Kaggle competition: Two Sigma: Using News to Predict Stock Movements
<SOS> metric: custom Kaggle data source: two-sigma-financial-news<choose_model_class>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
from sklearn.metrics import accuracy_score
from kaggle.competitions import twosigmanews
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.utils import conv_utils
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.engine import InputSpec
from keras import backend as K
from keras.layers import LeakyReLU
from keras.layers import ZeroPadding2D
from keras.losses import binary_crossentropy
import keras.callbacks as callbacks
from keras.callbacks import Callback
from keras.applications.xception import Xception
from keras.layers import multiply
import keras
from keras import optimizers
from keras.legacy import interfaces
from keras.utils.generic_utils import get_custom_objects
from keras.engine.topology import Input
from keras.engine.training import Model
from keras.layers.convolutional import Conv2D, UpSampling2D, Conv2DTranspose
from keras.layers.core import Activation, SpatialDropout2D
from keras.layers.merge import concatenate
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D
from keras.layers import Input,Dropout,BatchNormalization,Activation,Add
from keras.regularizers import l2
from keras.layers.core import Dense, Lambda
from keras.layers.merge import concatenate, add
from keras.layers import GlobalAveragePooling2D, Reshape, Dense, multiply, Permute
from keras.optimizers import SGD
AUTO = tf.data.experimental.AUTOTUNE
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 GCS_DS_PATH = KaggleDatasets().get_gcs_path('flower-classification-with-tpus')
 img_size=512
IMAGE_SIZE = [img_size, img_size]
EPOCHS = 20
BATCH_SIZE = 16 * strategy.num_replicas_in_sync<randomize_order>
env = twosigmanews.make_env() 
(market_train, _)= env.get_training_data()
EPOCHS_ = 20
LR_START = 0.0001
LR_MAX = 0.00005 * strategy.num_replicas_in_sync
LR_MIN = 0.000005
LR_RAMPUP_EPOCHS = 3
LR_SUSTAIN_EPOCHS = 3
LR_EXP_DECAY =.8
def lrfn(epoch):
if epoch < LR_RAMPUP_EPOCHS:
lr = np.random.random_sample() * 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(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]))<define_variables>
cat_cols = ['assetCode']
num_cols = ['volume', 'close', 'open', 'returnsClosePrevRaw1', 'returnsOpenPrevRaw1', 'returnsClosePrevMktres1',
'returnsOpenPrevMktres1', 'returnsClosePrevRaw10', 'returnsOpenPrevRaw10', 'returnsClosePrevMktres10',
'returnsOpenPrevMktres10']
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')
 VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')
 SKIP_VALIDATION = True
TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES<set_options>
train_indices, val_indices = train_test_split(market_train.index.values,test_size=0.25, random_state=23)
SKIP_VALIDATION = True
GCS_DS_PATH1 = KaggleDatasets().get_gcs_path('mytraindataset')
 TRAINING_FILENAMES_ = tf.io.gfile.glob(GCS_DS_PATH1 + '/*.tfrec')
 TRAINING_FILENAMES = TRAINING_FILENAMES + TRAINING_FILENAMES_<define_variables>
def encode(encoder, x):
len_encoder = len(encoder)
 try:
id = encoder[x]
except KeyError:
id = len_encoder
return id
encoders = [{} for cat in cat_cols]
for i, cat in enumerate(cat_cols):
print('encoding %s...' % cat, end=' ')
 encoders[i] = {l: id for id, l in enumerate(market_train.loc[train_indices, cat].astype(str ).unique())}
market_train[cat] = market_train[cat].astype(str ).apply(lambda x: encode(encoders[i], x))
print('Done')
 embed_sizes = [len(encoder)+ 1 for encoder in encoders]
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'] 
<categorify>
market_train[num_cols] = market_train[num_cols].fillna(0)
 print('scaling numerical columns')
 scaler = StandardScaler()
scaler = StandardScaler()
market_train[num_cols] = scaler.fit_transform(market_train[num_cols])
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.one_hot(example['class'], 104)
 return image, label
def read_labeled_tfrecord_train(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.one_hot(example['class'], 104)
