Spaces:
Sleeping
Sleeping
File size: 6,769 Bytes
25e624c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 |
# -*- coding: utf-8 -*-
"""
Run Comparative Evaluation - Compare models and test suites
Runs evaluations with:
1. Binary model (DistilBERT) + V1 test suite
2. Binary model (DistilBERT) + V2 test suite
3. Multi-emotion model (RoBERTa) + V1 test suite
4. Multi-emotion model (RoBERTa) + V2 test suite
Generates comparison reports
"""
import sys
import os
import time
from datetime import datetime
# Add parent to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from avatar.sentiment_emoji_map import EmojiMapper
from evaluation.accuracy_benchmark import AccuracyBenchmark
from evaluation.report_generator import ReportGenerator
def run_comparative_evaluation():
"""Run evaluation comparing models and test suites"""
print("=" * 80)
print("COMPARATIVE SENTIMENT ANALYSIS EVALUATION")
print("=" * 80)
print()
# Initialize common components
mapper = EmojiMapper()
report_gen = ReportGenerator(output_dir="evaluation/reports")
results = {}
# ========================================
# Test 1: Binary Model + V1 Suite
# ========================================
print("[1/4] Binary Model + V1 Test Suite")
print("-" * 50)
try:
from avatar.sentiment_transformer import SentimentAnalyzer as BinaryAnalyzer
from evaluation.emotion_test_suite import EmotionTestSuite
binary_analyzer = BinaryAnalyzer()
suite_v1 = EmotionTestSuite()
benchmark_v1 = AccuracyBenchmark(binary_analyzer, mapper)
print(f" Emotions: {suite_v1.get_emotion_count()}, Tests: {suite_v1.get_test_count()}")
start = time.time()
results["binary_v1"] = benchmark_v1.run_benchmark(suite_v1.EMOTION_TEST_DATA)
elapsed = time.time() - start
print(f" Accuracy: {results['binary_v1'].accuracy:.1%}")
print(f" Time: {elapsed:.2f}s")
print()
except Exception as e:
print(f" ❌ Error: {e}")
results["binary_v1"] = None
# ========================================
# Test 2: Binary Model + V2 Suite
# ========================================
print("[2/4] Binary Model + V2 Test Suite")
print("-" * 50)
try:
from evaluation.emotion_test_suite_v2 import EmotionTestSuiteV2
suite_v2 = EmotionTestSuiteV2()
benchmark_v2 = AccuracyBenchmark(binary_analyzer, mapper)
print(f" Emotions: {suite_v2.get_emotion_count()}, Tests: {suite_v2.get_test_count()}")
start = time.time()
results["binary_v2"] = benchmark_v2.run_benchmark(suite_v2.EMOTION_TEST_DATA)
elapsed = time.time() - start
print(f" Accuracy: {results['binary_v2'].accuracy:.1%}")
print(f" Time: {elapsed:.2f}s")
print()
except Exception as e:
print(f" ❌ Error: {e}")
results["binary_v2"] = None
# ========================================
# Test 3: Multi-Emotion Model + V1 Suite
# ========================================
print("[3/4] Multi-Emotion Model + V1 Test Suite")
print("-" * 50)
try:
from avatar.sentiment_multi_emotion import MultiEmotionAnalyzer
multi_analyzer = MultiEmotionAnalyzer()
benchmark_multi_v1 = AccuracyBenchmark(multi_analyzer, mapper)
print(f" Emotions: {suite_v1.get_emotion_count()}, Tests: {suite_v1.get_test_count()}")
start = time.time()
results["multi_v1"] = benchmark_multi_v1.run_benchmark(suite_v1.EMOTION_TEST_DATA)
elapsed = time.time() - start
print(f" Accuracy: {results['multi_v1'].accuracy:.1%}")
print(f" Time: {elapsed:.2f}s")
print()
except Exception as e:
print(f" ❌ Error: {e}")
print(f" (Install with: pip install transformers torch)")
results["multi_v1"] = None
# ========================================
# Test 4: Multi-Emotion Model + V2 Suite
# ========================================
print("[4/4] Multi-Emotion Model + V2 Test Suite")
print("-" * 50)
try:
benchmark_multi_v2 = AccuracyBenchmark(multi_analyzer, mapper)
print(f" Emotions: {suite_v2.get_emotion_count()}, Tests: {suite_v2.get_test_count()}")
start = time.time()
results["multi_v2"] = benchmark_multi_v2.run_benchmark(suite_v2.EMOTION_TEST_DATA)
elapsed = time.time() - start
print(f" Accuracy: {results['multi_v2'].accuracy:.1%}")
print(f" Time: {elapsed:.2f}s")
print()
except Exception as e:
print(f" ❌ Error: {e}")
results["multi_v2"] = None
# ========================================
# Generate Comparison Report
# ========================================
print("=" * 80)
print("COMPARISON SUMMARY")
print("=" * 80)
print()
print("| Configuration | Accuracy | Avg Time | Failed Emotions |")
print("|---------------------------|----------|----------|-----------------|")
configs = [
("Binary + V1 Suite", "binary_v1"),
("Binary + V2 Suite", "binary_v2"),
("Multi-Emotion + V1 Suite", "multi_v1"),
("Multi-Emotion + V2 Suite", "multi_v2"),
]
for name, key in configs:
r = results.get(key)
if r:
print(f"| {name:25} | {r.accuracy:7.1%} | {r.avg_inference_time_ms:6.2f}ms | {len(r.failed_emotions):15} |")
else:
print(f"| {name:25} | {'N/A':>7} | {'N/A':>8} | {'N/A':>15} |")
print()
# Show failed emotions comparison
print("Failed Emotions by Configuration:")
print("-" * 50)
for name, key in configs:
r = results.get(key)
if r and r.failed_emotions:
print(f"\n{name}:")
for em in r.failed_emotions[:10]: # Show first 10
acc = r.emotion_accuracy.get(em, 0)
print(f" ❌ {em}: {acc:.1%}")
# Save detailed reports
print()
print("=" * 80)
print("SAVING REPORTS")
print("=" * 80)
timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
for name, key in configs:
r = results.get(key)
if r:
safe_name = key.replace("_", "-")
md_path = report_gen.generate_markdown_report(
r,
filename=f"comparison_{safe_name}_{timestamp}.md"
)
print(f" Saved: {md_path}")
print()
print("=" * 80)
print("EVALUATION COMPLETE")
print("=" * 80)
return results
if __name__ == "__main__":
run_comparative_evaluation()
|