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sientia-dataops-laborious-l…/tests/laborious/activities/test_gates.py

1115 lines
35 KiB
Python

from unittest.mock import ANY, MagicMock, call, patch
from pandas import DataFrame
from pytest import fixture
from sientia_do.notifications.models import NotificationLevel
from sientia_do.observability.metrics_controller import MetricsController
from sientia_do.observability.sientia_monitoring import SientiaMonitoring
from laborious.activities.gates import Gates, input_filter_counters, input_filter_functions
@fixture(autouse=True)
def _passthrough_from_dict():
with patch(
'laborious.activities.gates.MinioDataFramePayload.from_dict', side_effect=lambda x: x
):
yield
def _minio_payload(retrieve_return, status=None):
"""
Build a MinioDataFramePayload-like test double with retrieve.
Args:
retrieve_return: Value returned from retrieve(minio_repo, metadata).
status: Optional status dict for MLflow response gate (payload.status).
Return:
MagicMock: Object with async retrieve and optional status.
"""
p = MagicMock()
p.retrieve = MagicMock(return_value=retrieve_return)
p.status = status
return p
@fixture
def gates_activity():
gates = Gates(
logger=MagicMock(),
notification_handler=MagicMock(),
metrics_controller=MagicMock(spec=MetricsController),
)
gates.error = MagicMock()
gates.debug = MagicMock()
gates.info = MagicMock()
gates.warning = MagicMock()
gates.critical = MagicMock()
gates.send_notification = MagicMock()
gates.emit_metric_sync = MagicMock()
return gates
metadata = {
'metadata': {
'model_id': 'test_model',
'model_name': 'test_model',
'workflow_name': 'test_workflow',
'schema_name': 'test_schedule',
},
}
def test_input_filter_counters_covers_every_input_filter_function():
# A filter added to input_filter_functions without a matching entry here would degrade to
# counter=0 (see the `.get(..., lambda *_: 0)` fallback in input_gate) instead of crashing —
# but this test should still catch the gap before it ships (review finding, 2026-09-30).
assert set(input_filter_counters) == set(input_filter_functions)
def test_input_gate_invalid_filter(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (None, 0, '', None)
gates_activity.error.assert_called_once_with(
'Filter INVALID_FILTER not found', metadata['metadata']
)
@patch('laborious.activities.gates.input_filter_functions')
def test_input_gate_filter_exception(mock_input_filter_functions, gates_activity):
# Arrange
mock_input_filter_functions.__contains__.return_value = True
mock_input_filter_functions.__getitem__.return_value = MagicMock(
side_effect=Exception('Test error')
)
input_data = {
**metadata,
'filters': {'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}}},
'data': _minio_payload(DataFrame({'value': []})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (None, 0, '', None)
gates_activity.send_notification.assert_called_once_with(
metadata=metadata['metadata'],
notification_id='INTPUT_GATE_ERROR__EMPTY_DATA',
message="Error in filter EMPTY_DATA:{'POLICY': 'STOP', 'CONFIG': {}}: \n Test error",
block='input_gate',
level=NotificationLevel.ERROR,
attachment_content=ANY,
)
def test_input_gate_no_filters(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (None, 0, '', None)
gates_activity.debug.assert_called()
def test_input_gate_with_filter(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}}},
'data': _minio_payload(DataFrame({'value': []})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
path_flag, confidence, comment, document = gates_activity.input_gate(input_data)
