vitor-aignosi
67fe4afaa6
Refactor and enhance the laborious workflow and utilities - Removed outdated test file `test_predictions_batch.py` from workflows. - Added `input_sample.json` for standardized input configuration. - Introduced `connectors_config.py` to manage database and service configurations. - Implemented a logging utility in `logger.py` for consistent logging across the application. - Created `policies.py` to define retry policies for workflows. - Developed comprehensive tests for `MLFlowRepository` in `test_model_repository.py`. - Added extensive tests for `OpcRepository` in `test_opc_repository.py`. - Updated `test_predictions_batch.py` to reflect new workflow structure and testing methodology.
606 lines
19 KiB
Python
606 lines
19 KiB
Python
from unittest.mock import ANY, MagicMock, patch
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from pandas import DataFrame
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from pytest import fixture, mark
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from laborious.activities.gates import Gates
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from sientia_do.notifications.models import NotificationLevel
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@fixture
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def gates():
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return Gates(
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logger=MagicMock(),
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notification_handler=MagicMock()
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)
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_specific_variables_null_values_with_stop_policy_only(
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input_filter_functions_mock,
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gates
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):
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specific_variables_null_values_mock = MagicMock(return_value=True)
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empty_data_mock = MagicMock(return_value=False)
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def functions_side_effect(x):
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if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
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return specific_variables_null_values_mock
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if x == 'path_confidence':
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return {
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'stop': -1,
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'continue': 2,
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'repeat': -1
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}
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return empty_data_mock
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input_filter_functions_mock.__getitem__.side_effect = functions_side_effect
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'stop',
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'VARIABLES': ['variable2']
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat']
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}
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result = await gates.input_gate(input_data)
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assert result == ('stop', -1, 'Input data with bad quality')
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input_args = specific_variables_null_values_mock.call_args
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assert input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert input_args[0][1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
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empty_data_mock.assert_not_called()
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_specific_variables_null_values_with_continue_policy_only(
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input_filter_functions_mock,
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gates
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):
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specific_variables_null_values_mock = MagicMock(return_value=True)
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empty_data_mock = MagicMock(return_value=False)
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def functions_side_effect(x):
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if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
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return specific_variables_null_values_mock
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if x == 'path_confidence':
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return {
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'stop': -1,
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'continue': 2,
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'repeat': -1
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}
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return empty_data_mock
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input_filter_functions_mock.__getitem__.side_effect = functions_side_effect
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'continue',
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'VARIABLES': ['variable2']
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat']
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}
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result = await gates.input_gate(input_data)
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assert result == ('continue', 2, 'Input data with bad quality')
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input_args = specific_variables_null_values_mock.call_args
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assert input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert input_args[0][1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
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empty_data_mock.assert_not_called()
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_specific_variables_null_values_no_filtered(
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input_filter_functions_mock,
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gates
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):
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specific_variables_null_values_mock = MagicMock(return_value=False)
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empty_data_mock = MagicMock(return_value=False)
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def functions_side_effect(x):
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if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
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return specific_variables_null_values_mock
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if x == 'path_confidence':
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return {
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'stop': -1,
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'continue': 2,
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'repeat': -1
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}
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return empty_data_mock
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input_filter_functions_mock.__getitem__.side_effect = functions_side_effect
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'stop',
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'VARIABLES': ['variable2']
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat']
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}
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result = await gates.input_gate(input_data)
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assert result == (None, 0, '')
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specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
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assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert specific_variables_null_values_input_args[0][
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1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
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empty_data_mock.assert_not_called()
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_one_stop_policy(
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input_filter_functions_mock,
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gates
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):
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specific_variables_null_values_mock = MagicMock(return_value=True)
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empty_data_mock = MagicMock(return_value=True)
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def functions_side_effect(x):
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if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
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return specific_variables_null_values_mock
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if x == 'path_confidence':
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return {
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'stop': -1,
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'continue': 2,
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'repeat': -1
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}
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return empty_data_mock
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input_filter_functions_mock.__getitem__.side_effect = functions_side_effect
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'stop',
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'VARIABLES': ['variable2']
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},
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'EMPTY_DATA': {
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'POLICY': 'continue',
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat']
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}
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result = await gates.input_gate(input_data)
