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from kurobako import problem


class BinhAndKornProblemFactory(problem.ProblemFactory):
    def specification(self):
        params = [
            problem.Var("x", problem.ContinuousRange(0, 5)),
            problem.Var("y", problem.ContinuousRange(0, 3)),
        ]
        return problem.ProblemSpec(

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import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
from scipy.special import erfcinv

import optuna


def objective(trial):
    # Suggest from U(0, 1) with Optuna.

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import argparse
import math
import time

import sqlalchemy

import optuna


class Profile:

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import lightgbm as lgb
import numpy as np

import pandas as pd
import sklearn.datasets
from sklearn.datasets import fetch_openml
import sklearn.metrics
from sklearn.model_selection import train_test_split

import optuna

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import optuna

def objective(trial):
    x = trial.suggest_int("x", 0, 2)
    y = trial.suggest_float("y", -1.0, 1.0)
    z = trial.suggest_float("z", 0.0, 1.5)
    return x ** 2 + y ** 3 - z ** 4

study = optuna.create_study(sampler=optuna.samplers.RandomSampler())
study.optimize(objective, n_trials=100)

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FROM optuna/optuna:py3.7-dev

RUN apt-get update

# Compile sqlite with a low SQLITE_MAX_VARIABLE_NUMBER.
RUN mkdir sqlite3_tmp
WORKDIR sqlite3_tmp
RUN wget https://www.sqlite.org/src/tarball/sqlite.tar.gz
RUN tar zxf sqlite.tar.gz
ENV CFLAGS -DSQLITE_ENABLE_FTS3 \

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import os

import optuna
from optuna import visualization
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from torchvision import datasets

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"""
Plots importances evaluated by fANOVA (AutoML.org) and Fanova (sklearn based implementation).

1. Loads data from csv containing parameter configuration-objective value pairs.
2. Loads search space definitions from csv.
3. Evaluates all single and pairwise importances between parameters.
4. Repeats evaluation N times (with different random seeds).
5. Plots the average over N evaluations.
"""
import argparse

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from collections import OrderedDict
import time

from matplotlib import pyplot as plt
import optuna
from optuna import samplers
from optuna.storages import RDBStorage
from optuna.storages import RedisStorage

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import logging
import threading
import time

import joblib
import tqdm


class TqdmLoggingHandler(logging.StreamHandler):
    def emit(self, record):