def assert_model_graph_structure(G):
""" verification on the structure of the graph """
# only one terminal node
if len(gh.get_terminal_nodes(G)) != 1:
raise ValueError("I should have only one terminal node")
# connex graph
if not gh.is_connected(G):
raise ValueError("the graph should be connected")
# no cycle
if gh.has_cycle(G):
raise ValueError("The graph shouldn't have any cycle")
for node in G.nodes:
if StepCategories.is_composition_step(node[0]):
if len(list(G.successors(node))) == 0:
raise ValueError("Composition node %s has no successor" % node)
for node in G.nodes:
if StepCategories.is_composition_step(node[0]):
successors = gh.get_all_successors(G, node)
predecessors = gh.get_all_predecessors(G, node)
if not gh.is_it_a_partition(list(G.nodes),
[successors, [node], predecessors]):
raise ValueError("Incorrect split around composition node %s" %
node)
def test_all_graphs_functions():
G = nx.DiGraph()
G = add_node_after(G, 1)
G = add_node_after(G, 2, 1)
G = add_node_after(G, 3, 2)
G = add_node_after(G, 4)
G = add_node_after(G, 5, 4)
G = add_node_after(G, 6, 5, 3)
assert set(get_terminal_nodes(G)) == {6}
assert set(get_starting_nodes(G)) == {1, 4}
assert set(get_all_successors(G, 1)) == {2, 3, 6}
assert set(get_all_successors(G, 2)) == {3, 6}
assert set(get_all_successors(G, 3)) == {6}
assert set(get_all_successors(G, 4)) == {5, 6}
assert set(get_all_successors(G, 5)) == {6}
assert set(get_all_successors(G, 6)) == set()
assert set(get_all_predecessors(G, 1)) == set()
assert set(get_all_predecessors(G, 2)) == {1}
assert set(get_all_predecessors(G, 3)) == {1, 2}
assert set(get_all_predecessors(G, 4)) == set()
assert set(get_all_predecessors(G, 5)) == {4}
assert set(get_all_predecessors(G, 6)) == {1, 2, 3, 4, 5}
def get_subpipeline(self, end_node, deepcopy_models=False):
""" create a New model that corresponds to the original GraphPipeline but with a new ending node
If the original GraphPipeline was fitted, the new model will also be fitted
Parameters
----------
end_node : str
the name of the node at which the new pipeline will stop. Must be in the Graph
deepcopy_models : boolean, default=False
if True will make a deepcopy of the models.
Returns
-------
new GraphPipeline instance
"""
self._complete_init()
if end_node not in self.complete_graph:
raise ValueError("the node '%s' isn't in the original graph" %
end_node)
# get all predecessors of nodes => to include in the graph
predecessors = get_all_predecessors(self.complete_graph, end_node)
nodes_to_keep = list(predecessors) + [end_node]
# Remark : we could separated into a submethod to create a subpipeline from a list of nodes
if len(nodes_to_keep) == 1:
assert end_node == nodes_to_keep[0]
return self._models[end_node]
# filter edges
edges_to_keep = []
for e1, e2 in get_two_by_two_edges(*self._edges):
if e1 in nodes_to_keep and e2 in nodes_to_keep:
edges_to_keep.append((e1, e2))
# I do that insteaf of :
# complete_graph_sub.subgraph(nodes_to_keep).edges
# beacause that way I preseve the order of the edges, which handle the concatenation order
# Retrieve sklearn model
if deepcopy_models:
models = {
node: deepcopy(self._models[node])
for node in nodes_to_keep
}
else:
models = {node: self._models[node] for node in nodes_to_keep}
# Change 'no_concat_nodes'
if self.no_concat_nodes is None:
no_concat_nodes = None
else:
no_concat_nodes = [
n for n in self.no_concat_nodes if n in nodes_to_keep
]
no_concat_nodes = type(self.no_concat_nodes)(no_concat_nodes)
if len(no_concat_nodes) == 0:
no_concat_nodes = None
###############################
### Create new pipeline ###
###############################
sub_pipeline = GraphPipeline(models=models,
edges=edges_to_keep,
verbose=self.verbose,
no_concat_nodes=no_concat_nodes)
# Internal modification to change the state
if self._preparation_done:
sub_pipeline._complete_init()
if not self._already_fitted:
return sub_pipeline
# here the pipeline was fitted
sub_pipeline._already_fitted = True
sub_pipeline._Xinput_features = deepcopy(
self._Xinput_features) #copy just to be safe
sub_pipeline._all_concat_order = dico_key_filter(
self._all_concat_order, lambda n: n in nodes_to_keep)
sub_pipeline._all_concat_type = dico_key_filter(