# Assert: the gate only computes the document now — it never writes anywhere, and never
# checks path_flag to decide whether to build one (that decision moved to the caller, see
# `PredictionMetadata.write_prediction_metadata` and `prediction_process.py`), so even a STOP
# resolution still gets its document built.
assert (path_flag, confidence, comment) == ('STOP', -1, 'Input data with bad quality')
assert document == {'filters': [{'name': 'EMPTY_DATA', 'policy': 'STOP', 'counter': 0}]}
gates_activity.debug.assert_called()
def test_input_gate_with_filter_lowercase_keys(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'EMPTY_DATA': {'policy': 'STOP', 'config': {}}},
'data': _minio_payload(DataFrame({'value': []})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (
'STOP',
-1,
'Input data with bad quality',
{'filters': [{'name': 'EMPTY_DATA', 'policy': 'STOP', 'counter': 0}]},
)
def test_input_gate_with_filter_capitalized_keys(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'EMPTY_DATA': {'Policy': 'STOP', 'Config': {}}},
'data': _minio_payload(DataFrame({'value': []})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (
'STOP',
-1,
'Input data with bad quality',
{'filters': [{'name': 'EMPTY_DATA', 'policy': 'STOP', 'counter': 0}]},
)
def test_input_gate_with_filter_not_caught(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}}},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (None, 0, '', None)
gates_activity.debug.assert_called()
def test_input_gate_repeat_also_builds_document(gates_activity):
# Arrange: REPEAT used to be treated like STOP (no document at all) — it isn't anymore, since
# REPEAT does insert a new `predictions` row (reusing the previous row's values), so it needs
# a document/metadata_id just like CONTINUE (2026-10-01 redesign).
input_data = {
**metadata,
'filters': {'EMPTY_DATA': {'POLICY': 'REPEAT', 'CONFIG': {}}},
'data': _minio_payload(DataFrame({'value': []})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.input_gate(input_data)
# Assert
assert result == (
'REPEAT',
-1,
'Input data with bad quality',
{'filters': [{'name': 'EMPTY_DATA', 'policy': 'REPEAT', 'counter': 0}]},
)
def test_input_gate_continue_builds_document_with_counter(gates_activity):
# Arrange: two rows with a null in one of the configured variables ('x'); a third row is
# clean, to confirm the counter reflects only the rows that actually matched the condition.
input_data = {
**metadata,
'filters': {
'SPECIFIC_VARIABLES_NULL_VALUES': {
'POLICY': 'CONTINUE',
'CONFIG': {'variables': ['x']},
}
},
'data': _minio_payload(
DataFrame({'variable': ['x', 'x', 'y'], 'value': [None, None, None]})
),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
path_flag, confidence, comment, document = gates_activity.input_gate(input_data)
# Assert
assert (path_flag, confidence, comment) == ('CONTINUE', 2, 'Input data with bad quality')
assert document == {
'filters': [{'name': 'SPECIFIC_VARIABLES_NULL_VALUES', 'policy': 'CONTINUE', 'counter': 2}]
}
def test_input_gate_multiple_filters_same_policy_lists_each(gates_activity):
