Kaggle competition: Petals to the Metal - Flower Classification on TPU
<SOS> metric: macrofscore Kaggle data source: petals-to-the-metal-flower-classification-on-tpu<set_options>
!pip install -q efficientnet
print("Tensorflow version " + tf.__version__)
 print("
Imported packages" )
%matplotlib inline 
<load_from_csv>
def seed_everything(seed=0):
random.seed(seed)
 np.random.seed(seed)
 tf.random.set_seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed )
df = pd.read_csv('/kaggle/input/titanic/train.csv')
 test_df = pd.read_csv('/kaggle/input/titanic/test.csv' )<groupby>
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Device:', tpu.master())
 tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 except:
strategy = tf.distribute.get_strategy()
print('Number of replicas:', strategy.num_replicas_in_sync )
embarked = df.groupby('Embarked' ).count() ['PassengerId']
embarked_max = embarked[embarked == embarked.max() ].index[0]
df.loc[df['Embarked'].isnull() , 'Embarked'] = embarked_max
embarked = test_df.groupby('Embarked' ).count() ['PassengerId']
embarked_max = embarked[embarked == embarked.max() ].index[0]
test_df.loc[test_df['Embarked'].isnull() , 'Embarked'] = embarked_max<feature_engineering>
IMAGE_SIZE = [512, 512]
HEIGHT = 512
WIDTH = 512
GCS_DS_PATH = KaggleDatasets().get_gcs_path()
GCS_PATH = GCS_DS_PATH + '/tfrecords-jpeg-512x512'
AUTO = tf.data.experimental.AUTOTUNE
EPOCHS = 30
CHANNELS = 3
model_path = f'model_{HEIGHT}x{WIDTH}.h5'
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
WARMUP_LEARNING_RATE = 1e-4 * strategy.num_replicas_in_sync
LEARNING_RATE = 3e-5 * strategy.num_replicas_in_sync
NUM_CLASSES = 104
ES_PATIENCE = 5
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']
avg = np.mean(df['Age'])
 test_avg = np.mean(test_df['Age'])
 df['Age'] = df['Age'].fillna(avg)
 test_df['Age'] = test_df['Age'].fillna(test_avg )<feature_engineering>
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, [*IMAGE_SIZE, 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
test_avg = np.mean(test_df['Fare'])
 test_df['Fare'] = test_df['Fare'].fillna(test_avg )<categorify>
def transform_rotation(image, height, rotation):
DIM = height
XDIM = DIM%2
rotation = rotation * tf.random.uniform([1],dtype='float32')
 rotation = math.pi * rotation / 180.
 c1 = tf.math.cos(rotation)
 s1 = tf.math.sin(rotation)
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 rotation_matrix = tf.reshape(tf.concat([c1,s1,zero, -s1,c1,zero, zero,zero,one],axis=0),[3,3])
 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(rotation_matrix,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])
 def transform_shear(image, height, shear):
DIM = height
XDIM = DIM%2
shear = shear * tf.random.uniform([1],dtype='float32')
 shear = math.pi * shear / 180.
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 c2 = tf.math.cos(shear)
 s2 = tf.math.sin(shear)
 shear_matrix = tf.reshape(tf.concat([one,s2,zero, zero,c2,zero, zero,zero,one],axis=0),[3,3])
 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(shear_matrix,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])
 def transform_shift(image, height, h_shift, w_shift):
DIM = height
XDIM = DIM%2
height_shift = h_shift * tf.random.uniform([1],dtype='float32')
 width_shift = w_shift * tf.random.uniform([1],dtype='float32')
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 shift_matrix = tf.reshape(tf.concat([one,zero,height_shift, zero,one,width_shift, zero,zero,one],axis=0),[3,3])
 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(shift_matrix,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] )
le = LabelEncoder()
le.fit(df['Sex'])
 df['Sex label'] = le.transform(df['Sex'])
 le.fit(test_df['Sex'])
 test_df['Sex label'] = le.transform(test_df['Sex'])
 le.fit(df['Embarked'])
 df['Embarked label'] = le.transform(df['Embarked'])
 le.fit(test_df['Embarked'])
 test_df['Embarked label'] = le.transform(test_df['Embarked'] )<prepare_x_and_y>
def data_augment(image, label):

 image = tf.image.random_flip_left_right(image)
 return image, label
def augment(image,label):

 p_rotation = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_pixel = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_shear = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_shift = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_crop = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 if p_spatial >=.2:
image = tf.image.random_flip_left_right(image)
 image = tf.image.random_flip_up_down(image)
 if p_rotate >.75:
image = tf.image.rot90(image, k=3)
 elif p_rotate >.5:
image = tf.image.rot90(image, k=2)
 elif p_rotate >.25:
image = tf.image.rot90(image, k=1)
 if p_rotation >=.3:
image = transform_rotation(image, height=HEIGHT, rotation=45.)
