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

Class IsotonicRegressionScikitsLearnNode



Isotonic regression model.

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

The isotonic regression optimization problem is defined by::


    min sum w_i (y[i] - y_[i]) ** 2

    subject to y_[i] <= y_[j] whenever X[i] <= X[j]
    and min(y_) = y_min, max(y_) = y_max

where:

    - - ``y[i]`` are inputs (real numbers)
    - - ``y_[i]`` are fitted
    - - ``X`` specifies the order.
    -   If ``X`` is non-decreasing then ``y_`` is non-decreasing.
    - - ``w[i]`` are optional strictly positive weights (default to 1.0)


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

**Parameters**

y_min : optional, default: None
    If not None, set the lowest value of the fit to y_min.

y_max : optional, default: None
    If not None, set the highest value of the fit to y_max.

increasing : boolean or string, optional, default: True
    If boolean, whether or not to fit the isotonic regression with y
    increasing or decreasing.

    The string value "auto" determines whether y should
    increase or decrease based on the Spearman correlation estimate's
    sign.

out_of_bounds : string, optional, default: "nan"
    The ``out_of_bounds`` parameter handles how x-values outside of the
    training domain are handled.  When set to "nan", predicted y-values
    will be NaN.  When set to "clip", predicted y-values will be
    set to the value corresponding to the nearest train interval endpoint.
    When set to "raise", allow ``interp1d`` to throw ValueError.


**Attributes**

``X_`` : ndarray (n_samples, )
    A copy of the input X.

``y_`` : ndarray (n_samples, )
    Isotonic fit of y.

``X_min_`` : float
    Minimum value of input array `X_` for left bound.

``X_max_`` : float
    Maximum value of input array `X_` for right bound.

``f_`` : function
    The stepwise interpolating function that covers the domain `X_`.

**Notes**

Ties are broken using the secondary method from Leeuw, 1977.

**References**

Isotonic Median Regression: A Linear Programming Approach
Nilotpal Chakravarti
Mathematics of Operations Research
Vol. 14, No. 2 (May, 1989), pp. 303-308

Isotone Optimization in R : Pool-Adjacent-Violators
Algorithm (PAVA) and Active Set Methods
Leeuw, Hornik, Mair
Journal of Statistical Software 2009

Correctness of Kruskal's algorithms for monotone regression with ties
Leeuw, Psychometrica, 1977

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Isotonic 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)
Transform new data by linear interpolation
 
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)

 

Isotonic regression model.

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

The isotonic regression optimization problem is defined by::


    min sum w_i (y[i] - y_[i]) ** 2

    subject to y_[i] <= y_[j] whenever X[i] <= X[j]
    and min(y_) = y_min, max(y_) = y_max

where:

    - - ``y[i]`` are inputs (real numbers)
    - - ``y_[i]`` are fitted
    - - ``X`` specifies the order.
    -   If ``X`` is non-decreasing then ``y_`` is non-decreasing.
    - - ``w[i]`` are optional strictly positive weights (default to 1.0)


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

**Parameters**

y_min : optional, default: None
    If not None, set the lowest value of the fit to y_min.

y_max : optional, default: None
    If not None, set the highest value of the fit to y_max.

increasing : boolean or string, optional, default: True
    If boolean, whether or not to fit the isotonic regression with y
    increasing or decreasing.

    The string value "auto" determines whether y should
    increase or decrease based on the Spearman correlation estimate's
    sign.

out_of_bounds : string, optional, default: "nan"
    The ``out_of_bounds`` parameter handles how x-values outside of the
    training domain are handled.  When set to "nan", predicted y-values
    will be NaN.  When set to "clip", predicted y-values will be
    set to the value corresponding to the nearest train interval endpoint.
    When set to "raise", allow ``interp1d`` to throw ValueError.


**Attributes**

``X_`` : ndarray (n_samples, )
    A copy of the input X.

``y_`` : ndarray (n_samples, )
    Isotonic fit of y.

``X_min_`` : float
    Minimum value of input array `X_` for left bound.

``X_max_`` : float
    Maximum value of input array `X_` for right bound.

``f_`` : function
    The stepwise interpolating function that covers the domain `X_`.

**Notes**

Ties are broken using the secondary method from Leeuw, 1977.

**References**

Isotonic Median Regression: A Linear Programming Approach
Nilotpal Chakravarti
Mathematics of Operations Research
Vol. 14, No. 2 (May, 1989), pp. 303-308

Isotone Optimization in R : Pool-Adjacent-Violators
Algorithm (PAVA) and Active Set Methods
Leeuw, Hornik, Mair
Journal of Statistical Software 2009

Correctness of Kruskal's algorithms for monotone regression with ties
Leeuw, Psychometrica, 1977

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)

 

Transform new data by linear interpolation

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

Parameters

T : array-like, shape=(n_samples,)
Data to transform.

Returns

T_ : array, shape=(n_samples,)
The transformed data
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.isotonic.IsotonicRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape=(n_samples,)
Training data.
y : array-like, shape=(n_samples,)
Training target.
sample_weight : array-like, shape=(n_samples,), optional, default: None
Weights. If set to None, all weights will be set to 1 (equal weights).

Returns

self : object
Returns an instance of self.

Notes

X is stored for future use, as transform needs X to interpolate new input data.

Overrides: Node.stop_training