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Regression examples

The snippets below show how to fit each predictor with a regression score_model.

ModalBoundaryClustering

from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sheshe import ModalBoundaryClustering

X, y = make_regression(n_samples=200, n_features=3, noise=0.1, random_state=0)
reg = LinearRegression().fit(X, y)
mbc = ModalBoundaryClustering(task="regression")
mbc.fit(X, y, score_model=reg)

ShuShu

from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sheshe import ShuShu

X, y = make_regression(n_samples=200, n_features=3, noise=0.1, random_state=0)
reg = LinearRegression().fit(X, y)
sh = ShuShu()
sh.fit(X, y, score_model=reg)

CheChe

from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sheshe import CheChe

X, y = make_regression(n_samples=200, n_features=3, noise=0.1, random_state=0)
reg = LinearRegression().fit(X, y)
cc = CheChe()
cc.fit(X, y, score_model=reg, max_pairs=3)

ModalScoutEnsemble

from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sheshe import ModalScoutEnsemble

X, y = make_regression(n_samples=200, n_features=3, noise=0.1, random_state=0)
base = LinearRegression().fit(X, y)
mse = ModalScoutEnsemble(base_estimator=LinearRegression(), task="regression")
mse.fit(X, y, score_model=base)

Fit times

Running experiments/regression_fit_times.py writes benchmark/regression_fit_times.csv with the following results:

predictor	fit_time_s	status
ModalBoundaryClustering	0.174340	ok
ShuShu		ValueError
CheChe	0.006064	ok
ModalScoutEnsemble	0.387224	ok