if p_shift >=.3:
image = transform_shift(image, height=HEIGHT, h_shift=15., w_shift=15.)
if p_shear >=.3:
image = transform_shear(image, height=HEIGHT, shear=20.)
if p_crop >.4:
crop_size = tf.random.uniform([], int(HEIGHT*.7), HEIGHT, dtype=tf.int32)
 image = tf.image.random_crop(image, size=[crop_size, crop_size, CHANNELS])
 elif p_crop >.7:
if p_crop >.9:
image = tf.image.central_crop(image, central_fraction=.7)
 elif p_crop >.8:
image = tf.image.central_crop(image, central_fraction=.8)
 else:
image = tf.image.central_crop(image, central_fraction=.9)
 image = tf.image.resize(image, size=[HEIGHT, WIDTH])
 if p_pixel >=.2:
if p_pixel >=.8:
image = tf.image.random_saturation(image, lower=0, upper=2)
 elif p_pixel >=.6:
image = tf.image.random_contrast(image, lower=.8, upper=2)
 elif p_pixel >=.4:
image = tf.image.random_brightness(image, max_delta=.2)
 else:
image = tf.image.adjust_gamma(image, gamma=.6)
 return image, label
def get_training_dataset(aug='old'):

 if aug == 'new':
aug_arg = augment
else:
aug_arg = data_augment
dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
 dataset = dataset.map(aug_arg, num_parallel_calls=AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_training_dataset_subset(ordered=False):

 dataset = load_dataset(TRAINING_FILENAMES, labeled=True, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
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_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 )
x_tr = df[['Pclass', 'Sex label', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked label']]
y_tr = df['Survived']
x_t = test_df[['Pclass', 'Sex label', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked label']]
x_tr =(x_tr - np.mean(x_tr)) /np.std(x_tr)
 x_t =(x_t - np.mean(x_t)) /np.std(x_t )<choose_model_class>
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()
def display_training_curves(training, validation, title, subplot):

 if subplot%10==1:
plt.subplots(figsize=(10,10), facecolor='
plt.tight_layout()
ax = plt.subplot(subplot)
 ax.set_facecolor('
ax.plot(training)
 ax.plot(validation)
 ax.set_title('model '+ title)
 ax.set_ylabel(title)
 ax.set_xlabel('epoch')
 ax.legend(['train', 'valid.'])