self._all_concat_type, lambda n: n in nodes_to_keep)
return sub_pipeline
def _rec_convert_graph_to_code_OLD2(Graph,
all_models_params,
models_dico,
model_name_mapping=None):
""" recursive function used to convert a Graph into a json code
See convert_graph_to_code
"""
### ** only one node in Graph : I'll return what was saved in models_dico ** ###
if len(Graph.nodes) == 1:
node = list(Graph.nodes)[0]
return models_dico[node]
node = _find_first_composition_node(Graph)
if node is not None:
predecessors = gh.get_all_predecessors(Graph, node)
successors = gh.get_all_successors(Graph, node)
if not gh.is_it_a_partition(list(Graph.nodes),
[predecessors, [node], successors]):
raise ValueError("Incorrect graph, wrong split around node %s" %
str(node))
else:
predecessors = []
successors = []
if node is None or len(successors) == 0:
### ** It's means I'll return a GraphPipeline ** ###
edges = gh.edges_from_graph(Graph)
if model_name_mapping is None:
model_name_mapping = _create_name_mapping(list(Graph.nodes))
# each node in graph will be mapped to a name within the GraphPipeline
models = {model_name_mapping[n]: models_dico[n] for n in Graph.nodes}
edges = [
tuple((model_name_mapping[e] for e in edge)) for edge in edges
]
return (SpecialModels.GraphPipeline, {
"models": models,
"edges": edges
})
Graph_bellow = Graph.subgraph(successors)
connected_Gbellow = gh.get_connected_graphs(Graph_bellow)
if len(predecessors) == 0 and len(connected_Gbellow) > 1:
return (
_klass_from_node(node),
[
_rec_convert_graph_to_code_OLD2(Gb, all_models_params,
models_dico,
model_name_mapping)
for Gb in connected_Gbellow
],
all_models_params[node],
)
elif len(predecessors) == 0 and len(connected_Gbellow) == 1:
return (
_klass_from_node(node),
_rec_convert_graph_to_code_OLD2(Graph_bellow, all_models_params,
models_dico, model_name_mapping),
all_models_params[node],
)
else:
G_bellow_and_node = Graph.subgraph([node] + successors)
G_above = Graph.subgraph(predecessors + [node])
models_dico[node] = _rec_convert_graph_to_code_OLD2(
G_bellow_and_node, all_models_params, models_dico,
model_name_mapping)
return _rec_convert_graph_to_code(G_above, all_models_params,
models_dico, model_name_mapping)
def _rec_convert_graph_to_code_OLD(G, all_params):
""" recursive function to convert a graph into a json representation """
if len(G.nodes) == 0:
return {}
### 1) Find First composition node
has_composition = False
for node in gh.iter_graph(G):
if StepCategories.is_composition_step(node[0]):
has_composition = True
break
return_gpipe = not has_composition
if has_composition:
### If there is a composition node, I need to split between what is above and what is bellow
predecessors = gh.get_all_predecessors(G, node)
successors = gh.get_all_successors(G, node)
if not gh.is_it_a_partition(list(G.nodes),
[predecessors, [node], successors]):
raise ValueError("Incorrect graph, wrong split around node %s" %
str(node))
if len(successors) == 0:
# If nothing bellow, I'll be able to return something
return_gpipe = True
if return_gpipe:
if len(G.nodes) > 1:
### I'll create a GraphPipeline object
edges = gh.edges_from_graph(G)
model_name_mapping = _create_name_mapping(list(G.nodes))
# each node in graph will be mapped to a name within the GraphPipeline
models = {model_name_mapping[n]: all_params[n] for n in G.nodes}
edges = [
tuple((model_name_mapping[e] for e in edge)) for edge in edges
]
return (SpecialModels.GraphPipeline, {
"models": models,
"edges": edges
})
else:
### Otherwise it is just the model_name with its parameters
return node[1][1], all_params[list(G.nodes)[0]]
G_above = G.subgraph(predecessors + [node])
G_bellow = G.subgraph(successors)
connected_Gbellow = gh.get_connected_graphs(G_bellow)
if len(connected_Gbellow) == 1:
# what is bellow is a 'connected graph' : it means that the composition need should be applied to One model
all_params[node] = _rec_convert_graph_to_code_OLD(G_bellow, all_params)
else:
# otherwise, the composition will be applied to a list of models
all_params[node] = [
_rec_convert_graph_to_code_OLD(g, all_params)
for g in connected_Gbellow
]
return _rec_convert_graph_to_code_OLD(G_above, all_params)