# Arrange: force both input filters to trigger (not reachable with today's real filter
# conditions — EMPTY_DATA and SPECIFIC_VARIABLES_NULL_VALUES are mutually exclusive) to lock
# in the list-building behavior approved by Vitor (2026-09-30): one entry per filter sharing
# the winning policy, instead of a single joined/aggregated value.
with patch('laborious.activities.gates.input_filter_functions') as mock_filters:
mock_filters.__contains__.return_value = True
mock_filters.__getitem__.return_value = MagicMock(return_value=True)
input_data = {
**metadata,
'filters': {
'SPECIFIC_VARIABLES_NULL_VALUES': {
'POLICY': 'CONTINUE',
'CONFIG': {'variables': ['x']},
},
'EMPTY_DATA': {'POLICY': 'CONTINUE', 'CONFIG': {}},
},
'data': _minio_payload(DataFrame({'variable': ['x'], 'value': [None]})),
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
path_flag, _, _, document = gates_activity.input_gate(input_data)
# Assert
assert path_flag == 'CONTINUE'
assert {f['name'] for f in document['filters']} == {
'SPECIFIC_VARIABLES_NULL_VALUES',
'EMPTY_DATA',
}
assert all(f['policy'] == 'CONTINUE' for f in document['filters'])
def test_mlflow_response_gate_invalid_filter(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
'data': _minio_payload(
{'content': {'message': 'success'}},
status={'success': True},
),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_response_gate(input_data)
# Assert
assert result == (None, 0, '')
@patch('laborious.activities.gates.mlflow_response_filter_functions')
def test_mlflow_response_gate_filter_exception(
mock_mlflow_response_filter_functions, gates_activity
):
# Arrange
mock_mlflow_response_filter_functions.__contains__.return_value = True
mock_mlflow_response_filter_functions.__getitem__.return_value = MagicMock(
side_effect=Exception('Test error')
)
input_data = {
**metadata,
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
'data': _minio_payload(
{'content': {'message': 'success'}},
status={'success': True},
),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_response_gate(input_data)
# Assert
assert result == (None, 0, '')
gates_activity.send_notification.assert_called_once_with(
metadata=metadata['metadata'],
notification_id='MLFLOW_GATE_RESPONSE_FILTER__INVALID_FILTER',
message="Error in filter INVALID_FILTER:{'POLICY': 'STOP'}: \n Test error",
block='mlflow_gate',
level=NotificationLevel.ERROR,
attachment_content=ANY,
)
def test_mlflow_response_gate_no_filters(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {},
'data': _minio_payload(
{'content': {'message': 'success'}},
status={'success': True},
),
'type': 'test',
'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_response_gate(input_data)
# Assert
assert result == (None, 0, '')
gates_activity.debug.assert_called()
def test_mlflow_response_gate_with_filter(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'API_ERROR': {'POLICY': 'STOP'}},
'data': _minio_payload(
{'content': {'message': 'API error occurred', 'traceback': 'error trace'}},
status={'success': False, 'message': 'API error occurred'},
),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_response_gate(input_data)
# Assert
assert result == ('STOP', -1, 'API error occurred')
gates_activity.debug.assert_called()
gates_activity.send_notification.assert_called()
def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'API_ERROR': {'Policy': 'STOP'}},
'data': _minio_payload(
{'content': {'message': 'API error occurred', 'traceback': 'error trace'}},
status={'success': False, 'message': 'API error occurred'},
),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_response_gate(input_data)
# Assert
assert result == ('STOP', -1, 'API error occurred')
def test_mlflow_response_gate_with_filter_not_caught(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'API_ERROR': {'POLICY': 'STOP'}},
'data': _minio_payload(
{'content': {'message': 'success'}},
status={'success': True},
),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_response_gate(input_data)
# Assert
assert result == (None, 0, '')
gates_activity.debug.assert_called()
