def _format_result_building(self, resulter=None):
"""Format and setting the resulter. The resulter manages all the
possibilities of measure construction we could do.
Parameters
----------
resulter: pst.BaseResulter
the object which manages all the possibilities of measure
construction we could do.
"""
self.resulter = DefaultResulter(self, resulter)
class FeaturesManager:
"""Method for retrieving features.
See also
--------
pySpatialTools.RetrieverManager
"""
__name__ = "pySpatialTools.FeaturesManager"
typefeat = "manager"
def _initialization(self):
"""Initialization of mutable class parameters."""
## Main objects to manage
self.descriptormodels = []
self.features = []
self.mode = None
self.selector = (0, 0), (0, 0), (0, 0)
## IO information
# self._variables = {} ## TO CHANGE
# self.featuresnames = [] ## TO CHANGE
self.out_features = []
self.k_perturb = 0
self._out = "ndarray" # dict
## IO managers
self._maps_input = []
self._maps_output = None
self._maps_vals_i = None
def __init__(
self,
features_objects,
mode=None,
maps_input=None,
maps_output=None,
maps_vals_i=None,
descriptormodels=None,
selectors=[None] * 3,
resulter=None,
):
"""Manager of features.
Parameters
----------
features_objects: list or pst.BaseFeatures
the features objects in order to be managed by the manage.
mode: str optional or None (default=None)
the mode we want to manage the different features. 'parallel' for
a parallel management or 'sequential' for a sequential management.
maps_input: function or pst.BaseSelector (default=None)
input map for the indices of transformed elements.
maps_output: function or pst.BaseSelector (default=None)
output map for the descriptors computed by the descriptormodels.
maps_vals_i: function or pst.BaseSelector (default=None)
the mapper from elements `i` to the values of the storing the
resulter measure `vals_i`.
descriptormodels: list or pst.BaseDescriptormodel (default=None)
the descriptormodels to be manage externally by that class.
selectors: list, tuple or pst.BaseSelector (default=[None]*3)
the selection information to manage the process.
resulter: pst.BaseResulter (default=None)
the object which manages all the possibilities of measure
construction we could do.
"""
self._initialization()
# out = out if out in ['ndarray', 'dict'] else None
# self._out = self._out if out is None else out
self._format_descriptormodel(descriptormodels)
self._format_maps(maps_input, maps_output, maps_vals_i)
self._format_features(features_objects)
self._format_mode(mode)
self._format_outfeatures()
self.set_selector(*selectors)
self._format_result_building(resulter)
def __getitem__(self, i_feat):
if i_feat < 0 or i_feat >= len(self.features):
raise IndexError("Not correct index for features.")
return self.features[i_feat]
def __len__(self):
return len(self.features)
@property
def shape(self):
"""As a mapper its shapes represents the size of the input and the
output stored in maps_vals_i.
Returns
-------
n_in: int or None
the number of values indices of the elements to retrieve and
compute their descriptors. If it is None, it is open (e.g. on-line
elements added)
n_out: int or None
the number of values indices of the resulter measure. In case of
None, it is an open container.
"""
return self._maps_vals_i.n_in, self._maps_vals_i.n_out
@property
def shape_measure(self):
"""The measures of the possible output measure.
Returns
-------
n_vals_i: int or None
the number of values indices of the resulter measure. In case of
None, it is an open container.
n_feats: int or None
the number of descriptors resultant in the measure.
ks: int
the number of perturbations plus the non-perturbated case.
"""
n_vals_i = self._maps_vals_i.n_out
n_feats = len(self.out_features) if self.out_features else None
return n_vals_i, n_feats, self.k_perturb + 1
# @property
# def nfeats(self):
# return len(self.variables)
################################ Formatters ###############################
###########################################################################
############################## Format features ############################
def _format_features(self, features_objects):
"""Formatter of features.
Parameters
----------
features_objects: list or pst.BaseFeatures
the features objects in order to be managed by the manage.
