Package mdp :: Package nodes :: Class RidgeCVScikitsLearnNode
[hide private]
[frames] | no frames]

Class RidgeCVScikitsLearnNode



Ridge regression with built-in cross-validation.

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

By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation.

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

**Parameters**

alphas : numpy array of shape [n_alphas]
    Array of alpha values to try.
    Small positive values of alpha improve the conditioning of the
    problem and reduce the variance of the estimates.
    Alpha corresponds to ``C^-1`` in other linear models such as
    LogisticRegression or LinearSVC.

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).

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

scoring : string, callable or None, optional, default: None
    A string (see model evaluation documentation) or
    a scorer callable object / function with signature
    ``scorer(estimator, X, y)``.

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


    - None, to use the efficient Leave-One-Out 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, if ``y`` is binary or multiclass,
    :class:`StratifiedKFold` used, else, :class:`KFold` is used.

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

gcv_mode : {None, 'auto', 'svd', eigen'}, optional
    Flag indicating which strategy to use when performing
    Generalized Cross-Validation. Options are::


        'auto' : use svd if n_samples > n_features or when X is a sparse
                 matrix, otherwise use eigen
        'svd' : force computation via singular value decomposition of X
                (does not work for sparse matrices)
        'eigen' : force computation via eigendecomposition of X^T X

    The 'auto' mode is the default and is intended to pick the cheaper
    option of the two depending upon the shape and format of the training
    data.

store_cv_values : boolean, default=False
    Flag indicating if the cross-validation values corresponding to
    each alpha should be stored in the `cv_values_` attribute (see
    below). This flag is only compatible with `cv=None` (i.e. using
    Generalized Cross-Validation).

**Attributes**

``cv_values_`` : array, shape = [n_samples, n_alphas] or         shape = [n_samples, n_targets, n_alphas], optional
    Cross-validation values for each alpha (if `store_cv_values=True` and         `cv=None`). After `fit()` has been called, this attribute will         contain the mean squared errors (by default) or the values of the         `{loss,score}_func` function (if provided in the constructor).

``coef_`` : array, shape = [n_features] or [n_targets, n_features]
    Weight vector(s).

``intercept_`` : float | array, shape = (n_targets,)
    Independent term in decision function. Set to 0.0 if
    ``fit_intercept = False``.

``alpha_`` : float
    Estimated regularization parameter.

See also

Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeClassifierCV: Ridge classifier with built-in cross validation

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Ridge regression with built-in cross-validation.
 
_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 Ridge regression model

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)

 

Ridge regression with built-in cross-validation.

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

By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation.

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

**Parameters**

alphas : numpy array of shape [n_alphas]
    Array of alpha values to try.
    Small positive values of alpha improve the conditioning of the
    problem and reduce the variance of the estimates.
    Alpha corresponds to ``C^-1`` in other linear models such as
    LogisticRegression or LinearSVC.

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).

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

scoring : string, callable or None, optional, default: None
    A string (see model evaluation documentation) or
    a scorer callable object / function with signature
    ``scorer(estimator, X, y)``.

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


    - None, to use the efficient Leave-One-Out 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, if ``y`` is binary or multiclass,
    :class:`StratifiedKFold` used, else, :class:`KFold` is used.

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

gcv_mode : {None, 'auto', 'svd', eigen'}, optional
    Flag indicating which strategy to use when performing
    Generalized Cross-Validation. Options are::


        'auto' : use svd if n_samples > n_features or when X is a sparse
                 matrix, otherwise use eigen
        'svd' : force computation via singular value decomposition of X
                (does not work for sparse matrices)
        'eigen' : force computation via eigendecomposition of X^T X

    The 'auto' mode is the default and is intended to pick the cheaper
    option of the two depending upon the shape and format of the training
    data.

store_cv_values : boolean, default=False
    Flag indicating if the cross-validation values corresponding to
    each alpha should be stored in the `cv_values_` attribute (see
    below). This flag is only compatible with `cv=None` (i.e. using
    Generalized Cross-Validation).

**Attributes**

``cv_values_`` : array, shape = [n_samples, n_alphas] or         shape = [n_samples, n_targets, n_alphas], optional
    Cross-validation values for each alpha (if `store_cv_values=True` and         `cv=None`). After `fit()` has been called, this attribute will         contain the mean squared errors (by default) or the values of the         `{loss,score}_func` function (if provided in the constructor).

``coef_`` : array, shape = [n_features] or [n_targets, n_features]
    Weight vector(s).

``intercept_`` : float | array, shape = (n_targets,)
    Independent term in decision function. Set to 0.0 if
    ``fit_intercept = False``.

``alpha_`` : float
    Estimated regularization parameter.

See also

Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeClassifierCV: Ridge classifier with built-in cross validation

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.ridge.RidgeCV 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 Ridge regression model

This node has been automatically generated by wrapping the sklearn.linear_model.ridge.RidgeCV 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] or [n_samples, n_targets]
Target values
sample_weight : float or array-like of shape [n_samples]
Sample weight

Returns

self : Returns self.

Overrides: Node.stop_training