def test_mlflow_content_gate_invalid_filter(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_content_gate(input_data)
# Assert
assert result == (None, 0, '')
@patch('laborious.activities.gates.mlflow_content_filter_functions')
def test_mlflow_content_gate_filter_exception(mock_mlflow_content_filter_functions, gates_activity):
# Arrange
mock_mlflow_content_filter_functions.__contains__.return_value = True
mock_mlflow_content_filter_functions.__getitem__.return_value = MagicMock(
side_effect=Exception('Test error')
)
input_data = {
**metadata,
'filters': {'API_ERROR': {'POLICY': 'STOP'}},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_content_gate(input_data)
# Assert
assert result == (None, 0, '')
gates_activity.debug.assert_called()
gates_activity.send_notification.assert_called_once_with(
metadata=metadata['metadata'],
notification_id='MLFLOW_GATE_CONTENT_FILTER__API_ERROR',
message="Error in filter API_ERROR:{'POLICY': 'STOP'}: \n Test error",
block='mlflow_gate',
level=NotificationLevel.ERROR,
attachment_content=ANY,
)
def test_mlflow_content_gate_no_filters(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'type': 'test',
'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_content_gate(input_data)
# Assert
assert result == (None, 0, '')
gates_activity.debug.assert_called()
def test_mlflow_content_gate_with_filter(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'NAN_VALUES': {'POLICY': 'STOP', 'CONFIG': {}}},
'data': _minio_payload(DataFrame({'value': [None, None, None]})),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_content_gate(input_data)
# Assert
assert result == ('STOP', -1, 'Transformed data not passed the content filter')
gates_activity.debug.assert_called()
gates_activity.send_notification.assert_called()
def test_mlflow_content_gate_with_filter_not_caught(gates_activity):
# Arrange
input_data = {
**metadata,
'filters': {'API_ERROR': {'POLICY': 'STOP'}},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
# Act
result = gates_activity.mlflow_content_gate(input_data)
# Assert
assert result == (None, 0, '')
gates_activity.debug.assert_called()
def test_mlflow_content_gate_filter_returns_false(gates_activity):
input_data = {
**metadata,
'filters': {'NAN_VALUES': {'POLICY': 'STOP', 'CONFIG': {}}},
'data': _minio_payload(DataFrame({'value': [1, 2, 3]})),
'type': 'test',
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
}
result = gates_activity.mlflow_content_gate(input_data)
assert result == (None, 0, '')
gates_activity.debug.assert_called()
def test_get_prediction_store_policy_invalid_policy(gates_activity):
# Arrange
prediction_store_policy = 'INVALID_POLICY'
# Act
policy_type, policy_value = gates_activity.get_prediction_store_policy(
prediction_store_policy, metadata
)
# Assert
assert policy_type == 'lts'
assert policy_value == 1
def test_get_prediction_store_policy_invalid_policy_value(gates_activity):
# Arrange
prediction_store_policy = 'abc:INVALID_VALUE'
# Act
policy_type, policy_value = gates_activity.get_prediction_store_policy(
prediction_store_policy, metadata
)
# Assert
assert policy_type == 'lts'
assert policy_value == 1
def test_get_prediction_store_policy_valid_policy_type(gates_activity):
# Arrange
prediction_store_policy = 'abc:1'
# Act
policy_type, policy_value = gates_activity.get_prediction_store_policy(
prediction_store_policy, metadata
)
# Assert
assert policy_type == 'lts'
assert policy_value == 1
def test_get_prediction_store_policy_valid_policy(gates_activity):
# Arrange
prediction_store_policy = 'erl:1'
# Act
policy_type, policy_value = gates_activity.get_prediction_store_policy(
prediction_store_policy, metadata
)
# Assert
assert policy_type == 'erl'
assert policy_value == 1
def test_format_prediction_no_timestamp(gates_activity):
# Arrange
input_data = {
**metadata,
'data': _minio_payload(
DataFrame(
{
'prediction': {'2023-05-26 11:12:27': 1},
'response_time': {'2023-05-26 11:12:27': 0.1},
}
)
),
'model_id': 'test_model',
'prediction_confidence': 0.9,
'prediction_store_policy': 'lts:1',
'timestamp': '2023-05-26 11:12:27',