"""
## 0. Format to list mode
# Format to list mode
if type(features_objects) != list:
features_objects = [features_objects]
# Format to feature objects
nfeat = len(features_objects)
for i in range(nfeat):
# features_objects[i] = self._auxformat_features(features_objects[i])
features_objects[i] = _featuresobject_parsing_creation(features_objects[i])
self.features = features_objects
## 1. Check input
if nfeat == 0:
msg = "Any feature object is input in the featureRetriever."
raise TypeError(msg)
## 2. Format kperturb
self._format_k_perturbs()
def _format_descriptormodel(self, descriptormodels=None):
"""Formatter of the descriptormodels.
Parameters
----------
descriptormodels: pst.BaseDescriptormodel (default=None)
the descriptormodels to manage externally of the features classes.
"""
if descriptormodels is None:
self.descriptormodels = [DummyDescriptor()]
else:
if type(descriptormodels) != list:
descriptormodels = [descriptormodels]
self.descriptormodels += descriptormodels
def _format_result_building(self, resulter=None):
"""Format and setting the resulter. The resulter manages all the
possibilities of measure construction we could do.
Parameters
----------
resulter: pst.BaseResulter
the object which manages all the possibilities of measure
construction we could do.
"""
self.resulter = DefaultResulter(self, resulter)
def _format_k_perturbs(self):
"""Format k perturbations."""
## 1. Format kperturb
kp = self[0].k_perturb
k_rei_bool = [self[i].k_perturb == kp for i in range(len(self))]
# Check k perturbations
if not all(k_rei_bool):
msg = "Not all the feature objects have the same perturbations."
raise Exception(msg)
self.k_perturb = kp
############################## Format IO maps #############################
def _format_map_vals_i(self, sp_typemodel):
"""Format mapper to indicate external val_i to aggregate result.
Parameters
----------
sp_typemodel: list, tuple, np.ndarray
the information to set the map_vals_i in order to obtain the
stored index for each element.
"""
if sp_typemodel is not None:
map_vals_i = create_mapper_vals_i(sp_typemodel, self)
# if type(sp_typemodel) == tuple:
# map_vals_i = create_mapper_vals_i(sp_typemodel, self)
# else:
# map_vals_i = create_mapper_vals_i(sp_typemodel, self)
self._maps_vals_i = map_vals_i
else:
self._maps_vals_i = create_mapper_vals_i(self._maps_vals_i, self)
def _format_maps(self, maps_input, maps_output, maps_vals_i):
"""Formatter of maps.
Parameters
----------
maps_input: function or pst.BaseSelector
input map for the indices of transformed elements.
maps_output: function or pst.BaseSelector
output map for the descriptors computed by the descriptormodels.
maps_vals_i: function or pst.BaseSelector
the mapper from elements `i` to the values of the storing the
resulter measure `vals_i`.
"""
## 1. Format input maps
if maps_input is None:
self._maps_input = [lambda i_info, k=0: i_info]
else:
if type(maps_input).__name__ == "function":
self._maps_input = [lambda i_info, k=0: maps_input(i_info, k)]
# else:
# self._maps_input = [maps_input]
## 2. Format output maps (TODO)
if maps_output is None:
self._maps_output = lambda self, feats: feats
else:
if type(maps_output).__name__ == "function":
self._maps_output = lambda s, feats: maps_output(s, feats)
# else:
# self._maps_output = maps_output
self._format_map_vals_i(maps_vals_i)
############################ Format mode utils ############################
def _format_mode(self, mode=None):
"""Format which mode is going to be this manager (sequential or
parallel). In sequential the features are options between which we
have to choose in order to get the outfeatures. In parallel we have to
use everyone and join the results into a final result.
Parameters
----------
mode: str optional or None (default=None)
the mode we want to manage the different features. 'parallel' for
a parallel management or 'sequential' for a sequential management.
