# pred_model_layer_1 = 256 # pred_model_layer_2 = 256 # pred_epochs = 100 np.set_printoptions(linewidth=200, threshold=(full_history_length + 1) * model_history_length * input_size) # unset with np.set_printoptions() # output location run_dir = os.path.join('dashboards', f'nn_dashboard', f'run_random_forest') if not os.path.exists(run_dir): os.makedirs(run_dir) stdout_add_file(os.path.join(run_dir, 'log_confirm.txt')) # ============ classifier: RandomForestClassifier = joblib.load( os.path.join(run_dir, 'random_forest_best.dump')) # 10-Fold Cross validation # print(np.mean(cross_val_score(clf, nn_dashboard_diff_none_train, nn_dashboard_diff_none_train_answers, cv=10))) bins = list(drange_inc(0, 1, '0.05')) # 5% point bin size bin_labels = list(range(1, 21)) # def binning(g): # return pd.Series(data={'actual': g.actual.sum(), 'count': len(g.index)}) # =======================
pred_model_layer_1 = 256 pred_model_layer_2 = 256 pred_epochs = 100 np.set_printoptions(linewidth=200, threshold=(full_history_length + 1) * model_history_length * input_size) # unset with np.set_printoptions() # output location run_dir = os.path.join('dashboards', f'pfa_dashboard', f'run_random_forest') if not os.path.exists(run_dir): os.makedirs(run_dir) stdout_add_file(os.path.join(run_dir, 'log.txt')) # ======================= # Get the data pfa_dashboard_diff_none_train: pd.DataFrame = pd.io.parsers.read_csv( os.path.join('dashboards', f'pfa_dashboard', f'pfa_dashboard_diff_none_train.csv'), delimiter=",", header=None) pfa_dashboard_diff_none_train_answers = pd.io.parsers.read_csv(os.path.join( 'dashboards', f'pfa_dashboard', f'pfa_dashboard_diff_none_train_answers.csv'), delimiter=",", header=None) # =======================
feature_num = 27 # <correct or not> + <26 features> lstm_layer_size = 64 epochs = 240 # output location run_dir = os.path.join('runs', f'run_t{history_length}_l{lstm_layer_size}_e{epochs}') score_dir = os.path.join('runs', f'run_t{history_length}_l{lstm_layer_size}_e{epochs}_score') if not os.path.exists(score_dir): os.makedirs(score_dir) # Setup some printing magic # https://stackoverflow.com/questions/11325019/how-to-output-to-the-console-and-file # https://stackoverflow.com/questions/7152762/how-to-redirect-print-output-to-a-file-using-python?noredirect=1&lq=1 stdout_add_file(os.path.join(score_dir, 'score.txt')) # we want to see everything in the prints np.set_printoptions(linewidth=200, threshold=(history_length + 1) * history_length * feature_num) # unset with np.set_printoptions() # =========== data answer_snapshots = read_numpy_3d_array_from_txt(os.path.join('outputs', f'snapshot_validate_l{history_length}.txt')) # input and outputs seq_in = answer_snapshots # we're using an auto encoder so the input is the output seq_out = seq_in # https://github.com/keras-team/keras/issues/4563