Package mdp :: Package nodes :: Class ElasticNetScikitsLearnNode

Class ElasticNetScikitsLearnNode


Linear Model trained with L1 and L2 prior as regularizer
This node has been automatically generated by wrapping the ``scikits.learn.linear_model.coordinate_descent.ElasticNet`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
rho=1 is the lasso penalty. Currently, rho <= 0.01 is not
reliable, unless you supply your own sequence of alpha.

**Parameters**

alpha : float
    Constant that multiplies the L1 term. Defaults to 1.0

rho : float
    The ElasticNet mixing parameter, with 0 < rho <= 1.

coef_: ndarray of shape n_features
    The initial coeffients to warm-start the optimization

fit_intercept: bool
    Whether the intercept should be estimated or not. If False, the
    data is assumed to be already centered.

**Notes**

To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a fortran contiguous numpy array.

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Linear Model trained with L1 and L2 prior as regularizer This node has been automatically generated by wrapping the ``scikits.learn.linear_model.coordinate_descent.ElasticNet`` class from the ``sklearn`` library.

_execute(self, x)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

_stop_training(self, **kwargs)
Concatenate the collected data in a single array.

execute(self, x)
Predict using the linear model This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.ElasticNet class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

stop_training(self, **kwargs)
Fit Elastic Net model with coordinate descent This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.ElasticNet class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from Cumulator

_train(self, *args)
Collect all input data in a list.

train(self, *args)
Collect all input data in a list.

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_check_train_args(self, x, *args, **kwargs)

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of numpy.dtype objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert y.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

is_invertible()
Return True if the node can be inverted, False otherwise.

bool

is_trainable()
Return True if the node can be trained, False otherwise.

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples:

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)


Linear Model trained with L1 and L2 prior as regularizer
This node has been automatically generated by wrapping the ``scikits.learn.linear_model.coordinate_descent.ElasticNet`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
rho=1 is the lasso penalty. Currently, rho <= 0.01 is not
reliable, unless you supply your own sequence of alpha.

**Parameters**

alpha : float
    Constant that multiplies the L1 term. Defaults to 1.0

rho : float
    The ElasticNet mixing parameter, with 0 < rho <= 1.

coef_: ndarray of shape n_features
    The initial coeffients to warm-start the optimization

fit_intercept: bool
    Whether the intercept should be estimated or not. If False, the
    data is assumed to be already centered.

**Notes**

To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a fortran contiguous numpy array.

Overrides: object.__init__

_execute(self, x)

Overrides: Node._execute

_get_supported_dtypes(self)

Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

Predict using the linear model This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.ElasticNet class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : numpy array of shape [n_samples, n_features]

Returns

C : array, shape = [n_samples]: Returns predicted values.

Overrides: Node.execute

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

is_trainable()
Static Method

Return True if the node can be trained, False otherwise.

Returns: bool: A boolean indicating whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

Fit Elastic Net model with coordinate descent This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.ElasticNet class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X: ndarray, (n_samples, n_features): Data
y: ndarray, (n_samples): Target
precompute : True | False | 'auto' | array-like: Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.
Xy : array-like, optional: Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomuted.
max_iter: int, optional: The maximum number of iterations
tol: float, optional: The tolerance for the optimization: if the updates are smaller than 'tol', the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

Notes

Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a fortran contiguous numpy array if necessary.

To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.

Overrides: Node.stop_training

Class ElasticNetScikitsLearnNode

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs) (Constructor)

_execute(self, x)

_get_supported_dtypes(self)

_stop_training(self, **kwargs)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self, **kwargs)

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

is_invertible()
Static Method

is_trainable()
Static Method