 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 load_dataset_train(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_train if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset
def get_training_dataset(do_aug=False):
dataset = load_dataset_train(TRAINING_FILENAMES, labeled=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 data_augment(image, label):
image = tf.image.random_flip_left_right(image)
 return image, label
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)
 NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 NUM_VALIDATION_IMAGES =(1 - SKIP_VALIDATION)* count_data_items(VALIDATION_FILENAMES)
 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)) 
<normalization>
categorical_inputs = []
for cat in cat_cols:
categorical_inputs.append(Input(shape=[1], name=cat))
categorical_embeddings = []
for i, cat in enumerate(cat_cols):
categorical_embeddings.append(Embedding(embed_sizes[i], 10 )(categorical_inputs[i]))
categorical_logits = Flatten()(categorical_embeddings[0])
 categorical_logits = Dense(32,activation='relu' )(categorical_logits)
 numerical_inputs = Input(shape=(11,), name='num')
 numerical_logits = numerical_inputs
numerical_logits = BatchNormalization()(numerical_logits)
 numerical_logits = Dense(128,activation='relu' )(numerical_logits)
 numerical_logits = Dense(64,activation='relu' )(numerical_logits)
 logits = Concatenate()([numerical_logits,categorical_logits])
 logits = Dense(64,activation='relu' )(logits)
 out = Dense(1, activation='sigmoid' )(logits)
 model = Model(inputs = categorical_inputs + [numerical_inputs], outputs=out)
 model.compile(optimizer='adam',loss=binary_crossentropy )
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))<categorify>
def get_input(market_train, indices):
X_num = market_train.loc[indices, num_cols].values
X = {'num':X_num}
for cat in cat_cols:
X[cat] = market_train.loc[indices, cat_cols].values
y =(market_train.loc[indices,'returnsOpenNextMktres10'] >= 0 ).values
r = market_train.loc[indices,'returnsOpenNextMktres10'].values
u = market_train.loc[indices, 'universe']
d = market_train.loc[indices, 'time'].dt.date
return X,y,r,u,d
X_train,y_train,r_train,u_train,d_train = get_input(market_train, train_indices)
 X_valid,y_valid,r_valid,u_valid,d_valid = get_input(market_train, val_indices )
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<create_dataframe>
class SWA(keras.callbacks.Callback):
def __init__(self, filepath, swa_epoch):
super(SWA, self ).__init__()
self.filepath = filepath
self.swa_epoch = swa_epoch
def on_train_begin(self, logs=None):
self.nb_epoch = self.params['epochs']
print('Stochastic weight averaging selected for last {} epochs.' 
.format(self.nb_epoch - self.swa_epoch))
def on_epoch_end(self, epoch, logs=None):
if epoch == self.swa_epoch:
self.swa_weights = self.model.get_weights()
elif epoch > self.swa_epoch:
for i in range(len(self.swa_weights)) :
self.swa_weights[i] =(self.swa_weights[i] * 
(epoch - self.swa_epoch)+ self.model.get_weights() [i])/(( epoch - self.swa_epoch)+ 1)
 else:
pass
def on_train_end(self, logs=None):
self.model.set_weights(self.swa_weights)
 print('Final model parameters set to stochastic weight average.')
 self.model.save_weights(self.filepath)
 print('Final stochastic averaged weights saved to file.' )
test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image )<train_model>
class SnapshotCallbackBuilder:
def __init__(self, nb_epochs, nb_snapshots, init_lr=0.1):
self.T = nb_epochs
self.M = nb_snapshots
self.alpha_zero = init_lr
def get_callbacks(self, model_prefix='Model'):
callback_list = [
callbacks.ModelCheckpoint("model.hdf5",monitor='val_my_iou_metric',
mode = 'max', save_best_only=True, verbose=1),
swa,
callbacks.LearningRateScheduler(schedule=self._cosine_anneal_schedule)
 ]
return callback_list
def _cosine_anneal_schedule(self, t):
cos_inner = np.pi *(t %(self.T // self.M))
cos_inner /= self.T // self.M
cos_out = np.cos(cos_inner)+ 1
return float(self.alpha_zero / 2 * cos_out )
training_dataset = get_training_dataset()
training_dataset = training_dataset.unbatch().batch(20)
 train_batch = iter(training_dataset )<train_model>

