coco_pipe.viz.plot_embedding#

coco_pipe.viz.plot_embedding(X_emb, labels=None, metadata=None, dims=(0, 1), title='Embedding', figsize=(10, 8), cmap=None, palette='deep', s=40, alpha=0.8, label_kind='categorical', metrics=None, metric_name=None, ax=None, random_state=None)#

Plot an explicit 2D or 3D embedding.

Parameters:

  • X_emb (ndarray) – Embedding array with shape (n_samples, n_dimensions).

  • labels (ndarray | None) – Optional label array aligned with samples used for color encoding.

  • metadata (dict[str, Any] | None) – Optional column-oriented metadata aligned with samples.

  • dims (tuple[int, int] | tuple[int, int, int]) – Column indices to use as plot axes. Two indices produce a 2D plot; three produce a 3D plot.

  • title (str) – Axes title.

  • figsize (tuple[float, float] | None) – Figure size used when creating new axes.

  • cmap (str | None) – Colormap used when label_kind="continuous".

  • palette (str) – Seaborn palette name used when label_kind="categorical".

  • s (int) – Scatter marker size.

  • alpha (float) – Scatter point opacity.

  • label_kind (Literal['categorical', 'continuous']) – "categorical" to color by class, "continuous" to apply a colormap to numeric labels.

  • metrics (dict[str, Any] | None) – Optional metrics mapping used to annotate the plot when metric_name is provided.

  • metric_name (str | None) – Name of the metric from metrics to display as an annotation.

  • ax (Axes | None) – Existing Matplotlib axes to draw into.

  • random_state (int | None) – Accepted for API compatibility; not used internally.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_embedding

Interactive Plotly version.

plot_metrics

Quality metric overview for the embedding run.

plot_shepard_diagram

Distance-preservation diagnostic.

plot_eigenvalues

Explained variance for linear reducers.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> X_emb = rng.normal(size=(50, 2))
>>> labels = np.arange(50) % 5
>>> fig, ax = viz.plot_embedding(X_emb, labels=labels)

Examples using coco_pipe.viz.plot_embedding#

Comprehensive Comparison of Dimensionality Reduction Methods

Comprehensive Comparison of Dimensionality Reduction Methods

Benchmarking Dimensionality Reduction: The Epistemology of Embeddings

Benchmarking Dimensionality Reduction: The Epistemology of Embeddings