bagging.py

"""
This example shows how to build models with bagging using the Committee model.
"""

from itertools import product

import numpy as np
from matplotlib import pyplot as plt
from modAL.models import ActiveLearner, Committee
from sklearn.neighbors import KNeighborsClassifier

# creating the dataset
im_width = 500
im_height = 500
data = np.zeros((im_height, im_width))
# each disk is coded as a triple (x, y, r), where x and y are the centers and r is the radius
disks = [(150, 150, 80), (200, 380, 50), (360, 200, 100)]
for i, j in product(range(im_width), range(im_height)):
    for x, y, r in disks:
        if (x-i)**2 + (y-j)**2 < r**2:
            data[i, j] = 1

# visualizing the dataset
with plt.style.context('seaborn-white'):
    plt.figure(figsize=(7, 7))
    plt.imshow(data)
    plt.title('The shapes to learn')
    plt.show()

# create the pool from the image
X_pool = np.transpose(
    [np.tile(np.asarray(range(data.shape[0])), data.shape[1]),
     np.repeat(np.asarray(range(data.shape[1])), data.shape[0])]
)
# map the intensity values against the grid
y_pool = np.asarray([data[P[0], P[1]] for P in X_pool])

# initial training data: 1000 random pixels
initial_idx = np.random.choice(range(len(X_pool)), size=1000)

# initializing the learners
n_learners = 3
learner_list = []
for _ in range(n_learners):
    learner = ActiveLearner(
        estimator=KNeighborsClassifier(n_neighbors=10),
        X_training=X_pool[initial_idx], y_training=y_pool[initial_idx],
        bootstrap_init=True
    )
    learner_list.append(learner)

# assembling the Committee
committee = Committee(learner_list)

# visualizing every learner in the Committee
with plt.style.context('seaborn-white'):
    plt.figure(figsize=(7*n_learners, 7))
    for learner_idx, learner in enumerate(committee):
        plt.subplot(1, n_learners, learner_idx+1)
        plt.imshow(learner.predict(X_pool).reshape(im_height, im_width))
        plt.title('Learner no. %d' % (learner_idx + 1))
    plt.show()

# visualizing the Committee's predictions
with plt.style.context('seaborn-white'):
    plt.figure(figsize=(7, 7))
    plt.imshow(committee.predict(X_pool).reshape(im_height, im_width))
    plt.title('Committee consensus predictions')
    plt.show()

# rebagging the data
committee.rebag()

# visualizing the learners in the retrained Committee
with plt.style.context('seaborn-white'):
    plt.figure(figsize=(7*n_learners, 7))
    for learner_idx, learner in enumerate(committee):
        plt.subplot(1, n_learners, learner_idx+1)
        plt.imshow(learner.predict(X_pool).reshape(im_height, im_width))
        plt.title('Learner no. %d after rebagging' % (learner_idx + 1))
    plt.show()

# refitting the model with bagging on a different set of data
new_idx = np.random.choice(range(len(X_pool)), size=100)
committee.fit(X_pool[new_idx], y_pool[new_idx], bootstrap=True)

# visualizing the learners in the retrained Committee
with plt.style.context('seaborn-white'):
    plt.figure(figsize=(7*n_learners, 7))
    for learner_idx, learner in enumerate(committee):
        plt.subplot(1, n_learners, learner_idx+1)
        plt.imshow(learner.predict(X_pool).reshape(im_height, im_width))
        plt.title('Learner no. %d after refitting' % (learner_idx + 1))
    plt.show()