}
# Act
result = gates_activity.format_prediction(input_data)
# Assert
assert result['prediction'] == {0: 1}
assert result['response_time'] == {0: ANY}
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
assert result['model_id'] == {0: 'test_model'}
assert result['prediction_confidence'] == {0: 0.9}
assert result['prediction_status'] == {0: 'Good'}
assert result['comments'] == {0: ''}
def test_format_prediction_with_timestamp_erl(gates_activity):
# Arrange
input_data = {
**metadata,
'data': _minio_payload(
DataFrame(
{
'prediction': {
'2023-05-26 11:12:27': 1,
'2023-05-26 11:12:28': 2,
'2023-05-26 11:12:29': 3,
},
'response_time': {
'2023-05-26 11:12:27': 0.1,
'2023-05-26 11:12:28': 0.2,
'2023-05-26 11:12:29': 0.3,
},
}
)
),
'model_id': 'test_model',
'prediction_confidence': 0.9,
'prediction_store_policy': 'erl:2',
'timestamp': '2023-05-26 11:12:27',
}
# Act
result = gates_activity.format_prediction(input_data)
# Assert
assert result['prediction'] == {0: 2, 1: 1}
assert result['response_time'] == {0: 0.2, 1: 0.1}
assert result['timestamp'] == {0: '2023-05-26 11:12:28', 1: '2023-05-26 11:12:27'}
assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
assert result['prediction_confidence'] == {0: 0.9, 1: 0.9}
assert result['prediction_status'] == {0: 'Good', 1: 'Good'}
assert result['comments'] == {0: '', 1: ''}
def test_format_prediction_with_timestamp_lts(gates_activity):
# Arrange
input_data = {
**metadata,
'data': _minio_payload(
DataFrame(
{
'prediction': {
'2023-05-26 11:12:27': 1,
'2023-05-26 11:12:28': 2,
'2023-05-26 11:12:29': 3,
},
'response_time': {
'2023-05-26 11:12:27': 0.1,
'2023-05-26 11:12:28': 0.2,
'2023-05-26 11:12:29': 0.3,
},
}
)
),
'model_id': 'test_model',
'prediction_confidence': 0.9,
'prediction_store_policy': 'lts:2',
'timestamp': '2023-05-26 11:12:27',
}
# Act
result = gates_activity.format_prediction(input_data)
# Assert
assert result['prediction'] == {0: 3, 1: 2}
assert result['response_time'] == {0: 0.3, 1: 0.2}
assert result['timestamp'] == {0: '2023-05-26 11:12:29', 1: '2023-05-26 11:12:28'}
assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
assert result['prediction_confidence'] == {0: 0.9, 1: 0.9}
assert result['prediction_status'] == {0: 'Good', 1: 'Good'}
assert result['comments'] == {0: '', 1: ''}
def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
# Arrange
input_data = {
**metadata,
'data': _minio_payload(
DataFrame(
{
'prediction': [1, 2, 3],
'response_time': [0.1, 0.2, 0.3],
'timestamp': [
'2023-05-26 11:12:27',
'2023-05-26 11:12:28',
'2023-05-26 11:12:29',
],
}
)
),
'model_id': 'test_model',
'prediction_confidence': 0.9,
'prediction_store_policy': 'lts:2',
'timestamp': '2023-05-26 11:12:27',
}
gates_activity.get_prediction_store_policy = MagicMock(return_value=('invalid', 1))
try:
gates_activity.format_prediction(input_data)
except ValueError as e:
assert str(e) == 'Invalid policy type: invalid'
else:
raise AssertionError('Expected ValueError')
@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=MagicMock)
def test_format_transformed_data_single_row(mock_from_dataframe, gates_activity):
# Arrange
payload_result = MagicMock()
mock_from_dataframe.return_value = payload_result
input_data = {
**metadata,
'data': _minio_payload(
DataFrame(
{
'var1': {'2023-05-26 11:12:27': 1.0},
'var2': {'2023-05-26 11:12:27': 2.0},
}
)
),
'model_id': 'test_model',
'model_name': 'test_model',
}
# Act
result = gates_activity.format_transformed_data(input_data)
# Assert
assert result is payload_result
mock_from_dataframe.assert_called_once()
kwargs = mock_from_dataframe.call_args.kwargs
assert kwargs['model_name'] == 'test_model'
assert kwargs['operation'] == 'transform'
assert kwargs['workflow_metadata'] == metadata['metadata']
assert kwargs['minio_repo'] is gates_activity.minio_repository
assert 'dataframe' in kwargs
gates_activity.info.assert_called()
@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=MagicMock)
def test_format_transformed_data_multiple_rows(mock_from_dataframe, gates_activity):
# Arrange
payload_result = MagicMock()
