Clustering Uncertainty-weighted Embeddings (CLUE)

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,
    plot_contour_for_samples,
)

from skactiveml.classifier import ParzenWindowClassifier
from skactiveml.pool import Clue

# Set a fixed random state for reproducibility.
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 = ParzenWindowClassifier(classes=[0, 1], random_state=random_state)
# Initialise the query strategy.
qs = Clue()

# Preparation for plotting: create a 2x2 grid of subplots.
fig, axs = plt.subplots(2, 2, constrained_layout=True)
feature_bound = [[min(X[:, 0]), min(X[:, 1])], [max(X[:, 0]), max(X[:, 1])]]
artists = [[] for j in range(5)]

# Active learning cycle.
n_cycles = 5
for c in range(n_cycles):
    # Train the classifier with the current labels.
    clf.fit(X, y)

    # Query the next batch of samples; retrieve both indices and utility values.
    query_idx, utilities = qs.query(X=X, y=y, clf=clf, batch_size=4, return_utilities=True)

    # Plot results on each subplot.
    for i, ax in enumerate(axs.flatten()):
        # Save current collections to identify new plot elements.
        coll_old = list(ax.collections)

        # Plot the utility contour for the current subplot.
        plot_contour_for_samples(
            X,
            utilities[i],
            res=25,
            feature_bound=feature_bound,
            replace_nan=None,
            ax=ax,
        )
        # Scatter all samples with true labels.
        ax.scatter(X[:, 0], X[:, 1], c=y_true, cmap="coolwarm", marker=".", zorder=2)
        # Highlight the labeled samples.
        X_labeled = X[labeled_indices(y)]
        ax.scatter(
            X_labeled[:, 0],
            X_labeled[:, 1],
            c="grey",
            alpha=0.8,
            marker=".",
            s=300,
        )
        # Overlay the decision boundary.
        ax = plot_decision_boundary(clf, feature_bound, ax=ax)
        # Set the title indicating the current batch and subplot index.
        ax.set_title(f"Batch {c+1}, Utilities[{i}]")

        # Collect new artists (plot elements) added during this cycle.
        for x in ax.collections:
            if x not in coll_old:
                artists[c].append(x)

    # Update the labels for the queried samples.
    y[query_idx] = y_true[query_idx]

# Create the animation using the collected artists.
ani = animation.ArtistAnimation(fig, artists, interval=1000, blit=True)

Once Loop Reflect

References:

The implementation of this strategy is based on Prabhu et al.1.

1

Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman. Active domain adaptation via clustering uncertainty-weighted embeddings. In IEEE/CVF Int. Conf. Comput. Vis., 8505–8514. 2021.