Show aggregated metrics in UI (Part 1) (#43)

* save

* vite build

use model_dump(mode="json")

Add run_id to EvalMetadataSchema for unique run identification

- Introduced run_id as an optional string to the EvalMetadataSchema to uniquely identify evaluation runs.
- Updated description to clarify the purpose of the run_id field.

Add run_id field to EvalMetadata for unique run identification

- Added run_id as an optional string to the EvalMetadata class to uniquely identify groups of evaluation rows.
- Updated the field description to clarify its purpose in relation to evaluation tests.

Fix evaluation result assignment in markdown highlighting test

- Updated the test_markdown_highlighting_evaluation function to assign the evaluation result directly to the row when no assistant response is found, ensuring proper handling of evaluation results.

Add run_id generation in evaluation_test for unique identification

- Integrated the generate_id function to create a run_id within the evaluation_test function.
- Passed the generated run_id to the evaluation function, ensuring unique identification of evaluation runs.

* Wrap logo image in a link to the Eval Protocol website for improved navigation.

* TODO: test the pivot table logic

* Refactor WebSocket log initialization message handling in logs_server.py

- Simplified the construction of the log initialization message by creating a data dictionary before sending it over the WebSocket, improving code readability.

* flatten json test

* refine pivot.ts

* Add support for composite columns in computePivot tests

- Introduced a new test case to validate the handling of multiple column fields in the computePivot function.
- Verified correct computation of cell values, row totals, column totals, and grand total for the pivot table with composite columns.

* assertion error means finished

* Refactor EvalMetadata and EvaluationRow models; add cohort_id, rollout_id, and run_id fields. Update evaluation_test to handle new identifiers and improve documentation on evaluation concepts.

* Add invocation_id field to EvaluationRow model and update corresponding schema in eval-protocol types. This enhances tracking of invocation context for evaluation rows.

* rename as its causing issues in pytest collection

* square up all the id madness and add a test

Author: Dylan Huang

eval_protocol/models.py

@@ -245,8 +245,8 @@ class EvaluationRow(BaseModel):
     supporting both row-wise batch evaluation and trajectory-based RL evaluation.
     """
 
-    # Core conversation data
-    messages: List[Message] = Field(description="List of messages in the conversation/trajectory.")
+    # Core OpenAI ChatCompletion compatible conversation data
+    messages: List[Message] = Field(description="List of messages in the conversation. Also known as a trajectory.")
 
     # Tool and function call information
     tools: Optional[List[Dict[str, Any]]] = Field(
@@ -264,6 +264,26 @@ class EvaluationRow(BaseModel):
         description="The status of the rollout.",
     )
 
+    invocation_id: Optional[str] = Field(
+        default_factory=generate_id,
+        description="The ID of the invocation that this row belongs to.",
+    )
+
+    cohort_id: Optional[str] = Field(
+        default_factory=generate_id,
+        description="The ID of the cohort that this row belongs to.",
+    )
+
+    rollout_id: Optional[str] = Field(
+        default_factory=generate_id,
+        description="The ID of the rollout that this row belongs to.",
+    )
+
+    run_id: Optional[str] = Field(
+        None,
+        description=("The ID of the run that this row belongs to."),
+    )
+
     # Ground truth reference (moved from EvaluateResult to top level)
     ground_truth: Optional[str] = Field(
         default=None, description="Optional ground truth reference for this evaluation."

eval_protocol/pytest/default_mcp_gym_rollout_processor.py

@@ -200,6 +200,8 @@ async def default_mcp_gym_rollout_processor(
     Returns:
         List of EvaluationRow objects with completed conversations
     """
+    if config.server_script_path is None:
+        raise ValueError("server_script_path is required for default_mcp_gym_rollout_processor")
     server = MCPServerManager(config.server_script_path, port=9700)
 
     try:

eval_protocol/pytest/evaluation_test.py

@@ -1,13 +1,14 @@
-import inspect
-import os
 import copy
+import inspect
 import math
+import os
 import statistics
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Literal, Optional
 
 import pytest
 
 from eval_protocol.dataset_logger import default_logger
+from eval_protocol.human_id import generate_id
 from eval_protocol.models import CompletionParams, EvalMetadata, EvaluationRow, InputMetadata, Message
 from eval_protocol.pytest.default_dataset_adapter import default_dataset_adapter
 from eval_protocol.pytest.default_no_op_rollout_process import default_no_op_rollout_processor
@@ -28,13 +29,14 @@
     aggregate,
     create_dynamically_parameterized_wrapper,
     execute_function,
+    log_eval_status_and_rows,
 )
+from eval_protocol.stats.confidence_intervals import compute_fixed_set_mu_ci
 
 from ..common_utils import load_jsonl
-from eval_protocol.stats.confidence_intervals import compute_fixed_set_mu_ci
 
