Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<define_variables>
DEVICE = "TPU"
CFG = dict(
 batch_size = 16,
read_size = 512,
crop_size = 464,
net_size = 464,
LR_START = 0.000005,
LR_MAX = 0.000020,
LR_MIN = 0.000001,
LR_RAMPUP_EPOCHS = 5,
LR_SUSTAIN_EPOCHS = 0,
LR_EXP_DECAY = 0.8,
epochs = 20,
rot = 180.0,
shr = 1.5,
hzoom = 6.0,
wzoom = 6.0,
hshift = 6.0,
wshift = 6.0,
optimizer = 'adam',
label_smooth_fac = 0.05,
tta_steps = 25
)
passengerID = unlabelled[['PassengerId']]<concatenate>
!pip install -q efficientnet
data = pd.concat([labelled, unlabelled], axis= 0, sort= False )<create_dataframe>
random.seed(a=42)
class_mean_age = data.pivot_table(values='Age', index='Pclass', aggfunc='median' )<count_missing_values>
BASEPATH = ".. /input/siim-isic-melanoma-classification"
df_train = pd.read_csv(os.path.join(BASEPATH, 'train.csv'))
df_test = pd.read_csv(os.path.join(BASEPATH, 'test.csv'))
df_sub = pd.read_csv(os.path.join(BASEPATH, 'sample_submission.csv'))
GCS_PATH = KaggleDatasets().get_gcs_path('melanoma-512x512')
 files_train = np.sort(np.array(tf.io.gfile.glob(GCS_PATH + '/train*.tfrec')))
 files_test = np.sort(np.array(tf.io.gfile.glob(GCS_PATH + '/test*.tfrec')) )
null_age = data['Age'].isnull()<feature_engineering>
if DEVICE == "TPU":
print("connecting to TPU...")
 try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
 except ValueError:
print("Could not connect to TPU")
 tpu = None
if tpu:
try:
print("initializing TPU...")
 tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 print("TPU initialized")
 except _:
print("failed to initialize TPU")
 else:
DEVICE = "GPU"
if DEVICE != "TPU":
print("Using default strategy for CPU and single GPU")
 strategy = tf.distribute.get_strategy()
if DEVICE == "GPU":
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
 AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
print(f'REPLICAS: {REPLICAS}' )
data.loc[null_age,'Age'] = data.loc[null_age,'Pclass'].apply(lambda x: class_mean_age.loc[x] )<count_missing_values>
def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = math.pi * rotation / 180.
 shear = math.pi * shear / 180.
 def get_3x3_mat(lst):
return tf.reshape(tf.concat([lst],axis=0), [3,3])
 c1 = tf.math.cos(rotation)
 s1 = tf.math.sin(rotation)
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 rotation_matrix = get_3x3_mat([c1, s1, zero,
-s1, c1, zero,
zero, zero, one])
 c2 = tf.math.cos(shear)
 s2 = tf.math.sin(shear)
 shear_matrix = get_3x3_mat([one, s2, zero,
zero, c2, zero,
zero, zero, one])
 zoom_matrix = get_3x3_mat([one/height_zoom, zero, zero,
zero, one/width_zoom, zero,
zero, zero, one])
 shift_matrix = get_3x3_mat([one, zero, height_shift,
zero, one, width_shift,
zero, zero, one])
 return K.dot(K.dot(rotation_matrix, shear_matrix),
K.dot(zoom_matrix, shift_matrix))
def transform(image, cfg):
DIM = cfg["read_size"]
XDIM = DIM%2
rot = cfg['rot'] * tf.random.normal([1], dtype='float32')
 shr = cfg['shr'] * tf.random.normal([1], dtype='float32')
 h_zoom = 1.0 + tf.random.normal([1], dtype='float32')/ cfg['hzoom']
w_zoom = 1.0 + tf.random.normal([1], dtype='float32')/ cfg['wzoom']
h_shift = cfg['hshift'] * tf.random.normal([1], dtype='float32')
 w_shift = cfg['wshift'] * tf.random.normal([1], dtype='float32')
 m = get_mat(rot,shr,h_zoom,w_zoom,h_shift,w_shift)
 x = tf.repeat(tf.range(DIM//2, -DIM//2,-1), DIM)
 y = tf.tile(tf.range(-DIM//2, DIM//2), [DIM])
 z = tf.ones([DIM*DIM], dtype='int32')
 idx = tf.stack([x,y,z])
 idx2 = K.dot(m, tf.cast(idx, dtype='float32'))
idx2 = K.cast(idx2, dtype='int32')
 idx2 = K.clip(idx2, -DIM//2+XDIM+1, DIM//2)
