How can I import all of sklearns regressors

Question

I'm doing some predictive modeling and would like to benchmark different kinds of regressors in scikit-learn, just to see what's out there and how they perform on a given prediction task.

I got inspired to do this by this kaggle kernel in which the author essentially manually imports a bunch of classifiers (about 10) and benchmarks them.

I'm having trouble finding a comprehensive list of imports for the regressors in sklearn so I'm trying to automatize the import statements to automatically return me a list of classes that I can use.

I tried to dynamically import the classes:

from importlib import import_module
import sklearn

def all_regressors():
    regressors=[]
    for module in sklearn.__all__:
        try:
            regressors.extend([cls for cls in import_module(f'sklearn.{module}').__all__ if 'Regress' in cls ])
        except:
            pass
    regressors.append(sklearn.svm.SVR)
    return regressors
print(all_regressors())

But i only get back the names as strings, rather than the classes:

['RandomForestRegressor', 'ExtraTreesRegressor', 'BaggingRegressor',
'GradientBoostingRegressor', 'AdaBoostRegressor', 
'GaussianProcessRegressor', 'IsotonicRegression', 'ARDRegression', 
'HuberRegressor', 'LinearRegression', 'LogisticRegression', 
'LogisticRegressionCV', 'PassiveAggressiveRegressor', 
'RandomizedLogisticRegression', 'SGDRegressor', 'TheilSenRegressor', 
'RANSACRegressor', 'MultiOutputRegressor', 'KNeighborsRegressor', 
'RadiusNeighborsRegressor', 'MLPRegressor', 'DecisionTreeRegressor', 
'ExtraTreeRegressor', <class 'sklearn.svm.classes.SVR'>]

How can I get the actual classes?

score 2 · Answer 1 · answered Oct 20 '17 at 15:19

I figured out i had to use getattr on the module object:

from importlib import import_module
import sklearn

def all_regressors():
    regressors=[]
    for module in sklearn.__all__:
        try:
            module = import_module(f'sklearn.{module}')
            regressors.extend([getattr(module,cls) for cls in module.__all__  if 'Regress' in cls ])
        except:
            pass
    regressors.append(sklearn.svm.SVR)
    return regressors
print(all_regressors())

[<class 'sklearn.ensemble.forest.RandomForestRegressor'>, <class 
'sklearn.ensemble.forest.ExtraTreesRegressor'>, <class 
'sklearn.ensemble.bagging.BaggingRegressor'>, <class 
'sklearn.ensemble.gradient_boosting.GradientBoostingRegressor'>,
<class 'sklearn.ensemble.weight_boosting.AdaBoostRegressor'>, <class 
'sklearn.gaussian_process.gpr.GaussianProcessRegressor'>, <class 
'sklearn.isotonic.IsotonicRegression'>, <class 
'sklearn.linear_model.bayes.ARDRegression'>, <class 
'sklearn.linear_model.huber.HuberRegressor'>, <class 
'sklearn.linear_model.base.LinearRegression'>, <class 
'sklearn.linear_model.logistic.LogisticRegression'>, <class 
'sklearn.linear_model.logistic.LogisticRegressionCV'>, <class 
'sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor'>, 
<class 'sklearn.linear_model.randomized_l1.RandomizedLogisticRegression'>, <class 
'sklearn.linear_model.stochastic_gradient.SGDRegressor'>, <class 
'sklearn.linear_model.theil_sen.TheilSenRegressor'>, <class 
'sklearn.linear_model.ransac.RANSACRegressor'>, <class 
'sklearn.multioutput.MultiOutputRegressor'>, <class 
'sklearn.neighbors.regression.KNeighborsRegressor'>, <class 
'sklearn.neighbors.regression.RadiusNeighborsRegressor'>, <class 
'sklearn.neural_network.multilayer_perceptron.MLPRegressor'>, <class 
'sklearn.tree.tree.DecisionTreeRegressor'>, <class 
'sklearn.tree.tree.ExtraTreeRegressor'>, <class 
'sklearn.svm.classes.SVR'>]

Some regressors seem to be missing from this list, e.g. Ridge and KernelRidge. A more complete is given at https://stackoverflow.com/a/42160314/2629879 — fhchl, May 12 '20 at 09:08

Fernando Wittmann · Accepted Answer · 2022-12-12T06:42:14.713

You can use all_estimators from sklearn.utils

from sklearn.utils import all_estimators

def get_all_regressors_sklearn():

    estimators = all_estimators(type_filter='regressor')

    all_regs = []
    for name, RegClass in estimators:
        print('Appending', name)
        try:
            reg = RegClass()
            all_regs.append(reg)
        except Exception as e:
            pass
    return all_regs

all_regs = get_all_regressors_sklearn()
print(all_regs)

Gives:

[ARDRegression(), AdaBoostRegressor(), BaggingRegressor(), BayesianRidge(), CCA(), DecisionTreeRegressor(), DummyRegressor(), ElasticNet(), ElasticNetCV(), ExtraTreeRegressor(), ExtraTreesRegressor(), GammaRegressor(), GaussianProcessRegressor(), GradientBoostingRegressor(), HistGradientBoostingRegressor(), HuberRegressor(), IsotonicRegression(), KNeighborsRegressor(), KernelRidge(), Lars(), LarsCV(), Lasso(), LassoCV(), LassoLars(), LassoLarsCV(), LassoLarsIC(), LinearRegression(), LinearSVR(), MLPRegressor(), MultiTaskElasticNet(), MultiTaskElasticNetCV(), MultiTaskLasso(), MultiTaskLassoCV(), NuSVR(), OrthogonalMatchingPursuit(), OrthogonalMatchingPursuitCV(), PLSCanonical(), PLSRegression(), PassiveAggressiveRegressor(), PoissonRegressor(), QuantileRegressor(), RANSACRegressor(), RadiusNeighborsRegressor(), RandomForestRegressor(), Ridge(), RidgeCV(), SGDRegressor(), SVR(), TheilSenRegressor(), TransformedTargetRegressor(), TweedieRegressor()]

Very cool, marked as correct answer! – Sebastian Wozny Dec 09 '22 at 13:28 — Sebastian Wozny, Dec 09 '22 at 13:28

How can I import all of sklearns regressors

2 Answers2