ソフトウェアエンジニアの技術ブログ：Software engineer tech blog

教師あり学習: 正解ラベルの付いたデータセット
教師なし学習: 正解ラベルの付いていないデータセット
-> 人間の解釈が必要な場合が多い

import numpy as np 
import matplotlib.pyplot as plt 
%matplotlib inline

from sklearn import datasets 
iris = datasets.load_iris()

print(iris.DESCR)

import numpy as np 
import numpy.random as random 
import scipy as sp 
import pandas as pd 
from pandas import Series, DataFrame 

from sklearn.model_selection import train_test_split 
from sklearn.datasets import load_iris 

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df["target"] = iris.target_names[iris.target]

df.head()

X = df.drop('target', axis=1)
Y = df['target']

X_train, X_test, y_train, y_test = train_test_split(X,Y,random_state=0)

# K-NN
from sklearn.neighbors import KNeighborsClassifier 
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

print("train score:",model.score(X_train,y_train))
print("test score:",model.score(X_test,y_test))

# decision tree
from sklearn.tree import DecisionTreeClassifier 
model = DecisionTreeClassifier(max_depth=3)
model.fit(X_train, y_train)

print("train score:",model.score(X_train,y_train))
print("test score:",model.score(X_test,y_test))

# SVM
from sklearn.svm import LinearSVC
model = LinearSVC()
model.fit(X_train, y_train)

print("train score:",model.score(X_train,y_train))
print("test score:",model.score(X_test,y_test))

# Linear Regression
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)

print("train score:",model.score(X_train,y_train))
print("test score:",model.score(X_test,y_test))

Github
https://github.com/intenseG/Pyaq

[vagrant@localhost igo]$ git clone https://github.com/intenseG/Pyaq.git
Initialized empty Git repository in /home/vagrant/python/igo/Pyaq/.git/
remote: Enumerating objects: 74, done.
remote: Total 74 (delta 0), reused 0 (delta 0), pack-reused 74
Unpacking objects: 100% (74/74), done.
[vagrant@localhost igo]$ cd Pyaq
[vagrant@localhost Pyaq]$ ls
LICENSE board.py learn.py pre_train search.py
README.md gtp.py model.py pyaq.py sgf.py

tensorflowが入っていることを確認します
[vagrant@localhost Pyaq]$ pip list

囲碁のデータセット
https://github.com/yenw/computer-go-dataset
http://www.yss-aya.com/ayaself/ayaself.html

pyaqの中身

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

from collections import Counter
import sys
from board import *
import gtp
import learn
import search


if __name__ == "__main__":
    args = sys.argv

    launch_mode = 0  # 0: gtp, 1: self, 2: learn
    byoyomi = 5.0
    main_time = 0.0
    quick = False
    random = False
    clean = False
    use_gpu = True

    for arg in args:
        if arg.find("self") >= 0:
            launch_mode = 1
        elif arg.find("learn") >= 0:
            launch_mode = 2
        elif arg.find("quick") >= 0:
            quick = True
        elif arg.find("random") >= 0:
            random = True
        elif arg.find("clean") >= 0:
            clean = True
        elif arg.find("main_time") >= 0:
            main_time = float(arg[arg.find("=") + 1:])
        elif arg.find("byoyomi") >= 0:
            byoyomi = float(arg[arg.find("=") + 1:])
        elif arg.find("cpu") >= 0:
            use_gpu = False

    if launch_mode == 0:
        gtp.call_gtp(main_time, byoyomi, quick, clean, use_gpu)

    elif launch_mode == 1:
        b = Board()
        if not random:
            tree = search.Tree("model.ckpt", use_gpu)

        while b.move_cnt < BVCNT * 2:
            prev_move = b.prev_move
            if random:
                move = b.random_play()
            elif quick:
                move = rv2ev(np.argmax(tree.evaluate(b)[0][0]))
                b.play(move, False)
            else:
                move, _ = tree.search(b, 0, clean=clean)
                b.play(move, False)

            b.showboard()
            if prev_move == PASS and move == PASS:
                break

        score_list = []
        b_cpy = Board()

        for i in range(256):
            b.copy(b_cpy)
            b_cpy.rollout(show_board=False)
            score_list.append(b_cpy.score())

        score = Counter(score_list).most_common(1)[0][0]
        if score == 0:
            result_str = "Draw"
        else:
            winner = "B" if score > 0 else "W"
            result_str = "%s+%.1f" % (winner, abs(score))
        sys.stderr.write("result: %s\n" % result_str)

    else:
        learn.learn(3e-4, 0.5, sgf_dir="sgf/", use_gpu=use_gpu, gpu_cnt=1)