from evalml.data_checks import (
DataCheck,
DataCheckAction,
DataCheckActionCode,
DataCheckMessageCode,
DataCheckWarning
)
from evalml.utils import infer_feature_types
[docs]class HighlyNullDataCheck(DataCheck):
"""Checks if there are any highly-null columns and rows in the input."""
[docs] def __init__(self, pct_null_threshold=0.95):
"""Checks if there are any highly-null columns and rows in the input.
Arguments:
pct_null_threshold(float): If the percentage of NaN values in an input feature exceeds this amount,
that column/row will be considered highly-null. Defaults to 0.95.
"""
if pct_null_threshold < 0 or pct_null_threshold > 1:
raise ValueError("pct_null_threshold must be a float between 0 and 1, inclusive.")
self.pct_null_threshold = pct_null_threshold
[docs] def validate(self, X, y=None):
"""Checks if there are any highly-null columns or rows in the input.
Arguments:
X (pd.DataFrame, np.ndarray): Features.
y (pd.Series, np.ndarray): Ignored.
Returns:
dict: dict with a DataCheckWarning if there are any highly-null columns or rows.
Example:
>>> import pandas as pd
>>> class SeriesWrap():
... def __init__(self, series):
... self.series = series
...
... def __eq__(self, series_2):
... return all(self.series.eq(series_2.series))
...
>>> df = pd.DataFrame({
... 'lots_of_null': [None, None, None, None, 5],
... 'no_null': [1, 2, 3, 4, 5]
... })
>>> null_check = HighlyNullDataCheck(pct_null_threshold=0.50)
>>> validation_results = null_check.validate(df)
>>> validation_results['warnings'][0]['details']['pct_null_cols'] = SeriesWrap(validation_results['warnings'][0]['details']['pct_null_cols'])
>>> highly_null_rows = SeriesWrap(pd.Series([0.5, 0.5, 0.5, 0.5]))
>>> assert validation_results== {"errors": [],\
"warnings": [{"message": "4 out of 5 rows are more than 50.0% null",\
"data_check_name": "HighlyNullDataCheck",\
"level": "warning",\
"code": "HIGHLY_NULL_ROWS",\
"details": {"pct_null_cols": highly_null_rows}},\
{"message": "Column 'lots_of_null' is 50.0% or more null",\
"data_check_name": "HighlyNullDataCheck",\
"level": "warning",\
"code": "HIGHLY_NULL_COLS",\
"details": {"column": "lots_of_null", "pct_null_rows": 0.8}}],\
"actions": [{"code": "DROP_ROWS", "metadata": {"rows": [0, 1, 2, 3]}},\
{"code": "DROP_COL",\
"metadata": {"column": "lots_of_null"}}]}
"""
results = {
"warnings": [],
"errors": [],
"actions": []
}
X = infer_feature_types(X)
percent_null_rows = X.isnull().mean(axis=1)
highly_null_rows = percent_null_rows[percent_null_rows >= self.pct_null_threshold]
if len(highly_null_rows) > 0:
warning_msg = f"{len(highly_null_rows)} out of {len(X)} rows are more than {self.pct_null_threshold*100}% null"
results["warnings"].append(DataCheckWarning(message=warning_msg,
data_check_name=self.name,
message_code=DataCheckMessageCode.HIGHLY_NULL_ROWS,
details={"pct_null_cols": highly_null_rows}).to_dict())
results["actions"].append(DataCheckAction(DataCheckActionCode.DROP_ROWS,
metadata={"rows": highly_null_rows.index.tolist()}).to_dict())
percent_null_cols = (X.isnull().mean()).to_dict()
highly_null_cols = {key: value for key, value in percent_null_cols.items() if value >= self.pct_null_threshold and value != 0}
warning_msg = "Column '{}' is {}% or more null"
results["warnings"].extend([DataCheckWarning(message=warning_msg.format(col_name, self.pct_null_threshold * 100),
data_check_name=self.name,
message_code=DataCheckMessageCode.HIGHLY_NULL_COLS,
details={"column": col_name, "pct_null_rows": highly_null_cols[col_name]}).to_dict()
for col_name in highly_null_cols])
results["actions"].extend([DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={"column": col_name}).to_dict()
for col_name in highly_null_cols])
return results