generated from allenai/python-package-template
-
Notifications
You must be signed in to change notification settings - Fork 591
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
eeb2733
commit 07466e1
Showing
2 changed files
with
161 additions
and
42 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,90 @@ | ||
import numpy as np | ||
|
||
from typing import Dict, List, Tuple, Optional | ||
|
||
def calculate_bootstrap_ci( | ||
test_scores: List[float], | ||
n_bootstrap: int = 1000, | ||
ci_level: float = 0.95 | ||
) -> Tuple[float, float]: | ||
""" | ||
Calculate bootstrap confidence interval for test scores. | ||
Args: | ||
test_scores: List of test scores (0.0 to 1.0 for each test) | ||
n_bootstrap: Number of bootstrap samples to generate | ||
ci_level: Confidence interval level (default: 0.95 for 95% CI) | ||
Returns: | ||
Tuple of (lower_bound, upper_bound) representing the confidence interval | ||
""" | ||
if not test_scores: | ||
return (0.0, 0.0) | ||
|
||
# Convert to numpy array for efficiency | ||
scores = np.array(test_scores) | ||
|
||
# Generate bootstrap samples | ||
bootstrap_means = [] | ||
for _ in range(n_bootstrap): | ||
# Sample with replacement | ||
sample = np.random.choice(scores, size=len(scores), replace=True) | ||
bootstrap_means.append(np.mean(sample)) | ||
|
||
# Calculate confidence interval | ||
alpha = (1 - ci_level) / 2 | ||
lower_bound = np.percentile(bootstrap_means, alpha * 100) | ||
upper_bound = np.percentile(bootstrap_means, (1 - alpha) * 100) | ||
|
||
return (lower_bound, upper_bound) | ||
|
||
|
||
def perform_permutation_test( | ||
scores_a: List[float], | ||
scores_b: List[float], | ||
n_permutations: int = 10000 | ||
) -> Tuple[float, float]: | ||
""" | ||
Perform a permutation test to determine if there's a significant difference | ||
between two sets of test scores. | ||
Args: | ||
scores_a: List of test scores for candidate A | ||
scores_b: List of test scores for candidate B | ||
n_permutations: Number of permutations to perform | ||
Returns: | ||
Tuple of (observed_difference, p_value) | ||
""" | ||
if not scores_a or not scores_b: | ||
return (0.0, 1.0) | ||
|
||
# Calculate observed difference in means | ||
observed_diff = np.mean(scores_a) - np.mean(scores_b) | ||
|
||
# Combine all scores | ||
combined = np.concatenate([scores_a, scores_b]) | ||
n_a = len(scores_a) | ||
n_combined = len(combined) | ||
|
||
# Perform permutation test | ||
count_greater_or_equal = 0 | ||
for _ in range(n_permutations): | ||
# Shuffle the combined array | ||
np.random.shuffle(combined) | ||
|
||
# Split into two groups of original sizes | ||
perm_a = combined[:n_a] | ||
perm_b = combined[n_a:] | ||
|
||
# Calculate difference in means | ||
perm_diff = np.mean(perm_a) - np.mean(perm_b) | ||
|
||
# Count how many permuted differences are >= to observed difference in absolute value | ||
if abs(perm_diff) >= abs(observed_diff): | ||
count_greater_or_equal += 1 | ||
|
||
# Calculate p-value | ||
p_value = count_greater_or_equal / n_permutations | ||
|
||
return (observed_diff, p_value) |