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