def create_standAlone_test_graph(self, test_ind, test_y):
print("Create testing graph")
self.test_ind = test_ind
self.test_y = test_y
self.num_test_events = len(test_ind)
self.uniq_ind_test, self.n_i_test, self.sq_sum_test, self.log_sum_test = utils_funcs.extract_event_tensor_Reileigh(
self.test_ind, self.test_y)
self.num_uniq_ind_test = len(self.uniq_ind_test)
# Integral Term
# sum_i < int_0^T lam_i>
# placeholders
self.entry_ind_test = tf.constant(self.uniq_ind_test, dtype=tf.int32)
self.entry_n_i_test = tf.constant(self.n_i_test, dtype=FLOAT_TYPE)
self.entry_sq_sum_test = tf.constant(self.sq_sum_test,
dtype=FLOAT_TYPE)
self.entry_log_sum_test = tf.constant(self.log_sum_test,
dtype=FLOAT_TYPE)
self.X_entries_test = utils_funcs.concat_embeddings(
self.tf_U, self.entry_ind_test)
# sample posterior base rate ( f )
self.Knm_entries_test = utils_funcs.kernel_cross_tf(
self.X_entries_test, self.tf_B, self.tf_log_amp_alpha,
self.tf_log_lengthscale_alpha)
self.gp_base_rate_entries_test = utils_funcs.sample_pst_f_tf_MLE(
self.tf_mu_alpha, self.Kmm_alpha, self.Knm_entries_test)
self.base_rate_entries_test = tf.exp(self.gp_base_rate_entries_test)
# int term 1, using entryEvent
self.int_part_test = tf.reduce_sum(self.base_rate_entries_test *
self.entry_sq_sum_test)
# event sum term 1
self.event_sum_test = tf.reduce_sum(self.gp_base_rate_entries_test *
self.entry_n_i_test +
self.entry_log_sum_test)
self.llk_test = self.event_sum_test - self.int_part_test
self.isTestGraphInitialized = True
return self
def create_standAlone_test_graph(self, test_ind, test_y):
print("Create testing graph")
self.test_ind = test_ind
self.test_y = test_y
self.num_test_events = len(test_ind)
self.uniq_ind_test, self.n_i_test, self.sq_sum_test, self.log_sum_test = utils_funcs.extract_event_tensor_Reileigh(
self.test_ind, self.test_y)
self.num_uniq_ind_test = len(self.uniq_ind_test)
# Integral Term
# sum_i < int_0^T lam_i>
# placeholders
self.entry_ind_test = tf.constant(self.uniq_ind_test, dtype=tf.int32)
self.entry_n_i_test = tf.constant(self.n_i_test, dtype=FLOAT_TYPE)
self.entry_sq_sum_test = tf.constant(self.sq_sum_test,
dtype=FLOAT_TYPE)
self.entry_log_sum_test = tf.constant(self.log_sum_test,
dtype=FLOAT_TYPE)
self.gp_base_rate_entries_test = utils_funcs.log_CP_base_rate(
self.tf_U, self.entry_ind_test)
self.base_rate_entries_test = tf.exp(self.gp_base_rate_entries_test)
# int term 1, using entryEvent
self.int_part_test = tf.reduce_sum(self.base_rate_entries_test *
self.entry_sq_sum_test)
# event sum term 1
self.event_sum_test = tf.reduce_sum(self.gp_base_rate_entries_test *
self.entry_n_i_test +
self.entry_log_sum_test)
self.llk_test = self.event_sum_test - self.int_part_test
self.isTestGraphInitialized = True
return self
def __init__(self, train_ind, train_y, init_config):
self.train_ind = train_ind
self.train_y = train_y
self.nmod = train_ind.shape[1]
self.uniq_ind, self.n_i, self.sq_sum, self.log_sum = utils_funcs.extract_event_tensor_Reileigh(
