Density-Diversity-Distribution-Distance Sampling

Note

The generated animation can be found at the bottom of the page.

Google Colab Note: If the notebook fails to run after installing the needed packages, try to restart the runtime (Ctrl + M) under Runtime -> Restart session.

Notebook Dependencies

Uncomment the following cell to install all dependencies for this tutorial.

# !pip install scikit-activeml

---

import numpy as np
from matplotlib import pyplot as plt, animation
from sklearn.datasets import make_blobs

from skactiveml.utils import MISSING_LABEL, labeled_indices, unlabeled_indices
from skactiveml.visualization import plot_utilities, plot_decision_boundary

from skactiveml.classifier import MixtureModelClassifier
from skactiveml.pool import FourDs
from sklearn.mixture import GaussianMixture

random_state = np.random.RandomState(0)

# Build a dataset.
X, y_true = make_blobs(
    n_samples=200,
    n_features=2,
    centers=[[0, 1], [-3, 0.5], [-1, -1], [2, 1], [1, -0.5]],
    cluster_std=0.7,
    random_state=random_state,
)
y_true = y_true % 2
y = np.full(shape=y_true.shape, fill_value=MISSING_LABEL)

# Initialise the classifier.
clf = MixtureModelClassifier(classes=[0, 1], mixture_model=GaussianMixture(n_components=5))

# Initialise the query strategy.
qs = FourDs()

# Preparation for plotting.
fig, ax = plt.subplots()
feature_bound = [[min(X[:, 0]), min(X[:, 1])], [max(X[:, 0]), max(X[:, 1])]]
artists = []

# Active learning cycle:
n_cycles = 20
for c in range(n_cycles):
    # Fit the classifier with current labels.
    clf.fit(X, y)

    # Query the next sample(s).
    query_idx = qs.query(X=X, y=y, clf=clf)

    # Capture the current plot state.
    coll_old = list(ax.collections)
    title = ax.text(
        0.5, 1.05,
        f"Decision boundary after acquiring {c} labels",
        size=plt.rcParams["axes.titlesize"],
        ha="center", transform=ax.transAxes,
    )

    # Update plot with utility values, samples, and decision boundary.
    X_labeled = X[labeled_indices(y)]
    ax = plot_utilities(
        qs,
        X=X, y=y, clf=clf,
        candidates=None,
        res=25,
        feature_bound=feature_bound,
        ax=ax,
    )
    ax.scatter(
        X[:, 0], X[:, 1], c=y_true, cmap="coolwarm", marker=".", zorder=2
    )
    ax.scatter(
        X_labeled[:, 0],
        X_labeled[:, 1],
        c="grey",
        alpha=0.8,
        marker=".",
        s=300,
    )
    ax = plot_decision_boundary(clf, feature_bound, ax=ax)

    coll_new = list(ax.collections)
    coll_new.append(title)
    artists.append([x for x in coll_new if x not in coll_old])

    # Update labels based on query.
    y[query_idx] = y_true[query_idx]

ani = animation.ArtistAnimation(fig, artists, interval=1000, blit=True)

Once Loop Reflect

References:

The implementation of this strategy is based on Reitmaier and Sick1.

1

Tobias Reitmaier and Bernhard Sick. Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS. Inf. Sci., 230:106–131, 2013.