Kaggle competition: Petals to the Metal - Flower Classification on TPU
<SOS> metric: macrofscore Kaggle data source: petals-to-the-metal-flower-classification-on-tpu<define_search_model>
print(tf.__version__ )
alphas_alt = [14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5]
alphas2 = [5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008]
e_alphas = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007]
e_l1ratio = [0.8, 0.85, 0.9, 0.95, 0.99, 1]
ridge = make_pipeline(RobustScaler() ,
RidgeCV(alphas=alphas_alt, cv=kfolds,))
lasso = make_pipeline(RobustScaler() ,
LassoCV(max_iter=1e7, alphas=alphas2,
random_state=42, cv=kfolds))
elasticnet = make_pipeline(RobustScaler() ,
ElasticNetCV(max_iter=1e7, alphas=e_alphas,
cv=kfolds, random_state=42, l1_ratio=e_l1ratio))
svr = make_pipeline(RobustScaler() ,
SVR(C= 20, epsilon= 0.008, gamma=0.0003,))
gbr = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,
max_depth=4, max_features='sqrt',
min_samples_leaf=15, min_samples_split=10,
loss='huber', random_state =42)
 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:linear', nthread=-1,
scale_pos_weight=1, seed=27,
reg_alpha=0.00006, random_state=42)
 stack_gen = StackingCVRegressor(regressors=(ridge, lasso, elasticnet,
gbr, xgboost, lightgbm),
meta_regressor=xgboost,
use_features_in_secondary=True)
<compute_test_metric>
def seed_everything(seed=0):
np.random.seed(seed)
 tf.random.set_seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 os.environ['TF_DETERMINISTIC_OPS'] = '1'
sns.set()
SEED = 17
seed_everything(SEED)
 warnings.filterwarnings("ignore" )
print('TEST score on CV')
 score = cv_rmse(ridge)
 print("Kernel Ridge score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = cv_rmse(lasso)
 print("Lasso score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = cv_rmse(elasticnet)
 print("ElasticNet score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = cv_rmse(svr)
 print("SVR score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = cv_rmse(lightgbm)
 print("Lightgbm score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = cv_rmse(gbr)
 print("GradientBoosting score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
 score = cv_rmse(xgboost)
 print("Xgboost score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()), datetime.now() ,)
<train_model>
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
 except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 else:
strategy = tf.distribute.get_strategy()
print("REPLICAS: ", strategy.num_replicas_in_sync )
print('START Fit')
 print(datetime.now() , 'StackingCVRegressor')
 stack_gen_model = stack_gen.fit(np.array(X), np.array(y))
print(datetime.now() , 'elasticnet')
 elastic_model_full_data = elasticnet.fit(X, y)
 print(datetime.now() , 'lasso')
 lasso_model_full_data = lasso.fit(X, y)
 print(datetime.now() , 'ridge')
 ridge_model_full_data = ridge.fit(X, y)
 print(datetime.now() , 'svr')
 svr_model_full_data = svr.fit(X, y)
 print(datetime.now() , 'GradientBoosting')
 gbr_model_full_data = gbr.fit(X, y)
 print(datetime.now() , 'xgboost')
 xgb_model_full_data = xgboost.fit(X, y)
 print(datetime.now() , 'lightgbm')
 lgb_model_full_data = lightgbm.fit(X, y)
<predict_on_test>
HEIGHT, WIDTH =(512, 512)
 CHANNELS = 3
N_CLASSES = 104
GCS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')+ '/tfrecords-jpeg-%sx%s' %(HEIGHT, WIDTH)
 TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
 VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')
 CLASSES = [
'pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',
'wild geranium', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood',
'globe thistle', 'snapdragon', "colt's foot", 'king protea', 'spear thistle',
'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily',
'balloon flower', 'giant white arum lily', 'fire lily', 'pincushion flower',
'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy',
'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william',
'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly',
'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip',
'lenten rose', 'barberton daisy', 'daffodil', 'sword lily', 'poinsettia',
'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy',
'common dandelion', 'petunia', 'wild pansy', 'primula', 'sunflower',
'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia',
'pink-yellow dahlia', 'cautleya spicata', 'japanese anemone', 'black-eyed susan',
'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris',
'windflower', 'tree poppy', 'gazania', 'azalea', 'water lily', 'rose',
'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily',
'anthurium', 'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose',
'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily',
'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea',
'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower',
'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose']
def blend_models_predict(X):
return(( 0.1 * elastic_model_full_data.predict(X)) + \ 
(0.1 * lasso_model_full_data.predict(X)) + \ 
(0.1 * ridge_model_full_data.predict(X)) + \ 