 
-def evaluation_test(
+def evaluation_test(  # noqa: C901
     *,
     model: List[ModelParam],
     input_messages: Optional[List[InputMessagesParam]] = None,
@@ -59,6 +61,37 @@ def evaluation_test(
 ]:
     """Decorator to create pytest-based evaluation tests.
 
+    Here are some key concepts to understand the terminology in EP:
+
+    - "invocation" is a single execution of a test function. An invocation can
+        generate 1 or more cohorts. Grouping by invocation might be useful to
+        aggregate eval scores across multiple invocations when you want to aggregate
+        scores across multiple datasets.
+    - "cohort" is a group of runs with for a combination of parameters. A single
+        cohort will have multiple runs if num_runs > 1.
+        1. If your evaluation_test has combinations of parameters, it will generate
+        multiple cohorts per combination of parameters.
+        2. A new execution of a test function will generate a new cohort.
+    - "run" is a group of rollouts. For multiple num_runs > 1, there will be
+        multiple "run_id"s.
+    - "rollout" is the execution/process that produces a "trajectory". You
+        "execute" multiple rollouts to generate a dataset of trajectories.
+    - "trajectory" is the result produced by a rollout — a list of OpenAI Chat
+        Completion messages (e.g. the "messages" field in EvaluationRow).
+    - "row" both the input and output of an evaluation. For example, in
+        tau-bench, a row is a task within the dataset that can be identified as
+        "airline_task_0" or "airline_task_1" etc. The "row_id" can be populated from
+        the dataset itself to identify a particular task you want to evaluate.  If
+        not provided, EP will generate a "row_id" for each row whenever you call the
+        evaluation test.
+    - "dataset" is a collection of rows (e.g. List[EvauluationRow])
+    - "eval" is a rubric implemented in the body of an @evaluation_test
+        decorated test. It simply produces a score from 0 to 1 and attached it
+        to the row as the "evaluation_result" field.
+
+    "invocation", "cohort", "run", "rollout", and "row" each have a unique ID
+    which can be used to easily group and identify your dataset by.
+
     Args:
         model: Model identifiers to query.
         input_messages: Messages to send to the model. This is useful if you
@@ -75,7 +108,7 @@ def evaluation_test(
         aggregation_method: How to aggregate scores across rows.
         threshold_of_success: If set, fail the test if the aggregated score is
             below this threshold.
-        num_runs: Number of times to repeat the evaluation.
+        num_runs: Number of times to repeat the rollout and evaluations.
         max_dataset_rows: Limit dataset to the first N rows.
         mcp_config_path: Path to MCP config file that follows MCPMultiClientConfiguration schema
         max_concurrent_rollouts: Maximum number of concurrent rollouts to run in parallel.
@@ -119,15 +152,15 @@ def decorator(
 
         def execute_with_params(
             test_func: TestFunction,
-            row: EvaluationRow | None = None,
-            input_dataset: List[EvaluationRow] | None = None,
+            processed_row: EvaluationRow | None = None,
+            processed_dataset: List[EvaluationRow] | None = None,
             evaluation_test_kwargs: Optional[EvaluationInputParam] = None,
         ):
             kwargs = {}
-            if input_dataset is not None:
-                kwargs["rows"] = input_dataset
-            if row is not None:
-                kwargs["row"] = row
+            if processed_dataset is not None:
+                kwargs["rows"] = processed_dataset
+            if processed_row is not None:
+                kwargs["row"] = processed_row
             if evaluation_test_kwargs is not None:
                 if "row" in evaluation_test_kwargs:
                     raise ValueError("'row' is a reserved parameter for the evaluation function")
@@ -176,7 +209,7 @@ def generate_combinations():
                         datasets = [[input_dataset]]  # type: ignore
             else:
                 datasets = [None]
-            params: List[Optional[RolloutInputParam]] = rollout_input_params if rollout_input_params is not None else [None]  # type: ignore
+            rips: List[Optional[RolloutInputParam]] = rollout_input_params if rollout_input_params is not None else [None]  # type: ignore
             # Apply EP_MAX_DATASET_ROWS to input_messages, but do NOT parameterize over
             # each row. Instead, pass the entire sliced list through in a single test run
             # so summaries aggregate all rows together (AIME-style behavior).
@@ -195,15 +228,15 @@ def generate_combinations():
             # Generate all combinations
             for m in model:
                 for ds in datasets:
-                    for ip in params:
+                    for rip in rips:
                         for im in messages:
                             for etk in kwargs:
                                 # if no dataset and no messages, raise an error
                                 if ds is None and im is None:
                                     raise ValueError(
                                         "No dataset or messages provided. Please provide at least one of input_dataset or input_messages."
                                     )
-                                combinations.append((m, ds, ip, im, etk))
+                                combinations.append((m, ds, rip, im, etk))
 
