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SIENTIAPDE-1646

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Update README, requirements, and E2E tests for improved configuration and functionality

- Enhanced the README with updated model configuration examples, including the addition of an alias for production.
- Removed the `requirements-light.txt` file and updated `requirements-local.txt` and `requirements.txt` to replace `asyncua` with `opcua`.
- Refactored E2E test scenarios to utilize scenario input files for better maintainability and clarity.
- Improved test coverage for MinIO offload functionality and added new helper functions for loading scenario inputs.
- Updated `values.yaml` to reflect new global configurations and environment variables for the laborious worker.
This commit is contained in:
Vitor Pimentel dos Santos
2026-05-07 17:02:25 -03:00
parent aaf647efdf
commit e6018af23f
51 changed files with 4408 additions and 2660 deletions
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197
tests/laborious/activities/test_model_metrics.py
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@@ -1,7 +1,7 @@
from unittest.mock import ANY, AsyncMock, MagicMock, patch
from unittest.mock import ANY, MagicMock, patch
from pandas import DataFrame
from pytest import fixture, mark
from pytest import fixture
from sientia_do.notifications.models import NotificationLevel
from laborious.activities.model_metrics import ModelMetrics
@@ -12,7 +12,7 @@ def model_metrics_activity():
model_metrics = ModelMetrics(
logger=MagicMock(),
notification_handler=MagicMock(),
metrics_controller=AsyncMock(),
metrics_controller=MagicMock(),
)
model_metrics.error = MagicMock()
model_metrics.debug = MagicMock()
@@ -20,8 +20,7 @@ def model_metrics_activity():
model_metrics.warning = MagicMock()
model_metrics.critical = MagicMock()
model_metrics.send_notification = MagicMock()
model_metrics.send_notification_async = AsyncMock()
model_metrics.emit_metric = AsyncMock()
model_metrics.emit_metric_sync = MagicMock()
model_metrics.get_core_labels = MagicMock(
return_value={
'pod_id': 'test_pod',
@@ -29,7 +28,7 @@ def model_metrics_activity():
'workflow_name': 'test_workflow',
}
)
model_metrics.observe_lag = AsyncMock()
model_metrics.observe_lag_sync = MagicMock()
model_metrics.pod_id = 'test_pod'
return model_metrics
@@ -44,8 +43,7 @@ metadata = {
}
@mark.asyncio
async def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
# Arrange
input_data = {
**metadata,
@@ -64,7 +62,7 @@ async def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
# Act & Assert
try:
await model_metrics_activity.calculate_drift(input_data)
model_metrics_activity.calculate_drift(input_data)
except ValueError as e:
assert str(e) == 'Invalid chunk period: invalid, must be "min" or "s"'
model_metrics_activity.error.assert_called_once_with(
@@ -74,10 +72,9 @@ async def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
raise AssertionError('Expected ValueError')
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_with_reference_data(
def test_calculate_drift_with_reference_data(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
# Arrange
@@ -107,7 +104,7 @@ async def test_calculate_drift_with_reference_data(
}
]
model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
reference_data = DataFrame(
{
@@ -142,7 +139,7 @@ async def test_calculate_drift_with_reference_data(
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
@@ -150,10 +147,18 @@ async def test_calculate_drift_with_reference_data(
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={'metric': 'method', 'statistic': 'value'}, inplace=True
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -161,10 +166,9 @@ async def test_calculate_drift_with_reference_data(
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_without_reference_data(
def test_calculate_drift_without_reference_data(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
# Arrange
@@ -194,7 +198,7 @@ async def test_calculate_drift_without_reference_data(
}
]
model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
target_data_dict = {
'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29'],
@@ -232,13 +236,13 @@ async def test_calculate_drift_without_reference_data(
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
assert result == mock_drift_df.to_dict.return_value
model_metrics_activity.warning.assert_called()
model_metrics_activity.send_notification_async.assert_called_once_with(
model_metrics_activity.send_notification.assert_called_once_with(
metadata=metadata['metadata'],
notification_id='MODEL_METRICS_REFERENCE_DATA_WARNING',
message='Using 30% first rows of target data as reference data',
@@ -247,10 +251,18 @@ async def test_calculate_drift_without_reference_data(
attachment_content=ANY,
)
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={'metric': 'method', 'statistic': 'value'}, inplace=True
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -258,16 +270,13 @@ async def test_calculate_drift_without_reference_data(
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_empty_drift_df(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
def test_calculate_drift_empty_drift_df(mock_to_datetime, mock_dataframe, model_metrics_activity):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
model_metrics_activity.get_drift_metrics = AsyncMock(return_value=DataFrame())
model_metrics_activity.get_drift_metrics = MagicMock(return_value=DataFrame())
reference_data = DataFrame(
{
@@ -302,7 +311,7 @@ async def test_calculate_drift_empty_drift_df(
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert result == []
@@ -311,10 +320,9 @@ async def test_calculate_drift_empty_drift_df(
)
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_empty_after_timestamp_filter(
def test_calculate_drift_empty_after_timestamp_filter(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
# Arrange
@@ -340,7 +348,7 @@ async def test_calculate_drift_empty_after_timestamp_filter(
mock_drift_df.__getitem__.side_effect = getitem_side_effect
model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
reference_data = DataFrame(
{
@@ -375,7 +383,7 @@ async def test_calculate_drift_empty_after_timestamp_filter(
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert result == []
@@ -383,17 +391,16 @@ async def test_calculate_drift_empty_after_timestamp_filter(
'No drift metrics found after dropping rows where timestamp is not in target data',
metadata['metadata'],
)
