def test(model, train, validation, test_data):
loss, accuracy = model.evaluate_generator(validation, steps=math.ceil(validation.n/config.BATCH_SIZE))
train_loss, train_accuracy = model.evaluate_generator(train, steps=math.ceil(train.n/config.BATCH_SIZE))
results = evaluate.get_results(model, validation)
probabilities = list(evaluate.transform_binary_probabilities(results))
labels = list(evaluate.get_labels(validation))
test_loss = 0
test_accuracy = 0
test_probabilities = []
test_labels = []
if len(test_data.classes) != 0:
test_loss, test_accuracy = model.evaluate_generator(test_data, steps=math.ceil(test_data.n/config.BATCH_SIZE))
test_results = evaluate.get_results(model, test_data)
test_probabilities = list(evaluate.transform_binary_probabilities(test_results))
test_labels = list(evaluate.get_labels(test_data))
return {
"train_accuracy": float(train_accuracy),
"train_loss": float(train_loss),
"accuracy": float(accuracy),
"loss": float(loss),
"test_accuracy": float(test_accuracy),
"test_loss": float(test_loss),
"probabilities": probabilities,
"labels": labels,
"test_probabilities": test_probabilities,
"test_labels": test_labels,
}
def predict(df):
basepath = '/media/user1/preprocessed'
features = []
truth_label = []
df.sort()
print(df)
label = np.zeros((1, ))
for name in df:
print(name)
label[0] = 0
vector = np.zeros(800)
if 'COR' in name or 'SUB' in name:
label[0] = 1
labels_df = pd.read_csv('./Book1.csv')
path = labels_df['ID'].tolist()
if name in path:
label[0] = 1
basedir = os.path.normpath(basepath)
print(basedir)
files = glob(basedir + '/' + name + '*.npy')
files.sort()
l = len(files)
if l == 0:
break
for i in range(l):
max_pro = 0
img = np.load(files[i])
s = img.shape
if s[0] != 224:
img = misc.imresize(img, (224, 224), 'bilinear')
img = np.stack((img, img, img), axis=2)
img = img[np.newaxis, :, :, :]
test_generator = Dataset(
img,
label,
augment=False,
shuffle=False,
input_form='t1',
seed=seed,
)
test_generator.reset()
test_results = evaluate.get_results(model, test_generator)
probabilities = list(
evaluate.transform_binary_probabilities(test_results))
vector[i] = probabilities[0]
features.append(vector)
truth_label.append(label[0])
return features, truth_label
def test_model(model, train, validation, test):
loss, accuracy = model.evaluate_generator(
validation, steps=math.ceil(len(validation) / config.BATCH_SIZE))
train_loss, train_accuracy = model.evaluate_generator(
train, steps=math.ceil(len(train) / config.BATCH_SIZE))
test_loss, test_accuracy = model.evaluate_generator(
test, steps=math.ceil(len(test) / config.BATCH_SIZE))
# think you mean train here
train.reset()
validation.reset()
test.reset()
results = evaluate.get_results(model, validation)
probabilities = list(evaluate.transform_binary_probabilities(results))
labels = list(evaluate.get_labels(validation))
test_results = evaluate.get_results(model, test)
test_probabilities = list(
evaluate.transform_binary_probabilities(test_results))
test_labels = list(evaluate.get_labels(test))
# think you mean train here
train.reset()
validation.reset()
test.reset()
return {
"train_accuracy": float(train_accuracy),
"train_loss": float(train_loss),
"accuracy": float(accuracy),
"loss": float(loss),
"test_accuracy": float(test_accuracy),
"test_loss": float(test_loss),
"probabilities": probabilities,
"labels": labels,
"test_probabilities": test_probabilities,
"test_labels": test_labels,
}
def test_model(model, train, validation, test): # , holdout_test):
train_loss, train_accuracy = model.evaluate_generator(
train, steps=math.ceil(len(train) / config.BATCH_SIZE))
loss, accuracy = model.evaluate_generator(
validation, steps=math.ceil(len(validation) / config.BATCH_SIZE))
test_loss, test_accuracy = model.evaluate_generator(
test, steps=math.ceil(len(test) / config.BATCH_SIZE))
# holdout_test_loss, holdout_test_accuracy = model.evaluate_generator(holdout_test, steps=math.ceil(len(holdout_test)/config.BATCH_SIZE))
train.reset()
validation.reset()
test.reset()
# holdout_test.reset()
# labels - ground truths
# results - predicted results from model
results = evaluate.get_results(model, validation)
probabilities = list(evaluate.transform_binary_probabilities(results))
labels = list(evaluate.get_labels(validation))
test_results = evaluate.get_results(model, test)
test_probabilities = list(
evaluate.transform_binary_probabilities(test_results))
test_labels = list(evaluate.get_labels(test))
# holdout_test_results = evaluate.get_results(model, holdout_test)
# holdout_test_probabilities = list(evaluate.transform_binary_probabilities(holdout_test_results))
# holdout_test_labels = list(evaluate.get_labels(holdout_test))
train.reset()
validation.reset()
test.reset()
# holdout_test.reset()
# get binary predictions
# holdout_binary_predictions = list(evaluate.transform_binary_predictions(holdout_test_results))
test_binary_predictions = list(
evaluate.transform_binary_predictions(test_results))
# get f1 score
test_f1_result = f1_score(test_labels, test_binary_predictions)
# holdout_f1_result = f1_score(holdout_test_labels, holdout_binary_predictions)
return {
"train_accuracy": float(train_accuracy),
"train_loss": float(train_loss),
"accuracy": float(accuracy),
"loss": float(loss),
"test_accuracy": float(test_accuracy),
"test_loss": float(test_loss),
# "holdout_test_accuracy": float(holdout_test_accuracy),
# "holdout_test_loss": float(holdout_test_loss),
"holdout_test_accuracy": float(0),
"holdout_test_loss": float(0),
"probabilities": probabilities,
"labels": labels,
"test_probabilities": test_probabilities,
"test_labels": test_labels,
# "holdout_test_probabilities": holdout_test_probabilities,
# "holdout_test_labels": holdout_test_labels,
"holdout_test_probabilities": 'na',
"holdout_test_labels": 'na',
"test_f1_result": test_f1_result,
# "holdout_f1_result": holdout_f1_result,
"holdout_f1_result": float(0),
}
test_features, test_label = relist(predict(test_set))
#test_features = np.load('./COR_test.npy')
#test_label = np.load('./COR_test_label.npy')
test_generator = Dataset(
test_features,
test_label,
augment=False,
shuffle=False,
input_form='t1',
seed=seed,
)
test_generator.reset()
test_results = evaluate.get_results(model, test_generator)
probabilities = list(evaluate.transform_binary_probabilities(test_results))
np.save('./test_slice_pro.npy', probabilities)
#test 5628
#validation 4593
#train 34585
lg_pred = np.zeros((len(probabilities)))
for i in range(len(probabilities)):
if probabilities[i] < 0.5:
lg_pred[i] = 0
else:
lg_pred[i] = 1
print("Accuracy: " + repr(accuracy_score(test_label, lg_pred)))
print("Average Precision Score: " +
repr(average_precision_score(test_label, lg_pred)))
print("Kappa: " + repr(cohen_kappa_score(test_label, lg_pred)))