Kaggle competition: Petals to the Metal - Flower Classification on TPU
<SOS> metric: macrofscore Kaggle data source: petals-to-the-metal-flower-classification-on-tpu<groupby>
print(tf.__version__)
 warnings.filterwarnings('ignore' )
train_data[['Embarked', 'Survived']].groupby(['Embarked'] ).mean()<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("No tpu present")
 print('Number of replicas:', strategy.num_replicas_in_sync)
 AUTO = tf.data.experimental.AUTOTUNE
train_data[['Embarked', 'Pclass']].groupby(['Embarked'] ).mean()<define_variables>
IMAGE_SIZE = [224, 224]
EPOCHS = 10
FOLDS = 5
SEED = 777
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
women_count = 0
women_survived_count = 0
for idx, row in train_data.iterrows() :
if row['Sex'] == 'female':
women_count += 1
if row['Survived'] == 1:
women_survived_count += 1
women_survived_count / women_count<define_variables>
GCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')
 GCS_PATH_SELECT = {
192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]
TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')+ tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec' )
predictions = []
for idx, row in test_data.iterrows() :
if row['Sex'] == 'female':
if row['Pclass'] == '1':
predictions.append(1)
 elif row['Pclass'] == '2':
if row['Embarked'] == 'C':
predictions.append(1)
 else:
predictions.append(0)
 else:
if row['Embarked'] == 'C' or row['Fare'] > 8:
predictions.append(1)
 else:
predictions.append(0)
 else:
if row['Age'] < 10 and row['Pclass'] != '3':
predictions.append(1)
 else:
predictions.append(0 )<feature_engineering>
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']
test_data['Survived'] = predictions<save_to_csv>
LR_START = 0.00001
LR_MAX = 0.00005 * strategy.num_replicas_in_sync
LR_MIN = 0.00001
LR_RAMPUP_EPOCHS = 5
LR_SUSTAIN_EPOCHS = 0
LR_EXP_DECAY =.8
def lrfn(epoch):
if epoch < LR_RAMPUP_EPOCHS:
lr =(LR_MAX - LR_START)/ LR_RAMPUP_EPOCHS * epoch + LR_START
elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:
lr = LR_MAX
else:
lr =(LR_MAX - LR_MIN)* LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS)+ LR_MIN
return lr
lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose = True)
 rng = [i for i in range(50 if EPOCHS<50 else EPOCHS)]
y = [lrfn(x)for x in rng]
plt.plot(rng, y)
 print("Learning rate schedule: {:.3g} to {:.3g} to {:.3g}".format(y[0], max(y), y[-1]))
test_data[['PassengerId', 'Survived']].to_csv('submission.csv', index=False )<set_options>
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
def data_augment(image, label):
image = tf.image.random_flip_left_right(image)
 return image, label
def get_training_dataset(dataset,do_aug=True):
dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 if do_aug: dataset = dataset.map(transform, 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(dataset):
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)
 NUM_TRAINING_IMAGES = int(count_data_items(TRAINING_FILENAMES)*(FOLDS-1.) /FOLDS)
 NUM_VALIDATION_IMAGES = int(count_data_items(TRAINING_FILENAMES)*(1./FOLDS))
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))
%matplotlib inline 
<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.
 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])
 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])
 zoom_matrix = tf.reshape(tf.concat([one/height_zoom,zero,zero, zero,one/width_zoom,zero, zero,zero,one],axis=0),[3,3])
 shift_matrix = tf.reshape(tf.concat([one,zero,height_shift, zero,one,width_shift, zero,zero,one],axis=0),[3,3])
 return K.dot(K.dot(rotation_matrix, shear_matrix), K.dot(zoom_matrix, shift_matrix))
test_df = pd.read_csv("/kaggle/input/titanic/test.csv")
 train_df = pd.read_csv("/kaggle/input/titanic/train.csv" )<sort_values>
def transform(image,label):
DIM = IMAGE_SIZE[0]
XDIM = DIM%2
rot = 15.* tf.random.normal([1],dtype='float32')
 shr = 5.* tf.random.normal([1],dtype='float32')
 h_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.
 w_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.
 h_shift = 16.* tf.random.normal([1],dtype='float32')
 w_shift = 16.* 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]),label
total = train_df.isnull().sum().sort_values(ascending=False)
 percent_1 = train_df.isnull().sum() /train_df.isnull().count() *100
percent_2 =(round(percent_1, 1)).sort_values(ascending=False)
 missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])
 missing_data.head(5 )<data_type_conversions>
def get_model() :
with strategy.scope() :
rnet = InceptionResNetV2(
 input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3),
weights='imagenet',
include_top=False 
)
 rnet.trainable = True
model = tf.keras.Sequential([
rnet,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax',dtype='float32')
 ])
 model.compile(
 optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 return model
def train_cross_validate(folds = 5):
histories = []
models = []
early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 3)
 kfold = KFold(folds, shuffle = True, random_state = SEED)
 for f,(trn_ind, val_ind)in enumerate(kfold.split(TRAINING_FILENAMES)) :
print() ; print('
print('
print('
train_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[trn_ind]['TRAINING_FILENAMES']), labeled = True)
 val_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[val_ind]['TRAINING_FILENAMES']), labeled = True, ordered = True)
 model = get_model()
history = model.fit(
 get_training_dataset(train_dataset),
steps_per_epoch = STEPS_PER_EPOCH,
epochs = EPOCHS,
callbacks = [lr_callback],
validation_data = get_validation_dataset(val_dataset),
verbose=2 
)
 models.append(model)
 histories.append(history)
 return histories, models
def train_and_predict(folds = 5):
test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image)
 print('Start training %i folds'%folds)
 histories, models = train_cross_validate(folds = folds)
 print('Computing predictions...')
