import logging
logging.basicConfig()
import downhill.base
downhill.base.logging.setLevel(20)

import efe
from efe.exp_generators import *
import efe.tools as tools
import time
if __name__ =="__main__":
	start_time = time.time()
	print(start_time)
	#Load data, ensure that data is at path: 'path'/'name'/[train|valid|test].txt
	dbpediaexp = build_data(name = 'dbpedia_1million_dataset',path = tools.cur_path + '/datasets/')


	#SGD hyper-parameters:
	params = Parameters(learning_rate = 0.8, 
						max_iter = 4000, 
						batch_size = len(dbpediaexp.train.values) / 100,  #Make 100 batches
						neg_ratio = 10, 
						valid_scores_every = 100,
						learning_rate_policy = 'adagrad',
						contiguous_sampling = False )

	#Here each model is identified by its name, i.e. the string of its class name in models.py
	#Parameters given here are the best ones for each model, validated from the grid-search described in the paper
	all_params = { "DistMult_Logistic_Model" : params } ; emb_size = 100; lmbda =0.01



	tools.logger.info( "Learning rate: " + str(params.learning_rate))
	tools.logger.info( "Max iter: " + str(params.max_iter))
	tools.logger.info( "Generated negatives ratio: " + str(params.neg_ratio))
	tools.logger.info( "Batch size: " + str(params.batch_size))


	#Then call a local grid search, here only with one value of rank and regularization
	dbpediaexp.grid_search_on_all_models(all_params, embedding_size_grid = [emb_size], lmbda_grid = [lmbda], nb_runs = 1)

	print("--- %s seconds ---" % (time.time() - start_time))
	#Print best averaged metrics:
	dbpediaexp.print_best_MRR_and_hits()
	print("--- %s saving embeddings ---")
	dbpediaexp.save_embeddings(model_s="DistMult_Logistic_Model",filename="DBpedia_DistMult_embeddings.txt")
	print("--- %s embeddings saved---")

import logging
logging.basicConfig()
import downhill.base
downhill.base.logging.setLevel(20)

import efe
from efe.exp_generators import *
import efe.tools as tools
import time
if __name__ =="__main__":
	start_time = time.time()
	print(start_time)
	#Load data, ensure that data is at path: 'path'/'name'/[train|valid|test].txt
	dbpediaexp = build_data(name = 'dbpedia_dataset',path = tools.cur_path + '/datasets/')


	#SGD hyper-parameters:
	params = Parameters(learning_rate = 0.8, 
						max_iter = 4000, 
						batch_size = len(dbpediaexp.train.values) / 100,  #Make 100 batches
						neg_ratio = 10, 
						valid_scores_every = 100,
						learning_rate_policy = 'adagrad',
						contiguous_sampling = False )

	#Here each model is identified by its name, i.e. the string of its class name in models.py
	#Parameters given here are the best ones for each model, validated from the grid-search described in the paper
	all_params = { "DistMult_Logistic_Model" : params } ; emb_size = 100; lmbda =0.01



	tools.logger.info( "Learning rate: " + str(params.learning_rate))
	tools.logger.info( "Max iter: " + str(params.max_iter))
	tools.logger.info( "Generated negatives ratio: " + str(params.neg_ratio))
	tools.logger.info( "Batch size: " + str(params.batch_size))


	#Then call a local grid search, here only with one value of rank and regularization
	dbpediaexp.grid_search_on_all_models(all_params, embedding_size_grid = [emb_size], lmbda_grid = [lmbda], nb_runs = 1)

	print("--- %s seconds ---" % (time.time() - start_time))
	#Print best averaged metrics:
	dbpediaexp.print_best_MRR_and_hits()
	print("--- %s saving embeddings ---")
	dbpediaexp.save_embeddings(model_s="DistMult_Logistic_Model",filename="DBpedia_DistMult_embeddings.txt")
	print("--- %s embeddings saved---")

Update dbpedia_run_DistMult.py