"""Data check that checks if there are any highly-null columns and rows in the input."""
from evalml.data_checks import (
DataCheck,
DataCheckAction,
DataCheckActionCode,
DataCheckMessageCode,
DataCheckWarning,
)
from evalml.utils import infer_feature_types
[docs]class HighlyNullDataCheck(DataCheck):
"""Check if there are any highly-null columns and rows in the input.
Args:
pct_null_col_threshold(float): If the percentage of NaN values in an input feature exceeds this amount,
that column will be considered highly-null. Defaults to 0.95.
pct_null_row_threshold(float): If the percentage of NaN values in an input row exceeds this amount,
that row will be considered highly-null. Defaults to 0.95.
"""
def __init__(self, pct_null_col_threshold=0.95, pct_null_row_threshold=0.95):
if pct_null_col_threshold < 0 or pct_null_col_threshold > 1:
raise ValueError(
"pct null column threshold must be a float between 0 and 1, inclusive."
)
if pct_null_row_threshold < 0 or pct_null_row_threshold > 1:
raise ValueError(
"pct null row threshold must be a float between 0 and 1, inclusive."
)
self.pct_null_col_threshold = pct_null_col_threshold
self.pct_null_row_threshold = pct_null_row_threshold
[docs] def validate(self, X, y=None):
"""Check if there are any highly-null columns or rows in the input.
Args:
X (pd.DataFrame, np.ndarray): Features.
y (pd.Series, np.ndarray): Ignored. Defaults to None.
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_col_threshold=0.50, pct_null_row_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_row_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_row_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_col_threshold and value != 0
}
warning_msg = "Column '{}' is {}% or more null"
results["warnings"].extend(
[
DataCheckWarning(
message=warning_msg.format(
col_name, self.pct_null_col_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