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assert result == ('stop', -1, 'Input data with bad quality')
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specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
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assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert specific_variables_null_values_input_args[0][
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1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
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empty_data_input_args = empty_data_mock.call_args
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assert empty_data_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert empty_data_input_args[0][1] == input_data['filters']['EMPTY_DATA']
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_one_continue_policy(
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input_filter_functions_mock,
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gates
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):
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specific_variables_null_values_mock = MagicMock(return_value=False)
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empty_data_mock = MagicMock(return_value=True)
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def functions_side_effect(x):
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if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
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return specific_variables_null_values_mock
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if x == 'path_confidence':
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return {
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'stop': -1,
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'continue': 2,
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'repeat': -1
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}
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return empty_data_mock
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input_filter_functions_mock.__getitem__.side_effect = functions_side_effect
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'stop',
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'VARIABLES': ['variable2']
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},
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'EMPTY_DATA': {
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'POLICY': 'continue',
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat']
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}
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result = await gates.input_gate(input_data)
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assert result == ('continue', 2, 'Input data with bad quality')
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specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
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assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert specific_variables_null_values_input_args[0][
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1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
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empty_data_input_args = empty_data_mock.call_args
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assert empty_data_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert empty_data_input_args[0][1] == input_data['filters']['EMPTY_DATA']
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_no_filtered(
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input_filter_functions_mock,
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gates
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):
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specific_variables_null_values_mock = MagicMock(return_value=False)
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empty_data_mock = MagicMock(return_value=False)
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def functions_side_effect(x):
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if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
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return specific_variables_null_values_mock
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if x == 'path_confidence':
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return {
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'stop': -1,
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'continue': 2,
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'repeat': -1
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}
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return empty_data_mock
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input_filter_functions_mock.__getitem__.side_effect = functions_side_effect
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'stop',
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'VARIABLES': ['variable2']
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},
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'EMPTY_DATA': {
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'POLICY': 'continue',
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat']
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}
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result = await gates.input_gate(input_data)
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assert result == (None, 0, '')
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specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
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assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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empty_data_input_args = empty_data_mock.call_args
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assert empty_data_input_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert empty_data_input_args[0][1] == input_data['filters']['EMPTY_DATA']
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@mark.asyncio
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@patch('laborious.activities.gates.input_filter_functions')
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async def test_input_gate_error(
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input_filter_functions_mock,
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gates
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):
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input_filter_functions_mock.__getitem__.side_effect = KeyError('test')
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input_data = {
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'filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'stop',
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'VARIABLES': ['variable2']
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat'],
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}
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result = await gates.input_gate(input_data)
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assert result == (None, 0, '')
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gates.notification_handler.build_and_send_notification.assert_called_once_with(
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notification_id='INTPUT_GATE_ERROR__SPECIFIC_VARIABLES_NULL_VALUES',
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message="Error in filter SPECIFIC_VARIABLES_NULL_VALUES:{'POLICY': 'stop', 'VARIABLES': ['variable2']}: \n 'test'",
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block='input_gate',
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level=NotificationLevel.ERROR,
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attachment_content=ANY
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)
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transform_filter_path_confidence = {
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'stop': -1,
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'continue': 255,
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'repeat': -1
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}
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@mark.asyncio
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@patch('laborious.activities.gates.mlflow_response_filter_functions')
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async def test_mlflow_response_gate_no_filtered(
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mlflow_response_filter_functions_mock,
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gates
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):
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api_error_filter_mock = MagicMock(return_value=False)
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def transform_filter_functions_side_effect(x: str):
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if x == 'API_ERROR':
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return api_error_filter_mock
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if x == 'path_confidence':
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return transform_filter_path_confidence
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mlflow_response_filter_functions_mock.__getitem__.side_effect = transform_filter_functions_side_effect
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input_data = {
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'filters': {
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'API_ERROR': {
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'POLICY': 'stop',
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat'],
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'type': 'predict'
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}
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result = await gates.mlflow_response_gate(input_data)
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assert result == (None, 0, '')
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api_error_filter_mock.assert_called_once_with(
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input_data['data'],
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input_data['filters']['API_ERROR']
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)