"""
## Format basic modes
if mode is None:
## Out setting
assert all([self[0]._out == e._out for e in self])
self._out = self[0]._out
## If open out_features
if not len(self[0].out_features):
n_f = len(self)
logi = [not len(self[i].out_features) for i in range(n_f)]
assert all(logi)
self.out_features = []
self.mode = None
else:
outs = self[0].out_features
logi = [self[i].out_features == outs for i in range(len(self))]
if all(logi):
n_f = len(self)
nulls = self[0]._nullvalue
assert [self[i]._nullvalue == nulls for i in range(n_f)]
self.mode = "sequential"
else:
self.mode = "parallel"
else:
## TODO: parallel and sequential TODO
assert mode in ["sequential", "parallel"]
self.mode = mode
def _format_outfeatures(self):
"""Format the output features of this manager."""
## If open out_features
logi = [type(self[i].out_features) == list for i in range(len(self))]
assert all(logi)
if len(self[0].out_features) == 0:
logi = [len(self[i].out_features) == 0 for i in range(len(self))]
assert all(logi)
self.out_features = []
## If close out_features
else:
## If mode parallel
if self.mode == "parallel":
outfeatures = []
for i in range(len(self)):
outfeatures.append(self[i].out_features)
self.out_features = outfeatures
## If mode sequential
elif self.mode == "sequential":
outs = self[0].out_features
logi = [self[i].out_features == outs for i in range(len(self))]
assert all(logi)
self.out_features = outs
############################# Format selectors ############################
def _format_selector(self, selector1, selector2=None, selector3=None):
"""Programable get_type_feats. It sets selection functions and
parameters.
Parameters
----------
selector1: tuple, np.ndarray or None (default=None)
the selection of the features for the element `i`.
selector2: tuple, np.ndarray or None (default=None)
the selection of the features for the neighs of the element `i`.
selector3: tuple, np.ndarray or None (default=None)
the selection of the descriptor models managed by that class.
"""
if selector1 is None:
self.get_type_feats = self._general_get_type_feat
self._get_input_features = self._get_input_features_general
self._get_output_features = self._get_output_features_general
self._complete_desc_i = self._complete_desc_i_general
else:
typ = type(selector1)
if selector2 is not None:
assert (type(selector2) == typ) and (type(selector3) == typ)
if typ == tuple:
if selector2 is None:
selector1, selector2, selector3 = selector1
self.selector = (selector1, selector2, selector3)
self.get_type_feats = self._static_get_type_feat
self._get_input_features = self._get_input_features_constant
self._get_output_features = self._get_output_features_constant
self._complete_desc_i = self._complete_desc_i_constant
else:
self.selector = Feat_RetrieverSelector(selector1, selector2, selector3)
self.get_type_feats = self._selector_get_type_feat
self._get_input_features = self._get_input_features_variable
self._get_output_features = self._get_output_features_variable
self._complete_desc_i = self._complete_desc_i_variable
self.selector.assert_correctness(self)
################################# Setters #################################
###########################################################################
def set_map_vals_i(self, _maps_vals_i):
"""Set how it maps each element of the input indices has to be
transformed in order to be obtain the stored index for each element.
Parameters
----------
maps_vals_i: function or pst.BaseSelector
the mapper from elements `i` to the values of the storing the
resulter measure `vals_i`.
"""
# self._maps_vals_i = _maps_vals_i
if type(_maps_vals_i) in [int, slice]:
_maps_vals_i = (self._maps_vals_i, _maps_vals_i)
self._format_map_vals_i(_maps_vals_i)
self._format_map_vals_i(_maps_vals_i)
def set_descriptormodels(self, descriptormodels):
"""Set descriptormodels.
Parameters
----------
descriptormodels: list or pst.BaseDescriptormodel
the descriptormodels to be manage externally by that class.
"""
self._format_descriptormodel(descriptormodels)
def set_selector(self, selector1, selector2=None, selector3=None):
"""Programable get_type_feats. It sets selection functions and
parameters.
Parameters
----------
selector1: tuple, np.ndarray or None (default=None)
the selection of the features for the element `i`.
selector2: tuple, np.ndarray or None (default=None)
the selection of the features for the neighs of the element `i`.
selector3: tuple, np.ndarray or None (default=None)
the selection of the descriptor models managed by that class.
