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"""Tests for evaluation postprocess functionality."""
from unittest.mock import Mock, patch
import pytest
from eval_protocol.models import (
EvaluationRow,
EvaluateResult,
EvalMetadata,
EvaluationThreshold,
ExecutionMetadata,
InputMetadata,
Message,
)
from eval_protocol.pytest.evaluation_test_postprocess import postprocess
from eval_protocol.stats.confidence_intervals import compute_fixed_set_mu_ci
class TestPostprocess:
"""Tests for postprocess function."""
def create_test_row(self, score: float, is_valid: bool = True) -> EvaluationRow:
"""Helper to create a test evaluation row."""
return EvaluationRow(
messages=[],
evaluation_result=EvaluateResult(score=score, is_score_valid=is_valid, reason="test"),
input_metadata=InputMetadata(completion_params={"model": "test-model"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=1,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_bootstrap_aggregation_with_valid_scores(self):
"""Test bootstrap aggregation with all valid scores and verify exact scores list."""
# Create test data: 2 runs with 2 rows each
all_results = [
[self.create_test_row(0.8), self.create_test_row(0.6)], # Run 1
[self.create_test_row(0.7), self.create_test_row(0.9)], # Run 2
]
mock_logger = Mock()
# Mock the aggregate function to capture the exact scores passed to it
with patch("eval_protocol.pytest.evaluation_test_postprocess.aggregate") as mock_aggregate:
mock_aggregate.return_value = 0.75 # Mock return value
postprocess(
all_results=all_results,
aggregation_method="bootstrap",
threshold=None,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_bootstrap",
num_runs=2,
experiment_duration_seconds=10.0,
)
# Check that aggregate was called with all individual scores in order
mock_aggregate.assert_called_once_with([0.8, 0.6, 0.7, 0.9], "bootstrap")
# Should call logger.log for each row
assert mock_logger.log.call_count == 4
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_bootstrap_aggregation_filters_invalid_scores(self):
"""Test that bootstrap aggregation excludes invalid scores and generates correct scores list."""
# Create test data with some invalid scores
all_results = [
[
self.create_test_row(0.8, is_valid=True),
self.create_test_row(0.0, is_valid=False), # Invalid - should be excluded
],
[
self.create_test_row(0.7, is_valid=True),
self.create_test_row(0.0, is_valid=False), # Invalid - should be excluded
],
]
mock_logger = Mock()
# Mock the aggregate function to capture the scores passed to it
with patch("eval_protocol.pytest.evaluation_test_postprocess.aggregate") as mock_aggregate:
mock_aggregate.return_value = 0.75 # Mock return value
postprocess(
all_results=all_results,
aggregation_method="bootstrap",
threshold=None,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_bootstrap_invalid",
num_runs=2,
experiment_duration_seconds=10.0,
)
# Check that aggregate was called with only valid scores
mock_aggregate.assert_called_once_with([0.8, 0.7], "bootstrap")
# Should still call logger.log for all rows (including invalid ones)
assert mock_logger.log.call_count == 4
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_mean_aggregation_with_valid_scores(self):
"""Test mean aggregation with all valid scores."""
all_results = [
[self.create_test_row(0.8), self.create_test_row(0.6)], # Run 1: mean = 0.7
[self.create_test_row(0.4), self.create_test_row(0.8)], # Run 2: mean = 0.6
]
mock_logger = Mock()
postprocess(
all_results=all_results,
aggregation_method="mean",
threshold=None,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_mean",
num_runs=2,
experiment_duration_seconds=10.0,
)
# Should call logger.log for each row
assert mock_logger.log.call_count == 4
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_mean_aggregation_filters_invalid_scores(self):
"""Test that mean aggregation excludes invalid scores from run averages."""
all_results = [
[
self.create_test_row(0.8, is_valid=True),
self.create_test_row(0.0, is_valid=False), # Invalid - excluded from run average
],
[
self.create_test_row(0.6, is_valid=True),
self.create_test_row(0.4, is_valid=True),
],
]
mock_logger = Mock()
postprocess(
all_results=all_results,
aggregation_method="mean",
threshold=None,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_mean_invalid",
num_runs=2,
experiment_duration_seconds=10.0,
)
# Should call logger.log for all rows
assert mock_logger.log.call_count == 4
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_empty_runs_are_skipped(self):
"""Test that runs with no valid scores are skipped."""
