name or "unknown" + model_id = log.model_info.id or "unknown" + parts = model_id.split("/", 1) + developer = parts[0] if len(parts) == 2 else "unknown" + model_name = parts[1] if len(parts) == 2 else model_id + out_dir = base_output / dataset / developer / model_name + out_dir.mkdir(parents=True, exist_ok=True) + return out_dir + + +def _write_log(log: Any, base_output: Path, eval_uuid: str | None = None) -> Path: + out_dir = _output_dir_for_log(base_output, log) + eval_uuid = eval_uuid or str(uuid.uuid4()) + out_file = out_dir / f"{eval_uuid}.json" + with out_file.open("w", encoding="utf-8") as file: + json.dump(log.model_dump(mode="json", exclude_none=True), file, indent=2) + return out_file + + +def _cmd_convert_lm_eval(args: argparse.Namespace) -> int: + from every_eval_ever.converters.lm_eval.adapter import LMEvalAdapter + from every_eval_ever.converters.lm_eval.instance_level_adapter import ( + LMEvalInstanceLevelAdapter, + ) + from every_eval_ever.converters.lm_eval.utils import find_samples_file + + adapter = LMEvalAdapter() + metadata = _common_metadata(args) + if args.inference_engine: + metadata["inference_engine"] = args.inference_engine + if args.inference_engine_version: + metadata["inference_engine_version"] = args.inference_engine_version + + log_path = Path(args.log_path) + metadata["parent_eval_output_dir"] = str(log_path.parent if log_path.is_file() else log_path) + if log_path.is_file(): + logs = adapter.transform_from_file(log_path, metadata) + elif log_path.is_dir(): + logs = adapter.transform_from_directory(log_path, metadata) + else: + raise FileNotFoundError(f"Path is not a file or directory: {log_path}") + + output_dir = Path(args.output_dir) + for log in logs: + eval_uuid = str(uuid.uuid4()) + if args.include_samples: + meta = adapter.get_eval_metadata(log.evaluation_id) + parent_dir = meta.get("parent_dir") + task_name = meta.get("task_name") + if parent_dir and task_name: + samples_file = find_samples_file(Path(parent_dir), task_name) + if samples_file: + detailed = LMEvalInstanceLevelAdapter().transform_and_save( + samples_path=samples_file, + evaluation_id=log.evaluation_id, + model_id=log.model_info.id, + task_name=task_name, + output_dir=str(_output_dir_for_log(output_dir, log)), + file_uuid=eval_uuid, + ) + log.detailed_evaluation_results = detailed + print(_write_log(log, output_dir, eval_uuid=eval_uuid)) + + print(f"Converted {len(logs)} evaluation log(s).") + return 0 + + +def _cmd_convert_inspect(args: argparse.Namespace) -> int: + from every_eval_ever.converters.inspect.adapter import InspectAIAdapter + + adapter = InspectAIAdapter() + metadata = _common_metadata(args) + metadata["file_uuid"] = str(uuid.uuid4()) + + log_path = Path(args.log_path) + if log_path.is_file(): + logs = [adapter.transform_from_file(log_path, metadata)] + elif log_path.is_dir(): + logs = adapter.transform_from_directory(log_path, metadata) + else: + raise FileNotFoundError(f"Path is not a file or directory: {log_path}") + + output_dir = Path(args.output_dir) + for log in logs: + print(_write_log(log, output_dir)) + + print(f"Converted {len(logs)} evaluation log(s).") + return 0 + + +def _cmd_convert_helm(args: argparse.Namespace) -> int: + from every_eval_ever.converters.helm.adapter import HELMAdapter + + adapter = HELMAdapter() + metadata = _common_metadata(args) + metadata["file_uuid"] = str(uuid.uuid4()) + + logs = adapter.transform_from_directory( + Path(args.log_path), + output_path=str(Path(args.output_dir) / "helm_output"), + metadata_args=metadata, + ) + output_dir = Path(args.output_dir) + for log in logs: + print(_write_log(log, output_dir)) + + print(f"Converted {len(logs)} evaluation log(s).") + return 0 + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="every_eval_ever", + description=( + "CLI for validating and converting evaluation results into the " + "Every Eval Ever schema." + ), + epilog=( + "Examples:\n" + " every_eval_ever convert lm_eval --log_path results.json --output_dir data\n" + " every_eval_ever convert inspect --log_path inspect_log.json --output_dir data\n" + " every_eval_ever convert helm --log_path helm_run_dir --output_dir data" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + subparsers = parser.add_subparsers(dest="command", required=True) + + validate_parser = subparsers.add_parser( + "validate", + help="Validate JSON and JSONL files with Pydantic models", + description=( + "Validate aggregate .json and instance-level .jsonl files " + "using the bundled Pydantic models." + ), + ) + validate_parser.add_argument( + "paths", + nargs="+", + help="One or more files or directories containing .json/.jsonl files.", + ) + validate_parser.add_argument( + "--max-errors", + type=int, + default=50, + help="Maximum errors to report per JSONL file.", + ) + validate_parser.add_argument( + "--format", + choices=["rich", "json", "github"], + default="rich", + dest="output_format", + help="Output format.", + ) + + check_duplicates_parser = subparsers.add_parser( + "check-duplicates", + help="Detect duplicate evaluation JSON entries", + description=( + "Detect duplicate evaluation entries while ignoring scrape-specific " + "keys (evaluation_id and retrieved_timestamp)." + ), + ) + check_duplicates_parser.add_argument( + "paths", + nargs="+", + help="One or more JSON files or directories containing JSON files.", + ) + + convert_parser = subparsers.add_parser( + "convert", + help="Convert source eval logs to every_eval_ever", + description="Convert outputs from supported eval frameworks into Every Eval Ever JSON.", + ) + convert_subparsers = convert_parser.add_subparsers(dest="source", required=True) + + for source in ["lm_eval", "inspect", "helm"]: + source_parser = convert_subparsers.add_parser( + source, + help=f"Convert {source} logs", + description=f"Convert {source} evaluation outputs to Every Eval Ever format.", + ) + source_parser.add_argument( + "--log_path", + "--log-path", + required=True, + help="Path to source log file or directory to convert.", + ) + source_parser.add_argument( + "--output_dir", + "--output-dir", + default="data", + help="Base output directory where converted files are written.", + ) + source_parser.add_argument( + "--source_organization_name", + "--source-organization-name", + default="unknown", + help="Organization name for source_metadata.source_organization_name.", + ) + source_parser.add_argument( + "--evaluator_relationship", + "--evaluator-relationship", + default="third_party", + choices=EVALUATOR_RELATIONSHIP_CHOICES, + help="Relationship between evaluator and model developer.", + ) + source_parser.add_argument( + "--source_organization_url", + "--source-organization-url", + default=None, + help="Optional organization URL for source metadata.", + ) + source_parser.add_argument( + "--source_organization_logo_url", + "--source-organization-logo-url", + default=None, + help="Optional organization logo URL for source metadata.", + ) + source_parser.add_argument( + "--eval_library_name", + "--eval-library-name", + default=source, + help="Evaluation library name recorded in eval_library.name.", + ) + source_parser.add_argument( + "--eval_library_version", + "--eval-library-version", + default="unknown", + help="Evaluation library version recorded in eval_library.version.", + ) + + if source == "lm_eval": + source_parser.add_argument( + "--include_samples", + "--include-samples", + action="store_true", + help="Also convert lm-eval sample JSONL into instance-level output.", + ) + source_parser.add_argument( + "--inference_engine", + "--inference-engine", + default=None, + help="Override inferred inference engine (e.g. vllm, transformers).", + ) + source_parser.add_argument( + "--inference_engine_version", + "--inference-engine-version", + default=None, + help="Inference engine version to record in model_info.inference_engine.version.", + ) + + return parser + + +def main(argv: list[str] | None = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + + if args.command == "validate": + from every_eval_ever.validate import main as validate_main + + return validate_main( + [ + *args.paths, + "--max-errors", + str(args.max_errors), + "--format", + args.output_format, + ] + ) + + if args.command == "check-duplicates": + from every_eval_ever.check_duplicate_entries import ( + main as check_duplicates_main, + ) + + return check_duplicates_main(args.paths) + + if args.command == "convert": + if args.source == "lm_eval": + return _cmd_convert_lm_eval(args) + if args.source == "inspect": + return _cmd_convert_inspect(args) + if args.source == "helm": + return _cmd_convert_helm(args) + + parser.print_help() + return 1 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv[1:])) diff --git a/eval_converters/README.md b/every_eval_ever/converters/README.md similarity index 90% rename from eval_converters/README.md rename to every_eval_ever/converters/README.md index 81f89b6a8..0f64b029a 100644 --- a/eval_converters/README.md +++ b/every_eval_ever/converters/README.md @@ -14,7 +14,7 @@ uv sync --extra all # + all ### Inspect -The conversion script from `Inspect AI` to the unified schema can be run using `eval_converters/inspect/__main__.py`. +The conversion script from `Inspect AI` to the unified schema can be run using `every_eval_ever/converters/inspect/__main__.py`. Using the `--log_path` argument, you can choose one of three ways to specify evaluations to convert: - Provide an `Inspect AI` evaluation log with the `.eval` extension (e.g., `2026-02-07T11-26-57+00-00_gaia_4V8zHbbRKpU5Yv2BMoBcjE.eval`) @@ -24,7 +24,7 @@ Using the `--log_path` argument, you can choose one of three ways to specify eva The exact command for converting an example evaluation log is: ```bash -uv run python3 -m eval_converters.inspect --log_path tests/data/inspect/2026-02-07T11-26-57+00-00_gaia_4V8zHbbRKpU5Yv2BMoBcjE.json +uv run --extra inspect every_eval_ever convert inspect --log_path tests/data/inspect/2026-02-07T11-26-57+00-00_gaia_4V8zHbbRKpU5Yv2BMoBcjE.json ``` @@ -60,10 +60,10 @@ options: ### HELM -You can convert HELM evaluation log into unified schema via `eval_converters/helm/__main__.py`. For example: +You can convert HELM evaluation log into unified schema via `every_eval_ever/converters/helm/__main__.py`. For example: ```bash -uv run python3 -m eval_converters.helm --log_path tests/data/helm/commonsense:dataset=hellaswag,method=multiple_choice_joint,model=eleutherai_pythia-1b-v0 +uv run --extra helm every_eval_ever convert helm --log_path tests/data/helm/commonsense:dataset=hellaswag,method=multiple_choice_joint,model=eleutherai_pythia-1b-v0 ``` The automatic conversion script requires following files generated by HELM to work correctly: @@ -104,12 +104,12 @@ options: ## lm-eval-harness -The conversion script from `lm-eval-harness` evaluation logs to the unified schema can be run using `eval_converters/lm_eval/__main__.py`. +The conversion script from `lm-eval-harness` evaluation logs to the unified schema can be run using `every_eval_ever/converters/lm_eval/__main__.py`. Using the `--log_path` argument, you can run a command like this: ```bash -uv run python -m eval_converters.lm_eval --log_path tests/data/lm_eval/results_2026-01-21T03-44-18.458309.json +uv run every_eval_ever convert lm_eval --log_path tests/data/lm_eval/results_2026-01-21T03-44-18.458309.json ``` @@ -156,4 +156,4 @@ options: