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Multi-domain Linear Monge Alignment — SKADA : Scikit Adaptation

This example illustrates the use of the MultiLinearMongeAlignmentAdapter

# Author: Remi Flamary
#
# License: BSD 3-Clause
# sphinx_gallery_thumbnail_number = 4

We generate a simple 2D concept drift dataset.

X, y, sample_domain = make_shifted_datasets(
    n_samples_source=20,
    n_samples_target=20,
    shift="concept_drift",
    noise=0.2,
    label="multiclass",
    random_state=42,
)


Xs, Xt, ys, yt = source_target_split(X, y, sample_domain=sample_domain)


plt.figure(5, (10, 5))
plt.subplot(1, 2, 1)
plt.scatter(Xs[:, 0], Xs[:, 1], c=ys, cmap="tab10", vmax=9, label="Source")
plt.title("Source data")
ax = plt.axis()

plt.subplot(1, 2, 2)
plt.scatter(Xt[:, 0], Xt[:, 1], c=yt, cmap="tab10", vmax=9, label="Target")
plt.title("Target data")
plt.axis(ax)

(np.float64(-2.3676169789533272), np.float64(4.53817259426296), np.float64(-1.7964573229829714), np.float64(4.336863129384494))

We train a simple SVC classifier on the source domain and evaluate its performance on the source and target domain. Performance is much lower on the target domain due to the shift. We also plot the decision boundary

clf = MultiLinearMongeAlignmentAdapter()
clf.fit(X, sample_domain=sample_domain)

X_adapt = clf.transform(X, sample_domain=sample_domain, allow_source=True)


plt.figure(5, (10, 3))
plt.subplot(1, 3, 1)
plt.scatter(Xs[:, 0], Xs[:, 1], c=ys, cmap="tab10", vmax=9, label="Source")
plt.title("Source data")
ax = plt.axis()

plt.subplot(1, 3, 2)
plt.scatter(Xt[:, 0], Xt[:, 1], c=yt, cmap="tab10", vmax=9, label="Target")
plt.title("Target data")
plt.axis(ax)

plt.subplot(1, 3, 3)
plt.scatter(
    X_adapt[sample_domain >= 0, 0],
    X_adapt[sample_domain >= 0, 1],
    c=y[sample_domain >= 0],
    marker="o",
    cmap="tab10",
    vmax=9,
    label="Source",
    alpha=0.5,
)
plt.scatter(
    X_adapt[sample_domain < 0, 0],
    X_adapt[sample_domain < 0, 1],
    c=y[sample_domain < 0],
    marker="x",
    cmap="tab10",
    vmax=9,
    label="Target",
    alpha=1,
)
plt.legend()
plt.title("Adapted data")

Text(0.5, 1.0, 'Adapted data')

Average accuracy on all domains: 0.9875

def get_multidomain_data(
    n_samples_source=100,
    n_samples_target=100,
    noise=0.1,
    random_state=None,
    n_sources=3,
    n_targets=2,
):
    np.random.seed(random_state)
    X, y, sample_domain = make_shifted_datasets(
        n_samples_source=n_samples_source,
        n_samples_target=n_samples_target,
        noise=noise,
        shift="concept_drift",
        label="multiclass",
        random_state=random_state,
    )
    for ns in range(n_sources - 1):
        Xi, yi, sample_domaini = make_shifted_datasets(
            n_samples_source=n_samples_source,
            n_samples_target=n_samples_target,
            noise=noise,
            shift="concept_drift",
            label="multiclass",
            random_state=random_state + ns,
            mean=np.random.randn(2),
            sigma=np.random.rand(2) * 0.5 + 0.5,
        )
        Xs, Xt, ys, yt = source_target_split(Xi, yi, sample_domain=sample_domaini)
        X = np.vstack([X, Xt])
        y = np.hstack([y, yt])
        sample_domain = np.hstack([sample_domain, np.ones(Xt.shape[0]) * (ns + 2)])

    for nt in range(n_targets - 1):
        Xi, yi, sample_domaini = make_shifted_datasets(
            n_samples_source=n_samples_source,
            n_samples_target=n_samples_target,
            noise=noise,
            shift="concept_drift",
            label="multiclass",
            random_state=random_state + nt + 42,
            mean=np.random.randn(2),
            sigma=np.random.rand(2) * 0.5 + 0.5,
        )
        Xs, Xt, ys, yt = source_target_split(Xi, yi, sample_domain=sample_domaini)
        X = np.vstack([X, Xt])
        y = np.hstack([y, yt])
        sample_domain = np.hstack([sample_domain, -np.ones(Xt.shape[0]) * (nt + 1)])

    return X, y, sample_domain


X, y, sample_domain = get_multidomain_data(
    n_samples_source=50,
    n_samples_target=50,
    noise=0.1,
    random_state=43,
    n_sources=3,
    n_targets=2,
)

Xs, Xt, ys, yt = source_target_split(X, y, sample_domain=sample_domain)


plt.figure(5, (10, 5))
plt.subplot(1, 2, 1)
plt.scatter(Xs[:, 0], Xs[:, 1], c=ys, cmap="tab10", vmax=9, label="Source")
plt.title("Source data")
ax = plt.axis()

plt.subplot(1, 2, 2)
plt.scatter(Xt[:, 0], Xt[:, 1], c=yt, cmap="tab10", vmax=9, label="Target")
plt.title("Target domains")
plt.axis(ax)

(np.float64(-2.310098338155625), np.float64(4.756925382279493), np.float64(-2.1443686989830857), np.float64(4.464886123797522))

clf = MultiLinearMongeAlignmentAdapter()
clf.fit(X, sample_domain=sample_domain)

X_adapt = clf.transform(X, sample_domain=sample_domain, allow_source=True)


plt.figure(5, (10, 3))
plt.subplot(1, 3, 1)
plt.scatter(Xs[:, 0], Xs[:, 1], c=ys, cmap="tab10", vmax=9, label="Source")
plt.title("Source data")
ax = plt.axis()

plt.subplot(1, 3, 2)
plt.scatter(Xt[:, 0], Xt[:, 1], c=yt, cmap="tab10", vmax=9, label="Target")
plt.title("Target data")
plt.axis(ax)

plt.subplot(1, 3, 3)
plt.scatter(
    X_adapt[sample_domain >= 0, 0],
    X_adapt[sample_domain >= 0, 1],
    c=y[sample_domain >= 0],
    marker="o",
    cmap="tab10",
    vmax=9,
    label="Source",
    alpha=0.5,
)
plt.scatter(
    X_adapt[sample_domain < 0, 0],
    X_adapt[sample_domain < 0, 1],
    c=y[sample_domain < 0],
    marker="x",
    cmap="tab10",
    vmax=9,
    label="Target",
    alpha=1,
)
plt.legend()
plt.axis(ax)
plt.title("Adapted data")

Text(0.5, 1.0, 'Adapted data')

Average accuracy on all domains: 1.0

Total running time of the script: (0 minutes 0.856 seconds)

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