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mura_train_hist.py
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mura_train_hist.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import tensorflow as tf
import json
import os
from lib.utils import get_logger
from lib import preprocessing2 as prep
from model.mgrnn_supervisor_all import MGRNNSupervisor
# flags
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer('batch_size', 20, 'Batch size')
flags.DEFINE_integer('cl_decay_steps', 100,
'Parameter to control the decay speed of probability of feeding groundth instead of model output.')
flags.DEFINE_integer('epochs', 200, 'Maximum number of epochs to train.')
flags.DEFINE_string('filter_type', 'laplacian', 'laplacian/random_walk/dual_random_walk.')
flags.DEFINE_string('activate_func', 'tanh', 'tanh/sigmoid/relu.')
flags.DEFINE_string('pool_type', '_mpool', '_mpool/_apool.')
flags.DEFINE_integer('horizon', 1, 'Maximum number of timestamps to prediction.')
flags.DEFINE_float('l1_decay', -1.0, 'L1 Regularization')
flags.DEFINE_float('lr_decay', -1.0, 'Learning rate decay.')
flags.DEFINE_integer('lr_decay_epoch', -1, 'The epoch that starting decaying the parameter.')
flags.DEFINE_integer('lr_decay_interval', -1, 'Interval beteween each deacy.')
flags.DEFINE_float('learning_rate', 0.001, 'Learning rate. -1: select by hyperopt tuning.')
flags.DEFINE_float('drop_out', 0.0, 'drop_out 0.0: select by hyperopt tuning.')
flags.DEFINE_bool('shuffle_training', False, 'shuffle_training False: select by hyperopt tuning.')
flags.DEFINE_string('log_dir', None, 'Log directory for restoring the model from a checkpoint.')
flags.DEFINE_string('loss_func', 'L2Norm', 'KL/L2/EMD: loss function.')
flags.DEFINE_string('optimizer', 'adam', 'adam/sgd/ada.')
flags.DEFINE_float('min_learning_rate', -1, 'Minimum learning rate')
flags.DEFINE_integer('nb_weeks', 17, 'How many week\'s data should be used for train/test.')
flags.DEFINE_integer('patience', -1,
'Maximum number of epochs allowed for non-improving validation error before early stopping.')
flags.DEFINE_integer('seq_len', 3, 'Sequence length.')
flags.DEFINE_integer('test_every_n_epochs', 10, 'Run model on the testing dataset every n epochs.')
flags.DEFINE_bool('coarsen', True, 'Apply coarsen on input data.')
flags.DEFINE_integer('coarsening_levels', 4, 'Number of coarsened graph.')
flags.DEFINE_integer('num_gpus', 2, 'How many GPUs to use.')
flags.DEFINE_bool('use_cpu_only', False, 'Set to true to only use cpu.')
flags.DEFINE_bool('use_curriculum_learning', None, 'Set to true to use Curriculum learning in decoding stage.')
flags.DEFINE_integer('verbose', -1, '1: to log individual sensor information.')
# flags for data related
flags.DEFINE_string('server_name', 'nyc', 'The name of dataset to be processed')
flags.DEFINE_string('borough', 'Manhattan', 'Selected area')
flags.DEFINE_string('zone', 'taxi_zone', 'map partition method')
flags.DEFINE_integer('sample_rate', 20, 'Sample rate to condense the data')
flags.DEFINE_string('mode', 'hist', 'avg: for single value, hist: for multi values')
flags.DEFINE_string('data_format', 'speed', 'speed or duration')
flags.DEFINE_bool('duration_log', False, 'Apply log10 to the data, True when data_form is duration')
flags.DEFINE_bool('fill_mean', False, 'Fill HA to the data, True when need to fill in mean values')
flags.DEFINE_bool('sparse_removal', False, 'Apply sparse removal to the data, True when need to remove sparse regions')
flags.DEFINE_string('scaler', 'maxmin', 'maxmin: MaxMinScaler, std: Standard scaler')
flags.DEFINE_string('config_filename', './conf/base_config.json', 'Configuration filename for restoring the model.')
flags.DEFINE_string('graph_pkl_filename', 'data/sensor_graph/adj_mx.pkl',
'Pickle file containing: sensor_ids, sensor_id_to_ind_map, dist_matrix')
flags.DEFINE_string('traffic_df_filename', 'data/df_highway_2012_4mon_sample.h5',
'Path to hdf5 pandas.DataFrame.')
flags.DEFINE_string('model_filename', None, 'model file name to restore.')
flags.DEFINE_string('model_dir', None, 'model dir to restore.')
