325 lines
11 KiB
Python
325 lines
11 KiB
Python
"""Unit tests for TrainingRepository."""
|
|
|
|
from io import BytesIO
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from pytest import fixture, raises
|
|
|
|
from model_manager.utils.models.train_model_params import TrainModelParams
|
|
from model_manager.utils.models.train_model_result import TrainModelResult
|
|
from model_manager.utils.repository.training_repository import TrainingRepository
|
|
|
|
|
|
@fixture
|
|
def logger():
|
|
"""Create a mock logger."""
|
|
return MagicMock()
|
|
|
|
|
|
@fixture
|
|
def training_repository(logger):
|
|
"""Create a TrainingRepository instance."""
|
|
return TrainingRepository(logger)
|
|
|
|
|
|
@fixture
|
|
def train_params():
|
|
"""Create sample training parameters."""
|
|
return TrainModelParams(
|
|
variable_columns=['feature1', 'feature2'],
|
|
lag_train=1,
|
|
lag_val=1,
|
|
target_variable='target',
|
|
rem_static_win=False,
|
|
low_lim={'feature1': 0.0, 'feature2': 0.0},
|
|
upp_lim={'feature1': 100.0, 'feature2': 100.0},
|
|
window=10,
|
|
use_scaler=True,
|
|
include_ar=False,
|
|
bucket_name='test-bucket',
|
|
file_name='test.csv',
|
|
line_separator='\n',
|
|
decimal_separator='.',
|
|
train_size=80,
|
|
shuffle=True,
|
|
experiment_run_id=123,
|
|
experiment_name='test_experiment',
|
|
removed_intervals=[],
|
|
)
|
|
|
|
|
|
@fixture
|
|
def sample_csv_data():
|
|
"""Create sample CSV data."""
|
|
csv_content = """feature1,feature2,target
|
|
1.0,2.0,10.0
|
|
2.0,3.0,15.0
|
|
3.0,4.0,20.0
|
|
4.0,5.0,25.0
|
|
5.0,6.0,30.0
|
|
6.0,7.0,35.0
|
|
7.0,8.0,40.0
|
|
8.0,9.0,45.0
|
|
9.0,10.0,50.0
|
|
10.0,11.0,55.0
|
|
"""
|
|
return BytesIO(csv_content.encode())
|
|
|
|
|
|
@patch('model_manager.utils.repository.training_repository.load_data')
|
|
@patch('model_manager.utils.repository.training_repository.DataPreprocessor')
|
|
@patch('model_manager.utils.repository.training_repository.split_train_test')
|
|
@patch('model_manager.utils.repository.training_repository.LinearRegressionModel')
|
|
def test_train_success(
|
|
mock_linear_model,
|
|
mock_split,
|
|
mock_preprocessor_class,
|
|
mock_load_data,
|
|
training_repository,
|
|
train_params,
|
|
sample_csv_data,
|
|
):
|
|
"""Test successful model training."""
|
|
# Setup mocks
|
|
mock_data = pd.DataFrame(
|
|
{'feature1': [1, 2, 3, 4, 5], 'feature2': [2, 3, 4, 5, 6], 'target': [10, 15, 20, 25, 30]}
|
|
)
|
|
mock_load_data.return_value = mock_data
|
|
|
|
mock_preprocessor = MagicMock()
|
|
mock_preprocessor_class.return_value = mock_preprocessor
|
|
mock_preprocessor.transform.return_value = mock_data
|
|
|
|
x_train = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [2, 3, 4]})
|
|
x_test = pd.DataFrame({'feature1': [4, 5], 'feature2': [5, 6]})
|
|
y_train = pd.Series([10, 15, 20], name='target')
|
|
y_test = pd.Series([25, 30], name='target')
|
|
mock_split.return_value = (x_train, x_test, y_train, y_test)
|
|
|
|
mock_model = MagicMock()
|
|
mock_linear_model.return_value = mock_model
|
|
|
|
mock_scaler = MagicMock()
|
|
mock_preprocessor.get_scaler.return_value = mock_scaler
|
|
|
|
# Execute
|
|
result = training_repository.train(sample_csv_data, train_params)
|
|
|
|
# Assertions
|
|
assert isinstance(result, TrainModelResult)
|
|
assert result.params == train_params
|
|
assert result.process_data == mock_preprocessor
|
|
assert result.regr == mock_model
|
|
mock_load_data.assert_called_once()
|
|
mock_preprocessor.fit.assert_called_once()
|
|
mock_model.fit.assert_called_once()
|
|
|
|
|
|
@patch('model_manager.utils.repository.training_repository.load_data')
|
|
def test_train_empty_data_after_transform(
|
|
mock_load_data, training_repository, train_params, sample_csv_data
|
|
):
|
|
"""Test training with empty data after transformation."""