(0.1 * svr_model_full_data.predict(X)) + \ 
(0.1 * gbr_model_full_data.predict(X)) + \ 
(0.15 * xgb_model_full_data.predict(X)) + \ 
(0.1 * lgb_model_full_data.predict(X)) + \ 
(0.25 * stack_gen_model.predict(np.array(X))))
print('RMSLE score on train data:')
 print(rmsle(y, blend_models_predict(X)))
 print('Predict submission', datetime.now() ,)
 submission = pd.read_csv(".. /input/sample_submission.csv")
 submission.iloc[:,1] = np.floor(np.expm1(blend_models_predict(X_sub)) )<save_to_csv>
AUTO = tf.data.experimental.AUTOTUNE
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [HEIGHT, WIDTH, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"class": tf.io.FixedLenFeature([], tf.int64),
}
example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 label = tf.cast(example['class'], tf.int32)
 return image, label
def read_unlabeled_tfrecord(example):
UNLABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"id": tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum
def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset
def data_augment(image, label):
image = tf.image.random_crop(image, size=[480, 480, CHANNELS])
 image = tf.image.random_flip_left_right(image, seed=SEED)
 return image, label
def get_training_dataset() :
dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
 dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_validation_dataset(ordered=False):
dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset
def get_train_valid_datasets() :
dataset = load_dataset(TRAINING_FILENAMES + VALIDATION_FILENAMES, labeled=True)
 dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n )
submission.to_csv("House_price_submission_v54.csv", index=False)
 print('Save submission', datetime.now() , )<load_from_csv>
np.set_printoptions(threshold=15, linewidth=80)
 def batch_to_numpy_images_and_labels(data):
images, labels = data
numpy_images = images.numpy()
numpy_labels = labels.numpy()
if numpy_labels.dtype == object:
numpy_labels = [None for _ in enumerate(numpy_images)]
return numpy_images, numpy_labels
def title_from_label_and_target(label, correct_label):
if correct_label is None:
return CLASSES[label], True
correct =(label == correct_label)
 return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '',
CLASSES[correct_label] if not correct else ''), correct
def display_one_flower(image, title, subplot, red=False, titlesize=16):
plt.subplot(*subplot)
 plt.axis('off')
 plt.imshow(image)
 if len(title)> 0:
plt.title(title, fontsize=int(titlesize)if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))
return(subplot[0], subplot[1], subplot[2]+1)
 def display_batch_of_images(databatch, predictions=None):

 images, labels = batch_to_numpy_images_and_labels(databatch)
 if labels is None:
labels = [None for _ in enumerate(images)]
rows = int(math.sqrt(len(images)))
 cols = len(images)//rows
FIGSIZE = 13.0
SPACING = 0.1
subplot=(rows,cols,1)
 if rows < cols:
plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))
else:
plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))
for i,(image, label)in enumerate(zip(images[:rows*cols], labels[:rows*cols])) :
title = '' if label is None else CLASSES[label]
correct = True
if predictions is not None:
title, correct = title_from_label_and_target(predictions[i], label)
 dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3
subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
 plt.tight_layout()
if label is None and predictions is None:
plt.subplots_adjust(wspace=0, hspace=0)
 else:
plt.subplots_adjust(wspace=SPACING, hspace=SPACING)
 plt.show()
train= pd.read_csv(".. /input/train.csv")
 test =pd.read_csv(".. /input/test.csv" )<count_duplicates>
NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
 NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 print('Number of training images %d' % NUM_TRAINING_IMAGES)
 print('Number of validation images %d' % NUM_VALIDATION_IMAGES)
 print('Number of test images %d' % NUM_TEST_IMAGES )
print("Train : "+str(train.shape))
idUn = len(set(train.Id))
idTo = train.shape[0]
idDup = idTo - idUn
print(str(idDup)+" duplicates available in this dataset" )<drop_column>
dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
 one_element = tf.data.Dataset.from_tensors(next(iter(dataset)))
 augmented_element = one_element.repeat().map(data_augment ).batch(25)
 display_batch_of_images(next(iter(augmented_element)) )
train = train.drop(train[(train['GrLivArea']>4000)&(train['SalePrice']<300000)].index)
<prepare_x_and_y>
class BiTModel(tf.keras.Model):
def __init__(self, num_classes, module):
super().__init__()
self.num_classes = num_classes
self.head = tf.keras.layers.Dense(num_classes, kernel_initializer='zeros')
 self.bit_model = module
def call(self, images):
bit_embedding = self.bit_model(images)
 return self.head(bit_embedding )
train_n = train.shape[0]
test_n = test.shape[0]
y_train = train.SalePrice.values
y_test = train['SalePrice']
all_data = pd.concat(( train,test),sort=False ).reset_index(drop = True)
 all_data.drop(['SalePrice'], axis=1, inplace = True)
 print("all_data size is : {}".format(all_data.shape))<sort_values>
