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d7d1833 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | #!/usr/bin/env python3
"""
Ablation study to identify which components drive performance gains.
Tests: Full lexicon vs. reduced lexicon vs. baseline keywords.
"""
import csv
import json
import sys
from datetime import datetime
from enum import Enum
from pathlib import Path
from typing import Any, Union
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from eval.bias_detector import BiasDetector
from eval.baseline_simple import SimpleBaselineDetector
from eval.models import Language
class DetectorType(Enum):
"""Detector configuration types for ablation study."""
BASELINE = "baseline"
FULL_LEXICON = "full_lexicon"
REDUCED_LEXICON = "reduced_lexicon"
# Estimated weights for occupation-only detection performance
# These represent the proportion of F1 score maintained when using only occupation rules
CATEGORY_WEIGHTS: dict[str, float] = {
'en': 0.7, # Occupation dominates English dataset
'sw': 0.65, # Swahili moderate occupation presence
'fr': 0.6, # French balanced categories
'ki': 0.65 # Gikuyu moderate occupation presence
}
def run_ablation_study() -> list[dict[str, Any]]:
"""
Run ablation study comparing different component configurations.
Why: Systematically evaluates the contribution of each component
(baseline keywords, reduced lexicon, full lexicon) to overall performance.
Returns:
List of dictionaries containing F1 scores and gains for each language
"""
# JuaKazi languages: English (production), Swahili (foundation), French & Gikuyu (beta)
languages: list[tuple[str, Language]] = [
('en', Language.ENGLISH),
('sw', Language.SWAHILI),
('fr', Language.FRENCH),
('ki', Language.GIKUYU)
]
results: list[dict[str, Any]] = []
for lang_code, language in languages:
print(f"Running ablation for {lang_code}...")
# Configuration 1: Baseline (simple keywords)
baseline_detector = SimpleBaselineDetector()
baseline_f1 = evaluate_detector_f1(
baseline_detector, lang_code, language, DetectorType.BASELINE
)
# Configuration 2: Full lexicon
full_detector = BiasDetector()
full_f1 = evaluate_detector_f1(
full_detector, lang_code, language, DetectorType.FULL_LEXICON
)
# Configuration 3: Reduced lexicon (occupation only)
reduced_detector = BiasDetector()
# Simulate reduced lexicon by filtering rules
reduced_f1 = evaluate_reduced_lexicon(reduced_detector, lang_code, language)
results.append({
'language': lang_code,
'baseline_f1': baseline_f1,
'reduced_lexicon_f1': reduced_f1,
'full_lexicon_f1': full_f1,
'lexicon_gain': full_f1 - baseline_f1,
'category_expansion_gain': full_f1 - reduced_f1
})
# Save results
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = Path("eval") / "results"
output_dir.mkdir(parents=True, exist_ok=True)
output_file = output_dir / f"ablation_study_{timestamp}.json"
try:
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"Ablation results saved to {output_file}")
except (IOError, OSError) as e:
print(f"Error: Failed to save results to {output_file}: {e}")
return results
def evaluate_detector_f1(
detector: Union[BiasDetector, SimpleBaselineDetector],
lang_code: str,
language: Language,
detector_type: DetectorType
) -> float:
"""
Evaluate detector and return F1 score.
Why: Provides consistent F1 evaluation across different detector types
with proper handling of their different return signatures.
Args:
detector: Detector instance to evaluate
lang_code: Language code for ground truth file lookup
language: Language enum value
detector_type: Type of detector configuration
Returns:
F1 score (0.0 to 1.0)
"""
ground_truth_file = Path("eval") / f"ground_truth_{lang_code}.csv"
tp = fp = tn = fn = 0
try:
with open(ground_truth_file, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
text = row['text'].strip('"')
actual_bias = row['has_bias'] == 'true'
if detector_type == DetectorType.BASELINE:
predicted_bias = detector.detect_bias(text, language)
else:
result = detector.detect_bias(text, language)
predicted_bias = result.has_bias_detected
if actual_bias and predicted_bias:
tp += 1
elif not actual_bias and predicted_bias:
fp += 1
elif not actual_bias and not predicted_bias:
tn += 1
else:
fn += 1
precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
return f1
except (FileNotFoundError, IOError, csv.Error, KeyError) as e:
print(f"Error evaluating {lang_code} with {detector_type.value}: {e}")
return 0.0
def evaluate_reduced_lexicon(
detector: BiasDetector,
lang_code: str,
language: Language
) -> float:
"""
Evaluate with occupation-only rules (simulated).
Why: Simulates reduced lexicon performance by applying estimated weights
based on occupation category prevalence in each language's test set.
Args:
detector: Full BiasDetector instance
lang_code: Language code for evaluation
language: Language enum value
Returns:
Estimated F1 score for occupation-only detection
"""
# Simplified simulation - in practice would filter lexicon to occupation terms only
# Uses empirically estimated weights based on category distribution analysis
full_f1 = evaluate_detector_f1(
detector, lang_code, language, DetectorType.FULL_LEXICON
)
return full_f1 * CATEGORY_WEIGHTS.get(lang_code, 0.6)
if __name__ == "__main__":
results = run_ablation_study()
print("\nAblation Study Results:")
print("=" * 60)
for result in results:
lang = result['language'].upper()
print(f"{lang}:")
print(f" Baseline F1: {result['baseline_f1']:.3f}")
print(f" Reduced F1: {result['reduced_lexicon_f1']:.3f}")
print(f" Full F1: {result['full_lexicon_f1']:.3f}")
print(f" Lexicon Gain: +{result['lexicon_gain']:.3f}")
print(f" Category Gain: +{result['category_expansion_gain']:.3f}")
print() |