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rnn_order_size.py
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import os
import numpy as np
import tensorflow as tf
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from data_frame import DataFrame
from tf_utils import lstm_layer, time_distributed_dense_layer, sequence_rmse
from tf_base_model import TFBaseModel
class DataReader(object):
def __init__(self, data_dir):
data_cols = [
'user_id',
'history_length',
'order_size_history',
'reorder_size_history',
'order_number_history',
'order_dow_history',
'order_hour_history',
'days_since_prior_order_history',
]
data = [np.load(os.path.join(data_dir, '{}.npy'.format(i)), mmap_mode='r') for i in data_cols]
self.test_df = DataFrame(columns=data_cols, data=data)
print self.test_df.shapes()
print 'loaded data'
self.train_df, self.val_df = self.test_df.train_test_split(train_size=0.9)
print 'train size', len(self.train_df)
print 'val size', len(self.val_df)
print 'test size', len(self.test_df)
def train_batch_generator(self, batch_size):
return self.batch_generator(
batch_size=batch_size,
df=self.train_df,
shuffle=True,
num_epochs=10000,
is_test=False
)
def val_batch_generator(self, batch_size):
return self.batch_generator(
batch_size=batch_size,
df=self.val_df,
shuffle=True,
num_epochs=10000,
is_test=False
)
def test_batch_generator(self, batch_size):
return self.batch_generator(
batch_size=batch_size,
df=self.test_df,
shuffle=False,
num_epochs=1,
is_test=True
)
def batch_generator(self, batch_size, df, shuffle=True, num_epochs=10000, is_test=False):
batch_gen = df.batch_generator(batch_size, shuffle=shuffle, num_epochs=num_epochs, allow_smaller_final_batch=is_test)
for batch in batch_gen:
batch['order_dow_history'] = np.roll(batch['order_dow_history'], -1, axis=1)
batch['order_hour_history'] = np.roll(batch['order_hour_history'], -1, axis=1)
batch['days_since_prior_order_history'] = np.roll(batch['days_since_prior_order_history'], -1, axis=1)
batch['order_number_history'] = np.roll(batch['order_number_history'], -1, axis=1)
batch['next_reorder_size'] = np.roll(batch['reorder_size_history'], -1, axis=1)
if not is_test:
batch['history_length'] = batch['history_length'] - 1
yield batch
class rnn(TFBaseModel):
def __init__(self, lstm_size=300, **kwargs):
self.lstm_size = lstm_size
super(rnn, self).__init__(**kwargs)
def calculate_loss(self):
x = self.get_input_sequences()
preds = self.calculate_outputs(x)
loss = sequence_rmse(self.next_reorder_size, preds, self.history_length, 100)
return loss
def get_input_sequences(self):
self.user_id = tf.placeholder(tf.int32, [None])
self.history_length = tf.placeholder(tf.int32, [None])
self.label = tf.placeholder(tf.int32, [None])
self.eval_set = tf.placeholder(tf.int32, [None])
self.order_size_history = tf.placeholder(tf.int32, [None, 100])
self.reorder_size_history = tf.placeholder(tf.int32, [None, 100])
self.order_number_history = tf.placeholder(tf.int32, [None, 100])
self.order_dow_history = tf.placeholder(tf.int32, [None, 100])
self.order_hour_history = tf.placeholder(tf.int32, [None, 100])
self.days_since_prior_order_history = tf.placeholder(tf.int32, [None, 100])
self.next_reorder_size = tf.placeholder(tf.int32, [None, 100])
self.keep_prob = tf.placeholder(tf.float32)
self.is_training = tf.placeholder(tf.bool)
order_dow_history = tf.one_hot(self.order_dow_history, 8)
order_hour_history = tf.one_hot(self.order_hour_history, 25)
days_since_prior_order_history = tf.one_hot(self.days_since_prior_order_history, 31)
order_size_history = tf.one_hot(self.order_size_history, 60)
reorder_size_history = tf.one_hot(self.reorder_size_history, 50)
order_number_history = tf.one_hot(self.order_number_history, 101)
order_dow_history_scalar = tf.expand_dims(tf.cast(self.order_dow_history, tf.float32) / 8.0, 2)
order_hour_history_scalar = tf.expand_dims(tf.cast(self.order_hour_history, tf.float32) / 25.0, 2)
days_since_prior_order_history_scalar = tf.expand_dims(tf.cast(self.days_since_prior_order_history, tf.float32) / 31.0, 2)
order_size_history_scalar = tf.expand_dims(tf.cast(self.order_size_history, tf.float32) / 60.0, 2)
reorder_size_history_scalar = tf.expand_dims(tf.cast(self.reorder_size_history, tf.float32) / 50.0, 2)
order_number_history_scalar = tf.expand_dims(tf.cast(self.order_number_history, tf.float32) / 100.0, 2)
x = tf.concat([
order_dow_history,
order_hour_history,
days_since_prior_order_history,
order_size_history,
reorder_size_history,
order_number_history,
order_dow_history_scalar,
order_hour_history_scalar,
days_since_prior_order_history_scalar,
order_size_history_scalar,
reorder_size_history_scalar,
order_number_history_scalar,
], axis=2)
return x
def calculate_outputs(self, x):
h = lstm_layer(x, self.history_length, self.lstm_size, scope='lstm-1')
h_final = time_distributed_dense_layer(h, 50, activation=tf.nn.relu, scope='dense-1')
y_hat = tf.squeeze(time_distributed_dense_layer(h_final, 1, scope='dense2'), 2)
final_temporal_idx = tf.stack([tf.range(tf.shape(self.history_length)[0]), self.history_length - 1], axis=1)
self.final_states = tf.gather_nd(h_final, final_temporal_idx)
self.final_predictions = tf.gather_nd(y_hat, final_temporal_idx)
self.prediction_tensors = {
'user_ids': self.user_id,
'final_states': self.final_states,
'predictions': self.final_predictions
}
return y_hat
if __name__ == '__main__':
base_dir = './'
dr = DataReader(data_dir=os.path.join(base_dir, 'data'))
nn = rnn(
reader=dr,
log_dir=os.path.join(base_dir, 'logs'),
checkpoint_dir=os.path.join(base_dir, 'checkpoints'),
prediction_dir=os.path.join(base_dir, 'predictions'),
optimizer='adam',
learning_rate=.001,
lstm_size=300,
batch_size=128,
num_training_steps=200000,
early_stopping_steps=10000,
warm_start_init_step=0,
regularization_constant=0.0,
keep_prob=1.0,
enable_parameter_averaging=False,
num_restarts=2,
min_steps_to_checkpoint=1000,
log_interval=20,
num_validation_batches=4,
)
nn.fit()
nn.restore()
nn.predict()