latent_dim = 50 print(K.tensorflow_backend._get_available_gpus()) if dblp.preprocessed_dataset_exist() and dblp.train_test_indices_exist(): dataset = dblp.load_preprocessed_dataset() train_test_indices = dblp.load_train_test_indices() else: if not dblp.ae_data_exist(file_path='../dataset/ae_dataset.pkl'): dblp.extract_data(filter_journals=True, skill_size_filter=min_skill_size, member_size_filter=min_member_size) if not dblp.preprocessed_dataset_exist( ) or not dblp.train_test_indices_exist(): dblp.dataset_preprocessing( dblp.load_ae_dataset(file_path='../dataset/ae_dataset.pkl'), seed=seed, kfolds=k_fold) dataset = dblp.load_preprocessed_dataset() train_test_indices = dblp.load_train_test_indices() # k_fold Cross Validation cvscores = [] # Defining evaluation scores holders for train data r_at_k_all_train = dblp_eval.init_eval_holder( evaluation_k_set ) # all r@k of instances in one fold and one k_evaluation_set r_at_k_overall_train = dblp_eval.init_eval_holder( evaluation_k_set ) # overall r@k of instances in one fold and one k_evaluation_set
#nn settings epochs = 300 back_propagation_batch_size = 64 training_batch_size = 6000 min_skill_size = 0 min_member_size = 0 encoding_dim = 1000 print(K.tensorflow_backend._get_available_gpus()) t2v_model = Team2Vec() t2v_model = load_T2V_model(t2v_model) embedding_dim = t2v_model.model.vector_size if dblp.ae_data_exist(file_path='../dataset/ae_t2v_dim{}_tSkill_dataset.pkl'.format(embedding_dim)): dataset = dblp.load_ae_dataset(file_path='../dataset/ae_t2v_dim{}_tSkill_dataset.pkl'.format(embedding_dim)) else: if not dblp.ae_data_exist(file_path='../dataset/ae_dataset.pkl'): dblp.extract_data(filter_journals=True, skill_size_filter=min_skill_size, member_size_filter=min_member_size) if not dblp.preprocessed_dataset_exist() or not dblp.train_test_indices_exist(): dblp.dataset_preprocessing(dblp.load_ae_dataset(file_path='../dataset/ae_dataset.pkl'), seed=seed, kfolds=k_fold) preprocessed_dataset = dblp.load_preprocessed_dataset() dblp.nn_t2v_dataset_generator(t2v_model, preprocessed_dataset, output_file_path='../dataset/ae_t2v_dim{}_tSkill_dataset.pkl'.format(embedding_dim), mode='skill') del preprocessed_dataset dataset = dblp.load_ae_dataset(file_path='../dataset/ae_t2v_dim{}_tSkill_dataset.pkl'.format(embedding_dim)) # reparameterization trick # instead of sampling from Q(z|X), sample epsilon = N(0,I)
print('Skill embedding options') t2v_model_skill = Team2Vec() t2v_model_skill = load_T2V_model(t2v_model_skill) embedding_dim_skill = t2v_model_skill.model.vector_size print('User embedding options') t2v_model_user = Team2Vec() t2v_model_user = load_T2V_model(t2v_model_user) embedding_dim_user = t2v_model_user.model.vector_size if dblp.ae_data_exist( file_path='../dataset/ae_t2v_dimSkill{}_dimUser{}_tFull_dataset.pkl'. format(embedding_dim_skill, embedding_dim_user)): dataset = dblp.load_ae_dataset( file_path='../dataset/ae_t2v_dimSkill{}_dimUser{}_tFull_dataset.pkl'. format(embedding_dim_skill, embedding_dim_user)) else: if not dblp.ae_data_exist(file_path='../dataset/ae_dataset.pkl'): dblp.extract_data(filter_journals=True, skill_size_filter=min_skill_size, member_size_filter=min_member_size) if not dblp.preprocessed_dataset_exist( ) or not dblp.train_test_indices_exist(): dblp.dataset_preprocessing( dblp.load_ae_dataset(file_path='../dataset/ae_dataset.pkl'), seed=seed, kfolds=k_fold) preprocessed_dataset = dblp.load_preprocessed_dataset() dblp.nn_t2v_dataset_generator(
back_propagation_batch_size = 64 training_batch_size = 6000 min_skill_size = 0 min_member_size = 0 latent_dim = 2 print(tf.test.is_gpu_available()) t2v_model = Team2Vec() t2v_model = load_T2V_model(t2v_model) embedding_dim = t2v_model.model.vector_size if dblp.ae_data_exist(file_path='../dataset/ae_t2v_dim{}_tSkill_dataset.pkl'. format(embedding_dim)): dataset = dblp.load_ae_dataset( file_path='../dataset/ae_t2v_dim{}_tSkill_dataset.pkl'.format( embedding_dim)) else: if not dblp.ae_data_exist(file_path='../dataset/ae_dataset.pkl'): dblp.extract_data(filter_journals=True, skill_size_filter=min_skill_size, member_size_filter=min_member_size) if not dblp.preprocessed_dataset_exist( ) or not dblp.train_test_indices_exist(): dblp.dataset_preprocessing( dblp.load_ae_dataset(file_path='../dataset/ae_dataset.pkl'), seed=seed, kfolds=k_fold) preprocessed_dataset = dblp.load_preprocessed_dataset() dblp.nn_t2v_dataset_generator(
#nn settings epochs = 2000 back_propagation_batch_size = 32 training_batch_size = 6000 min_skill_size = 0 min_member_size = 0 latent_dim = 2 beta = 30 print(tf.test.is_gpu_available()) m2v_path = '../dataset/embedding_dict.pkl' if dblp.ae_data_exist(file_path='../dataset/ae_e_m2v_tSkill_dataset.pkl'): dataset = dblp.load_ae_dataset(file_path='../dataset/ae_e_m2v_tSkill_dataset.pkl') else: if not dblp.ae_data_exist(file_path='../dataset/ae_dataset.pkl'): dblp.extract_data(filter_journals=True, skill_size_filter=min_skill_size, member_size_filter=min_member_size, output_dir='../dataset/ae_dataset.pkl') if not dblp.preprocessed_dataset_exist(file_path='../dataset/dblp_preprocessed_dataset.pkl') or not dblp.train_test_indices_exist(file_path='../dataset/Train_Test_indices.pkl'): dblp.dataset_preprocessing(dblp.load_ae_dataset(file_path='../dataset/ae_dataset.pkl'), indices_dict_file_path='../dataset/Train_Test_indices.pkl', preprocessed_dataset_file_path='../dataset/dblp_preprocessed_dataset.pkl', seed=seed, kfolds=k_fold) preprocessed_dataset = dblp.load_preprocessed_dataset(file_path='../dataset/dblp_preprocessed_dataset.pkl') dblp.nn_m2v_embedding_dataset_generator(model_path=m2v_path, dataset=preprocessed_dataset, output_file_path='../dataset/ae_e_m2v_tSkill_dataset.pkl', mode='skill', max_length=22) del preprocessed_dataset dataset = dblp.load_ae_dataset(file_path='../dataset/ae_e_m2v_tSkill_dataset.pkl') # reparameterization trick # instead of sampling from Q(z|X), sample epsilon = N(0,I)