L1 Normalization - It may be defined as the normalization technique that modifies the dataset values in a way that in each row the sum of the absolute values will always be up to
It may be defined as the normalization technique that modifies the dataset values in a way that in each row the sum of the absolute values will always be up to 1. It is also called the Least Absolute Deviations.
In this example, we use L1 Normalize technique to normalize the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded and then with the help of the Normalizer class, it will be normalized.
The first few lines of the following script are the same as we have written in previous chapters while loading CSV data.
from pandas import read_csv from numpy import set_printoptions from sklearn.preprocessing import Normalizer path = r'C:\pima-indians-diabetes.csv' names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = read_csv (path, names=names) array = dataframe.values
Now, we can use the Normalizer class with L1 to normalize the data.
Data_normalizer = Normalizer(norm='l1').fit(array) Data_normalized = Data_normalizer.transform(array)
We can also summarize the data for output as per our choice. Here, we are setting the precision to 2 and showing the first 3 rows in the output.
set_printoptions(precision=2) print ("\nNormalized data:\n", Data_normalized [0:3])
Normalized data: [[0.02 0.43 0.21 0.1 0. 0.1 0. 0.14 0. ] [0. 0.36 0.28 0.12 0. 0.11 0. 0.13 0. ] [0.03 0.59 0.21 0. 0. 0.07 0. 0.1 0. ]]