smly

diff --git a/examples/pruning/lightgbm_integration.py b/examples/pruning/lightgbm_integration.py
index 8e623772..4c0c315c 100644
--- a/examples/pruning/lightgbm_integration.py
+++ b/examples/pruning/lightgbm_integration.py
@@ -21,6 +21,15 @@ import optuna
 # FYI: Objective functions can take additional arguments
 # (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
 def objective(trial):
+    def custom_accuracy_pct(preds, data):
+        y_true = data.get_label()

smly

import numpy as np

from .functions import l2norm_numpy


def qe_dba(
    feats_test, feats_index, sims, topk_idx, alpha=3.0, qe=True, dba=True
):
    # alpha-query expansion (DBA)
    feats_concat = np.concatenate([feats_test, feats_index], axis=0)

smly

• ref: https://datatracker.ietf.org/doc/html/rfc2324

$ curl -I https://eng.mil.ru/
HTTP/1.1 418
Date: Thu, 24 Feb 2022 07:58:56 GMT
Content-Length: 0
Connection: keep-alive
Server: Ministry of Defence of the Russian Federation

smly

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Usage: switchbot [OPTIONS]

Options:
  -d, --device [light|speaker.tv|speaker.mpd|aircon]
                                  [required]
  --on
  --off

smly

"""
An example to check the AUC score on a validation set for each 10 epochs.
I hope it will be helpful for optimizing number of epochs.
"""
# -*- coding: utf-8 -*-
import logging

from sklearn.metrics import roc_auc_score
from keras.callbacks import Callback

smly

[I 2020-04-16 15:04:56,418] Using an existing study with name 'parallel' instead of creating a new one.
feature_fraction, val_score: inf:   0%|                                                                                                                                                                                                                                                                  | 0/7 [00:00<?, ?it/s][W 2020-04-16 15:04:56,643] Setting status of trial#15 as TrialState.FAIL because of the following error: AttributeError("'numpy.float64' object has no attribute 'translate'")
Traceback (most recent call last):
  File "/home/smly/gitws/optuna/optuna/study.py", line 682, in _run_trial
    result = func(trial)
  File "/home/smly/gitws/optuna/optuna/integration/lightgbm_tuner/optimize.py", line 229, in __call__
    param_value = min(trial.suggest_uniform("feature_fraction", 0.4, 1.0 + EPS), 1.0)
  File "/home/smly/gitws/optuna/optuna/trial.py", line 550, in suggest_uniform
    return self._suggest

smly

   col  target
0  Cat       1
1  Dog       1
2  Dog       0
3  Dog       0
4  Fox       0
<class 'cudf.core.dataframe.DataFrame'>
   col  target  col_le
0  Cat       1       0
1  Dog       1       1

smly

diff --git a/optuna/integration/lightgbm_tuner/optimize.py b/optuna/integration/lightgbm_tuner/optimize.py
index f11c8326..4434e51c 100644
--- a/optuna/integration/lightgbm_tuner/optimize.py
+++ b/optuna/integration/lightgbm_tuner/optimize.py
@@ -112,7 +112,14 @@ class BaseTuner(object):
         else:
             raise NotImplementedError
 
+        if self.lgbm_params.get("metric") == "None":
+            if len(booster.best_score[valid_name].keys()) > 0:

smly

import optuna.integration.lightgbm as lgb
import numpy as np
import optuna


def test_run() -> None:
    params = {
        "objective": "binary",
        "metric": "binary_logloss",
    }  # type: Dict

smly

import numpy as np
import sklearn.datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import mlflow

import optuna
import optuna.integration.lightgbm as lgb