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Config.py
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#coding:utf-8
import numpy as np
import tensorflow as tf
import os
import time
import datetime
import ctypes
import json
class Config(object):
def __init__(self):
self.lib = ctypes.cdll.LoadLibrary("./release/Base.so")
self.lib.sampling.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int64, ctypes.c_int64, ctypes.c_int64]
self.lib.getHeadBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
self.lib.getTailBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
self.lib.testHead.argtypes = [ctypes.c_void_p]
self.lib.testTail.argtypes = [ctypes.c_void_p]
self.lib.getTestBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
self.lib.getValidBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
self.lib.getBestThreshold.argtypes = [ctypes.c_void_p, ctypes.c_void_p]
self.lib.test_triple_classification.argtypes = [ctypes.c_void_p, ctypes.c_void_p]
self.test_flag = False
self.in_path = None
self.out_path = None
self.bern = 0
self.hidden_size = 100
self.ent_size = self.hidden_size
self.rel_size = self.hidden_size
self.train_times = 0
self.margin = 1.0
self.nbatches = 100
self.negative_ent = 1
self.negative_rel = 0
self.workThreads = 1
self.alpha = 0.001
self.lmbda = 0.000
self.log_on = 1
self.exportName = None
self.importName = None
self.export_steps = 0
self.opt_method = "SGD"
self.optimizer = None
self.test_link_prediction = False
self.test_triple_classification = False
def init(self):
self.trainModel = None
if self.in_path != None:
self.lib.setInPath(ctypes.create_string_buffer(self.in_path, len(self.in_path) * 2))
self.lib.setBern(self.bern)
self.lib.setWorkThreads(self.workThreads)
self.lib.randReset()
self.lib.importTrainFiles()
self.relTotal = self.lib.getRelationTotal()
self.entTotal = self.lib.getEntityTotal()
self.trainTotal = self.lib.getTrainTotal()
self.testTotal = self.lib.getTestTotal()
self.validTotal = self.lib.getValidTotal()
self.batch_size = self.lib.getTrainTotal() / self.nbatches
self.batch_seq_size = self.batch_size * (1 + self.negative_ent + self.negative_rel)
self.batch_h = np.zeros(self.batch_size * (1 + self.negative_ent + self.negative_rel), dtype = np.int64)
self.batch_t = np.zeros(self.batch_size * (1 + self.negative_ent + self.negative_rel), dtype = np.int64)
self.batch_r = np.zeros(self.batch_size * (1 + self.negative_ent + self.negative_rel), dtype = np.int64)
self.batch_y = np.zeros(self.batch_size * (1 + self.negative_ent + self.negative_rel), dtype = np.float32)
self.batch_h_addr = self.batch_h.__array_interface__['data'][0]
self.batch_t_addr = self.batch_t.__array_interface__['data'][0]
self.batch_r_addr = self.batch_r.__array_interface__['data'][0]
self.batch_y_addr = self.batch_y.__array_interface__['data'][0]
if self.test_link_prediction:
self.lib.importTestFiles()
self.lib.importTypeFiles()
self.test_h = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
self.test_t = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
self.test_r = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
self.test_h_addr = self.test_h.__array_interface__['data'][0]
self.test_t_addr = self.test_t.__array_interface__['data'][0]
self.test_r_addr = self.test_r.__array_interface__['data'][0]
if self.test_triple_classification:
self.lib.importTestFiles()
self.lib.importTypeFiles()
self.test_pos_h = np.zeros(self.lib.getTestTotal(), dtype = np.int64)
self.test_pos_t = np.zeros(self.lib.getTestTotal(), dtype = np.int64)
self.test_pos_r = np.zeros(self.lib.getTestTotal(), dtype = np.int64)
self.test_neg_h = np.zeros(self.lib.getTestTotal(), dtype = np.int64)
self.test_neg_t = np.zeros(self.lib.getTestTotal(), dtype = np.int64)
self.test_neg_r = np.zeros(self.lib.getTestTotal(), dtype = np.int64)
self.test_pos_h_addr = self.test_pos_h.__array_interface__['data'][0]
self.test_pos_t_addr = self.test_pos_t.__array_interface__['data'][0]
self.test_pos_r_addr = self.test_pos_r.__array_interface__['data'][0]
