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c54dcef | 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 | """Tests for evaluation metrics."""
import pytest
from core.eval import RAGEvaluator, BenchmarkDataset
from core.eval_utils import generate_evaluation_report
import pandas as pd
from pathlib import Path
import tempfile
def test_hit_at_k():
"""Test Hit@k metric."""
evaluator = RAGEvaluator()
retrieved = ["doc1", "doc2", "doc3", "doc4", "doc5"]
relevant = ["doc2", "doc6"]
assert evaluator.hit_at_k(retrieved, relevant, k=5) == 1.0
assert evaluator.hit_at_k(retrieved, relevant, k=1) == 0.0
def test_precision_recall():
"""Test Precision and Recall metrics."""
evaluator = RAGEvaluator()
retrieved = ["doc1", "doc2", "doc3"]
relevant = ["doc2", "doc3", "doc4"]
precision = evaluator.precision_at_k(retrieved, relevant, k=3)
recall = evaluator.recall_at_k(retrieved, relevant, k=3)
assert precision == pytest.approx(2/3, 0.01)
assert recall == pytest.approx(2/3, 0.01)
def test_mrr():
"""Test Mean Reciprocal Rank."""
evaluator = RAGEvaluator()
retrieved = ["doc1", "doc2", "doc3"]
relevant = ["doc2"]
mrr = evaluator.mrr(retrieved, relevant)
assert mrr == pytest.approx(0.5, 0.01) # 1/2
def test_semantic_similarity():
"""Test semantic similarity metric."""
evaluator = RAGEvaluator()
answer = "The patient needs proper identification."
reference = "Patient identification is required."
similarity = evaluator.semantic_similarity(answer, reference)
assert 0 <= similarity <= 1
assert similarity > 0.5 # Should be reasonably similar
def test_benchmark_dataset():
"""Test benchmark dataset functionality."""
dataset = BenchmarkDataset()
hospital_queries = dataset.get_sample_hospital_queries()
bank_queries = dataset.get_sample_bank_queries()
fluid_queries = dataset.get_sample_fluid_simulation_queries()
assert len(hospital_queries) > 0
assert len(bank_queries) > 0
assert len(fluid_queries) > 0
assert "query" in hospital_queries[0]
assert "domain" in hospital_queries[0]
def test_evaluation_report_generation():
"""Test evaluation report generation."""
# Create sample evaluation data
data = {
'query': ['Query 1', 'Query 2', 'Query 3'],
'base_retrieval_time': [0.05, 0.06, 0.04],
'base_total_time': [1.5, 2.0, 1.8],
'hier_retrieval_time': [0.03, 0.04, 0.03],
'hier_total_time': [1.0, 1.2, 1.1],
'speedup': [1.5, 1.67, 1.64]
}
with tempfile.TemporaryDirectory() as temp_dir:
# Save sample CSV
csv_path = Path(temp_dir) / "test_eval.csv"
df = pd.DataFrame(data)
df.to_csv(csv_path, index=False)
# Generate report
stats = generate_evaluation_report(str(csv_path))
# Verify statistics
assert stats['total_queries'] == 3
assert stats['avg_speedup'] > 1.0
assert stats['hier_wins'] == 3
# Verify files created
assert Path(str(csv_path).replace('.csv', '_report_charts.png')).exists()
assert Path(str(csv_path).replace('.csv', '_report_summary.md')).exists()
def test_evaluate_rag_pipeline():
"""Test complete RAG pipeline evaluation."""
evaluator = RAGEvaluator()
# Mock RAG result
rag_result = {
"answer": "Patient admission requires ID verification.",
"contexts": [
{"id": "doc1", "document": "Text about admission", "metadata": {}},
{"id": "doc2", "document": "Text about verification", "metadata": {}}
],
"retrieval_time": 0.05,
"generation_time": 1.2,
"total_time": 1.25,
"pipeline": "Hier-RAG"
}
relevant_ids = ["doc1", "doc3"]
reference_answer = "Admission requires identification."
metrics = evaluator.evaluate_rag_pipeline(
rag_result,
relevant_ids,
reference_answer,
k_values=[1, 3, 5]
)
assert "hit@1" in metrics
assert "hit@3" in metrics
assert "mrr" in metrics
assert "semantic_similarity" in metrics
assert metrics["retrieval_time"] == 0.05
def test_empty_results():
"""Test evaluation with empty results."""
evaluator = RAGEvaluator()
retrieved = []
relevant = ["doc1", "doc2"]
assert evaluator.hit_at_k(retrieved, relevant, k=5) == 0.0
assert evaluator.precision_at_k(retrieved, relevant, k=5) == 0.0
assert evaluator.mrr(retrieved, relevant) == 0.0 |