def test_simple_path_construction():
G = AnalysisGraph.from_indra_statements_json_file("tests/data/indra_statements_format.json")
G.add_node("c0")
G.add_node("c1")
G.add_node("c2")
print("Nodes of the graph:")
G.print_nodes()
G.add_edge((("", 1, "c0"), ("", 1, "c1")))
G.add_edge((("", 1, "c1"), ("", 1, "c2")))
G.add_edge((("", 1, "c0"), ("", 1, "c2")))
G.add_edge(
(("", 1, "c3"), ("", 1, "c1"))
) # Creates a loop 1 -> 2 -> 3 -> 1
print("Edges of the graph:")
G.print_edges()
G.find_all_paths()
G.print_all_paths()
G.print_cells_affected_by_beta(0, 1)
G.print_cells_affected_by_beta(1, 2)
G2 = AnalysisGraph.from_indra_statements_json_file(
"tests/data/indra_statements_format.json"
)
def test_remove_edge():
causal_fragments = [(("small", 1, tension), ("large", -1, food_security))]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
G.print_all_paths()
print("\nRemoving edge - invalid source")
with pytest.raises(IndexError):
G.remove_edge(source="invalid", target=food_security)
print("\nRemoving edge - invalid target")
with pytest.raises(IndexError):
G.remove_edge(source=tension, target="invalid")
print("\nRemoving edge - source and target inverted target")
G.remove_edge(source=food_security, target=tension)
G.print_nodes()
print("\nRemoving edge - correct")
G.remove_edge(source=tension, target=food_security)
G.print_nodes()
G.print_edges()
G.to_png()
def test_remove_edges():
causal_fragments = [(("small", 1, tension), ("large", -1, food_security))]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
G.print_all_paths()
edges_to_remove = [
("invalid_src_1", food_security),
("invalid_src_2", food_security),
(tension, "invalid_tgt1"),
(tension, "invalid_tgt2"),
("invalid_src_2", "invalid_tgt_2"),
("invalid_src_3", "invalid_tgt3"),
(food_security, tension),
(tension, food_security),
]
print("\nRemoving edges")
G.remove_edges(edges_to_remove)
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
G.print_all_paths()
def test_prune():
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n0"), ("large", -1, "n2")),
(("small", 1, "n0"), ("large", -1, "n3")),
(("small", 1, "n2"), ("large", -1, "n1")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n1")),
# (("small", 1, "n4"), ("large", -1, "n2")),
# (("small", 1, "n2"), ("large", -1, "n3")),
]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
print("\nBefore pruning")
G.print_all_paths()
cutoff = 2
G.prune(cutoff)
print("\nAfter pruning")
G.print_all_paths()
def test_remove_edges():
causal_fragments = [(("small", 1, "UN/events/human/conflict"),
("large", -1, "UN/entities/human/food/food_security"))
]
print('\n\n\n\n')
print('\nCreating CAG')
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
edges_to_remove = [
('invalid_src_1', "UN/entities/human/food/food_security"),
('invalid_src_2', "UN/entities/human/food/food_security"),
('UN/events/human/conflict', 'invalid_tgt1'),
('UN/events/human/conflict', 'invalid_tgt2'),
('invalid_src_2', 'invalid_tgt_2'),
('invalid_src_3', 'invalid_tgt3'),
("UN/entities/human/food/food_security", 'UN/events/human/conflict'),
('UN/events/human/conflict', "UN/entities/human/food/food_security"),
]
print('\nRemoving edges')
G.remove_edges(edges_to_remove)
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
def test_cpp_extensions_preds():
statements = [(
("large", -1, "UN/entities/human/financial/economic/inflation"),
("small", 1, "UN/events/human/human_migration"),
)]
G = AnalysisGraph.from_causal_fragments(statements)
G.map_concepts_to_indicators()
G["UN/events/human/human_migration"].replace_indicator(
"Net migration", "New asylum seeking applicants", "UNHCR")
G.to_png()
# Now we can specify how to initialize betas. Posible values are:
# InitialBeta.ZERO
# InitialBeta.ONE
# InitialBeta.HALF
# InitialBeta.MEAN
# InitialBeta.RANDOM - A random value between [-1, 1]
G.train_model(2015,
1,
2015,
12,
10,
10,
initial_beta=InitialBeta.ZERO,