 idx3 = tf.stack([DIM//2-idx2[0,], DIM//2-1+idx2[1,]])
 d = tf.gather_nd(image, tf.transpose(idx3))
return tf.reshape(d,[DIM, DIM,3] )
data.Age.isnull().sum()<create_dataframe>
def read_labeled_tfrecord(example):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
'patient_id' : tf.io.FixedLenFeature([], tf.int64),
'sex' : tf.io.FixedLenFeature([], tf.int64),
'age_approx' : tf.io.FixedLenFeature([], tf.int64),
'anatom_site_general_challenge': tf.io.FixedLenFeature([], tf.int64),
'diagnosis' : tf.io.FixedLenFeature([], tf.int64),
'target' : tf.io.FixedLenFeature([], tf.int64)
 }
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['target']
def read_unlabeled_tfrecord(example, return_image_name):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['image_name'] if return_image_name else 0
def prepare_image(img, cfg=None, augment=True):
img = tf.image.decode_jpeg(img, channels=3)
 img = tf.image.resize(img, [cfg['read_size'], cfg['read_size']])
 img = tf.cast(img, tf.float32)/ 255.0
if augment:
img = transform(img, cfg)
 img = tf.image.random_crop(img, [cfg['crop_size'], cfg['crop_size'], 3])
 img = tf.image.random_flip_left_right(img)
 img = tf.image.random_hue(img, 0.01)
 img = tf.image.random_saturation(img, 0.7, 1.3)
 img = tf.image.random_contrast(img, 0.8, 1.2)
 img = tf.image.random_brightness(img, 0.1)
 else:
img = tf.image.central_crop(img, cfg['crop_size'] / cfg['read_size'])
 img = tf.image.resize(img, [cfg['net_size'], cfg['net_size']])
 img = tf.reshape(img, [cfg['net_size'], cfg['net_size'], 3])
 return img
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1))
for filename in filenames]
return np.sum(n )
class_mean_fare = data.pivot_table(values= 'Fare', index= 'Pclass', aggfunc='median' )<count_missing_values>
def get_dataset(files, cfg, augment = False, shuffle = False, repeat = False,
labeled=True, return_image_names=True):
ds = tf.data.TFRecordDataset(files, num_parallel_reads=AUTO)
 ds = ds.cache()
if repeat:
ds = ds.repeat()
if shuffle:
ds = ds.shuffle(1024*8)
 opt = tf.data.Options()
opt.experimental_deterministic = False
ds = ds.with_options(opt)
 if labeled:
ds = ds.map(read_labeled_tfrecord, num_parallel_calls=AUTO)
 else:
ds = ds.map(lambda example: read_unlabeled_tfrecord(example, return_image_names),
num_parallel_calls=AUTO)
 ds = ds.map(lambda img, imgname_or_label:(prepare_image(img, augment=augment, cfg=cfg),
imgname_or_label),
num_parallel_calls=AUTO)
 ds = ds.batch(cfg['batch_size'] * REPLICAS)
 ds = ds.prefetch(AUTO)
 return ds
null_fare = data['Fare'].isnull()<feature_engineering>
def show_dataset(thumb_size, cols, rows, ds):
mosaic = PIL.Image.new(mode='RGB', size=(thumb_size*cols +(cols-1),
thumb_size*rows +(rows-1)))
 for idx, data in enumerate(iter(ds)) :
img, target_or_imgid = data
ix = idx % cols
iy = idx // cols
img = np.clip(img.numpy() * 255, 0, 255 ).astype(np.uint8)
 img = PIL.Image.fromarray(img)
 img = img.resize(( thumb_size, thumb_size), resample=PIL.Image.BILINEAR)
 mosaic.paste(img,(ix*thumb_size + ix,
iy*thumb_size + iy))
display(mosaic)
 ds = get_dataset(files_train, CFG ).unbatch().take(12*5)
 show_dataset(64, 12, 5, ds )
data.loc[null_fare, 'Fare'] = data.loc[null_fare, 'Pclass'].apply(lambda x: class_mean_fare.loc[x] )<count_missing_values>
ds = tf.data.TFRecordDataset(files_train, num_parallel_reads=AUTO)
 ds = ds.take(1 ).cache().repeat()
ds = ds.map(read_labeled_tfrecord, num_parallel_calls=AUTO)
 ds = ds.map(lambda img, target:(prepare_image(img, cfg=CFG, augment=True), target),
num_parallel_calls=AUTO)
 ds = ds.take(12*5)
 ds = ds.prefetch(AUTO)
 show_dataset(64, 12, 5, ds )