lm_eval, helm) --eval_library_version EVAL_LIBRARY_VERSION Version of the evaluation library -``` \ No newline at end of file +``` diff --git a/every_eval_ever/converters/__init__.py b/every_eval_ever/converters/__init__.py new file mode 100644 index 000000000..1b7ca553f --- /dev/null +++ b/every_eval_ever/converters/__init__.py @@ -0,0 +1 @@ +SCHEMA_VERSION = '0.2.2' diff --git a/eval_converters/common/__init__.py b/every_eval_ever/converters/common/__init__.py similarity index 100% rename from eval_converters/common/__init__.py rename to every_eval_ever/converters/common/__init__.py diff --git a/eval_converters/common/adapter.py b/every_eval_ever/converters/common/adapter.py similarity index 97% rename from eval_converters/common/adapter.py rename to every_eval_ever/converters/common/adapter.py index b1462aabf..6c7712f09 100644 --- a/eval_converters/common/adapter.py +++ b/every_eval_ever/converters/common/adapter.py @@ -7,8 +7,8 @@ from pathlib import Path from typing import Any, Dict, List, Tuple, Union -from eval_converters.common.error import AdapterError, TransformationError -from eval_types import EvaluationLog +from every_eval_ever.converters.common.error import AdapterError, TransformationError +from every_eval_ever.eval_types import EvaluationLog @dataclass class AdapterMetadata: diff --git a/eval_converters/common/error.py b/every_eval_ever/converters/common/error.py similarity index 100% rename from eval_converters/common/error.py rename to every_eval_ever/converters/common/error.py diff --git a/eval_converters/common/utils.py b/every_eval_ever/converters/common/utils.py similarity index 100% rename from eval_converters/common/utils.py rename to every_eval_ever/converters/common/utils.py diff --git a/eval_converters/helm/__init__.py b/every_eval_ever/converters/helm/__init__.py similarity index 100% rename from eval_converters/helm/__init__.py rename to every_eval_ever/converters/helm/__init__.py diff --git a/eval_converters/helm/__main__.py b/every_eval_ever/converters/helm/__main__.py similarity index 96% rename from eval_converters/helm/__main__.py rename to every_eval_ever/converters/helm/__main__.py index 93861056e..d09707a2f 100644 --- a/eval_converters/helm/__main__.py +++ b/every_eval_ever/converters/helm/__main__.py @@ -7,17 +7,14 @@ from typing import Any, Dict, List, Union try: - from eval_converters.helm.adapter import HELMAdapter + from every_eval_ever.converters.helm.adapter import HELMAdapter except ImportError as exc: raise SystemExit( "The 'crfm-helm' package is required to use the HELM converter.\n" "Install it with: uv sync --extra helm" ) from exc -from eval_types import ( - EvaluatorRelationship, - EvaluationLog -) +from every_eval_ever.eval_types import EvaluatorRelationship, EvaluationLog def parse_args(): parser = ArgumentParser() @@ -125,4 +122,4 @@ def save_evaluation_log( file_uuid ) else: - print("Missing unified schema result!") \ No newline at end of file + print("Missing unified schema result!") diff --git a/eval_converters/helm/adapter.py b/every_eval_ever/converters/helm/adapter.py similarity index 86% rename from eval_converters/helm/adapter.py rename to every_eval_ever/converters/helm/adapter.py index ad7c1914a..25f641dff 100644 --- a/eval_converters/helm/adapter.py +++ b/every_eval_ever/converters/helm/adapter.py @@ -3,17 +3,28 @@ import datetime from typing import Any, Dict, List, Tuple from pathlib import Path -from dacite import from_dict -from helm.benchmark.metrics.metric import PerInstanceStats -from helm.benchmark.adaptation.scenario_state import AdapterSpec, RequestState, ScenarioState -from helm.benchmark.metrics.statistic import Stat -from helm.benchmark.config_registry import register_builtin_configs_from_helm_package -from helm.benchmark.model_deployment_registry import get_model_deployment -from helm.benchmark.run_spec import RunSpec -from helm.common.codec import from_json - -from eval_types import ( +_HELM_IMPORT_ERROR: Exception | None = None +try: + from dacite import from_dict + from helm.benchmark.metrics.metric import PerInstanceStats + from helm.benchmark.adaptation.scenario_state import ( + AdapterSpec, + RequestState, + ScenarioState, + ) + from helm.benchmark.metrics.statistic import Stat + from helm.benchmark.config_registry import register_builtin_configs_from_helm_package + from helm.benchmark.model_deployment_registry import get_model_deployment + from helm.benchmark.run_spec import RunSpec + from helm.common.codec import from_json +except Exception as ex: # pragma: no cover - exercised only when optional deps missing + _HELM_IMPORT_ERROR = ex + from_dict = None # type: ignore[assignment] + PerInstanceStats = AdapterSpec = RequestState = ScenarioState = Stat = RunSpec = Any # type: ignore[assignment] + get_model_deployment = register_builtin_configs_from_helm_package = from_json = None # type: ignore[assignment] + +from every_eval_ever.eval_types import ( DetailedEvaluationResults, EvalLibrary, EvaluationLog, @@ -32,19 +43,29 @@ Uncertainty ) -from instance_level_types import ( +from every_eval_ever.instance_level_types import ( InstanceLevelEvaluationLog ) -from eval_converters.common.adapter import AdapterMetadata, BaseEvaluationAdapter, SupportedLibrary -from eval_converters.common.utils import sha256_file -from eval_converters.helm.utils import extract_reasoning -from eval_converters.helm.instance_level_adapter import ( +from every_eval_ever.converters.common.adapter import AdapterMetadata, BaseEvaluationAdapter, SupportedLibrary +from every_eval_ever.converters.common.utils import sha256_file +from every_eval_ever.converters.helm.utils import extract_reasoning +from every_eval_ever.converters.helm.instance_level_adapter import ( HELMInstanceLevelDataAdapter ) -from eval_converters import SCHEMA_VERSION +from every_eval_ever.converters import SCHEMA_VERSION + + +def _require_helm_dependencies() -> None: + if _HELM_IMPORT_ERROR is not None: + raise ImportError( + "HELM converter dependencies are missing. " + "Install with: pip install 'every_eval_ever[helm]'" + ) from _HELM_IMPORT_ERROR + -register_builtin_configs_from_helm_package() +if register_builtin_configs_from_helm_package is not None: + register_builtin_configs_from_helm_package() class HELMAdapter(BaseEvaluationAdapter): @@ -301,7 +322,7 @@ def _transform_single( if request_states: parent_eval_output_dir = metadata_args.get('parent_eval_output_dir') - detailed_results_id = f'{metadata_args.get('file_uuid')}_samples' + detailed_results_id = f"{metadata_args.get('file_uuid')}_samples" model_dev, model_name = model_info.id.split('/', 1) evaluation_dir = f'{parent_eval_output_dir}/{source_data.dataset_name}/{model_dev}/{model_name}' @@ -349,4 +370,7 @@ def _transform_single( detailed_evaluation_results=detailed_evaluation_results ) - return eval_log \ No newline at end of file + return eval_log + def __init__(self, strict_validation: bool = True): + _require_helm_dependencies() + super().__init__(strict_validation) diff --git a/eval_converters/helm/instance_level_adapter.py b/every_eval_ever/converters/helm/instance_level_adapter.py similarity index 84% rename from eval_converters/helm/instance_level_adapter.py rename to every_eval_ever/converters/helm/instance_level_adapter.py index 3f4192f81..e1c94bd36 100644 --- a/eval_converters/helm/instance_level_adapter.py +++ b/every_eval_ever/converters/helm/instance_level_adapter.py @@ -1,13 +1,24 @@ import json -from helm.benchmark.adaptation.scenario_state import ( - RequestState -) - from pathlib import Path -from typing import List, Tuple +from typing import Any, List, Tuple + +_HELM_IMPORT_ERROR: Exception | None = None +try: + from helm.benchmark.adaptation.scenario_state import RequestState +except Exception as ex: # pragma: no cover - exercised only when optional deps missing + _HELM_IMPORT_ERROR = ex + RequestState = Any # type: ignore[assignment] + + +def _require_helm_dependencies() -> None: + if _HELM_IMPORT_ERROR is not None: + raise ImportError( + "HELM converter dependencies are missing. " + "Install with: pip install 'every_eval_ever[helm]'" + ) from _HELM_IMPORT_ERROR -from instance_level_types import ( +from every_eval_ever.instance_level_types import ( AnswerAttributionItem, Evaluation, Input, @@ -18,9 +29,9 @@ TokenUsage, ) -from eval_converters import SCHEMA_VERSION -from eval_converters.common.utils import sha256_string -from eval_converters.helm.utils import extract_all_reasonings +from every_eval_ever.converters import SCHEMA_VERSION +from every_eval_ever.converters.common.utils import sha256_string +from every_eval_ever.converters.helm.utils import extract_all_reasonings class HELMInstanceLevelDataAdapter: @@ -31,6 +42,7 @@ def __init__( hash_algorithm: str, evaluation_dir: str ): + _require_helm_dependencies() self.evaluation_id = evaulation_id self.format = format self.hash_algorithm = hash_algorithm @@ -140,4 +152,4 @@ def convert_instance_level_logs( )) self._save_json(instance_level_logs) - return self.path, len(instance_level_logs) \ No newline at end of file + return self.path, len(instance_level_logs) diff --git a/eval_converters/helm/utils.py b/every_eval_ever/converters/helm/utils.py similarity index 74% rename from eval_converters/helm/utils.py rename to every_eval_ever/converters/helm/utils.py index 5f12500fa..f2846e2a4 100644 --- a/eval_converters/helm/utils.py +++ b/every_eval_ever/converters/helm/utils.py @@ -1,5 +1,9 @@ -from typing import List -from helm.benchmark.adaptation.scenario_state import RequestState +from typing import Any, List + +try: + from helm.benchmark.adaptation.scenario_state import RequestState +except Exception: # pragma: no cover - exercised only when optional deps missing + RequestState = Any # type: ignore[assignment] def extract_reasoning(request_state: RequestState) -> str | None: if request_state.result and request_state.result.completions: @@ -23,4 +27,4 @@ def extract_all_reasonings(request_state: RequestState) -> List[str] | None: getattr(getattr(c, "thinking", None), "text", None) for c in request_state.result.completions if getattr(c, "thinking", None) is not None - ] \ No newline at end of file + ] diff --git a/eval_converters/inspect/__init__.py b/every_eval_ever/converters/inspect/__init__.py similarity index 100% rename from eval_converters/inspect/__init__.py rename to every_eval_ever/converters/inspect/__init__.py diff --git a/eval_converters/inspect/__main__.py b/every_eval_ever/converters/inspect/__main__.py similarity index 97% rename from eval_converters/inspect/__main__.py rename to every_eval_ever/converters/inspect/__main__.py index 116e0143c..3dd076698 100644 --- a/eval_converters/inspect/__main__.py +++ b/every_eval_ever/converters/inspect/__main__.py @@ -9,18 +9,15 @@ try: from inspect_ai.log import list_eval_logs - from eval_converters.inspect.adapter import InspectAIAdapter + from every_eval_ever.converters.inspect.adapter import InspectAIAdapter except ImportError as exc: raise SystemExit( "The 'inspect-ai' package is required to use the Inspect AI converter.