self.test_neg_h_addr = self.test_neg_h.__array_interface__['data'][0]
self.test_neg_t_addr = self.test_neg_t.__array_interface__['data'][0]
self.test_neg_r_addr = self.test_neg_r.__array_interface__['data'][0]
self.valid_pos_h = np.zeros(self.lib.getValidTotal(), dtype = np.int64)
self.valid_pos_t = np.zeros(self.lib.getValidTotal(), dtype = np.int64)
self.valid_pos_r = np.zeros(self.lib.getValidTotal(), dtype = np.int64)
self.valid_neg_h = np.zeros(self.lib.getValidTotal(), dtype = np.int64)
self.valid_neg_t = np.zeros(self.lib.getValidTotal(), dtype = np.int64)
self.valid_neg_r = np.zeros(self.lib.getValidTotal(), dtype = np.int64)
self.valid_pos_h_addr = self.valid_pos_h.__array_interface__['data'][0]
self.valid_pos_t_addr = self.valid_pos_t.__array_interface__['data'][0]
self.valid_pos_r_addr = self.valid_pos_r.__array_interface__['data'][0]
self.valid_neg_h_addr = self.valid_neg_h.__array_interface__['data'][0]
self.valid_neg_t_addr = self.valid_neg_t.__array_interface__['data'][0]
self.valid_neg_r_addr = self.valid_neg_r.__array_interface__['data'][0]
def get_ent_total(self):
return self.entTotal
def get_rel_total(self):
return self.relTotal
def set_lmbda(self, lmbda):
self.lmbda = lmbda
def set_optimizer(self, optimizer):
self.optimizer = optimizer
def set_opt_method(self, method):
self.opt_method = method
def set_test_link_prediction(self, flag):
self.test_link_prediction = flag
def set_test_triple_classification(self, flag):
self.test_triple_classification = flag
def set_log_on(self, flag):
self.log_on = flag
def set_alpha(self, alpha):
self.alpha = alpha
def set_in_path(self, path):
self.in_path = path
def set_out_files(self, path):
self.out_path = path
def set_bern(self, bern):
self.bern = bern
def set_dimension(self, dim):
self.hidden_size = dim
self.ent_size = dim
self.rel_size = dim
def set_ent_dimension(self, dim):
self.ent_size = dim
def set_rel_dimension(self, dim):
self.rel_size = dim
def set_train_times(self, times):
self.train_times = times
def set_nbatches(self, nbatches):
self.nbatches = nbatches
def set_margin(self, margin):
self.margin = margin
def set_work_threads(self, threads):
self.workThreads = threads
def set_ent_neg_rate(self, rate):
self.negative_ent = rate
def set_rel_neg_rate(self, rate):
self.negative_rel = rate
def set_import_files(self, path):
self.importName = path
def set_export_files(self, path, steps = 0):
self.exportName = path
self.export_steps = steps
def set_export_steps(self, steps):
self.export_steps = steps
def sampling(self):
self.lib.sampling(self.batch_h_addr, self.batch_t_addr, self.batch_r_addr, self.batch_y_addr, self.batch_size, self.negative_ent, self.negative_rel)
def save_tensorflow(self):
with self.graph.as_default():
with self.sess.as_default():
self.saver.save(self.sess, self.exportName)
def restore_tensorflow(self):
with self.graph.as_default():
with self.sess.as_default():
self.saver.restore(self.sess, self.importName)
def export_variables(self, path = None):
with self.graph.as_default():
with self.sess.as_default():
if path == None:
self.saver.save(self.sess, self.exportName)
else:
self.saver.save(self.sess, path)
def import_variables(self, path = None):
with self.graph.as_default():
with self.sess.as_default():
if path == None:
self.saver.restore(self.sess, self.importName)
else:
self.saver.restore(self.sess, path)
def get_parameter_lists(self):
return self.trainModel.parameter_lists
def get_parameters_by_name(self, var_name):
with self.graph.as_default():
with self.sess.as_default():
if var_name in self.trainModel.parameter_lists:
return self.sess.run(self.trainModel.parameter_lists[var_name])
else:
return None
def get_parameters(self, mode = "numpy"):
res = {}
lists = self.get_parameter_lists()
for var_name in lists:
if mode == "numpy":
res[var_name] = self.get_parameters_by_name(var_name)
else:
res[var_name] = self.get_parameters_by_name(var_name).tolist()
return res
def save_parameters(self, path = None):
if path == None:
path = self.out_path
f = open(path, "w")
