toshihikoyanase

"""
Optuna example that optimizes multi-layer perceptrons using PyTorch distributed.

In this example, we optimize the validation accuracy of hand-written digit recognition using
PyTorch distributed data parallel and MNIST. We optimize the neural network architecture as well
as the optimizer configuration. As it is too time consuming to use the whole MNIST dataset, we
here use a small subset of it.

You can execute this example with mpirun command as follows:
    $ mpirun -n 2 python pytorch_distributed_simple.py

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import torch
import torch.distributed as dist
import torch.multiprocessing as mp


def example(rank, world_size):
    dist.init_process_group("gloo", rank=rank, world_size=world_size)

    if rank == 0:
        z = torch.tensor([True], dtype=torch.bool)

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def _dominates_both_feasible(extended: np.ndarray, const_extended: np.ndarray) -> np.ndarray:
    return np.logical_and(
        np.logical_and(
            np.all(const_extended <= 0, axis=2),
            np.all(np.swapaxes(const_extended, 0, 1) <= 0, axis=2),
        ), np.logical_and(
            np.all(extended <= np.swapaxes(extended, 0, 1), axis=2),
            np.any(extended < np.swapaxes(extended, 0, 1), axis=2),
        )
    )

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Overview

This document explains how to test migrated schema of Optuna's RDBStorage. The test consists of following two parts:

1. Test for values
2. Test for schema

Each section describes the test instruction for each database.

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import sklearn.datasets
from sklearn.model_selection import train_test_split

import optuna.integration.lightgbm as lgb


if __name__ == "__main__":
    X, y = sklearn.datasets.load_boston(return_X_y=True)
    train_x, val_x, train_y, val_y = train_test_split(X, y, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)

toshihikoyanase

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

import optuna.integration.lightgbm as lgb


if __name__ == "__main__":
    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)

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import logging
import os

import optuna


logging.basicConfig()
logging.getLogger("sqlalchemy.engine").setLevel(logging.INFO)

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from multiprocessing import Pool
import os
import sys

import optuna


def f(x, y):
    return (x - 3) ** 2 + y

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import optuna
from optuna import distributions
from optuna.samplers import TPESampler
from optuna.study import StudyDirection
from optuna.trial import TrialState

import numpy as np

def default_gamma(x):
    # type: (int) -> int