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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Title
Step1:  Load Iris Dataset
Step2:  Make Iris Dataset Imbalanced
Step3:  Upsampling Minority Class To Match Majority
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Python Code:
# Load libraries
import numpy as np
from sklearn.datasets import load_iris
Explanation: Title: Handling Imbalanced Classes With Upsampling
Slug: handling_imbalanced_classes_with_upsampling
Summary: How to handle imbalanced classes with upsampling during machine learning in Python. 
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Structured Data
Authors: Chris Albon
<a alt="Upsampling" href="https://machinelearningflashcards.com">
    <img src="handling_imbalanced_classes_with_upsampling/Upsampling_print.png" class="flashcard center-block">
</a>
In upsampling, for every observation in the majority class, we randomly select an observation from the minority class with replacement. The end result is the same number of observations from the minority and majority classes.
Preliminaries
End of explanation
# Load iris data
iris = load_iris()
# Create feature matrix
X = iris.data
# Create target vector
y = iris.target
Explanation: Load Iris Dataset
End of explanation
# Remove first 40 observations
X = X[40:,:]
y = y[40:]
# Create binary target vector indicating if class 0
y = np.where((y == 0), 0, 1)
# Look at the imbalanced target vector
y
Explanation: Make Iris Dataset Imbalanced
End of explanation
# Indicies of each class' observations
i_class0 = np.where(y == 0)[0]
i_class1 = np.where(y == 1)[0]
# Number of observations in each class
n_class0 = len(i_class0)
n_class1 = len(i_class1)
# For every observation in class 1, randomly sample from class 0 with replacement
i_class0_upsampled = np.random.choice(i_class0, size=n_class1, replace=True)
# Join together class 0's upsampled target vector with class 1's target vector
np.concatenate((y[i_class0_upsampled], y[i_class1]))
Explanation: Upsampling Minority Class To Match Majority
End of explanation
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