outliers_data_check

Data check that checks if there are any outliers in input data by using IQR to determine score anomalies.

Module Contents

Classes Summary

OutliersDataCheck

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

Contents

class evalml.data_checks.outliers_data_check.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.

Methods

get_boxplot_data	Returns box plot information for the given data.
name	Return a name describing the data check.
validate	Check if there are any outliers in a dataframe by using IQR to determine column anomalies. Column with anomalies are considered to contain outliers.

static get_boxplot_data(data_)

Returns box plot information for the given data.

Parameters

data (pd.Series, np.ndarray) – Input data.

Returns

A payload of box plot statistics.

Return type

dict

name(cls)

Return a name describing the data check.

validate(self, X, y=None)

Check 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) – Input features.

• y (pd.Series, np.ndarray) – Ignored. Defaults to None.

Returns

A dictionary with warnings if any columns have outliers.

Return type

dict

Examples

>>> 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"], "rows": [3], "column_indices": {"z": [3]}}}],
...     "errors": [],
...     "actions": [{"code": "DROP_ROWS", "metadata": {"rows": [3], "columns": None}}]}
...
...
>>> box_plot_data = OutliersDataCheck.get_boxplot_data(df['z'])
>>> box_plot_data["score"] = round(box_plot_data["score"], 2)
>>> assert box_plot_data == {
...     'score': 0.89,
...     'pct_outliers': 0.2,
...     'values': {'q1': -4.0,
...                'median': -3.0,
...                'q3': -2.0,
...                'low_bound': -7.0,
...                'high_bound': 1.0,
...                'low_values': [-1201],
...                'high_values': [],
...                'low_indices': [3],
...                'high_indices': []}}

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