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R9868/src/main.cpp
f157e09e2ed2pyeddl_0.9.0

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main.cpp
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#include <string>
#include "database.h"
#include "basicnet.h"
#define NB_CHNS 4
// Processing parameters
const double validation_size = 0.15;
const int test_stride = 640; // Corresponds to 50% overlap, can be set to 1280 to have no overlap
// Model parameters
const double lr = 0.00075;
const int epochs = 1;
const int batch_size = 32;
int main()
{
std::cout << "[INFO] Reading Dataset..." << std::endl;
Database database;
std::cout << "[INFO] Reading Dataset Completed" << std::endl;
std::cout << "[INFO] Building Net..." << std::endl;
BasicNet basic_net;
std::cout << "[INFO] Building Net Completed" << std::endl;
// Count n. unique patients
// Instance a result class
// for each patient:
// write info
// test_data = data of current patient
// train_data = Database EXCEPT data of current patient
// .......
/*
patients = np.unique(data.patient)
#Load seizure times
seizures = pd.read_csv(data_folder+"seizures.csv", delimiter='\t')
seizures_ = seizures[seizures.Patient.isin(patients)]
seizures_["length"] = seizures_.apply(lambda x: (x.end_seizure - x.start_seizure), axis=1)
results = {}
for patient in patients:
print("Patient: ", patient)
patient_data = data[data.patient == patient]
files = np.unique(patient_data.file)
print(' ', len(files), ' files.')
test_data = patient_data
train_data = data.drop(patient_data.index)
#Build train/test set by cutting each signals in pieces of 5 seconds, with 50% overlap
x_train, y_train, x_test, y_test = cut_signal_data(train_data, test_data, stride_test=test_stride)
#Shuffle and balance classes
x_train, y_train = shuffle(x_train, y_train)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=validation_size, stratify = y_train)
x_train = np.array(x_train)
x_val = np.array(x_val)
// x_data->reshape_({x_data->shape[0], NB_CHNS, 1280, 1)});
x_train = x_train.reshape((len(x_train),NB_CHNS,1280, 1))
x_val = x_val.reshape((len(x_val),NB_CHNS,1280, 1))
#Create and train new model
model = getModel()
es = EarlyStopping(monitor='val_loss', min_delta=0.005, patience=20, restore_best_weights=True)
train(model, x_train, y_train, [x_val, y_val], epochs=epochs, batch_size = batch_size)
x_test = np.array(x_test)
lengths = [len(x[0]) for x in x_test]
x_test_splitted = np.array_split(x_test, len(files))
y_test_splitted = np.array_split(np.array(y_test), len(files))
for x_test, y_test, file in zip(x_test_splitted, y_test_splitted, files):
x_test = np.array(x_test)
x_test = x_test.reshape((len(x_test),NB_CHNS,1280, 1))
predict(model, x_test, y_test)
pred = model.predict(x_test) - 0.25
#show_confusion_matrix(y_test, pred, str(patient) +' '+ str(file))
print("Accuracy (segments) :", compute_accuracy(pred, y_test))
detection_time = compute_detect_time(np.max(np.rint(pred), axis=1), y_test, [3,4])
print("Detection time window (positive if 23<d<45) :", detection_time)
seizure_length = seizures_.length[(seizures_.File == file) & (seizures_.Patient == patient)].values
plt.plot(np.linspace(0, len(y_test)-1, len(y_test)),np.max(np.rint(pred), axis=1),'.')
plt.axvline(x=int(len(y_test)/2), color='k')
if(len(seizure_length)==1):
seizure_chunk = np.rint(seizure_length/5)
plt.axvline(x=int(len(y_test)/2)+seizure_chunk, color='k')
plt.axvline(detection_time, color='r')
plt.title(str(patient)+" "+str(file))
plt.show()
#Record accuracy (temp measure. Will change)
if(str(patient) not in results.keys()):
results[str(patient)] = []
results[str(patient)].append([str(file), detection_time, compute_accuracy(pred, y_test)])
detection_times = []
counter = 0
for patient in results.keys():
for res in results[patient]:
counter+=1
detect_time = res[1]
if((detect_time>23) and (detect_time<=45)):
detection_times.append(detect_time)
plt.hist(detection_times, bins=23, range=[23,46])
plt.xlabel("Per segments of 5 seconds")
plt.show()
detected = len(detection_times)/counter
print("Detection accuracy: ", detected)
*/
}
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