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

Logistic Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Implements L1 and L2 regularized logistic regression.

Parameters

penalty : string, 'l1' or 'l2'
Used to specify the norm used in the penalization
dual : boolean
Dual or primal formulation. Dual formulation is only implemented for l2 penalty.
C : float
Specifies the strength of the regularization. The smaller it is the bigger in the regularization.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be added the decision function
intercept_scaling : float, default: 1
when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased
tol: float, optional
tolerance for stopping criteria

Attributes

coef_ : array, shape = [n_classes-1, n_features]
Coefficient of the features in the decision function.
intercept_ : array, shape = [n_classes-1]
intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True

See also

LinearSVC

Notes

The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.

References

LIBLINEAR -- A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Logistic Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Implements L1 and L2 regularized logistic regression.
list
_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.
 
_label(self, x)
 
_stop_training(self, **kwargs)
Transform the data and labels lists to array objects and reshape them.
 
label(self, x)
Predict target values of X according to the fitted model. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
 
stop_training(self, **kwargs)
Fit the model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression 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 ClassifierCumulator
 
_check_train_args(self, x, labels)
 
_train(self, x, labels)
Cumulate all input data in a one dimensional list.
 
train(self, x, labels)
Cumulate all input data in a one dimensional list.
    Inherited from ClassifierNode
 
_execute(self, x)
 
_prob(self, x, *args, **kargs)
 
execute(self, x)
Process the data contained in x.
 
prob(self, x, *args, **kwargs)
This function does classification or regression on a test vector T given a model with probability information. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
 
rank(self, x, threshold=None)
Returns ordered list with all labels ordered according to prob(x) (e.g., [[3 1 2], [2 1 3], ...]).
    Inherited from PreserveDimNode
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
    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)
 
_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)
 
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)

 

Logistic Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Implements L1 and L2 regularized logistic regression.

Parameters

penalty : string, 'l1' or 'l2'
Used to specify the norm used in the penalization
dual : boolean
Dual or primal formulation. Dual formulation is only implemented for l2 penalty.
C : float
Specifies the strength of the regularization. The smaller it is the bigger in the regularization.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be added the decision function
intercept_scaling : float, default: 1
when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased
tol: float, optional
tolerance for stopping criteria

Attributes

coef_ : array, shape = [n_classes-1, n_features]
Coefficient of the features in the decision function.
intercept_ : array, shape = [n_classes-1]
intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True

See also

LinearSVC

Notes

The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.

References

LIBLINEAR -- A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/

Overrides: object.__init__

_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.
Returns: list
The list of dtypes supported by this node.
Overrides: Node._get_supported_dtypes

_label(self, x)

 
Overrides: ClassifierNode._label

_stop_training(self, **kwargs)

  
Transform the data and labels lists to array objects and reshape them.
Overrides: Node._stop_training

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

label(self, x)

 

Predict target values of X according to the fitted model. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression 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]

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

stop_training(self, **kwargs)

 

Fit the model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression 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 vector, where n_samples in the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
class_weight : {dict, 'auto'}, optional
Weights associated with classes. If not given, all classes are supposed to have weight one.

Returns

self : object
Returns self.
Overrides: Node.stop_training

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Modular toolkit for Data Processing MDP
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