 def display_confusion_matrix(cmat, score, precision, recall):

 plt.figure(figsize=(15,15))
ax = plt.gca()
ax.matshow(cmat, cmap='Reds')
 ax.set_xticks(range(len(CLASSES)))
 ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})
 plt.setp(ax.get_xticklabels() , rotation=45, ha="left", rotation_mode="anchor")
 ax.set_yticks(range(len(CLASSES)))
 ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})
 plt.setp(ax.get_yticklabels() , rotation=45, ha="right", rotation_mode="anchor")
 titlestring = ""
if score is not None:
titlestring += 'f1 = {:.3f} '.format(score)
 if precision is not None:
titlestring += '
precision = {:.3f} '.format(precision)
 if recall is not None:
titlestring += '
recall = {:.3f} '.format(recall)
 if len(titlestring)> 0:
ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'
plt.show()
parameters = {
'max_depth': range(2, 5, 1),
'n_estimators': range(100, 600, 10),
'learning_rate': [0.01, 0.05],
'colsample_bytree': [.9, 1]
}
gbm = XGBClassifier()
model = GridSearchCV(
 estimator=gbm,
param_grid=parameters,
scoring = 'accuracy',
n_jobs = 10,
cv = 10,
verbose=True 
)
<find_best_params>
def create_model(input_shape, NUM_CLASSES):
pretrained_model = efn.EfficientNetB6(
 weights='noisy-student',
include_top=False ,
input_shape=input_shape 
)
 pretrained_model.trainable = False
model = tf.keras.Sequential([
pretrained_model,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 return model
print("Лучшие параметры: ", model.best_params_)
 print("Лучшая точность: ", xgb_random.best_score_ )<train_model>
NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 train_dataset = get_training_dataset_subset(ordered=True)
 y_train = next(iter(train_dataset.unbatch().map(lambda image, label: label ).batch(NUM_TRAINING_IMAGES)) ).numpy()
print(f'Number of training images {NUM_TRAINING_IMAGES}')
 NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
 valid_dataset = get_validation_dataset(ordered=True)
 y_valid = next(iter(valid_dataset.unbatch().map(lambda image, label: label ).batch(NUM_VALIDATION_IMAGES)) ).numpy()
print(f'Number of validation images {NUM_VALIDATION_IMAGES}')
 NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 print(f'Number of test images {NUM_TEST_IMAGES}')
 test_dataset = get_test_dataset(ordered=True )
model = XGBClassifier(n_estimators=180, max_depth = 3 , learning_rate= 0.01, colsample_bytree = 1)
 model.fit(x_tr, y_tr)
 predictions = model.predict(x_t)
 pr = pd.DataFrame(test_df['PassengerId'])
 pr['Survived'] = pd.DataFrame(predictions )<save_to_csv>
train_iter = iter(train_dataset.unbatch().batch(20))
train_batch = next(train_iter)
 display_batch_of_images(train_batch)
pr.to_csv('submission.csv', index = False )<import_modules>
valid_iter = iter(valid_dataset.unbatch().batch(20))
valid_batch = next(valid_iter)
 display_batch_of_images(valid_batch)
plotly.offline.init_notebook_mode()
%matplotlib inline
def RMSLE(pred,actual):
return np.sqrt(np.mean(np.power(( np.log(pred+1)-np.log(actual+1)) ,2)) )<load_from_csv>
test_iter = iter(test_dataset.unbatch().batch(20))
test_batch = next(test_iter)
 display_batch_of_images(test_batch)
pd.set_option('mode.chained_assignment', None)
 test = pd.read_csv(".. /input/covid19-global-forecasting-week-2/test.csv")
 train = pd.read_csv(".. /input/covid19-global-forecasting-week-2/train.csv")
 train['Province_State'].fillna('', inplace=True)
 test['Province_State'].fillna('', inplace=True)
 train['Date'] = pd.to_datetime(train['Date'])
 test['Date'] = pd.to_datetime(test['Date'])
 train = train.sort_values(['Country_Region','Province_State','Date'])
 test = test.sort_values(['Country_Region','Province_State','Date'] )<categorify>
display_batch_of_images(next(iter(get_training_dataset().unbatch().batch(20))))
train[['ConfirmedCases', 'Fatalities']] = train.groupby(['Country_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']].transform('cummax')
<feature_engineering>
display_batch_of_images(next(iter(get_training_dataset(aug='new' ).unbatch().batch(20))))
feature_day = [1,20,50,100,200,500,1000]
def CreateInput(data):
feature = []
for day in feature_day:
data.loc[:,'Number day from ' + str(day)+ ' case'] = 0