"""
self._format_selector(selector1, selector2, selector3)
################################# Getters #################################
###########################################################################
def compute_descriptors(self, i, neighs_info, k=None, feat_selectors=None):
"""General compute descriptors for descriptormodel class.
Parameters
----------
i: np.ndarray or list
the indices of the elements `i`.
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: int or list (default=None)
the perturbations indices we wantto get.
feat_selectors: list, tuple or pst.BaseSelector (default=None)
the selection information.
Returns
-------
descriptors: list
the descriptors for each perturbation and element.
vals_i: list or np.ndarray
the store information index of each element `i`.
"""
## 0. Prepare list of k
ks = list(range(self.k_perturb + 1)) if k is None else k
ks = [ks] if type(ks) == int else ks
i_input = [i] if type(i) == int else i
# if type(feat_selectors) == tuple:
# feat_selectors = [feat_selectors]
neighs_info = ensuring_neighs_info(neighs_info, k)
# sh = neighs_info.shape
# print sh, len(i_input), len(ks), ks, i_input
# assert(len(i_input) == sh[1])
# assert(len(ks) == sh[0])
## 1. Prepare selectors
t_feat_in, t_feat_out, t_feat_des = self.get_type_feats(i, feat_selectors)
# print t_feat_in, t_feat_out, t_feat_des
## 2. Get pfeats (pfeats 2dim array (krein, jvars))
desc_i = self._get_input_features(i_input, ks, t_feat_in)
print i_input, ks, self._get_input_features, type(t_feat_in)
print "." * 20, desc_i
desc_neigh = self._get_output_features(neighs_info, ks, t_feat_out)
# print i, ks, i_input, neighs_info, neighs_info.ks, neighs_info.idxs
## 3. Map vals_i
vals_i = self._get_vals_i(i, ks)
# print '+'*10, vals_i, desc_neigh, desc_i
## 4. Complete descriptors (TODO)
descriptors = self._complete_desc_i(i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_des)
# descriptors =\
# self.descriptormodel.complete_desc_i(i, neighs_info, desc_i,
# desc_neigh, vals_i)
## 5. Join descriptors
descriptors = self._join_descriptors(descriptors)
return descriptors, vals_i
########################## Main manager functions #########################
## Interaction with resulter object
def initialization_desc(self):
"""Wrapper to the resulter initialization descriptor measure function.
Returns
-------
null_descriptor: dict, list or np.ndarray
the empty null descriptor for the given measure.
"""
return self.resulter.initialization_desc()
def initialization_output(self):
"""Wrapper to the resulter initialization measure resultant.
Returns
-------
measure: np.ndarray or list
the transformed measure computed by the whole spatial descriptor
model.
"""
return self.resulter.initialization_output()
def _join_descriptors(self, descriptors):
"""Wrapper to the resulter initialization measure resultant.
Parameters
----------
descriptors: list
the final descriptors after computing the associated union of the
descriptors of `i` with the ones of its neighbourhood.
Returns
-------
descriptors: list
the final descriptors after formatting properly.
"""
return self.resulter._join_descriptors(descriptors)
def add2result(self, measure, desc_i, vals_i):
"""Wrapper to the resulter for adding the new descriptors computed to
the resultant measure.
Parameters
----------
measure: np.ndarray or list
the transformed measure computed by the whole spatial descriptor
model.
desc_i: np.ndarray, list or dict or others
the spatial descriptors associated to the element `i`.
vals_i: list or np.ndarray
the store information index of each element `i`.
Returns
-------
measure: np.ndarray or list
the transformed measure computed by the whole spatial descriptor
model.
"""
return self.resulter.add2result(measure, desc_i, vals_i)
def to_complete_measure(self, measure):
"""Wrapper to the resulter for the completing function.
Parameters
----------
measure: np.ndarray or list
the measure computed by the whole spatial descriptor model.
