def transform(self, **Xy):
"""
Parameter
---------
Xy: dictionary
parameters for fit and transform
"""
if conf.KW_SPLIT_TRAIN_TEST in Xy:
Xy_train, Xy_test = train_test_split(Xy)
Xy_out = dict()
# Train fit
res = self.estimator.fit(**_sub_dict(Xy_train, self.in_args_fit))
# Train predict
Xy_out_tr = _as_dict(self.estimator.predict(**_sub_dict(Xy_train,
self.in_args_predict)),
keys=self.out_args_predict)
Xy_out_tr = _dict_suffix_keys(Xy_out_tr,
suffix=conf.SEP + conf.TRAIN + conf.SEP + conf.PREDICTION)
Xy_out.update(Xy_out_tr)
# Test predict
Xy_out_te = _as_dict(self.estimator.predict(**_sub_dict(Xy_test,
self.in_args_predict)),
keys=self.out_args_predict)
Xy_out_te = _dict_suffix_keys(Xy_out_te,
suffix=conf.SEP + conf.TEST + conf.SEP + conf.PREDICTION)
Xy_out.update(Xy_out_te)
## True test
Xy_test_true = _sub_dict(Xy_test, self.out_args_predict)
Xy_out_true = _dict_suffix_keys(Xy_test_true,
suffix=conf.SEP + conf.TEST + conf.SEP + conf.TRUE)
Xy_out.update(Xy_out_true)
else:
res = self.estimator.fit(**_sub_dict(Xy, self.in_args_fit))
# catch args_transform in ds, transform, store output in a dict
Xy_out = _as_dict(self.estimator.predict(**_sub_dict(Xy,
self.in_args_predict)),
keys=self.out_args_predict)
Xy_out = _dict_suffix_keys(Xy_out,
suffix=conf.SEP + conf.PREDICTION)
## True test
Xy_true = _sub_dict(Xy, self.out_args_predict)
Xy_out_true = _dict_suffix_keys(Xy_true,
suffix=conf.SEP + conf.TRUE)
Xy_out.update(Xy_out_true)
return Xy_out
def transform(self, **Xy):
"""
Parameter
---------
Xy: dictionary
parameters for fit and transform
"""
is_fit_predict = False
is_fit_transform = False
if (hasattr(self.wrapped_node, "transform") and
hasattr(self.wrapped_node, "predict")):
if not self.children:
# leaf node
is_fit_predict = True
else:
# internal node
is_fit_transform = True
elif hasattr(self.wrapped_node, "transform"):
is_fit_transform = True
elif hasattr(self.wrapped_node, "predict"):
is_fit_predict = True
if is_fit_transform:
Xy_train, Xy_test = train_test_split(Xy)
if Xy_train is not Xy_test:
res = self.wrapped_node.fit(**_sub_dict(Xy_train,
self.in_args_fit))
Xy_out_tr = self._wrapped_node_transform(**Xy_train)
Xy_out_te = self._wrapped_node_transform(**Xy_test)
Xy_out = train_test_merge(Xy_out_tr, Xy_out_te)
else:
res = self.wrapped_node.fit(**_sub_dict(Xy, self.in_args_fit))
Xy_out = self._wrapped_node_transform(**Xy)
# update ds with transformed values
Xy.update(Xy_out)
return Xy
elif is_fit_predict:
Xy_train, Xy_test = train_test_split(Xy)
if Xy_train is not Xy_test:
Xy_out = dict()
res = self.wrapped_node.fit(**_sub_dict(Xy_train,
self.in_args_fit))
Xy_out_tr = self._wrapped_node_predict(**Xy_train)
Xy_out_tr = _dict_suffix_keys(
Xy_out_tr,
suffix=conf.SEP + conf.TRAIN + conf.SEP + conf.PREDICTION)
Xy_out.update(Xy_out_tr)
# Test predict
Xy_out_te = self._wrapped_node_predict(**Xy_test)
Xy_out_te = _dict_suffix_keys(
Xy_out_te,
