make_imbalance function

An illustration of the make_imbalance function

# Authors: Dayvid Oliveira
#          Christos Aridas
#          Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT

import matplotlib.pyplot as plt
from sklearn.datasets import make_moons

from imblearn.datasets import make_imbalance

print(__doc__)


def plot_decoration(ax):
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()
    ax.spines['left'].set_position(('outward', 10))
    ax.spines['bottom'].set_position(('outward', 10))
    ax.set_xlim([-4, 4])


# Generate the dataset
X, y = make_moons(n_samples=200, shuffle=True, noise=0.5, random_state=10)

# Two subplots, unpack the axes array immediately
f, axs = plt.subplots(2, 3)

axs = [a for ax in axs for a in ax]

axs[0].scatter(X[y == 0, 0], X[y == 0, 1], label="Class #0", alpha=0.5)
axs[0].scatter(X[y == 1, 0], X[y == 1, 1], label="Class #1", alpha=0.5)
axs[0].set_title('Original set')
plot_decoration(axs[0])

ratios = [0.9, 0.75, 0.5, 0.25, 0.1]
for i, ratio in enumerate(ratios, start=1):
    ax = axs[i]

    X_, y_ = make_imbalance(X, y, ratio=ratio, min_c_=1)
    ax.scatter(X_[y_ == 0, 0], X_[y_ == 0, 1], label="Class #0", alpha=0.5)
    ax.scatter(X_[y_ == 1, 0], X_[y_ == 1, 1], label="Class #1", alpha=0.5)
    ax.set_title('ratio = {}'.format(ratio))
    plot_decoration(ax)

plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 0.674 seconds)