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Code import - branch release/SIENTIAPDE-1645

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gitea_admin
2026-08-05 13:53:37 +00:00
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tests/utils/models/test_train_model_params.py Normal file
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"""Unit tests for TrainModelParams (current schema)."""
import copy
from unittest.mock import patch
import pytest
from model_manager.utils.models.train_model_params import (
DEFAULT_TRAIN_DATE_FORMAT,
TrainModelParams,
validate_frontend_date_format,
)
@pytest.fixture
def minimal_model_metadata() -> dict:
"""Minimal truthy metadata so validate_business_rules passes schema lookup."""
return {'schemas': {'components': {'schemas': {}}}}
@pytest.fixture
def valid_train_params_dict(minimal_model_metadata) -> dict:
"""Valid dictionary for TrainModelParams.from_dict."""
return {
'variable_columns': ['var1', 'var2'],
'target_variable': 'target',
'bucket_name': 'test-bucket',
'file_name': 'test-file.csv',
'line_separator': ',',
'decimal_separator': '.',
'date_column': 'timestamp',
'date_format': 'yyyy-MM-dd HH:mm:ss',
'train_size': 80,
'shuffle': True,
'random_state': 42,
'experiment_run_id': 1,
'model_name': 'Linear Regression',
'val_file_name': None,
'data_model_kwargs': {},
'model_kwargs': {},
'opt_params': {},
'model_type': 'linear_regression',
'model_id': None,
'model_metadata': minimal_model_metadata,
}
def test_from_dict_success(valid_train_params_dict):
"""from_dict builds params and experiment_name from model_name."""
params = TrainModelParams.from_dict(valid_train_params_dict)
assert params.variable_columns == ['var1', 'var2']
assert params.target_variable == 'target'
assert params.bucket_name == 'test-bucket'
assert params.experiment_run_id == 1
assert params.experiment_name == 'Linear Regression'
assert params.model_metadata is valid_train_params_dict['model_metadata']
def test_from_dict_date_format_omitted_uses_default(valid_train_params_dict):
"""Missing date_format defaults to DEFAULT_TRAIN_DATE_FORMAT."""
d = copy.deepcopy(valid_train_params_dict)
del d['date_format']
params = TrainModelParams.from_dict(d)
assert params.date_format == DEFAULT_TRAIN_DATE_FORMAT
def test_from_dict_date_format_blank_uses_default(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['date_format'] = ' '
params = TrainModelParams.from_dict(d)
assert params.date_format == DEFAULT_TRAIN_DATE_FORMAT
def test_from_dict_superfluous_date_column_camel_key_is_ignored(valid_train_params_dict):
"""Only snake_case keys are read; dateColumn does not populate date_column."""
d = copy.deepcopy(valid_train_params_dict)
d['dateColumn'] = 'wrong_name'
params = TrainModelParams.from_dict(d)
assert params.date_column == 'timestamp'
def test_from_dict_missing_date_column_raises(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
del d['date_column']
with pytest.raises(ValueError, match='date_column is required'):
TrainModelParams.from_dict(d)
def test_from_dict_date_format_non_string_raises(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['date_format'] = 12345
with pytest.raises(TypeError, match='date_format must be a string'):
TrainModelParams.from_dict(d)
def test_from_dict_coerces_experiment_run_id_string(valid_train_params_dict):
"""Numeric string experiment_run_id is coerced to int."""
d = copy.deepcopy(valid_train_params_dict)
d['experiment_run_id'] = '42'
params = TrainModelParams.from_dict(d)
assert params.experiment_run_id == 42
def test_from_dict_model_metadata_none(valid_train_params_dict):
"""model_metadata may be None before load_model_metadata activity."""
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = None
params = TrainModelParams.from_dict(d)
assert params.model_metadata is None
def test_coerce_experiment_run_id_rejects_bool():
"""Boolean must not be accepted as experiment_run_id."""
with pytest.raises(TypeError, match='experiment_run_id must be an integer'):
TrainModelParams._coerce_experiment_run_id(True)
def test_parse_optional_model_metadata_rejects_list():
"""model_metadata must be dict or None."""
