"""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.
"""
import numpy as np
import pandas as pd
from evalml.data_checks import (
DataCheck,
DataCheckError,
DataCheckMessageCode,
DataCheckWarning,
)
from evalml.utils import infer_feature_types
[docs]class ClassImbalanceDataCheck(DataCheck):
"""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.
Args:
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
"""
def __init__(self, threshold=0.1, min_samples=100, num_cv_folds=3, test_size=None):
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 min_samples <= 0:
raise ValueError(
"Provided value min_samples {} is not greater than 0".format(
min_samples,
),
)
self.min_samples = min_samples
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
if test_size is not None:
if not (isinstance(test_size, (int, float)) and 0 < test_size <= 1):
raise ValueError(
"Parameter test_size must be a number between 0 and less than or equal to 1",
)
self.test_size = test_size
else:
self.test_size = 1
[docs] def 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.
Args:
X (pd.DataFrame, np.ndarray): Features. Ignored.
y (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.
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) == []
"""
messages = []
original_vc = pd.Series(y).value_counts(sort=True)
y = infer_feature_types(y)
new_vc = y.value_counts(sort=True)
if str(y.ww.logical_type) not in ["Boolean", "BooleanNullable"]:
# If the inferred logical type is not in Boolean/BooleanNullable, then a
# mapping to the original values is not necessary.
after_to_before_inference_mapping = {new: new for new in new_vc.keys()}
else:
# If the inferred logical type is in Boolean/BooleanNullable, then a
# mapping to the original values will be needed for the data check messages
after_to_before_inference_mapping = {
new: old for old, new in zip(original_vc.keys(), new_vc.keys())
}
# Needed for checking severe imbalance to verify values present below threshold
before_to_after_inference_mapping = {
old: new for new, old in after_to_before_inference_mapping.items()
}
fold_counts = y.value_counts(normalize=False, sort=True)
fold_counts = np.floor(fold_counts * self.test_size).astype(int)
if len(fold_counts) == 0:
return messages
# 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 = [
after_to_before_inference_mapping.get(each)
for each in below_threshold_folds.index.tolist()
]
error_msg = "The number of instances of these targets is less than 2 * the number of cross folds = {} instances: {}"
messages.append(
DataCheckError(
message=error_msg.format(
self.cv_folds,
sorted(below_threshold_values),
),
data_check_name=self.name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
details={"target_values": sorted(below_threshold_values)},
).to_dict(),
)
counts = fold_counts / (fold_counts + fold_counts.values[0])
below_threshold = counts.where(counts < self.threshold).dropna()
# if there are items that occur less than the threshold, add them to the list of results
if len(below_threshold):
below_threshold_values = [
after_to_before_inference_mapping.get(each)
for each in below_threshold.index.tolist()
]
warning_msg = "The following labels fall below {:.0f}% of the target: {}"
messages.append(
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},
).to_dict(),
)
sample_counts = fold_counts.where(fold_counts < self.min_samples).dropna()
if len(below_threshold) and len(sample_counts):
sample_count_values = [
after_to_before_inference_mapping.get(each)
for each in sample_counts.index.tolist()
]
severe_imbalance = [
v
for v in sample_count_values
if before_to_after_inference_mapping.get(v) in below_threshold
]
warning_msg = "The following labels in the target have severe class imbalance because they fall under {:.0f}% of the target and have less than {} samples: {}"
messages.append(
DataCheckWarning(
message=warning_msg.format(
self.threshold * 100,
self.min_samples,
severe_imbalance,
),
data_check_name=self.name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
details={"target_values": severe_imbalance},
).to_dict(),
)
return messages