 check_point = ModelCheckpoint('model.hdf5',verbose=True, save_best_only=True)
 early_stop = EarlyStopping(patience=5,verbose=True)
 model.fit(X_train,y_train.astype(int),
validation_data=(X_valid,y_valid.astype(int)) ,
epochs=5,
verbose=True,
callbacks=[early_stop,check_point] )
print("Training data shapes:")
 for image, label in get_training_dataset().take(3):
print(image.numpy().shape, label.numpy().shape)
 print("Training data label examples:", label.numpy())
 print("Test data shapes:")
 for image, idnum in get_test_dataset().take(3):
print(image.numpy().shape, idnum.numpy().shape)
 print("Test data IDs:", idnum.numpy().astype('U'))<train_model>
r_valid = r_valid.clip(-1,1)
 x_t_i = confidence_valid * r_valid * u_valid
data = {'day' : d_valid, 'x_t_i' : x_t_i}
df = pd.DataFrame(data)
 x_t = df.groupby('day' ).sum().values.flatten()
mean = np.mean(x_t)
 std = np.std(x_t)
 score_valid = mean / std
print(score_valid )
def binary_focal_loss(gamma=2., alpha=.25):

 def binary_focal_loss_fixed(y_true, y_pred):

 pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
epsilon = K.epsilon()
pt_1 = K.clip(pt_1, epsilon, 1.- epsilon)
 pt_0 = K.clip(pt_0, epsilon, 1.- epsilon)
 return -K.sum(alpha * K.pow(1.- pt_1, gamma)* K.log(pt_1)) \
-K.sum(( 1 - alpha)* K.pow(pt_0, gamma)* K.log(1.- pt_0))
return binary_focal_loss_fixed
def categorical_focal_loss(gamma=2., alpha=.25):

 def categorical_focal_loss_fixed(y_true, y_pred):

 y_pred /= K.sum(y_pred, axis=-1, keepdims=True)
 epsilon = K.epsilon()
y_pred = K.clip(y_pred, epsilon, 1.- epsilon)
 cross_entropy = -y_true * K.log(y_pred)
 loss = alpha * K.pow(1 - y_pred, gamma)* cross_entropy
return K.sum(loss, axis=1)
 return categorical_focal_loss_fixed 
<choose_model_class>
days = env.get_prediction_days()
<choose_model_class><EOS> 
n_days = 0
prep_time = 0
prediction_time = 0
packaging_time = 0
predicted_confidences = np.array([])
 for(market_obs_df, news_obs_df, predictions_template_df)in days:
n_days +=1
print(n_days,end=' ')
 t = time.time()
market_obs_df['assetCode_encoded'] = market_obs_df[cat].astype(str ).apply(lambda x: encode(encoders[i], x))
market_obs_df[num_cols] = market_obs_df[num_cols].fillna(0)
 market_obs_df[num_cols] = scaler.transform(market_obs_df[num_cols])
 X_num_test = market_obs_df[num_cols].values
X_test = {'num':X_num_test}
X_test['assetCode'] = market_obs_df['assetCode_encoded'].values
prep_time += time.time() - t
t = time.time()
market_prediction = model.predict(X_test)[:,0]*2 -1
predicted_confidences = np.concatenate(( predicted_confidences, market_prediction))
prediction_time += time.time() -t
t = time.time()
preds = pd.DataFrame({'assetCode':market_obs_df['assetCode'],'confidence':market_prediction})
 predictions_template_df = predictions_template_df.merge(preds,how='left' ).drop('confidenceValue',axis=1 ).fillna(0 ).rename(columns={'confidence':'confidenceValue'})
 env.predict(predictions_template_df)
 packaging_time += time.time() - t
env.write_submission_file()
total = prep_time + prediction_time + packaging_time
print(f'Preparing Data: {prep_time:.2f}s')
 print(f'Making Predictions: {prediction_time:.2f}s')
 print(f'Packing: {packaging_time:.2f}s')
 print(f'Total: {total:.2f}s' )