e3f3d49a3b
<!-- .github/pull_request_template.md --> ## Description This PR contains eval framework changes due to the autooptimizer integration ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced answer generation now returns structured answer details. - Search functionality accepts configurable prompt inputs. - Option to generate a metrics dashboard from evaluations. - Corpus building tasks now support adjustable chunk settings for greater flexibility. - New task retrieval functionality allows for flexible task configuration. - Introduced new methods for creating and managing metrics dashboards. - **Refactor/Chore** - Streamlined API signatures and reorganized module interfaces for better consistency. - Updated import paths to reflect new module structure. - **Tests** - Updated test scenarios to align with new configurations and parameter adjustments. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
import pytest
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from typing import Optional
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import sys
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from unittest.mock import patch, MagicMock
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import unittest
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import numpy as np
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from cognee.eval_framework.analysis.metrics_calculator import bootstrap_ci
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with patch.dict(
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sys.modules,
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{"deepeval": MagicMock(), "deepeval.test_case": MagicMock()},
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):
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from cognee.eval_framework.evaluation.metrics.exact_match import ExactMatchMetric
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from cognee.eval_framework.evaluation.metrics.f1 import F1ScoreMetric
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class MockTestCase:
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def __init__(self, actual_output: Optional[str], expected_output: Optional[str]):
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self.actual_output = actual_output
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self.expected_output = expected_output
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@pytest.fixture
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def metrics():
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return {
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"exact_match": ExactMatchMetric(),
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"f1": F1ScoreMetric(),
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}
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@pytest.mark.parametrize(
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"actual, expected, expected_exact_score, expected_f1_range",
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[
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("Hello World", "Hello World", 1.0, (1.0, 1.0)),
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("Hello World", "hello world", 1.0, (1.0, 1.0)),
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("Hello World", "Hello World", 0.0, (0.0, 1.0)),
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(" Hello World ", "Hello World", 1.0, (1.0, 1.0)),
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("", "Hello World", 0.0, (0.0, 0.0)),
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("Hello World", "", 0.0, (0.0, 0.0)),
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("", "", 1.0, (1.0, 1.0)),
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("Hello World", "Goodbye World", 0.0, (0.0, 1.0)),
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("Hello", "Hello World", 0.0, (0.0, 1.0)),
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("Hello, World!", "hello, world!", 1.0, (1.0, 1.0)),
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("123", "123", 1.0, (1.0, 1.0)),
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("123", "456", 0.0, (0.0, 0.0)),
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("Café", "café", 1.0, (1.0, 1.0)),
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("Café", "Cafe", 0.0, (0.0, 0.0)),
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],
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)
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def test_metrics(metrics, actual, expected, expected_exact_score, expected_f1_range):
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test_case = MockTestCase(actual, expected)
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exact_match_score = metrics["exact_match"].measure(test_case)
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assert exact_match_score == expected_exact_score, (
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f"Exact match failed for '{actual}' vs '{expected}'"
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)
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f1_score = metrics["f1"].measure(test_case)
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assert expected_f1_range[0] <= f1_score <= expected_f1_range[1], (
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f"F1 score failed for '{actual}' vs '{expected}'"
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)
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class TestBootstrapCI(unittest.TestCase):
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def test_bootstrap_ci_basic(self):
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scores = [1, 2, 3, 4, 5]
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mean, lower, upper = bootstrap_ci(scores, num_samples=1000, confidence_level=0.95)
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self.assertAlmostEqual(mean, np.mean(scores), places=2)
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self.assertLessEqual(lower, mean)
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self.assertGreaterEqual(upper, mean)
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def test_bootstrap_ci_single_value(self):
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scores = [3, 3, 3, 3, 3]
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mean, lower, upper = bootstrap_ci(scores, num_samples=1000, confidence_level=0.95)
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self.assertEqual(mean, 3)
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self.assertEqual(lower, 3)
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self.assertEqual(upper, 3)
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def test_bootstrap_ci_empty_list(self):
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mean, lower, upper = bootstrap_ci([])
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self.assertTrue(np.isnan(mean))
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self.assertTrue(np.isnan(lower))
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self.assertTrue(np.isnan(upper))
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