self.train_ind, self.train_y)
self.num_entries = len(self.uniq_ind)
#self.log_file_name = init_config['log_file_name']
self.init_U = init_config['U']
self.batch_size_entry = init_config['batch_size_entry']
self.learning_rate = init_config['learning_rate']
self.GP_SCOPE_NAME = "gp_params"
#GP parameters
with tf.variable_scope(self.GP_SCOPE_NAME):
# Embedding params
self.tf_U = [
tf.Variable(self.init_U[k], dtype=FLOAT_TYPE)
for k in range(self.nmod)
]
# Integral Term
# sum_i < int_0^T lam_i>
# placeholders
self.batch_entry_ind = tf.placeholder(
dtype=tf.int32, shape=[self.batch_size_entry, self.nmod])
self.batch_entry_n_i = tf.placeholder(dtype=FLOAT_TYPE,
shape=[self.batch_size_entry, 1])
self.batch_entry_log_sum = tf.placeholder(
dtype=FLOAT_TYPE, shape=[self.batch_size_entry, 1])
self.batch_entry_sq_sum = tf.placeholder(
dtype=FLOAT_TYPE, shape=[self.batch_size_entry, 1])
self.tf_T = tf.constant(self.train_y[-1][0] - self.train_y[0][0],
dtype=FLOAT_TYPE)
self.tf_T0 = tf.constant(self.train_y[0][0], dtype=FLOAT_TYPE)
self.tf_T1 = tf.constant(self.train_y[-1][0], dtype=FLOAT_TYPE)
# sample posterior base rate ( f )
self.gp_base_rate_entries = utils_funcs.log_CP_base_rate(
self.tf_U, self.batch_entry_ind)
self.base_rate_entries = tf.exp(self.gp_base_rate_entries)
#int term 1, using entryEvent
self.int_part1 = self.num_entries / self.batch_size_entry * tf.reduce_sum(
self.base_rate_entries * self.batch_entry_sq_sum)
# event sum term 1
self.eventSum = (self.batch_entry_n_i * self.gp_base_rate_entries +
self.batch_entry_log_sum)
self.event_sum_part1 = self.num_entries / self.batch_size_entry * (
tf.reduce_sum(self.eventSum))
self.ELBO = self.event_sum_part1 - self.int_part1
self.neg_ELBO = -self.ELBO
self.ELBO_hist = []
# setting
self.min_opt = tf.train.AdamOptimizer(self.learning_rate)
self.min_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.GP_SCOPE_NAME)
#print( self.min_params) ##
self.min_step = self.min_opt.minimize(self.neg_ELBO,
var_list=self.min_params)
# GPU settings
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
self.entries_ind_y_gnrt = utils_funcs.DataGenerator(
self.uniq_ind,
np.concatenate([self.n_i, self.sq_sum, self.log_sum], axis=1))
self.isTestGraphInitialized = False
def __init__(self, train_ind, train_y, init_config):
self.train_ind = train_ind
self.train_y = train_y
self.nmod = train_ind.shape[1]
self.uniq_ind, self.n_i, self.sq_sum, self.log_sum = utils_funcs.extract_event_tensor_Reileigh(
self.train_ind, self.train_y)
self.num_entries = len(self.uniq_ind)
#self.log_file_name = init_config['log_file_name']
self.init_U = init_config['U']
self.batch_size_entry = init_config['batch_size_entry']
self.learning_rate = init_config['learning_rate']
# VI Sparse GP
self.B = init_config['inducing_B'] # init with k-means, [len_B, rank]
self.len_B = len(self.B)
self.GP_SCOPE_NAME = "gp_params"
#GP parameters
with tf.variable_scope(self.GP_SCOPE_NAME):
# Embedding params
self.tf_U = [
