RandomSampleImputer¶
API Reference¶
- class feature_engine.imputation.RandomSampleImputer(random_state=None, seed='general', seeding_method='add', variables=None)[source]¶
The RandomSampleImputer() replaces missing data with a random sample extracted from the variables in the training set.
The RandomSampleImputer() works with both numerical and categorical variables.
Note
The Random samples used to replace missing values may vary from execution to execution. This may affect the results of your work. This, it is advisable to set a seed.
There are 2 ways in which the seed can be set in the RandomSampleImputer():
If seed = ‘general’ then the random_state can be either None or an integer. The seed will be used as the random_state and all observations will be imputed in one go. This is equivalent to
pandas.sample(n, random_state=seed)
where n is the number of observations with missing data.If seed = ‘observation’, then the random_state should be a variable name or a list of variable names. The seed will be calculated observation per observation, either by adding or multiplying the seeding variable values, and passed to the random_state. Then, a value will be extracted from the train set using that seed and used to replace the NAN in particular observation. This is the equivalent of
pandas.sample(1, random_state=var1+var2)
if the ‘seeding_method’ is set to ‘add’ orpandas.sample(1, random_state=var1*var2)
if the ‘seeding_method’ is set to ‘multiply’.For more details on why this functionality is important refer to the course Feature Engineering for Machine Learning in Udemy: https://www.udemy.com/feature-engineering-for-machine-learning/
Note, if the variables indicated in the random_state list are not numerical the imputer will return an error. Note also that the variables indicated as seed should not contain missing values.
This estimator stores a copy of the training set when the fit() method is called. Therefore, the object can become quite heavy. Also, it may not be GDPR compliant if your training data set contains Personal Information. Please check if this behaviour is allowed within your organisation.
- Parameters
- random_state: int, str or list, default=None
The random_state can take an integer to set the seed when extracting the random samples. Alternatively, it can take a variable name or a list of variables, which values will be used to determine the seed observation per observation.
- seed: str, default=’general’
Indicates whether the seed should be set for each observation with missing values, or if one seed should be used to impute all observations in one go.
general: one seed will be used to impute the entire dataframe. This is equivalent to setting the seed in pandas.sample(random_state).
observation: the seed will be set for each observation using the values of the variables indicated in the random_state for that particular observation.
- seeding_method: str, default=’add’
If more than one variable are indicated to seed the random sampling per observation, you can choose to combine those values as an addition or a multiplication. Can take the values ‘add’ or ‘multiply’.
- variables: list, default=None
The list of variables to be imputed. If None, the imputer will select all variables in the train set.
Attributes
X_:
Copy of the training dataframe from which to extract the random samples.
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:
Make a copy of the dataframe
transform:
Impute missing data.
fit_transform:
Fit to the data, then transform it.
- fit(X, y=None)[source]¶
Makes a copy of the train set. Only stores a copy of the variables to impute. This copy is then used to randomly extract the values to fill the missing data during transform.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training dataset. Only a copy of the indicated variables will be stored in the transformer.
- y: None
y is not needed in this imputation. You can pass None or y.
- Returns
- self
- Raises
- TypeError
If the input is not a Pandas DataFrame
- transform(X)[source]¶
Replace missing data with random values taken from the train set.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The dataframe to be transformed.
- Returns
- X: pandas dataframe of shape = [n_samples, n_features]
The dataframe without missing values in the transformed variables.
- rtype
DataFrame
..
- Raises
- TypeError
If the input is not a Pandas DataFrame
Example¶
The RandomSampleImputer() replaces missing data with a random sample extracted from the variable. It works with both numerical and categorical variables. A list of variables can be indicated, or the imputer will automatically select all variables in the train set.
A seed can be set to a pre-defined number and all observations will be replaced in batch. Alternatively, a seed can be set using the values of 1 or more numerical variables. In this case, the observations will be imputed individually, one at a time, using the values of the variables as a seed.
For example, if the observation shows variables color: np.nan, height: 152, weight:52, and we set the imputer as:
RandomSampleImputer(random_state=['height', 'weight'],
seed='observation',
seeding_method='add'))
the observation will be replaced using pandas sample as follows:
observation.sample(1, random_state=int(152+52))
More details on how to use the RandomSampleImputer():
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from feature_engine.imputation import RandomSampleImputer
# Load dataset
data = pd.read_csv('houseprice.csv')
# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1),
data['SalePrice'],
test_size=0.3,
random_state=0
)
# set up the imputer
imputer = RandomSampleImputer(
random_state=['MSSubClass', 'YrSold'],
seed='observation',
seeding_method='add'
)
# fit the imputer
imputer.fit(X_train)
# transform the data
train_t = imputer.transform(X_train)
test_t = imputer.transform(X_test)
fig = plt.figure()
ax = fig.add_subplot(111)
X_train['LotFrontage'].plot(kind='kde', ax=ax)
train_t['LotFrontage'].plot(kind='kde', ax=ax, color='red')
lines, labels = ax.get_legend_handles_labels()
ax.legend(lines, labels, loc='best')