\n" "Install it with: uv sync --extra inspect" ) from exc -from eval_types import ( - EvaluatorRelationship, - EvaluationLog -) -from instance_level_types import InstanceLevelEvaluationLog +from every_eval_ever.eval_types import EvaluatorRelationship, EvaluationLog +from every_eval_ever.instance_level_types import InstanceLevelEvaluationLog logger = logging.getLogger(__name__) diff --git a/eval_converters/inspect/adapter.py b/every_eval_ever/converters/inspect/adapter.py similarity index 91% rename from eval_converters/inspect/adapter.py rename to every_eval_ever/converters/inspect/adapter.py index 31fd14d24..e586eecf5 100644 --- a/eval_converters/inspect/adapter.py +++ b/every_eval_ever/converters/inspect/adapter.py @@ -3,27 +3,42 @@ import uuid import logging -from inspect_ai.log import ( - EvalDataset, - EvalLog, - EvalMetric, - EvalResults, - EvalSample, - EvalSampleSummary, - EvalScore, - EvalStats, - EvalSpec, - list_eval_logs, - read_eval_log, - read_eval_log_sample, - read_eval_log_sample_summaries, -) -from inspect_ai.log import EvalPlan as InspectEvalPlan from pathlib import Path from typing import Any, Dict, List, Tuple, Union from urllib.parse import urlparse -from eval_types import ( +_INSPECT_IMPORT_ERROR: Exception | None = None +try: + from inspect_ai.log import ( + EvalDataset, + EvalLog, + EvalMetric, + EvalResults, + EvalSample, + EvalSampleSummary, + EvalScore, + EvalStats, + EvalSpec, + list_eval_logs, + read_eval_log, + read_eval_log_sample, + read_eval_log_sample_summaries, + ) + from inspect_ai.log import EvalPlan as InspectEvalPlan +except Exception as ex: # pragma: no cover - exercised only when optional deps missing + _INSPECT_IMPORT_ERROR = ex + EvalDataset = EvalLog = EvalMetric = EvalResults = EvalSample = EvalSampleSummary = EvalScore = EvalStats = EvalSpec = Any # type: ignore[assignment] + InspectEvalPlan = Any # type: ignore[assignment] + + +def _require_inspect_dependencies() -> None: + if _INSPECT_IMPORT_ERROR is not None: + raise ImportError( + "Inspect converter dependencies are missing. " + "Install with: pip install 'every_eval_ever[inspect]'" + ) from _INSPECT_IMPORT_ERROR + +from every_eval_ever.eval_types import ( AgenticEvalConfig, AvailableTool, DetailedEvaluationResults, @@ -51,25 +66,25 @@ Uncertainty ) -from eval_converters.common.adapter import ( +from every_eval_ever.converters.common.adapter import ( AdapterMetadata, BaseEvaluationAdapter, SupportedLibrary ) -from eval_converters.common.error import AdapterError -from eval_converters.common.utils import ( +from every_eval_ever.converters.common.error import AdapterError +from every_eval_ever.converters.common.utils import ( convert_timestamp_to_unix_format, get_current_unix_timestamp ) -from eval_converters.inspect.instance_level_adapter import ( +from every_eval_ever.converters.inspect.instance_level_adapter import ( InspectInstanceLevelDataAdapter ) -from eval_converters.common.utils import sha256_file -from eval_converters.inspect.utils import ( +from every_eval_ever.converters.common.utils import sha256_file +from every_eval_ever.converters.inspect.utils import ( extract_model_info_from_model_path ) -from eval_converters import SCHEMA_VERSION +from every_eval_ever.converters import SCHEMA_VERSION logger = logging.getLogger(__name__) @@ -79,6 +94,10 @@ class InspectAIAdapter(BaseEvaluationAdapter): Adapter for transforming evaluation outputs from the Inspect AI library into the unified schema format. """ + def __init__(self, strict_validation: bool = True): + _require_inspect_dependencies() + super().__init__(strict_validation) + @property def metadata(self) -> AdapterMetadata: return AdapterMetadata( diff --git a/eval_converters/inspect/instance_level_adapter.py b/every_eval_ever/converters/inspect/instance_level_adapter.py similarity index 89% rename from eval_converters/inspect/instance_level_adapter.py rename to every_eval_ever/converters/inspect/instance_level_adapter.py index 6c5bd52e9..57893be7f 100644 --- a/eval_converters/inspect/instance_level_adapter.py +++ b/every_eval_ever/converters/inspect/instance_level_adapter.py @@ -1,18 +1,30 @@ import json -from inspect_ai.model import ( - ChatMessage, - ChatMessageAssistant, - ChatMessageTool, - ChatMessageUser, - ModelUsage -) -from inspect_ai.log import ( - EvalSample -) from pathlib import Path from typing import Any, List, Tuple -from instance_level_types import ( +_INSPECT_IMPORT_ERROR: Exception | None = None +try: + from inspect_ai.model import ( + ChatMessage, + ChatMessageAssistant, + ChatMessageTool, + ChatMessageUser, + ModelUsage, + ) + from inspect_ai.log import EvalSample +except Exception as ex: # pragma: no cover - exercised only when optional deps missing + _INSPECT_IMPORT_ERROR = ex + ChatMessage = ChatMessageAssistant = ChatMessageTool = ChatMessageUser = ModelUsage = EvalSample = Any # type: ignore[assignment] + + +def _require_inspect_dependencies() -> None: + if _INSPECT_IMPORT_ERROR is not None: + raise ImportError( + "Inspect converter dependencies are missing. " + "Install with: pip install 'every_eval_ever[inspect]'" + ) from _INSPECT_IMPORT_ERROR + +from every_eval_ever.instance_level_types import ( AnswerAttributionItem, Evaluation, Input, @@ -25,12 +37,13 @@ ToolCall ) -from eval_converters import SCHEMA_VERSION -from eval_converters.common.utils import sha256_string +from every_eval_ever.converters import SCHEMA_VERSION +from every_eval_ever.converters.common.utils import sha256_string class InspectInstanceLevelDataAdapter: def __init__(self, evaulation_id: str, format: str, hash_algorithm: str, evaluation_dir: str): + _require_inspect_dependencies() self.evaluation_id = evaulation_id self.format = format self.hash_algorithm = hash_algorithm diff --git a/eval_converters/inspect/utils.py b/every_eval_ever/converters/inspect/utils.py similarity index 98% rename from eval_converters/inspect/utils.py rename to every_eval_ever/converters/inspect/utils.py index 740a45099..b6808ffca 100644 --- a/eval_converters/inspect/utils.py +++ b/every_eval_ever/converters/inspect/utils.py @@ -5,11 +5,11 @@ from pydantic import BaseModel from typing import Dict, Type, List -from eval_types import ( +from every_eval_ever.eval_types import ( InferenceEngine, ModelInfo ) -from eval_converters.common.utils import get_model_organization_info +from every_eval_ever.converters.common.utils import get_model_organization_info class ModelPathHandler: diff --git a/eval_converters/lm_eval/__init__.py b/every_eval_ever/converters/lm_eval/__init__.py similarity index 100% rename from eval_converters/lm_eval/__init__.py rename to every_eval_ever/converters/lm_eval/__init__.py diff --git a/eval_converters/lm_eval/__main__.py b/every_eval_ever/converters/lm_eval/__main__.py similarity index 100% rename from eval_converters/lm_eval/__main__.py rename to every_eval_ever/converters/lm_eval/__main__.py diff --git a/eval_converters/lm_eval/adapter.py b/every_eval_ever/converters/lm_eval/adapter.py similarity index 98% rename from eval_converters/lm_eval/adapter.py rename to every_eval_ever/converters/lm_eval/adapter.py index 15cd2373b..5d2b00b1a 100644 --- a/eval_converters/lm_eval/adapter.py +++ b/every_eval_ever/converters/lm_eval/adapter.py @@ -4,14 +4,14 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Union -from eval_converters import SCHEMA_VERSION -from eval_converters.common.adapter import ( +from every_eval_ever.converters import SCHEMA_VERSION +from every_eval_ever.converters.common.adapter import ( AdapterMetadata, BaseEvaluationAdapter, SupportedLibrary, ) -from eval_converters.common.utils import get_current_unix_timestamp -from eval_types import ( +from every_eval_ever.converters.common.utils import get_current_unix_timestamp +from every_eval_ever.eval_types import ( EvalLibrary, EvaluationLog, EvaluationResult, diff --git a/eval_converters/lm_eval/instance_level_adapter.py b/every_eval_ever/converters/lm_eval/instance_level_adapter.py similarity index 97% rename from eval_converters/lm_eval/instance_level_adapter.py rename to every_eval_ever/converters/lm_eval/instance_level_adapter.py index 221f168aa..1fb052be4 100644 --- a/eval_converters/lm_eval/instance_level_adapter.py +++ b/every_eval_ever/converters/lm_eval/instance_level_adapter.py @@ -5,9 +5,9 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Union -from eval_converters import SCHEMA_VERSION -from eval_types import DetailedEvaluationResults, Format, HashAlgorithm -from instance_level_types import ( +from every_eval_ever.converters import SCHEMA_VERSION +from every_eval_ever.eval_types import DetailedEvaluationResults, Format, HashAlgorithm +from every_eval_ever.instance_level_types import ( AnswerAttributionItem, Evaluation, Input, diff --git a/eval_converters/lm_eval/utils.py b/every_eval_ever/converters/lm_eval/utils.py similarity index 100% rename from eval_converters/lm_eval/utils.py rename to every_eval_ever/converters/lm_eval/utils.py diff --git a/eval_types.py b/every_eval_ever/eval_types.py similarity index 99% rename from eval_types.py rename to every_eval_ever/eval_types.py index c72fb500c..e8b4a15b3 100644 --- a/eval_types.py +++ b/every_eval_ever/eval_types.py @@ -1,6 +1,6 @@ # generated by datamodel-codegen: # filename: eval.schema.json -# timestamp: 2026-03-18T22:31:31+00:00 +# timestamp: 2026-03-19T20:30:15+00:00 from __future__ import annotations from enum import Enum diff --git a/utils/helpers/__init__.py b/every_eval_ever/helpers/__init__.py similarity index 100% rename from utils/helpers/__init__.py rename to every_eval_ever/helpers/__init__.py diff --git a/utils/helpers/developer.py b/every_eval_ever/helpers/developer.py similarity index 100% rename from utils/helpers/developer.py rename to every_eval_ever/helpers/developer.py diff --git a/utils/helpers/fetch.py b/every_eval_ever/helpers/fetch.py similarity index 100% rename from utils/helpers/fetch.py rename to every_eval_ever/helpers/fetch.py diff --git a/utils/helpers/io.py b/every_eval_ever/helpers/io.py similarity index 97% rename from utils/helpers/io.py rename to every_eval_ever/helpers/io.py index 678c5bb3d..2427229b8 100644 --- a/utils/helpers/io.py +++ b/every_eval_ever/helpers/io.py @@ -5,7 +5,7 @@ from pathlib import Path from typing import Union -from eval_types import EvaluationLog +from every_eval_ever.eval_types import EvaluationLog def sanitize_filename(name: str) -> str: diff --git a/utils/helpers/schema.py b/every_eval_ever/helpers/schema.py similarity index 97% rename from utils/helpers/schema.py rename to every_eval_ever/helpers/schema.py index 08d66e4ac..25706beea 100644 --- a/utils/helpers/schema.py +++ b/every_eval_ever/helpers/schema.py @@ -3,7 +3,7 @@ import time from typing import Any, Dict, List, Optional -from eval_types import ( +from every_eval_ever.eval_types import ( EvaluationLog, EvaluationResult, EvaluatorRelationship, @@ -113,6 +113,7 @@ def make_source_metadata( source_type: str = "documentation", evaluator_relationship: EvaluatorRelationship = EvaluatorRelationship.third_party, organization_url: Optional[str] = None, + additional_details: Optional[Dict[str, str]] = None, ) -> SourceMetadata: """ Create SourceMetadata for an evaluation source. @@ -123,6 +124,7 @@ def make_source_metadata( source_type: Either "documentation" or "evaluation_run" evaluator_relationship: Relationship to model developer organization_url: Optional URL for the organization + additional_details: Optional extra metadata key-values Returns: Configured SourceMetadata instance @@ -133,6 +135,7 @@ def make_source_metadata( source_organization_name=organization_name, source_organization_url=organization_url, evaluator_relationship=evaluator_relationship, + additional_details=additional_details, ) diff --git a/instance_level_types.py b/every_eval_ever/instance_level_types.py similarity index 99% rename from instance_level_types.py rename to every_eval_ever/instance_level_types.py index 819ad4919..e43b5b12f 100644 --- a/instance_level_types.py +++ b/every_eval_ever/instance_level_types.py @@ -1,6 +1,6 @@ # generated by datamodel-codegen: # filename: instance_level_eval.schema.json -# timestamp: 2026-03-18T22:31:33+00:00 +# timestamp: 2026-03-19T20:30:15+00:00 from __future__ import annotations from enum import Enum diff --git a/every_eval_ever/schema.py b/every_eval_ever/schema.py new file mode 100644 index 000000000..677bb8a0a --- /dev/null +++ b/every_eval_ever/schema.py @@ -0,0 +1,41 @@ +"""Helpers for loading bundled JSON schemas.""" + +from __future__ import annotations + +import json +from importlib import resources +from pathlib import Path +from typing import Any + + +def schema_text(name: str = "eval.schema.json") -> str: + with schema_path(name) as path: + return path.read_text(encoding="utf-8") + + +def schema_json(name: str = "eval.schema.json") -> dict[str, Any]: + return json.loads(schema_text(name)) + + +class _SchemaPathContext: + """Context manager for a bundled schema file. + + If the package is installed on disk, this usually yields the existing schema + path directly. For zipped or other non-filesystem imports, it materializes the + resource as a temporary file for the lifetime of the context. + """ + + def __init__(self, name: str = "eval.schema.json") -> None: + resource = resources.files("every_eval_ever.schemas").joinpath(name) + self._context = resources.as_file(resource) + + def __enter__(self) -> Path: + return self._context.__enter__() + + def __exit__(self, exc_type, exc_value, traceback) -> None: + self._context.__exit__(exc_type, exc_value, traceback) + return None + + +def schema_path(name: str = "eval.schema.json") -> _SchemaPathContext: + return _SchemaPathContext(name) diff --git a/every_eval_ever/schemas/__init__.py b/every_eval_ever/schemas/__init__.py new file mode 100644 index 000000000..e9f4cde4a --- /dev/null +++ b/every_eval_ever/schemas/__init__.py @@ -0,0 +1 @@ +"""Bundled JSON schema files for every_eval_ever.""" diff --git a/every_eval_ever/schemas/eval.schema.json b/every_eval_ever/schemas/eval.schema.json new file mode 100644 index 000000000..1c7f17271 --- /dev/null +++ b/every_eval_ever/schemas/eval.schema.json @@ -0,0 +1,760 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "version": "0.2.2", + "type": "object", + "description": "Schema for storing and validating LLMs evaluation data, including model configuration, prompts, instances, Output, and evaluation metrics", + "required": [ + "schema_version", + "evaluation_id", + "retrieved_timestamp", + "source_metadata", + "model_info", + "eval_library", + "evaluation_results" + ], + "additionalProperties": false, + "properties": { + "schema_version": { + "type": "string", + "description": "Version of the schema used for this evaluation data" + }, + "evaluation_id": { + "type": "string", + "description": "Unique identifier for this specific evaluation run. Use eval_name/model_id/retrieved_timestamp format" + }, + "evaluation_timestamp": { + "type": "string", + "description": "Timestamp for when the evaluation was run" + }, + "retrieved_timestamp": { + "type": "string", + "description": "Timestamp for when this record was created - using Unix Epoch time format" + }, + "source_metadata": { + "type": "object", + "description": "Metadata about the source of the leaderboard data", + "required": [ + "source_type", + "source_organization_name", + "evaluator_relationship" + ], + "properties": { + "source_name": { + "type": "string", + "description": "Name of the source (e.g. title of the source leaderboard or name of the platform used for the evaluation)." + }, + "source_type": { + "type": "string", + "enum": [ + "documentation", + "evaluation_run" + ], + "description": "Whether the data comes from a direct evaluation run or from documentation" + }, + "source_organization_name": { + "type": "string", + "description": "Name of the organization that provides the data" + }, + "source_organization_url": { + "type": "string", + "description": "URL for the organization that provides the data" + }, + "source_organization_logo_url": { + "type": "string", + "description": "URL for the Logo for the organization that provides the data" + }, + "evaluator_relationship": { + "type": "string", + "description": "Relationship between the evaluator and the model", + "enum": [ + "first_party", + "third_party", + "collaborative", + "other" + ] + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "eval_library": { + "type": "object", + "description": "Evaluation library/framework used to run the evaluation", + "properties": { + "name": { + "type": "string", + "description": "Name of the evaluation library (e.g. inspect_ai, lm_eval, helm)" + }, + "version": { + "type": "string", + "description": "Version of the evaluation library. Use 'unknown' if the version is not available." + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + }, + "required": [ + "name", + "version" + ] + }, + "model_info": { + "$ref": "#/$defs/model_info" + }, + "evaluation_results": { + "type": "array", + "description": "Array of evaluation results", + "items": { + "type": "object", + "required": [ + "evaluation_name", + "source_data", + "metric_config", + "score_details" + ], + "properties": { + "evaluation_result_id": { + "type": "string", + "description": "Stable identifier for this metric result inside an evaluation run. Recommended deterministic join key for instance-level records." + }, + "evaluation_name": { + "type": "string", + "description": "Name of the evaluation" + }, + "source_data": { + "description": "Source of dataset for this evaluation: URL, HuggingFace dataset, or private/custom dataset.", + "oneOf": [ + { + "$ref": "#/$defs/source_data_url" + }, + { + "$ref": "#/$defs/source_data_hf" + }, + { + "$ref": "#/$defs/source_data_private" + } + ] + }, + "evaluation_timestamp": { + "type": "string", + "description": "Timestamp for when the evaluations were run" + }, + "metric_config": { + "type": "object", + "description": "Details about the metric", + "required": [ + "lower_is_better" + ], + "properties": { + "evaluation_description": { + "type": "string", + "description": "Description of the evaluation" + }, + "metric_id": { + "type": "string", + "description": "Stable metric identifier for joining/deduping/querying. Use a canonical global id when applicable (e.g. accuracy, f1_macro, auroc, rmse, pass_at_k). For benchmark/leaderboard-specific metrics, use a namespaced id (e.g. rewardbench.overall, lmarena.elo)." + }, + "metric_name": { + "type": "string", + "description": "Display name for the metric (e.g. Accuracy, F1-macro, pass@1)." + }, + "metric_kind": { + "type": "string", + "description": "Normalized metric family/type used for safe aggregation (e.g. accuracy, f1, auroc, rmse, mae, pass_rate, elo)." + }, + "metric_unit": { + "type": "string", + "description": "Unit of the metric values (e.g. proportion, percent, points, ms, tokens)." + }, + "metric_parameters": { + "type": "object", + "description": "Metric-specific parameters (e.g. {\"k\": 1} for pass@k).", + "additionalProperties": { + "type": [ + "string", + "number", + "boolean", + "null" + ] + } + }, + "lower_is_better": { + "type": "boolean", + "description": "Whether a lower score is better" + }, + "score_type": { + "type": "string", + "description": "Type of score", + "enum": [ + "binary", + "continuous", + "levels" + ] + }, + "level_names": { + "type": "array", + "description": "Names of the score levels", + "items": { + "type": "string" + } + }, + "level_metadata": { + "type": "array", + "description": "Additional Description for each Score Level", + "items": { + "type": "string" + } + }, + "has_unknown_level": { + "type": "boolean", + "description": "Indicates whether there is an Unknown Level - if True, then a score of -1 will be treated as Unknown" + }, + "min_score": { + "type": "number", + + "description": "Minimum possible score for continuous metric" + }, + "max_score": { + "type": "number", + "description": "Maximum possible score for continuous metric" + }, + "llm_scoring": { + "type": "object", + "description": "Configuration when LLM is used as scorer/judge", + "required": [ + "judges", + "input_prompt" + ], + "properties": { + "judges": { + "type": "array", + "description": "LLM judge(s) - single item for judge, multiple for jury", + "items": { + "$ref": "#/$defs/judge_config" + }, + "minItems": 1 + }, + "input_prompt": { + "type": "string", + "description": "Prompt template used for judging" + }, + "aggregation_method": { + "type": "string", + "enum": [ + "majority_vote", + "average", + "weighted_average", + "median" + ], + "description": "How to aggregate scores when multiple judges" + }, + "expert_baseline": { + "type": "number", + "description": "Expert/human baseline score for comparison" + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + }, + "if": { + "properties": { + "score_type": { + "const": "levels" + } + } + }, + "then": { + "required": [ + "level_names", + "has_unknown_level" + ] + }, + "else": { + "if": { + "properties": { + "score_type": { + "const": "continuous" + } + } + }, + "then": { + "required": [ + "min_score", + "max_score" + ] + } + } + }, + "score_details": { + "type": "object", + "description": "The score for the evaluation and related details", + "required": [ + "score" + ], + "properties": { + "score": { + "type": "number", + "description": "The score for the evaluation" + }, + "details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + }, + "uncertainty": { + "type": "object", + "description": "Quantification of uncertainty around the reported score", + "properties": { + "standard_error": { + "type": "object", + "description": "Standard error of the score estimate (SE_mean = standard_deviation / sqrt(num_samples))", + "properties": { + "value": { + "type": "number", + "description": "The standard error value" + }, + "method": { + "type": "string", + "description": "How the standard error was computed (e.g. 