Returns
-------
measure: np.ndarray or list
the transformed measure computed by the whole spatial descriptor
model.
"""
return self.resulter.to_complete_measure(measure)
######################## Interaction with features ########################
############################# Input features ##############################
def _get_input_features_constant(self, i, k, typefeats=(0, 0)):
"""Get 'input' features. Get the features of the elements of which we
want to study their neighbourhood. We consider constant typefeats.
That case is under the assumption of constant selection of features.
Parameters
----------
i: int, np.ndarray or list
the indices of the elements.
k: list
perturbations indices we want to get.
typefeats: tuple
the selectors for the features of the elements `i`.
Returns
-------
feats_i: np.ndarray or list
the features of the elements `i`.
"""
## Input mapping
i_input = self._maps_input[typefeats[0]](i)
## Retrieve features
feats_i = self.features[typefeats[1]].compute((i_input, k))
## Outformat
feats_i = self._maps_output(self, feats_i)
return feats_i
def _get_input_features_variable(self, i, k, typefeats=(0, 0)):
"""Get 'input' features. Get the features of the elements of which we
want to study their neighbourhood. We consider constant typefeats.
That case is under the assumption of variable selection of features.
Parameters
----------
i: int, np.ndarray or list
the indices of the elements.
k: list
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the elements `i`.
Returns
-------
feats_i: np.ndarray or list
the features of the elements `i`.
"""
## Preparing input
i = [[i]] if type(i) == int else i
i_l = len(i)
k_l = 1 if type(k) == int else len(k)
typefeats = [typefeats] * i_l if type(typefeats) == tuple else typefeats
feats_i = [[] for kl in range(k_l)]
print "`" * 20, i_l
for j in range(i_l):
## Input mapping
i_j = [i[j]] if type(i[j]) == int else i[j]
i_input = self._maps_input[typefeats[j][0]](i_j)
## Retrieve features
feats_ij = self.features[typefeats[j][1]].compute((i_input, k))
print feats_ij, i_input, k
## Outformat
feats_ij = self._maps_output(self, feats_ij)
print feats_ij
for k_j in range(len(feats_ij)):
feats_i[k_j].append(feats_ij[k_j][0])
assert len(feats_i) == k_l
assert len(feats_i[0]) == i_l
return feats_i
def _get_input_features_general(self, i, k, typefeats=(0, 0)):
"""Get 'input' features. Get the features of the elements of which we
want to study their neighbourhood. We dont consider anything.
That case is under no assumtions of selection.
Parameters
----------
i: int, np.ndarray or list
the indices of the elements.
k: list or np.ndarray
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the elements `i`.
Returns
-------
feats_i: np.ndarray or list
the features of the elements `i`.
"""
if type(typefeats) == list:
feats_i = self._get_input_features_variable(i, k, typefeats)
else:
feats_i = self._get_input_features_constant(i, k, typefeats)
return feats_i
############################# Output features #############################
def _get_output_features_constant(self, neighs_info, k, typefeats=(0, 0)):
"""Get 'output' features. Get the features of the elements in the
neighbourhood of the elements we want to study. That case is under
assumption of the constant selection of features.
Parameters
----------
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: list or np.ndarray
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the neighs of elements `i`.
Returns
-------
feats_neighs: np.ndarray or list
the features of the neighs of the elements `i`.
"""
## Neighs info as an object
neighs_info = ensuring_neighs_info(neighs_info, k)
## Input mapping
neighs_info = self._maps_input[typefeats[0]](neighs_info)
## Features retrieve
feats_neighs = self.features[typefeats[1]].compute((neighs_info, k))
## Outformat
feats_neighs = self._maps_output(self, feats_neighs)
return feats_neighs
def _get_output_features_variable(self, neighs_info, k, typefeats=(0, 0)):
"""Get 'output' features. Get the features of the elements in the
neighbourhood of the elements we want to study. That case is under
assumption of the variable selection of features.
