from .class_imbalance_data_check import ClassImbalanceDataCheck
from .data_checks import DataChecks
from .datetime_nan_data_check import DateTimeNaNDataCheck
from .highly_null_data_check import HighlyNullDataCheck
from .id_columns_data_check import IDColumnsDataCheck
from .invalid_targets_data_check import InvalidTargetDataCheck
from .natural_language_nan_data_check import NaturalLanguageNaNDataCheck
from .no_variance_data_check import NoVarianceDataCheck
from .target_distribution_data_check import TargetDistributionDataCheck
from .target_leakage_data_check import TargetLeakageDataCheck
from evalml.problem_types import ProblemTypes, handle_problem_types
[docs]class DefaultDataChecks(DataChecks):
"""A collection of basic data checks that is used by AutoML by default.
Includes:
- `HighlyNullDataCheck`
- `HighlyNullRowsDataCheck`
- `IDColumnsDataCheck`
- `TargetLeakageDataCheck`
- `InvalidTargetDataCheck`
- `NoVarianceDataCheck`
- `ClassImbalanceDataCheck` (for classification problem types)
- `DateTimeNaNDataCheck`
- `NaturalLanguageNaNDataCheck`
- `TargetDistributionDataCheck`
Arguments:
problem_type (str): The problem type that is being validated. Can be regression, binary, or multiclass.
objective (str or ObjectiveBase): Name or instance of the objective class.
n_splits (int): The number of splits as determined by the data splitter being used. Defaults to 3.
"""
_DEFAULT_DATA_CHECK_CLASSES = [
HighlyNullDataCheck,
IDColumnsDataCheck,
TargetLeakageDataCheck,
InvalidTargetDataCheck,
NoVarianceDataCheck,
NaturalLanguageNaNDataCheck,
DateTimeNaNDataCheck,
]
def __init__(self, problem_type, objective, n_splits=3):
if handle_problem_types(problem_type) in [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]:
super().__init__(
self._DEFAULT_DATA_CHECK_CLASSES + [TargetDistributionDataCheck],
data_check_params={
"InvalidTargetDataCheck": {
"problem_type": problem_type,
"objective": objective,
},
},
)
else:
super().__init__(
self._DEFAULT_DATA_CHECK_CLASSES + [ClassImbalanceDataCheck],
data_check_params={
"InvalidTargetDataCheck": {
"problem_type": problem_type,
"objective": objective,
},
"ClassImbalanceDataCheck": {"num_cv_folds": n_splits},
},
)