use_continuous=False)
preds = G.generate_prediction(2015, 1, 2016, 12)
pred_plot(preds, "New asylum seeking applicants", save_as="pred_plot.pdf")
def test_delete_indicator():
statements = [(
("large", -1, "UN/entities/human/financial/economic/inflation"),
("small", 1, "UN/events/human/human_migration"),
)]
G = AnalysisGraph.from_causal_fragments(statements)
print("\n")
G.print_nodes()
G.map_concepts_to_indicators()
G.print_indicators()
print("\n")
G["UN/events/human/human_migration"].replace_indicator(
"Net migration", "New asylum seeking applicants", "UNHCR")
G.print_indicators()
print("\n")
G.set_indicator("UN/events/human/human_migration", "Net Migration",
"MITRE12")
G.print_indicators()
print("\n")
G.delete_indicator("UN/events/human/human_migration",
"New asylum seeking applicants")
G.print_indicators()
print("\n")
G.set_indicator(
"UN/events/human/human_migration",
"New asylum seeking applicants",
"UNHCR",
)
G.delete_all_indicators("UN/events/human/human_migration")
G.print_indicators()
def test_merge():
causal_fragments = [
(("small", 1, tension), ("large", -1, food_security)),
(("small", 1, displacement), ("small", 1, tension)),
(("small", 1, displacement), ("large", -1, food_security)),
(("small", 1, tension), ("small", 1, crop_production)),
(("large", -1, food_security), ("small", 1, crop_production)),
(
("small", 1, "UN/events/human/economic_crisis"),
("small", 1, tension),
),
(
("small", 1, "UN/events/weather/precipitation"),
("large", -1, food_security),
),
(("large", -1, food_security), ("small", 1, inflation)),
]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
print("\nBefore merging")
G.print_all_paths()
G.print_nodes()
print("\nAfter merging")
G.merge_nodes(food_security, tension)
G.print_all_paths()
G.print_nodes()
def test_remove_node():
causal_fragments = [(("small", 1, "UN/events/human/conflict"),
("large", -1, "UN/entities/human/food/food_security"))
]
print('\n\n\n\n')
print('\nCreating CAG')
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
print('\nRemoving an invalid concept')
G.remove_node(concept='invalid')
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
print('\nRemoving a valid concept')
G.remove_node(concept='UN/events/human/conflict')
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
def createNewModel():
""" Create a new Delphi model. """
data = json.loads(request.data)
if os.environ.get("CI") == "true":
# When running in a continuous integration run, we set the sampling
# resolution to be small to prevent timeouts.
res = 5
elif os.environ.get("DELPHI_N_SAMPLES") is not None:
# We also enable setting the sampling resolution through the
# environment variable "DELPHI_N_SAMPLES", for development and testing
# purposes.
res = int(os.environ["DELPHI_N_SAMPLES"])
else:
# If neither "CI" or "DELPHI_N_SAMPLES" is set, we default to a
# sampling resolution of 1000.
# TODO - we might want to set the default sampling resolution with some
# kind of heuristic, based on the number of nodes and edges. - Adarsh
res = 1000
G = AnalysisGraph.from_causemos_json_string(request.data, res)
model = DelphiModel(id=data["id"],
model=G.serialize_to_json_string(verbose=False))
db.session.merge(model)
db.session.commit()
response = json.loads(G.generate_create_model_response())
return jsonify(response)
def test_subgraph_between():
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n1"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n3")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n0"), ("large", -1, "n5")),
(("small", 1, "n5"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n4")),
(("small", 1, "n0"), ("large", -1, "n7")),
(("small", 1, "n7"), ("large", -1, "n4")),
(("small", 1, "n0"), ("large", -1, "n4")),
(("small", 1, "n0"), ("large", -1, "n8")),
(("small", 1, "n8"), ("large", -1, "n9")),
(("small", 1, "n10"), ("large", -1, "n0")),
(("small", 1, "n4"), ("large", -1, "n12")),