Parameters
----------
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: list or np.ndarray
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the neighs of elements `i`.
Returns
-------
feats_neighs: np.ndarray or list
the features of the neighs of the elements `i`.
"""
## Neighs info as an object
neighs_info = ensuring_neighs_info(neighs_info, k)
## Loop for all typefeats
i_l = len(neighs_info.iss)
# print i_l, neighs_info.iss, neighs_info.idxs
k_l = 1 if type(k) == int else len(k)
typefeats = [typefeats] * i_l if type(typefeats) == tuple else typefeats
feats_neighs = [[] for kl in range(k_l)]
for j in range(i_l):
## Input mapping
neighs_info_j = neighs_info.get_copy_iss_by_ind(j)
neighs_info_j = self._maps_input[typefeats[j][0]](neighs_info_j)
## Features retrieve
feats_neighs_j = self.features[typefeats[j][1]].compute((neighs_info_j, k))
## Outformat
feats_neighs_j = self._maps_output(self, feats_neighs_j)
## Store
for k_j in range(len(feats_neighs_j)):
feats_neighs[k_j].append(feats_neighs_j[k_j][0])
assert len(feats_neighs) == k_l
assert len(feats_neighs[0]) == i_l
return feats_neighs
def _get_output_features_general(self, neighs_info, k, typefeats=(0, 0)):
"""Get 'output' features. Get the features of the elements in the
neighbourhood of the elements we want to study. That case is under
no assumption of selection.
Parameters
----------
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: list or np.ndarray
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the neighs of elements `i`.
Returns
-------
feats_neighs: np.ndarray or list
the features of the neighs of the elements `i`.
"""
if type(typefeats) == list:
feats_neighs = self._get_output_features_variable(neighs_info, k, typefeats)
else:
feats_neighs = self._get_output_features_constant(neighs_info, k, typefeats)
return feats_neighs
########################### Descriptor features ###########################
def _complete_desc_i_constant(self, i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_desc):
"""Complete descriptors by interaction of point features and
neighbourhood features. That unction is under assumption of constant
selection.
Parameters
----------
i: int, list or np.ndarray
the index information.
neighs_info:
the neighbourhood information of each `i`.
desc_i: np.ndarray or list of dict
the descriptors associated to the elements `iss` for each
perturbation `k`.
desc_neighs: np.ndarray or list of dict
the descriptors associated to the neighbourhood elements of each
`iss` for each perturbation `k`.
vals_i: list or np.ndarray
the storable index information for each perturbation `k`.
t_feat_desc: tuple or list
the selection of the features and descriptormodel to compute the
descriptor from desc_i and desc_neighs.
Returns
-------
descriptors: list
the descriptors for each perturbation and element.
"""
if t_feat_desc[0]:
descriptors = self.features[t_feat_desc[1]].complete_desc_i(i, neighs_info, desc_i, desc_neigh, vals_i)
else:
descriptors = self.descriptormodels[t_feat_desc[1]].complete_desc_i(
i, neighs_info, desc_i, desc_neigh, vals_i
)
return descriptors
def _complete_desc_i_variable(self, i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_desc):
"""Complete descriptors by interaction of point features and
neighbourhood features. Assumption of variable t_feat_desc. That
function is under assumption of variable selection.
Parameters
----------
i: int, list or np.ndarray
the index information.
neighs_info:
the neighbourhood information of each `i`.
desc_i: np.ndarray or list of dict
the descriptors associated to the elements `iss` for each
perturbation `k`.
desc_neighs: np.ndarray or list of dict
the descriptors associated to the neighbourhood elements of each
`iss` for each perturbation `k`.
vals_i: list or np.ndarray
the storable index information for each perturbation `k`.
t_feat_desc: tuple or list
the selection of the features and descriptormodel to compute the
descriptor from desc_i and desc_neighs.
Returns
-------
descriptors: list
the descriptors for each perturbation and element.
