time_start=0, classification_col=c) x_train, y_train = timestamped_to_vector(train, vector_col=v, time_start=0, classification_col=c) # Random search with thresholding rand_params = Configs.get_all() expt = Experiments.Experiment(rand_params, search_algorithm="random", data=(x_train, y_train), folds=10, folder_name="random_search_reults", thresholding=True, threshold=0.5) # parameter configurations A_B_C = Configs.get_A_B_C # Ensemble model ensemble_config = Experiments.Ensemble_configurations( list(A_B_C.values()), x_test=x_test, y_test=y_test, x_train=x_train, y_train=y_train, folder_name="test_train_results", batch_size=64) ensemble_config.run_experiments()
params_A["step"] = [1, 2, 3, 4, 5] # snapshot intervals (s) increase_snap_expt = Experiments.Increase_Snaphot_Experiment( params_A, search_algorithm="grid", data=(x, y), folds=10, folder_name="increase_intervals", thresholding=False, run_on_factors=True) increase_snap_expt.run_experiments() # Ensemble model ensemble_config = Experiments.Ensemble_configurations( list(A_B_C.values()), data=(x, y), folds=10, folder_name="ensemble_ABC", batch_size=58) ensemble_config.run_experiments() # Time step ensemble model params_A["step"] = [1] ensemble_sub_seq = Experiments.Ensemble_sub_sequences( params_A, data=(x, y), folds=10, folder_name="ensemble_sub_sequence") ensemble_sub_seq.run_experiments() # Leave out test data for config_description in A_B_C: params = A_B_C[config_description] params["sequence_length"] = [4, 8, 16] omit_test = Experiments.Omit_test_data(