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 (optional, default=1.345)) – The tuning constant for Huber’s t function

  • maxiter (int (optional, default=50)) – The maximum number of iterations to try

  • tol (float (optional, default=1e-08)) – The convergence tolerance of the estimate

  • fit_intercept (bool (optional, default=True)) – Whether to fit an intercept in this model

  • cov_type (one of {‘H1’, ‘H2’, or ‘H3’} (optional, 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]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([t, maxiter, tol, fit_intercept, …])

Initialize self.

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) – 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) – 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) – 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