sklearn.isotonic.IsotonicRegression¶
- class sklearn.isotonic.IsotonicRegression(y_min=None, y_max=None, increasing=True, out_of_bounds='nan')¶
Isotonic regression model.
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)
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_.
References
Isotonic Median Regression: A Linear Programming Approach Nilotpal Chakravarti Mathematics of Operations Research Vol. 14, No. 2 (May, 1989), pp. 303-308
Methods
fit(X, y[, sample_weight, weight]) Fit the model using X, y as training data. fit_transform(X, y[, sample_weight, weight]) Fit model and transform y by linear interpolation. get_params([deep]) Get parameters for this estimator. predict(T) Predict new data by linear interpolation. score(X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of this estimator. transform(T) Transform new data by linear interpolation - __init__(y_min=None, y_max=None, increasing=True, out_of_bounds='nan')¶
- fit(X, y, sample_weight=None, weight=None)¶
Fit the model using X, y as training data.
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.
- fit_transform(X, y, sample_weight=None, weight=None)¶
Fit model and transform y by linear interpolation.
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 equal to 1 (equal weights).
Returns: `y_` : array, shape=(n_samples,)
The transformed data.
Notes
X doesn’t influence the result of fit_transform. It is however stored for future use, as transform needs X to interpolate new input data.
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- predict(T)¶
Predict new data by linear interpolation.
Parameters: T : array-like, shape=(n_samples,)
Data to transform.
Returns: `T_` : array, shape=(n_samples,)
Transformed data.
- score(X, y, sample_weight=None)¶
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,)
True values for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
- transform(T)¶
Transform new data by linear interpolation
Parameters: T : array-like, shape=(n_samples,)
Data to transform.
Returns: `T_` : array, shape=(n_samples,)
The transformed data
