作業中のメモ

よく「計算機」を使って作業をする.知らなかったことを中心にまとめるつもり.

分類問題を解く ~データの用意と可視化~

どうも,筆者です.

久しぶりの更新である.今回は,気象庁が公開している過去の気象データを用いて,分類問題を解く.まず,分類用データがないため,データの用意をする.

とある都市の気象データを以下に示す.

気温(),湿度(),日照時間(h)
平均,平均,平均
20.9,47,9
20.5,40,14
20.7,47,13
21.5,65,3.3
19.3,75,4.2
21.6,68,8.3
21,74,0
22.6,70,10.5
22.8,79,1.4
24.5,62,13.4
24.3,62,10.8
23.2,75,0.2
21.7,87,0
24,70,7.1
24.2,70,3.7
21.7,84,0
24.1,70,3.8
25.9,54,13.4
22.7,81,0.2
25.1,71,5.3
25.2,74,6.3
24.4,83,0.6
24.8,76,7.6
22.5,79,0.4
23,86,0
23.3,66,8.6
24.2,60,8
20.7,83,0.2
22.3,89,0
25,81,1
27.7,70,11.2
29,67,9.3
27.8,75,3.1
27.9,73,7.3
27.9,68,9.5
25.9,77,3
29.9,63,11.6
27.1,74,0
22.3,94,0
26.7,74,9.4
29.3,51,12.4
28.2,64,0.6
27.1,83,0.5
28.1,69,9
25.5,66,5.5
25.6,72,0.9
27.1,75,0.5
28.2,60,13.5
28.7,53,13.8
28.5,58,13.5
27.7,65,7
26.3,71,3.5
25.1,68,2.1
25.4,70,3.7
26.3,64,4.8
22.7,87,0
24.9,85,1.7
26.8,74,3.2
27.2,69,6.8
28,63,5.8
29.1,61,9.8
27.9,74,8.8
26.5,79,6.4
27.9,73,7.5
29,70,10.1
29.3,69,11.2
29.8,67,10.1
29.5,67,8.7
31,56,12.8
30.1,55,11.9
28.9,51,11
28.5,62,11.7
28.3,66,6.2
28.4,63,5.6
28,73,3.5
27.9,74,2.8
29.8,61,10.2
30.9,62,7.5
29.3,73,5.6
29.6,69,10.5
29.6,68,8.5
29.1,68,9.3
30.9,62,5
30.4,61,7.7
27.8,76,2.3
28.1,71,4.2
28.9,65,9.2
26.6,73,1.4
25.3,84,0.8
26.9,75,6.1
24.3,67,7
26.8,55,12.1
23.8,64,5.3
19.9,57,8.9
19.3,47,12.8
20.2,43,13.8
20.9,41,14
21.1,58,8.9
19.4,78,0
19.3,82,2.6
22.6,57,10.7
23.1,57,8.6
21.7,39,6.2
20.7,44,12.7
20.7,47,13.6
21.9,47,14
23.3,46,13.8
22.8,48,13
22.3,55,8.9
22,65,0
25.6,48,13.7
25,57,7
21,86,0.2
23.5,59,4
24.7,63,12.6
24.4,70,5.4
21.9,90,0
25,60,6.8
24.6,72,1.5
24,81,3.2
24.8,76,0.7
22.9,90,0.5
26.6,79,2.7
27.8,74,5.5
29.1,67,7.2
26.9,78,0.8
26,73,1.7
26.9,61,12.7
28.4,55,13.6
28.9,56,11
27.6,73,0.1
27.5,77,3.6
28.2,73,6.7
28.6,76,3.5
27.9,79,5.6
27.9,74,6.3
28.8,66,9.2
29.7,63,8.6
27.8,72,2.6
29.3,61,10.8
28.2,67,8.8
28.5,69,9.1
28.9,67,7.5
29,68,8
27,81,0.4
28.6,75,3.4
27,89,0.1
29.3,78,4.9
26.3,82,0
28.5,73,5.5
29,72,4.3
27,81,0
29.2,72,8.7
29.8,65,7
27.8,66,3.3
27.7,69,3.3
29.3,69,6.1
29.2,75,3.1
29.9,70,10.2
27.3,83,0.1
27.4,73,6.6
29.7,57,12.5
29.5,67,3.5
27.1,89,0.9
27.9,74,7.3
28,62,4.8
27.5,66,0.6
24.9,85,0
25.9,85,1.2
28,74,4.6
26.7,85,3.7
28.2,70,9.8
28.1,73,6
28.5,72,6.3
28,74,2.3
27.8,77,5.5
30.8,62,10.9
30.2,65,8
27.5,58,2.8
26.8,62,10
27.3,72,1
28.7,74,3.5
28.9,64,9.2
27.3,51,11.5

このデータを下記の条件に合致する場合は1,合致しない場合は0とラベルを振る. 【条件】「気温が24℃以上」かつ「湿度が50%以上」かつ「日照時間が3h以上」

また,ここで利用するデータは「気温」と「湿度」のみとする.気温,湿度,上記の条件を適用した結果を以下に示す.

