Source code for evalml.data_checks.sparsity_data_check

from evalml.data_checks import (
    DataCheck,
    DataCheckAction,
    DataCheckActionCode,
    DataCheckMessageCode,
    DataCheckWarning,
)
from evalml.problem_types import handle_problem_types, is_multiclass
from evalml.utils.woodwork_utils import infer_feature_types

warning_too_unique = "Input columns ({}) for {} problem type are too sparse."


[docs]class SparsityDataCheck(DataCheck): """Checks if there are any columns with sparsely populated values in the input. Arguments: problem_type (str or ProblemTypes): The specific problem type to data check for. 'multiclass' or 'time series multiclass' is the only accepted problem type. threshold (float): The threshold value, or percentage of each column's unique values, below which, a column exhibits sparsity. Should be between 0 and 1. unique_count_threshold (int): The minimum number of times a unique value has to be present in a column to not be considered "sparse." Defaults to 10. """ def __init__(self, problem_type, threshold, unique_count_threshold=10): self.problem_type = handle_problem_types(problem_type) if not is_multiclass(self.problem_type): raise ValueError("Sparsity is only defined for multiclass problem types.") self.threshold = threshold if threshold < 0 or threshold > 1: raise ValueError("Threshold must be a float between 0 and 1, inclusive.") self.unique_count_threshold = unique_count_threshold if unique_count_threshold < 0 or not isinstance(unique_count_threshold, int): raise ValueError("Unique count threshold must be positive integer.")
[docs] def validate(self, X, y=None): """Calculates what percentage of each column's unique values exceed the count threshold and compare that percentage to the sparsity threshold stored in the class instance. Arguments: X (pd.DataFrame, np.ndarray): Features. y (pd.Series, np.ndarray): Ignored. Returns: dict: dict with a DataCheckWarning if there are any sparse columns. Example: >>> import pandas as pd >>> df = pd.DataFrame({ ... 'sparse': [float(x) for x in range(100)], ... 'not_sparse': [float(1) for x in range(100)] ... }) >>> sparsity_check = SparsityDataCheck(problem_type="multiclass", threshold=0.5, unique_count_threshold=10) >>> assert sparsity_check.validate(df) == {"errors": [],\ "warnings": [{"message": "Input columns (sparse) for multiclass problem type are too sparse.",\ "data_check_name": "SparsityDataCheck",\ "level": "warning",\ "code": "TOO_SPARSE",\ "details": {"column": "sparse", 'sparsity_score': 0.0}}],\ "actions": [{"code": "DROP_COL",\ "metadata": {"column": "sparse"}}]} """ results = {"warnings": [], "errors": [], "actions": []} X = infer_feature_types(X) res = X.apply( SparsityDataCheck.sparsity_score, count_threshold=self.unique_count_threshold, ) too_sparse_cols = [col for col in res.index[res < self.threshold]] results["warnings"].extend( [ DataCheckWarning( message=warning_too_unique.format(col_name, self.problem_type), data_check_name=self.name, message_code=DataCheckMessageCode.TOO_SPARSE, details={"column": col_name, "sparsity_score": res.loc[col_name]}, ).to_dict() for col_name in too_sparse_cols ] ) results["actions"].extend( [ DataCheckAction( action_code=DataCheckActionCode.DROP_COL, metadata={"column": col_name}, ).to_dict() for col_name in too_sparse_cols ] ) return results
[docs] @staticmethod def sparsity_score(col, count_threshold=10): """This function calculates a sparsity score for the given value counts by calculating the percentage of unique values that exceed the count_threshold. Arguments: col (pd.Series): Feature values. count_threshold (int): The number of instances below which a value is considered sparse. Default is 10. Returns: (float): Sparsity score, or the percentage of the unique values that exceed count_threshold. """ counts = col.value_counts() score = sum(counts > count_threshold) / counts.size return score