"""
## Preparing input
i_l = 1 if type(i) == int else len(i)
i = [i] * i_l if type(i) == int else i
k_l = len(desc_i)
if type(t_feat_desc) != list:
t_feat_desc = [t_feat_desc] * i_l
## Sequential computation
descriptors = [[] for kl in range(k_l)]
print vals_i, desc_i, self.k_perturb
print "joe", i_l, len(desc_i), len(desc_neigh), len(vals_i)
for j in range(i_l):
neighs_info_j = neighs_info.get_copy_iss_by_ind(j)
vals_ij = [vals_i[k][j] for k in range(len(vals_i))]
desc_ij = [desc_i[k][j] for k in range(len(desc_i))]
desc_neighj = [desc_neigh[k][j] for k in range(len(desc_neigh))]
if t_feat_desc[j][0]:
descriptors_j = self.features[t_feat_desc[j][1]].complete_desc_i(
i[j], neighs_info_j, desc_ij, desc_neighj, vals_ij
)
else:
descriptors_j = self.descriptormodels[t_feat_desc[j][1]].complete_desc_i(
i[j], neighs_info_j, desc_ij, desc_neighj, vals_ij
)
for k_j in range(k_l):
descriptors[k_j].append(descriptors_j[k_j])
return descriptors
def _complete_desc_i_general(self, i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_desc):
"""Complete descriptors by interaction of point features and
neighbourhood features. No assumptions about iss and t_feat_desc.
That function has not assumtions of selection features and
descriptormodel.
Parameters
----------
i: int, list or np.ndarray
the index information.
neighs_info:
the neighbourhood information of each `i`.
desc_i: np.ndarray or list of dict
the descriptors associated to the elements `iss` for each
perturbation `k`.
desc_neighs: np.ndarray or list of dict
the descriptors associated to the neighbourhood elements of each
`iss` for each perturbation `k`.
vals_i: list or np.ndarray
the storable index information for each perturbation `k`.
t_feat_desc: tuple or list
the selection of the features and descriptormodel to compute the
descriptor from desc_i and desc_neighs.
Returns
-------
descriptors: list
the descriptors for each perturbation and element.
"""
if type(t_feat_desc) == list:
descriptors = self._complete_desc_i_variable(i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_desc)
else:
descriptors = self._complete_desc_i_constant(i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_desc)
return descriptors
############################### Map_vals_i ###############################
def _get_vals_i(self, i, ks):
"""Get indice to store the final result.
Parameters
----------
i: int, list or np.ndarray
the index information.
ks: list
perturbations indices we want to get.
Returns
-------
vals_i: list or np.ndarray
the storable index information for each perturbation `k`.
"""
#### TODO: extend
## 0. Prepare variable needed
vals_i = []
## 1. Loop over possible ks and compute vals_i
for k in ks:
vals_i.append(self._maps_vals_i.apply(self, i, k))
## WARNING: TOTEST
vals_i = np.array(vals_i)
# print vals_i
assert len(vals_i.shape) == 2
assert len(vals_i) == len(ks)
assert len(np.array([i]).ravel()) == vals_i.shape[1]
return vals_i
################################# Type_feat ###############################
###########################################################################
## Formatting the selection of path from i information for features
## retrieving.
##
## See also:
## ---------
## pst.RetrieverManager
#########################
def _general_get_type_feat(self, i, typefeats_i=None):
"""Format properly general typefeats selector information. That
function has not assumption of selection information.
Parameters
----------
i: int, list or np.ndarray
the index information.
typefeats_i: list, tuple or None (default=None)
the selector information of all the selections we have to do
in that process for each element `i`.
Returns
-------
typefeats_i: tuple or list
the selectors for the features of the elements `i`.
typefeats_nei: tuple or list
the selectors for the features of the neighs of elements `i`.
typefeats_desc: tuple or list
the selection of the features and descriptormodel to compute the
descriptor from desc_i and desc_neighs.
