# make_imbalance functionΒΆ

An illustration of the make_imbalance function

Out:

```
```

```# Authors: Dayvid Oliveira
#          Christos Aridas
#          Guillaume Lemaitre <[email protected]>

from collections import Counter

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])

def ratio_func(y, multiplier, minority_class):
target_stats = Counter(y)
return {minority_class: int(multiplier * target_stats[minority_class])}

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

X_, y_ = make_imbalance(X, y, sampling_strategy=ratio_func,
**{"multiplier": multiplier,
"minority_class": 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('sampling_strategy = {}'.format(multiplier))
plot_decoration(ax)

plt.tight_layout()
plt.show()
```

Total running time of the script: ( 0 minutes 0.889 seconds)

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