Temperature,Humidity,Status
20.9,47,0
20.5,40,0
20.7,47,0
21.5,65,0
19.3,75,0
21.6,68,0
21,74,0
22.6,70,0
22.8,79,0
24.5,62,1
24.3,62,1
23.2,75,0
21.7,87,0
24,70,1
24.2,70,1
21.7,84,0
24.1,70,1
25.9,54,1
22.7,81,0
25.1,71,1
25.2,74,1
24.4,83,0
24.8,76,1
22.5,79,0
23,86,0
23.3,66,0
24.2,60,1
20.7,83,0
22.3,89,0
25,81,0
27.7,70,1
29,67,1
27.8,75,1
27.9,73,1
27.9,68,1
25.9,77,1
29.9,63,1
27.1,74,0
22.3,94,0
26.7,74,1
29.3,51,1
28.2,64,0
27.1,83,0
28.1,69,1
25.5,66,1
25.6,72,0
27.1,75,0
28.2,60,1
28.7,53,1
28.5,58,1
27.7,65,1
26.3,71,1
25.1,68,0
25.4,70,1
26.3,64,1
22.7,87,0
24.9,85,0
26.8,74,1
27.2,69,1
28,63,1
29.1,61,1
27.9,74,1
26.5,79,1
27.9,73,1
29,70,1
29.3,69,1
29.8,67,1
29.5,67,1
31,56,1
30.1,55,1
28.9,51,1
28.5,62,1
28.3,66,1
28.4,63,1
28,73,1
27.9,74,0
29.8,61,1
30.9,62,1
29.3,73,1
29.6,69,1
29.6,68,1
29.1,68,1
30.9,62,1
30.4,61,1
27.8,76,0
28.1,71,1
28.9,65,1
26.6,73,0
25.3,84,0
26.9,75,1
24.3,67,1
26.8,55,1
23.8,64,0
19.9,57,0
19.3,47,0
20.2,43,0
20.9,41,0
21.1,58,0
19.4,78,0
19.3,82,0
22.6,57,0
23.1,57,0
21.7,39,0
20.7,44,0
20.7,47,0
21.9,47,0
23.3,46,0
22.8,48,0
22.3,55,0
22,65,0
25.6,48,0
25,57,1
21,86,0
23.5,59,0
24.7,63,1
24.4,70,1
21.9,90,0
25,60,1
24.6,72,0
24,81,1
24.8,76,0
22.9,90,0
26.6,79,0
27.8,74,1
29.1,67,1
26.9,78,0
26,73,0
26.9,61,1
28.4,55,1
28.9,56,1
27.6,73,0
27.5,77,1
28.2,73,1
28.6,76,1
27.9,79,1
27.9,74,1
28.8,66,1
29.7,63,1
27.8,72,0
29.3,61,1
28.2,67,1
28.5,69,1
28.9,67,1
29,68,1
27,81,0
28.6,75,1
27,89,0
29.3,78,1
26.3,82,0
28.5,73,1
29,72,1
27,81,0
29.2,72,1
29.8,65,1
27.8,66,1
27.7,69,1
29.3,69,1
29.2,75,1
29.9,70,1
27.3,83,0
27.4,73,1
29.7,57,1
29.5,67,1
27.1,89,0
27.9,74,1
28,62,1
27.5,66,0
24.9,85,0
25.9,85,0
28,74,1
26.7,85,1
28.2,70,1
28.1,73,1
28.5,72,1
28,74,0
27.8,77,1
30.8,62,1
30.2,65,1
27.5,58,0
26.8,62,1
27.3,72,0
28.7,74,1
28.9,64,1
27.3,51,1

これを「train_data.csv」として保存する.また,テスト用のデータも同様に用意する.下記にとある時期の気象データを示す.

21.3,62,13.4
22.4,61,13.1
22.9,62,10.6
23.9,60,12.9
23.6,64,7
19.4,93,0
22.6,81,5.5
22.6,82,1.4
24.1,69,10.8
21.3,78,0
24.1,76,0.3
23.7,63,11.5
22.7,56,13.8
22.6,62,8.7
21.6,77,0
21.2,60,9.5
21.8,67,4
21,77,4.6
23.1,73,5.4
20.9,96,0
23.3,71,2.5
24.5,51,12.3
20.3,79,0.1
24.3,62,8.5
27.4,52,13
26.6,69,9.2
27.1,72,1.6
26.8,75,2.8
26.4,78,2.3
27.3,75,5.2
27.9,68,10.5
28,66,7.4
27.2,68,5.8
25.7,84,0
24.6,93,0
24.3,93,0
25.5,84,0.2
26.9,73,6
27.7,68,8.8
28.5,65,11
29.7,59,7.1
29.4,58,3.3
29.7,58,8.7
30.8,54,13
31.2,57,12.8
31.5,60,12.2
31,64,10.1
32,61,10.6
31,65,8
30.8,63,11.1
30.5,63,10.6
32.7,51,13.5
33.3,46,13.1
32.2,52,12.6
30.8,63,6.7
29.3,62,5.3
28.4,58,5
29.2,59,3.7
28.4,68,5.8
29.6,58,11.6
29.9,61,7.2
31,58,11.5
33.1,51,12.5
32.8,45,12.9
31.7,52,10.7
32.7,53,9.8
33,49,12.9
29.6,58,3
30.2,52,10.9
31.5,54,11.7
30.8,61,3.6
31.3,61,7.5
28.2,74,3
29.6,70,6.5
30.2,65,8.1
26.9,79,0.4
27.9,74,2.4
26.5,38,13.1
25,50,9.2
25.5,52,10
26.2,65,2.3
28.9,70,6.8
30.4,60,10.2
28.8,67,6.6
28,79,0.2
29.8,70,5.5
31.1,57,12.3
31.1,52,11.8
29.5,58,2.9
30.1,59,9.5
29.7,61,7.9
28.6,69,4