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
# When the timestamp filter empties the dataframe, the rename/drop pipeline
# is short-circuited, so neither ``drop`` nor ``rename`` should run.
mock_drift_df.drop.assert_not_called()
mock_drift_df.rename.assert_not_called()
mock_drift_df.__getitem__.assert_called()
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_success_min(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
def test_calculate_drift_success_min(mock_to_datetime, mock_dataframe, model_metrics_activity):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
@@ -421,7 +428,7 @@ async def test_calculate_drift_success_min(
}
]
model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
reference_data = DataFrame(
{
@@ -456,7 +463,7 @@ async def test_calculate_drift_success_min(
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
@@ -464,10 +471,18 @@ async def test_calculate_drift_success_min(
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={'metric': 'method', 'statistic': 'value'}, inplace=True
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -475,10 +490,9 @@ async def test_calculate_drift_success_min(
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metrics_activity):
def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metrics_activity):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
@@ -506,7 +520,7 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
}
]
model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
reference_data = DataFrame(
{
@@ -541,7 +555,7 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
@@ -549,10 +563,18 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={'metric': 'method', 'statistic': 'value'}, inplace=True
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -560,16 +582,15 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@mark.asyncio
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
async def test_calculate_drift_get_drift_metrics_error(
def test_calculate_drift_get_drift_metrics_error(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
model_metrics_activity.get_drift_metrics = AsyncMock(
model_metrics_activity.get_drift_metrics = MagicMock(
side_effect=Exception('Get drift metrics error')
)
@@ -606,14 +627,14 @@ async def test_calculate_drift_get_drift_metrics_error(
}
# Act
result = await model_metrics_activity.calculate_drift(input_data)
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert result == []
model_metrics_activity.error.assert_called_once_with(
'Error getting drift metrics: Get drift metrics error', metadata['metadata']
)
model_metrics_activity.send_notification_async.assert_called_once_with(
model_metrics_activity.send_notification.assert_called_once_with(
metadata=metadata['metadata'],
notification_id='MODEL_METRICS_GET_DRIFT_METRICS_ERROR',
message='Error getting drift metrics: Get drift metrics error',
@@ -623,12 +644,11 @@ async def test_calculate_drift_get_drift_metrics_error(
)
@mark.asyncio
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.metrics')
async def test_get_drift_metrics_success(
def test_get_drift_metrics_success(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
):
# Arrange
@@ -668,7 +688,7 @@ async def test_get_drift_metrics_success(
).columns
# Act
result = await model_metrics_activity.get_drift_metrics(
result = model_metrics_activity.get_drift_metrics(
reference_data=reference_data,
target_data=target_data,
target_name='target',
@@ -681,16 +701,15 @@ async def test_get_drift_metrics_success(
# Assert
assert isinstance(result, DataFrame)
model_metrics_activity.debug.assert_called()
model_metrics_activity.observe_lag.assert_called()
model_metrics_activity.emit_metric.assert_called()
model_metrics_activity.observe_lag_sync.assert_called()
model_metrics_activity.emit_metric_sync.assert_called()
@mark.asyncio
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.metrics')
async def test_get_drift_metrics_univariate_error(
def test_get_drift_metrics_univariate_error(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
):
# Arrange
@@ -722,7 +741,7 @@ async def test_get_drift_metrics_univariate_error(
# Act & Assert
try:
await model_metrics_activity.get_drift_metrics(
model_metrics_activity.get_drift_metrics(
reference_data=reference_data,
target_data=target_data,
target_name='target',
@@ -736,19 +755,18 @@ async def test_get_drift_metrics_univariate_error(
model_metrics_activity.error.assert_called_once_with(
'Error detecting univariate drift: Univariate drift error', metadata['metadata']
)
model_metrics_activity.emit_metric.assert_called_with(
model_metrics_activity.emit_metric_sync.assert_called_with(
metric_object=mock_metrics.MODEL_ANALYZE_ERROR_COUNT, tags=ANY
)
else:
raise AssertionError('Expected Exception')
@mark.asyncio
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.metrics')
async def test_get_drift_metrics_multivariate_error(
def test_get_drift_metrics_multivariate_error(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
):
mock_time.return_value = 1000.0
@@ -769,7 +787,7 @@ async def test_get_drift_metrics_multivariate_error(