if(train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].count() > 0):
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].max()
else:
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].min()
for i in range(0, len(data)) :
if(data['Date'].iloc[i] > fromday):
day_denta = data['Date'].iloc[i] - fromday
data['Number day from ' + str(day)+ ' case'].iloc[i] = day_denta.days
feature = feature + ['Number day from ' + str(day)+ ' case']
return data[feature]
pred_data_all = pd.DataFrame()
with tqdm(total=len(train['Country_Region'].unique())) as pbar:
for country in train['Country_Region'].unique() :
for province in train[(train['Country_Region'] == country)]['Province_State'].unique() :
df_train = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]
df_test = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
X_train = CreateInput(df_train)
 y_train_confirmed = df_train['ConfirmedCases'].ravel()
y_train_fatalities = df_train['Fatalities'].ravel()
X_pred = CreateInput(df_test)
 feature_use = X_pred.columns[0]
for i in range(X_pred.shape[1] - 1,0,-1):
if(X_pred.iloc[0,i] > 0):
feature_use = X_pred.columns[i]
break
idx = X_train[X_train[feature_use] == 0].shape[0]
adjusted_X_train = X_train[idx:][feature_use].values.reshape(-1, 1)
 adjusted_y_train_confirmed = y_train_confirmed[idx:]
adjusted_y_train_fatalities = y_train_fatalities[idx:]
adjusted_X_pred = X_pred[feature_use].values.reshape(-1, 1)
 model = make_pipeline(PolynomialFeatures(2), BayesianRidge())
 model.fit(adjusted_X_train,adjusted_y_train_confirmed)
 y_hat_confirmed = model.predict(adjusted_X_pred)
 model.fit(adjusted_X_train,adjusted_y_train_fatalities)
 y_hat_fatalities = model.predict(adjusted_X_pred)
 pred_data = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
pred_data['ConfirmedCases_hat'] = y_hat_confirmed
pred_data['Fatalities_hat'] = y_hat_fatalities
pred_data_all = pred_data_all.append(pred_data)
 pbar.update(1)
 df_val = pd.merge(pred_data_all,train[['Date','Country_Region','Province_State','ConfirmedCases','Fatalities']],on=['Date','Country_Region','Province_State'], how='left')
 df_val.loc[df_val['Fatalities_hat'] < 0,'Fatalities_hat'] = 0
df_val.loc[df_val['ConfirmedCases_hat'] < 0,'ConfirmedCases_hat'] = 0
df_val_1 = df_val.copy()<compute_test_metric>
with strategy.scope() :
model = create_model(( None, None, CHANNELS), NUM_CLASSES)
 model.compile(
 optimizer=optimizers.Adam(lr=WARMUP_LEARNING_RATE),
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'], 
)
 model.summary()
RMSLE(df_val[(df_val['ConfirmedCases'].isnull() == False)]['ConfirmedCases'].values,df_val[(df_val['ConfirmedCases'].isnull() == False)]['ConfirmedCases_hat'].values )<compute_test_metric>
STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
warmup_history = model.fit(x=get_training_dataset() ,
steps_per_epoch=STEPS_PER_EPOCH,
validation_data=get_validation_dataset() ,
epochs=3,
verbose=2 ).history
RMSLE(df_val[(df_val['Fatalities'].isnull() == False)]['Fatalities'].values,df_val[(df_val['Fatalities'].isnull() == False)]['Fatalities_hat'].values )<groupby>
for layer in model.layers:
layer.trainable = True
checkpoint = ModelCheckpoint(model_path, monitor='val_lines', mode='min', save_best_only=True)
 es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE,
restore_best_weights=True, verbose=1)
 lr_callback = LearningRateScheduler(exponential_schedule_with_warmup, verbose=0)
 callback_list = [checkpoint, es, lr_callback]
model.compile(optimizer=optimizers.Adam(lr=LEARNING_RATE),
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
country = "Vietnam"
df_val = df_val_1
df_val[df_val['Country_Region'] == country].groupby(['Date','Country_Region'] ).sum().reset_index()<save_model>
history = model.fit(
 get_training_dataset() ,
validation_data=valid_dataset,
epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
callbacks=[lr_callback],
verbose=2
).history
animator.save('confirm_animation.gif', writer='imagemagick', fps=2)
 display(Image(url='confirm_animation.gif'))<feature_engineering>
cmdataset = get_validation_dataset(ordered=True)
 images_ds = cmdataset.map(lambda image, label: image)
 labels_ds = cmdataset.map(lambda image, label: label ).unbatch()
cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES)) ).numpy()
cm_probabilities = model.predict(images_ds)
 cm_predictions = np.argmax(cm_probabilities, axis=-1)
 labels = range(len(CLASSES))
cmat = confusion_matrix(
 cm_correct_labels,
cm_predictions,
labels=labels, 
)
 cmat =(cmat.T / cmat.sum(axis=1)).T
feature_day = [1,20,50,100,200,500,1000]
def CreateInput(data):
feature = []
for day in feature_day:
data.loc[:,'Number day from ' + str(day)+ ' case'] = 0
if(train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].count() > 0):
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].max()
else:
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].min()
for i in range(0, len(data)) :
if(data['Date'].iloc[i] > fromday):
day_denta = data['Date'].iloc[i] - fromday
data['Number day from ' + str(day)+ ' case'].iloc[i] = day_denta.days
feature = feature + ['Number day from ' + str(day)+ ' case']
return data[feature]
pred_data_all = pd.DataFrame()
with tqdm(total=len(train['Country_Region'].unique())) as pbar:
for country in train['Country_Region'].unique() :
for province in train[(train['Country_Region'] == country)]['Province_State'].unique() :
with warnings.catch_warnings() :
warnings.filterwarnings("ignore")
 df_train = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]
df_test = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
X_train = CreateInput(df_train)
 y_train_confirmed = df_train['ConfirmedCases'].ravel()
y_train_fatalities = df_train['Fatalities'].ravel()
X_pred = CreateInput(df_test)
 feature_use = X_pred.columns[0]
for i in range(X_pred.shape[1] - 1,0,-1):
if(X_pred.iloc[0,i] > 0):
feature_use = X_pred.columns[i]
break
idx = X_train[X_train[feature_use] == 0].shape[0]
adjusted_X_train = X_train[idx:][feature_use].values.reshape(-1, 1)
 adjusted_y_train_confirmed = y_train_confirmed[idx:]
adjusted_y_train_fatalities = y_train_fatalities[idx:]
pred_data = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
max_train_date = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].max()
min_test_date = pred_data['Date'].min()
model = ExponentialSmoothing(adjusted_y_train_confirmed, trend = 'additive' ).fit()
y_hat_confirmed = model.forecast(pred_data[pred_data['Date'] > max_train_date].shape[0])
 y_train_confirmed = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['Date'] >= min_test_date)]['ConfirmedCases'].values
y_hat_confirmed = np.concatenate(( y_train_confirmed,y_hat_confirmed), axis = 0)
 model = ExponentialSmoothing(adjusted_y_train_fatalities, trend = 'additive' ).fit()
y_hat_fatalities = model.forecast(pred_data[pred_data['Date'] > max_train_date].shape[0])
 y_train_fatalities = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['Date'] >= min_test_date)]['Fatalities'].values
y_hat_fatalities = np.concatenate(( y_train_fatalities,y_hat_fatalities), axis = 0)
 pred_data['ConfirmedCases_hat'] = y_hat_confirmed
pred_data['Fatalities_hat'] = y_hat_fatalities
pred_data_all = pred_data_all.append(pred_data)
 pbar.update(1)
 df_val = pd.merge(pred_data_all,train[['Date','Country_Region','Province_State','ConfirmedCases','Fatalities']],on=['Date','Country_Region','Province_State'], how='left')
 df_val.loc[df_val['Fatalities_hat'] < 0,'Fatalities_hat'] = 0
df_val.loc[df_val['ConfirmedCases_hat'] < 0,'ConfirmedCases_hat'] = 0
df_val_2 = df_val.copy()<feature_engineering>
score = f1_score(
 cm_correct_labels,
cm_predictions,
labels=labels,
average='macro', 
)
 precision = precision_score(
 cm_correct_labels,
cm_predictions,
labels=labels,
average='macro', 
)
 recall = recall_score(
 cm_correct_labels,
cm_predictions,
labels=labels,
average='macro',
)
feature_day = [1,20,50,100,200,500,1000]
def CreateInput(data):
feature = []
for day in feature_day:
data.loc[:,'Number day from ' + str(day)+ ' case'] = 0
if(train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].count() > 0):
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].max()
else:
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].min()
for i in range(0, len(data)) :
if(data['Date'].iloc[i] > fromday):
day_denta = data['Date'].iloc[i] - fromday