(("small", 1, "n12"), ("large", -1, "n13")),
(("small", 1, "n13"), ("large", -1, "n4")),
]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
G.print_nodes()
G.print_name_to_vertex()
# G.print_all_paths()
cutoff = 3
src = "n0"
tgt = "n4"
print(
"\nSubgraph with inbetween hops less than or equal {} between source node {} and target node {}".format(
cutoff, src, tgt
)
)
try:
G_sub = G.get_subgraph_for_concept_pair(src, tgt, cutoff)
# G_sub.find_all_paths()
except IndexError:
print("Incorrect source or target concept")
return
print("\n\nTwo Graphs")
print("The original")
G.print_nodes()
G.print_name_to_vertex()
print()
print("The subgraph")
G_sub.print_nodes()
G_sub.print_name_to_vertex()
def createNewICM():
""" Create a new ICM"""
data = json.loads(request.data)
G = AnalysisGraph.from_uncharted_json_serialized_dict(data)
G.assemble_transition_model_from_gradable_adjectives()
G.sample_from_prior()
G.to_sql(app=current_app)
_metadata = ICMMetadata.query.filter_by(id=G.id).first().deserialize()
del _metadata["model_id"]
return jsonify(_metadata)
def createNewModel():
""" Create a new Delphi model. """
data = json.loads(request.data)
G = AnalysisGraph.from_causemos_json_string(request.data)
G.id = data["id"]
model = DelphiModel(id=data["id"], model=G.to_json_string())
db.session.merge(model)
db.session.commit()
edge_weights = G.get_edge_weights_for_causemos_viz()
return jsonify({"status": "success", "relations": edge_weights})
def create_base_CAG(uncharted_json_file):
G = AnalysisGraph.from_uncharted_json_file(uncharted_json_file)
G.merge_nodes(
"wm/concept/causal_factor/condition/food_security",
"wm/concept/causal_factor/condition/food_insecurity",
same_polarity=False,
)
G = G.get_subgraph_for_concept(
"wm/concept/causal_factor/condition/food_insecurity", inward=True)
G.map_concepts_to_indicators()
return G
def test_inference():
causal_fragments = [(("small", 1, tension), ("large", -1, food_security))]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
def getModelStatus(modelID):
query_result = DelphiModel.query.filter_by(id=modelID).first()
if not query_result:
return jsonify(json.loads('{"status": "invalid model id"}'))
model = query_result.model
G = AnalysisGraph.deserialize_from_json_string(model, verbose=False)
response = json.loads(G.generate_create_model_response())
return jsonify(response)
def test_simple_path_construction():
G = AnalysisGraph.from_json_file("tests/data/indra_statements_format.json")
G.add_node('c0')
G.add_node('c1')
G.add_node('c2')
#G.add_node('c3')
#G.add_node('c4')
print('Nodes of the graph:')
G.print_nodes()
G.add_edge((("", 1, "c0"), ("", 1, "c1")))
G.add_edge((("", 1, "c1"), ("", 1, "c2")))
#G.add_edge((("", 1, "c1"), ("", 1, "c3")))
#G.add_edge((("", 1, "c2"), ("", 1, "c3")))
G.add_edge((("", 1, "c0"), ("", 1, "c2")))
G.add_edge(
(("", 1, "c3"), ("", 1, "c1"))) # Creates a loop 1 -> 2 -> 3 -> 1
'''
G.add_edge(0,1)
G.add_edge(1,2)
#G.add_edge(1,3)
#G.add_edge(2,3)
G.add_edge(0,2)
G.add_edge(3,1) # Creates a loop 1 -> 2 -> 3 -> 1
'''
print('Edges of the graph:')
G.print_edges()
G.find_all_paths()
G.print_all_paths()
G.print_cells_affected_by_beta(0, 1)
G.print_cells_affected_by_beta(1, 2)
G2 = AnalysisGraph.from_json_file(
"tests/data/indra_statements_format.json")
#G2.initialize( True )
'''
def createNewModel():
""" Create a new Delphi model. """
if os.environ.get("CI") == "true":
# When running in a continuous integration run, we set the sampling
# resolution to be small to prevent timeouts.
kde_kernels = 5
sampling_resolution = 5
burn = 5
elif os.environ.get("DELPHI_N_SAMPLES") is not None:
# We also enable setting the sampling resolution through the
# environment variable "DELPHI_N_SAMPLES", for development and testing
# purposes.
# NOTE: When creating, this environment variable is named incorrectly!
kde_kernels = int(os.environ["DELPHI_N_SAMPLES"])
sampling_resolution = 100
burn = 100
else:
# If neither "CI" or "DELPHI_N_SAMPLES" is set, we default to a
# sampling resolution of 1000.
# TODO - we might want to set the default sampling resolution with some
# kind of heuristic, based on the number of nodes and edges. - Adarsh
kde_kernels = 1000
sampling_resolution = 1000
burn = 10000
data = json.loads(request.data)
G = AnalysisGraph.from_causemos_json_string(request.data,
belief_score_cutoff=0,
grounding_score_cutoff=0,
kde_kernels=kde_kernels)
model = DelphiModel(id=data["id"],
model=G.serialize_to_json_string(verbose=False))
db.session.merge(model)
db.session.commit()
response = json.loads(G.generate_create_model_response())
# executor.submit_stored(data["id"], train_model, G, data["id"])
try:
proc = multiprocessing.Process(target=train_model,
args=(G, data["id"],
sampling_resolution, burn,
current_app),
name='training')
proc.start()
except multiprocessing.ProcessError:
print("Error: unable to start training process")
response['status'] = 'server error: training'
return jsonify(response)
def create_base_CAG(causemos_create_model,
belief_score_cutoff=0,
grounding_score_cutoff=0,
kde_kernels=4):
if causemos_create_model:
G = AnalysisGraph.from_causemos_json_file(causemos_create_model,
belief_score_cutoff,
grounding_score_cutoff,
kde_kernels)
else:
statements = [(
("large", 1,
"wm/concept/indicator_and_reported_property/agriculture/Crop_Production"
),
("small", -1,
"wm/concept/causal_factor/condition/food_insecurity"),
)]
G = AnalysisGraph.from_causal_fragments(statements)
G.map_concepts_to_indicators()
curate_indicators(G)
return G
def test_inference():
causal_fragments = [(("small", 1, "UN/events/human/conflict"),
("large", -1, "UN/entities/human/food/food_security"))
]
print('\n\n\n\n')
print('\nCreating CAG')
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
print('\nSample from proposal debug')
def test_merge():
causal_fragments = [
(("small", 1, "UN/events/human/conflict"),
("large", -1, "UN/entities/human/food/food_security")),
(("small", 1, "UN/events/human/human_migration"),
("small", 1, "UN/events/human/conflict")),
(("small", 1, "UN/events/human/human_migration"),
("large", -1, "UN/entities/human/food/food_security")),
(("small", 1, "UN/events/human/conflict"),
("small", 1, "UN/entities/natural/crop_technology/product")),
(("large", -1, "UN/entities/human/food/food_security"),
("small", 1, "UN/entities/natural/crop_technology/product")),
(("small", 1, "UN/events/human/economic_crisis"),
("small", 1, "UN/events/human/conflict")),
(("small", 1, "UN/events/weather/precipitation"),
("large", -1, "UN/entities/human/food/food_security")),
(("small", 1, "UN/entities/human/financial/economic/inflation"),
("small", 1, "UN/events/human/conflict")),
(("large", -1, "UN/entities/human/food/food_security"),
("small", 1, "UN/entities/human/financial/economic/inflation")),
]
'''
("large", -1, "UN/entities/human/food/food_security")
("small", 1, "UN/events/human/conflict")
("small", 1, "UN/events/human/human_migration")
("small", 1, "UN/entities/natural/crop_technology/product")
("small", 1, "UN/events/human/economic_crisis")
("small", 1, "UN/events/weather/precipitation")
("small", 1, "UN/entities/human/financial/economic/inflation")
'''
print('\n\n\n\n')
print('\nCreating CAG')
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
print('\nBefore merging')
G.print_all_paths()
G.print_nodes()
print('\nAfter mergning')
#G.merge_nodes( "UN/events/human/conflict", "UN/entities/human/food/food_security")
G.merge_nodes("UN/entities/human/food/food_security",
"UN/events/human/conflict")
G.print_all_paths()
G.print_nodes()
def test_remove_edge():
causal_fragments = [(("small", 1, "UN/events/human/conflict"),
("large", -1, "UN/entities/human/food/food_security"))
]
print('\n\n\n\n')
print('\nCreating CAG')
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
print('\nRemoving edge - invalid source')
G.remove_edge(source='invalid',
target="UN/entities/human/food/food_security")
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
print('\nRemoving edge - invalid target')
G.remove_edge(source='UN/events/human/conflict', target='invalid')
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
print('\nRemoving edge - source and target inverted target')
G.remove_edge(source="UN/entities/human/food/food_security",
