def _approx_cross_validation_pre_calculation(
self,
X,
y,
groups,
scoring,
cv,
verbose,
fit_params_step,
return_predict,
method,
no_scoring,
stopping_round,
stopping_threshold,
nodes_not_to_crossvalidate,
nodes_cant_cv_transform,
kwargs_step,
):
""" sub-method to loop through the nodes of the pipeline and pre-compute everything that can be pre-computed """
data_dico = {} # Will contain transformed blocks at each node
nodes_done = set()
for node in self._nodes_order:
concat_at_this_node = self.no_concat_nodes is None or node not in self.no_concat_nodes
if not concat_at_this_node:
raise NotImplementedError(
"Approx cross-validation does't work if no concatenation (node %s)"
% str(node))
nodes_done.add(node)
if self.verbose:
print("start processing node %s ..." % node)
### Debugging Help ###
# if getattr(self,"_return_before_node",None) is not None and getattr(self,"_return_before_node",None) == node:
# return data_dico
model = self._models[node]
predecessors = list(self.complete_graph.predecessors(node))
# Carefull : here it is not necessary always in the same order
#### I'll use the order in which the edges were given
# Concatenation : alphabetical order
if len(predecessors) == 0:
#########################
### No predecessors ###
#########################
# ==> Apply on original data
lastX = X
elif len(predecessors) == 1:
########################
### One predecessor ###
########################
# ==> Apply on data coming out of last node
lastX = data_dico[predecessors[0]]
# data_dico[node] = model.fit_transform(lastX, y, **fit_params_step[node] )
elif len(predecessors) > 1:
#######################
### More than one ###
#######################
# ==> concat all the predecessors node and apply it
### Fix concatenation order ###
edges_number = self._get_edges_number(predecessors, node)
predecessors = sorted(predecessors,
key=lambda p:
(edges_number.get(p, -1), p))
self._all_concat_order[node] = predecessors
all_lastX = [
data_dico[predecessor] for predecessor in predecessors
]
if self.verbose:
print("start aggregation...")
# if do_fit:
output_type = guess_output_type(all_lastX)
self._all_concat_type[node] = output_type
# else:
# output_type = self._all_concat_type[node]
has_none = False
for x in all_lastX:
if x is None:
has_none = True
break
# None in all_lastX
if has_none:
lastX = None
else:
lastX = generic_hstack(all_lastX, output_type=output_type)
if node != self._terminal_node and lastX is not None:
# This is not the end of the graph
if node not in nodes_not_to_crossvalidate and node not in nodes_cant_cv_transform:
### 1) Node should BE crossvalitaded ...
### 2) ... and we CAN use 'cv_transform'
if self.verbose:
print("do crossvalidation on %s" % node)
_, data_dico[node] = cross_validation(
model,
lastX,
y,
groups=groups,
cv=cv,
verbose=verbose,
fit_params=fit_params_step[node],
return_predict=True,
method="transform",
no_scoring=True,
stopping_round=None,
stopping_threshold=None,
**kwargs_step[node])
elif node not in nodes_not_to_crossvalidate and node in nodes_cant_cv_transform:
### 1) Node should BE crossvalitated ...
### 2) ... but we can't use 'cv_transform'
if self.verbose:
print("can't do node %s" % node)
data_dico[node] = None # Can't compute this node
else:
### Node that shouldn't be cross-validated ###
if self.verbose:
print("skip crossvalidation on %s" % node)
cloned_model = clone(model)
if groups is not None and function_has_named_argument(
cloned_model.fit_transform, "groups"):
data_dico[node] = cloned_model.fit_transform(
lastX, y, groups, **fit_params_step[node])
else:
data_dico[node] = cloned_model.fit_transform(
lastX, y, **fit_params_step[node])
elif lastX is not None:
### CV no matter what at the last node ###
# if node not in nodes_not_to_crossvalidate and node not in nodes_cant_cv_transform:
#
# # This is the last node of the Graph
# result = approx_cross_validation( model, lastX, y, groups = groups, scoring = scoring, cv = cv ,
# verbose = verbose, fit_params = fit_params_step[node],
# return_predict = return_predict , method = method, no_scoring = no_scoring,
# stopping_round = stopping_round, stopping_threshold = stopping_threshold,
# **kwargs_step[node])
#
# elif node not in nodes_not_to_crossvalidate and node in nodes_cant_cv_transform:
# pass
#
# else:
# This is the last node of the Graph
result = cross_validation(
model,
lastX,
y,
groups=groups,
scoring=scoring,
cv=cv,
verbose=verbose,
fit_params=fit_params_step[node],
return_predict=return_predict,
method=method,
no_scoring=no_scoring,
stopping_round=stopping_round,
stopping_threshold=stopping_threshold,
**kwargs_step[node])
# Rmk : if we do that so column regarding the time of fit are 'false' : they will only account for the time spent in the last node
return True, data_dico, result
# return result
else:
###
if self.verbose:
print("can't compute node %s because lastX is None" % node)
data_dico[node] = None
# return result
return False, data_dico, None # None : no result yet
def _fit_transform(self,
X,
y=None,
groups=None,
method=None,
fit_params=None):
""" main method of GraphPipeline, handles the fit and predict of object """
do_fit = method in ("fit", "fit_transform", "fit_predict")
if not self._already_fitted and not do_fit:
raise NotFittedError("Please fit the model before")
# Split fit_params into a 'step-by-step' dictionnary
fit_params_step = {name: {} for name in self.complete_graph.nodes}
if fit_params is not None:
for key, value in fit_params.items():
step, param = key.split("__", 1)
fit_params_step[step][param] = value
data_dico = {} # Will contain transformed blocks at each node
