"""A default set of data checks that can be used for a variety of datasets."""
from .class_imbalance_data_check import ClassImbalanceDataCheck
from .data_checks import DataChecks
from .datetime_format_data_check import DateTimeFormatDataCheck
from .id_columns_data_check import IDColumnsDataCheck
from .invalid_target_data_check import InvalidTargetDataCheck
from .no_variance_data_check import NoVarianceDataCheck
from .null_data_check import NullDataCheck
from .target_distribution_data_check import TargetDistributionDataCheck
from .target_leakage_data_check import TargetLeakageDataCheck
from .ts_parameters_data_check import TimeSeriesParametersDataCheck
from .ts_splitting_data_check import TimeSeriesSplittingDataCheck
from evalml.problem_types import (
ProblemTypes,
handle_problem_types,
is_classification,
is_time_series,
)
[docs]class DefaultDataChecks(DataChecks):
"""A collection of basic data checks that is used by AutoML by default.
Includes:
- `NullDataCheck`
- `HighlyNullRowsDataCheck`
- `IDColumnsDataCheck`
- `TargetLeakageDataCheck`
- `InvalidTargetDataCheck`
- `NoVarianceDataCheck`
- `ClassImbalanceDataCheck` (for classification problem types)
- `TargetDistributionDataCheck` (for regression problem types)
- `DateTimeFormatDataCheck` (for time series problem types)
- 'TimeSeriesParametersDataCheck' (for time series problem types)
- `TimeSeriesSplittingDataCheck` (for time series classification problem types)
Args:
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.
datetime_column (str): The name of the column containing datetime information to be used for time series problems.
Default to "index" indicating that the datetime information is in the index of X or y.
"""
_DEFAULT_DATA_CHECK_CLASSES = [
NullDataCheck,
IDColumnsDataCheck,
TargetLeakageDataCheck,
InvalidTargetDataCheck,
NoVarianceDataCheck,
]
def __init__(self, problem_type, objective, n_splits=3, problem_configuration=None):
default_checks = self._DEFAULT_DATA_CHECK_CLASSES
data_check_params = {}
if is_time_series(problem_type):
if problem_configuration is None:
raise ValueError(
"problem_configuration cannot be None for time series problems!"
)
if is_classification(problem_type):
default_checks = default_checks + [TimeSeriesSplittingDataCheck]
data_check_params.update(
{
"TimeSeriesSplittingDataCheck": {
"problem_type": problem_type,
"n_splits": n_splits,
}
}
)
default_checks = default_checks + [
DateTimeFormatDataCheck,
TimeSeriesParametersDataCheck,
]
data_check_params.update(
{
"DateTimeFormatDataCheck": {
"datetime_column": problem_configuration["time_index"],
},
"TimeSeriesParametersDataCheck": {
"problem_configuration": problem_configuration,
"n_splits": n_splits,
},
}
)
if handle_problem_types(problem_type) in [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]:
default_checks = default_checks + [TargetDistributionDataCheck]
data_check_params.update(
{
"InvalidTargetDataCheck": {
"problem_type": problem_type,
"objective": objective,
}
}
)
else:
default_checks = default_checks + [ClassImbalanceDataCheck]
data_check_params.update(
{
"InvalidTargetDataCheck": {
"problem_type": problem_type,
"objective": objective,
},
"ClassImbalanceDataCheck": {"num_cv_folds": n_splits},
}
)
super().__init__(
default_checks,
data_check_params=data_check_params,
)