mock_from_dataframe.return_value = payload_result
input_data = {
**metadata,
'data': _minio_payload(
DataFrame(
{
'var1': {
'2023-05-26 11:12:27': 1.0,
'2023-05-26 11:12:28': 2.0,
},
'var2': {
'2023-05-26 11:12:27': 3.0,
'2023-05-26 11:12:28': 4.0,
},
}
)
),
'model_id': 'test_model',
'model_name': 'test_model',
}
# Act
result = gates_activity.format_transformed_data(input_data)
# Assert
assert result is payload_result
mock_from_dataframe.assert_called_once()
kwargs = mock_from_dataframe.call_args.kwargs
assert kwargs['model_name'] == 'test_model'
assert kwargs['operation'] == 'transform'
assert kwargs['workflow_metadata'] == metadata['metadata']
assert kwargs['minio_repo'] is gates_activity.minio_repository
assert 'dataframe' in kwargs
gates_activity.info.assert_called()
@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=MagicMock)
def test_format_transformed_data_empty_data(mock_from_dataframe, gates_activity):
# Arrange
payload_result = MagicMock()
mock_from_dataframe.return_value = payload_result
input_data = {
**metadata,
'data': _minio_payload(DataFrame()),
'model_id': 'test_model',
'model_name': 'test_model',
}
# Act
result = gates_activity.format_transformed_data(input_data)
# Assert
assert result is payload_result
mock_from_dataframe.assert_called_once()
kwargs = mock_from_dataframe.call_args.kwargs
assert kwargs['model_name'] == 'test_model'
assert kwargs['operation'] == 'transform'
assert kwargs['workflow_metadata'] == metadata['metadata']
assert kwargs['minio_repo'] is gates_activity.minio_repository
assert 'dataframe' in kwargs
gates_activity.info.assert_called()
def test_format_default_prediction(gates_activity):
# Arrange
input_data = {
**metadata,
'timestamp': '2023-05-26 11:12:27',
'model_id': 'test_model',
'prediction_confidence': 0.1,
'comment': 'Test comment',
}
# Act
result = gates_activity.format_default_prediction(input_data)
# Assert
assert result['prediction'] == {0: 0}
assert result['response_time'] == {0: 0}
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
assert result['model_id'] == {0: 'test_model'}
assert result['prediction_confidence'] == {0: 0.1}
assert result['prediction_status'] == {0: 'Bad'}
assert result['comments'] == {0: 'Test comment'}
gates_activity.debug.assert_called()
def test_format_retrain_report(gates_activity):
# Arrange
input_data = {
**metadata,
'experiment_response': {
'success': True,
'timestamp': '2023-05-26 11:12:27',
'message': 'success',
},
'update_report': {
'version': '1.0.0',
'mlflow_run_id': 'test_mlflow_run_id',
'mlflow_experiment_id': 'test_mlflow_experiment_id',
},
'model_id': 'test_model',
'model_name': 'test_model',
}
# Act
result = gates_activity.format_retrain_report(input_data)
# Assert
assert result['model_id'] == {0: 'test_model'}
assert result['model_name'] == {0: 'test_model'}
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
assert result['status'] == {0: 'success'}
assert result['version'] == {0: '1.0.0'}
assert result['mlflow_run_id'] == {0: 'test_mlflow_run_id'}
assert result['mlflow_experiment_id'] == {0: 'test_mlflow_experiment_id'}
def test_format_retrain_report_failure(gates_activity):
# Arrange
input_data = {
**metadata,
'experiment_response': {
'success': False,
'timestamp': '2023-05-26 11:12:27',
'message': 'failure',
},
'update_report': {
'version': '1.0.0',
'mlflow_run_id': 'test_mlflow_run_id',
'mlflow_experiment_id': 'test_mlflow_experiment_id',
},
'model_id': 'test_model',
'model_name': 'test_model',
}
# Act
result = gates_activity.format_retrain_report(input_data)
# Assert
assert result['model_id'] == {0: 'test_model'}
assert result['model_name'] == {0: 'test_model'}
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
assert result['status'] == {0: 'failure'}
assert 'version' not in result
assert 'mlflow_run_id' not in result
assert 'mlflow_experiment_id' not in result
gates_activity.info.assert_called()
gates_activity.debug.assert_called()
@patch('laborious.activities.gates.metrics')
def test_write_metrics(mock_metrics, gates_activity):
"""Test write_metrics method."""