with pytest.raises(TypeError, match='model_metadata must be a dict or None'):
TrainModelParams._parse_optional_model_metadata([])
def test_check_none_raises_value_error():
with pytest.raises(ValueError, match='test_field is required'):
TrainModelParams._check_none(None, str, 'test_field')
def test_check_none_raises_type_error():
with pytest.raises(TypeError, match='test_field must be of type str'):
TrainModelParams._check_none(123, str, 'test_field')
def test_validate_business_rules_success(valid_train_params_dict):
params = TrainModelParams.from_dict(valid_train_params_dict)
params.validate_business_rules()
def test_validate_business_rules_missing_model_metadata(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = None
params = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match='model_metadata is required'):
params.validate_business_rules()
def test_validate_business_rules_train_size_out_of_range(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['train_size'] = 5
params = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match='train_size must be between'):
params.validate_business_rules()
def test_validate_business_rules_empty_variable_columns(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['variable_columns'] = []
params = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match='variable_columns cannot be empty'):
params.validate_business_rules()
def test_validate_business_rules_empty_target(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['target_variable'] = ' '
params = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match='target_variable cannot be empty'):
params.validate_business_rules()
def test_validate_business_rules_whitespace_date_column(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['date_column'] = ' '
params = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match='date_column cannot be empty'):
params.validate_business_rules()
def test_from_dict_missing_required_key(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
del d['bucket_name']
with pytest.raises(ValueError, match='bucket_name is required'):
TrainModelParams.from_dict(d)
def test_to_dict_roundtrip_keys(valid_train_params_dict):
params = TrainModelParams.from_dict(valid_train_params_dict)
d = params.to_dict()
assert 'variable_columns' in d
assert d['experiment_run_id'] == 1
def test_coerce_experiment_run_id_float():
assert TrainModelParams._coerce_experiment_run_id(2.0) == 2
def test_coerce_experiment_run_id_none_raises():
with pytest.raises(ValueError, match='experiment_run_id is required'):
TrainModelParams._coerce_experiment_run_id(None)
def test_coerce_experiment_run_id_invalid_type():
with pytest.raises(TypeError, match='integer or numeric string'):
TrainModelParams._coerce_experiment_run_id([1])
def test_validate_model_param_schema_validation_error(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = {
'schemas': {
'components': {
'schemas': {
'data_model': {
'type': 'object',
'properties': {'x': {'type': 'integer'}},
'required': ['x'],
},
}
}
}
}
p = TrainModelParams.from_dict(d)
p.data_model_kwargs = {}
with pytest.raises(ValueError, match='Model parameters validation failed'):
p.validate_business_rules()
def test_validate_model_param_unexpected_validator_error(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = {
'schemas': {
'components': {
'schemas': {
'data_model': {'type': 'object'},
}
}
}
}
p = TrainModelParams.from_dict(d)
with patch('model_manager.utils.models.train_model_params.Draft202012Validator') as m:
m.return_value.validate.side_effect = RuntimeError('boom')
with pytest.raises(RuntimeError, match='boom'):
p.validate_business_rules()
def test_validate_business_rules_date_format_invalid(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['date_format'] = 'not-an-allowed-format'
p = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match='Invalid date_format'):
p.validate_business_rules()
def test_validate_required_strings_whitespace_bucket_file_model(valid_train_params_dict):
for field, msg in [
('bucket_name', 'bucket_name cannot be empty'),
('file_name', 'file_name cannot be empty'),
('model_name', 'model_name cannot be empty'),
]:
d = copy.deepcopy(valid_train_params_dict)
d[field] = ' '
p = TrainModelParams.from_dict(d)
with pytest.raises(ValueError, match=msg):
p.validate_business_rules()
def test_validate_model_param_only_data_model_schema(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = {
'schemas': {'components': {'schemas': {'data_model': {'type': 'object'}}}}
}
p = TrainModelParams.from_dict(d)
p.data_model_kwargs = {}
p.validate_business_rules()
def test_validate_model_param_only_model_schema(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = {'schemas': {'components': {'schemas': {'model': {'type': 'object'}}}}}
p = TrainModelParams.from_dict(d)
p.model_kwargs = {}
p.validate_business_rules()
def test_validate_model_param_only_opt_params_schema(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = {
'schemas': {'components': {'schemas': {'opt_params': {'type': 'object'}}}}
}
p = TrainModelParams.from_dict(d)
p.opt_params = {}
p.validate_business_rules()
def test_validate_model_param_all_schema_branches(valid_train_params_dict):
d = copy.deepcopy(valid_train_params_dict)
d['model_metadata'] = {
'schemas': {
'components': {
'schemas': {
'data_model': {'type': 'object'},
'model': {'type': 'object'},
'opt_params': {'type': 'object'},
}
}
}
}
p = TrainModelParams.from_dict(d)
p.data_model_kwargs = {}
p.model_kwargs = {}
p.opt_params = {}
p.validate_business_rules()
def test_validate_frontend_date_format_whitespace_returns():
validate_frontend_date_format(' ')
def test_validate_frontend_date_format_valid_returns():
validate_frontend_date_format('dd/MM/yyyy HH:mm:ss')