|
|
mock_data = pd.DataFrame({'feature1': [], 'feature2': [], 'target': []})
|
|
mock_load_data.return_value = mock_data
|
|
|
|
with patch.object(training_repository, 'init_data_preprocessor') as mock_init:
|
|
mock_preprocessor = MagicMock()
|
|
mock_init.return_value = mock_preprocessor
|
|
mock_preprocessor.transform.return_value = pd.DataFrame()
|
|
|
|
with raises(ValueError, match='Data view is empty after transformation'):
|
|
training_repository.train(sample_csv_data, train_params)
|
|
|
|
|
|
def test_init_scaler_dict_with_minmax_scaler(training_repository, train_params):
|
|
"""Test scaler dict initialization with MinMaxScaler."""
|
|
mock_preprocessor = MagicMock()
|
|
mock_scaler = MagicMock()
|
|
mock_scaler.x_min = [0.0, 1.0]
|
|
mock_scaler.x_max = [10.0, 11.0]
|
|
mock_scaler.y_min = 5.0
|
|
mock_scaler.y_max = 50.0
|
|
mock_preprocessor.get_scaler.return_value = mock_scaler
|
|
|
|
# Patch isinstance to return True for MinMaxScaler
|
|
with patch(
|
|
'model_manager.utils.repository.training_repository.isinstance',
|
|
side_effect=lambda obj, cls: cls.__name__ == 'MinMaxScaler',
|
|
):
|
|
result = training_repository.init_scaler_dict(mock_preprocessor, train_params)
|
|
|
|
assert result is not None
|
|
assert 'feature1' in result
|
|
assert 'feature2' in result
|
|
assert 'target' in result
|
|
assert result['feature1'] == {'min': 0.0, 'max': 10.0}
|
|
assert result['feature2'] == {'min': 1.0, 'max': 11.0}
|
|
assert result['target'] == {'min': 5.0, 'max': 50.0}
|
|
|
|
|
|
def test_init_scaler_dict_with_z_scaler(training_repository, train_params):
|
|
"""Test scaler dict initialization with Z_Scaler."""
|
|
mock_preprocessor = MagicMock()
|
|
mock_scaler = MagicMock()
|
|
mock_scaler.create_dict.return_value = {'mean': 5.0, 'std': 2.0}
|
|
mock_preprocessor.get_scaler.return_value = mock_scaler
|
|
|
|
# Patch isinstance to return True for Z_Scaler
|
|
with patch(
|
|
'model_manager.utils.repository.training_repository.isinstance',
|
|
side_effect=lambda obj, cls: cls.__name__ == 'Z_Scaler',
|
|
):
|
|
result = training_repository.init_scaler_dict(mock_preprocessor, train_params)
|
|
|
|
assert result == {'mean': 5.0, 'std': 2.0}
|
|
mock_scaler.create_dict.assert_called_once()
|
|
|
|
|
|
def test_init_scaler_dict_without_scaler(training_repository):
|
|
"""Test scaler dict initialization when use_scaler is False."""
|
|
train_params_no_scaler = TrainModelParams(
|
|
variable_columns=['feature1'],
|
|
lag_train=1,
|
|
lag_val=1,
|
|
target_variable='target',
|
|
rem_static_win=False,
|
|
low_lim={'feature1': 0.0},
|
|
upp_lim={'feature1': 100.0},
|
|
window=10,
|
|
use_scaler=False,
|
|
include_ar=False,
|
|
bucket_name='test',
|
|
file_name='test.csv',
|
|
line_separator='\n',
|
|
decimal_separator='.',
|
|
train_size=80,
|
|
shuffle=True,
|
|
experiment_run_id=123,
|
|
experiment_name='test',
|
|
removed_intervals=[],
|
|
)
|
|
|
|
mock_preprocessor = MagicMock()
|
|
result = training_repository.init_scaler_dict(mock_preprocessor, train_params_no_scaler)
|
|
|
|
assert result == {}
|
|
|
|
|
|
@patch('model_manager.utils.repository.training_repository.mse')
|
|
@patch('model_manager.utils.repository.training_repository.mae')
|
|
@patch('model_manager.utils.repository.training_repository.r2')
|
|
def test_after_train_calculation_with_scaler(
|
|
mock_r2, mock_mae, mock_mse, training_repository, train_params
|
|
):
|
|
"""Test post-training calculations with scaler."""
