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