Package mdp :: Package nodes :: Class LarsCVScikitsLearnNode
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Class LarsCVScikitsLearnNode



Cross-validated Least Angle Regression model

This node has been automatically generated by wrapping the ``sklearn.linear_model.least_angle.LarsCV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <least_angle_regression>`.

**Parameters**

fit_intercept : boolean
    whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations
    (e.g. data is expected to be already centered).

positive : boolean (default=False)
    Restrict coefficients to be >= 0. Be aware that you might want to
    remove fit_intercept which is set True by default.

verbose : boolean or integer, optional
    Sets the verbosity amount

normalize : boolean, optional, default False
    If True, the regressors X will be normalized before regression.

copy_X : boolean, optional, default True
    If ``True``, X will be copied; else, it may be overwritten.

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.

max_iter: integer, optional
    Maximum number of iterations to perform.

cv : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:


    - None, to use the default 3-fold cross-validation,
    - integer, to specify the number of folds.
    - An object to be used as a cross-validation generator.
    - An iterable yielding train/test splits.

    For integer/None inputs, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

max_n_alphas : integer, optional
    The maximum number of points on the path used to compute the
    residuals in the cross-validation

n_jobs : integer, optional
    Number of CPUs to use during the cross validation. If ``-1``, use
    all the CPUs

eps : float, optional
    The machine-precision regularization in the computation of the
    Cholesky diagonal factors. Increase this for very ill-conditioned
    systems.


**Attributes**

``coef_`` : array, shape (n_features,)
    parameter vector (w in the formulation formula)

``intercept_`` : float
    independent term in decision function

``coef_path_`` : array, shape (n_features, n_alphas)
    the varying values of the coefficients along the path

``alpha_`` : float
    the estimated regularization parameter alpha

``alphas_`` : array, shape (n_alphas,)
    the different values of alpha along the path

``cv_alphas_`` : array, shape (n_cv_alphas,)
    all the values of alpha along the path for the different folds

``cv_mse_path_`` : array, shape (n_folds, n_cv_alphas)
    the mean square error on left-out for each fold along the path
    (alpha values given by ``cv_alphas``)

``n_iter_`` : array-like or int
    the number of iterations run by Lars with the optimal alpha.

See also

lars_path, LassoLars, LassoLarsCV

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Cross-validated Least Angle Regression model
 
_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
 
stop_training(self, **kwargs)
Fit the model using X, y as training data.

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 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.
 
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)

 

Cross-validated Least Angle Regression model

This node has been automatically generated by wrapping the ``sklearn.linear_model.least_angle.LarsCV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <least_angle_regression>`.

**Parameters**

fit_intercept : boolean
    whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations
    (e.g. data is expected to be already centered).

positive : boolean (default=False)
    Restrict coefficients to be >= 0. Be aware that you might want to
    remove fit_intercept which is set True by default.

verbose : boolean or integer, optional
    Sets the verbosity amount

normalize : boolean, optional, default False
    If True, the regressors X will be normalized before regression.

copy_X : boolean, optional, default True
    If ``True``, X will be copied; else, it may be overwritten.

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.

max_iter: integer, optional
    Maximum number of iterations to perform.

cv : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:


    - None, to use the default 3-fold cross-validation,
    - integer, to specify the number of folds.
    - An object to be used as a cross-validation generator.
    - An iterable yielding train/test splits.

    For integer/None inputs, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

max_n_alphas : integer, optional
    The maximum number of points on the path used to compute the
    residuals in the cross-validation

n_jobs : integer, optional
    Number of CPUs to use during the cross validation. If ``-1``, use
    all the CPUs

eps : float, optional
    The machine-precision regularization in the computation of the
    Cholesky diagonal factors. Increase this for very ill-conditioned
    systems.


**Attributes**

``coef_`` : array, shape (n_features,)
    parameter vector (w in the formulation formula)

``intercept_`` : float
    independent term in decision function

``coef_path_`` : array, shape (n_features, n_alphas)
    the varying values of the coefficients along the path

``alpha_`` : float
    the estimated regularization parameter alpha

``alphas_`` : array, shape (n_alphas,)
    the different values of alpha along the path

``cv_alphas_`` : array, shape (n_cv_alphas,)
    all the values of alpha along the path for the different folds

``cv_mse_path_`` : array, shape (n_folds, n_cv_alphas)
    the mean square error on left-out for each fold along the path
    (alpha values given by ``cv_alphas``)

``n_iter_`` : array-like or int
    the number of iterations run by Lars with the optimal alpha.

See also

lars_path, LassoLars, LassoLarsCV

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 sklearn.linear_model.least_angle.LarsCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.

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.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

Fit the model using X, y as training data.

This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LarsCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,)
Target values.

Returns

self : object
returns an instance of self.
Overrides: Node.stop_training