`igl.models`

scikit-learn-compatible estimators: classifier, regressor, autoencoder.

`igl.models.classifier.IGLClassifier`

Bases: _BaseIGLEstimator[CrossEntropyLoss], ClassifierMixin

scikit-learn-compatible classifier built on :class:igl.IGLModule.

Parameters:

Name	Type	Description	Default
`max_dim`	`int`	Latent dimension `d_max` for Matryoshka truncation.	`16`
`n_anchors`	`int \| None`	Anchor count. `None` defers to `config` or default 64.	`None`
`n_scales`	`int \| None`	Scale count. `None` defers to `config` or 4.	`None`
`operator`	`OperatorNameLike \| None`	Kernel operator. `None` defers to `config` or `OperatorName.GAUSSIAN`.	`None`
`normalize`	`NormalizeModeLike \| None`	Φ-normalization mode.	`None`
`encoder_hidden`	`int \| tuple[int, ...] \| None`	Encoder `hidden` shorthand (`int` for uniform width, tuple for per-layer widths).	`None`
`encoder_depth`	`int \| None`	Encoder depth shorthand.	`None`
`config`	`IGLConfig \| None`	Optional :class:`igl.IGLConfig`. Explicit kwargs override it.	`None`
`random_state`	`int \| None`	Optional integer seed for reproducible training.	`None`
`validation_fraction`	`float \| None`	If set, hold out this fraction of training data for early-stopping and dimension-curve evaluation.	`0.2`

Attributes:

Name	Type	Description
`classes_`		Sorted unique training labels.
`n_features_in_`		Ambient input dimension seen during fit.
`module_`		The underlying :class:`igl.IGLModule`.
`history_`		:class:`igl.TrainingHistory` from the trainer.
`dimension_curve_`		`{k: error_rate}` from :func:`igl.eval_dimension_curve` post-fit.
`effective_dimension_`		Detected elbow `k` (the discovered `d_eff`).
`scaler_`		:class:`sklearn.preprocessing.StandardScaler` used on inputs.

`fit(x, y)`

Fit the classifier on (x, y).

`predict(x)`

Predict class labels for x.

`predict_proba(x)`

Predict class probabilities for x.

`igl.models.regressor.IGLRegressor`

Bases: _BaseIGLEstimator[MSELoss], RegressorMixin

scikit-learn-compatible regressor.

Supports scalar and multi-output regression. Targets y may be a 1-D array ([n_samples]) for scalar regression, or 2-D ([n_samples, n_outputs]) for multi-output. The output dimension is inferred from y in :meth:fit.

Args mirror :class:IGLClassifier except for validation_fraction.

Attributes:

Name	Type	Description
`n_features_in_`		Ambient input dimension seen during fit.
`n_outputs_`		Number of output dimensions inferred from `y`.
`module_`		The underlying :class:`igl.IGLModule`.
`history_`		:class:`igl.TrainingHistory` from the trainer.
`dimension_curve_`		`{k: mse}` from :func:`igl.eval_dimension_curve`.
`effective_dimension_`		Discovered `d_eff`.
`scaler_`		:class:`sklearn.preprocessing.StandardScaler`.

`fit(x, y)`

Fit the regressor on (x, y).

`predict(x)`

Predict targets for x. Returns 1-D if fit with scalar targets.

`igl.models.autoencoder.IGLAutoencoder`

Bases: _BaseIGLEstimator[MSELoss], TransformerMixin

Train an IGL model with y = x (reconstruction).

The :meth:fit method ignores its y argument and uses x as both input and target.

Attributes:

Name	Type	Description
`n_features_in_`		Ambient input dimension.
`n_outputs_`		Equal to `n_features_in_`.
`module_`		The underlying :class:`igl.IGLModule`.
`history_,`	`(dimension_curve_, effective_dimension_, scaler_)`	same as :class:`IGLRegressor`.

`fit(x, y=None)`

Fit the autoencoder. y is ignored; the scaled x is the target.

Reconstruction is therefore measured in the StandardScaler-scaled feature space. :meth:reconstruct inverse-transforms back to the original feature space for users who need real-space outputs.

`reconstruct(x)`

Inverse-transform the reconstruction back to the original feature space.

`transform(x)`

Return the reconstructed input (in the scaled feature space).