def getOptimalT_helper(model, ValDataGen, valid_nums):
lastFullValPred = np.empty((0, 28))
lastFullValLabels = np.empty((0, 28))
for i in tqdm(range(valid_nums)):
img, lbl = next(ValDataGen)
scores = model.predict(img)
lastFullValPred = np.append(lastFullValPred, scores, axis=0)
lastFullValLabels = np.append(lastFullValLabels, lbl, axis=0)
print(lastFullValPred.shape, lastFullValLabels.shape)
rng = np.arange(0, 1, 0.001)
f1s = np.zeros((rng.shape[0], 28))
for j, t in enumerate(tqdm(rng)):
for i in range(28):
p = np.array(lastFullValPred[:, i] > t, dtype=np.int8)
scoref1 = off1(lastFullValLabels[:, i], p, average='binary')
f1s[j, i] = scoref1
print(np.max(f1s, axis=0))
print(np.mean(np.max(f1s, axis=0)))
T = np.empty(28)
for i in range(28):
T[i] = rng[np.where(f1s[:, i] == np.max(f1s[:, i]))[0][0]]
print(T)
return T, np.mean(np.max(f1s, axis=0))
def getOptimalT(self, model, val_gen):
last_full_val_pred = np.empty((0, 28))
last_full_val_labels = np.empty((0, 28))
for i in tqdm(range(len(val_gen))):
data_im, data_label = val_gen[i]
scores = model.predict(data_im)
last_full_val_pred = np.append(last_full_val_pred, scores, axis=0)
last_full_val_labels = np.append(last_full_val_labels,
data_label,
axis=0)
print(last_full_val_pred.shape, last_full_val_labels.shape)
rng = np.arange(0, 1, 0.001)
f1s = np.zeros((rng.shape[0], 28))
for j, t in enumerate(tqdm(rng)):
for i in range(28):
p = np.array(last_full_val_pred[:, i] > t, dtype=np.int8)
#scoref1 = K.eval(f1_score(fullValLabels[:,i], p, average='binary'))
scoref1 = off1(last_full_val_labels[:, i], p, average='binary')
f1s[j, i] = scoref1
print(np.max(f1s, axis=0))
print(np.mean(np.max(f1s, axis=0)))
T = np.empty(28)
for i in range(28):
T[i] = rng[np.where(f1s[:, i] == np.max(f1s[:, i]))[0][0]]
#print('Choosing threshold: ', T, ', validation F1-score: ', max(f1s))
print(T)
return T, np.mean(np.max(f1s, axis=0))
def f1_full_validation(self, y_true, y_pred, class_num, save_path=None):
"""
:param y_true:
:param y_pred:
:param class_num:
:param save_path:
"""
rng = np.arange(0, 1, 0.001)
f1s = np.zeros((rng.shape[0], class_num))
# reshape, if problem is mutli-label problem
y_true = np.reshape(y_true, (y_true.shape[0], -1, class_num))
y_pred = np.reshape(y_pred, (y_pred.shape[0], -1, class_num))
for j, t in enumerate(tqdm(rng, 'Updating the F1 threshold')):
for i in range(class_num):
p = np.array(y_pred[..., i] > t, dtype=np.int8)
scoref1 = off1(y_true[..., i].reshape(-1),
p.reshape(-1),
average='binary')
f1s[j, i] = scoref1
print('Individual F1-scores for each class:')
print(np.max(f1s, axis=0))
print('Macro F1-score CV =', np.mean(np.max(f1s, axis=0)))
T = np.empty(class_num * y_true.shape[1])
F_T = np.empty(class_num)
# Duplicate the threshold j times
for j in range(y_true.shape[1]):
for i in range(class_num):
T[i + j * class_num] = rng[np.where(
f1s[:, i] == np.max(f1s[:, i]))[0][0]]
F_T[i] = rng[np.where(f1s[:, i] == np.max(f1s[:, i]))[0][0]]
print('Probability threshold maximizing CV F1-score for each class:')
print(F_T)
self.threshold = T
self.final_t = F_T
def evaluate():
print("Loading weights ...")
