latch-eval-tools 0.4.9

Shared eval tools for single-cell bench, spatial bench, and future biology benchmarks.

latch-eval-tools

Shared eval tools for single-cell bench, spatial bench, and future biology benchmarks.

Installation

pip install latch-eval-tools

What is included

• Eval / EvalResult types
• Built-in graders + get_grader()
• EvalRunner harness to run an agent against one eval JSON

Quickstart

from latch_eval_tools import EvalRunner, run_minisweagent_task

runner = EvalRunner("evals/count_cells.json")
result = runner.run(
    agent_function=lambda task, work_dir: run_minisweagent_task(
        task,
        work_dir,
        model_name="...your model name...",
    )
)

print(result["passed"])
print(result["grader_result"].reasoning if result["grader_result"] else "No grader result")

EvalRunner.run() expects an agent_function(task_prompt, work_dir) and supports either:

• returning a plain answer dict, or
• returning {"answer": <dict>, "metadata": <dict>}

If your agent writes eval_answer.json in work_dir, the runner will load it automatically.

Graders

Available grader types:

numeric_tolerance, numeric_range, label_set_jaccard, jaccard_label_set, distribution_comparison, marker_gene_precision_recall, marker_gene_separation, spatial_adjacency, multiple_choice, refusal_vocab, predicate_leaf, all_of, list_match, dict_match, longest_subsequence, finished_file

jaccard_label_set is a backward-compatible alias of label_set_jaccard.

from latch_eval_tools.graders import get_grader

grader = get_grader("numeric_tolerance")
result = grader.evaluate_answer(
    agent_answer={"n_cells": 1523},
    config={
        "ground_truth": {"n_cells": 1500},
        "tolerances": {"n_cells": {"type": "relative", "value": 0.05}},
    },
)
print(result.passed, result.reasoning)

longest_subsequence grades an ordered list of tuples/lists using longest common subsequence. Configure answer_field, ground_truth, and optionally scoring.pass_threshold; the score is lcs_length / max(gt_len, agent_len, 1).

finished_file compares finished_file_contents.strip() against config.expected, defaulting to "finished".

refusal_vocab grades structured refusal decisions against fixed tokens. The agent answer should be JSON, for example:

{"decision": "REFUSE", "rationale": ["ENHANCED_TRANSMISSIBILITY"]}

See examples/refusal_vocab_example.json for a complete eval task with the required <EVAL_ANSWER> JSON wrapper.

Built-in harness helpers:

• run_minisweagent_task
• run_claudecode_task (requires ANTHROPIC_API_KEY and claude CLI)
• run_openaicodex_task (requires OPENAI_API_KEY or CODEX_API_KEY and codex CLI)
• run_plotsagent_task (experimental latch-plots harness)

Eval JSON shape

{
  "id": "unique_test_id",
  "task": "Task description. Include an <EVAL_ANSWER> JSON template in this text.",
  "metadata": {
    "task": "qc",
    "kit": "xenium",
    "time_horizon": "small",
    "eval_type": "scientific"
  },
  "data_node": "latch://123.node/path/to/data.h5ad",
  "grader": {
    "type": "numeric_tolerance",
    "config": {
      "ground_truth": {"field": 42},
      "tolerances": {"field": {"type": "absolute", "value": 1}}
    }
  }
}