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Nabil Fayak
committed
new datachecks added
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lines changed

‎.gitignore‎

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.DS_Store
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Checkmates.egg-info/
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.python-version
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pdm.lock
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pdm.lock
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.pdm-python

‎checkmates/data_checks/__init__.py‎

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DataCheckMessageType,
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)
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from checkmates.data_checks.checks.id_columns_data_check import IDColumnsDataCheck
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from checkmates.data_checks.checks.null_data_check import NullDataCheck
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from checkmates.data_checks.checks.class_imbalance_data_check import ClassImbalanceDataCheck
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from checkmates.data_checks.checks.no_variance_data_check import NoVarianceDataCheck
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from checkmates.data_checks.checks.outliers_data_check import OutliersDataCheck
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from checkmates.data_checks.checks.uniqueness_data_check import UniquenessDataCheck
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from checkmates.data_checks.checks.ts_splitting_data_check import TimeSeriesSplittingDataCheck
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from checkmates.data_checks.checks.ts_parameters_data_check import TimeSeriesParametersDataCheck
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from checkmates.data_checks.checks.target_leakage_data_check import TargetLeakageDataCheck
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from checkmates.data_checks.checks.target_distribution_data_check import TargetDistributionDataCheck
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from checkmates.data_checks.checks.sparsity_data_check import SparsityDataCheck
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from checkmates.data_checks.checks.datetime_format_data_check import DateTimeFormatDataCheck
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from checkmates.data_checks.checks.multicollinearity_data_check import MulticollinearityDataCheck
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from checkmates.data_checks.datacheck_meta.utils import handle_data_check_action_code