             return combinations
 
@@ -216,12 +249,12 @@ def generate_combinations():
         # Create parameter tuples for pytest.mark.parametrize
         param_tuples = []
         for combo in combinations:
-            model_name, dataset, params, messages, etk = combo
+            model_name, dataset, rip, messages, etk = combo
             param_tuple = [model_name]
             if input_dataset is not None:
                 param_tuple.append(dataset)
             if rollout_input_params is not None:
-                param_tuple.append(params)
+                param_tuple.append(rip)
             if input_messages is not None:
                 param_tuple.append(messages)
             if evaluation_test_kwargs is not None:
@@ -242,11 +275,20 @@ def generate_combinations():
         # Create wrapper function with exact signature that pytest expects
         def create_wrapper_with_signature() -> Callable:
             # Create the function body that will be used
+            invocation_id = generate_id()
+
             def wrapper_body(**kwargs):
                 model_name = kwargs["model"]
                 eval_metadata = None
                 all_results: List[EvaluationRow] = []
 
+                cohort_id = generate_id()
+
+                def _log_eval_error(
+                    status: Literal["finished", "error"], rows: Optional[List[EvaluationRow]] | None, passed: bool
+                ) -> None:
+                    log_eval_status_and_rows(eval_metadata, rows, status, passed, default_logger)
+
                 try:
                     # Handle dataset loading
                     data: List[EvaluationRow] = []
@@ -283,6 +325,7 @@ def wrapper_body(**kwargs):
                     # into input_params (e.g., '{"temperature":0,"extra_body":{"reasoning":{"effort":"low"}}}').
                     try:
                         import json as _json
+
                         _env_override = os.getenv("EP_INPUT_PARAMS_JSON")
                         if _env_override:
                             override_obj = _json.loads(_env_override)
@@ -320,6 +363,8 @@ def wrapper_body(**kwargs):
                         row.input_metadata.session_data["mode"] = mode
                         # Initialize eval_metadata for each row
                         row.eval_metadata = eval_metadata
+                        row.cohort_id = cohort_id
+                        row.invocation_id = invocation_id
 
                         # has to be done in the pytest main process since it's
                         # used to determine whether this eval has stopped
@@ -339,14 +384,25 @@ def wrapper_body(**kwargs):
                     for _ in range(num_runs):
                         # Regenerate outputs each run by deep-copying the pristine dataset
                         # so model responses are not reused across runs.
-                        fresh_rows = [copy.deepcopy(r) for r in data]
-                        input_dataset = execute_function(rollout_processor, rows=fresh_rows, config=config)
+                        run_id = generate_id()
+                        fresh_dataset = [copy.deepcopy(r) for r in data]
+
+                        # apply new run_id to fresh_dataset
+                        for row in fresh_dataset:
+                            row.run_id = run_id
+
+                        # generate new rollout_id for each row
+                        for row in fresh_dataset:
+                            row.rollout_id = generate_id()
+
+                        processed_dataset = execute_function(rollout_processor, rows=fresh_dataset, config=config)
+
                         if mode == "pointwise":
                             # Pointwise mode: apply the evaluator function to each row
-                            for row in input_dataset:
+                            for row in processed_dataset:
                                 result = execute_with_params(
                                     test_func,
-                                    row=row,
+                                    processed_row=row,
                                     evaluation_test_kwargs=kwargs.get("evaluation_test_kwargs") or {},
                                 )
                                 if result is None or not isinstance(result, EvaluationRow):
@@ -358,7 +414,7 @@ def wrapper_body(**kwargs):
                             # Batch mode: call the test function with the full dataset
                             results = execute_with_params(
                                 test_func,
-                                input_dataset=input_dataset,
+                                processed_dataset=processed_dataset,
                                 evaluation_test_kwargs=kwargs.get("evaluation_test_kwargs") or {},
                             )
                             if results is None:
@@ -430,6 +486,7 @@ def wrapper_body(**kwargs):
                         # Aggregate per-metric mean and 95% CI when available
                         metrics_summary: Dict[str, Dict[str, float]] = {}
                         from collections import defaultdict
+
                         metric_scores: Dict[str, list] = defaultdict(list)
                         for r in all_results:
                             if r.evaluation_result and r.evaluation_result.metrics:
@@ -470,7 +527,10 @@ def wrapper_body(**kwargs):
                                 )
                             # As per project convention, avoid printing per-metric CI lines to reduce noise
                         if summary_path:
-                            import json, pathlib, time, re
+                            import json
+                            import pathlib
+                            import re
+                            import time
 