|
|
# Setup mock train result
|
|
mock_train_result = MagicMock(spec=TrainModelResult)
|
|
mock_train_result.params = train_params
|
|
mock_train_result.x_train = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [2, 3, 4]})
|
|
mock_train_result.x_test = pd.DataFrame({'feature1': [4, 5], 'feature2': [5, 6]})
|
|
mock_train_result.y_train = pd.Series([10, 15, 20], name='target')
|
|
mock_train_result.y_test = pd.Series([25, 30], name='target')
|
|
|
|
mock_regr = MagicMock()
|
|
mock_regr.predict.return_value = np.array([24.5, 29.5])
|
|
mock_train_result.regr = mock_regr
|
|
|
|
mock_scaler = MagicMock()
|
|
mock_scaler.denormalize_single_input.side_effect = lambda x, col: x
|
|
mock_scaler.denormalize_predictions.side_effect = lambda x, col: x
|
|
|
|
mock_process_data = MagicMock()
|
|
mock_process_data.get_scaler.return_value = mock_scaler
|
|
mock_train_result.process_data = mock_process_data
|
|
|
|
# Setup metric mocks
|
|
mock_mse.return_value = 0.5
|
|
mock_mae.return_value = 0.3
|
|
mock_r2.return_value = 0.95
|
|
|
|
# Execute
|
|
result = training_repository.after_train_calculation(train_params, mock_train_result)
|
|
|
|
# Assertions
|
|
assert result == mock_train_result
|
|
assert result.mse_val == 0.5
|
|
assert result.mae_val == 0.3
|
|
assert result.r2_val == 0.95
|
|
assert result.y_pred is not None
|
|
mock_regr.predict.assert_called_once()
|
|
mock_mse.assert_called_once()
|
|
mock_mae.assert_called_once()
|
|
mock_r2.assert_called_once()
|
|
|
|
|
|
@patch('model_manager.utils.repository.training_repository.mse')
|
|
@patch('model_manager.utils.repository.training_repository.mae')
|
|
@patch('model_manager.utils.repository.training_repository.r2')
|
|
def test_after_train_calculation_without_scaler(mock_r2, mock_mae, mock_mse, training_repository):
|
|
"""Test post-training calculations without scaler."""
|
|
train_params_no_scaler = TrainModelParams(
|
|
variable_columns=['feature1'],
|
|
lag_train=1,
|
|
lag_val=1,
|
|
target_variable='target',
|
|
rem_static_win=False,
|
|
low_lim={'feature1': 0.0},
|
|
upp_lim={'feature1': 100.0},
|
|
window=10,
|
|
use_scaler=False,
|
|
include_ar=False,
|
|
bucket_name='test',
|
|
file_name='test.csv',
|
|
line_separator='\n',
|
|
decimal_separator='.',
|
|
train_size=80,
|
|
shuffle=True,
|
|
experiment_run_id=123,
|
|
experiment_name='test',
|
|
removed_intervals=[],
|
|
)
|
|
|
|
mock_train_result = MagicMock(spec=TrainModelResult)
|
|
mock_train_result.params = train_params_no_scaler
|
|
mock_train_result.x_train = pd.DataFrame({'feature1': [1, 2, 3]})
|
|
mock_train_result.x_test = pd.DataFrame({'feature1': [4, 5]})
|
|
mock_train_result.y_train = pd.Series([10, 15, 20], name='target')
|
|
mock_train_result.y_test = pd.Series([25, 30], name='target')
|
|
|
|
mock_regr = MagicMock()
|
|
mock_regr.predict.return_value = np.array([24.5, 29.5])
|
|
mock_train_result.regr = mock_regr
|
|
|
|
# Setup metric mocks
|
|
mock_mse.return_value = 0.5
|
|
mock_mae.return_value = 0.3
|
|
mock_r2.return_value = 0.95
|
|
|
|
# Execute
|
|
result = training_repository.after_train_calculation(train_params_no_scaler, mock_train_result)
|
|
|
|
# Assertions
|
|
assert result.mse_val == 0.5
|
|
assert result.mae_val == 0.3
|
|
assert result.r2_val == 0.95
|
|
|
|
|
|
@patch('model_manager.utils.repository.training_repository.DataPreprocessor')
|
|
def test_init_data_preprocessor(mock_preprocessor_class, training_repository, train_params):
|
|
"""Test DataPreprocessor initialization."""
|
|
mock_preprocessor = MagicMock()
|
|
mock_preprocessor_class.return_value = mock_preprocessor
|
|
|
|
result = training_repository.init_data_preprocessor(train_params)
|
|
|
|
assert result == mock_preprocessor
|
|
mock_preprocessor_class.assert_called_once()
|
|
call_kwargs = mock_preprocessor_class.call_args[1]
|
|
assert call_kwargs['target_variable'] == 'target'
|
|
assert call_kwargs['input_columns'] == ['feature1', 'feature2']
|
|
assert call_kwargs['low_lim'] == {'feature1': 0.0, 'feature2': 0.0}
|
|
assert call_kwargs['upp_lim'] == {'feature1': 100.0, 'feature2': 100.0}
|