suffix=conf.SEP + conf.TEST + conf.SEP + conf.PREDICTION)
Xy_out.update(Xy_out_te)
## True test
Xy_test_true = _sub_dict(Xy_test, self.out_args_predict)
Xy_out_true = _dict_suffix_keys(
Xy_test_true,
suffix=conf.SEP + conf.TEST + conf.SEP + conf.TRUE)
Xy_out.update(Xy_out_true)
else:
res = self.wrapped_node.fit(**_sub_dict(Xy, self.in_args_fit))
Xy_out = self._wrapped_node_predict(**Xy)
Xy_out = _dict_suffix_keys(
Xy_out,
suffix=conf.SEP + conf.PREDICTION)
## True test
Xy_true = _sub_dict(Xy, self.out_args_predict)
Xy_out_true = _dict_suffix_keys(
Xy_true,
suffix=conf.SEP + conf.TRUE)
Xy_out.update(Xy_out_true)
return Xy_out
else:
raise ValueError("%s should implement either transform or predict"
% self.wrapped_node.__class__.__name__)
def transform(self, **Xy):
"""
Parameter
---------
Xy: dictionary
parameters for fit and transform
"""
is_fit_predict = False
is_fit_transform = False
if (hasattr(self.wrapped_node, "transform") and
hasattr(self.wrapped_node, "predict")):
if not self.children:
# leaf node
is_fit_predict = True
else:
# internal node
is_fit_transform = True
elif hasattr(self.wrapped_node, "transform"):
is_fit_transform = True
elif hasattr(self.wrapped_node, "predict"):
is_fit_predict = True
if is_fit_transform:
Xy_train, Xy_test = train_test_split(Xy)
if Xy_train is not Xy_test:
res = self.wrapped_node.fit(**_sub_dict(Xy_train,
self.in_args_fit))
Xy_out_tr = self._wrapped_node_transform(**Xy_train)
Xy_out_te = self._wrapped_node_transform(**Xy_test)
Xy_out = train_test_merge(Xy_out_tr, Xy_out_te)
else:
res = self.wrapped_node.fit(**_sub_dict(Xy, self.in_args_fit))
Xy_out = self._wrapped_node_transform(**Xy)
# update ds with transformed values
Xy.update(Xy_out)
return Xy
elif is_fit_predict:
Xy_train, Xy_test = train_test_split(Xy)
if Xy_train is not Xy_test:
Xy_out = dict()
res = self.wrapped_node.fit(**_sub_dict(Xy_train,
self.in_args_fit))
Xy_out_tr = self._wrapped_node_predict(**Xy_train)
Xy_out_tr = _dict_suffix_keys(
Xy_out_tr,
suffix=conf.SEP + conf.TRAIN + conf.SEP + conf.PREDICTION)
Xy_out.update(Xy_out_tr)
# Test predict
Xy_out_te = self._wrapped_node_predict(**Xy_test)
Xy_out_te = _dict_suffix_keys(
Xy_out_te,
suffix=conf.SEP + conf.TEST + conf.SEP + conf.PREDICTION)
Xy_out.update(Xy_out_te)
## True test
Xy_test_true = _sub_dict(Xy_test, self.out_args_predict)
Xy_out_true = _dict_suffix_keys(
Xy_test_true,
suffix=conf.SEP + conf.TEST + conf.SEP + conf.TRUE)
Xy_out.update(Xy_out_true)
else:
res = self.wrapped_node.fit(**_sub_dict(Xy, self.in_args_fit))
Xy_out = self._wrapped_node_predict(**Xy)
Xy_out = _dict_suffix_keys(
Xy_out,
suffix=conf.SEP + conf.PREDICTION)
## True test
Xy_true = _sub_dict(Xy, self.out_args_predict)
Xy_out_true = _dict_suffix_keys(
Xy_true,
suffix=conf.SEP + conf.TRUE)
Xy_out.update(Xy_out_true)
return Xy_out
else:
raise ValueError("%s should implement either transform or predict"
% self.wrapped_node.__class__.__name__)
