smly

def objective(trial):
    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)
 
    param = {
        'objective': 'binary',
        'metric': 'binary_logloss',
        'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
        'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),

smly

import sklearn.datasets                                                                                                                                                                                                                        
import sklearn.metrics
import xgboost as xgb

import optuna


# FYI: Objective functions can take additional arguments
# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
def objective(trial):

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 pandas as pd
import lightgbm as lgb

# データをロード
df_train = pd.read_csv('./train.csv')
df_test = pd.read_csv('./test.csv')

# データフレームを「訓練」「テスト」の順番に連結する
df = pd.concat([df_train, df_test], sort=False)

smly

"""
Public LB: 0.50456
"""
from collections import Counter

import lightgbm as lgb
import ml_metrics
import numpy as np
import pandas as pd
from sklearn.feature_extraction import DictVectorizer

smly

# Python script using data from DonorsChoose.org Application Screening
# Forked from https://www.kaggle.com/opanichev/lightgbm-and-tf-idf-starter

# Original version: Validation score: 0.7791025740062782
# - Private Score 0.78470
# - Public Score 0.79516
# Tuned version: Validation score: 0.7799395759019241
# - Private Score 0.78622
# - Public Score 0.79535

smly

diff --git a/file.c b/file.c
index 6069770..76bb5e6 100644
--- a/file.c
+++ b/file.c
@@ -8526,12 +8526,14 @@ guess_save_name(Buffer *buf, char *path)
        if ((p = checkHeader(buf, "Content-Disposition:")) != NULL &&
            (q = strcasestr(p, "filename")) != NULL &&
            (q == p || IS_SPACE(*(q - 1)) || *(q - 1) == ';') &&
-           matchattr(q, "filename", 8, &name))
-           path = name->ptr;

smly

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

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

smly

Keybase proof

I hereby claim:

• I am smly on github.
• I am smly (https://keybase.io/smly) on keybase.
• I have a public key ASCQq0Kd4zZ_oWh3AhmyznhCAKx1FsnvZNx5n2pKyl5ejgo

To claim this, I am signing this object:

smly

# -*- coding: utf-8 -*-
"""
Original code: prep.py on https://github.com/anlthms/sp-2016.git

* invalid media stream: 2_1418_0.3.wav
* valid media stream: 2_1418_0.2.wav

md5 checksum
-------------
964a651d454ff8a2b77ccd35e20d0805  /opt/nih_seizure/train_2.zip