utils.send_line(f'nround_mean: {nround_mean}') result = f"CV wloss: {np.mean(wloss_list)} + {np.std(wloss_list)}" print(result) utils.send_line(result) for i, y_pred in enumerate(y_preds): y_pred = pd.DataFrame(utils_metric.softmax(y_pred.astype(float).values)) if i == 0: tmp = y_pred else: tmp += y_pred tmp /= len(y_preds) y_preds = tmp.copy().values.astype(float) a_score = utils_metric.akiyama_metric(y.values, y_preds) print(f'akiyama_metric: {a_score}') utils.send_line(f'akiyama_metric: {a_score}') # ============================================================================= # model # ============================================================================= gc.collect() np.random.seed(SEED) model_all = [] for i in range(LOOP):
print(result) utils.send_line(result) for i,y_pred in enumerate(y_preds): y_pred = pd.DataFrame(utils_metric.softmax(y_pred.astype(float).values)) if i==0: oof = y_pred else: oof += y_pred oof /= len(y_preds) oof.to_pickle(f'../data/oof_{__file__}.pkl') oof = oof.copy().values.astype(float) a_score = utils_metric.akiyama_metric(y.values, oof) print(f'akiyama_metric: {a_score}') utils.send_line(f'akiyama_metric: {a_score}') utils.plot_confusion_matrix(__file__, oof) # ============================================================================= # weight # ============================================================================= import utils_post y_true = pd.get_dummies(y)