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|
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pytest
from unittest.mock import MagicMock, patch
from azure.core.credentials import TokenCredential
from azure.ai.evaluation import AzureOpenAIModelConfiguration
from azure.ai.evaluation._evaluators._relevance import RelevanceEvaluator
@pytest.fixture(scope="session")
def test_model_config() -> AzureOpenAIModelConfiguration:
"""Mock model configuration for tests."""
return AzureOpenAIModelConfiguration(
azure_endpoint="https://test.openai.azure.com",
api_key="test-key",
api_version="2024-02-15-preview",
azure_deployment="test-deployment"
)
@pytest.fixture(scope="function")
def mock_credential():
"""Mock credential for tests."""
return MagicMock(spec=TokenCredential)
@pytest.mark.unittest
class TestConversationThresholdBehavior:
"""Test threshold behavior in conversation evaluators."""
@patch(
"azure.ai.evaluation._evaluators._relevance._relevance."
"RelevanceEvaluator.__call__"
)
def test_relevance_evaluator_with_conversation(
self, mock_call, test_model_config
):
"""Test relevance evaluator with conversation input."""
mock_result = {
"relevance": 4.0,
"relevance_result": "PASS",
"evaluation_per_turn": {
"relevance": [4.0]
}
}
mock_call.return_value = mock_result
evaluator = RelevanceEvaluator(
model_config=test_model_config,
threshold=3
)
conversation = {
"messages": [
{"role": "user", "content": "What is the weather like?"},
{"role": "assistant", "content": "It's sunny today."}
]
}
result = evaluator(conversation=conversation)
assert result["relevance"] > 0.0
assert "evaluation_per_turn" in result
@pytest.mark.unittest
class TestMultipleEvaluatorThresholds:
"""Test multiple evaluators with different thresholds."""
@patch(
"azure.ai.evaluation._evaluators._relevance._relevance."
"RelevanceEvaluator.__call__"
)
def test_evaluators_with_different_thresholds(
self, mock_call, test_model_config
):
"""Test that evaluators can have different thresholds."""
mock_result = {
"relevance": 4.0,
"relevance_result": "PASS",
"evaluation_per_turn": {
"relevance": [4.0]
}
}
mock_call.return_value = mock_result
evaluator1 = RelevanceEvaluator(
model_config=test_model_config,
threshold=2
)
evaluator2 = RelevanceEvaluator(
model_config=test_model_config,
threshold=4
)
conversation = {
"messages": [
{"role": "user", "content": "What is the time?"},
{"role": "assistant", "content": "It is 3pm."}
]
}
result1 = evaluator1(conversation=conversation)
result2 = evaluator2(conversation=conversation)
assert evaluator1._threshold != evaluator2._threshold
assert "relevance" in result1
assert "relevance" in result2
assert "evaluation_per_turn" in result1
assert "evaluation_per_turn" in result2
@patch(
"azure.ai.evaluation._evaluators._relevance._relevance."
"RelevanceEvaluator.__call__"
)
def test_threshold_comparison_behavior(
self, mock_call, test_model_config
):
"""Test how different thresholds affect evaluation results."""
mock_result_high = {
"relevance": 4.5,
"relevance_result": "PASS",
"evaluation_per_turn": {
"relevance": [4.5]
}
}
mock_result_low = {
"relevance": 2.5,
"relevance_result": "FAIL",
"evaluation_per_turn": {
"relevance": [2.5]
}
}
mock_call.side_effect = [mock_result_high, mock_result_low]
strict_evaluator = RelevanceEvaluator(
model_config=test_model_config,
threshold=4
)
lenient_evaluator = RelevanceEvaluator(
model_config=test_model_config,
threshold=2
)
conversation = {
"messages": [
{"role": "user", "content": "What is the capital of France?"},
{
"role": "assistant",
"content": "Paris is the capital of France."
}
]
}
strict_result = strict_evaluator(conversation=conversation)
lenient_result = lenient_evaluator(conversation=conversation)
assert strict_evaluator._threshold > lenient_evaluator._threshold
assert strict_result["relevance"] > lenient_result["relevance"]
assert "evaluation_per_turn" in strict_result
assert "evaluation_per_turn" in lenient_result
@pytest.mark.unittest
class TestEvaluatorsCombinedWithSample:
"""Test evaluators with sample conversations and thresholds."""
def test_sample_evaluators_threshold_setup(self, test_model_config):
"""Test setting up evaluators with different thresholds."""
evaluators = [
RelevanceEvaluator(
model_config=test_model_config,
threshold=threshold
)
for threshold in [1, 3, 5]
]
for i, evaluator in enumerate(evaluators):
assert evaluator._threshold == [1, 3, 5][i]
@patch(
"azure.ai.evaluation._evaluators._relevance._relevance."
"RelevanceEvaluator.__call__"
)
def test_sample_evaluators_passing_thresholds(
self, mock_call, test_model_config
):
"""Test evaluators with passing threshold values."""
mock_result = {
"relevance": 4.0,
"relevance_result": "PASS",
"evaluation_per_turn": {
"relevance": [4.0]
}
}
mock_call.return_value = mock_result
evaluator = RelevanceEvaluator(
model_config=test_model_config,
threshold=3
)
conversations = [
{
"messages": [
{"role": "user", "content": "What is 2+2?"},
{"role": "assistant", "content": "2+2 equals 4."}
]
},
{
"messages": [
{"role": "user", "content": "Who wrote Romeo and Juliet?"},
{
"role": "assistant",
"content": "Shakespeare was the author."
}
]
}
]
for conversation in conversations:
result = evaluator(conversation=conversation)
assert "relevance" in result
assert result["relevance"] == 4.0
assert "evaluation_per_turn" in result
|
