09ee92f100
- Added new ignore rule for Ruff to allow temporary paths in tests. - Introduced MyPy overrides for specific modules to ignore errors. - Refactored `Cleanup` and `ExperimentTracking` classes to remove async keywords from methods, improving consistency in method signatures. - Updated `Training` class methods to handle synchronous operations, enhancing performance and clarity. - Adjusted `requirements.txt` to remove unnecessary Git dependency, streamlining project setup.
265 lines
9.1 KiB
Python
265 lines
9.1 KiB
Python
"""Unit tests for TrainModelParams (current schema)."""
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import copy
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from unittest.mock import patch
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import pytest
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from model_manager.utils.models.train_model_params import TrainModelParams
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@pytest.fixture
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def minimal_model_metadata() -> dict:
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"""Minimal truthy metadata so validate_business_rules passes schema lookup."""
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return {'schemas': {'components': {'schemas': {}}}}
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@pytest.fixture
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def valid_train_params_dict(minimal_model_metadata) -> dict:
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"""Valid dictionary for TrainModelParams.from_dict."""
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return {
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'variable_columns': ['var1', 'var2'],
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'target_variable': 'target',
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'bucket_name': 'test-bucket',
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'file_name': 'test-file.csv',
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'line_separator': ',',
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'decimal_separator': '.',
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'date_column': None,
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'date_format': None,
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'train_size': 80,
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'shuffle': True,
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'random_state': 42,
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'experiment_run_id': 1,
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'model_name': 'Linear Regression',
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'val_file_name': None,
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'data_model_kwargs': {},
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'model_kwargs': {},
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'opt_params': {},
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'model_type': 'linear_regression',
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'model_id': None,
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'model_metadata': minimal_model_metadata,
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}
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def test_from_dict_success(valid_train_params_dict):
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"""from_dict builds params and experiment_name from model_name."""
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params = TrainModelParams.from_dict(valid_train_params_dict)
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assert params.variable_columns == ['var1', 'var2']
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assert params.target_variable == 'target'
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assert params.bucket_name == 'test-bucket'
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assert params.experiment_run_id == 1
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assert params.experiment_name == 'Linear Regression_experiment'
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assert params.model_metadata is valid_train_params_dict['model_metadata']
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def test_from_dict_coerces_experiment_run_id_string(valid_train_params_dict):
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"""Numeric string experiment_run_id is coerced to int."""
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d = copy.deepcopy(valid_train_params_dict)
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d['experiment_run_id'] = '42'
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params = TrainModelParams.from_dict(d)
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assert params.experiment_run_id == 42
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def test_from_dict_model_metadata_none(valid_train_params_dict):
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"""model_metadata may be None before load_model_metadata activity."""
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = None
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params = TrainModelParams.from_dict(d)
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assert params.model_metadata is None
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def test_coerce_experiment_run_id_rejects_bool():
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"""Boolean must not be accepted as experiment_run_id."""
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with pytest.raises(TypeError, match='experiment_run_id must be an integer'):
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TrainModelParams._coerce_experiment_run_id(True)
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def test_parse_optional_model_metadata_rejects_list():
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"""model_metadata must be dict or None."""