これを下記の条件に従いラベルを振る.合致する場合は1,合致しない場合は0とする. 【条件】「気温が24℃以上」かつ「湿度が50%以上」かつ「日照時間が3h以上」

また,ここで利用するデータは「気温」と「湿度」のみとする.気温,湿度,上記の条件を適用した結果を以下に示す.

Temperature,Humidity,Status
21.3,62,0
22.4,61,0
22.9,62,0
23.9,60,0
23.6,64,0
19.4,93,0
22.6,81,0
22.6,82,0
24.1,69,1
21.3,78,0
24.1,76,0
23.7,63,0
22.7,56,0
22.6,62,0
21.6,77,0
21.2,60,0
21.8,67,0
21,77,0
23.1,73,0
20.9,96,0
23.3,71,0
24.5,51,1
20.3,79,0
24.3,62,1
27.4,52,1
26.6,69,1
27.1,72,0
26.8,75,0
26.4,78,0
27.3,75,1
27.9,68,1
28,66,1
27.2,68,1
25.7,84,0
24.6,93,0
24.3,93,0
25.5,84,0
26.9,73,1
27.7,68,1
28.5,65,1
29.7,59,1
29.4,58,1
29.7,58,1
30.8,54,1
31.2,57,1
31.5,60,1
31,64,1
32,61,1
31,65,1
30.8,63,1
30.5,63,1
32.7,51,1
33.3,46,0
32.2,52,1
30.8,63,1
29.3,62,1
28.4,58,1
29.2,59,1
28.4,68,1
29.6,58,1
29.9,61,1
31,58,1
33.1,51,1
32.8,45,0
31.7,52,1
32.7,53,1
33,49,0
29.6,58,1
30.2,52,1
31.5,54,1
30.8,61,1
31.3,61,1
28.2,74,1
29.6,70,1
30.2,65,1
26.9,79,0
27.9,74,0
26.5,38,0
25,50,1
25.5,52,1
26.2,65,0
28.9,70,1
30.4,60,1
28.8,67,1
28,79,0
29.8,70,1
31.1,57,1
31.1,52,1
29.5,58,0
30.1,59,1
29.7,61,1
28.6,69,1

これを「test_data.csv」として保存する.

下記のスクリプトでデータを可視化する.

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

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def get_data(target_df):
    X = np.array([target_df['Temperature'].tolist(), target_df['Humidity'].tolist()], dtype=np.float64).T
    y = np.array(target_df['Status'].tolist(), dtype=np.int32)
    return X, y

def visualization(X, y, marker='o'):
    fig, ax = plt.subplots(figsize=(10, 10))
    x1_min, x1_max = X[:, 0].min()-1, X[:, 0].max()+1
    x2_min, x2_max = X[:, 1].min()-1, X[:, 1].max()+1
    x1_mesh, x2_mesh = np.meshgrid(np.arange(x1_min, x1_max, 0.1),np.arange(x2_min, x2_max, 0.1))
    ax.scatter(X[y==0, 0], X[y==0, 1], c='blue', marker=marker, cmap='jet')
    ax.scatter(X[y==1, 0], X[y==1, 1], c='red', marker=marker, cmap='jet')
    ax.set_xlim(x1_mesh.min(), x1_mesh.max())
    ax.set_ylim(x2_mesh.min(), x2_mesh.max())
    ax.set_xlabel('Temperature')
    ax.set_ylabel('Humidity')
    return fig, ax

if __name__ == '__main__':
    train_df = pd.read_csv('train_data.csv', header=0)
    test_df = pd.read_csv('test_data.csv', header=0)
    train_X, train_y = get_data(train_df)
    test_X, test_y = get_data(test_df)

    for (X, y, name, marker) in [(train_X, train_y, 'train', 'o'), (test_X, test_y, 'test', '^')]:
        fig, ax = visualization(X, y, marker=marker)
        ax.set_title('{} data'.format(name))
        plt.savefig('visualization_{}_data.png'.format(name))
        plt.close(fig)

結果は下記のようになる.

f:id:mathematicsphysical:20190802001956p:plain
学習用データ

f:id:mathematicsphysical:20190802002019p:plain
テスト用データ

今回はここまでとする.以降,時間を見つけて分類問題を解く.