Use the estimator interface

SOM provides fit, transform, predict, score, get_params and set_params, so a map can be used the way a scikit-learn estimator is used. train remains the primary way to train and is not going away.

som = python_som.SOM(x=10, y=10, input_len=4, random_seed=42)
som.weight_initialization(mode=WeightInit.LINEAR, data=X)

som.fit(X, n_iteration=100, mode=TrainingMode.BATCH)  # returns the map, so calls chain
labels = som.predict(X)  # (n_samples,)  flat node index
distances = som.transform(X)  # (n_samples, x*y)
som.score(X)  # negated quantization error

What maps onto what

Modelled on KMeans, which is the right precedent: a SOM is a topologically-constrained k-means.

KMeans	SOM
fit(X, y=None)	same	returns the estimator
transform(X) → (n, n_clusters)	(n, x*y)	distance to every node
predict(X) → (n,)	(n,)	flat node index
score(X)	same	negated, so larger is better
cluster_centers_	weights_
inertia_	quantization_error_	None before training
n_features_in_	same

predict returns a flat index

Not (row, column). A 1-D array of labels is what scorers, confusion_matrix and cross_val_score all assume, so returning coordinates would read better for a grid and compose with nothing.

rows, columns = np.unravel_index(som.predict(X), som.get_shape())

winner(x) still returns (row, column) for a single sample.

Two differences from scikit-learn worth knowing

fit continues rather than resetting. scikit-learn estimators conventionally discard their fitted state on a second fit. This one does not: a SOM's models are its state, and train has always continued from wherever they were. Calling fit twice trains twice.

The models exist before training. Initialization is a separate step (weight_initialization), so weights_ is available from construction. There is no "not fitted" state to raise about.

set_params, and what it will not change

som.set_params(learning_rate=0.1, neighborhood_radius=3.0)

The rates, the radii, the decays and the distance function can be changed. The grid shape and input_len cannot, and asking raises:

som.set_params(x=20)
# ValueError: 'x' cannot be changed after construction: the models would no longer match it.

Silently rebuilding would discard trained weights, which is the kind of help nobody asks for.

set_params is what set_learning_rate and set_neighborhood_radius become. Both still work and are removed in 1.0.0.

Full scikit-learn integration

These method names alone are not enough for Pipeline.predict, GridSearchCV or cross_val_score. Since scikit-learn 1.7 those paths require __sklearn_tags__, which in practice means inheriting BaseEstimator:

approach	clone	Pipeline.fit	Pipeline.predict	GridSearchCV	cross_val_score
these methods alone	ok	ok	fails	fails	fails
python_som.sklearn.SOMEstimator	ok	ok	ok	ok	ok

So a proper adapter ships separately, and scikit-learn stays an optional dependency:

pip install "python-som[sklearn]"

from python_som.sklearn import SOMEstimator

GridSearchCV(SOMEstimator(x=10, y=10), {"x": [8, 10, 12]}, cv=3).fit(X)

Use the methods on SOM when you are calling them yourself; use the adapter when scikit-learn is doing the calling.