def __init__(self, **kwargs):
"""
set _node_transformer, _edge_transformer, tdifNodeTextVectorizer
"""
FeatureDefinition.__init__(self)
nbTypes = self._getTypeNumber(kwargs)
block_transformer = FeatureUnion(
[ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!
(
"xywh",
Pipeline([
('selector', NodeTransformerXYWH_v2()),
#v1 ('xywh', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling
('xywh',
QuantileTransformer(n_quantiles=self.n_QUANTILES,
copy=False)
) #use in-place scaling
])),
(
"neighbors",
Pipeline([
('selector', NodeTransformerNeighbors()),
#v1 ('neighbors', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling
('neighbors',
QuantileTransformer(n_quantiles=self.n_QUANTILES,
copy=False)
) #use in-place scaling
])),
(
"1hot",
Pipeline([('1hot', Node1HotFeatures()
) #does the 1-hot encoding directly
]))
])
grid_line_transformer = GridLine_NodeTransformer()
self._node_transformer = TransformerListByType(
[block_transformer, grid_line_transformer])
edge_BB_transformer = FeatureUnion(
[ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!
("1hot",
Pipeline([('1hot',
Edge1HotFeatures(PageNumberSimpleSequenciality()))
])),
("boolean", Pipeline([('boolean', EdgeBooleanFeatures_v2())])),
(
"numerical",
Pipeline([
('selector', EdgeNumericalSelector()),
#v1 ('numerical', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling
('numerical',
QuantileTransformer(n_quantiles=self.n_QUANTILES,
copy=False)
) #use in-place scaling
]))
])
edge_BL_transformer = Block2GridLine_EdgeTransformer()
edge_LL_transformer = GridLine2GridLine_EdgeTransformer()
self._edge_transformer = TransformerListByType([
edge_BB_transformer,
edge_BL_transformer,
edge_BL_transformer, # useless but required
edge_LL_transformer
])
self.tfidfNodeTextVectorizer = None #tdifNodeTextVectorizer
