OutlierTrimmer

API Reference

class feature_engine.outliers.OutlierTrimmer(capping_method='gaussian', tail='right', fold=3, variables=None, missing_values='raise')[source]

The OutlierTrimmer() removes observations with outliers from the dataset.

It works only with numerical variables. A list of variables can be indicated. Alternatively, the OutlierTrimmer() will select all numerical variables.

The OutlierTrimmer() first calculates the maximum and /or minimum values beyond which a value will be considered an outlier, and thus removed.

Limits are determined using:

  • a Gaussian approximation

  • the inter-quantile range proximity rule

  • percentiles.

Gaussian limits:

  • right tail: mean + 3* std

  • left tail: mean - 3* std

IQR limits:

  • right tail: 75th quantile + 3* IQR

  • left tail: 25th quantile - 3* IQR

where IQR is the inter-quartile range: 75th quantile - 25th quantile.

percentiles or quantiles:

  • right tail: 95th percentile

  • left tail: 5th percentile

You can select how far out to cap the maximum or minimum values with the parameter ‘fold’.

If capping_method='gaussian' fold gives the value to multiply the std.

If capping_method='iqr' fold is the value to multiply the IQR.

If capping_method='quantile', fold is the percentile on each tail that should be censored. For example, if fold=0.05, the limits will be the 5th and 95th percentiles. If fold=0.1, the limits will be the 10th and 90th percentiles.

The transformer first finds the values at one or both tails of the distributions (fit).

The transformer then removes observations with outliers from the dataframe (transform).

Parameters
capping_method: str, default=gaussian

Desired capping method. Can take ‘gaussian’, ‘iqr’ or ‘quantiles’.

‘gaussian’: the transformer will find the maximum and / or minimum values to cap the variables using the Gaussian approximation.

‘iqr’: the transformer will find the boundaries using the IQR proximity rule.

‘quantiles’: the limits are given by the percentiles.

tail: str, default=right

Whether to cap outliers on the right, left or both tails of the distribution. Can take ‘left’, ‘right’ or ‘both’.

fold: int or float, default=3

How far out to to place the capping values. The number that will multiply the std or IQR to calculate the capping values. Recommended values, 2 or 3 for the gaussian approximation, or 1.5 or 3 for the IQR proximity rule.

If capping_method=’quantile’, then ‘fold’ indicates the percentile. So if fold=0.05, the limits will be the 95th and 5th percentiles. Note: Outliers will be removed up to a maximum of the 20th percentiles on both sides. Thus, when capping_method=’quantile’, then ‘fold’ takes values between 0 and 0.20.

variables: list, default=None

The list of variables for which the outliers will be removed If None, the transformer will find and select all numerical variables.

missing_values: string, default=’raise’

Indicates if missing values should be ignored or raised. Sometimes we want to remove outliers in the raw, original data, sometimes, we may want to remove outliers in the already pre-transformed data. If missing_values=’ignore’, the transformer will ignore missing data when learning the capping parameters or transforming the data. If missing_values=’raise’ the transformer will return an error if the training or the datasets to transform contain missing values.

Attributes

right_tail_caps_:

Dictionary with the maximum values above which values will be removed.

left_tail_caps_ :

Dictionary with the minimum values below which values will be removed.

variables_:

The group of variables that will be transformed.

n_features_in_:

The number of features in the train set used in fit.

Methods

fit:

Find maximum and minimum values.

transform:

Remove outliers.

fit_transform:

Fit to the data. Then transform it.

transform(X)[source]

Remove observations with outliers from the dataframe.

Parameters
Xpandas dataframe of shape = [n_samples, n_features]

The data to be transformed.

Returns
Xpandas dataframe of shape = [n_samples, n_features]

The dataframe without outlier observations.

rtype

DataFrame ..

Raises
TypeError

If the input is not a Pandas DataFrame

ValueError

If the dataframe is not of same size as that used in fit()

Example

Removes values beyond predefined minimum and maximum values from the data set. The minimum and maximum values can be calculated in 1 of 3 different ways:

Gaussian limits:

right tail: mean + 3* std

left tail: mean - 3* std

IQR limits:

right tail: 75th quantile + 3* IQR

left tail: 25th quantile - 3* IQR

where IQR is the inter-quartile range: 75th quantile - 25th quantile.

percentiles or quantiles:

right tail: 95th percentile

left tail: 5th percentile

See the API Reference for more details.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine.outliers import OutlierTrimmer

# Load dataset
def load_titanic():
    data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')
    data = data.replace('?', np.nan)
    data['cabin'] = data['cabin'].astype(str).str[0]
    data['pclass'] = data['pclass'].astype('O')
    data['embarked'].fillna('C', inplace=True)
    data['fare'] = data['fare'].astype('float')
    data['fare'].fillna(data['fare'].median(), inplace=True)
    data['age'] = data['age'].astype('float')
    data['age'].fillna(data['age'].median(), inplace=True)
    return data

data = load_titanic()

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
            data.drop(['survived', 'name', 'ticket'], axis=1),
            data['survived'], test_size=0.3, random_state=0)

# set up the capper
capper = OutlierTrimmer(capping_method='iqr', tail='right', fold=1.5, variables=['age', 'fare'])

# fit the capper
capper.fit(X_train)

# transform the data
train_t= capper.transform(X_train)
test_t= capper.transform(X_test)

capper.right_tail_caps_
{'age': 53.0, 'fare': 66.34379999999999}
train_t[['fare', 'age']].max()
fare    65.0
age     53.0
dtype: float64