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)
weight = utils_post.get_weight(y_true, oof, eta=0.1, nround=9999)
# =============================================================================
# model
# =============================================================================
gc.collect()
np.random.seed(SEED)
model_all = []
for i in range(LOOP):
y_pred = pd.concat([y_pred_gal, y_pred_exgal],
ignore_index=True).fillna(0)
y_pred = y_pred[[f'class_{c}' for c in utils_metric.classes]]
loss = utils_metric.multi_weighted_logloss(y.values, y_pred.values)
# =============================================================================
# weight
# =============================================================================
import utils_post
y_true = pd.get_dummies(y)
weight = utils_post.get_weight(y_true, y_pred.values, eta=0.1, nround=9999)
weight = np.append(weight, 1)
print(list(weight))
# =============================================================================
# confusion matrix
# =============================================================================
import matplotlib as mpl
mpl.use('Agg')
from matplotlib import pyplot as plt
from tqdm import tqdm
from sklearn.metrics import confusion_matrix
import itertools
unique_y = np.unique(y)
import optuna
from multiprocessing import cpu_count, Pool
# =============================================================================
# weight
# =============================================================================
oof = pd.read_pickle(
'../FROM_MYTEAM/oof_v103_068_lgb__v103_062_nn__specz_avg.pkl')
oof = oof.copy().values.astype(float)
y = utils.load_target().target
y_ohe = pd.get_dummies(y)
weight = utils_post.get_weight(y_ohe, oof, eta=0.1, nround=9999)
print(f'weight: np.array({list(weight)})')
weight = utils_post.get_weight(y_ohe,
oof,
eta=0.1,
nround=9999,
based_true=False)
print(f'weight: np.array({list(weight)})')
# =============================================================================
# one by one
# =============================================================================