feature_dico = {} # Will contain the get_feature_names() of each node
if do_fit:
input_features = getattr(X, "columns", None)
if input_features is not None:
input_features = list(input_features)
self._Xinput_features = input_features
else:
input_features = self._Xinput_features
nodes_done = set()
for node in self._nodes_order:
nodes_done.add(node)
if self.verbose:
print("start processing node %s ..." % node)
### Debugging Help ###
if (getattr(self, "_return_before_node", None) is not None
and getattr(self, "_return_before_node", None) == node):
return data_dico
model = self._models[node]
predecessors = list(self.complete_graph.predecessors(node))
# Carefull : here it is not necessary always in the same order
#### I'll use the order in which the edges were given
# Concatenation : alphabetical order
concat_at_this_node = self.no_concat_nodes is None or node not in self.no_concat_nodes
if len(predecessors) == 0:
#########################
### No predecessors ###
#########################
if concat_at_this_node:
lastX = X
else:
lastX = {"_data": X}
# ==> Apply on original data
last_features = input_features
elif len(predecessors) == 1:
########################
### One predecessor ###
########################
# ==> Apply on data coming out of last node
if concat_at_this_node:
lastX = data_dico[predecessors[0]]
else:
lastX = {
predecessor: data_dico[predecessor]
for predecessor in predecessors
}
last_features = feature_dico[predecessors[0]]
elif len(predecessors) > 1:
#######################
### More than one ###
#######################
# ==> concat all the predecessors node and apply it
### Fix concatenation order ###
if do_fit:
edges_number = self._get_edges_number(predecessors, node)
predecessors = sorted(predecessors,
key=lambda p:
(edges_number.get(p, -1), p))
self._all_concat_order[node] = predecessors
else:
predecessors = self._all_concat_order[node]
all_lastX = [
data_dico[predecessor] for predecessor in predecessors
]
all_last_features = [
feature_dico[predecessor] for predecessor in predecessors
]
if all_last_features is None or None in all_last_features:
last_features = None
else:
last_features = unlist(all_last_features)
# all_columns_names = [try_to_find_features_names( self._models[predecessor], input_features = input_features)
# for predecessor, input_features in zip(predecessors, all_last_features)]
# for predecessor, input_features in zip(predecessors,all_last_features):
# try_to_find_features_names( self._models[predecessor], input_features = input_features)
if self.verbose:
print("start aggregation...")
if do_fit:
output_type = guess_output_type(all_lastX)
self._all_concat_type[node] = output_type
else:
output_type = self._all_concat_type[node]
if concat_at_this_node:
lastX = generic_hstack(all_lastX,
output_type=output_type,
all_columns_names=all_last_features)
else:
lastX = {
predecessor: data_dico[predecessor]
for predecessor in predecessors
}
if node != self._terminal_node:
# This is not the end of the graph
if do_fit:
if groups is not None and function_has_named_argument(
model.fit_transform, "groups"):
data_dico[node] = model.fit_transform(
lastX, y, groups=groups, **fit_params_step[node])
else:
data_dico[node] = model.fit_transform(
lastX, y, **fit_params_step[node])
# ICI : on pourrait sauté le fit pour certains models dans le fit params
# Quelque-chose comme :
# if node in preffited_models:
#
# self._model[node] = preffited_models[node]
# model = preffited_models[node]
# + copy model into pipeline
# data_dico[node] = model.transform(lastX, y)
# else:
# data_dico[node] = model.fit_transform(lastX, y, **fit_params_step[node] )
else:
data_dico[node] = model.transform(lastX)
feature_dico[node] = try_to_find_features_names(
model, input_features=last_features)
else:
# This is the last node of the Graph
if method == "fit":
if groups is not None and function_has_named_argument(
model.fit, "groups"):
model.fit(lastX, y, groups, **fit_params_step[node])
else:
model.fit(lastX, y, **fit_params_step[node])
result = self
elif method == "fit_predict":
if groups is not None and function_has_named_argument(
model.fit_predict, "groups"):
result = model.fit_predict(lastX, y, groups,
**fit_params_step[node])
else:
result = model.fit_predict(lastX, y,
**fit_params_step[node])
elif method == "fit_transform":
if groups is not None and function_has_named_argument(
model.fit_transform, "groups"):
result = model.fit_transform(lastX, y, groups,
**fit_params_step[node])
else:
result = model.fit_transform(lastX, y,
**fit_params_step[node])
elif method == "transform":
result = model.transform(lastX)
elif method == "predict":
result = model.predict(lastX)
elif method == "predict_proba":
result = model.predict_proba(lastX)
elif method == "predict_log_proba":
result = model.predict_log_proba(lastX)
elif method == "decision_function":
result = model.decision_function(lastX)
elif method == "score":
result = model.score(lastX, y)
else:
raise ValueError("I don't know that kind of method '%s' " %
method)
feature_dico[node] = try_to_find_features_names(
model, input_features=last_features)
return result
#######################
#### Dico cleaning ####
#######################
# I'll do a step of cleaning to remove useless blocks in memory
# I need to remove data in nodes that wont be accessed anymore
still_usefull = set()
for n in self.complete_graph.nodes:
if n in nodes_done:
continue
p = list(self.complete_graph.predecessors(n))
still_usefull.update(p)
for n in data_dico.keys():
if data_dico[n] is None:
continue
if n not in still_usefull:
if self.verbose:
print("deleting useless node %s" % n)
data_dico[n] = None
