econml.sklearn_extensions.linear_model.StatsModelsRLM

class econml.sklearn_extensions.linear_model.StatsModelsRLM(t=1.345, maxiter=50, tol=1e-08, fit_intercept=True, cov_type='H1')[source]

Bases: econml.sklearn_extensions.linear_model._StatsModelsWrapper

Class which mimics robust linear regression from the statsmodels package.

Parameters

t (float, default 1.345) – The tuning constant for Huber’s t function
maxiter (int, default 50) – The maximum number of iterations to try
tol (float, default 1e-08) – The convergence tolerance of the estimate
fit_intercept (bool, default True) – Whether to fit an intercept in this model
cov_type ({‘H1’, ‘H2’, or ‘H3’}, default ‘H1’) – Indicates how the covariance matrix is estimated. See statsmodels.robust.robust_linear_model.RLMResults for more information.

__init__(t=1.345, maxiter=50, tol=1e-08, fit_intercept=True, cov_type='H1')[source]

Methods

`__init__`([t, maxiter, tol, fit_intercept, ...])
`coef__interval`([alpha])	Gets a confidence interval bounding the fitted coefficients.
`fit`(X, y)	Fits the model.
`get_params`([deep])	Get parameters for this estimator.
`intercept__interval`([alpha])	Gets a confidence interval bounding the intercept(s) (or 0 if no intercept was fit).
`predict`(X)	Predicts the output given an array of instances.
`predict_interval`(X[, alpha])	Gets a confidence interval bounding the prediction.
`prediction_stderr`(X)	Gets the standard error of the predictions.
`set_params`(**params)	Set the parameters of this estimator.

Attributes

`coef_`	Get the model's coefficients on the covariates.
`coef_stderr_`	Gets the standard error of the fitted coefficients.
`intercept_`	Get the intercept(s) (or 0 if no intercept was fit).
`intercept_stderr_`	Gets the standard error of the intercept(s) (or 0 if no intercept was fit).

coef__interval(alpha=0.05)

Gets a confidence interval bounding the fitted coefficients.

Parameters: alpha (float, default 0.05) – The confidence level. Will calculate the alpha/2-quantile and the (1-alpha/2)-quantile of the parameter distribution as confidence interval
Returns: coef__interval – The lower and upper bounds of the confidence interval of the coefficients
Return type: {tuple ((p, d) array, (p,d) array), tuple ((d,) array, (d,) array)}

fit(X, y)[source]

Fits the model.

Parameters

X ((N, d) nd array_like) – co-variates
y ((N,) nd array_like or (N, p) array_like) – output variable

Returns

self

Return type

StatsModelsRLM

get_params(deep=True)

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

intercept__interval(alpha=0.05)

Gets a confidence interval bounding the intercept(s) (or 0 if no intercept was fit).

Parameters: alpha (float, default 0.05) – The confidence level. Will calculate the alpha/2-quantile and the (1-alpha/2)-quantile of the parameter distribution as confidence interval
Returns: intercept__interval – The lower and upper bounds of the confidence interval of the intercept(s)
Return type: {tuple ((p,) array, (p,) array), tuple (float, float)}

predict(X)

Predicts the output given an array of instances.

Parameters: X ((n, d) array_like) – The covariates on which to predict
Returns: predictions – The predicted mean outcomes
Return type: {(n,) array, (n,p) array}

predict_interval(X, alpha=0.05)

Gets a confidence interval bounding the prediction.

Parameters

X ((n, d) array_like) – The covariates on which to predict
alpha (float, default 0.05) – The confidence level. Will calculate the alpha/2-quantile and the (1-alpha/2)-quantile of the parameter distribution as confidence interval

Returns

prediction_intervals – The lower and upper bounds of the confidence intervals of the predicted mean outcomes

Return type

{tuple ((n,) array, (n,) array), tuple ((n,p) array, (n,p) array)}

prediction_stderr(X)

Gets the standard error of the predictions.

Parameters: X ((n, d) array_like) – The covariates at which to predict
Returns: prediction_stderr – The standard error of each coordinate of the output at each point we predict
Return type: (n, p) array_like

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

property coef_

Get the model’s coefficients on the covariates.

Returns: coef_ – The coefficients of the variables in the linear regression. If label y was p-dimensional, then the result is a matrix of coefficents, whose p-th row containts the coefficients corresponding to the p-th coordinate of the label.
Return type: {(d,), (p, d)} nd array_like

property coef_stderr_

Gets the standard error of the fitted coefficients.

Returns: coef_stderr_ – The standard error of the coefficients
Return type: {(d,), (p, d)} nd array_like

property intercept_

Get the intercept(s) (or 0 if no intercept was fit).

Returns: intercept_ – The intercept of the linear regresion. If label y was p-dimensional, then the result is a vector whose p-th entry containts the intercept corresponding to the p-th coordinate of the label.
Return type: float or (p,) nd array_like

property intercept_stderr_

Gets the standard error of the intercept(s) (or 0 if no intercept was fit).

Returns: intercept_stderr_ – The standard error of the intercept(s)
Return type: float or (p,) nd array_like