fix faulty header of random search selector

README.md

@@ -1,5 +1,5 @@
 
-![Coverage](https://img.shields.io/badge/Coverage-95%25-brightgreen)
+![Coverage](https://img.shields.io/badge/Coverage-96%25-brightgreen)
 [![CI](https://github.com/automl/promptolution/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/automl/promptolution/actions/workflows/ci.yml)
 [![Docs](https://github.com/automl/promptolution/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/automl/promptolution/actions/workflows/docs.yml)
 ![Code Style](https://img.shields.io/badge/Code%20Style-black-black)

promptolution/exemplar_selectors/random_search_selector.py

@@ -1,5 +1,7 @@
 """Random search exemplar selector."""
 
+import random
+
 from promptolution.exemplar_selectors.base_exemplar_selector import BaseExemplarSelector
 from promptolution.utils.prompt import Prompt
 
@@ -11,14 +13,15 @@ class RandomSearchSelector(BaseExemplarSelector):
     evaluates their performance, and selects the best performing set.
     """
 
-    def select_exemplars(self, prompt: Prompt, n_trials: int = 5) -> Prompt:
+    def select_exemplars(self, prompt: Prompt, n_examples: int = 5, n_trials: int = 5) -> Prompt:
         """Select exemplars using a random search strategy.
 
         This method generates multiple sets of random examples, evaluates their performance
         when combined with the original prompt, and returns the best performing set.
 
         Args:
-            prompt (str): The input prompt to base the exemplar selection on.
+            prompt (Prompt): The input prompt to base the exemplar selection on.
+            n_examples (int, optional): The number of exemplars to select. Defaults to 5.
             n_trials (int, optional): The number of random trials to perform. Defaults to 5.
 
         Returns:
@@ -29,8 +32,9 @@ def select_exemplars(self, prompt: Prompt, n_trials: int = 5) -> Prompt:
 
         for _ in range(n_trials):
             result = self.task.evaluate(prompt, self.predictor, eval_strategy="subsample")
-            seq = result.sequences
-            prompt_with_examples = Prompt(prompt.instruction, [seq[0][0]])
+            seq = result.sequences[0]
+            examples = random.sample(list(seq), n_examples)
+            prompt_with_examples = Prompt(prompt.instruction, examples)
             # evaluate prompts as few shot prompt
             result = self.task.evaluate(prompt_with_examples, self.predictor, eval_strategy="subsample")
             score = float(result.agg_scores[0])

tests/exemplar_selectors/test_exemplar_selectors.py

@@ -0,0 +1,76 @@
+"""Tests for exemplar selectors."""
+
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+
+from tests.mocks.mock_predictor import MockPredictor
+
+from promptolution.exemplar_selectors.random_search_selector import RandomSearchSelector
+from promptolution.tasks.base_task import EvalResult
+from promptolution.utils.prompt import Prompt
+
+
+def make_eval_result(sequences, score):
+    n = len(sequences)
+    return EvalResult(
+        scores=np.array([[score] * n], dtype=float),
+        agg_scores=np.array([score], dtype=float),
+        sequences=np.array([sequences], dtype=object),
+        input_tokens=np.array([[1.0] * n], dtype=float),
+        output_tokens=np.array([[1.0] * n], dtype=float),
+        agg_input_tokens=np.array([1.0], dtype=float),
+        agg_output_tokens=np.array([1.0], dtype=float),
+    )
+
+
+@pytest.fixture
+def task_and_predictor():
+    task = MagicMock()
+    pred = MockPredictor()
+    return task, pred
+
+
+def test_random_search_selector_respects_n_examples(task_and_predictor):
+    task, pred = task_and_predictor
+    sequences = [f"ex_{i}" for i in range(10)]
+    task.evaluate.return_value = make_eval_result(sequences, score=0.8)
+
+    selector = RandomSearchSelector(task, pred)
+    result = selector.select_exemplars(Prompt("Classify:"), n_examples=3, n_trials=1)
+
+    assert len(result.few_shots) == 3
+    assert all(ex in sequences for ex in result.few_shots)
+
+
+def test_random_search_selector_returns_best_trial(task_and_predictor):
+    task, pred = task_and_predictor
+    sequences = [f"ex_{i}" for i in range(5)]
+
+    # First trial scores low, second scores high
+    task.evaluate.side_effect = [
+        make_eval_result(sequences, score=0.3),  # zero-shot eval trial 1
+        make_eval_result(sequences, score=0.3),  # few-shot eval trial 1
+        make_eval_result(sequences, score=0.9),  # zero-shot eval trial 2
+        make_eval_result(sequences, score=0.9),  # few-shot eval trial 2
+    ]
+
+    selector = RandomSearchSelector(task, pred)
+    result = selector.select_exemplars(Prompt("Classify:"), n_examples=2, n_trials=2)
+
+    assert len(result.few_shots) == 2
+    assert result.few_shots != []
+
+
+def test_random_search_selector_n_examples_kwarg(task_and_predictor):
+    """Regression test: calling with n_examples as keyword arg must not raise TypeError."""
+    task, pred = task_and_predictor
+    sequences = [f"ex_{i}" for i in range(5)]
+    task.evaluate.return_value = make_eval_result(sequences, score=0.5)
+
+    selector = RandomSearchSelector(task, pred)
+    # This call pattern is what triggered the original bug report
+    result = selector.select_exemplars(prompt=Prompt("Classify:"), n_examples=2)
+
+    assert len(result.few_shots) == 2