).columns
try:
await model_metrics_activity.get_drift_metrics(
model_metrics_activity.get_drift_metrics(
reference_data=reference_data,
target_data=target_data,
target_name='target',
@@ -783,19 +801,18 @@ async def test_get_drift_metrics_multivariate_error(
model_metrics_activity.error.assert_called_once_with(
'Error detecting multivariate drift: Multivariate drift error', metadata['metadata']
)
model_metrics_activity.emit_metric.assert_called_with(
model_metrics_activity.emit_metric_sync.assert_called_with(
metric_object=mock_metrics.MODEL_ANALYZE_ERROR_COUNT, tags=ANY
)
else:
raise AssertionError('Expected Exception')
@mark.asyncio
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.metrics')
async def test_get_drift_metrics_dataframe_error(
def test_get_drift_metrics_dataframe_error(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
):
mock_time.return_value = 1000.0
@@ -817,7 +834,7 @@ async def test_get_drift_metrics_dataframe_error(
).columns
try:
await model_metrics_activity.get_drift_metrics(
model_metrics_activity.get_drift_metrics(
reference_data=reference_data,
target_data=target_data,
target_name='target',
@@ -831,15 +848,14 @@ async def test_get_drift_metrics_dataframe_error(
model_metrics_activity.error.assert_called_once_with(
'Error getting drift metrics: Dataframe error', metadata['metadata']
)
model_metrics_activity.emit_metric.assert_called_with(
model_metrics_activity.emit_metric_sync.assert_called_with(
metric_object=mock_metrics.MODEL_ANALYZE_ERROR_COUNT, tags=ANY
)
else:
raise AssertionError('Expected Exception')
@mark.asyncio
async def test_calculate_simple_metrics_success_all_metrics(model_metrics_activity):
def test_calculate_simple_metrics_success_all_metrics(model_metrics_activity):
# Arrange
target_data = DataFrame(
{
@@ -858,7 +874,7 @@ async def test_calculate_simple_metrics_success_all_metrics(model_metrics_activi
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 4
@@ -877,8 +893,7 @@ async def test_calculate_simple_metrics_success_all_metrics(model_metrics_activi
model_metrics_activity.debug.assert_called_once()
@mark.asyncio
async def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity):
def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity):
# Arrange
target_data = DataFrame(
{
@@ -897,7 +912,7 @@ async def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 1
@@ -911,8 +926,7 @@ async def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity
)
@mark.asyncio
async def test_calculate_simple_metrics_success_mse_only(model_metrics_activity):
def test_calculate_simple_metrics_success_mse_only(model_metrics_activity):
# Arrange
target_data = DataFrame(
{
@@ -931,7 +945,7 @@ async def test_calculate_simple_metrics_success_mse_only(model_metrics_activity)
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 1
@@ -945,8 +959,7 @@ async def test_calculate_simple_metrics_success_mse_only(model_metrics_activity)
)
@mark.asyncio
async def test_calculate_simple_metrics_success_mae_only(model_metrics_activity):
def test_calculate_simple_metrics_success_mae_only(model_metrics_activity):
# Arrange
target_data = DataFrame(
{
@@ -965,7 +978,7 @@ async def test_calculate_simple_metrics_success_mae_only(model_metrics_activity)
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 1
@@ -979,8 +992,7 @@ async def test_calculate_simple_metrics_success_mae_only(model_metrics_activity)
)
@mark.asyncio
async def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
# Arrange
target_data = DataFrame(
{
@@ -999,7 +1011,7 @@ async def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 1
@@ -1013,8 +1025,7 @@ async def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
)
@mark.asyncio
async def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
# Arrange
# All target values are the same, so ss_tot will be 0
target_data = DataFrame(
@@ -1034,7 +1045,7 @@ async def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 1
@@ -1049,8 +1060,7 @@ async def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
)
@mark.asyncio
async def test_calculate_simple_metrics_success_multiple_metrics_subset(model_metrics_activity):
def test_calculate_simple_metrics_success_multiple_metrics_subset(model_metrics_activity):
# Arrange
target_data = DataFrame(
{
@@ -1069,7 +1079,7 @@ async def test_calculate_simple_metrics_success_multiple_metrics_subset(model_me
}
# Act
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
# Assert
assert len(result['metric']) == 2
@@ -1084,8 +1094,7 @@ async def test_calculate_simple_metrics_success_multiple_metrics_subset(model_me
)
@mark.asyncio
async def test_calculate_simple_metrics_unknown_metric_ignored(model_metrics_activity):
def test_calculate_simple_metrics_unknown_metric_ignored(model_metrics_activity):
target_data = DataFrame(
{
'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28'],
@@ -1102,7 +1111,7 @@ async def test_calculate_simple_metrics_unknown_metric_ignored(model_metrics_act
'interval_minutes': 5,
}
result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
assert len(result['metric']) == 1
assert result['metric'].values[0] == 'rmse'