import glob
import os
list_of_files = glob.glob('*.h5') # * means all if need specific format then *.csv
latest_file = max(list_of_files, key=os.path.getctime)
model = load_model(latest_file, custom_objects={'f1_measure': f1_measure})
fullValGen = valid_gen
lastFullValPred = np.empty((0, 28))
lastFullValLabels = np.empty((0, 28))
for i in tqdm(range(len(fullValGen))):
im, lbl = fullValGen[i]
scores = model.predict(im)
lastFullValPred = np.append(lastFullValPred, scores, axis=0)
lastFullValLabels = np.append(lastFullValLabels, lbl, axis=0)
print(lastFullValPred.shape, lastFullValLabels.shape)
from sklearn.metrics import f1_score as off1, f1_score
rng = np.arange(0, 1, 0.001)
f1s = np.zeros((rng.shape[0], 28))
for j, t in enumerate(tqdm(rng)):
for i in range(28):
p = np.array(lastFullValPred[:, i] > t, dtype=np.int8)
scoref1 = off1(lastFullValLabels[:, i], p, average='binary')
f1s[j, i] = scoref1
print('Individual F1-scores for each class:')
print(np.max(f1s, axis=0))
print('Macro F1-score CV =', np.mean(np.max(f1s, axis=0)))
T = np.empty(28)
for i in range(28):
T[i] = rng[np.where(f1s[:, i] == np.max(f1s[:, i]))[0][0]]
print('Probability threshold maximizing CV F1-score for each class:')
print(T)
pathsTest, labelsTest = testDataset()
testg = ProteinDataGenerator(pathsTest, labelsTest, BATCH_SIZE, SHAPE)
submit = pd.read_csv(DATA_DIR + 'sample_submission.csv')
P = np.zeros((pathsTest.shape[0], 28))
for i in tqdm(range(len(testg))):
images, labels = testg[i]
score = model.predict(images)
P[i * BATCH_SIZE:i * BATCH_SIZE + score.shape[0]] = score
PP = np.array(P)
prediction = []
for row in tqdm(range(submit.shape[0])):
str_label = ''
for col in range(PP.shape[1]):
if (PP[row, col] < T[col]):
str_label += ''
else:
str_label += str(col) + ' '
prediction.append(str_label.strip())
submit['Predicted'] = np.array(prediction)
ts = str(int(time.time()))
submit.to_csv('model' + ts + '.csv', index=False)
lastFullValLabels = np.empty((0, 28))
for i in tqdm(range(len(fullValGen))):
im, lbl = fullValGen[i]
scores = bestModel.predict(im)
lastFullValPred = np.append(lastFullValPred, scores, axis=0)
lastFullValLabels = np.append(lastFullValLabels, lbl, axis=0)
print(lastFullValPred.shape, lastFullValLabels.shape)
rng = np.arange(0, 1, 0.001)
f1s = np.zeros((rng.shape[0], 28))
for j, t in enumerate(tqdm(rng)):
for i in range(28):
p = np.array(lastFullValPred[:, i] > t, dtype=np.int8)
scoref1 = off1(lastFullValLabels[:, i], p, average='binary')
f1s[j, i] = scoref1
print('Individual F1-scores for each class:')
print(np.max(f1s, axis=0))
print('Macro F1-score CV =', np.mean(np.max(f1s, axis=0)))
plt.plot(rng, f1s)
T = np.empty(28)
for i in range(28):
T[i] = rng[np.where(f1s[:, i] == np.max(f1s[:, i]))[0][0]]
if T[i] < 0.01:
T[i] = 0.01
print('Probability threshold maximizing CV F1-score for each class:')
print(T)
def generateSubmitFile(model=None):
pathsTest, labelsTest = getTestDataset()
if not model:
model = load_model('./base.model',
custom_objects={'f1': f1}) # , 'f1_loss': f1_loss})
paths, labels = getTrainDataset()
testg = ProteinDataGenerator(pathsTest, labelsTest, BATCH_SIZE, SHAPE)
lastTrainIndex = int((1 - VAL_RATIO) * paths.shape[0])
pathsVal = paths[lastTrainIndex:]
labelsVal = labels[lastTrainIndex:]
vg = ProteinDataGenerator(pathsVal,
labelsVal,
BATCH_SIZE,
SHAPE,
use_cache=True,
shuffle=False)
submit = pd.read_csv(sample_dir)
P = np.zeros((pathsTest.shape[0], 28))
for i in tqdm(range(len(testg))):
images, labels = testg[i]
score = model.predict(images)
P[i * BATCH_SIZE:i * BATCH_SIZE + score.shape[0]] = score
rng = np.arange(0, 1, 0.001)
f1s = np.zeros((rng.shape[0], 28))
lastFullValPred = np.empty((0, 28))
np.random.seed(SEED)
keys = np.arange(paths.shape[0], dtype=np.int)
np.random.shuffle(keys)
fullValGen = vg
lastFullValLabels = np.empty((0, 28))
for i in tqdm(range(len(fullValGen))):
im, lbl = fullValGen[i]
scores = model.predict(im)
lastFullValPred = np.append(lastFullValPred, scores, axis=0)
lastFullValLabels = np.append(lastFullValLabels, lbl, axis=0)
print(lastFullValPred.shape, lastFullValLabels.shape)
for j, t in enumerate(tqdm(rng)):
for i in range(28):
p = np.array(lastFullValPred[:, i] > t, dtype=np.int8)
scoref1 = off1(lastFullValLabels[:, i], p, average='binary')
f1s[j, i] = scoref1
PP = np.array(P)
prediction = []
T = np.empty(28)
for i in range(28):
T[i] = rng[np.where(f1s[:, i] == np.max(f1s[:, i]))[0][0]]
print('Probability threshold maximizing CV F1-score for each class:')
print(T)
for row in tqdm(range(submit.shape[0])):
str_label = ''
for col in range(PP.shape[1]):
if (PP[row, col] < T[col]):
str_label += ''
else:
str_label += str(col) + ' '
prediction.append(str_label.strip())
submit['Predicted'] = np.array(prediction)
submit.to_csv('submit.csv', index=False)