'analytic', 'bootstrap', 'jackknife')" + } + }, + "required": [ + "value" + ] + }, + "confidence_interval": { + "type": "object", + "description": "Lower and upper bounds for the metric at a given confidence level.", + "properties": { + "lower": { + "type": "number", + "description": "Lower bound of the confidence interval" + }, + "upper": { + "type": "number", + "description": "Upper bound of the confidence interval" + }, + "confidence_level": { + "type": "number", + "description": "Confidence level (e.g. 0.95 for a 95% confidence interval)", + "minimum": 0, + "maximum": 1 + }, + "method": { + "type": "string", + "description": "How the confidence interval was computed" + } + }, + "required": [ + "lower", + "upper" + ] + }, + "standard_deviation": { + "type": "number", + "description": "Standard deviation of the per-sample scores" + }, + "num_samples": { + "type": "integer", + "description": "Number of samples used to compute the uncertainty estimates" + }, + "num_bootstrap_samples": { + "type": "integer", + "description": "Number of bootstrap resamples used, if bootstrap method was applied" + } + } + } + } + }, + "generation_config": { + "type": "object", + "properties": { + "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" + }, + "execution_command": { + "type": "string", + "description": "Command used to run the model to generate results" + }, + "reasoning": { + "type": "boolean", + "description": "Whether reasoning orchain-of-thought was used to generate results" + }, + "prompt_template": { + "type": "string", + "description": "Input prompt template for task (should contain agentic info if needed)." + }, + "agentic_eval_config": { + "type": "object", + "description": "General configuration for agentic evaluations.", + "properties": { + "available_tools": { + "type": "array", + "description": "List of all available tools with their configurations", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "e.g. bash, calculator, ..." + }, + "description": { + "type": "string" + }, + "parameters": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + } + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "eval_plan": { + "type": "object", + "description": "Plan (solvers) used in evaluation. Solvers are crucial parts of Inspect evaluations which can serve a wide variety of purposes like providing system prompts, prompt engineering, model generation or multi-turn dialog.", + "properties": { + "name": { + "type": "string" + }, + "steps": { + "type": "array", + "description": "Array of evaluation plan steps (each step is a JSON-serialized string)", + "items": { + "type": "string" + } + }, + "config": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "eval_limits": { + "type": "object", + "description": "Listed evaluation limits like time limit, message limit, token limit.", + "properties": { + "time_limit": { + "type": "integer", + "description": "Time limit for evaluation." + }, + "message_limit": { + "type": "integer", + "description": "Message limit for evaluation." + }, + "token_limit": { + "type": "integer", + "description": "Token limit for evaluation." + } + } + }, + "sandbox": { + "type": "object", + "properties": { + "type": { + "type": "string", + "description": "Type of sandbox e.g. docker" + }, + "config": { + "type": "string", + "description": "Config file name/path e.g. compose.yaml. TODO or full config? Not sure based on the Inspect docs" + } + } + }, + "max_attempts": { + "type": "integer", + "description": "Maximum number of submission attempts (default 1).", + "default": 1 + }, + "incorrect_attempt_feedback": { + "type": "string", + "description": "Feedback from the model after incorrect attempt." + } + }, + "additionalProperties": false + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + } + } + } + }, + "detailed_evaluation_results": { + "type": "object", + "description": "Reference to the evaluation results for all individual samples in the evaluation", + "properties": { + "format": { + "type": "string", + "description": "Format of the detailed evaluation results", + "enum": [ + "jsonl", + "json" + ] + }, + "file_path": { + "type": "string", + "description": "Path to the detailed evaluation results file" + }, + "hash_algorithm": { + "type": "string", + "description": "Hash algorithm used for checksum and sample_hash in instance-level data", + "enum": [ + "sha256", + "md5" + ] + }, + "checksum": { + "type": "string", + "description": "Checksum value of the file" + }, + "total_rows": { + "type": "integer", + "description": "Total number of rows in the detailed evaluation results file" + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + } + }, + "$defs": { + "judge_config": { + "type": "object", + "description": "Configuration for a single LLM judge/juror", + "required": [ + "model_info" + ], + "properties": { + "model_info": { + "$ref": "#/$defs/model_info" + }, + "temperature": { + "type": "number" + }, + "weight": { + "type": "number", + "description": "Weight of this judge's score in aggregation (used in jury)" + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "model_info": { + "type": "object", + "description": "Complete model specification including basic information, technical configuration and inference settings", + "required": [ + "name", + "id" + ], + "properties": { + "name": { + "type": "string", + "description": "Model name provided by evaluation source" + }, + "id": { + "type": "string", + "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", + "description": "Name of organization that provides the model (e.g. 'OpenAI')" + }, + "inference_platform": { + "type": "string", + "description": "Name of inference platform which provides an access to models by API to run the evaluations or provides models weights to run them locally (e.g. HuggingFace, Bedrock, Together AI)" + }, + "inference_engine": { + "type": "object", + "description": "Name of inference engine which provides an access to optimized models to use them for local evaluations (e.g. vLLM, Ollama).", + "properties": { + "name": { + "type": "string", + "description": "Name of the inference engine" + }, + "version": { + "type": "string", + "description": "Version of the inference engine" + } + } + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "source_data_url": { + "type": "object", + "description": "URL source for the evaluation data", + "required": [ + "dataset_name", + "source_type", + "url" + ], + "properties": { + "dataset_name": { + "type": "string", + "description": "Name of the source dataset" + }, + "source_type": { + "const": "url" + }, + "url": { + "type": "array", + "items": { + "type": "string" + }, + "minItems": 1, + "description": "URL(s) for the source of the evaluation data" + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "source_data_hf": { + "type": "object", + "description": "Details about HuggingFace dataset used for evaluation", + "required": [ + "dataset_name", + "source_type" + ], + "properties": { + "dataset_name": { + "type": "string", + "description": "Name of the source dataset" + }, + "source_type": { + "const": "hf_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": "string" + } + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "source_data_private": { + "type": "object", + "description": "Generic source data when neither URL array nor HuggingFace dataset applies", + "required": [ + "dataset_name", + "source_type" + ], + "properties": { + "dataset_name": { + "type": "string", + "description": "Name of the source dataset" + }, + "source_type": { + "const": "other" + }, + "additional_details": { + "type": "object", + "description": "Additional parameters (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + } + } +} diff --git a/every_eval_ever/schemas/instance_level_eval.schema.json b/every_eval_ever/schemas/instance_level_eval.schema.json new file mode 100644 index 000000000..8701eb8f0 --- /dev/null +++ b/every_eval_ever/schemas/instance_level_eval.schema.json @@ -0,0 +1,338 @@ +{ "$schema": "http://json-schema.org/draft-07/schema#", + "version": "instance_level_eval_0.2.2", + "type": "object", + "description": "Schema for storing instance-level evaluation data for LLM evaluations, supporting single-turn, multi-turn, and agentic interactions", + "required": [ + "schema_version", + "evaluation_id", + "model_id", + "evaluation_name", + "sample_id", + "interaction_type", + "input", + "answer_attribution", + "evaluation" + ], + "additionalProperties": false, + "properties": { + "schema_version": { + "type": "string", + "description": "Version of the schema used for this instance data" + }, + "evaluation_id": { + "type": "string", + "description": "Foreign key linking to the aggregate evaluation JSON. Must match the evaluation_id in the aggregate file." + }, + "model_id": { + "type": "string", + "description": "Identifier of the model in HuggingFace format (e.g. meta-llama/Llama-3.2-1B-Instruct)" + }, + "evaluation_name": { + "type": "string", + "description": "The specific eval name, ideally unique (e.g. GSM8K, mmlu_physics). Primarily for display/filtering when evaluation_result_id is unavailable. If an evaluation run reports multiple aggregate results/metrics for the same sample, emit multiple instance records (one per aggregate result) rather than trying to attach a single instance record to many aggregate results." + }, + "evaluation_result_id": { + "type": "string", + "description": "Preferred deterministic foreign key to aggregate evaluation_results[].evaluation_result_id. This is intended to point to exactly one aggregate evaluation result; if a single underlying interaction/sample contributes to multiple aggregate results, emit multiple instance records with different evaluation_result_id values." + }, + "sample_id": { + "type": "string", + "description": "Question/sample identifier from the original dataset (e.g. gsm8k_0001)" + }, + "sample_hash": { + "type": ["string", "null"], + "description": "Hash of (input.raw + input.reference) to ensure comparison is between the same sample across models, in case sample_id is not consistent" + }, + "interaction_type": { + "type": "string", + "enum": ["single_turn", "multi_turn", "agentic"], + "description": "Type of interaction: single_turn for simple Q&A, multi_turn for conversations, agentic for tool-using agents" + }, + "input": { + "type": "object", + "description": "Input data for the evaluation sample", + "required": ["raw", "reference"], + "properties": { + "raw": { + "type": "string", + "description": "The raw input as defined in the eval" + }, + "formatted": { + "type": ["string", "null"], + "description": "Includes chat template, CoT and all relevant modifications - basically the un-tokenized version of what the model sees" + }, + "reference": { + "type": "array", + "description": "Ground truths or reference answers for comparison/scoring", + "items": { "type": "string" } + }, + "choices": { + "type": ["array", "null"], + "description": "Optional list of choices for multiple-choice questions", + "items": { + "type": "string" + } + } + } + }, + "output": { + "type": ["object", "null"], + "description": "Output data - only used for single_turn interactions, null for multi_turn/agentic", + "required": ["raw"], + "properties": { + "raw": { + "type": "array", + "description": "Complete model responses", + "items": { "type": "string" } + }, + "reasoning_trace": { + "type": ["array", "null"], + "description": "Reasoning traces of the model if applicable (e.g. chain-of-thought tokens)", + "items": { "type": "string" } + } + } + }, + "messages": { + "type": ["array", "null"], + "description": "Full message transcript - used for multi_turn and agentic, null for single_turn. Contains all system, user, assistant, and tool messages in order.", + "items": { + "type": "object", + "required": ["turn_idx", "role"], + "properties": { + "turn_idx": { + "type": "integer", + "minimum": 0, + "description": "Index starting from 0 indicating the position in the conversation" + }, + "role": { + "type": "string", + "description": "Role of the speaker (e.g. user, assistant, system, tool)" + }, + "content": { + "type": ["string", "null"], + "description": "The actual raw text for that particular turn (can be null if empty)" + }, + "reasoning_trace": { + "type": ["string", "null"], + "description": "Reasoning trace for that particular turn if applicable" + }, + "tool_calls": { + "type": ["array", "null"], + "description": "List of tool invocations for this turn, if applicable", + "items": { + "type": "object", + "required": ["id", "name"], + "properties": { + "id": { + "type": "string", + "description": "Unique identifier for the tool call" + }, + "name": { + "type": "string", + "description": "Name of tool/function" + }, + "arguments": { + "type": ["object", "null"], + "description": "Arguments used to call the tool (all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + } + }, + "tool_call_id": { + "type": ["array", "null"], + "description": "Reference to the tool call ID(s) this message has the content payload for.", + "items": { + "type": "string" + } + } + } + } + }, + "answer_attribution": { + "type": "array", + "description": "Information about how the answer was extracted from the model output", + "items": { + "type": "object", + "required": ["turn_idx", "source", "extracted_value", "extraction_method", "is_terminal"], + "properties": { + "turn_idx": { + "type": "integer", + "minimum": 0, + "description": "Turn index in messages. 