 probabilities = np.average([models[i].predict(test_images_ds)for i in range(folds)], axis = 0)
 predictions = np.argmax(probabilities, axis=-1)
 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='')
 return histories, models
histories_inception, inception_model = train_and_predict(folds = FOLDS )
data = [train_df, test_df]
for dataset in data:
dataset['relatives'] = dataset['SibSp'] + dataset['Parch']
dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0
dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1
dataset['not_alone'] = dataset['not_alone'].astype(int)
 train_df['not_alone'].value_counts()<drop_column>
def get_model() :
with strategy.scope() :
rnet = NASNetLarge(
 input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3),
weights='imagenet',
include_top=False 
)
 rnet.trainable = True
model = tf.keras.Sequential([
rnet,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax',dtype='float32')
 ])
 model.compile(
 optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 return model
def train_cross_validate(folds = 5):
histories = []
models = []
early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 3)
 kfold = KFold(folds, shuffle = True, random_state = SEED)
 for f,(trn_ind, val_ind)in enumerate(kfold.split(TRAINING_FILENAMES)) :
print() ; print('
print('
print('
train_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[trn_ind]['TRAINING_FILENAMES']), labeled = True)
 val_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[val_ind]['TRAINING_FILENAMES']), labeled = True, ordered = True)
 model = get_model()
history = model.fit(
 get_training_dataset(train_dataset),
steps_per_epoch = STEPS_PER_EPOCH,
epochs = EPOCHS,
callbacks = [lr_callback],
validation_data = get_validation_dataset(val_dataset),
verbose=2 
)
 models.append(model)
 histories.append(history)
 return histories, models
def train_and_predict(folds = 5):
test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image)
 print('Start training %i folds'%folds)
 histories, models = train_cross_validate(folds = folds)
 print('Computing predictions...')
 probabilities = np.average([models[i].predict(test_images_ds)for i in range(folds)], axis = 0)
 predictions = np.argmax(probabilities, axis=-1)
 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='')
 return histories, models
train_df = train_df.drop(['PassengerId'], axis=1 )<categorify>
%%time
all_labels = []; all_prob = []; all_pred = []
kfold = KFold(FOLDS, shuffle = True, random_state = SEED)
 for j,(trn_ind, val_ind)in enumerate(kfold.split(TRAINING_FILENAMES)) :
print('Inferring fold',j+1,'validation images...')
 VAL_FILES = list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[val_ind]['TRAINING_FILENAMES'])
 NUM_VALIDATION_IMAGES = count_data_items(VAL_FILES)
 cmdataset = get_validation_dataset(load_dataset(VAL_FILES, labeled = True, ordered = True))
images_ds = cmdataset.map(lambda image, label: image)
 labels_ds = cmdataset.map(lambda image, label: label ).unbatch()
all_labels.append(next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES)) ).numpy())
 prob = inception_model[j].predict(images_ds)
 all_prob.append(prob)
 all_pred.append(np.argmax(prob, axis=-1))
cm_correct_labels = np.concatenate(all_labels)
 cm_probabilities = np.concatenate(all_prob)
 cm_predictions = np.concatenate(all_pred )
deck = {"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7, "U": 8}
data = [train_df, test_df]
for dataset in data:
dataset['Cabin'] = dataset['Cabin'].fillna("U0")
 dataset['Deck'] = dataset['Cabin'].map(lambda x: re.compile("([a-zA-Z]+)" ).search(x ).group())
 dataset['Deck'] = dataset['Deck'].map(deck)
 dataset['Deck'] = dataset['Deck'].fillna(0)
 dataset['Deck'] = dataset['Deck'].astype(int)
 train_df = train_df.drop(['Cabin'], axis=1)
 test_df = test_df.drop(['Cabin'], axis=1 )<data_type_conversions>
cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))
 score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 display_confusion_matrix(cmat, score, precision, recall)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall)) ; print()
data = [train_df, test_df]
for dataset in data:
mean = train_df["Age"].mean()
std = test_df["Age"].std()
is_null = dataset["Age"].isnull().sum()
rand_age = np.random.randint(mean - std, mean + std, size = is_null)
 age_slice = dataset["Age"].copy()
age_slice[np.isnan(age_slice)] = rand_age
dataset["Age"] = age_slice
dataset["Age"] = train_df["Age"].astype(int)
 train_df["Age"].isnull().sum()<categorify>