class_imbalance_data_check

Data check that checks 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.

Use for classification problems.

Module Contents

Classes Summary

ClassImbalanceDataCheck

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. Use for classification problems.

Contents

class evalml.data_checks.class_imbalance_data_check.ClassImbalanceDataCheck(threshold=0.1, min_samples=100, num_cv_folds=3, test_size=None)

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. Use for classification problems.

Parameters

• threshold (float) – The minimum threshold allowed for class imbalance before a warning is raised. This threshold is calculated by comparing the number of samples in each class to the sum of samples in that class and the majority class. For example, a multiclass case with [900, 900, 100] samples per classes 0, 1, and 2, respectively, would have a 0.10 threshold for class 2 (100 / (900 + 100)). Defaults to 0.10.

• min_samples (int) – The minimum number of samples per accepted class. If the minority class is both below the threshold and min_samples, then we consider this severely imbalanced. Must be greater than 0. Defaults to 100.

• num_cv_folds (int) – The number of cross-validation folds. Must be positive. Choose 0 to ignore this warning. Defaults to 3.

• test_size (None, float, int) – Percentage of test set size. Used to calculate class imbalance prior to splitting the data into training and validation/test sets.

Raises

• ValueError – If threshold is not within 0 and 0.5

• ValueError – If min_samples is not greater than 0

• ValueError – If number of cv folds is negative

• ValueError – If test_size is not between 0 and 1

Methods

name	Return a name describing the data check.
validate	Check if any target labels are imbalanced beyond a threshold for binary and multiclass problems.

name(cls)

Return a name describing the data check.

validate(self, X, y)

Check if any target labels are imbalanced beyond a threshold for binary and multiclass problems.

Ignores NaN values in target labels if they appear.

Parameters

• X (pd.DataFrame, np.ndarray) – Features. Ignored.

• y (pd.Series, np.ndarray) – Target labels to check for imbalanced data.

Returns

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.

Return type

dict

Examples

>>> import pandas as pd
...
>>> X = pd.DataFrame()
>>> y = pd.Series([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

In this binary example, the target class 0 is present in fewer than 10% (threshold=0.10) of instances, and fewer than 2 * the number of cross folds (2 * 3 = 6). Therefore, both a warning and an error are returned as part of the Class Imbalance Data Check. In addition, if a target is present with fewer than min_samples occurrences (default is 100) and is under the threshold, a severe class imbalance warning will be raised.

>>> class_imb_dc = ClassImbalanceDataCheck(threshold=0.10)
>>> assert class_imb_dc.validate(X, y) == [
...     {
...         "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], "rows": None, "columns": None},
...         "action_options": []
...     },
...     {
...         "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], "rows": None, "columns": None},
...         "action_options": []
...     },
...     {
...         "message": "The following labels in the target have severe class imbalance because they fall under 10% of the target and have less than 100 samples: [0]",
...         "data_check_name": "ClassImbalanceDataCheck",
...         "level": "warning",
...         "code": "CLASS_IMBALANCE_SEVERE",
...         "details": {"target_values": [0], "rows": None, "columns": None},
...         "action_options": []
...     }
... ]

In this multiclass example, the target class 0 is present in fewer than 30% of observations, however with 1 cv fold, the minimum number of instances required is 2 * 1 = 2. Therefore a warning, but not an error, is raised.

>>> y = pd.Series([0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2])
>>> class_imb_dc = ClassImbalanceDataCheck(threshold=0.30, min_samples=5, num_cv_folds=1)
>>> assert class_imb_dc.validate(X, y) == [
...     {
...         "message": "The following labels fall below 30% of the target: [0]",
...         "data_check_name": "ClassImbalanceDataCheck",
...         "level": "warning",
...         "code": "CLASS_IMBALANCE_BELOW_THRESHOLD",
...         "details": {"target_values": [0], "rows": None, "columns": None},
...         "action_options": []
...     },
...     {
...         "message": "The following labels in the target have severe class imbalance because they fall under 30% of the target and have less than 5 samples: [0]",
...         "data_check_name": "ClassImbalanceDataCheck",
...         "level": "warning",
...         "code": "CLASS_IMBALANCE_SEVERE",
...         "details": {"target_values": [0], "rows": None, "columns": None},
...         "action_options": []
...     }
... ]
...
>>> y = pd.Series([0, 0, 1, 1, 1, 1, 2, 2, 2, 2])
>>> class_imb_dc = ClassImbalanceDataCheck(threshold=0.30, num_cv_folds=1)
>>> assert class_imb_dc.validate(X, y) == []