def main(): # set path iris_path = [ 'DataAnalysisProjectDesign/Experiment1/iris_train.arff', 'DataAnalysisProjectDesign/Experiment1/iris_test.arff' ] adult_path = [ 'DataAnalysisProjectDesign/Experiment1/adult_train.arff', 'DataAnalysisProjectDesign/Experiment1/adult_test.arff' ] # get choice data_choice = input('Enter 1 for iris DT; Enter 2 for adult DT:') tree_choice = input('Enter 1 for ID3; Enter 2 for CART:') path = select_dataset(data_choice, iris_path, adult_path) # create train data instance train_data_obj = Data(path[0]) train_data_obj.load_data() train_data_obj.fill_missing_data() # create test data instance test_data_obj = Data(path[1]) test_data_obj.clear_memory() test_data_obj.load_data() test_data_obj.fill_missing_data() tree = dt_router(train_data_obj, test_data_obj, tree_choice) tree.test() conf_mat, judge = tree.get_conf_mat() return tree, conf_mat, judge
def main(): # set path iris_path = 'DataAnalysisProjectDesign/Experiment2/iris_train.arff' adult_path = 'DataAnalysisProjectDesign/Experiment2/adult_train.arff' # get choice data_choice = input('Enter 1 for iris; Enter 2 for adult:') dt_num = int(input('Enter your expected tree number:')) path = select_dataset(data_choice,iris_path,adult_path) # create data instance data_obj = Data(path) data_obj.load_data() data_obj.fill_missing_data() # create random forest rf = RandomForest( data=data_obj, dt_num=dt_num ) rf.bagging() rf.train_rf() correct_rate,conf_mat = rf.test_rf() return dt_num,correct_rate,conf_mat
def main(): path = 'DM_Experiment4/iris.arff' choice = input('Use KMeans Enter 1;Use DBSCAN Enter 2:') # load data data_obj = Data(path=path) data_obj.load_data() algorithm_router(choice, data_obj)
def run_model(): d = Data(LANG, DEVorTEST, GLOVE_FILE, ELMO_FILE, MODEL, DEP_ADJACENCY_GCN, POSITION_EMBED) d.load_data( DATAPATH ) # This loads train, dev, and test if available, and also word2vec and ELMo where relevant model = Tagger(d, d.max_length, d.input_dim, d.n_poses, d.n_classes, initial_weight) tagger = getattr(model, MODEL)() # choose the specified tagging model T = Train_Test(POS, MODEL, tagger, d) if DEVorTEST == "CROSS_VAL": T.cross_validation(EPOCHS, BATCH_SIZE, DATAPATH) else: T.train(EPOCHS, BATCH_SIZE) T.test(DATAPATH ) # We pass DATAPATH to this function to be used for evaluation
def run_model(): # args: lang, train, dev, test, word2vec_dir, elmo_dir, model_name d = Data(LANG_TR, LANG_DEV, LANG_TS, DEVorTEST, WV_DIR, ELMO_PATH, MODEL, DEP_ADJACENCY_GCN, DEP_INFO, POS) d.load_data( DATAPATH ) # This loads train, dev (if available), test (if available) and also word2vec and ELMo # args: max_length, n_poses, n_classes, initial_weight='' model = Tagger(d, initial_weight) tagger = getattr(model, MODEL)() # choose the specified tagging model print(tagger) T = Train_Test(POS, W2V, MODEL, tagger, d, DEVorTEST) if DEVorTEST == "CROSS_VAL": T.cross_validation(EPOCHS, BATCH_SIZE, DATAPATH) else: T.train(EPOCHS, BATCH_SIZE) T.test( DATAPATH ) # We give the Data_path to this function, just for it to return the evaluation for us