Versioned Metadata for Reproducibility

openjury/evaluate.py

@@ -1,7 +1,7 @@
 import json
 import re
 from dataclasses import dataclass
-from datetime import datetime
+from datetime import datetime, timezone
 from pathlib import Path
 
 import numpy as np
@@ -10,6 +10,7 @@
 from langchain_core.language_models.llms import LLM
 
 from openjury.instruction_dataset import load_instructions
+from openjury.repro import write_run_metadata, _to_jsonable
 from openjury.utils import (
     read_df,
     data_root,
@@ -65,6 +66,25 @@ def load_judge_system_and_user_prompt(
     return system_prompt, user_prompt_template
 
 
+def resolve_judge_prompts(
+    *,
+    provide_explanation: bool,
+    system_prompt: str | None = None,
+    user_prompt_template: str | None = None,
+) -> tuple[str, str]:
+    default_system_prompt, default_user_prompt_template = (
+        load_judge_system_and_user_prompt(provide_explanation=provide_explanation)
+    )
+    return (
+        system_prompt if system_prompt is not None else default_system_prompt,
+        (
+            user_prompt_template
+            if user_prompt_template is not None
+            else default_user_prompt_template
+        ),
+    )
+
+
 def evaluate_completions(
     dataset: str = "alpaca-eval",
     judge_chat_model: LLM = None,
@@ -88,6 +108,7 @@ def evaluate_completions(
     exceeding context limit
     :return:
     """
+    run_started_at = datetime.now(timezone.utc)
     local_path_tables = data_root / "tables"
     download_hf(name=dataset, local_path=local_path_tables)
 
@@ -138,6 +159,11 @@ def get_output(df_outputs: pd.DataFrame, dataset: str, method: str):
 
         judge_chat_model = Together(model="meta-llama/Llama-3.3-70B-Instruct-Turbo")
 
+    (
+        judge_system_prompt,
+        judge_user_prompt_template,
+    ) = resolve_judge_prompts(provide_explanation=provide_explanation)
+
     annotations = annotate_battles(
         judge_chat_model=judge_chat_model,
         instructions=instructions.tolist(),
@@ -174,7 +200,37 @@ def get_output(df_outputs: pd.DataFrame, dataset: str, method: str):
     output_folder.mkdir(parents=True, exist_ok=True)
     pd.DataFrame(annotations).to_csv(output_folder / "annotations.csv", index=False)
     with open(output_folder / "results.json", "w") as f:
-        json.dump(results, f)
+        json.dump(_to_jsonable(results), f, allow_nan=False)
+
+    run_metadata = {
+        "dataset": dataset,
+        "method_A": method_A,
+        "method_B": method_B,
+        "num_annotations": num_annotations,
+        "n_annotations": len(instructions),
+        "use_tqdm": use_tqdm,
+        "truncate_input_chars": truncate_input_chars,
+        "provide_explanation": provide_explanation,
+    }
+
+    try:
+        write_run_metadata(
+            output_dir=output_folder,
+            entrypoint="openjury.evaluate.evaluate_completions",
+            run=run_metadata,
+            results=results,
+            input_payloads={
+                "instruction_index": instructions.index.tolist(),
+                "instructions": instructions.tolist(),
+                "completions_A": completions_A.loc[instructions.index].tolist(),
+                "completions_B": completions_B.loc[instructions.index].tolist(),
+            },
+            judge_system_prompt=judge_system_prompt,
+            judge_user_prompt_template=judge_user_prompt_template,
+            started_at_utc=run_started_at,
+        )
+    except OSError as e:
+        print(f"Warning: failed to write run metadata: {e}")
 
 
 @dataclass
@@ -227,14 +283,11 @@ def annotate_battles(
     # alternatively pass list of tuples
     assert len(instructions) == len(completions_A) == len(completions_B)
 
