save results locally

lang_agent/eval/evaluator.py

@@ -3,6 +3,10 @@ from typing import Type, Literal
 import tyro
 from loguru import logger
 import functools
+import os
+import os.path as osp
+import glob
+import pandas as pd
 
 from lang_agent.config import InstantiateConfig
 from lang_agent.pipeline import Pipeline, PipelineConfig
@@ -10,9 +14,6 @@ from lang_agent.eval.validator import ValidatorConfig, Validator
 
 from langsmith import Client
 
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableLambda
-
 @tyro.conf.configure(tyro.conf.SuppressFixed)
 @dataclass
 class EvaluatorConfig(InstantiateConfig):
@@ -27,6 +28,8 @@ class EvaluatorConfig(InstantiateConfig):
     dataset_name:Literal["Toxic Queries"] = "dev_langagent"
     """name of the dataset to evaluate"""
 
+    log_dir:str = "logs"
+
     pipe_config: PipelineConfig = field(default_factory=PipelineConfig)
 
     validator_config: ValidatorConfig = field(default_factory=ValidatorConfig)
@@ -49,7 +52,6 @@ class Evaluator:
     def evaluate(self):
         logger.info("running experiment")
         
-        
         inp_fnc = self.validator.get_inp_fnc(self.config.dataset_name)
         runnable = functools.partial(inp_fnc, pipeline=self.pipeline)
 
@@ -59,10 +61,34 @@ class Evaluator:
             evaluators=self.validator.get_val_fnc(self.config.dataset_name),
             experiment_prefix=self.config.experiment_prefix,
             description=self.config.experiment_desc,
-            max_concurrency=4
+            max_concurrency=4,
+            upload_results=False
         )
 
 
-        
+    def save_results(self):
+        os.makedirs(self.config.log_dir, exist_ok=True)
+
+        assert hasattr(self, "result"), "NO RESULTS, run evaluate() before saving results"
+
+        head_path = osp.join(self.config.log_dir, f"{self.dataset.name}-{self.config.experiment_prefix}")
+        n_exp = len(glob.glob(f"{head_path}*"))
+        exp_save_f = osp.join(f"{head_path}-{n_exp}.csv")
+
+        df = self.result.to_pandas()
+        df.to_csv(exp_save_f, index=False)
+
+        metric_col = [e for e in df.columns if "feedback" in e]
+
+        df_curr_m = df[metric_col].mean()
+
+        metric_f = osp.join(self.config.log_dir, "0_exp_metrics.csv")  # start with 0 for first file in folder
+        if osp.exists(metric_f):
+            df_m = pd.read_csv(metric_f)
+            df_m = pd.concat([df_m, df_curr_m])
+        else:
+            df_m = df_curr_m
+        
+        df_m.to_csv(metric_f, index=False)