Dual Strategy for Active Learning#

Idea: DUAL is simple meta-policy that switches between two base query strategies instead of only combining their scores.

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 sklearn.model_selection import train_test_split

from skactiveml.utils import MISSING_LABEL, is_labeled, simple_batch
from skactiveml.visualization import plot_decision_boundary, \
    plot_contour_for_samples

from sklearn.linear_model import LogisticRegression
from sklearn.mixture import GaussianMixture
from skactiveml.classifier import SklearnClassifier
from skactiveml.pool import UncertaintySampling

random_state = np.random.RandomState(0)

# Build a dataset.
X_true, y_clusters = make_blobs(
    n_samples=400,
    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_clusters % 2

X_pool, X_test, y_pool, y_test = train_test_split(
    X_true, y_true, test_size=0.25, random_state=random_state
)

X = X_pool
y = np.full(shape=y_pool.shape, fill_value=MISSING_LABEL)

# Initialise the classifier.
clf = SklearnClassifier(LogisticRegression(), classes=np.unique(y_true))
# Initialise the query strategy.
qs = UncertaintySampling(method='least_confident', random_state=random_state)
gmm = GaussianMixture(init_params='kmeans', n_components=5)
gmm.fit(X)
density = np.exp(gmm.score_samples(X))
delta = 0.1
u_max = -np.inf
switching_point = False

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

# The active learning cycle:
n_cycles = 20
for c in range(n_cycles):
    # Fit the classifier.
    clf.fit(X, y)

    # Get labeled samples.
    X_labeled = X[is_labeled(y)]

    # Query the next sample(s).
    if not switching_point:
        # DWUS
        query_idx, utils = qs.query(X=X, y=y, clf=clf, utility_weight=density, return_utilities=True)
        utilities = utils[0]
        switching_point = utilities[query_idx[0]] - u_max < delta
        u_max = utilities[query_idx[0]]
        strategy = "DWUS"
    else:
        # DWUS + US
        utils_US = qs.query(X=X, y=y, clf=clf, return_utilities=True)[1][0]
        err = np.nanmean(utils_US)
        utilities = (1-err)*utils_US + err*density
        query_idx = simple_batch(utilities, random_state)
        strategy = "DWUS + US"

    # Plot the labeled data.
    coll_old = list(ax.collections)
    title = ax.text(
        0.5, 1.05,
        f"Decision boundary after acquring {c} labels with {strategy}\n"
        f"Test Accuracy: {clf.score(X_test, y_test):.4f}",
        size=plt.rcParams["axes.titlesize"], ha="center",
        transform=ax.transAxes
    )
    ax = plot_contour_for_samples(X, utilities, feature_bound=feature_bound,
                                  res=31, ax=ax, replace_nan=None)
    ax.scatter(X[:, 0], X[:, 1], c=y_pool, cmap="coolwarm", marker=".",
               zorder=2)
    ax.scatter(X_labeled[:, 0], X_labeled[:, 1], c="grey", alpha=.8,
               marker=".", s=300)
    ax = plot_decision_boundary(clf, feature_bound, ax=ax)
    ax.set_xlabel('Feature 1')
    ax.set_ylabel('Feature 2')

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

    # Label the queried samples.
    y[query_idx] = y_pool[query_idx]

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

References:

The implementation of this strategy is based on Donmez et al.[1].

Total running time of the script: (0 minutes 5.575 seconds)

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Dual Strategy for Active Learning#

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