Welcome to LassoNet’s documentation!¶

Installation¶

pip install lassonet

API¶

class lassonet.LassoNetRegressor(*, hidden_dims=(100,), lambda_start='auto', lambda_seq=None, gamma=0.0, gamma_skip=0.0, path_multiplier=1.02, M=10, groups=None, dropout=0, batch_size=None, optim=None, n_iters=(1000, 100), patience=(100, 10), tol=0.99, backtrack=False, val_size=None, device=None, verbose=1, random_state=None, torch_seed=None)¶

Use LassoNet as regressor

Parameters:

hidden_dims (tuple of int, default=(100,)) – Shape of the hidden layers.
lambda_start (float, default='auto') – First value on the path. Leave ‘auto’ to estimate it automatically.
lambda_seq (iterable of float) – If specified, the model will be trained on this sequence of values, until all coefficients are zero. The dense model will always be trained first. Note: lambda_start and path_multiplier will be ignored.
gamma (float, default=0.0) – l2 penalization on the network
gamma_skip (float, default=0.0) – l2 penalization on the skip connection
path_multiplier (float, default=1.02) – Multiplicative factor ($1 + \epsilon$) to increase the penalty parameter over the path
M (float, default=10.0) – Hierarchy parameter.
groups (None or list of lists) – Use group LassoNet regularization. groups is a list of list such that groups[i] contains the indices of the features in the i-th group.
dropout (float, default = None)
batch_size (int, default=None) – If None, does not use batches. Batches are shuffled at each epoch.
optim (torch optimizer or tuple of 2 optimizers, default=None) – Optimizer for initial training and path computation. Default is Adam(lr=1e-3), SGD(lr=1e-3, momentum=0.9).
n_iters (int or pair of int, default=(1000, 100)) – Maximum number of training epochs for initial training and path computation. This is an upper-bound on the effective number of epochs, since the model uses early stopping.
patience (int or pair of int or None, default=(100, 10)) – Number of epochs to wait without improvement during early stopping.
tol (float, default=0.99) – Minimum improvement for early stopping: new objective < tol * old objective.
backtrack (bool, default=False) – If true, ensures the objective function decreases.
val_size (float, default=None) – Proportion of data to use for early stopping. 0 means that training data is used. To disable early stopping, set patience=None. Default is 0.1 for all models except Cox for which training data is used. If X_val and y_val are given during training, it will be ignored.
device (torch device, default=None) – Device on which to train the model using PyTorch. Default: GPU if available else CPU
verbose (int, default=1)
random_state – Random state for validation
torch_seed – Torch state for model random initialization

fit(X, y, *, X_val=None, y_val=None, dense_only=False)¶: Train the model. Note that if lambda_ is not given, the trained model will most likely not use any feature. If dense_only is True, will only train a dense model.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

path(X, y, *, X_val=None, y_val=None, lambda_seq=None, lambda_max=inf, return_state_dicts=False, callback=None, disable_lambda_warning=False) → List[HistoryItem]¶

Train LassoNet on a lambda_ path. The path is defined by the class parameters: start at lambda_start and increment according to path_multiplier. The path will stop when no feature is being used anymore. callback will be called at each step on (model, history)

If you pass lambda_seq=[], only the dense model will be trained.

score(X, y, sample_weight=None)¶

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – $R^2$ of self.predict(X) w.r.t. y.

Return type:

float

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_val parameter in fit.
dense_only (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for dense_only parameter in fit.
y_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_val parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LassoNetRegressor¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object

stability_selection(X, y, n_models=20) → Tuple[List[List[HistoryItem]], Tensor, Tuple[Tensor, LongTensor]]¶

Compute stability selection paths to be passed to lassonet.utils.selection_probability.

