def test_unnest_tuple():
examples = [
((1, 2, 3), (1, 2, 3)),
((1, (2, 3)), (1, 2, 3)),
((1, (2, (3))), (1, 2, 3)),
(((1, ), (2, ), (3, 4)), (1, 2, 3, 4)),
]
for nested_tuple, unnested_tuple in examples:
assert unnest_tuple(nested_tuple) == unnested_tuple
def add_columns_selector(Graph, var_type_node_dico, var_type_columns_dico,
all_models_params):
""" include columns selector where it is needed
Either modify the graph by adding new edge with selector model
Or modify all_models_params to include a 'columns_to_use' parameter
Parameters
----------
Graph : nx.DiGraph
Graph representation the model
var_type_node_dico : dict
dictionnary indicating which node works on which type of columns.
keys = node of Graph (ie : name of models)
values = variable type
var_type_columns_dico : dict
dictionnary indicating which type of variable corresponds to which columns
keys = variable type
values = list of columns
all_models_params : dict
dictionnary indicating the parameter of each models, it will be modified to include 'columns_to_use'
keys = node of Graph (ie: name of models)
values = parameters of each models
WILL BE MODIFIED in PLACE !
Returns
-------
modified Graph
modified all_params
"""
nodes_no_composition = [
n for n in Graph.nodes if not StepCategories.is_composition_step(n[0])
]
sub_Graph = Graph.subgraph(nodes_no_composition)
starting_nodes = gh.get_starting_nodes(sub_Graph)
for node in starting_nodes:
vtype = var_type_node_dico[node]
if _must_include_selector(node[1]):
if vtype is not None:
name_of_cat = "Selector_%s" % unnest_tuple(vtype)
new_node = (name_of_cat, (name_of_cat,
SpecialModels.ColumnsSelector))
if new_node in Graph.nodes:
raise ValueError(
"Please check, I have duplicate names : %s" %
str(new_node))
Graph = gh.insert_node_above(Graph, node, new_node=new_node)
all_models_params[new_node] = {
"columns_to_use": _get_columns(vtype,
var_type_columns_dico)
}
else:
pass # nothing to do : the transformer would need a selector BUT vtype is None which means I can apply to everything
else:
if vtype is not None:
all_models_params[node]["columns_to_use"] = _get_columns(
vtype, var_type_columns_dico)
return Graph, all_models_params
def create_graphical_representation(steps):
""" from a an OrderedDict of steps create a Graphical reprensetation of the model we'll use """
# Rmk : il faut a priori, mettre les numero de l'etape dans le graph
# + mettre les labels correct
# comme ça on pourra avoir plusieurs noeud avec le meme nom (Ex : Scaler...)
### 1) Split Composion Steps vs Rest
all_composition_steps = []
all_others = []
for (step_name, model_name), var_type in steps.items():
if StepCategories.is_composition_step(step_name):
all_composition_steps.append((step_name, model_name, var_type))
else:
all_others.append((step_name, model_name, var_type))
### 2) Create Graph for non-composition step
new_steps = OrderedDict()
G = nx.DiGraph()
for step_name, model_name, var_type in all_others:
# for name,var_type in steps.items():
unested_var_type = unnest_tuple(var_type)
terminal_nodes = gh.get_terminal_nodes(
G
) # Terminal links : I'll add the new step on one (or more) of those
ending_node_type = {
unnest_tuple(steps[node]): node
for node in terminal_nodes
}
node_name = (step_name, model_name) # 2-uple
if node_name in G.nodes:
raise ValueError("This node already exists '(%s,%s)'" % node_name)
# 1) Soit je rattache le nouveau a UN noeud terminal
# 2) Soit je cree une nouvelle branche (nouveau noeud ratacher a rien)
# 3) Soit je rattache a PLUSIEURS noeud terminaux
elif unested_var_type in ending_node_type:
### 1) I already have a branch of this type
last_node = ending_node_type[unested_var_type]
G = gh.add_node_after(G, node_name, last_node)
### I don't have a branch ###
else:
all_candidates = [(t, n) for t, n in ending_node_type.items()
if tuple_include(t, unested_var_type)]
# I need to look where I want to plug it #
if len(all_candidates) == 0:
### 2) Je dois creer une nouvelle branche : aucun noeud ###
G = gh.add_node_after(G, node_name)
else:
### 3) Je rattache a plusieurs noeuds
### Ici : il faut parfois rajouter un noeud en AMONT, si on a des types qui n'ont pas ete rajouter
types_added = unnest_tuple([t for t, n in all_candidates])
types_not_added = diff(unested_var_type, types_added)
if len(types_not_added) > 0:
name_of_cat = "Selector_%s" % unnest_tuple(types_not_added)
new_node = (name_of_cat, (name_of_cat,
SpecialModels.ColumnsSelector))
G = gh.add_node_after(G, new_node)
new_steps[
new_node] = types_not_added # I also must dynamically add the node to the list of steps
all_candidates = all_candidates + [
(types_not_added, new_node)
]
G = gh.add_node_after(G, node_name,
*[n for t, n in all_candidates])
### 3) Include composition node on top
for step_name, model_name, _ in reversed(all_composition_steps):
starting_nodes = gh.get_starting_nodes(G)
for n in starting_nodes:
G.add_edge((step_name, model_name), n)
### 4) Verify the Graph structure
for (step_name, model_name), _ in steps.items():
if (step_name, model_name) not in G:
raise ValueError("'(%s , %s)' should be in graph" %
(step_name, model_name))
# all nodes were in the steps
for node in G.nodes():
if node not in steps and node not in new_steps:
raise ValueError("'(%s,%s)' shouldn't be in graph" % node)
assert_model_graph_structure(G)
return G, new_steps
