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[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

`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_)[source]¶

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

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]
... })
>>> 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': []}
...     }

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

validate(self, X, y=None)[source]¶

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

The column “z” has an outlier so a warning is added to alert the user of its location.

>>> 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",
...                  "data_check_name": "OutliersDataCheck",
...                  "metadata": {"rows": [3], "columns": None}}]}

no_variance_data_check

sparsity_data_check