LR = 3e-3
SCHEDULE_LENGTH = 500
STEPS_PER_EPOCH = 50
BATCH_SIZE = 32 * strategy.num_replicas_in_sync
LR = 0.003 * BATCH_SIZE / 512
SCHEDULE_LENGTH = SCHEDULE_LENGTH * 512 / BATCH_SIZE
EPOCHS = int(SCHEDULE_LENGTH / STEPS_PER_EPOCH )
all_data.isnull().sum().sort_values(ascending=False)
<sort_values>
with strategy.scope() :
GCS_PATH_TO_SAVEDMODEL = KaggleDatasets().get_gcs_path('bit-tf-hub-models')+ '/BiT TF Hub Models/bit_m-r101x1_1'
loaded_model = tf.saved_model.load(GCS_PATH_TO_SAVEDMODEL)
 model = BiTModel(num_classes=N_CLASSES, module=loaded_model )
all_data_na_values = all_data.isnull().sum()
all_data_na_values = all_data_na_values.drop(all_data_na_values[all_data_na_values == 0].index ).sort_values(ascending=False)[:30]
all_data_na =(all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index ).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio' :all_data_na,'Missing Values' :all_data_na_values})
 missing_data.head(20 )<categorify>
SCHEDULE_BOUNDARIES = [int(SCHEDULE_LENGTH * 0.3), int(SCHEDULE_LENGTH * 0.6), int(SCHEDULE_LENGTH * 0.9)]
lr_schedule = tf.keras.optimizers.schedules \ 
.PiecewiseConstantDecay(
 boundaries=SCHEDULE_BOUNDARIES, values=[LR, LR*0.1, LR*0.001, LR*0.0001] 
)
 optimizer = tf.keras.optimizers.SGD(learning_rate=lr_schedule, momentum=0.9)
 loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 model.compile(
 optimizer=optimizer,
loss = loss_fn,
metrics=['sparse_categorical_accuracy']
)
all_data['PoolQC'] = all_data['PoolQC'].fillna("None")
 all_data['MiscFeature'] = all_data['MiscFeature'].fillna("None")
 all_data["Alley"] = all_data["Alley"].fillna("None")
 all_data["Fence"] = all_data["Fence"].fillna("None")
 all_data["FireplaceQu"] = all_data["FireplaceQu"].fillna("None")
 all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median()))
for col in('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):
all_data[col] = all_data[col].fillna('None')
 for col in('GarageYrBlt', 'GarageArea', 'GarageCars'):
all_data[col] = all_data[col].fillna(0)
 for col in('BsmtCond', 'BsmtExposure', 'BsmtQual', 'BsmtFinType2', 'BsmtFinType1'):
all_data[col] = all_data[col].fillna('None')
 for col in('BsmtHalfBath', 'BsmtFullBath', 'TotalBsmtSF', 'BsmtUnfSF', 'BsmtFinSF2', 'BsmtFinSF1'):
all_data[col] = all_data[col].fillna(0)
 all_data["MasVnrType"] = all_data["MasVnrType"].fillna("None")
 all_data["MasVnrArea"] = all_data["MasVnrArea"].fillna(0)
 all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode() [0])
 all_data = all_data.drop(['Utilities'], axis=1)
 all_data["Functional"] = all_data["Functional"].fillna("Typ")
 all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode() [0])
 all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode() [0])
 all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode() [0])
 all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode() [0])
 all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode() [0])
<sort_values>
history = model.fit(x=get_training_dataset() ,
steps_per_epoch=STEPS_PER_EPOCH,
validation_data=get_validation_dataset() ,
epochs=EPOCHS, verbose=1 )
all_data_na_values = all_data.isnull().sum()
all_data_na_values = all_data_na_values.drop(all_data_na_values[all_data_na_values == 0].index ).sort_values(ascending=False)[:30]
all_data_na =(all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index ).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio' :all_data_na,'Missing Values' :all_data_na_values,'Data_type':all_data_na.dtype})
 missing_data.head() 
<data_type_conversions>
val_ds = get_validation_dataset(ordered=True)
 val_images_ds = val_ds.map(lambda image, label: image)
 val_labels_ds = val_ds.map(lambda image, label: label ).unbatch()
val_correct_labels = next(iter(val_labels_ds.batch(NUM_VALIDATION_IMAGES)) ).numpy()
test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image )
all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)
 all_data['OverallCond'] = all_data['OverallCond'].astype(str)
 all_data['YrSold'] = all_data['YrSold'].astype(str)
 all_data['MoSold'] = all_data['MoSold'].astype(str )<categorify>
val_probabilities = model.predict(val_images_ds)
 val_predictions = np.argmax(val_probabilities, axis=-1)
 score_sch = f1_score(val_correct_labels, val_predictions, labels=range(len(CLASSES)) , average='macro')
 precision_sch = precision_score(val_correct_labels, val_predictions, labels=range(len(CLASSES)) , average='macro')
 recall_sch = recall_score(val_correct_labels, val_predictions, labels=range(len(CLASSES)) , average='macro')
 test_probabilities = model.predict(test_images_ds)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score_sch, precision_sch, recall_sch))
<feature_engineering><EOS> 
print('[INFO] Computing predictions...')
 probabilities = test_probabilities
predictions = np.argmax(probabilities, axis=-1)
 print('[INFO] Generating submission.csv file...')
 test_ids_ds = test_ds.map(lambda image, idnum: idnum ).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES)) ).numpy().astype('U')
 np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='' )