-    (
-        default_system_prompt,
-        default_user_prompt_template,
-    ) = load_judge_system_and_user_prompt(provide_explanation=provide_explanation)
-    if system_prompt is None:
-        system_prompt = default_system_prompt
-    if user_prompt_template is None:
-        user_prompt_template = default_user_prompt_template
+    system_prompt, user_prompt_template = resolve_judge_prompts(
+        provide_explanation=provide_explanation,
+        system_prompt=system_prompt,
+        user_prompt_template=user_prompt_template,
+    )
 
     prompt_template = ChatPromptTemplate.from_messages(
         [("system", system_prompt), ("user", user_prompt_template)]

openjury/generate_and_evaluate.py

@@ -5,18 +5,22 @@
 
 import argparse
 import json
-import os
-from dataclasses import dataclass, asdict, field
-from datetime import datetime
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
 from functools import partial
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
 
-from openjury.evaluate import annotate_battles, PairScore
+from openjury.evaluate import (
+    annotate_battles,
+    PairScore,
+    resolve_judge_prompts,
+)
 from openjury.generate import generate_instructions, generate_base
 from openjury.instruction_dataset import load_instructions
+from openjury.repro import write_run_metadata, _to_jsonable
 from openjury.utils import data_root, read_df, download_hf
 from openjury.utils import make_model, cache_function_dataframe
 
@@ -272,6 +276,7 @@ def main(args: CliArgs):
     3) create annotations
     """
 
+    run_started_at = datetime.now(timezone.utc)
     print(
         f"Using dataset {args.dataset} and evaluating models {args.model_A} and {args.model_B}."
     )
@@ -382,6 +387,13 @@ def main(args: CliArgs):
         # the default system prompt of annotate is to compare instruction tuned models.
 
         system_prompt = None
+    (
+        effective_judge_system_prompt,
+        judge_user_prompt_template,
+    ) = resolve_judge_prompts(
+        provide_explanation=args.provide_explanation,
+        system_prompt=system_prompt,
+    )
     annotations = annotate_battles(
         judge_chat_model=judge_chat_model,
         instructions=instructions.head(n_instructions).tolist(),
@@ -416,10 +428,6 @@ def main(args: CliArgs):
     res_folder = Path(args.result_folder) / name
     res_folder.mkdir(parents=True, exist_ok=True)
 
-    # save argument for results analysis
-    with open(res_folder / f"args-{name}.json", "w") as f:
-        json.dump(asdict(args), f, indent=2)
-
     print(f"Saving results to {res_folder}")
     df = pd.DataFrame(annotations)
     df["instruction_index"] = instructions.head(n_instructions).index.tolist()
@@ -476,14 +484,36 @@ def main(args: CliArgs):
         "num_ties": num_ties,
         "num_missing": num_battles - (num_losses + num_wins + num_ties),
         "preferences": prefs.tolist(),
-        "date": str(datetime.now().isoformat()),
-        "user": os.getenv("USER", ""),
     }
     print(f"{args.model_A} vs {args.model_B} judged by {args.judge_model}")
     print_results(results)
 
     with open(res_folder / f"results-{name}.json", "w") as f:
-        json.dump(results, f, indent=2)
+        json.dump(_to_jsonable(results), f, indent=2, allow_nan=False)
+
+    eval_instruction_index = instructions.head(n_instructions).index.tolist()
+    eval_instructions = instructions.head(n_instructions).tolist()
+    eval_completions_A = completions_A.head(n_instructions).tolist()
+    eval_completions_B = completions_B.head(n_instructions).tolist()
+
+    try:
+        write_run_metadata(
+            output_dir=res_folder,
+            entrypoint="openjury.generate_and_evaluate.main",
+            run=asdict(args),
+            results=results,
+            input_payloads={
+                "instruction_index": eval_instruction_index,
+                "instructions": eval_instructions,
+                "completions_A": eval_completions_A,
+                "completions_B": eval_completions_B,
+            },
+            judge_system_prompt=effective_judge_system_prompt,
+            judge_user_prompt_template=judge_user_prompt_template,
+            started_at_utc=run_started_at,
+        )
+    except OSError as e:
+        print(f"Warning: failed to write run metadata: {e}")
 
     return prefs