def eval_func(space, space_fixed):
try:
space = {**space, **space_fixed}
prince = diseaseslib.PrioritizationPRINCE(
bipartite_network=bip_diseases,
query_nodes_name="diseaseId",
leave_p_out=priorization_params["leave_p_out"],
max_evals=priorization_params["n_evaluations"],
similarity_lower_threshold=space["threshold"],
mode=space["mode"],
to_undirected=space["to_directed"])
l_seeds, l_seeds_weight, l_target_genes, l_true_target_genes = prince.generate_data_to_prioritize(
bip_diseases, gl)
localeval = evaluators.AUROClinkage(
node_names=genes_in_data,
l_seeds=l_seeds,
l_targets=l_targets,
l_true_targets=l_true_targets,
l_seeds_weight=l_seeds_weight,
alpha=priorization_params["alpha"],
laplacian_exponent=priorization_params["lambda exponent"],
tol=1e-08,
max_iter=priorization_params["max_iter"],
max_fpr=priorization_params["max_fpr"],
auroc_normalized=priorization_params["auroc_normalize"])
val = localeval.evaluate(dict_of_networks["PPI"])
print(val)
return val
except AssertionError:
print(space)
return -1 # mark that something went wrong
def eval_func(space):
return evaluators.AUROClinkage(node_names=genes_in_data,
l_seeds=l_seeds,
l_targets=l_targets,
l_true_targets=l_true_targets,
l_seeds_weight=l_seeds_weight,
alpha=space["alpha"],
laplacian_exponent=priorization_params["lambda exponent"], # here is the varaible
tol=1e-08,
max_iter=priorization_params["max_iter"],
max_fpr=priorization_params["max_fpr"],
auroc_normalized=priorization_params["auroc_normalize"]).evaluate(dict_of_networks["PPI"])
gl.filter_genes(genes_in_data)
prioritize = diseaseslib.PreparePrioritization(leave_p_out=priorization_params["leave_p_out"],
max_evals=priorization_params["n_evaluations"])
l_seeds, l_seeds_weight, l_targets, l_true_targets = prioritize.generate_data_to_prioritize(d, gl)
###############################################################################
# BP+PPI CC+PPI
###############################################################################
evaluator = evaluators.AUROClinkage(node_names=genes_in_data,
l_seeds=l_seeds,
l_targets=l_targets,
l_true_targets=l_true_targets,
l_seeds_weight=l_seeds_weight,
alpha=priorization_params["alpha"],
laplacian_exponent=priorization_params["lambda exponent"],
tol=1e-08,
max_iter=priorization_params["max_iter"],
max_fpr=priorization_params["max_fpr"],
auroc_normalized=priorization_params["auroc_normalize"])
############################################################
t0=time()
auc_ppi = evaluator.evaluate(dict_of_networks["PPI"])
print("Time: ", time()-t0)
print(auc_ppi)
t0 = time()
auc_bp = evaluator.evaluate(dict_of_networks["BP"])
print(p)
l_true_targets = [[int(np.random.choice(targets, size=1, p=p))]
for targets in l_targets]
print("Target list:")
print(l_targets)
print("True targets")
print(l_true_targets)
# --------------------------
evaluator = evaluators.AUROClinkage(node_names=list(range(N)),
l_seeds_dict=l_seeds_dict,
l_targets=l_targets,
l_true_targets=l_true_targets,
alpha=alpha,
tol=1e-08,
max_iter=max_iter,
max_fpr=max_fpr,
laplacian_exponent=laplacian_exponent,
auroc_normalized=False,
k_fold=3)
print(evaluator.metric_name)
optimizer = Optimizer(
optimization_name=evaluator.metric_name + "_" + integrator.__name__,
path2files="/home/crux/Downloads",
space=Optimizer.get_integrator_space(integrator=integrator),
objective_function=lambda sp: Optimizer.gamma_objective_function(
sp, evaluator=evaluator, integrator=integrator),
max_evals=max_evals,
maximize=True)