                             def _sanitize_filename(text: str) -> str:
                                 safe = re.sub(r"[^A-Za-z0-9._-]+", "-", text.strip())
@@ -487,7 +547,11 @@ def _extract_effort_tag(params: dict) -> str | None:
                                             return str(eb["reasoning"]["effort"]).lower()
                                         if "reasoning_effort" in eb:
                                             return str(eb["reasoning_effort"]).lower()
-                                    if "reasoning" in params and isinstance(params["reasoning"], dict) and "effort" in params["reasoning"]:
+                                    if (
+                                        "reasoning" in params
+                                        and isinstance(params["reasoning"], dict)
+                                        and "effort" in params["reasoning"]
+                                    ):
                                         return str(params["reasoning"]["effort"]).lower()
                                 except Exception:
                                     return None
@@ -529,25 +593,11 @@ def _extract_effort_tag(params: dict) -> str | None:
                             agg_score >= threshold_of_success
                         ), f"Aggregated score {agg_score:.3f} below threshold {threshold_of_success}"
 
+                except AssertionError:
+                    _log_eval_error("finished", data if "data" in locals() else None, passed=False)
+                    raise
                 except Exception:
-                    # Update eval metadata status to error and log it
-                    if eval_metadata is not None:
-                        eval_metadata.status = "error"
-                        eval_metadata.passed = False
-
-                        # Create a minimal result row to log the error if we don't have any results yet
-                        if not data:
-                            error_row = EvaluationRow(messages=[], eval_metadata=eval_metadata, evaluation_result=None)
-                            default_logger.log(error_row)
-                        else:
-                            # Update existing results with error status
-                            for r in data:
-                                if r.eval_metadata is not None:
-                                    r.eval_metadata.status = "error"
-                                    r.eval_metadata.passed = False
-                                default_logger.log(r)
-
-                    # Re-raise the exception to maintain pytest behavior
+                    _log_eval_error("error", data if "data" in locals() else None, passed=False)
                     raise
 
             return create_dynamically_parameterized_wrapper(test_func, wrapper_body, test_param_names)

eval_protocol/pytest/utils.py

@@ -1,6 +1,9 @@
 import asyncio
 import inspect
-from typing import Any, Callable, List, Literal
+from typing import Any, Callable, List, Literal, Optional
+
+from eval_protocol.dataset_logger.dataset_logger import DatasetLogger
+from eval_protocol.models import EvalMetadata, EvaluationRow
 
 
 def execute_function(func: Callable, **kwargs) -> Any:
@@ -92,3 +95,32 @@ def wrapper(**kwargs):
     wrapper.__signature__ = inspect.Signature(parameters)
 
     return wrapper
+
+
+def log_eval_status_and_rows(
+    eval_metadata: Optional[EvalMetadata],
+    rows: Optional[List[EvaluationRow]] | None,
+    status: Literal["finished", "error"],
+    passed: bool,
+    logger: DatasetLogger,
+) -> None:
+    """Update eval status and emit rows to the given logger.
+
+    If no rows are provided, emits a minimal placeholder row so downstream
+    consumers still observe a terminal status.
+    """
+    if eval_metadata is None:
+        return
+
+    eval_metadata.status = status
+    eval_metadata.passed = passed
+
+    rows_to_log: List[EvaluationRow] = rows or []
+    if not rows_to_log:
+        error_row = EvaluationRow(messages=[], eval_metadata=eval_metadata, evaluation_result=None)
+        logger.log(error_row)
+    else:
+        for r in rows_to_log:
+            if r.eval_metadata is not None:
+                r.eval_metadata.status = status
+            logger.log(r)