bagging.py

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

from itertools import product

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

np.random.seed(0)

# 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

# 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)

# rebagging the data
committee.rebag()

# 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)