PROBA = True
PROCESSING = True
LEN = 12
WIDTH = 2
FOLDS = 3
PARTITIONS = 2
MOD, r = divmod(LEN, FOLDS)
assert r == 0
lg = LayerGenerator()
data = Data('subset', PROBA, PROCESSING, PARTITIONS, FOLDS)
X, y = data.get_data((LEN, WIDTH), MOD)
(F, wf), (P, wp) = data.ground_truth(X, y, subsets=PARTITIONS)
layer = lg.get_layer('subset', PROBA, PROCESSING, PARTITIONS, FOLDS)
lc = lg.get_layer_container('subset', PROBA, PROCESSING, PARTITIONS, FOLDS)
layer.indexer.fit(X)
cache = Cache(X, y, data)
def test_layer_fit():
"""[Parallel | Subset | Prep | Proba] test layer fit."""
layer_fit(layer, cache, F, wf)
def test_layer_predict():
"""[Parallel | Subset | Prep | Proba] test layer predict."""
layer_predict(layer, cache, P, wp)
PROBA = False
PROCESSING = True
LEN = 6
WIDTH = 2
FOLDS = 3
MOD, r = divmod(LEN, FOLDS)
assert r == 0
lg = LayerGenerator()
data = Data('stack', PROBA, PROCESSING, FOLDS)
X, y = data.get_data((LEN, WIDTH), MOD)
(F, wf), (P, wp) = data.ground_truth(X, y)
layer = lg.get_layer('stack', PROBA, PROCESSING, FOLDS)
lc = lg.get_layer_container('stack', PROBA, PROCESSING, FOLDS)
layer.indexer.fit(X)
cache = Cache(X, y, data)
def test_layer_fit():
"""[Parallel | Stack | Prep] test layer fit."""
layer_fit(layer, cache, F, wf)
def test_layer_predict():
"""[Parallel | Stack | Prep] test layer predict."""
layer_predict(layer, cache, P, wp)
PROBA = True
PROCESSING = False
LEN = 6
WIDTH = 2
FOLDS = 3
MOD, r = divmod(LEN, FOLDS)
assert r == 0
lg = LayerGenerator()
data = Data('blend', PROBA, PROCESSING)
X, y = data.get_data((LEN, WIDTH), MOD)
(F, wf), (P, wp) = data.ground_truth(X, y)
layer = lg.get_layer('blend', PROBA, PROCESSING)
lc = lg.get_layer_container('blend', PROBA, PROCESSING)
lc_p = lg.get_layer_container('blend',
PROBA,
PROCESSING,
propagate_features=[1])
layer.indexer.fit(X)
cache = Cache(X, y, data)
def test_layer_fit():
"""[Parallel | Blend | No Prep | Proba] test layer fit."""
layer_fit(layer, cache, F, wf)
:licence: MIT
"""
import numpy as np
from mlens.externals.sklearn.base import clone
from mlens.utils.checks import check_ensemble_build, assert_valid_estimator, \
check_is_fitted, assert_correct_format, check_initialized
from mlens.utils.dummy import LayerGenerator, OLS, Scale
from mlens.utils.exceptions import LayerSpecificationError, \
LayerSpecificationWarning, NotFittedError, ParallelProcessingError, \
ParallelProcessingWarning
lg = LayerGenerator()
LAYER = lg.get_layer('stack', False, True)
LAYER_CONTAINER = lg.get_layer_container('stack', False, True)
class Tmp(object):
"""Temporary class for mimicking ParallelProcessing status."""
def __init__(self, lyr, __initialized__, __fitted__):
self.__initialized__ = __initialized__
self.__fitted__ = __fitted__
self.layers = lyr
class Lyr(object):
"""Temporary layer class for storing raise on exception."""
def __init__(self, raise_on_exception):
self.raise_on_exception = raise_on_exception