all_results = [
[self.create_test_row(0.8, is_valid=True)], # Run 1: has valid score
[self.create_test_row(0.0, is_valid=False)], # Run 2: no valid scores - should be skipped
]
mock_logger = Mock()
postprocess(
all_results=all_results,
aggregation_method="mean",
threshold=None,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_empty_runs",
num_runs=2,
experiment_duration_seconds=10.0,
)
# Should still call logger.log for all rows
assert mock_logger.log.call_count == 2
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_all_invalid_scores(self):
"""Test behavior when all scores are invalid."""
all_results = [
[self.create_test_row(0.0, is_valid=False), self.create_test_row(0.0, is_valid=False)],
]
mock_logger = Mock()
postprocess(
all_results=all_results,
aggregation_method="bootstrap",
threshold=None,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_all_invalid",
num_runs=1,
experiment_duration_seconds=10.0,
)
# Should still call logger.log for all rows
assert mock_logger.log.call_count == 2
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_threshold_all_zero_scores_fail(self):
"""When all scores are 0.0 and threshold.success is 0.01, postprocess should fail."""
all_results = [
[self.create_test_row(0.0), self.create_test_row(0.0)],
]
mock_logger = Mock()
threshold = EvaluationThreshold(success=0.01, standard_error=None)
with pytest.raises(AssertionError) as excinfo:
postprocess(
all_results=all_results,
aggregation_method="mean",
threshold=threshold,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_threshold_all_zero",
num_runs=1,
experiment_duration_seconds=10.0,
)
# Sanity check on the assertion message
assert "below threshold" in str(excinfo.value)
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_threshold_equal_score_passes(self):
"""When agg_score equals threshold.success (0.01), postprocess should pass."""
all_results = [
[self.create_test_row(0.01)],
]
mock_logger = Mock()
threshold = EvaluationThreshold(success=0.01, standard_error=None)
# Should not raise
postprocess(
all_results=all_results,
aggregation_method="mean",
threshold=threshold,
active_logger=mock_logger,
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_threshold_equal_score",
num_runs=1,
experiment_duration_seconds=10.0,
)
class TestBootstrapEquivalence:
def test_bootstrap_equivalence_pandas_vs_pure_python(self):
import random
import pandas as pd
from eval_protocol.pytest.evaluation_test_utils import calculate_bootstrap_scores as py_bootstrap
# Deterministic synthetic scores
rng = random.Random(123)
scores = [rng.random() for _ in range(100)]
n_boot = 1000
seed = 42
# Old (pandas) style bootstrap: resample full column with replacement
df = pd.DataFrame({"score": scores})
pandas_means = [
df.sample(frac=1.0, replace=True, random_state=seed + i)["score"].mean() for i in range(n_boot)
]
pandas_boot_mean = sum(pandas_means) / len(pandas_means)
# New pure-python implementation
py_boot_mean = py_bootstrap(scores, n_boot=n_boot, seed=seed)
# They estimate the same quantity; allow small Monte Carlo tolerance
assert abs(pandas_boot_mean - py_boot_mean) < 0.02
class TestComputeFixedSetMuCi:
"""Tests for compute_fixed_set_mu_ci function."""
@patch.dict("os.environ", {"EP_NO_UPLOAD": "1"}) # Disable uploads
def test_compute_fixed_set_mu_ci_with_flattened_results(self):
"""Test that postprocess correctly calls compute_fixed_set_mu_ci with flattened all_results structure."""
