Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<define_search_model>
DEVICE = "TPU"
CFG = dict(
 net_count = 5,
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
)
ridge = make_pipeline(RobustScaler() , RidgeCV(alphas=alphas_rid, cv=kfolds))
lasso = make_pipeline(RobustScaler() , LassoCV(max_iter=1e7, alphas=alphas_las, random_state=42, cv=kfolds))
elasticnet = make_pipeline(RobustScaler() , ElasticNetCV(max_iter=1e7, alphas=e_alphas, cv=kfolds, l1_ratio=e_l1ratio))
svr = make_pipeline(RobustScaler() , SVR(C= 20, epsilon= 0.008, gamma=0.0003,))
lightgbm = LGBMRegressor(objective='regression',
num_leaves=4,
learning_rate=0.01,
n_estimators=5000,
max_bin=200,
bagging_fraction=0.75,
bagging_freq=5,
bagging_seed=7,
feature_fraction=0.2,
feature_fraction_seed=7,
verbose=-1, 
)
 xgboost = XGBRegressor(learning_rate=0.01,n_estimators=3460,
max_depth=3, min_child_weight=0,
gamma=0, subsample=0.7,
colsample_bytree=0.7,
objective='reg:squarederror', nthread=-1,
scale_pos_weight=1, seed=27,
reg_alpha=0.00006)
 stack_gen = StackingCVRegressor(regressors=(ridge, lasso, elasticnet, xgboost, lightgbm),
meta_regressor=xgboost,
use_features_in_secondary=True )<compute_train_metric>
!pip install -q efficientnet
score = rmse_cv(ridge)
 print("Ridge: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = rmse_cv(lasso)
 print("Lasso: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,
score = rmse_cv(elasticnet)
 print("Elastic Net: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = rmse_cv(svr)
 print("SVR: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = rmse_cv(lightgbm)
 print("lightgmb: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = rmse_cv(xgboost)
 print("XGBoost: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() , )<train_model>
random.seed(a=42)
stack_gen_model = stack_gen.fit(np.array(X), np.array(y))
elastic_model_full_data = elasticnet.fit(X, y)
 lasso_model_full_data = lasso.fit(X, y)
 ridge_model_full_data = ridge.fit(X, y)
 svr_model_full_data = svr.fit(X, y)
 xgb_model_full_data = xgboost.fit(X, y)
 lgb_model_full_data = lightgbm.fit(X, y )<predict_on_test>
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-256x256')
 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')) )
def blend_models_predict(X):
return(( 0.1 * elastic_model_full_data.predict(X)) + \ 
(0.05 * lasso_model_full_data.predict(X)) + \ 
(0.2 * ridge_model_full_data.predict(X)) + \ 
(0.1 * svr_model_full_data.predict(X)) + \ 
(0.15 * xgb_model_full_data.predict(X)) + \ 
(0.1 * lgb_model_full_data.predict(X)) + \ 
(0.3 * stack_gen_model.predict(np.array(X))))<compute_test_metric>
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}' )
print('RMSLE score on train data:')
 print(rmse(y, blend_models_predict(X)) )<load_from_csv>
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] )
print('Predict submission')
 submission = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/sample_submission.csv")
 submission.iloc[:,1] = np.floor(np.expm1(blend_models_predict(X_sub)) )<load_from_csv>
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 )
print('Blend with Top Kernels submissions
')
 sub_1 = pd.read_csv(".. /input/top-sub/S1.csv")
 sub_2 = pd.read_csv(".. /input/top-sub/S2.csv")
 sub_3 = pd.read_csv(".. /input/top-sub/S3.csv")
 submission.iloc[:,1] = np.floor(( 0.25 * np.floor(np.expm1(blend_models_predict(X_sub)))) + 
(0.25 * sub_1.iloc[:,1])+ 
(0.25 * sub_2.iloc[:,1])+ 
(0.25 * sub_3.iloc[:,1]))<save_to_csv>
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
q1 = submission['SalePrice'].quantile(0.005)
 q2 = submission['SalePrice'].quantile(0.995)
 submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*0.77)
 submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*1.1)
 submission.to_csv("submission.csv", index=False )<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 )
train_dir = ".. /input/train.csv"
test_dir = ".. /input/test.csv"<load_from_csv>
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 )
df = pd.read_csv(train_dir)
 test_df = pd.read_csv(test_dir)
 print("Total number of instance : ",len(df))
df.isna().sum()<drop_column>
ds = get_dataset(files_test, CFG, labeled=False ).unbatch().take(12*5)
 show_dataset(64, 12, 5, ds )
df.drop(["Cabin"], axis = 1, inplace = True)
 test_df.drop(["Cabin"], axis = 1, inplace = True)
 df.drop(["Ticket"], axis = 1, inplace = True)
 test_df.drop(["Ticket"], axis = 1, inplace = True)
 df.info()
df.head(10 )<drop_column>
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
df.drop("PassengerId", axis = 1, inplace = True)
 test_df.drop("PassengerId", axis = 1, inplace = True )<categorify>
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
df["Sex"].replace("male", 0, inplace = True)
 test_df["Sex"].replace("male", 0, inplace = True)
 df["Sex"].replace("female", 1, inplace = True)
 test_df["Sex"].replace("female", 1, inplace = True)
 df["Embarked"].replace(["S","C","Q"],[0,1,2], inplace = True)
 test_df["Embarked"].replace(["S","C","Q"],[0,1,2], inplace = True )<feature_engineering>
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
def create_family_ranges(df):
familysize = []
for members in df["n_fam_mem"]:
if members == 0:
familysize.append(0)
 elif members > 0 and members <=4:
familysize.append(1)
 elif members > 4:
familysize.append(2)
 return familysize
famsize = create_family_ranges(df)
 df["familysize"] = famsize
test_famsize = create_family_ranges(test_df)
 test_df["familysize"] = test_famsize<categorify>
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)] )
def age_to_int(df):
agelist = df["Age"].values.tolist()
for i in range(len(agelist)) :
if agelist[i] < 18 and agelist[i] >= 0:
agelist[i] = 0
elif agelist[i] >= 18 and agelist[i] < 60:
agelist[i] = 1
elif agelist[i]>=60 and agelist[i]<200:
agelist[i] = 2
else:
agelist[i] = -1
ageint = pd.DataFrame(agelist)
 return ageint<data_type_conversions>
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 )
ageint = age_to_int(df)
 df["Ageint"] = ageint
df.drop("Age", axis = 1, inplace = True)
 test_ageint = age_to_int(test_df)
 test_df["Ageint"] = test_ageint
test_df.drop("Age", axis = 1, inplace = True)
<categorify>
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())] )
def conv_fare_ranges(df):
fare_ranges = []
for fare in df.actual_fare:
if fare < 7:
fare_ranges.append(0)
 elif fare >=7 and fare < 14:
fare_ranges.append(1)
 elif fare >=14 and fare < 30:
fare_ranges.append(2)
 elif fare >=30 and fare < 50:
fare_ranges.append(3)
 elif fare >=50:
fare_ranges.append(4)
 return fare_ranges
fare_ranges = conv_fare_ranges(df)
 df["fare_ranges"] = fare_ranges
test_fare_ranges = conv_fare_ranges(test_df)
 test_df["fare_ranges"] = test_fare_ranges<categorify>
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 )
<count_values><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 )