data.Fare.isnull().sum()<count_values>
ds = get_dataset(files_test, CFG, labeled=False ).unbatch().take(12*5)
 show_dataset(64, 12, 5, ds )
data.Embarked.value_counts()<data_type_conversions>
def get_lr_callback(cfg):
lr_start = cfg['LR_START']
lr_max = cfg['LR_MAX'] * strategy.num_replicas_in_sync
lr_min = cfg['LR_MIN']
lr_ramp_ep = cfg['LR_RAMPUP_EPOCHS']
lr_sus_ep = cfg['LR_SUSTAIN_EPOCHS']
lr_decay = cfg['LR_EXP_DECAY']
def lrfn(epoch):
if epoch < lr_ramp_ep:
lr =(lr_max - lr_start)/ lr_ramp_ep * epoch + lr_start
elif epoch < lr_ramp_ep + lr_sus_ep:
lr = lr_max
else:
lr =(lr_max - lr_min)* lr_decay**(epoch - lr_ramp_ep - lr_sus_ep)+ lr_min
return lr
lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=False)
 return lr_callback
data['Embarked'] = data.Embarked.fillna('S' )<count_missing_values>
def get_model(cfg, model):
model_input = tf.keras.Input(shape=(cfg['net_size'], cfg['net_size'], 3), name='imgIn')
 dummy = tf.keras.layers.Lambda(lambda x:x )(model_input)
 outputs = []
constructor = getattr(efn, model)
 x = constructor(include_top=False, weights='imagenet',
input_shape=(cfg['net_size'], cfg['net_size'], 3),
pooling='avg' )(dummy)
 x = tf.keras.layers.Dense(1, activation='sigmoid' )(x)
 outputs.append(x)
 model = tf.keras.Model(model_input, outputs, name='aNetwork')
 model.summary()
return model
data.Embarked.isnull().sum()<feature_engineering>
def compile_new_model(cfg, model):
with strategy.scope() :
model = get_model(cfg, model)
 losses = tf.keras.losses.BinaryCrossentropy(label_smoothing = cfg['label_smooth_fac'])
 model.compile(
 optimizer = cfg['optimizer'],
loss = losses,
metrics = [tf.keras.metrics.AUC(name='auc')])
 return model
data['Title'] = data.Name.apply(lambda x : x[x.find(',')+2:x.find('.')] )<count_values>
ds_train = get_dataset(files_train, CFG, augment=True, shuffle=True, repeat=True)
 ds_train = ds_train.map(lambda img, label:(img, tuple([label])))
 steps_train = count_data_items(files_train)/(CFG['batch_size'] * REPLICAS)
 model_B6 = compile_new_model(CFG, 'EfficientNetB6')
 print("
Begin Training Models")
 Done Training model_B5
")
 model_B6.load_weights('/kaggle/input/melanoma-weights/enetb6.h5')
 print("
Done Training model_B6
")
 model_B6.save_weights('enetb6.h5')
 print('Weights saved!')
 Done Training model_B7
" )
data.Title.value_counts()<count_values>
CFG['batch_size'] = 256
cnt_test = count_data_items(files_test)
 steps = cnt_test /(CFG['batch_size'] * REPLICAS)* CFG['tta_steps']
ds_testAug = get_dataset(files_test, CFG, augment=True, repeat=True,
labeled=False, return_image_names=False)
 preds_B6 = model_B6.predict(ds_testAug, verbose=1, steps=steps)
rare_titles =(data['Title'].value_counts() < 10 )<feature_engineering>
preds = np.stack(preds_B6)
 preds = preds[:,:cnt_test* CFG['tta_steps']]
preds = preds[:df_test.shape[0]*CFG['tta_steps']]
preds = np.stack(np.split(preds, CFG['tta_steps']),axis=1)
 preds = np.mean(preds, axis=1)
 preds_B6 = preds.reshape(-1 )
data['Title'] = data['Title'].apply(lambda x : 'Other' if rare_titles.loc[x] == True else x )<feature_engineering>
preds = preds_B6
data['FamilySize'] = data['SibSp'] + data['Parch'] + 1<feature_engineering>
ds = get_dataset(files_test, CFG, augment=False, repeat=False,
labeled=False, return_image_names=True)
 image_names = np.array([img_name.numpy().decode("utf-8")
 for img, img_name in iter(ds.unbatch())] )
<feature_engineering><EOS> 
submission = pd.DataFrame(dict(
 image_name = image_names,
target = preds))
submission = submission.sort_values('image_name')
 submission.to_csv(f'submission_ensemble.csv', index=False)
 submission.head()