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Modular toolkit for Data Processing MDP
Package mdp :: Package nodes :: Class LassoScikitsLearnNode
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Class LassoScikitsLearnNode

Linear Model trained with L1 prior as regularizer (aka the Lasso) This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.Lasso class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Technically the Lasso model is optimizing the same objective function as the Elastic Net with rho=1.0 (no L2 penalty).

Parameters

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

Attributes

coef_ : array, shape = [n_features]
parameter vector (w in the fomulation formula)
intercept_ : float
independent term in decision function.

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.Lasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
Lasso(alpha=0.1, fit_intercept=True)
>>> print clf.coef_
[ 0.85  0.  ]
>>> print clf.intercept_
0.15

See also

LassoLARS

Notes

The algorithm used to fit the model is coordinate descent.

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 prior as regularizer (aka the Lasso) This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.Lasso class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Technically the Lasso model is optimizing the same objective function as the Elastic Net with rho=1.0 (no L2 penalty).
 
_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.Lasso 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.Lasso 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 prior as regularizer (aka the Lasso) This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.Lasso class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Technically the Lasso model is optimizing the same objective function as the Elastic Net with rho=1.0 (no L2 penalty).

Parameters

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

Attributes

coef_ : array, shape = [n_features]
parameter vector (w in the fomulation formula)
intercept_ : float
independent term in decision function.

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.Lasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
Lasso(alpha=0.1, fit_intercept=True)
>>> print clf.coef_
[ 0.85  0.  ]
>>> print clf.intercept_
0.15

See also

LassoLARS

Notes

The algorithm used to fit the model is coordinate descent.

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.Lasso 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.Lasso 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

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Modular toolkit for Data Processing MDP
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