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

Overrides: object.__init__

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

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

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