outliers_data_check¶

Module Contents¶

Classes Summary¶

OutliersDataCheck

Checks if there are any outliers in input data by using IQR to determine score anomalies. Columns with score anomalies are considered to contain outliers.

Contents¶

class evalml.data_checks.outliers_data_check.OutliersDataCheck[source]¶

Checks if there are any outliers in input data by using IQR to determine score anomalies. Columns with score anomalies are considered to contain outliers.

Methods

`name`	Returns a name describing the data check.
`validate`	Checks if there are any outliers in a dataframe by using IQR to determine column anomalies. Column with anomalies are considered to contain outliers.

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

validate(self, X, y=None)[source]¶

Checks if there are any outliers in a dataframe by using IQR to determine column anomalies. Column with anomalies are considered to contain outliers.

Parameters

X (pd.DataFrame, np.ndarray) – Features
y (pd.Series, np.ndarray) – Ignored.

Returns

A dictionary with warnings if any columns have outliers.

Return type

dict

Example

>>> import pandas as pd
>>> df = pd.DataFrame({
...     'x': [1, 2, 3, 4, 5],
...     'y': [6, 7, 8, 9, 10],
...     'z': [-1, -2, -3, -1201, -4]
... })
>>> outliers_check = OutliersDataCheck()
>>> assert outliers_check.validate(df) == {"warnings": [{"message": "Column(s) 'z' are likely to have outlier data.",                                                                     "data_check_name": "OutliersDataCheck",                                                                     "level": "warning",                                                                     "code": "HAS_OUTLIERS",                                                                     "details": {"columns": ["z"]}}],                                                       "errors": [],                                                       "actions": []}

no_variance_data_check sparsity_data_check