0 for single_turn" + }, + "source": { + "type": "string", + "description": "Source of the extracted value (e.g. 'output.raw' or 'messages[turn_idx].content')" + }, + "extracted_value": { + "type": "string", + "description": "Value that was extracted" + }, + "extraction_method": { + "type": "string", + "description": "Method used to extract the value (e.g. regex, exact_match, llm_judge, custom)" + }, + "is_terminal": { + "type": "boolean", + "description": "Whether this is the final answer (false if intermediate outputs are used to build up to a final answer)" + } + } + } + }, + "evaluation": { + "type": "object", + "description": "Evaluation results and scoring data", + "required": ["score", "is_correct"], + "properties": { + "score": { + "type": "number", + "description": "Instance-level score" + }, + "is_correct": { + "type": "boolean", + "description": "Whether the final answer is correct" + }, + "num_turns": { + "type": ["integer", "null"], + "minimum": 1, + "description": "Number of turns in the interaction" + }, + "tool_calls_count": { + "type": ["integer", "null"], + "minimum": 0, + "description": "Count of tool calls across all turns in messages" + } + } + }, + "token_usage": { + "type": ["object", "null"], + "description": "Token usage for the model completion", + "required": ["input_tokens", "output_tokens", "total_tokens"], + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0, + "description": "Total input tokens used" + }, + "output_tokens": { + "type": "integer", + "minimum": 0, + "description": "Total output tokens used" + }, + "total_tokens": { + "type": "integer", + "minimum": 0, + "description": "Total tokens used" + }, + "input_tokens_cache_write": { + "type": ["integer", "null"], + "minimum": 0, + "description": "Number of tokens written to the cache" + }, + "input_tokens_cache_read": { + "type": ["integer", "null"], + "minimum": 0, + "description": "Number of tokens retrieved from the cache" + }, + "reasoning_tokens": { + "type": ["integer", "null"], + "minimum": 0, + "description": "Number of tokens used for reasoning" + } + } + }, + "performance": { + "type": ["object", "null"], + "description": "Performance and latency metrics", + "properties": { + "latency_ms": { + "type": ["number", "null"], + "minimum": 0, + "description": "Total latency in milliseconds" + }, + "time_to_first_token_ms": { + "type": ["number", "null"], + "minimum": 0, + "description": "Time to first token in milliseconds" + }, + "generation_time_ms": { + "type": ["number", "null"], + "minimum": 0, + "description": "Time for generation in milliseconds" + }, + "additional_details": { + "type": ["object", "null"], + "description": "Additional performance metrics (key-value pairs, all values must be strings)", + "additionalProperties": { + "type": "string" + } + } + } + }, + "error": { + "type": ["string", "null"], + "description": "Information about any error that occurred (e.g. timeout, refusal, API error)" + }, + "metadata": { + "type": ["object", "null"], + "description": "Optional metadata about the sample (e.g. subject, difficulty, tags)", + "additionalProperties": { + "type": "string" + } + } + }, + "allOf": [ + { + "if": { + "properties": { + "interaction_type": { + "const": "single_turn" + } + } + }, + "then": { + "required": ["output"], + "properties": { + "output": { + "type": "object", + "not": { + "type": "null" + } + }, + "messages": { + "type": "null" + } + } + } + }, + { + "if": { + "properties": { + "interaction_type": { + "enum": ["multi_turn", "agentic"] + } + } + }, + "then": { + "required": ["messages"], + "properties": { + "output": { + "type": "null" + }, + "messages": { + "type": "array", + "not": { + "type": "null" + } + }, + "metrics": { + "required": ["num_turns"] + } + } + } + } + ] +} diff --git a/validate.py b/every_eval_ever/validate.py similarity index 93% rename from validate.py rename to every_eval_ever/validate.py index 6f9587579..ac80ea80d 100644 --- a/validate.py +++ b/every_eval_ever/validate.py @@ -5,11 +5,11 @@ instance-level (_samples.jsonl) files against InstanceLevelEvaluationLog. Usage: - uv run python validate.py data/benchmark/dev/model/uuid.json - uv run python validate.py data/benchmark/dev/model/uuid_samples.jsonl - uv run python validate.py data/benchmark/dev/model/ # directory recurse - uv run python validate.py --format json data/*.json - uv run python validate.py --max-errors 10 data/*_samples.jsonl + uv run python -m every_eval_ever validate data/benchmark/dev/model/uuid.json + uv run python -m every_eval_ever validate data/benchmark/dev/model/uuid_samples.jsonl + uv run python -m every_eval_ever validate data/benchmark/dev/model/ # directory recurse + uv run python -m every_eval_ever validate --format json data/*.json + uv run python -m every_eval_ever validate --max-errors 10 data/*_samples.jsonl """ from __future__ import annotations @@ -25,8 +25,8 @@ from rich.panel import Panel from rich.text import Text -from eval_types import EvaluationLog -from instance_level_types import InstanceLevelEvaluationLog +from every_eval_ever.eval_types import EvaluationLog +from every_eval_ever.instance_level_types import InstanceLevelEvaluationLog DEFAULT_MAX_ERRORS = 50 @@ -319,7 +319,7 @@ def render_report_github(reports: list[ValidationReport]) -> str: # --------------------------------------------------------------------------- -def main() -> None: +def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser( prog="eee-validate", description="Validate EEE schema files using Pydantic models", @@ -342,12 +342,12 @@ def main() -> None: dest="output_format", help="Output format (default: rich)", ) - args = parser.parse_args() + args = parser.parse_args(argv) file_paths = expand_paths(args.paths) if not file_paths: print("No files found to validate.", file=sys.stderr) - sys.exit(1) + return 1 reports = [validate_file(fp, max_errors=args.max_errors) for fp in file_paths] @@ -365,9 +365,8 @@ def main() -> None: if output: print(output) - if any(not r.valid for r in reports): - sys.exit(1) + return 1 if any(not r.valid for r in reports) else 0 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/instance_level_eval.schema.json b/instance_level_eval.schema.json deleted file mode 100644 index 8701eb8f0..000000000 --- a/instance_level_eval.schema.json +++ /dev/null @@ -1,338 +0,0 @@ -{ "$schema": "http://json-schema.org/draft-07/schema#", - "version": "instance_level_eval_0.2.2", - "type": "object", - "description": "Schema for storing instance-level evaluation data for LLM evaluations, supporting single-turn, multi-turn, and agentic interactions", - "required": [ - "schema_version", - "evaluation_id", - "model_id", - "evaluation_name", - "sample_id", - "interaction_type", - "input", - "answer_attribution", - "evaluation" - ], - "additionalProperties": false, - "properties": { - "schema_version": { - "type": "string", - "description": "Version of the schema used for this instance data" - }, - "evaluation_id": { - "type": "string", - "description": "Foreign key linking to the aggregate evaluation JSON. Must match the evaluation_id in the aggregate file." - }, - "model_id": { - "type": "string", - "description": "Identifier of the model in HuggingFace format (e.g. meta-llama/Llama-3.2-1B-Instruct)" - }, - "evaluation_name": { - "type": "string", - "description": "The specific eval name, ideally unique (e.g. GSM8K, mmlu_physics). Primarily for display/filtering when evaluation_result_id is unavailable. If an evaluation run reports multiple aggregate results/metrics for the same sample, emit multiple instance records (one per aggregate result) rather than trying to attach a single instance record to many aggregate results." - }, - "evaluation_result_id": { - "type": "string", - "description": "Preferred deterministic foreign key to aggregate evaluation_results[].evaluation_result_id. This is intended to point to exactly one aggregate evaluation result; if a single underlying interaction/sample contributes to multiple aggregate results, emit multiple instance records with different evaluation_result_id values." - }, - "sample_id": { - "type": "string", - "description": "Question/sample identifier from the original dataset (e.g. gsm8k_0001)" - }, - "sample_hash": { - "type": ["string", "null"], - "description": "Hash of (input.raw + input.reference) to ensure comparison is between the same sample across models, in case sample_id is not consistent" - }, - "interaction_type": { - "type": "string", - "enum": ["single_turn", "multi_turn", "agentic"], - "description": "Type of interaction: single_turn for simple Q&A, multi_turn for conversations, agentic for tool-using agents" - }, - "input": { - "type": "object", - "description": "Input data for the evaluation sample", - "required": ["raw", "reference"], - "properties": { - "raw": { - "type": "string", - "description": "The raw input as defined in the eval" - }, - "formatted": { - "type": ["string", "null"], - "description": "Includes chat template, CoT and all relevant modifications - basically the un-tokenized version of what the model sees" - }, - "reference": { - "type": "array", - "description": "Ground truths or reference answers for comparison/scoring", - "items": { "type": "string" } - }, - "choices": { - "type": ["array", "null"], - "description": "Optional list of choices for multiple-choice questions", - "items": { - "type": "string" - } - } - } - }, - "output": { - "type": ["object", "null"], - "description": "Output data - only used for single_turn interactions, null for multi_turn/agentic", - "required": ["raw"], - "properties": { - "raw": { - "type": "array", - "description": "Complete model responses", - "items": { "type": "string" } - }, - "reasoning_trace": { - "type": ["array", "null"], - "description": "Reasoning traces of the model if applicable (e.g. chain-of-thought tokens)", - "items": { "type": "string" } - } - } - }, - "messages": { - "type": ["array", "null"], - "description": "Full message transcript - used for multi_turn and agentic, null for single_turn. Contains all system, user, assistant, and tool messages in order.", - "items": { - "type": "object", - "required": ["turn_idx", "role"], - "properties": { - "turn_idx": { - "type": "integer", - "minimum": 0, - "description": "Index starting from 0 indicating the position in the conversation" - }, - "role": { - "type": "string", - "description": "Role of the speaker (e.g. user, assistant, system, tool)" - }, - "content": { - "type": ["string", "null"], - "description": "The actual raw text for that particular turn (can be null if empty)" - }, - "reasoning_trace": { - "type": ["string", "null"], - "description": "Reasoning trace for that particular turn if applicable" - }, - "tool_calls": { - "type": ["array", "null"], - "description": "List of tool invocations for this turn, if applicable", - "items": { - "type": "object", - "required": ["id", "name"], - "properties": { - "id": { - "type": "string", - "description": "Unique identifier for the tool call" - }, - "name": { - "type": "string", - "description": "Name of tool/function" - }, - "arguments": { - "type": ["object", "null"], - "description": "Arguments used to call the tool (all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - } - }, - "tool_call_id": { - "type": ["array", "null"], - "description": "Reference to the tool call ID(s) this message has the content payload for.", - "items": { - "type": "string" - } - } - } - } - }, - "answer_attribution": { - "type": "array", - "description": "Information about how the answer was extracted from the model output", - "items": { - "type": "object", - "required": ["turn_idx", "source", "extracted_value", "extraction_method", "is_terminal"], - "properties": { - "turn_idx": { - "type": "integer", - "minimum": 0, - "description": "Turn index in messages. 