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
n_models (int) – Number of models to train

Returns:

oracle (int) – Ideal number of features to select.
order (LongTensor) – Order of the selected features.
wrong (Tensor) – Expected number of wrong features.
paths (List[List[HistoryItem]])
prob (torch.Tensor) – Tensor of shape (n_steps, n_features) containing the selection probability of each feature at lambda value.

class lassonet.LassoNetClassifier(class_weight=None, **kwargs)¶

Use LassoNet as classifier

Parameters:

class_weight (iterable of float, default=None) – If specified, weights for different classes in training. There must be one number per class.
hidden_dims (tuple of int, default=(100,)) – Shape of the hidden layers.
lambda_start (float, default='auto') – First value on the path. Leave ‘auto’ to estimate it automatically.
lambda_seq (iterable of float) – If specified, the model will be trained on this sequence of values, until all coefficients are zero. The dense model will always be trained first. Note: lambda_start and path_multiplier will be ignored.
gamma (float, default=0.0) – l2 penalization on the network
gamma_skip (float, default=0.0) – l2 penalization on the skip connection
path_multiplier (float, default=1.02) – Multiplicative factor ($1 + \epsilon$) to increase the penalty parameter over the path
M (float, default=10.0) – Hierarchy parameter.
groups (None or list of lists) – Use group LassoNet regularization. groups is a list of list such that groups[i] contains the indices of the features in the i-th group.
dropout (float, default = None)
batch_size (int, default=None) – If None, does not use batches. Batches are shuffled at each epoch.
optim (torch optimizer or tuple of 2 optimizers, default=None) – Optimizer for initial training and path computation. Default is Adam(lr=1e-3), SGD(lr=1e-3, momentum=0.9).
n_iters (int or pair of int, default=(1000, 100)) – Maximum number of training epochs for initial training and path computation. This is an upper-bound on the effective number of epochs, since the model uses early stopping.
patience (int or pair of int or None, default=(100, 10)) – Number of epochs to wait without improvement during early stopping.
tol (float, default=0.99) – Minimum improvement for early stopping: new objective < tol * old objective.
backtrack (bool, default=False) – If true, ensures the objective function decreases.
val_size (float, default=None) – Proportion of data to use for early stopping. 0 means that training data is used. To disable early stopping, set patience=None. Default is 0.1 for all models except Cox for which training data is used. If X_val and y_val are given during training, it will be ignored.
device (torch device, default=None) – Device on which to train the model using PyTorch. Default: GPU if available else CPU
verbose (int, default=1)
random_state – Random state for validation
torch_seed – Torch state for model random initialization

fit(X, y, *, X_val=None, y_val=None, dense_only=False)¶: Train the model. Note that if lambda_ is not given, the trained model will most likely not use any feature. If dense_only is True, will only train a dense model.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

path(X, y, *, X_val=None, y_val=None, lambda_seq=None, lambda_max=inf, return_state_dicts=False, callback=None, disable_lambda_warning=False) → List[HistoryItem]¶

Train LassoNet on a lambda_ path. The path is defined by the class parameters: start at lambda_start and increment according to path_multiplier. The path will stop when no feature is being used anymore. callback will be called at each step on (model, history)

If you pass lambda_seq=[], only the dense model will be trained.

score(X, y, sample_weight=None)¶

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_val parameter in fit.
dense_only (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for dense_only parameter in fit.
y_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_val parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LassoNetClassifier¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object

stability_selection(X, y, n_models=20) → Tuple[List[List[HistoryItem]], Tensor, Tuple[Tensor, LongTensor]]¶

Compute stability selection paths to be passed to lassonet.utils.selection_probability.

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
n_models (int) – Number of models to train

Returns:

oracle (int) – Ideal number of features to select.
order (LongTensor) – Order of the selected features.
wrong (Tensor) – Expected number of wrong features.
paths (List[List[HistoryItem]])
prob (torch.Tensor) – Tensor of shape (n_steps, n_features) containing the selection probability of each feature at lambda value.

class lassonet.LassoNetCoxRegressor(tie_approximation=None, **kwargs)¶

Use LassoNet for Cox regression

y has two dimensions: durations and events

Parameters:

tie_approximation (str) – Tie approximation for the Cox model, must be one of (“breslow”, “efron”).
hidden_dims (tuple of int, default=(100,)) – Shape of the hidden layers.
lambda_start (float, default='auto') – First value on the path. Leave ‘auto’ to estimate it automatically.
lambda_seq (iterable of float) – If specified, the model will be trained on this sequence of values, until all coefficients are zero. The dense model will always be trained first. Note: lambda_start and path_multiplier will be ignored.
gamma (float, default=0.0) – l2 penalization on the network
gamma_skip (float, default=0.0) – l2 penalization on the skip connection
path_multiplier (float, default=1.02) – Multiplicative factor ($1 + \epsilon$) to increase the penalty parameter over the path
M (float, default=10.0) – Hierarchy parameter.
groups (None or list of lists) – Use group LassoNet regularization. groups is a list of list such that groups[i] contains the indices of the features in the i-th group.
dropout (float, default = None)
batch_size (int, default=None) – If None, does not use batches. Batches are shuffled at each epoch.
optim (torch optimizer or tuple of 2 optimizers, default=None) – Optimizer for initial training and path computation. Default is Adam(lr=1e-3), SGD(lr=1e-3, momentum=0.9).
n_iters (int or pair of int, default=(1000, 100)) – Maximum number of training epochs for initial training and path computation. This is an upper-bound on the effective number of epochs, since the model uses early stopping.
patience (int or pair of int or None, default=(100, 10)) – Number of epochs to wait without improvement during early stopping.
tol (float, default=0.99) – Minimum improvement for early stopping: new objective < tol * old objective.
backtrack (bool, default=False) – If true, ensures the objective function decreases.
val_size (float, default=None) – Proportion of data to use for early stopping. 0 means that training data is used. To disable early stopping, set patience=None. Default is 0.1 for all models except Cox for which training data is used. If X_val and y_val are given during training, it will be ignored.
device (torch device, default=None) – Device on which to train the model using PyTorch. Default: GPU if available else CPU
verbose (int, default=1)
random_state – Random state for validation
torch_seed – Torch state for model random initialization

fit(X, y, *, X_val=None, y_val=None, dense_only=False)¶: Train the model. Note that if lambda_ is not given, the trained model will most likely not use any feature. If dense_only is True, will only train a dense model.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

path(X, y, *, X_val=None, y_val=None, lambda_seq=None, lambda_max=inf, return_state_dicts=False, callback=None, disable_lambda_warning=False) → List[HistoryItem]¶

Train LassoNet on a lambda_ path. The path is defined by the class parameters: start at lambda_start and increment according to path_multiplier. The path will stop when no feature is being used anymore. callback will be called at each step on (model, history)

If you pass lambda_seq=[], only the dense model will be trained.

score(X_test, y_test)¶: Concordance index

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_val parameter in fit.
dense_only (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for dense_only parameter in fit.
y_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_val parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, X_test: bool | None | str = '$UNCHANGED$', y_test: bool | None | str = '$UNCHANGED$') → LassoNetCoxRegressor¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_test (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_test parameter in score.
y_test (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_test parameter in score.

Returns:

self – The updated object.

Return type:

object

stability_selection(X, y, n_models=20) → Tuple[List[List[HistoryItem]], Tensor, Tuple[Tensor, LongTensor]]¶

Compute stability selection paths to be passed to lassonet.utils.selection_probability.

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
n_models (int) – Number of models to train

Returns:

oracle (int) – Ideal number of features to select.
order (LongTensor) – Order of the selected features.
wrong (Tensor) – Expected number of wrong features.
paths (List[List[HistoryItem]])
prob (torch.Tensor) – Tensor of shape (n_steps, n_features) containing the selection probability of each feature at lambda value.

class lassonet.LassoNetRegressorCV(cv=None, **kwargs)¶

See BaseLassoNet for the parameters

cvint, cross-validation generator or iterable, default=None: Determines the cross-validation splitting strategy. Default is 5-fold cross-validation. See <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html>

fit(X, y)¶: Train the model. Note that if lambda_ is not given, the trained model will most likely not use any feature.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

path(X, y, *, return_state_dicts=True)¶

Train LassoNet on a lambda_ path. The path is defined by the class parameters: start at lambda_start and increment according to path_multiplier. The path will stop when no feature is being used anymore. callback will be called at each step on (model, history)

If you pass lambda_seq=[], only the dense model will be trained.

score(X, y, sample_weight=None)¶

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – $R^2$ of self.predict(X) w.r.t. y.

Return type:

float

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_val parameter in fit.
dense_only (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for dense_only parameter in fit.
y_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_val parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LassoNetRegressorCV¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object

stability_selection(X, y, n_models=20) → Tuple[List[List[HistoryItem]], Tensor, Tuple[Tensor, LongTensor]]¶

Compute stability selection paths to be passed to lassonet.utils.selection_probability.