"""
if typefeats_i is None:
typefeats_i, typefeats_nei, typefeats_desc = self.selector
if type(i) == list:
typefeats_i = [typefeats_i] * len(i)
typefeats_nei = [typefeats_nei] * len(i)
typefeats_desc = [typefeats_desc] * len(i)
else:
if type(i) == list:
typefeats_input = typefeats_i
typefeats_i, typefeats_nei, typefeats_desc = [], [], []
for j in range(len(i)):
typefeats_i.append(typefeats_input[j][0])
typefeats_nei.append(typefeats_input[j][1])
typefeats_desc.append(typefeats_input[j][2])
else:
typefeats_i, typefeats_nei, typefeats_desc = typefeats_i
# if type(i) == int:
# typefeats_i, typefeats_nei, typefeats_desc = typefeats_i
# else:
# typefeat = typefeats_i
# typefeats_i, typefeats_nei, typefeats_desc = [], [], []
# for j in range(len(i)):
# typefeats_i.append(typefeat[j][0])
# typefeats_nei.append(typefeat[j][1])
# typefeats_desc.append(typefeat[j][2])
return typefeats_i, typefeats_nei, typefeats_desc
def _static_get_type_feat(self, i, typefeats_i=None):
"""Format properly typefeats selector information. That function has
assumptions of static selection.
Parameters
----------
i: int, list or np.ndarray
the index information.
typefeats_i: list, tuple or None (default=None)
the selector information of all the selections we have to do
in that process for each element `i`.
Returns
-------
typefeats_i: tuple or list
the selectors for the features of the elements `i`.
typefeats_nei: tuple or list
the selectors for the features of the neighs of elements `i`.
typefeats_desc: tuple or list
the selection of the features and descriptormodel to compute the
descriptor from desc_i and desc_neighs.
"""
typefeats_i, typefeats_nei, typefeats_desc = self.selector
# if type(i) == list:
# typefeats_i = [typefeats_i]*len(i)
# typefeats_nei = [typefeats_nei]*len(i)
# typefeats_desc = [typefeats_desc]*len(i)
return typefeats_i, typefeats_nei, typefeats_desc
def _selector_get_type_feat(self, i, typefeats_i=None):
"""Get information only from selector.
Parameters
----------
i: int, list or np.ndarray
the index information.
typefeats_i: list, tuple or None (default=None)
the selector information of all the selections we have to do
in that process for each element `i`.
Returns
-------
typefeats_i: tuple or list
the selectors for the features of the elements `i`.
typefeats_nei: tuple or list
the selectors for the features of the neighs of elements `i`.
typefeats_desc: tuple or list
the selection of the features and descriptormodel to compute the
descriptor from desc_i and desc_neighs.
"""
selector_i = format_selection(self.selector[i])
typefeats_i, typefeats_nei, typefeats_desc = selector_i
# if type(i) == int:
# typefeats_i, typefeats_nei, typefeats_desc = self.selector[i]
# else:
# typefeats_i, typefeats_nei, typefeats_desc = [], [], []
# selectors_i = self.selector[i]
# print selectors_i
# for j in range(len(i)):
# typefeats_i.append(selectors_i[j][0])
# typefeats_nei.append(selectors_i[j][1])
# typefeats_desc.append(selectors_i[j][2])
return typefeats_i, typefeats_nei, typefeats_desc
######################## Perturbation management ##########################
###########################################################################
def add_perturbations(self, perturbations):
"""Adding perturbations to features.
Parameters
----------
perturbations: list or pst.BasePerturbation
the perturbation information.
"""
## 1. Apply perturbations
for i_ret in range(len(self.features)):
self.features[i_ret].add_perturbations(perturbations)
## 2. Format kperturb
self._format_k_perturbs()
## 3. Reformat resulter
self._format_result_building()
######################### Aggregation management ##########################
###########################################################################
def add_aggregations(self, aggfeatures):
"""Add aggregations to featuremanager. Only it is useful this function
if there is only one retriever previously and we are aggregating the
first one.
Parameters
----------
aggfeatures: np.ndarray or list
the aggregated features to be stored in order to be managed.
"""
self.features.append(aggfeatures)