target='UN/events/human/conflict')
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
print('\nRemoving edge - correct')
G.remove_edge(source='UN/events/human/conflict',
target="UN/entities/human/food/food_security")
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
G.print_all_paths()
G.to_png()
def test_remove_node():
causal_fragments = [(("small", 1, tension), ("large", -1, food_security))]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print("\nRemoving an invalid concept")
with pytest.raises(IndexError):
G.remove_node(concept="invalid")
print("\nRemoving a valid concept")
G.remove_node(concept=tension)
G.print_nodes()
def runExperiment(request, experiment_id, experiment_type, model, app):
G = AnalysisGraph.deserialize_from_json_string(model, verbose=False)
if experiment_type == "PROJECTION":
runProjectionExperiment(request, experiment_id, G, app)
elif experiment_type == "GOAL_OPTIMIZATION":
# Not yet implemented
pass
elif experiment_type == "SENSITIVITY_ANALYSIS":
# Not yet implemented
pass
elif experiment_type == "MODEL_VALIDATION":
# Not yet implemented
pass
elif experiment_type == "BACKCASTING":
# Not yet implemented
pass
else:
# Unknown experiment type
pass
def test_remove_nodes():
causal_fragments = [(("small", 1, tension), ("large", -1, food_security))]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
G.print_all_paths()
print("\nRemoving a several concepts, some valid, some invalid")
G.remove_nodes(concepts=set(["invalid1", tension, "invalid2"]))
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
G.print_all_paths()
def runExperiment(request, modelID, experiment_id):
request_body = request.get_json()
experiment_type = request_body["experimentType"]
query_result = DelphiModel.query.filter_by(id=modelID).first()
if not query_result:
# Model ID not in database. Should be an incorrect model ID
result = CauseMosAsyncExperimentResult.query.filter_by(
id=experiment_id).first()
result.status = "failed"
db.session.merge(result)
db.session.commit()
return
model = query_result.model
trained = json.loads(model)["trained"]
G = AnalysisGraph.deserialize_from_json_string(model, verbose=False)
if experiment_type == "PROJECTION":
runProjectionExperiment(request, modelID, experiment_id, G, trained)
elif experiment_type == "GOAL_OPTIMIZATION":
# Not yet implemented
pass
elif experiment_type == "SENSITIVITY_ANALYSIS":
# Not yet implemented
pass
elif experiment_type == "MODEL_VALIDATION":
# Not yet implemented
pass
elif experiment_type == "BACKCASTING":
# Not yet implemented
pass
else:
# Unknown experiment type
pass
def test_cpp_extensions():
G = AnalysisGraph.from_json_file("tests/data/indra_statements_format.json")
def test_subgraph():
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n1"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n3")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n5")),
(("small", 1, "n5"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n7")),
(("small", 1, "n7"), ("large", -1, "n8")),
(("small", 1, "n0"), ("large", -1, "n9")),
(("small", 1, "n9"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n10")),
(("small", 1, "n10"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n11")),
(("small", 1, "n11"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n12")),
(("small", 1, "n12"), ("large", -1, "n8")),
(("small", 1, "n13"), ("large", -1, "n14")),
(("small", 1, "n14"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n15")),
(("small", 1, "n15"), ("large", -1, "n16")),
(("small", 1, "n5"), ("large", -1, "n3")), # Creates a loop
]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print("\nName to vertex ID map entries")
G.print_name_to_vertex()
G.print_nodes()
G.print_name_to_vertex()
hops = 2
node = "n4"
print(
"\nSubgraph of {} hops beginning at node {} graph".format(hops, node)
)
try:
G_sub = G.get_subgraph_for_concept(node, False, hops)
except IndexError:
print("Concept {} is not in the CAG!".format(node))
return
print("\n\nTwo Graphs")
print("The original")
G.print_nodes()
G.print_name_to_vertex()
print("The subgraph")
G_sub.print_nodes()
G_sub.print_name_to_vertex()
print("\nSubgraph of {} hops ending at node {} graph".format(hops, node))
G_sub = G.get_subgraph_for_concept(node, True, hops)
