from evalml.data_checks import ( DataCheck, DataCheckError, DataCheckMessageCode, DataCheckWarning ) from evalml.utils import _convert_woodwork_types_wrapper, infer_feature_types [docs]class ClassImbalanceDataCheck(DataCheck): """Checks if any target labels are imbalanced beyond a threshold. Use for classification problems""" [docs] def __init__(self, threshold=0.1, num_cv_folds=3): """Check if any of the target labels are imbalanced, or if the number of values for each target are below 2 times the number of cv folds Arguments: threshold (float): The minimum threshold allowed for class imbalance before a warning is raised. A perfectly balanced dataset would have a threshold of (1/n_classes), ie 0.50 for binary classes. Defaults to 0.10 num_cv_folds (int): The number of cross-validation folds. Must be positive. Choose 0 to ignore this warning. """ if threshold <= 0 or threshold > 0.5: raise ValueError("Provided threshold {} is not within the range (0, 0.5]".format(threshold)) self.threshold = threshold if num_cv_folds < 0: raise ValueError("Provided number of CV folds {} is less than 0".format(num_cv_folds)) self.cv_folds = num_cv_folds * 2 [docs] def validate(self, X, y): """Checks if any target labels are imbalanced beyond a threshold for binary and multiclass problems Ignores NaN values in target labels if they appear. Arguments: X (ww.DataTable, pd.DataFrame, np.ndarray): Features. Ignored. y (ww.DataColumn, pd.Series, np.ndarray): Target labels to check for imbalanced data. Returns: dict: Dictionary with DataCheckWarnings if imbalance in classes is less than the threshold, and DataCheckErrors if the number of values for each target is below 2 * num_cv_folds. Example: >>> import pandas as pd >>> X = pd.DataFrame() >>> y = pd.Series([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) >>> target_check = ClassImbalanceDataCheck(threshold=0.10) >>> assert target_check.validate(X, y) == {"errors": [{"message": "The number of instances of these targets is less than 2 * the number of cross folds = 6 instances: [0]",\ "data_check_name": "ClassImbalanceDataCheck",\ "level": "error",\ "code": "CLASS_IMBALANCE_BELOW_FOLDS",\ "details": {"target_values": [0]}}],\ "warnings": [{"message": "The following labels fall below 10% of the target: [0]",\ "data_check_name": "ClassImbalanceDataCheck",\ "level": "warning",\ "code": "CLASS_IMBALANCE_BELOW_THRESHOLD",\ "details": {"target_values": [0]}}]} """ messages = { "warnings": [], "errors": [] } y = infer_feature_types(y) y = _convert_woodwork_types_wrapper(y.to_series()) fold_counts = y.value_counts(normalize=False) # search for targets that occur less than twice the number of cv folds first below_threshold_folds = fold_counts.where(fold_counts < self.cv_folds).dropna() if len(below_threshold_folds): below_threshold_values = below_threshold_folds.index.tolist() error_msg = "The number of instances of these targets is less than 2 * the number of cross folds = {} instances: {}" DataCheck._add_message(DataCheckError(message=error_msg.format(self.cv_folds, below_threshold_values), data_check_name=self.name, message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS, details={"target_values": below_threshold_values}), messages) counts = fold_counts / fold_counts.sum() below_threshold = counts.where(counts < self.threshold).dropna() # if there are items that occur less than the threshold, add them to the list of messages if len(below_threshold): below_threshold_values = below_threshold.index.tolist() warning_msg = "The following labels fall below {:.0f}% of the target: {}" DataCheck._add_message(DataCheckWarning(message=warning_msg.format(self.threshold * 100, below_threshold_values), data_check_name=self.name, message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD, details={"target_values": below_threshold_values}), messages) return messages