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Merge pull request #32 from PYFTS/feature/som_fts

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adaptação da FTS para receber métodos de transformação multivariada
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Petrônio Cândido de Lima e Silva 2020-12-10 20:50:28 -03:00 committed by GitHub
commit 62c4d67f27
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 15 additions and 2 deletions
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2
pyFTS/common/Transformations.py
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@ -15,6 +15,8 @@ class Transformation(object):
def __init__(self, **kwargs):
self.is_invertible = True
self.is_multivariate = False
"""detemine if this transformation can be applied to multivariate data"""
self.minimal_length = 1
self.name = ''

6
pyFTS/common/transformations/som.py
View File

@ -18,6 +18,7 @@ class SOMTransformation(Transformation):
self.data: pd.DataFrame = None
self.grid_dimension: Tuple = grid_dimension
self.pbc = kwargs.get('PBC', True)
self.is_multivariate = True
# debug attributes
self.name = 'Kohonen Self Organizing Maps FTS'
@ -25,8 +26,6 @@ class SOMTransformation(Transformation):
def apply(self,
data: pd.DataFrame,
endogen_variable=None,
names: Tuple[str] = ('x', 'y'),
param=None,
**kwargs):
"""
@ -45,6 +44,9 @@ class SOMTransformation(Transformation):
"""
endogen_variable = kwargs.get('endogen_variable', None)
names = kwargs.get('names', ('x', 'y'))
if endogen_variable not in data.columns:
endogen_variable = None
cols = data.columns[:-1] if endogen_variable is None else [col for col in data.columns if

9
pyFTS/models/multivariate/mvfts.py
View File

@ -35,6 +35,12 @@ class MVFTS(fts.FTS):
self.name = "Multivariate FTS"
self.uod_clip = False
def append_transformation(self, transformation, **kwargs):
if not transformation.is_multivariate:
raise Exception('The transformation is not multivariate')
self.transformations.append(transformation)
self.transformations_param.append(kwargs)
def append_variable(self, var):
"""
Append a new endogenous variable to the model
@ -53,6 +59,9 @@ class MVFTS(fts.FTS):
def apply_transformations(self, data, params=None, updateUoD=False, **kwargs):
ndata = data.copy(deep=True)
for ct, transformation in enumerate(self.transformations):
ndata = transformation.apply(ndata, **self.transformations_param[ct])
for var in self.explanatory_variables:
try:
values = ndata[var.data_label].values #if isinstance(ndata, pd.DataFrame) else ndata[var.data_label]