Plot dataset source domain and shifted target domain

This illustrates the make_shifted_dataset() dataset generator. Each method consists of generating source data and shifted target data. We illustrate here: covariate shift, target shift, concept drift, and sample bias. See detailed description of each shift in [1].

[1]

Moreno-Torres, J. G., Raeder, T., Alaiz-Rodriguez, R., Chawla, N. V., and Herrera, F. (2012). A unifying view on dataset shift in classification. Pattern recognition, 45(1):521-530.

# Author: Ruben Bueno <ruben.bueno@polytechnique.edu>
#
# License: BSD 3-Clause

import matplotlib.pyplot as plt

from skada import source_target_split
from skada.datasets import make_shifted_datasets

def plot_shifted_dataset(shift, random_state=42):
    """Plot source and shifted target data for a given type of shift.

    The possible shifts are 'covariate_shift', 'target_shift',
    'concept_drift' or 'subspace'.

    We use here the same random seed for multiple calls to
    ensure same distributions.
    """
    X, y, sample_domain = make_shifted_datasets(
        n_samples_source=20,
        n_samples_target=20,
        shift=shift,
        noise=0.3,
        label="regression",
        random_state=random_state,
    )
    X_source, X_target, y_source, y_target = source_target_split(
        X, y, sample_domain=sample_domain
    )

    fig, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
    fig.suptitle(shift.replace("_", " ").title(), fontsize=14)
    plt.subplots_adjust(bottom=0.15)
    ax1.scatter(
        X_source[:, 0],
        X_source[:, 1],
        c=y_source,
        vmax=max(y),
        alpha=0.5,
        cmap="autumn",
    )
    ax1.set_title("Source data")
    ax1.set_xlabel("Feature 1")
    ax1.set_ylabel("Feature 2")

    s = ax2.scatter(
        X_target[:, 0],
        X_target[:, 1],
        c=y_target,
        vmax=max(y),
        alpha=0.5,
        cmap="autumn",
    )
    ax2.set_title("Target data")
    ax2.set_xlabel("Feature 1")
    ax2.set_ylabel("Feature 2")

    fig.subplots_adjust(right=0.8)
    cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.80])
    cb = fig.colorbar(s, cax=cbar_ax)
    cb.set_label("y-value")

    plt.show()

for shift in ["covariate_shift", "target_shift", "concept_drift", "subspace"]:
    plot_shifted_dataset(shift)

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