0 for single_turn" - }, - "source": { - "type": "string", - "description": "Source of the extracted value (e.g. 'output.raw' or 'messages[turn_idx].content')" - }, - "extracted_value": { - "type": "string", - "description": "Value that was extracted" - }, - "extraction_method": { - "type": "string", - "description": "Method used to extract the value (e.g. regex, exact_match, llm_judge, custom)" - }, - "is_terminal": { - "type": "boolean", - "description": "Whether this is the final answer (false if intermediate outputs are used to build up to a final answer)" - } - } - } - }, - "evaluation": { - "type": "object", - "description": "Evaluation results and scoring data", - "required": ["score", "is_correct"], - "properties": { - "score": { - "type": "number", - "description": "Instance-level score" - }, - "is_correct": { - "type": "boolean", - "description": "Whether the final answer is correct" - }, - "num_turns": { - "type": ["integer", "null"], - "minimum": 1, - "description": "Number of turns in the interaction" - }, - "tool_calls_count": { - "type": ["integer", "null"], - "minimum": 0, - "description": "Count of tool calls across all turns in messages" - } - } - }, - "token_usage": { - "type": ["object", "null"], - "description": "Token usage for the model completion", - "required": ["input_tokens", "output_tokens", "total_tokens"], - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0, - "description": "Total input tokens used" - }, - "output_tokens": { - "type": "integer", - "minimum": 0, - "description": "Total output tokens used" - }, - "total_tokens": { - "type": "integer", - "minimum": 0, - "description": "Total tokens used" - }, - "input_tokens_cache_write": { - "type": ["integer", "null"], - "minimum": 0, - "description": "Number of tokens written to the cache" - }, - "input_tokens_cache_read": { - "type": ["integer", "null"], - "minimum": 0, - "description": "Number of tokens retrieved from the cache" - }, - "reasoning_tokens": { - "type": ["integer", "null"], - "minimum": 0, - "description": "Number of tokens used for reasoning" - } - } - }, - "performance": { - "type": ["object", "null"], - "description": "Performance and latency metrics", - "properties": { - "latency_ms": { - "type": ["number", "null"], - "minimum": 0, - "description": "Total latency in milliseconds" - }, - "time_to_first_token_ms": { - "type": ["number", "null"], - "minimum": 0, - "description": "Time to first token in milliseconds" - }, - "generation_time_ms": { - "type": ["number", "null"], - "minimum": 0, - "description": "Time for generation in milliseconds" - }, - "additional_details": { - "type": ["object", "null"], - "description": "Additional performance metrics (key-value pairs, all values must be strings)", - "additionalProperties": { - "type": "string" - } - } - } - }, - "error": { - "type": ["string", "null"], - "description": "Information about any error that occurred (e.g. timeout, refusal, API error)" - }, - "metadata": { - "type": ["object", "null"], - "description": "Optional metadata about the sample (e.g. subject, difficulty, tags)", - "additionalProperties": { - "type": "string" - } - } - }, - "allOf": [ - { - "if": { - "properties": { - "interaction_type": { - "const": "single_turn" - } - } - }, - "then": { - "required": ["output"], - "properties": { - "output": { - "type": "object", - "not": { - "type": "null" - } - }, - "messages": { - "type": "null" - } - } - } - }, - { - "if": { - "properties": { - "interaction_type": { - "enum": ["multi_turn", "agentic"] - } - } - }, - "then": { - "required": ["messages"], - "properties": { - "output": { - "type": "null" - }, - "messages": { - "type": "array", - "not": { - "type": "null" - } - }, - "metrics": { - "required": ["num_turns"] - } - } - } - } - ] -} diff --git a/instance_level_eval.schema.json b/instance_level_eval.schema.json new file mode 120000 index 000000000..7e10082d4 --- /dev/null +++ b/instance_level_eval.schema.json @@ -0,0 +1 @@ +every_eval_ever/schemas/instance_level_eval.schema.json \ No newline at end of file diff --git a/post_codegen.py b/post_codegen.py index cd523c3d3..103653be3 100644 --- a/post_codegen.py +++ b/post_codegen.py @@ -1,11 +1,11 @@ """ -Post-codegen patches for eval_types.py and instance_level_types.py. +Post-codegen patches for every_eval_ever/eval_types.py and every_eval_ever/instance_level_types.py. Run after datamodel-codegen to re-apply model validators that codegen cannot generate. Usage: - uv run datamodel-codegen --input eval.schema.json --output eval_types.py ... - uv run datamodel-codegen --input instance_level_eval.schema.json --output instance_level_types.py ... + uv run datamodel-codegen --input every_eval_ever/schemas/eval.schema.json --output every_eval_ever/eval_types.py ... + uv run datamodel-codegen --input every_eval_ever/schemas/instance_level_eval.schema.json --output every_eval_ever/instance_level_types.py ... uv run python post_codegen.py """ @@ -19,7 +19,7 @@ PATCHES = [ { - "file": "instance_level_types.py", + "file": "every_eval_ever/instance_level_types.py", "import_add": "model_validator", "class_name": "InstanceLevelEvaluationLog", "validator": ''' @@ -47,7 +47,7 @@ def validate_interaction_type_consistency(self): ''', }, { - "file": "eval_types.py", + "file": "every_eval_ever/eval_types.py", "import_add": "model_validator", "class_name": "MetricConfig", "validator": ''' @@ -75,7 +75,7 @@ def validate_score_type_requirements(self): # --------------------------------------------------------------------------- DISCRIMINATOR_PATCH = { - "file": "eval_types.py", + "file": "every_eval_ever/eval_types.py", "target_line": " source_data: SourceDataUrl | SourceDataHf | SourceDataPrivate = Field(", "replacement": ' source_data: Annotated[SourceDataUrl | SourceDataHf | SourceDataPrivate, Discriminator("source_type")] = Field(', "imports": ["Annotated", "Discriminator"], @@ -140,7 +140,7 @@ def apply_discriminator_patch(patch: dict) -> None: content = path.read_text() # Check if the specific replacement has already been applied - if patch.get("replacement") and patch["replacement"] in content: + if patch["replacement"] in content: print(f" {patch['file']}: discriminator already patched, skipping") return @@ -174,8 +174,8 @@ def apply_discriminator_patch(patch: dict) -> None: f" {patch['file']}: warning: multiple ({occurrences}) occurrences of " "target line found; patching all occurrences" ) - content = content.replace(target_line, patch["replacement"]) + content = content.replace(target_line, patch["replacement"]) path.write_text(content) print(f" {patch['file']}: patched source_data with Discriminator") diff --git a/pyproject.toml b/pyproject.toml index a1cc8bf26..8ee714086 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools>=69", "wheel"] +build-backend = "setuptools.build_meta" + [project] name = "every-eval-ever" version = "0.2.2" @@ -24,9 +28,20 @@ all = [ "every-eval-ever[helm]", ] +[project.scripts] +every_eval_ever = "every_eval_ever.cli:main" + +[tool.setuptools] +include-package-data = true + +[tool.setuptools.packages.find] +include = ["every_eval_ever*"] + +[tool.setuptools.package-data] +every_eval_ever = ["schemas/*.json"] [dependency-groups] dev = [ "datamodel-code-generator>=0.43.1", -"pytest>=9.0.2", + "pytest>=9.0.2", "ruff>=0.12.2", ] diff --git a/tests/test_check_duplicate_entries.py b/tests/test_check_duplicate_entries.py index 64180bb4d..450349e7b 100644 --- a/tests/test_check_duplicate_entries.py +++ b/tests/test_check_duplicate_entries.py @@ -1,12 +1,10 @@ -import importlib.util import json -import sys from pathlib import Path import pytest +from every_eval_ever import check_duplicate_entries as check_module -MODULE_PATH = Path(__file__).resolve().parents[1] / "utils" / "check_duplicate_entries.py" DATA_ROOT = Path(__file__).resolve().parents[1] / "data" SAMPLE_FILES = [ Path(__file__).resolve().parent / "data" / "98ea850e-7019-4728-a558-8b1819ec47c2.json", @@ -14,15 +12,6 @@ ] -@pytest.fixture(scope="module") -def check_module(): - spec = importlib.util.spec_from_file_location("check_duplicate_entries", MODULE_PATH) - module = importlib.util.module_from_spec(spec) - assert spec.loader is not None - spec.loader.exec_module(module) - return module - - @pytest.fixture(scope="module") def sample_payloads(): missing = [path for path in SAMPLE_FILES if not path.exists()] @@ -48,7 +37,7 @@ def simulate_rescrape(payload: dict) -> dict: return cloned -def test_normalized_hash_ignores_keys_and_list_order(check_module, sample_payloads): +def test_normalized_hash_ignores_keys_and_list_order(sample_payloads): payload_a = clone_payload(sample_payloads[0]) payload_b = simulate_rescrape(sample_payloads[0]) @@ -57,7 +46,7 @@ def test_normalized_hash_ignores_keys_and_list_order(check_module, sample_payloa ) -def test_normalized_hash_detects_real_changes(check_module, sample_payloads): +def test_normalized_hash_detects_real_changes(sample_payloads): payload_a = clone_payload(sample_payloads[0]) payload_c = clone_payload(sample_payloads[0]) payload_c["evaluation_id"] = "eval-c" @@ -72,7 +61,7 @@ def test_normalized_hash_detects_real_changes(check_module, sample_payloads): ) -def test_expand_paths_returns_json_files(check_module, tmp_path): +def test_expand_paths_returns_json_files(tmp_path): top = tmp_path / "top.json" nested_dir = tmp_path / "nested" nested_dir.mkdir() @@ -94,7 +83,7 @@ def test_expand_paths_returns_json_files(check_module, tmp_path): def test_main_reports_duplicates( - check_module, sample_payloads, tmp_path, capsys, monkeypatch + sample_payloads, tmp_path, capsys ): payload = sample_payloads[0] file_a = tmp_path / "a.json" @@ -103,9 +92,6 @@ def test_main_reports_duplicates( write_json(file_b, simulate_rescrape(payload)) - monkeypatch.setattr(sys, "argv", ["check_duplicate_entries", str(file_a), str(file_b)]) - with pytest.raises(SystemExit) as excinfo: - check_module.main() - assert