Training Space

Before running the Bayesian optimisation loop, ALSEBO selects a small initial training set — a diverse subset of the candidate pool that is sent for experimental measurement first. The quality of this initial batch directly affects how quickly the surrogate model learns the fitness landscape.

Why diversity matters

A GP surrogate trained on a clustered (non-diverse) initial set will have high uncertainty everywhere except in the region it was trained on, causing the acquisition function to explore only that region in early rounds. By spreading the initial measurements across the full sequence landscape, the surrogate gains a coarse but global picture of the fitness surface from the very first round.

Sampling strategy

alsebo.training_space.sample_initial_training_sequnces() implements a two-stage approach:

Stage 1 — Dimensionality reduction

The high-dimensional feature vectors from seq_space.csv are projected to 2-D using either t-SNE or PCA:

Method	Behaviour	When to use
"TSNE"	Non-linear; preserves local neighbourhood structure. Sequences that are functionally similar cluster together.	Default. Best when the feature space has non-linear structure (e.g. DCA or ESM features).
"PCA"	Linear; preserves global variance. Fast and deterministic.	Good starting point when the feature space is already low-dimensional (e.g. "latent" featurization with 2-D VAE).

Stage 2 — k-means clustering + centroid selection

The 2-D projection is partitioned into k clusters (where k = training_seq_size). For each cluster, the sequence closest to the centroid is selected as the representative:

kmeans = KMeans(n_clusters=k, random_state=42).fit(gen_seq_X_2d)
for cluster_id in range(k):
    cluster_points = np.where(labels == cluster_id)[0]
    centroid = kmeans.cluster_centers_[cluster_id]
    dists = np.linalg.norm(gen_seq_X_2d[cluster_points] - centroid, axis=1)
    best_idx = cluster_points[np.argmin(dists)]

This guarantees that the k selected sequences are as spread out as possible across the projected landscape.

from alsebo.training_space import sample_initial_training_sequnces

sample_initial_training_sequnces(
    exp_dir="./experiment/",
    training_seq_size=20,
    manipold="TSNE",          # or "PCA"
)
# writes experiment/training_seqs.csv

Recording experimental results

After measuring the selected sequences, attach the objective values to create the first training CSV with alsebo.training_space.generate_sequence_training_file():

from alsebo.training_space import generate_sequence_training_file

obj_config = {
    "names":      ["fitness"],
    "directions": ["max"],
}

obj_values = [
    [0.45], [0.67], [0.31], ...   # one inner list per sequence
]

generate_sequence_training_file(
    exp_dir="./experiment/",
    obj_config=obj_config,
    obj_values=obj_values,
)
# writes experiment/seq_exp_data.csv

The obj_config dictionary

obj_config is passed through the entire ALSEBO pipeline and controls how objectives are interpreted:

Key	Description
"names"	List of column names for each objective in the CSV, e.g. ["fitness"] or ["fitness", "thermostability"].
"directions"	List of "max" or "min" per objective. Minimisation objectives are sign-flipped internally before UCB scoring.
"weights"	(optional) List of floats summing to 1 for multi-objective scalarisation. Defaults to equal weights if omitted.