delete pandas dep in base_eval use csv instead

scripts/base_eval.py

@@ -1,5 +1,5 @@
 """
-Evlauate the CORE metric for a given model.
+Evaluate the CORE metric for a given model.
 
 Run on a single GPU:
 python base_eval.py
@@ -10,14 +10,13 @@ torchrun --nproc_per_node=8 base_eval.py
 The script will print the CORE metric to the console.
 """
 import os
-import sys
+import csv
 import time
 import json
 import random
 import yaml
 from contextlib import nullcontext
 
-import pandas as pd
 import torch
 
 from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, autodetect_device_type
@@ -26,13 +25,12 @@ from nanochat.checkpoint_manager import load_model
 from nanochat.core_eval import evaluate_task
 
 # -----------------------------------------------------------------------------
-# nanoChat specific function dealing with I/O etc.
+# nanochat specific function dealing with I/O etc.
 
 def evaluate_model(model, tokenizer, device, max_per_task=-1):
     """
     Evaluate a base model on the CORE benchmark.
     - max_per_task: crop the data to this many examples per task for testing (-1 = disable)
-    TODO: clean up this function, delete the need for all the files, for pandas dependency, etc.
     """
     # Load config and task metadata
     base_dir = get_base_dir()
@@ -43,7 +41,15 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
     with open(config_path, 'r') as f:
         config = yaml.safe_load(f)
     tasks = config['icl_tasks']
-    eval_metadata = pd.read_csv(eval_meta_data)
+
+    # Load random baseline values from eval metadata
+    random_baselines = {}
+    with open(eval_meta_data, 'r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            task_name = row['Eval Task']
+            random_baseline = row['Random baseline']
+            random_baselines[task_name] = float(random_baseline)
 
     # Evaluate each task
     results = {}
@@ -75,8 +81,7 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
         accuracy = evaluate_task(model, tokenizer, data, device, task_meta)
 
         results[label] = accuracy
-        row = eval_metadata[eval_metadata["Eval Task"] == label]
-        random_baseline = row["Random baseline"].values[0]
+        random_baseline = random_baselines[label]
         centered_result = (accuracy - 0.01 * random_baseline) / (1.0 - 0.01 * random_baseline)
         centered_results[label] = centered_result
         end_time = time.time()