def run(cls, job, eval=True):
"""Run a test"""
if job == 'fit':
lr, _ = est.get_learner(cls, True, False)
lr.dtype = np.float64
else:
lr = run(cls, 'fit', False)
data = Data(cls, True, False, True)
X, y = data.get_data((25, 4), 3)
if job in ['fit', 'transform']:
(F, wf), _ = data.ground_truth(X, y, data.indexer.partitions)
else:
_, (F, wf) = data.ground_truth(X, y, data.indexer.partitions)
args = {'fit': [X, y], 'transform': [X], 'predict': [X]}[job]
P = _run(lr, job, *args, return_preds=True)
if not eval:
return lr
np.testing.assert_array_equal(P, F)
if job in ['fit', 'transform']:
lrs = lr.sublearners
else:
lrs = lr.learner
w = [obj.estimator.coef_ for obj in lrs]
np.testing.assert_array_equal(w, wf)
from mlens.utils.dummy import OLS, Scale
from mlens.utils.exceptions import NotFittedError
from mlens.parallel import make_group
from mlens.estimators import LearnerEstimator, TransformerEstimator, LayerEnsemble
from mlens.externals.sklearn.base import clone
try:
from sklearn.utils.estimator_checks import check_estimator
run_sklearn = True
except ImportError:
check_estimator = None
run_sklearn = False
data = Data('stack', False, True)
X, y = data.get_data((25, 4), 3)
(F, wf), (P, wp) = data.ground_truth(X, y)
Est = LayerEnsemble
est = LayerEnsemble(make_group(FoldIndex(), ESTIMATORS, PREPROCESSING),
dtype=np.float64)
class Tmp(Est):
"""Temporary class
Wrapper to get full estimator on no-args instantiation. For compatibility
with older Scikit-learn versions.
"""
def __init__(self):
args = {
LearnerEstimator: (OLS(), FoldIndex()),
"""ML-ENSEMBLE
"""
import numpy as np
from mlens.testing.dummy import OLS, ECM
from mlens.testing import Data
from mlens.ensemble import Subsemble, SuperLearner
data = Data('subsemble', False, False, partitions=2, folds=3)
X, y = data.get_data((30, 4), 3)
data.indexer.fit(X)
(F, wf), (P, wp) = data.ground_truth(X, y, data.indexer.partitions)
def test_subset_fit():
"""[Subsemble] 'fit' and 'predict' runs correctly."""
meta = OLS()
meta.fit(F, y)
g = meta.predict(P)
ens = Subsemble()
ens.add(ECM, partitions=2, folds=3, dtype=np.float64)
ens.add_meta(OLS(), dtype=np.float64)
ens.fit(X, y)
pred = ens.predict(X)
np.testing.assert_array_equal(pred, g)
