evalml.data_checks.HighlyNullDataCheck.validate¶
-
HighlyNullDataCheck.validate(X, y=None)[source]¶ Checks if there are any highly-null columns or rows in the input.
- Parameters
X (ww.DataTable, pd.DataFrame, np.ndarray) – Data
y (ww.DataColumn, pd.Series, np.ndarray) – Ignored.
- Returns
dict with a DataCheckWarning if there are any highly-null columns or rows.
- Return type
dict
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"}}]}