Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<prepare_output>
DEVICE = "TPU"
CFG = dict(
 net_count = 7,
batch_size = 16,
read_size = 256,
crop_size = 250,
net_size = 224,
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 = 12,
rot = 180.0,
shr = 2.0,
hzoom = 8.0,
wzoom = 8.0,
hshift = 8.0,
wshift = 8.0,
optimizer = 'adam',
label_smooth_fac = 0.05,
tta_steps = 25
)
bucket = storage.Bucket(storage_client, name=BUCKET_NAME)
 if not bucket.exists() :
bucket.create(location=BUCKET_REGION )<load_pretrained>
!pip install -q efficientnet
def upload_blob(bucket_name, source_file_name, destination_blob_name):

 bucket = storage_client.get_bucket(bucket_name)
 blob = bucket.blob(destination_blob_name)
 blob.upload_from_filename(source_file_name)
 print('File {} uploaded to {}.'.format(
 source_file_name,
destination_blob_name))
def download_to_kaggle(bucket_name,destination_directory,file_name,prefix=None):

 os.makedirs(destination_directory, exist_ok = True)
 full_file_path = os.path.join(destination_directory, file_name)
 blobs = storage_client.list_blobs(bucket_name,prefix=prefix)
 for blob in blobs:
blob.download_to_filename(full_file_path )<save_to_csv>
random.seed(a=42)
train_df[['id','text','target']].to_csv('/kaggle/working/train.csv', index=False)
 test_df[['id','text']].to_csv('/kaggle/working/test.csv', index=False )<load_pretrained>
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')) )
upload_blob(BUCKET_NAME, '/kaggle/working/train.csv', 'train.csv')
 upload_blob(BUCKET_NAME, '/kaggle/working/test.csv', 'test.csv' )<create_dataframe>
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}' )
dataset_display_name = 'tweet_disaster_cleaned'
new_dataset = False
try:
dataset = tables_client.get_dataset(dataset_display_name=dataset_display_name)
 except:
new_dataset = True
dataset = tables_client.create_dataset(dataset_display_name )<train_model>
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] )
if new_dataset:
gcs_input_uris = ['gs://' + BUCKET_NAME + '/train.csv']
import_data_operation = tables_client.import_data(
 dataset=dataset,
gcs_input_uris=gcs_input_uris 
)
 print('Dataset
import_data_operation.result()<define_variables>
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 )
ID_COLUMN = 'id'<choose_model_class>
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
TRAIN_BUDGET = 1000
model = None
model_display_name = 'tweet_disaster_model_clean'
try:
model = tables_client.get_model(model_display_name=model_display_name)
 except:
response = tables_client.create_model(
 model_display_name,
dataset=dataset,
train_budget_milli_node_hours=TRAIN_BUDGET,
exclude_column_spec_names=[TARGET_COLUMN,ID_COLUMN] 
)
 print('Create model operation: {}'.format(response.operation))
model = response.result()
print(model )<define_variables>
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 )
gcs_input_uris = 'gs://' + BUCKET_NAME + '/test.csv'
gcs_output_uri_prefix = 'gs://' + BUCKET_NAME + '/predictions'
batch_predict_response = tables_client.batch_predict(
 model=model,
gcs_input_uris=gcs_input_uris,
gcs_output_uri_prefix=gcs_output_uri_prefix, 
)
 print('Batch prediction operation: {}'.format(batch_predict_response.operation))
batch_predict_result = batch_predict_response.result()
batch_predict_response.metadata<load_pretrained>
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 )
gcs_output_folder = batch_predict_response.metadata.batch_predict_details.output_info.gcs_output_directory.replace('gs://' + BUCKET_NAME + '/','')
 download_to_kaggle(BUCKET_NAME,'/kaggle/working','submissions.csv', prefix=gcs_output_folder )<load_from_csv>
ds = get_dataset(files_test, CFG, labeled=False ).unbatch().take(12*5)
 show_dataset(64, 12, 5, ds )
preds_df = pd.read_csv("/kaggle/working/submissions.csv")
 preds_df = preds_df.sort_values(by=['id'])
 preds_df['target'] =(preds_df['target_1_score'] >= 0.5 ).astype(int )<save_to_csv>
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
preds_df[['id','target']].to_csv("submission.csv", index=False )<feature_engineering>
def get_model(cfg):
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 = []
for i in range(cfg['net_count']):
constructor = getattr(efn, f'EfficientNetB{i}')
 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
tfidf_vectorizer = feature_extraction.text.TfidfVectorizer(ngram_range =(1,2), stop_words='english',strip_accents='unicode' )<categorify>
def compile_new_model(cfg):
with strategy.scope() :
model = get_model(cfg)
 losses = [tf.keras.losses.BinaryCrossentropy(label_smoothing = cfg['label_smooth_fac'])
 for i in range(cfg['net_count'])]
model.compile(
 optimizer = cfg['optimizer'],
loss = losses,
metrics = [tf.keras.metrics.AUC(name='auc')])
 return model
train_vectors = tfidf_vectorizer.fit_transform(train_df["text"])
 test_vectors = tfidf_vectorizer.transform(test_df["text"] )<find_best_model_class>
ds_train = get_dataset(files_train, CFG, augment=True, shuffle=True, repeat=True)
 ds_train = ds_train.map(lambda img, label:(img, tuple([label] * CFG['net_count'])))
 steps_train = count_data_items(files_train)/(CFG['batch_size'] * REPLICAS)
 model = compile_new_model(CFG)
 history = model.fit(ds_train,
verbose = 1,
steps_per_epoch = steps_train,
epochs = CFG['epochs'],
callbacks = [get_lr_callback(CFG)] )
feature_cols = ['keyword', 'location']
X = train_df[feature_cols]
y = train_df.target
one_hot_encoded_training_predictors = pd.get_dummies(X)
 clf = RandomForestClassifier(n_estimators = 100)
 scores = model_selection.cross_val_score(clf, one_hot_encoded_training_predictors, y, cv=5, scoring="f1")
 scores<train_model>
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)
 probs = model.predict(ds_testAug, verbose=1, steps=steps)
 probs = np.stack(probs)
 probs = probs[:,:cnt_test * CFG['tta_steps']]
probs = np.stack(np.split(probs, CFG['tta_steps'], axis=1), axis=1)
 probs = np.mean(probs, axis=1 )
clf.fit(one_hot_encoded_training_predictors, y )<choose_model_class>
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())] )
clf = linear_model.RidgeClassifier() 
<compute_train_metric>
for i in range(CFG["net_count"]):
submission = pd.DataFrame(dict(
 image_name = image_names,
target = probs[i,:,0]))
submission = submission.sort_values('image_name')
 submission.to_csv(f'submission_model_{i}.csv', index=False )
<train_model><EOS> 
submission = pd.DataFrame(dict(
 image_name = image_names,
target = np.mean(probs[:,:,0], axis=0)))
 submission = submission.sort_values('image_name')
 submission.to_csv('submission_models_blended.csv', index=False )