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
n_models (int) – Number of models to train

Returns:

oracle (int) – Ideal number of features to select.
order (LongTensor) – Order of the selected features.
wrong (Tensor) – Expected number of wrong features.
paths (List[List[HistoryItem]])
prob (torch.Tensor) – Tensor of shape (n_steps, n_features) containing the selection probability of each feature at lambda value.

class lassonet.LassoNetClassifierCV(cv=None, **kwargs)¶

See BaseLassoNet for the parameters

cvint, cross-validation generator or iterable, default=None: Determines the cross-validation splitting strategy. Default is 5-fold cross-validation. See <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html>

fit(X, y)¶: Train the model. Note that if lambda_ is not given, the trained model will most likely not use any feature.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

path(X, y, *, return_state_dicts=True)¶

Train LassoNet on a lambda_ path. The path is defined by the class parameters: start at lambda_start and increment according to path_multiplier. The path will stop when no feature is being used anymore. callback will be called at each step on (model, history)

If you pass lambda_seq=[], only the dense model will be trained.

score(X, y, sample_weight=None)¶

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_val parameter in fit.
dense_only (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for dense_only parameter in fit.
y_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_val parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LassoNetClassifierCV¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object

stability_selection(X, y, n_models=20) → Tuple[List[List[HistoryItem]], Tensor, Tuple[Tensor, LongTensor]]¶

Compute stability selection paths to be passed to lassonet.utils.selection_probability.

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
n_models (int) – Number of models to train

Returns:

oracle (int) – Ideal number of features to select.
order (LongTensor) – Order of the selected features.
wrong (Tensor) – Expected number of wrong features.
paths (List[List[HistoryItem]])
prob (torch.Tensor) – Tensor of shape (n_steps, n_features) containing the selection probability of each feature at lambda value.

class lassonet.LassoNetCoxRegressorCV(cv=None, **kwargs)¶

See BaseLassoNet for the parameters

cvint, cross-validation generator or iterable, default=None: Determines the cross-validation splitting strategy. Default is 5-fold cross-validation. See <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html>

fit(X, y)¶: Train the model. Note that if lambda_ is not given, the trained model will most likely not use any feature.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

path(X, y, *, return_state_dicts=True)¶

Train LassoNet on a lambda_ path. The path is defined by the class parameters: start at lambda_start and increment according to path_multiplier. The path will stop when no feature is being used anymore. callback will be called at each step on (model, history)

If you pass lambda_seq=[], only the dense model will be trained.

score(X_test, y_test)¶: Concordance index

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_val parameter in fit.
dense_only (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for dense_only parameter in fit.
y_val (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_val parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, X_test: bool | None | str = '$UNCHANGED$', y_test: bool | None | str = '$UNCHANGED$') → LassoNetCoxRegressorCV¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_test (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_test parameter in score.
y_test (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_test parameter in score.

Returns:

self – The updated object.

Return type:

object

stability_selection(X, y, n_models=20) → Tuple[List[List[HistoryItem]], Tensor, Tuple[Tensor, LongTensor]]¶

Compute stability selection paths to be passed to lassonet.utils.selection_probability.

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
n_models (int) – Number of models to train

Returns:

oracle (int) – Ideal number of features to select.
order (LongTensor) – Order of the selected features.
wrong (Tensor) – Expected number of wrong features.
paths (List[List[HistoryItem]])
prob (torch.Tensor) – Tensor of shape (n_steps, n_features) containing the selection probability of each feature at lambda value.

lassonet.plot_path(model, X_test, y_test, *, score_function=None)¶

Plot the evolution of the model on the path, namely: - lambda - number of selected variables - score

Requires to have called model.path(return_state_dicts=True) beforehand.

Parameters:

model (LassoNetClassifier or LassoNetRegressor)
X_test (array-like)
y_test (array-like)
score_function (function or None) – if None, use score_function=model.score score_function must take as input X_test, y_test

lassonet.lassonet_path(X, y, task, *, X_val=None, y_val=None, **kwargs)¶

Parameters:

X (array-like of shape (n_samples, n_features)) – Training data
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values
task (str, must be "classification" or "regression") – Task
X_val (array-like of shape (n_samples, n_features)) – Validation data
y_val (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Validation values
parameters. (See BaseLassoNet for the other)

Welcome to LassoNet’s documentation!¶

Installation¶

API¶

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