with torch.no_grad():
model.eval()
y_hat = model.predict(x_test)
y_hat = np.argmax(y_hat, axis=1)
# auc = sklearn.metrics.roc_auc_score(y_test, np.asarray(y_hat).flatten(), multi_class='ovo')
acc = sklearn.metrics.accuracy_score(y_test, np.asarray(y_hat).flatten())
f1 = sklearn.metrics.f1_score(y_test, np.asarray(y_hat).flatten(), average='macro')
experiment["model"] = model.name
experiment["auc"] = 0
experiment["acc"] = acc
experiment["f1"] = f1
experiment["num_genes"] = len(x_train.columns)
experiment["time_elapsed"] = str(time.time() - start_time)
results = record_result(results, experiment, filename)
print(experiment)
except Exception as e:
print("------------------------------------")
print("Exception, x shape: ", x_train.shape)
# print(str( gene in list(neighbors.nodes)))
print(x_train.shape, y_train.shape, adj.shape)
print(e)
logging.error(logging.traceback.format_exc())
print("------------------------------------")
# cleanup
model.best_model = None
del model
torch.cuda.empty_cache()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.fit(X_train, y_train, adj)
model.eval()
with torch.no_grad():
y_hat = model.predict(X_test)
auc = sklearn.metrics.roc_auc_score(y_test, np.argmax(y_hat,
axis=1))
acc = sklearn.metrics.accuracy_score(y_test,
np.argmax(y_hat, axis=1))
print("auc:", auc, " acc: ", acc)
experiment["auc"] = auc
experiment["acc"] = acc
results.append(experiment)
if auc > best_auc:
best_auc = copy.deepcopy(auc)
best_auc_model = copy.deepcopy(model)
if acc > best_acc:
best_acc = copy.deepcopy(acc)
best_acc_model = copy.deepcopy(model)
model.best_model = None # cleanup
del model
torch.cuda.empty_cache()
except Exception:
tb = traceback.format_exc()
experiment['error'] = tb
print(tb)
print(fixed_params)
print(*results, sep="\n")
print("best AUC: ", best_auc, " best ACC: ", best_acc)