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with pytest.raises(TypeError, match='model_metadata must be a dict or None'):
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TrainModelParams._parse_optional_model_metadata([])
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def test_check_none_raises_value_error():
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with pytest.raises(ValueError, match='test_field is required'):
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TrainModelParams._check_none(None, str, 'test_field')
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def test_check_none_raises_type_error():
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with pytest.raises(TypeError, match='test_field must be of type str'):
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TrainModelParams._check_none(123, str, 'test_field')
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def test_validate_business_rules_success(valid_train_params_dict):
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params = TrainModelParams.from_dict(valid_train_params_dict)
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params.validate_business_rules()
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def test_validate_business_rules_missing_model_metadata(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = None
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params = TrainModelParams.from_dict(d)
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with pytest.raises(ValueError, match='model_metadata is required'):
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params.validate_business_rules()
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def test_validate_business_rules_train_size_out_of_range(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['train_size'] = 5
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params = TrainModelParams.from_dict(d)
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with pytest.raises(ValueError, match='train_size must be between'):
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params.validate_business_rules()
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def test_validate_business_rules_empty_variable_columns(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['variable_columns'] = []
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params = TrainModelParams.from_dict(d)
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with pytest.raises(ValueError, match='variable_columns cannot be empty'):
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params.validate_business_rules()
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def test_validate_business_rules_empty_target(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['target_variable'] = ' '
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params = TrainModelParams.from_dict(d)
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with pytest.raises(ValueError, match='target_variable cannot be empty'):
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params.validate_business_rules()
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def test_from_dict_missing_required_key(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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del d['bucket_name']
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with pytest.raises(ValueError, match='bucket_name is required'):
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TrainModelParams.from_dict(d)
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def test_to_dict_roundtrip_keys(valid_train_params_dict):
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params = TrainModelParams.from_dict(valid_train_params_dict)
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d = params.to_dict()
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assert 'variable_columns' in d
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assert d['experiment_run_id'] == 1
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def test_coerce_experiment_run_id_float():
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assert TrainModelParams._coerce_experiment_run_id(2.0) == 2
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def test_coerce_experiment_run_id_none_raises():
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with pytest.raises(ValueError, match='experiment_run_id is required'):
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TrainModelParams._coerce_experiment_run_id(None)
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def test_coerce_experiment_run_id_invalid_type():
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with pytest.raises(TypeError, match='integer or numeric string'):
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TrainModelParams._coerce_experiment_run_id([1])
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def test_validate_model_param_schema_validation_error(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = {
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'schemas': {
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'components': {
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'schemas': {
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'data_model': {
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'type': 'object',
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'properties': {'x': {'type': 'integer'}},
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'required': ['x'],
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},
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}
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}
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}
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}
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p = TrainModelParams.from_dict(d)
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p.data_model_kwargs = {}
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with pytest.raises(ValueError, match='Model parameters validation failed'):
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p.validate_business_rules()
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def test_validate_model_param_unexpected_validator_error(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = {
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'schemas': {
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'components': {
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'schemas': {
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'data_model': {'type': 'object'},
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}
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}
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}
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}
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p = TrainModelParams.from_dict(d)
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with patch('model_manager.utils.models.train_model_params.Draft202012Validator') as m:
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m.return_value.validate.side_effect = RuntimeError('boom')
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with pytest.raises(RuntimeError, match='boom'):
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p.validate_business_rules()
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def test_validate_business_rules_date_format_invalid(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['date_format'] = 'not-an-allowed-format'
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p = TrainModelParams.from_dict(d)
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with pytest.raises(ValueError, match='Invalid date_format'):
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p.validate_business_rules()
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def test_validate_required_strings_whitespace_bucket_file_model(valid_train_params_dict):
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for field, msg in [
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('bucket_name', 'bucket_name cannot be empty'),
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('file_name', 'file_name cannot be empty'),
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('model_name', 'model_name cannot be empty'),
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]:
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d = copy.deepcopy(valid_train_params_dict)
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d[field] = ' '
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p = TrainModelParams.from_dict(d)
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with pytest.raises(ValueError, match=msg):
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p.validate_business_rules()
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def test_validate_model_param_only_data_model_schema(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = {
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'schemas': {'components': {'schemas': {'data_model': {'type': 'object'}}}}
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}
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p = TrainModelParams.from_dict(d)
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p.data_model_kwargs = {}
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p.validate_business_rules()
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def test_validate_model_param_only_model_schema(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = {'schemas': {'components': {'schemas': {'model': {'type': 'object'}}}}}
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p = TrainModelParams.from_dict(d)
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p.model_kwargs = {}
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p.validate_business_rules()
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def test_validate_model_param_only_opt_params_schema(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = {
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'schemas': {'components': {'schemas': {'opt_params': {'type': 'object'}}}}
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}
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p = TrainModelParams.from_dict(d)
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p.opt_params = {}
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p.validate_business_rules()
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def test_validate_model_param_all_schema_branches(valid_train_params_dict):
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d = copy.deepcopy(valid_train_params_dict)
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d['model_metadata'] = {
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'schemas': {
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'components': {
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'schemas': {
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'data_model': {'type': 'object'},
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'model': {'type': 'object'},
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'opt_params': {'type': 'object'},
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}
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}
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}
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}
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p = TrainModelParams.from_dict(d)
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p.data_model_kwargs = {}
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p.model_kwargs = {}
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p.opt_params = {}
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p.validate_business_rules()
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