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].
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="multiclass",
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,
cmap="tab10",
vmax=10,
alpha=0.5,
)
ax1.set_title("Source data")
ax1.set_xlabel("Feature 1")
ax1.set_ylabel("Feature 2")
ax2.scatter(
X_source[:, 0],
X_source[:, 1],
c=y_source,
cmap="tab10",
vmax=10,
alpha=0.1,
)
ax2.scatter(
X_target[:, 0],
X_target[:, 1],
c=y_target,
cmap="tab10",
vmax=10,
alpha=0.5,
)
ax2.set_title("Target data")
ax2.set_xlabel("Feature 1")
ax2.set_ylabel("Feature 2")
plt.show()
for shift in [
"covariate_shift",
"target_shift",
"concept_drift",
"subspace",
]:
plot_shifted_dataset(shift)
Total running time of the script: (0 minutes 0.598 seconds)
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