q1_run1 = EvaluationRow(
messages=[Message(role="user", content="What is 2+2?")],
evaluation_result=EvaluateResult(score=0.5, is_score_valid=True, reason="correct"),
input_metadata=InputMetadata(row_id="q1", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q1_run2 = EvaluationRow(
messages=[Message(role="user", content="What is 2+2?")],
evaluation_result=EvaluateResult(score=0.4, is_score_valid=True, reason="incorrect"),
input_metadata=InputMetadata(row_id="q1", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q1_run3 = EvaluationRow(
messages=[Message(role="user", content="What is 2+2?")],
evaluation_result=EvaluateResult(score=0.45, is_score_valid=True, reason="incorrect"),
input_metadata=InputMetadata(row_id="q1", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q2_run1 = EvaluationRow(
messages=[Message(role="user", content="What is 3+3?")],
evaluation_result=EvaluateResult(score=0.8, is_score_valid=True, reason="incorrect"),
input_metadata=InputMetadata(row_id="q2", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q2_run2 = EvaluationRow(
messages=[Message(role="user", content="What is 3+3?")],
evaluation_result=EvaluateResult(score=0.9, is_score_valid=True, reason="correct"),
input_metadata=InputMetadata(row_id="q2", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q2_run3 = EvaluationRow(
messages=[Message(role="user", content="What is 3+3?")],
evaluation_result=EvaluateResult(score=0.95, is_score_valid=True, reason="correct"),
input_metadata=InputMetadata(row_id="q2", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q3_run1 = EvaluationRow(
messages=[Message(role="user", content="What is 4+4?")],
evaluation_result=EvaluateResult(score=0.1, is_score_valid=True, reason="incorrect"),
input_metadata=InputMetadata(row_id="q3", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q3_run2 = EvaluationRow(
messages=[Message(role="user", content="What is 4+4?")],
evaluation_result=EvaluateResult(score=0.2, is_score_valid=True, reason="correct"),
input_metadata=InputMetadata(row_id="q3", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q3_run3_valid = EvaluationRow(
messages=[Message(role="user", content="What is 4+4?")],
evaluation_result=EvaluateResult(score=0.3, is_score_valid=True, reason="correct"),
input_metadata=InputMetadata(row_id="q3", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
q3_run3_invalid = EvaluationRow(
messages=[Message(role="user", content="What is 4+4?")],
evaluation_result=EvaluateResult(score=0.3, is_score_valid=False, reason="correct"),
input_metadata=InputMetadata(row_id="q3", completion_params={"model": "test"}),
execution_metadata=ExecutionMetadata(),
eval_metadata=EvalMetadata(
name="test",
description="test",
version="1.0",
status=None,
num_runs=3,
aggregation_method="mean",
passed_threshold=None,
passed=None,
),
)
rows = [[q1_run1, q2_run1, q3_run1], [q1_run2, q2_run2, q1_run3], [q2_run3, q3_run2, q3_run3_valid]]
rows_with_invalid_score = [
[q1_run1, q2_run1, q3_run1],
[q1_run2, q2_run2, q1_run3],
[q2_run3, q3_run2, q3_run3_invalid],
]
# Store results for assertions
first_result = None
second_result = None
# Test first case (all valid scores)
with patch("eval_protocol.pytest.evaluation_test_postprocess.compute_fixed_set_mu_ci") as mock_ci:
mock_ci.side_effect = lambda input_rows, **kwargs: compute_fixed_set_mu_ci(input_rows, **kwargs)
postprocess(
all_results=rows,
aggregation_method="mean",
threshold=None,
active_logger=Mock(),
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_ci_flattened",
num_runs=3,
experiment_duration_seconds=10.0,
)
first_result = mock_ci.return_value
# Test second case (with invalid score)
with patch("eval_protocol.pytest.evaluation_test_postprocess.compute_fixed_set_mu_ci") as mock_ci:
mock_ci.side_effect = lambda input_rows, **kwargs: compute_fixed_set_mu_ci(input_rows, **kwargs)
postprocess(
all_results=rows_with_invalid_score,
aggregation_method="mean",
threshold=None,
active_logger=Mock(),
mode="pointwise",
completion_params={"model": "test-model"},
test_func_name="test_ci_flattened_invalid",
num_runs=3,
experiment_duration_seconds=10.0,
)
second_result = mock_ci.return_value
# Assert exact values
# First case: (0.5111111111111111, 0.18101430525778583, 0.8412079169644363, 0.168416737680268)
if first_result and len(first_result) == 4:
mu_hat1, ci_low1, ci_high1, se1 = first_result
assert abs(mu_hat1 - 0.5111111111111111) < 1e-10
assert abs(ci_low1 - 0.18101430525778583) < 1e-10
assert abs(ci_high1 - 0.8412079169644363) < 1e-10
assert abs(se1 - 0.168416737680268) < 1e-10
# Second case: (0.49444444444444446, 0.13494616580367125, 0.8539427230852177, 0.18341748910243533)
if second_result and len(second_result) == 4:
mu_hat2, ci_low2, ci_high2, se2 = second_result
assert abs(mu_hat2 - 0.49444444444444446) < 1e-10
assert abs(ci_low2 - 0.13494616580367125) < 1e-10
assert abs(ci_high2 - 0.8539427230852177) < 1e-10
assert abs(se2 - 0.18341748910243533) < 1e-10