# flags for the graph construction
flags.DEFINE_integer('hopk', 4, 'Hopk to construct the adjacent matrix')
flags.DEFINE_integer('sigma', 12, 'sigma used to construct the adj matrix')
flags.DEFINE_float('trace_ratio', 0.0001, 'Trace ratio in loss')
flags.DEFINE_bool('is_restore', False, 'Whether in training or test model directly')
def main(_):
# Reads graph data.
with open(FLAGS.config_filename) as f:
# load configuration
data_model_config = json.load(f)
data_config = data_model_config['data']
# load data: include graph and data array
for name in ['server_name', 'hopk', 'sigma', 'mode', 'zone', 'coarsen',
'coarsening_levels', 'borough', 'data_format', 'duration_log',
'sample_rate']:
data_config[name] = getattr(FLAGS, name)
data_config['window_size'] = getattr(FLAGS, 'seq_len')
data_config['predict_size'] = getattr(FLAGS, 'horizon')
data_config['base_dir'] = os.path.join(data_config['base_dir'],
data_config['server_name'],
data_config['borough'],
data_config['zone'])
data_config['data_dir'] = os.path.join(data_config['base_dir'],
'S{}'.format(data_config['sample_rate']),
data_config['data_format'],
data_config['mode'],
'W{}_P{}'.format(data_config['window_size'],
data_config['predict_size']),
'rm{}'.format(data_config['data_rm_ratio'])
)
logger = get_logger('./logs/', 'info.log')
logger.info('Loading graph...')
dataset = prep.NYCData(**data_config)
logger.info('Loading graph tensor data with {} Mean-Fill...'.format(FLAGS.fill_mean))
dataset_f = dataset.gcnn_lstm_data_construction(sparse_removal=FLAGS.sparse_removal)
adj_mx = dataset.adj_matrix
dist_mx = dataset.dist_matrix
logger.info('Construct model and train...')
nodes = dataset.nodes
print("Number of edges is ", len(nodes))
print("Shape of adjacency matrix is ", adj_mx.shape)
supervisor_config = data_model_config['model']
supervisor_config['output_dim'] = dataset.output_dim
supervisor_config['start_date'] = data_config['start_date']
# setting for training
supervisor_config['use_cpu_only'] = FLAGS.use_cpu_only
if FLAGS.log_dir:
supervisor_config['log_dir'] = FLAGS.log_dir
if FLAGS.use_curriculum_learning is not None:
supervisor_config['use_curriculum_learning'] = FLAGS.use_curriculum_learning
if FLAGS.loss_func:
supervisor_config['loss_func'] = FLAGS.loss_func
if FLAGS.filter_type:
supervisor_config['filter_type'] = FLAGS.filter_type
# Overwrites space with specified parameters.
for name in ['batch_size', 'cl_decay_steps', 'epochs', 'horizon', 'learning_rate', 'l1_decay',
'lr_decay', 'lr_decay_epoch', 'lr_decay_interval', 'sample_rate', 'min_learning_rate',
'patience', 'seq_len', 'test_every_n_epochs', 'verbose', 'coarsen', 'coarsening_levels',
'zone', 'scaler', 'data_format', 'num_gpus', 'mode', 'fill_mean', 'activate_func',
'hopk', 'sigma', 'drop_out', 'shuffle_training', 'optimizer', 'pool_type',
'trace_ratio']:
if type(getattr(FLAGS, name)) == str or getattr(FLAGS, name) >= 0:
supervisor_config[name] = getattr(FLAGS, name)
tf_config = tf.ConfigProto(allow_soft_placement=True)
if FLAGS.use_cpu_only:
tf_config = tf.ConfigProto(device_count={'GPU': 0})
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
supervisor = MGRNNSupervisor(traffic_reading_df=dataset_f, adj_mx=adj_mx,
config=supervisor_config,
origin_df_file=dataset.origin_df_file,
nodes=nodes,
coarsed_dict=dataset.coarsed_dict)
if not FLAGS.is_restore:
supervisor.train(sess=sess)
else:
supervisor.test_and_write_results(
sess=sess, model_filename=FLAGS.model_filename,
model_dir=FLAGS.model_dir, dist_mx=dist_mx)
if __name__ == '__main__':
tf.app.run()