toshihikoyanase

#!/usr/bin/env python
import tweepy
import datetime
import time
# See http://blog.unfindable.net/archives/4257                                                        

locationsL=[-180,-90,180,90]

class StreamListener(tweepy.StreamListener):
    def __init__(self, api=None):

toshihikoyanase

mkdir tmp
for f in *b.zip
do 
    echo $f
    pushd tmp
    unzip ../$f
    popd
done

mkdir utf8

toshihikoyanase

#! /usr/bin/env python
# -*- coding: utf-8 -*-
import argparse


def run():
    args = parse_args()
    print("Input path: {0}".format(args.input_path))

toshihikoyanase

"""
Optuna example that optimizes a simple quadratic function.

In this example, we demonstrate how to import existing experimental results 
and continue the optimization.

We have the following two ways to execute this example:

(1) Execute this code directly.
    $ python quadratic_trial_import.py

toshihikoyanase

"""
Optuna example that optimizes a simple quadratic function.

In this example, we demonstrate how to import existing experimental results
and continue the optimization.

We have the following two ways to execute this example:

(1) Execute this code directly.
    $ python quadratic_change_range.py

toshihikoyanase

toshihikoyanase

Settings: (sigopt/evalset/auc-test-suites)

• Problems: 38
• Metrics: best

Solver	Borda	Firsts
(a) optuna#tpe-faster	0	37
(b) optuna#tpe-latest	1	38

Plot of best value

toshihikoyanase

import optuna
import sklearn
import sklearn.datasets
import sklearn.neural_network

def objective(trial):

    # ネットワーク構造の決定
    n_layers = trial.suggest_int('n_layers', 1, 4)
    layers = []

toshihikoyanase

import ...

def objective(trial):
    ...
    alpha = trial.suggest_loguniform('alpha', 1e-5, 1e-1)
    clf = sklearn.linear_model.SGDClassifier(alpha=alpha)

    for step in range(100):
        clf.partial_fit(train_x, train_y, classes=classes)

toshihikoyanase

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