excinfo.value.code == 1 + assert check_module.main([str(file_a), str(file_b)]) == 1 captured = capsys.readouterr().out assert "Found duplicate entries" in captured diff --git a/tests/test_helm_adapter.py b/tests/test_helm_adapter.py index 18c2836d9..4074515a8 100644 --- a/tests/test_helm_adapter.py +++ b/tests/test_helm_adapter.py @@ -4,8 +4,8 @@ from pathlib import Path import tempfile -from eval_converters.helm.adapter import HELMAdapter -from eval_types import ( +from every_eval_ever.converters.helm.adapter import HELMAdapter +from every_eval_ever.eval_types import ( EvaluationLog, EvaluatorRelationship, SourceDataHf, diff --git a/tests/test_helm_instance_level_adapter.py b/tests/test_helm_instance_level_adapter.py index 88247157e..dc40075e8 100644 --- a/tests/test_helm_instance_level_adapter.py +++ b/tests/test_helm_instance_level_adapter.py @@ -5,9 +5,9 @@ import tempfile from pathlib import Path -from eval_converters.helm.adapter import HELMAdapter -from eval_types import EvaluatorRelationship -from instance_level_types import InstanceLevelEvaluationLog, InteractionType +from every_eval_ever.converters.helm.adapter import HELMAdapter +from every_eval_ever.eval_types import EvaluatorRelationship +from every_eval_ever.instance_level_types import InstanceLevelEvaluationLog, InteractionType def _load_instance_level_data(adapter, filepath, metadata_args): @@ -17,9 +17,9 @@ def _load_instance_level_data(adapter, filepath, metadata_args): output_path=str(Path(metadata_args['parent_eval_output_dir']) / 'helm_output'), metadata_args=metadata_args ) - + converted_eval = converted_eval_list[0] - + instance_level_path = Path(converted_eval.detailed_evaluation_results.file_path) instance_logs = [] with instance_level_path.open('r', encoding='utf-8') as f: @@ -27,13 +27,13 @@ def _load_instance_level_data(adapter, filepath, metadata_args): if line.strip(): data = json.loads(line) instance_logs.append(InstanceLevelEvaluationLog.model_validate(data)) - + return converted_eval, instance_logs def test_mmlu_instance_level(): adapter = HELMAdapter() - + with tempfile.TemporaryDirectory() as tmpdir: metadata_args = { 'source_organization_name': 'TestOrg', @@ -43,38 +43,38 @@ def test_mmlu_instance_level(): } converted_eval, instance_logs = _load_instance_level_data( - adapter, - 'tests/data/helm/mmlu:subject=philosophy,method=multiple_choice_joint,model=openai_gpt2', + adapter, + 'tests/data/helm/mmlu:subject=philosophy,method=multiple_choice_joint,model=openai_gpt2', metadata_args ) - + assert len(instance_logs) == 10 log = instance_logs[0] - - assert log.schema_version == '0.2.1' + + assert log.schema_version == '0.2.2' assert log.evaluation_id == 'test_mmlu_samples' assert log.model_id == 'openai/gpt2' assert log.evaluation_name == 'mmlu' assert log.sample_id == 'id147' assert len(log.sample_hash) == 64 assert log.interaction_type == InteractionType.single_turn - + assert log.input.raw.startswith('The') assert log.input.reference == ['internalmeaning'] - + assert log.output.raw == [' D'] - + assert log.messages is None - + assert len(log.answer_attribution) == 1 assert log.answer_attribution[0].turn_idx == 0 assert log.answer_attribution[0].source == 'output.raw' assert log.answer_attribution[0].extraction_method == 'exact_match' assert log.answer_attribution[0].is_terminal is True - + assert log.evaluation.score == 0.0 assert log.evaluation.is_correct is False - + assert log.token_usage.input_tokens > 0 assert log.token_usage.output_tokens > 0 assert log.token_usage.total_tokens > 0 @@ -82,7 +82,7 @@ def test_mmlu_instance_level(): def test_hellaswag_instance_level(): adapter = HELMAdapter() - + with tempfile.TemporaryDirectory() as tmpdir: metadata_args = { 'source_organization_name': 'TestOrg', @@ -92,33 +92,33 @@ def test_hellaswag_instance_level(): } converted_eval, instance_logs = _load_instance_level_data( - adapter, - 'tests/data/helm/commonsense:dataset=hellaswag,method=multiple_choice_joint,model=eleutherai_pythia-1b-v0', + adapter, + 'tests/data/helm/commonsense:dataset=hellaswag,method=multiple_choice_joint,model=eleutherai_pythia-1b-v0', metadata_args ) - + assert len(instance_logs) == 10 log = instance_logs[0] - - assert log.schema_version == '0.2.1' + + assert log.schema_version == '0.2.2' assert log.model_id == 'eleutherai/pythia-1b-v0' assert log.evaluation_name == 'hellaswag' assert log.interaction_type == InteractionType.single_turn - + assert len(log.input.choices) == 4 - + assert log.output.raw == [' B'] assert log.messages is None - + assert log.evaluation.score == 0.0 assert log.evaluation.is_correct is False - + assert log.performance.generation_time_ms > 0 def test_narrativeqa_instance_level(): adapter = HELMAdapter() - + with tempfile.TemporaryDirectory() as tmpdir: metadata_args = { 'source_organization_name': 'TestOrg', @@ -128,26 +128,26 @@ def test_narrativeqa_instance_level(): } converted_eval, instance_logs = _load_instance_level_data( - adapter, - 'tests/data/helm/narrative_qa:model=openai_gpt2', + adapter, + 'tests/data/helm/narrative_qa:model=openai_gpt2', metadata_args ) - + assert len(instance_logs) == 5 log = instance_logs[0] - - assert log.schema_version == '0.2.1' + + assert log.schema_version == '0.2.2' assert log.model_id == 'openai/gpt2' assert log.evaluation_name == 'narrativeqa' assert log.interaction_type == InteractionType.single_turn - + assert log.input.reference == ['The school Mascot', 'the schools mascot'] - + assert log.output.raw == [' Olive.'] assert log.messages is None - + assert log.evaluation.score == 0.0 assert log.evaluation.is_correct is False - + assert len(log.answer_attribution) == 1 assert log.answer_attribution[0].extraction_method == 'exact_match' diff --git a/tests/test_inspect_adapter.py b/tests/test_inspect_adapter.py index d8932208a..f7c0204b7 100644 --- a/tests/test_inspect_adapter.py +++ b/tests/test_inspect_adapter.py @@ -5,9 +5,9 @@ from pathlib import Path import tempfile -from eval_converters.inspect.adapter import InspectAIAdapter -from eval_converters.inspect.utils import extract_model_info_from_model_path -from eval_types import ( +from every_eval_ever.converters.inspect.adapter import InspectAIAdapter +from every_eval_ever.converters.inspect.utils import extract_model_info_from_model_path +from every_eval_ever.eval_types import ( EvaluationLog, EvaluatorRelationship, SourceDataHf, diff --git a/tests/test_inspect_instance_level_adapter.py b/tests/test_inspect_instance_level_adapter.py index cdfb86a7f..c867b50fd 100644 --- a/tests/test_inspect_instance_level_adapter.py +++ b/tests/test_inspect_instance_level_adapter.py @@ -5,10 +5,12 @@ import tempfile from pathlib import Path -from eval_converters.inspect.adapter import InspectAIAdapter -from eval_converters.inspect.instance_level_adapter import InspectInstanceLevelDataAdapter -from eval_types import EvaluatorRelationship -from instance_level_types import InstanceLevelEvaluationLog, InteractionType +from every_eval_ever.converters.inspect.adapter import InspectAIAdapter +from every_eval_ever.eval_types import EvaluatorRelationship +from every_eval_ever.instance_level_types import InstanceLevelEvaluationLog, InteractionType +from every_eval_ever.converters.inspect.instance_level_adapter import ( + InspectInstanceLevelDataAdapter, +) from inspect_ai.model import ChatMessageAssistant, ChatMessageUser, ContentText diff --git a/tests/test_lm_eval_adapter.py b/tests/test_lm_eval_adapter.py index 0ce86d238..113442586 100644 --- a/tests/test_lm_eval_adapter.py +++ b/tests/test_lm_eval_adapter.py @@ -1,10 +1,10 @@ import tempfile from pathlib import Path -from eval_converters.lm_eval.adapter import LMEvalAdapter -from eval_converters.lm_eval.instance_level_adapter import LMEvalInstanceLevelAdapter -from eval_converters.lm_eval.utils import parse_model_args, find_samples_file -from eval_types import ( +from every_eval_ever.converters.lm_eval.adapter import LMEvalAdapter +from every_eval_ever.converters.lm_eval.instance_level_adapter import LMEvalInstanceLevelAdapter +from every_eval_ever.converters.lm_eval.utils import parse_model_args, find_samples_file +from every_eval_ever.eval_types import ( EvaluationLog, EvaluatorRelationship, SourceDataHf, diff --git a/tests/test_validate.py b/tests/test_validate.py index d2e5b9fbc..7a0ef7abc 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -8,7 +8,7 @@ import pytest -from validate import ( +from every_eval_ever.validate import ( ValidationReport, expand_paths, validate_aggregate, @@ -23,7 +23,7 @@ # --------------------------------------------------------------------------- VALID_AGGREGATE: dict = { - "schema_version": "0.2.1", + "schema_version": "0.2.2", "evaluation_id": "test/model/123", "retrieved_timestamp": "1234567890", "source_metadata": { @@ -51,7 +51,7 @@ } VALID_SINGLE_TURN: dict = { - "schema_version": "0.2.1", + "schema_version": "instance_level_eval_0.2.2", "evaluation_id": "test/model/123", "model_id": "org/test-model", "evaluation_name": "test_eval", @@ -72,7 +72,7 @@ } VALID_MULTI_TURN: dict = { - "schema_version": "0.2.1", + "schema_version": "instance_level_eval_0.2.2", "evaluation_id": "test/model/123", "model_id": "org/test-model", "evaluation_name": "test_eval", diff --git a/utils/global-mmlu-lite/adapter.py b/utils/global-mmlu-lite/adapter.py index eb8f24771..06de65a9f 100644 --- a/utils/global-mmlu-lite/adapter.py +++ b/utils/global-mmlu-lite/adapter.py @@ -12,7 +12,7 @@ import time from typing import List -from eval_types import ( +from every_eval_ever.eval_types import ( ConfidenceInterval, EvalLibrary, EvaluationLog, @@ -25,11 +25,7 @@ Uncertainty, ) -import sys -from pathlib import Path -sys.path.insert(0, str(Path(__file__).parent.parent)) - -from helpers import ( +from every_eval_ever.helpers import ( fetch_json, get_developer, make_source_metadata, diff --git a/utils/helm/adapter.py b/utils/helm/adapter.py index 69c190a90..014c8449f 100644 --- a/utils/helm/adapter.py +++ b/utils/helm/adapter.py @@ -19,7 +19,7 @@ from collections import defaultdict from typing import Any, Dict, List, Tuple -from eval_types import ( +from every_eval_ever.eval_types import ( EvalLibrary, EvaluationLog, EvaluationResult, @@ -32,11 +32,7 @@ SourceDataUrl ) -import sys -from pathlib import Path -sys.path.insert(0, str(Path(__file__).parent.parent)) - -from helpers import ( +from every_eval_ever.helpers import ( fetch_json, get_developer, make_model_info, diff --git a/utils/hfopenllm_v2/adapter.py b/utils/hfopenllm_v2/adapter.py index 1754e17c5..92c72cded 100644 --- a/utils/hfopenllm_v2/adapter.py +++ b/utils/hfopenllm_v2/adapter.py @@ -9,10 +9,9 @@ """ import time -from pathlib import Path from typing import Any, Dict, List -from eval_types import ( +from every_eval_ever.eval_types import ( EvalLibrary, EvaluationLog, EvaluationResult, @@ -23,10 +22,7 @@ SourceDataHf, ) -import sys -sys.path.insert(0, str(Path(__file__).parent.parent)) - -from helpers import ( +from every_eval_ever.helpers import ( fetch_json, get_developer, make_model_info, diff --git a/utils/rewardbench/adapter.py b/utils/rewardbench/adapter.py index acfd0bf93..3e4ac001f 100644 --- a/utils/rewardbench/adapter.py +++ b/utils/rewardbench/adapter.py @@ -16,7 +16,7 @@ from pathlib import Path from typing import Any, Dict, List, Optional -from eval_types import ( +from 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