highly_null_data_check¶

Data check that checks if there are any highly-null columns and rows in the input.

Module Contents¶

Classes Summary¶

HighlyNullDataCheck

Check if there are any highly-null columns and rows in the input.

Contents¶

class evalml.data_checks.highly_null_data_check.HighlyNullDataCheck(pct_null_col_threshold=0.95, pct_null_row_threshold=0.95)[source]¶

Check if there are any highly-null columns and rows in the input.

Parameters

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.

Methods

`get_null_column_information`	Finds columns that are considered highly null (percentage null is greater than threshold) and returns dictionary mapping column name to percentage null and dictionary mapping column name to null indices.
`get_null_row_information`	Finds rows that are considered highly null (percentage null is greater than threshold).
`name`	Return a name describing the data check.
`validate`	Check if there are any highly-null columns or rows in the input.

static get_null_column_information(X, pct_null_col_threshold=0.0)[source]¶

Finds columns that are considered highly null (percentage null is greater than threshold) and returns dictionary mapping column name to percentage null and dictionary mapping column name to null indices.

Parameters

X (pd.DataFrame) – DataFrame to check for highly null columns.
pct_null_col_threshold (float) – Percentage threshold for a column to be considered null. Defaults to 0.0.

Returns

Tuple containing: dictionary mapping column name to its null percentage and dictionary mapping column name to null indices in that column.

Return type

tuple

static get_null_row_information(X, pct_null_row_threshold=0.0)[source]¶

Finds rows that are considered highly null (percentage null is greater than threshold).

Parameters

X (pd.DataFrame) – DataFrame to check for highly null rows.
pct_null_row_threshold (float) – Percentage threshold for a row to be considered null. Defaults to 0.0.

Returns

Series containing the percentage null for each row.

Return type

pd.Series

name(cls)¶: Return a name describing the data check.

validate(self, X, y=None)[source]¶

Check if there are any highly-null columns or rows in the input.

Parameters

X (pd.DataFrame, np.ndarray) – Features.
y (pd.Series, np.ndarray) – Ignored. Defaults to None.

Returns

dict with a DataCheckWarning if there are any highly-null columns or rows.

Return type

dict

Examples

>>> 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))

With pct_null_col_threshold set to 0.50, any column that has 50% or more of its observations set to null will be included in the warning, as well as the percentage of null values identified (“all_null”: 1.0, “lots_of_null”: 0.8).

>>> df = pd.DataFrame({
...     'all_null': [None, pd.NA, None, None, None],
...     'lots_of_null': [None, None, None, None, 5],
...     'few_null': ["near", "far", pd.NaT, "wherever", "nowhere"],
...     'no_null': [1, 2, 3, 4, 5]
... })
...
>>> highly_null_dc = HighlyNullDataCheck(pct_null_col_threshold=0.50)
>>> assert highly_null_dc.validate(df) == {
...     'warnings': [{'message': "Columns 'all_null', 'lots_of_null' are 50.0% or more null",
...                   'data_check_name': 'HighlyNullDataCheck',
...                   'level': 'warning',
...                   'details': {'columns': ['all_null', 'lots_of_null'],
...                               'rows': None,
...                               'pct_null_rows': {'all_null': 1.0, 'lots_of_null': 0.8}},
...                   'code': 'HIGHLY_NULL_COLS'}],
...     'errors': [],
...     'actions': [{'code': 'DROP_COL',
...                  'data_check_name': 'HighlyNullDataCheck',
...                  'metadata': {'columns': ['all_null', 'lots_of_null'], 'rows': None}}]}

With pct_null_row_threshold set to 0.50, any row with 50% or more of its respective column values set to null will included in the warning, as well as the offending rows (“rows”: [0, 1, 2, 3]). Since the default value for pct_null_col_threshold is 0.95, “all_null” is also included in the warnings since the percentage of null values in that row is over 95%.

>>> highly_null_dc = HighlyNullDataCheck(pct_null_row_threshold=0.50)
>>> validation_results = highly_null_dc.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, 0.75, 0.5]))
>>> assert validation_results == {
...     'warnings': [{'message': '4 out of 5 rows are 50.0% or more null',
...                   'data_check_name': 'HighlyNullDataCheck',
...                   'level': 'warning',
...                   'details': {'columns': None,
...                               'rows': [0, 1, 2, 3],
...                               'pct_null_cols': highly_null_rows},
...                   'code': 'HIGHLY_NULL_ROWS'},
...                  {'message': "Columns 'all_null' are 95.0% or more null",
...                   'data_check_name': 'HighlyNullDataCheck',
...                   'level': 'warning',
...                   'details': {'columns': ['all_null'],
...                               'rows': None,
...                               'pct_null_rows': {'all_null': 1.0}},
...                   'code': 'HIGHLY_NULL_COLS'}],
...     'errors': [],
...     'actions': [{'code': 'DROP_ROWS',
...                  'data_check_name': 'HighlyNullDataCheck',
...                  'metadata': {'columns': None, 'rows': [0, 1, 2, 3]}},
...                 {'code': 'DROP_COL',
...                  'data_check_name': 'HighlyNullDataCheck',
...                  'metadata': {'columns': ['all_null'], 'rows': None}}]}

default_data_checks

id_columns_data_check