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)
