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)
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)
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) lc_p = lg.get_layer_container('subset', PROBA, PROCESSING, PARTITIONS, FOLDS, propagate_features=[1]) layer.indexer.fit(X) cache = Cache(X, y, data) def test_layer_fit(): """[Parallel | Subset | No Prep | Proba] test layer fit.""" layer_fit(layer, cache, F, wf)
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 = 2 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) lc_p = lg.get_layer_container('stack', PROBA, PROCESSING, FOLDS, propagate_features=[1]) layer.indexer.fit(X) cache = Cache(X, y, data) def test_layer_fit(): """[Parallel | Stack | No Prep | Proba ] test layer fit.""" layer_fit(layer, cache, F, wf)
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) 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) def test_layer_predict(): """[Parallel | Blend | No Prep | Proba] test layer predict.""" layer_predict(layer, cache, P, wp)
""" 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