Sketch-map example using pre-selected high-dimensional landmarks

This example demonstrates a minimal, sphinx-gallery friendly usage of the SketchMap estimator. It loads the provided landmark file, fits the estimator on the landmark set (using per-sample sample_weights) and plots the resulting 2D embedding.

Notes

• The lightweight Python implementation of Sketch-map lives in skmatter.decomposition._sketchmap.

• The example fits only the landmark set (callers that want a subset should pre-select and pass that array to fit).

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm

from skmatter.decomposition import SketchMap

Load example landmark data. The file included with the examples contains high-dimensional descriptors and the last column holds a per-sample weight.

data_file = "highd-landmarks"
data = np.loadtxt(data_file)
X_land = data[:, :-1]
weights = data[:, -1]

Fit SketchMap on the landmark set and provide sample_weights. We fit on the landmarks only here (no FPS or internal selection).

sm = SketchMap(n_components=2, auto_histogram=True, preopt_steps=50)
sm.fit(X_land, sample_weights=weights, mixing_ratio=0.2, n_components=2)
T = sm.embedding_

Plot the resulting embedding colored by the per-sample weight.

cmap = cm.get_cmap("viridis")
fig, ax = plt.subplots(figsize=(6, 5))
sc = ax.scatter(T[:, 0], T[:, 1], c=weights, s=40, cmap=cmap, edgecolor="k")
ax.set_title("Sketch-map embedding (example)")
ax.set_xlabel("Sketchmap 1")
ax.set_ylabel("Sketchmap 2")
cbar = fig.colorbar(sc, ax=ax)
cbar.set_label("landmark weight")
fig.tight_layout()
plt.show()

/home/docs/checkouts/readthedocs.org/user_builds/scikit-matter/checkouts/284/examples/decomposition/sketchmap.py:48: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.
  cmap = cm.get_cmap("viridis")