SimpleImputer

path: sklearn.impute.SimpleImputer

description: this primitive is an imputation transformer for filling missing values.

see json.

argument	type	description
parameters
X	numpy.ndarray	n-dimensional sequence of values
hyperparameters
missing_values	int, float, str, numpy.nan, or None	the placeholder for the missing values. All occurrences of missing_values will be imputed
strategy	str	the imputation strategy. If mean, then replace missing values using the mean along each column. Can only be used with numeric data. If median, then replace missing values using the median along each column. Can only be used with numeric data. If most_frequent, then replace missing using the most frequent value along each column. Can be used with strings or numeric data. If constant, then replace missing values with fill_value. Can be used with strings or numeric data.
fill_value	int, float, or str	when strategy == "constant", fill_value is used to replace all occurrences of missing_values. If left to the default, fill_value will be 0 when imputing numerical data and “missing_value” for strings or object data types
verbose	bool	controls the verbosity of the imputer
copy	bool	if True, a copy of X will be created
output
X	numpy.ndarray	a transformed version of X

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: X = np.array([1] * 4 + [np.nan]).reshape(-1, 1)

In [4]: primitive = load_primitive('sklearn.impute.SimpleImputer',
   ...:     arguments={"X": X})
   ...: 

In [5]: primitive.fit()

In [6]: primitive.produce(X=X)
Out[6]: 
array([[1.],
       [1.],
       [1.],
       [1.],
       [1.]])

fillna MinMaxScaler