Cont metrics (#1475)

* add brier_score

* process brier_score

* brier score is working for N-sized class

* fxied brier score

* add TED to BigBench and Brier score to MMLU

* format

* Update metrics.py

* Update task.py

* Update generate_until_template_yaml

* Delete lm_eval/tasks/bigbench/aux_metric.py

* Update generate_until_template_yaml

* Update _default_template_yaml

* Update _generate_configs.py

* Update _generate_configs.py

* Update _generate_configs.py

* fix (format?)

* format?

* format, once more

---------

Co-authored-by: Hailey Schoelkopf <[email protected]>

lm_eval/api/metrics.py

@@ -116,6 +116,25 @@ def ter(items):
     return sacrebleu.corpus_ter(preds, refs).score
 
 
+@register_aggregation("brier_score")
+def brier_score(items):  # This is a passthrough function
+    gold, predictions = list(zip(*items))
+    gold = list(gold)
+    gold_one_hot = np.eye(np.max(gold) + 1)[gold]
+    predictions = list(zip(*items))[1]
+    return np.mean(np.sum((predictions - gold_one_hot) ** 2, axis=1))
+
+
+@register_metric(
+    metric="brier_score",
+    higher_is_better=False,
+    output_type=["multiple_choice"],
+    aggregation="brier_score",
+)
+def brier_score_fn(items):  # This is a passthrough function
+    return items
+
+
 @register_metric(
     metric="acc",
     higher_is_better=True,

lm_eval/api/task.py

@@ -1227,12 +1227,21 @@ def process_results(self, doc, results):
                 # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly
                 exact_match = int(is_greedy[gold]) if gold != -100 else 0
 
+            prob_norm = utils.softmax(lls)
+
+            # TODO use keyword arguments to the metric?
+            # gold, pred, norm stuff, the original lls,
             result_dict = {
                 **({"acc": acc} if "acc" in use_metric else {}),
                 **({"f1": (gold, pred)} if "f1" in use_metric else {}),
                 **({"mcc": (gold, pred)} if "mcc" in use_metric else {}),
                 **({"acc_norm": acc_norm} if "acc_norm" in use_metric else {}),
                 **({"exact_match": exact_match} if "exact_match" in use_metric else {}),
+                **(
+                    {"brier_score": (gold, prob_norm)}
+                    if "brier_score" in use_metric
+                    else {}
+                ),
             }
 
             if "acc_mutual_info" in use_metric:

lm_eval/tasks/ammlu/_generate_configs.py

@@ -1,10 +1,10 @@
 """
 Take in a YAML, and output all other splits with this YAML
 """
-import os
-import yaml
 import argparse
+import os
 
+import yaml
 from tqdm import tqdm
 
 
@@ -68,6 +68,7 @@
     "world_religions": "العلوم الانسانية",
 }
 
+
 def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--base_yaml_path", required=True)
@@ -95,9 +96,7 @@ def parse_args():
         if args.cot_prompt_path is not None:
             description = cot_file[subject_eng]
         else:
-            description = (
-                f"فم بعملية التقييم في مجال {category} \n\n"
-            )
+            description = f"فم بعملية التقييم في مجال {category} \n\n"
 
         yaml_dict = {
             "include": base_yaml_name,

lm_eval/utils.py

@@ -11,6 +11,7 @@
 from pathlib import Path
 from typing import Any, Callable, List
 
+import numpy as np
 import yaml
 from jinja2 import BaseLoader, Environment, StrictUndefined
 
@@ -104,6 +105,12 @@ def pattern_match(patterns, source_list):
     return sorted(list(task_names))
 
 
+def softmax(x):
+    """Compute softmax values for each sets of scores in x."""
+    e_x = np.exp(x - np.max(x))
+    return e_x / e_x.sum()
+
+
 def general_detokenize(string):
     string = string.replace(" n't", "n't")
     string = string.replace(" )", ")")