def __init__(self):
args = {
LearnerEstimator: (OLS(), FoldIndex()),
LayerEnsemble: (make_group(FoldIndex(), ESTIMATORS,
PREPROCESSING), ),
TransformerEstimator: (Scale(), FoldIndex())
}[Est]
super(Tmp, self).__init__(*args)
def test_data():
"""[Parallel | Learner] Test data"""
idx = INDEXERS['subsemble']()
lyr = Layer('layer-scorer').push(make_group(idx, ECM, None))
for lr in lyr.learners:
lr.scorer = scorer
run(lyr, 'fit', X, y, return_preds=True)
repr = lyr.data.__repr__()
assert lyr.raw_data
assert isinstance(lyr.raw_data, list)
assert isinstance(lyr.data, dict)
assert repr
assert 'score' in repr
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()),
LayerEnsemble: (make_group(FoldIndex(), ESTIMATORS,
PREPROCESSING), ),
TransformerEstimator: (Scale(), FoldIndex())
To parallelize the implementation, we can use the :class:`Layer` class. A layer is a handle that will run any number of :class:`Group` instances attached to it in parallel. Each group in turn is a wrapper around a ``indexer-transformers-estimators`` triplet. Basics ------ So, to fit our two learners in parallel, we first need a :class:`Group` object to handle them. """ from mlens.parallel import Layer, Group, make_group, run from mlens.utils.dummy import OLS, Scale from mlens.index import FoldIndex indexer = FoldIndex(folds=2) group = make_group(indexer, [OLS(1), OLS(2)], None) ############################################################################ # This ``group`` object is now a complete description of how to fit our two # learners using the prescribed indexing method. # # To train the estimators, we need feed the group to a :class:`Layer` instance: import numpy as np np.random.seed(2) X = np.arange(20).reshape(10, 2) y = np.random.rand(10) layer = Layer(stack=group)
ESTIMATORS_PROBA_2 = {'sc2': [('offs2', LogisticRegression(offset=2)),
('null2', LogisticRegression())],
'no2': [('offs2', LogisticRegression(offset=2)),
('null2', LogisticRegression())]}
def scorer(p, y): return np.mean(p - y)
data = Data('stack', True, True)
X, y = data.get_data((25, 4), 3)
idx1 = INDEXERS['stack']()
g1 = make_group(
idx1, ESTIMATORS_PROBA_1, PREPROCESSING_1,
learner_kwargs={'proba': True, 'verbose': True},
transformer_kwargs={'verbose': True})
idx2 = INDEXERS['subsemble']()
g2 = make_group(
idx2, ESTIMATORS_PROBA_2, PREPROCESSING_2,
learner_kwargs={'proba': False, 'verbose': True},
transformer_kwargs={'verbose': True})
layer = Layer('layer')
def test_push_1():
"""[Parallel | Layer] Testing single push"""
assert not layer.__stack__