tf.Variable(self.init_U[k], dtype=FLOAT_TYPE)
for k in range(self.nmod)
]
# pseudo inputs
self.tf_B = tf.Variable(self.B, dtype=FLOAT_TYPE)
# pseudo outputs
self.tf_mu_alpha = tf.Variable(np.zeros([self.len_B, 1]),
dtype=FLOAT_TYPE)
self.tf_Ltril_alpha = tf.Variable(np.eye(self.len_B),
dtype=FLOAT_TYPE)
self.tf_log_lengthscale_alpha = tf.Variable(np.zeros(
[self.B.shape[1], 1]),
dtype=FLOAT_TYPE)
self.tf_log_amp_alpha = tf.Variable(0, dtype=FLOAT_TYPE)
self.Kmm_alpha = utils_funcs.kernel_cross_tf(
self.tf_B, self.tf_B, self.tf_log_amp_alpha,
self.tf_log_lengthscale_alpha)
self.Kmm_alpha = self.Kmm_alpha + MATRIX_JITTER * tf.linalg.eye(
self.len_B, dtype=FLOAT_TYPE)
self.Var_alpha = self.tf_Ltril_alpha @ tf.transpose(
self.tf_Ltril_alpha)
# KL terms
self.KL_alpha = utils_funcs.KL_q_p_tf(self.Kmm_alpha, self.Var_alpha,
self.tf_Ltril_alpha,
self.tf_mu_alpha, self.len_B)
# Integral Term
# sum_i < int_0^T lam_i>
# placeholders
self.batch_entry_ind = tf.placeholder(
dtype=tf.int32, shape=[self.batch_size_entry, self.nmod])
self.batch_entry_n_i = tf.placeholder(dtype=FLOAT_TYPE,
shape=[self.batch_size_entry, 1])
self.batch_entry_log_sum = tf.placeholder(
dtype=FLOAT_TYPE, shape=[self.batch_size_entry, 1])
self.batch_entry_sq_sum = tf.placeholder(
dtype=FLOAT_TYPE, shape=[self.batch_size_entry, 1])
self.X_entries = utils_funcs.concat_embeddings(self.tf_U,
self.batch_entry_ind)
self.tf_T = tf.constant(self.train_y[-1][0] - self.train_y[0][0],
dtype=FLOAT_TYPE)
self.tf_T0 = tf.constant(self.train_y[0][0], dtype=FLOAT_TYPE)
self.tf_T1 = tf.constant(self.train_y[-1][0], dtype=FLOAT_TYPE)
# sample posterior base rate ( f )
self.Knm_entries = utils_funcs.kernel_cross_tf(
self.X_entries, self.tf_B, self.tf_log_amp_alpha,
self.tf_log_lengthscale_alpha)
self.gp_base_rate_entries = utils_funcs.sample_pst_f_tf(
self.tf_mu_alpha, self.tf_Ltril_alpha, self.Kmm_alpha,
self.Knm_entries, self.tf_log_amp_alpha, MATRIX_JITTER)
self.base_rate_entries = tf.exp(self.gp_base_rate_entries)
#int term 1, using entryEvent
self.int_part1 = self.num_entries / self.batch_size_entry * tf.reduce_sum(
self.base_rate_entries * self.batch_entry_sq_sum)
# event sum term 1
self.eventSum = (self.batch_entry_n_i * self.gp_base_rate_entries +
self.batch_entry_log_sum)
self.event_sum_part1 = self.num_entries / self.batch_size_entry * (
tf.reduce_sum(self.eventSum))
self.ELBO = self.event_sum_part1 - self.int_part1 - self.KL_alpha
self.neg_ELBO = -self.ELBO
self.ELBO_hist = []
# setting
self.min_opt = tf.train.AdamOptimizer(self.learning_rate)
self.min_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.GP_SCOPE_NAME)
#print( self.min_params) ##
self.min_step = self.min_opt.minimize(self.neg_ELBO,
var_list=self.min_params)
# GPU settings
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
self.entries_ind_y_gnrt = utils_funcs.DataGenerator(
self.uniq_ind,
np.concatenate([self.n_i, self.sq_sum, self.log_sum], axis=1))
self.isTestGraphInitialized = False