ETIA.CausalLearning.regressors package

Submodules

ETIA.CausalLearning.regressors.LinearRegressor module

class LinearRegression_

Bases: object

Wrapper class for setting up a LinearRegression model with custom parameters.

set_regressor_params(parameters)

Configures and returns a LinearRegression object. Currently, LinearRegression does not require parameters in this method.

set_regressor_params(parameters)

Configures and returns a LinearRegression object.

Parameters:

parameters (dict) – A dictionary containing the model parameters (though LinearRegression does not currently use parameters in this implementation).

Returns:

A LinearRegression object configured with default parameters.

Return type:

LinearRegression

Examples

>>> params = {}
>>> regressor = LinearRegression_().set_regressor_params(params)
>>> print(regressor)
LinearRegression()

ETIA.CausalLearning.regressors.RandomForestRegressor module

class RandomForestRegressor_

Bases: object

Wrapper class for setting up a RandomForestRegressor model with custom parameters.

set_regressor_params(parameters)

Configures and returns a RandomForestRegressor object with the specified parameters.

set_regressor_params(parameters)

Configures and returns a RandomForestRegressor object with the specified parameters.

Parameters:

parameters (dict) –

A dictionary containing the following keys:

• ’n_trees’: int, The number of trees in the forest.

• ’min_samples_leaf’: int or float, The minimum number of samples required to be at a leaf node.

• ’max_depth’: int, The maximum depth of the tree.

Returns:

A RandomForestRegressor object configured with the specified parameters.

Return type:

RandomForestRegressor

Examples

>>> params = {'n_trees': 100, 'min_samples_leaf': 0.1, 'max_depth': 10}
>>> regressor = RandomForestRegressor_().set_regressor_params(params)
>>> print(regressor)
RandomForestRegressor(max_depth=10, min_samples_leaf=0.1)

ETIA.CausalLearning.regressors.regressors module