def main(self): """ Method executing the whole pipeline. """ ## # get data ## if (self.dataset in DataLoader.default_datasets or os.path.exists(self.dataset)): dataLoader = DataLoader(DataLoader.default_datasets[self.dataset]) data = dataLoader.load() self.model_params['img_size'] = data.get_dimensions() self.model_params['label_size'] = data.get_label_dimensions() else: print("Dataset " + self.dataset + " does not exist. Aborting...") return -1 ### # Potential Graph creation ### if not os.path.exists(os.path.join(self.model_folder, 'model.meta')): builder = GraphBuilder() builder.build_graph(self.model_name, self.model_params) ### # Network training ### if self.do_training: network = Network(self.model_name, self.model_folder, self.opt, self.opt_params, self.num_epochs, self.batch_size, data, self.summary_folder, self.summary_intervals, self.complete_set, self.keep_prob, self.l2_reg, self.clip_gradient, self.clip_value) network.load_and_train() ### # Evaluation ### if self.do_eval: evaluator = Evaluation(data, self.model_folder, self.summary_folder, self.model_name, self.summary_folder, self.batch_size, **self.eval_params) evaluator.evaluate() print('Finished Evaluation.') ### # Tensorboard ### if self.tensorboard and self.do_training: print("Opening Tensorboard") os.system("tensorboard --logdir=" + self.summary_folder)
def test_build_graph(self): #relations = pd.read_csv('..\\..\\..\\Data\\combined_course_structure.csv') #node_names = list(relations['postreq']) #print(node_names) #gb = GraphBuilder(node_names, relations) #g = gb.build_graph() #print(g.get_node('Calculus and Analytic Geometry I').get_parents()[0].get_name()) relations = pd.read_csv('..\\..\\..\\Data\\combined_course_structure.csv') data = pd.read_csv('..\\..\\ExcelFiles\\courses_and_grades.csv') node_names = list(data.columns) gb = GraphBuilder(node_names, relations) g = gb.build_graph() print(g)
from bayesian_network import BayesianNetwork from knowledge_base import KnowledgeBase from graph_builder import GraphBuilder if __name__ == "__main__": _data_file_path = '..\\ExcelFiles\\courses_and_grades.csv' _relations_file_path = '..\\..\\Data\\combined_course_structure.csv' knowledge_base = KnowledgeBase(_relations_file_path, _data_file_path) builder = GraphBuilder() builder = builder.build_nodes(list(knowledge_base.get_data().columns)) builder = builder.add_parents(knowledge_base.get_relations()) builder = builder.add_children() builder = builder.build_edges() graph = builder.build_graph() nodes = graph.get_nodes() # bayes_net = BayesianNetwork(knowledge_base, graph) # print(bayes_net.get_graph().get_node('Calculus and Analytic Geometry I').get_parents())