print("\n\nTwo Graphs")
print("The original")
G.print_nodes()
G.print_name_to_vertex()
print(
"\nSubgraph of {} hops beginning at node {} graph".format(hops, node)
)
G.get_subgraph_for_concept(node, False, hops)
def test_debug():
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n0"), ("large", -1, "n2")),
(("small", 1, "n0"), ("large", -1, "n3")),
(("small", 1, "n2"), ("large", -1, "n1")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n1")),
]
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n1"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n3")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n5")),
(("small", 1, "n5"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n7")),
(("small", 1, "n7"), ("large", -1, "n8")),
(("small", 1, "n0"), ("large", -1, "n9")),
(("small", 1, "n9"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n10")),
(("small", 1, "n10"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n11")),
(("small", 1, "n11"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n12")),
(("small", 1, "n12"), ("large", -1, "n8")),
(("small", 1, "n13"), ("large", -1, "n14")),
(("small", 1, "n14"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n15")),
(("small", 1, "n15"), ("large", -1, "n16")),
(("small", 1, "n5"), ("large", -1, "n3")), # Creates a loop
]
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n1"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n3")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n5")),
(("small", 1, "n5"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n7")),
(("small", 1, "n7"), ("large", -1, "n8")),
(("small", 1, "n0"), ("large", -1, "n3")),
]
print("\n\n\n\n")
print("\nCreating CAG")
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print("\nBefore pruning")
G.print_all_paths()
hops = 3
node = "n0"
print(
f"\nSubgraph of {hops} hops beginning at node {node} graph"
)
try:
G_sub = G.get_subgraph_for_concept(node, False, hops)
except IndexError:
print(f"Concept {node} is not in the CAG!")
return
G_sub.find_all_paths()
G_sub.print_nodes()
def test_subgraph():
causal_fragments = [ # Center node is n4
(("small", 1, "n0"), ("large", -1, "n1")),
(("small", 1, "n1"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n3")),
(("small", 1, "n3"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n5")),
(("small", 1, "n5"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n7")),
(("small", 1, "n7"), ("large", -1, "n8")),
#(("small", 1, "n8"), ("large", -1, "n9")),
#(("small", 1, "n9"), ("large", -1, "n0")),
(("small", 1, "n0"), ("large", -1, "n9")),
(("small", 1, "n9"), ("large", -1, "n2")),
(("small", 1, "n2"), ("large", -1, "n10")),
(("small", 1, "n10"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n11")),
(("small", 1, "n11"), ("large", -1, "n6")),
(("small", 1, "n6"), ("large", -1, "n12")),
(("small", 1, "n12"), ("large", -1, "n8")),
(("small", 1, "n13"), ("large", -1, "n14")),
(("small", 1, "n14"), ("large", -1, "n4")),
(("small", 1, "n4"), ("large", -1, "n15")),
(("small", 1, "n15"), ("large", -1, "n16")),
(("small", 1, "n5"), ("large", -1, "n3")), # Creates a loop
]
print('\n\n\n\n')
print('\nCreating CAG')
G = AnalysisGraph.from_causal_fragments(causal_fragments)
G.find_all_paths()
G.print_nodes()
print('\nName to vertex ID map entries')
G.print_name_to_vertex()
#G.remove_nodes(set(['n0', 'n1', 'n2', 'n3', 'n4']))
#G.remove_nodes(set(['n2', 'n3', 'n4']))
#G.remove_nodes(set(['n9', 'n8', 'n7', 'n6', 'n5']))
G.print_nodes()
G.print_name_to_vertex()
hops = 3
node = 'n4'
print('\nSubgraph of {} hops beginning at node {} graph'.format(
hops, node))
try:
G_sub = G.get_subgraph_for_concept(node, False, hops)
except IndexError:
print('Concept {} is not in the CAG!'.format(node))
return
print('\n\nTwo Graphs')
print('The original')
G.print_nodes()
G.print_name_to_vertex()
#G.print_all_paths()
print()
print('The subgraph')
G_sub.print_nodes()
G_sub.print_name_to_vertex()
#G_sub.print_all_paths()
print('\nSubgraph of {} hops ending at node {} graph'.format(hops, node))
G_sub = G.get_subgraph_for_concept(node, True, hops)
print('\n\nTwo Graphs')
print('The original')
G.print_nodes()
G.print_name_to_vertex()
print('\nSubgraph of {} hops beginning at node {} graph'.format(
hops, node))
G.get_subgraph_for_concept(node, False, hops)