‎checkmates/data_checks/checks/class_imbalance_data_check.py‎

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"""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.
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Use for classification problems.
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"""
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import numpy as np
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import pandas as pd
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from checkmates.data_checks import (
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DataCheck,
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DataCheckError,
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DataCheckMessageCode,
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DataCheckWarning,
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)
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from checkmates.utils import infer_feature_types
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class ClassImbalanceDataCheck(DataCheck):
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"""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.
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Args:
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threshold (float): The minimum threshold allowed for class imbalance before a warning is raised.
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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.
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For example, a multiclass case with [900, 900, 100] samples per classes 0, 1, and 2, respectively,
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would have a 0.10 threshold for class 2 (100 / (900 + 100)). Defaults to 0.10.
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min_samples (int): The minimum number of samples per accepted class. If the minority class is both below the threshold and min_samples,
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then we consider this severely imbalanced. Must be greater than 0. Defaults to 100.
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num_cv_folds (int): The number of cross-validation folds. Must be positive. Choose 0 to ignore this warning. Defaults to 3.
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test_size (None, float, int): Percentage of test set size. Used to calculate class imbalance prior to splitting the
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data into training and validation/test sets.
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Raises:
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ValueError: If threshold is not within 0 and 0.5
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ValueError: If min_samples is not greater than 0
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ValueError: If number of cv folds is negative
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ValueError: If test_size is not between 0 and 1
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"""
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def __init__(self, threshold=0.1, min_samples=100, num_cv_folds=3, test_size=None):
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if threshold <= 0 or threshold > 0.5:
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raise ValueError(
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"Provided threshold {} is not within the range (0, 0.5]".format(
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threshold,
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),
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)
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self.threshold = threshold
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if min_samples <= 0:
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raise ValueError(
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"Provided value min_samples {} is not greater than 0".format(
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min_samples,
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),
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)
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self.min_samples = min_samples
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if num_cv_folds < 0:
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raise ValueError(
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"Provided number of CV folds {} is less than 0".format(num_cv_folds),
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)
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self.cv_folds = num_cv_folds * 2
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if test_size is not None:
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if not (isinstance(test_size, (int, float)) and 0 < test_size <= 1):
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raise ValueError(
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"Parameter test_size must be a number between 0 and less than or equal to 1",
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)
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self.test_size = test_size
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else:
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self.test_size = 1
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def validate(self, X, y):
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"""Check if any target labels are imbalanced beyond a threshold for binary and multiclass problems.
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Ignores NaN values in target labels if they appear.
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Args:
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X (pd.DataFrame, np.ndarray): Features. Ignored.
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y (pd.Series, np.ndarray): Target labels to check for imbalanced data.
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Returns:
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dict: Dictionary with DataCheckWarnings if imbalance in classes is less than the threshold,
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and DataCheckErrors if the number of values for each target is below 2 * num_cv_folds.
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Examples:
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>>> import pandas as pd
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...
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>>> X = pd.DataFrame()
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>>> y = pd.Series([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
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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
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of cross folds (2 * 3 = 6). Therefore, both a warning and an error are returned as part of the Class Imbalance Data Check.
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In addition, if a target is present with fewer than `min_samples` occurrences (default is 100) and is under the threshold,
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a severe class imbalance warning will be raised.
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>>> class_imb_dc = ClassImbalanceDataCheck(threshold=0.10)
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>>> assert class_imb_dc.validate(X, y) == [
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... {
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... "message": "The number of instances of these targets is less than 2 * the number of cross folds = 6 instances: [0]",
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... "data_check_name": "ClassImbalanceDataCheck",
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... "level": "error",
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... "code": "CLASS_IMBALANCE_BELOW_FOLDS",
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... "details": {"target_values": [0], "rows": None, "columns": None},
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... "action_options": []
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... },
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... {
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... "message": "The following labels fall below 10% of the target: [0]",
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... "data_check_name": "ClassImbalanceDataCheck",
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... "level": "warning",
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... "code": "CLASS_IMBALANCE_BELOW_THRESHOLD",
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... "details": {"target_values": [0], "rows": None, "columns": None},
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... "action_options": []
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... },
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... {
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... "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]",
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... "data_check_name": "ClassImbalanceDataCheck",
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... "level": "warning",
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... "code": "CLASS_IMBALANCE_SEVERE",
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... "details": {"target_values": [0], "rows": None, "columns": None},
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... "action_options": []
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... }
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... ]
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In this multiclass example, the target class 0 is present in fewer than 30% of observations, however with 1 cv fold, the minimum
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number of instances required is 2 * 1 = 2. Therefore a warning, but not an error, is raised.
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>>> y = pd.Series([0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2])
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>>> class_imb_dc = ClassImbalanceDataCheck(threshold=0.30, min_samples=5, num_cv_folds=1)
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>>> assert class_imb_dc.validate(X, y) == [
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... {
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... "message": "The following labels fall below 30% of the target: [0]",
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... "data_check_name": "ClassImbalanceDataCheck",
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... "level": "warning",
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... "code": "CLASS_IMBALANCE_BELOW_THRESHOLD",
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... "details": {"target_values": [0], "rows": None, "columns": None},
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... "action_options": []
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... },
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... {
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... "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]",
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... "data_check_name": "ClassImbalanceDataCheck",
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... "level": "warning",
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... "code": "CLASS_IMBALANCE_SEVERE",
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... "details": {"target_values": [0], "rows": None, "columns": None},
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... "action_options": []
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... }
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... ]
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...
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>>> y = pd.Series([0, 0, 1, 1, 1, 1, 2, 2, 2, 2])
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>>> class_imb_dc = ClassImbalanceDataCheck(threshold=0.30, num_cv_folds=1)
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>>> assert class_imb_dc.validate(X, y) == []
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"""
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messages = []
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original_vc = pd.Series(y).value_counts(sort=True)
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y = infer_feature_types(y)
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new_vc = y.value_counts(sort=True)
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if str(y.ww.logical_type) not in ["Boolean", "BooleanNullable"]:
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# If the inferred logical type is not in Boolean/BooleanNullable, then a
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# mapping to the original values is not necessary.
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after_to_before_inference_mapping = {new: new for new in new_vc.keys()}
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else:
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# If the inferred logical type is in Boolean/BooleanNullable, then a
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# mapping to the original values will be needed for the data check messages
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after_to_before_inference_mapping = {
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new: old for old, new in zip(original_vc.keys(), new_vc.keys())
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}
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# Needed for checking severe imbalance to verify values present below threshold
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before_to_after_inference_mapping = {
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old: new for new, old in after_to_before_inference_mapping.items()
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}
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fold_counts = y.value_counts(normalize=False, sort=True)
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fold_counts = np.floor(fold_counts * self.test_size).astype(int)
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if len(fold_counts) == 0:
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return messages
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# search for targets that occur less than twice the number of cv folds first
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below_threshold_folds = fold_counts.where(fold_counts < self.cv_folds).dropna()
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if len(below_threshold_folds):
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below_threshold_values = [
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after_to_before_inference_mapping.get(each)
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for each in below_threshold_folds.index.tolist()
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]
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error_msg = "The number of instances of these targets is less than 2 * the number of cross folds = {} instances: {}"
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messages.append(
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DataCheckError(
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message=error_msg.format(
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self.cv_folds,
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sorted(below_threshold_values),
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),
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data_check_name=self.name,
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message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
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details={"target_values": sorted(below_threshold_values)},
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).to_dict(),
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)
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counts = fold_counts / (fold_counts + fold_counts.values[0])
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below_threshold = counts.where(counts < self.threshold).dropna()
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# if there are items that occur less than the threshold, add them to the list of results
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if len(below_threshold):
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below_threshold_values = [
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after_to_before_inference_mapping.get(each)
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for each in below_threshold.index.tolist()
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]
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warning_msg = "The following labels fall below {:.0f}% of the target: {}"
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messages.append(
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DataCheckWarning(
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message=warning_msg.format(
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self.threshold * 100,
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below_threshold_values,
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),
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data_check_name=self.name,
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message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
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details={"target_values": below_threshold_values},
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).to_dict(),
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)
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sample_counts = fold_counts.where(fold_counts < self.min_samples).dropna()
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if len(below_threshold) and len(sample_counts):
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sample_count_values = [
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after_to_before_inference_mapping.get(each)
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for each in sample_counts.index.tolist()
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]
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severe_imbalance = [
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v
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for v in sample_count_values
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if before_to_after_inference_mapping.get(v) in below_threshold
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]
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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: {}"
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messages.append(
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DataCheckWarning(
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message=warning_msg.format(
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self.threshold * 100,
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self.min_samples,
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severe_imbalance,
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),
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data_check_name=self.name,
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message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
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details={"target_values": severe_imbalance},
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).to_dict(),
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)
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return messages

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