data['Number day from ' + str(day)+ ' case'].iloc[i] = day_denta.days
feature = feature + ['Number day from ' + str(day)+ ' case']
return data[feature]
pred_data_all = pd.DataFrame()
with tqdm(total=len(train['Country_Region'].unique())) as pbar:
for country in train['Country_Region'].unique() :
for province in train[(train['Country_Region'] == country)]['Province_State'].unique() :
with warnings.catch_warnings() :
warnings.filterwarnings("ignore")
 df_train = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]
df_test = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
X_train = CreateInput(df_train)
 y_train_confirmed = df_train['ConfirmedCases'].ravel()
y_train_fatalities = df_train['Fatalities'].ravel()
X_pred = CreateInput(df_test)
 feature_use = X_pred.columns[0]
for i in range(X_pred.shape[1] - 1,0,-1):
if(X_pred.iloc[0,i] > 0):
feature_use = X_pred.columns[i]
break
idx = X_train[X_train[feature_use] == 0].shape[0]
adjusted_X_train = X_train[idx:][feature_use].values.reshape(-1, 1)
 adjusted_y_train_confirmed = y_train_confirmed[idx:]
adjusted_y_train_fatalities = y_train_fatalities[idx:]
idx = X_pred[X_pred[feature_use] == 0].shape[0]
adjusted_X_pred = X_pred[idx:][feature_use].values.reshape(-1, 1)
 pred_data = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
max_train_date = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].max()
min_test_date = pred_data['Date'].min()
model = SARIMAX(adjusted_y_train_confirmed, order=(1,1,0),
measurement_error=True ).fit(disp=False)
 y_hat_confirmed = model.forecast(pred_data[pred_data['Date'] > max_train_date].shape[0])
 y_train_confirmed = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['Date'] >= min_test_date)]['ConfirmedCases'].values
y_hat_confirmed = np.concatenate(( y_train_confirmed,y_hat_confirmed), axis = 0)
 model = SARIMAX(adjusted_y_train_fatalities, order=(1,1,0),
measurement_error=True ).fit(disp=False)
 y_hat_fatalities = model.forecast(pred_data[pred_data['Date'] > max_train_date].shape[0])
 y_train_fatalities = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['Date'] >= min_test_date)]['Fatalities'].values
y_hat_fatalities = np.concatenate(( y_train_fatalities,y_hat_fatalities), axis = 0)
 pred_data['ConfirmedCases_hat'] = y_hat_confirmed
pred_data['Fatalities_hat'] = y_hat_fatalities
pred_data_all = pred_data_all.append(pred_data)
 pbar.update(1)
 df_val = pd.merge(pred_data_all,train[['Date','Country_Region','Province_State','ConfirmedCases','Fatalities']],on=['Date','Country_Region','Province_State'], how='left')
 df_val.loc[df_val['Fatalities_hat'] < 0,'Fatalities_hat'] = 0
df_val.loc[df_val['ConfirmedCases_hat'] < 0,'ConfirmedCases_hat'] = 0
df_val_3 = df_val.copy()<compute_test_metric>
dataset = get_validation_dataset()
dataset = dataset.unbatch().batch(20)
 batch = iter(dataset )
method_list = ['Poly Bayesian Ridge','Exponential Smoothing','SARIMA']
method_val = [df_val_1,df_val_2,df_val_3]
for i in range(0,3):
df_val = method_val[i]
method_score = [method_list[i]] + [RMSLE(df_val[(df_val['ConfirmedCases'].isnull() == False)]['ConfirmedCases'].values,df_val[(df_val['ConfirmedCases'].isnull() == False)]['ConfirmedCases_hat'].values)] + [RMSLE(df_val[(df_val['Fatalities'].isnull() == False)]['Fatalities'].values,df_val[(df_val['Fatalities'].isnull() == False)]['Fatalities_hat'].values)]
print(method_score )<save_to_csv>
images, labels = next(batch)
 probabilities = model.predict(images)
 predictions = np.argmax(probabilities, axis=-1)
 display_batch_of_images(( images, labels), predictions )
df_val = df_val_3
submission = df_val[['ForecastId','ConfirmedCases_hat','Fatalities_hat']]
submission.columns = ['ForecastId','ConfirmedCases','Fatalities']
submission = submission.round({'ConfirmedCases': 0, 'Fatalities': 0})
 submission.to_csv('submission.csv', index=False)
 submission<filter>
test_ds = get_test_dataset(ordered=True)
 print('Computing predictions...')
 test_images_ds = test_ds.map(lambda image, idnum: image)
 probabilities = model.predict(test_images_ds)
 predictions = np.argmax(probabilities, axis=-1)
 print(predictions )
<import_modules><EOS> 
print('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='', 
)