f.write(json.dumps(self.get_parameters("list")))
f.close()
def set_parameters_by_name(self, var_name, tensor):
with self.graph.as_default():
with self.sess.as_default():
if var_name in self.trainModel.parameter_lists:
self.trainModel.parameter_lists[var_name].assign(tensor).eval()
def set_parameters(self, lists):
for i in lists:
self.set_parameters_by_name(i, lists[i])
def set_model(self, model):
self.model = model
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf.Session()
with self.sess.as_default():
initializer = tf.contrib.layers.xavier_initializer(uniform = True)
with tf.variable_scope("model", reuse=None, initializer = initializer):
self.trainModel = self.model(config = self)
if self.optimizer != None:
pass
elif self.opt_method == "Adagrad" or self.opt_method == "adagrad":
self.optimizer = tf.train.AdagradOptimizer(learning_rate = self.alpha, initial_accumulator_value=1e-20)
elif self.opt_method == "Adadelta" or self.opt_method == "adadelta":
self.optimizer = tf.train.AdadeltaOptimizer(self.alpha)
elif self.opt_method == "Adam" or self.opt_method == "adam":
self.optimizer = tf.train.AdamOptimizer(self.alpha)
else:
self.optimizer = tf.train.GradientDescentOptimizer(self.alpha)
grads_and_vars = self.optimizer.compute_gradients(self.trainModel.loss)
self.train_op = self.optimizer.apply_gradients(grads_and_vars)
self.saver = tf.train.Saver()
self.sess.run(tf.initialize_all_variables())
def train_step(self, batch_h, batch_t, batch_r, batch_y):
feed_dict = {
self.trainModel.batch_h: batch_h,
self.trainModel.batch_t: batch_t,
self.trainModel.batch_r: batch_r,
self.trainModel.batch_y: batch_y
}
_, loss = self.sess.run([self.train_op, self.trainModel.loss], feed_dict)
return loss
def test_step(self, test_h, test_t, test_r):
feed_dict = {
self.trainModel.predict_h: test_h,
self.trainModel.predict_t: test_t,
self.trainModel.predict_r: test_r,
}
predict = self.sess.run(self.trainModel.predict, feed_dict)
return predict
def run(self):
with self.graph.as_default():
with self.sess.as_default():
if self.importName != None:
self.restore_tensorflow()
for times in range(self.train_times):
res = 0.0
for batch in range(self.nbatches):
self.sampling()
res += self.train_step(self.batch_h, self.batch_t, self.batch_r, self.batch_y)
if self.log_on:
print times
print res
if self.exportName != None and (self.export_steps!=0 and times % self.export_steps == 0):
self.save_tensorflow()
if self.exportName != None:
self.save_tensorflow()
if self.out_path != None:
self.save_parameters(self.out_path)
def test(self):
with self.graph.as_default():
with self.sess.as_default():
if self.importName != None:
self.restore_tensorflow()
if self.test_link_prediction:
total = self.lib.getTestTotal()
for times in range(total):
self.lib.getHeadBatch(self.test_h_addr, self.test_t_addr, self.test_r_addr)
res = self.test_step(self.test_h, self.test_t, self.test_r)
self.lib.testHead(res.__array_interface__['data'][0])
self.lib.getTailBatch(self.test_h_addr, self.test_t_addr, self.test_r_addr)
res = self.test_step(self.test_h, self.test_t, self.test_r)
self.lib.testTail(res.__array_interface__['data'][0])
if self.log_on:
print times
self.lib.test_link_prediction()
if self.test_triple_classification:
self.lib.getValidBatch(self.valid_pos_h_addr, self.valid_pos_t_addr, self.valid_pos_r_addr, self.valid_neg_h_addr, self.valid_neg_t_addr, self.valid_neg_r_addr)
res_pos = self.test_step(self.valid_pos_h, self.valid_pos_t, self.valid_pos_r)
res_neg = self.test_step(self.valid_neg_h, self.valid_neg_t, self.valid_neg_r)
self.lib.getBestThreshold(res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0])
self.lib.getTestBatch(self.test_pos_h_addr, self.test_pos_t_addr, self.test_pos_r_addr, self.test_neg_h_addr, self.test_neg_t_addr, self.test_neg_r_addr)
res_pos = self.test_step(self.test_pos_h, self.test_pos_t, self.test_pos_r)
res_neg = self.test_step(self.test_neg_h, self.test_neg_t, self.test_neg_r)
self.lib.test_triple_classification(res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0])