def connect_tuple_wrappers(node, output_type):
if not isinstance(output_type, Tuple):
return
for index in range(0, len(output_type.inner)):
internal_input = output_type.inner[index]
for other_clss in registry:
annotations = _get_annotations(other_clss)
other_input = annotations.input
if not (conforms(internal_input, other_input) and other_clss != node):
continue
# `other_class` has input compatible with one element in the Tuple
# build the output `Tuple[..., internal_output, ...]` of the wrapper class
internal_output = annotations.output
output_tuple = list(output_type.inner)
output_tuple[index] = internal_output
output_tuple_type = Tuple(*output_tuple)
# dynamic class representing the wrapper algorithm
if (index, output_type, output_tuple_type) in list_tuples:
continue
other_wrapper = build_composite_tuple(
index, output_type, output_tuple_type
)
list_tuples.add((index, output_type, output_tuple_type))
registry.append(other_wrapper)
open_nodes.append(other_wrapper)
G.add_edge(node, other_wrapper)
def build(internal_output, depth):
if internal_output in types_seen:
return
for other_clss in registry:
annotations = _get_annotations(other_clss)
if annotations in list_pairs:
continue
other_input = annotations.input
other_output = annotations.output
if other_input == other_output:
continue
if not conforms(internal_output, other_input):
continue
other_wrapper = build_composite_list(other_input, other_output, depth)
list_pairs.add(annotations)
registry.append(other_wrapper)
types_queue.append(_get_annotations(other_wrapper).output)
def _has_output(clss, output):
return conforms(_get_annotations(clss).output, output)
def _has_input(clss, input):
return conforms(input, _get_annotations(clss).input)
def build_pipelines(input, output, registry) -> "PipelineBuilder":
"""
Creates a `PipelineBuilder` instance that generates all pipelines
from `input` to `output` types.
##### Parameters
- `input`: type descriptor for the desired input.
- `output`: type descriptor for the desired output.
- `registry`: list of available classes to build the pipelines.
"""
# warnings.warn(
# "This method is deprecated and not under use by AutoGOAL's"
# " internal API anymore, use `build_pipeline_graph` instead.",
# category=DeprecationWarning,
# stacklevel=2,
# )
list_pairs = set()
types_queue = []
if isinstance(input, Tuple):
types_queue.extend(input.inner)
else:
types_queue.append(input)
if isinstance(output, Tuple):
types_queue.extend(output.inner)
else:
types_queue.append(output)
types_seen = set()
while types_queue:
output_type = types_queue.pop(0)
def build(internal_output, depth):
if internal_output in types_seen:
return
for other_clss in registry:
annotations = _get_annotations(other_clss)
if annotations in list_pairs:
continue
other_input = annotations.input
other_output = annotations.output
if other_input == other_output:
continue
if not conforms(internal_output, other_input):
continue
other_wrapper = build_composite_list(other_input, other_output, depth)
list_pairs.add(annotations)
registry.append(other_wrapper)
types_queue.append(_get_annotations(other_wrapper).output)
depth = 0
while isinstance(output_type, List):
if output_type.depth() >= MAX_LIST_DEPTH:
break
depth += 1
output_type = output_type.inner
build(output_type, depth)
types_seen.add(output_type)
logger.debug("Output type", output_type)
list_tuples = set()
def connect_tuple_wrappers(node, output_type):
if not isinstance(output_type, Tuple):
return
for index in range(0, len(output_type.inner)):
internal_input = output_type.inner[index]
for other_clss in registry:
annotations = _get_annotations(other_clss)
other_input = annotations.input
if not (conforms(internal_input, other_input) and other_clss != node):
continue
# `other_class` has input compatible with one element in the Tuple
# build the output `Tuple[..., internal_output, ...]` of the wrapper class
internal_output = annotations.output
output_tuple = list(output_type.inner)
output_tuple[index] = internal_output
output_tuple_type = Tuple(*output_tuple)
# dynamic class representing the wrapper algorithm
if (index, output_type, output_tuple_type) in list_tuples:
continue
other_wrapper = build_composite_tuple(
index, output_type, output_tuple_type
)
list_tuples.add((index, output_type, output_tuple_type))
registry.append(other_wrapper)
open_nodes.append(other_wrapper)
G.add_edge(node, other_wrapper)
G = Graph()
open_nodes = []
closed_nodes = set()
# Enqueue open nodes
for clss in registry:
if conforms(input, _get_annotations(clss).input):
open_nodes.append(clss)
G.add_edge(GraphSpace.Start, clss)
connect_tuple_wrappers(GraphSpace.Start, input)
if GraphSpace.Start not in G:
raise TypeError("There are no classes compatible with input type:%r." % input)
while open_nodes:
clss = open_nodes.pop(0)
if clss in closed_nodes:
continue
closed_nodes.add(clss)
output_type = _get_annotations(clss).output
for other_clss in registry:
other_input = _get_annotations(other_clss).input
if conforms(output_type, other_input) and other_clss != clss:
open_nodes.append(other_clss)
G.add_edge(clss, other_clss)
connect_tuple_wrappers(clss, output_type)
if conforms(output_type, output):
G.add_edge(clss, GraphSpace.End)
if GraphSpace.End not in G:
raise TypeError(
"No pipelines can be constructed from input:%r to output:%r."
% (input, output)
)
reachable_from_end = set(nx.dfs_preorder_nodes(G.reverse(False), GraphSpace.End))
unreachable_nodes = set(G.nodes) - reachable_from_end
G.remove_nodes_from(unreachable_nodes)
if not GraphSpace.Start in G:
raise TypeError(
"No pipelines can be constructed from input:%r to output:%r."
% (input, output)
)
return PipelineBuilder(G, registry)
def type_is_guaranteed(input_type):
for other_type in types:
if conforms(other_type, input_type):
return True
return False