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gates.notification_handler.build_and_send_notification.assert_not_called()
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@mark.asyncio
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@patch('laborious.activities.gates.mlflow_response_filter_functions')
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async def test_mlflow_response_gate_filtered(
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mlflow_response_filter_functions_mock,
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gates
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):
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api_error_filter_mock = MagicMock(return_value=True)
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def transform_filter_functions_side_effect(x: str):
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if x == 'API_ERROR':
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return api_error_filter_mock
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if x == 'path_confidence':
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return transform_filter_path_confidence
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mlflow_response_filter_functions_mock.__getitem__.side_effect = transform_filter_functions_side_effect
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input_data = {
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'filters': {
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'API_ERROR': {
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'POLICY': 'continue',
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}
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},
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'data': {
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'success': False,
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'content': {
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'message': 'Error',
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'traceback': 'Error'
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}
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},
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'path_priority': ['stop', 'continue', 'repeat'],
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'type': 'predict'
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}
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result = await gates.mlflow_response_gate(input_data)
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assert result == ('continue', 255, "Error")
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api_error_filter_mock.assert_called_once_with(
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input_data['data'],
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input_data['filters']['API_ERROR']
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)
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gates.notification_handler.build_and_send_notification.assert_called_once_with(
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notification_id='PREDICT_GATE_RESPONSE_FILTER__API_ERROR',
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message=input_data['data']['content']['message'],
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block='mlflow_gate',
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level=NotificationLevel.WARNING,
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attachment_content=input_data['data']['content']['traceback']
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)
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@mark.asyncio
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@patch('laborious.activities.gates.mlflow_content_filter_functions')
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async def test_mlflow_content_gate_no_filtered(
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mlflow_content_filter_functions_mock,
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gates
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):
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nan_values_filter_mock = MagicMock(return_value=False)
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def transform_filter_functions_side_effect(x: str):
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if x == 'NAN_VALUES':
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return nan_values_filter_mock
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if x == 'path_confidence':
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return transform_filter_path_confidence
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mlflow_content_filter_functions_mock.__getitem__.side_effect = transform_filter_functions_side_effect
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input_data = {
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'filters': {
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'NAN_VALUES': {
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'POLICY': 'repeat',
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}
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},
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'data': {
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'variable': ['variable1', 'variable2'],
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'value': [1, 2]
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},
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'path_priority': ['stop', 'continue', 'repeat'],
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'type': 'predict'
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}
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result = await gates.mlflow_content_gate(input_data)
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assert result == (None, 0, '')
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nan_values_filter_mock_args = nan_values_filter_mock.call_args
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assert nan_values_filter_mock_args[0][0].equals(DataFrame(
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{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
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assert nan_values_filter_mock_args[0][1] == input_data['filters']['NAN_VALUES']
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gates.notification_handler.build_and_send_notification.assert_not_called()
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@mark.asyncio
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@patch('laborious.activities.gates.mlflow_content_filter_functions')
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|
async def test_mlflow_content_gate_filtered(
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mlflow_content_filter_functions_mock,
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gates
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):
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nan_values_filter_mock = MagicMock(return_value=True)
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def transform_filter_functions_side_effect(x: str):
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if x == 'NAN_VALUES':
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return nan_values_filter_mock
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if x == 'path_confidence':
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return transform_filter_path_confidence
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|
mlflow_content_filter_functions_mock.__getitem__.side_effect = \
|
|
transform_filter_functions_side_effect
|
|
|
|
input_data = {
|
|
'filters': {
|
|
'NAN_VALUES': {
|
|
'POLICY': 'repeat',
|
|
}
|
|
},
|
|
'data': {
|
|
'variable': ['variable1', 'variable2'],
|
|
'value': [1, 2]
|
|
},
|
|
'path_priority': ['stop', 'continue', 'repeat'],
|
|
'type': 'predict'
|
|
}
|
|
|
|
result = await gates.mlflow_content_gate(input_data)
|
|
assert result == (
|
|
'repeat', -1, "Transformed data not passed the content filter")
|
|
|
|
nan_values_filter_mock_args = nan_values_filter_mock.call_args
|
|
assert nan_values_filter_mock_args[0][0].equals(DataFrame(
|
|
{'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
|
|
assert nan_values_filter_mock_args[0][1] == input_data['filters']['NAN_VALUES']
|
|
|
|
gates.notification_handler.build_and_send_notification.assert_called_once_with(
|
|
notification_id='PREDICT_GATE_CONTENT_FILTER__NAN_VALUES',
|
|
message="Data not passed the content filter NAN_VALUES:{'POLICY': 'repeat'}",
|
|
block='mlflow_gate',
|
|
level=NotificationLevel.WARNING,
|
|
attachment_content=DataFrame(input_data['data']).to_string()
|
|
)
|
|
|
|
|
|
@mark.asyncio
|
|
async def test_format_prediction(
|
|
gates
|
|
):
|
|
input_data = {
|
|
'data': {
|
|
'variable': ['variable1', 'variable2'],
|
|
'value': [1, 2]
|
|
},
|
|
'timestamp': '2021-01-01',
|
|
'model_id': 'model_id',
|
|
'prediction_confidence': 0.95
|
|
}
|
|
|
|
expected_output = DataFrame(input_data['data'])
|
|
expected_output['timestamp'] = input_data['timestamp']
|
|
expected_output['model_id'] = input_data['model_id']
|
|
expected_output['prediction_confidence'] = input_data['prediction_confidence']
|
|
expected_output['prediction_status'] = 'Good'
|
|
expected_output['comment'] = ''
|
|
|
|
result = await gates.format_prediction(input_data)
|
|
assert result == expected_output.to_dict()
|
|
|
|
|
|
@mark.asyncio
|
|
async def test_format_default_prediction(
|
|
gates
|
|
):
|
|
input_data = {
|
|
'timestamp': '2021-01-01',
|
|
'model_id': 'model_id',
|
|
'prediction_confidence': 0.95,
|
|
'comment': 'Comment'
|
|
}
|
|
|
|
expected_output = DataFrame({
|
|
'prediction': [0],
|
|
'response_time': [0],
|
|
'timestamp': [input_data['timestamp']],
|
|
'model_id': [input_data['model_id']],
|
|
'prediction_confidence': [input_data['prediction_confidence']],
|
|
'prediction_status': ['Bad'],
|
|
'comment': [input_data['comment']]
|
|
})
|
|
|
|
result = await gates.format_default_prediction(input_data)
|
|
assert result == expected_output.to_dict()
|
|
|
|
|
|
@mark.asyncio
|
|
async def test_get_last_timestamp(
|
|
gates
|
|
):
|
|
input_data = {
|
|
'data': {
|
|
'variable': ['variable1', 'variable2'],
|
|
'value': [1, 2],
|
|
'timestamp': ['2021-01-01', '2021-01-02']
|
|
}
|
|
}
|
|
|
|
result = await gates.get_last_timestamp(input_data)
|
|
assert result == '2021-01-02'
|