input_data = {
**metadata,
'prediction': {
'prediction': [1, 2, 3],
'prediction_confidence': [0.9, 0.8, 0.7],
'response_time': [0.1, 0.2, 0.3],
},
'opc_metrics': {'server1': {'tag1': 0.1, 'tag2': 0.2}},
}
gates_activity.write_metrics(input_data)
core_tags = {
'pod_id': gates_activity.pod_id,
'runtime': gates_activity.runtime,
'operation_type': 'predict',
'model_name': metadata['metadata']['model_name'],
'workflow_name': metadata['metadata']['workflow_name'],
}
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTIONS_WRITTEN_COUNT,
tags=core_tags,
),
]
)
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTION_CONFIDENCE_MONITOR,
method='set',
tags=core_tags,
value=0.9,
),
]
)
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTION_RESPONSE_TIME_MONITOR,
method='observe',
tags=core_tags,
value=0.1,
),
]
)
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTION_OPC_WRITING_COUNT,
tags={
**core_tags,
'opc_server_id': 'server1',
'tag': 'tag1',
},
),
]
)
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
method='observe',
tags={
**core_tags,
'opc_server_id': 'server1',
'tag': 'tag1',
},
value=0.1,
),
]
)
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTION_OPC_WRITING_COUNT,
tags={
**core_tags,
'opc_server_id': 'server1',
'tag': 'tag2',
},
),
]
)
gates_activity.emit_metric_sync.assert_has_calls(
[
call(
metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
method='observe',
tags={
**core_tags,
'opc_server_id': 'server1',
'tag': 'tag2',
},
value=0.2,
),
]
)
@patch('laborious.activities.gates.metrics')
def test_write_metrics_with_none_opc_response_time(mock_metrics, gates_activity):
"""Test write_metrics method with None response_time in opc_metrics."""
input_data = {
**metadata,
'prediction': {
'prediction': [1],
'prediction_confidence': [0.9],
'response_time': [0.1],
},
'opc_metrics': {'server1': {'tag1': 0.1, 'tag2': None}},
}
gates_activity.write_metrics(input_data)
# Verify that metrics for tag1 are emitted
gates_activity.emit_metric_sync.assert_any_call(
metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
method='observe',
tags={
'pod_id': gates_activity.pod_id,
'runtime': gates_activity.runtime,
'operation_type': 'predict',
'model_name': metadata['metadata']['model_name'],
'workflow_name': metadata['metadata']['workflow_name'],
'opc_server_id': 'server1',
'tag': 'tag1',
},
value=0.1,
)
# Verify that metrics for tag2 (with None response_time) are NOT emitted
calls = [
c
for c in gates_activity.emit_metric_sync.call_args_list
if len(c[1].get('tags', {})) > 0 and c[1]['tags'].get('tag') == 'tag2'
]
assert len(calls) == 0, 'Metrics should not be emitted for None response_time'
@patch.object(SientiaMonitoring, 'shutdown')
def test_close_disposes_minio_repository(mock_shutdown):
"""
``Gates.close`` should close the optional MinIO client and clear the repository reference.
"""
minio = MagicMock()
gates = Gates(
minio_repository=minio,
logger=MagicMock(),
notification_handler=MagicMock(),
metrics_controller=MagicMock(spec=MetricsController),
)
gates.close()
minio.close.assert_called_once()
assert gates.minio_repository is None
# Other instances collected by GC during the test also hit the patched shutdown.
assert mock_shutdown.call_args_list.count(call(gates)) == 1
@patch.object(SientiaMonitoring, 'shutdown')
def test_close_without_minio_repository(mock_shutdown):
"""When no MinIO repository is configured, ``close`` only shuts down monitoring."""
gates = Gates(
minio_repository=None,
logger=MagicMock(),
notification_handler=MagicMock(),
metrics_controller=MagicMock(spec=MetricsController),
)
gates.close()
# Other instances collected by GC during the test also hit the patched shutdown.
assert mock_shutdown.call_args_list.count(call(gates)) == 1