Задача.

Загрузить набор данных Wine.

Первый атрибут -- номер класса.

Построить изображения проекций (на одном рисунке) набора данных на избранные пары значений признаков (выбрать некоторым образом 12 различных пар признаков).

Методом главных компонент постройте сокращенный набор, содержащий наиболее существенные признаки (главные компоненты).

Построить изображения проекций (на одном рисунке) набора данных по всем парам выбранных новых признаков

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

attrs = """class_id
Alcohol
Malic acid
Ash
Alcalinity of ash
Magnesium
Total phenols
Flavanoids
Nonflavanoid phenols
Proanthocyanins
Color intensity
Hue
OD280/OD315 of diluted wines
Proline""".split('\n')
print(attrs)

['class_id', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']

wine_df=pd.read_csv("data/wine.data", sep=",", names=attrs)

wine_df

List=[]
for i in range(13):
    for j in range(i+1,13):
        List.append([i+1,j+1])

pair=[]
i=0
while i < 12:
    tmp = rnd.choice(List)
    if tmp not in pair:
        pair.append(tmp)
        i=i+1
    else:
        continue
print(pair)

[[4, 7], [7, 13], [5, 11], [2, 8], [1, 10], [10, 13], [3, 8], [6, 7], [7, 11], [4, 10], [4, 11], [8, 11]]

plt.figure(figsize=(18.0, 10.0))
for i in range(12):
    plt.subplot(3, 4, i+1)
    plt.scatter(wine_df[attrs[pair[i][0]]], wine_df[attrs[pair[i][1]]], s=25, c=wine_df[attrs[0]])
    plt.xlabel(attrs[pair[i][0]])
    plt.ylabel(attrs[pair[i][1]])
    plt.grid(1)
plt.tight_layout()
plt.show()

	class_id	Alcohol	Malic acid	Ash	Alcalinity of ash	Magnesium	Total phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color intensity	Hue	OD280/OD315 of diluted wines	Proline
0	1	14.23	1.71	2.43	15.6	127	2.80	3.06	0.28	2.29	5.640000	1.04	3.92	1065
1	1	13.20	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.380000	1.05	3.40	1050
2	1	13.16	2.36	2.67	18.6	101	2.80	3.24	0.30	2.81	5.680000	1.03	3.17	1185
3	1	14.37	1.95	2.50	16.8	113	3.85	3.49	0.24	2.18	7.800000	0.86	3.45	1480
4	1	13.24	2.59	2.87	21.0	118	2.80	2.69	0.39	1.82	4.320000	1.04	2.93	735
5	1	14.20	1.76	2.45	15.2	112	3.27	3.39	0.34	1.97	6.750000	1.05	2.85	1450
6	1	14.39	1.87	2.45	14.6	96	2.50	2.52	0.30	1.98	5.250000	1.02	3.58	1290
7	1	14.06	2.15	2.61	17.6	121	2.60	2.51	0.31	1.25	5.050000	1.06	3.58	1295
8	1	14.83	1.64	2.17	14.0	97	2.80	2.98	0.29	1.98	5.200000	1.08	2.85	1045
9	1	13.86	1.35	2.27	16.0	98	2.98	3.15	0.22	1.85	7.220000	1.01	3.55	1045
10	1	14.10	2.16	2.30	18.0	105	2.95	3.32	0.22	2.38	5.750000	1.25	3.17	1510
11	1	14.12	1.48	2.32	16.8	95	2.20	2.43	0.26	1.57	5.000000	1.17	2.82	1280
12	1	13.75	1.73	2.41	16.0	89	2.60	2.76	0.29	1.81	5.600000	1.15	2.90	1320
13	1	14.75	1.73	2.39	11.4	91	3.10	3.69	0.43	2.81	5.400000	1.25	2.73	1150
14	1	14.38	1.87	2.38	12.0	102	3.30	3.64	0.29	2.96	7.500000	1.20	3.00	1547
15	1	13.63	1.81	2.70	17.2	112	2.85	2.91	0.30	1.46	7.300000	1.28	2.88	1310
16	1	14.30	1.92	2.72	20.0	120	2.80	3.14	0.33	1.97	6.200000	1.07	2.65	1280
17	1	13.83	1.57	2.62	20.0	115	2.95	3.40	0.40	1.72	6.600000	1.13	2.57	1130
18	1	14.19	1.59	2.48	16.5	108	3.30	3.93	0.32	1.86	8.700000	1.23	2.82	1680
19	1	13.64	3.10	2.56	15.2	116	2.70	3.03	0.17	1.66	5.100000	0.96	3.36	845
20	1	14.06	1.63	2.28	16.0	126	3.00	3.17	0.24	2.10	5.650000	1.09	3.71	780
21	1	12.93	3.80	2.65	18.6	102	2.41	2.41	0.25	1.98	4.500000	1.03	3.52	770
22	1	13.71	1.86	2.36	16.6	101	2.61	2.88	0.27	1.69	3.800000	1.11	4.00	1035
23	1	12.85	1.60	2.52	17.8	95	2.48	2.37	0.26	1.46	3.930000	1.09	3.63	1015
24	1	13.50	1.81	2.61	20.0	96	2.53	2.61	0.28	1.66	3.520000	1.12	3.82	845
25	1	13.05	2.05	3.22	25.0	124	2.63	2.68	0.47	1.92	3.580000	1.13	3.20	830
26	1	13.39	1.77	2.62	16.1	93	2.85	2.94	0.34	1.45	4.800000	0.92	3.22	1195
27	1	13.30	1.72	2.14	17.0	94	2.40	2.19	0.27	1.35	3.950000	1.02	2.77	1285
28	1	13.87	1.90	2.80	19.4	107	2.95	2.97	0.37	1.76	4.500000	1.25	3.40	915
29	1	14.02	1.68	2.21	16.0	96	2.65	2.33	0.26	1.98	4.700000	1.04	3.59	1035
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
148	3	13.32	3.24	2.38	21.5	92	1.93	0.76	0.45	1.25	8.420000	0.55	1.62	650
149	3	13.08	3.90	2.36	21.5	113	1.41	1.39	0.34	1.14	9.400000	0.57	1.33	550
150	3	13.50	3.12	2.62	24.0	123	1.40	1.57	0.22	1.25	8.600000	0.59	1.30	500
151	3	12.79	2.67	2.48	22.0	112	1.48	1.36	0.24	1.26	10.800000	0.48	1.47	480
152	3	13.11	1.90	2.75	25.5	116	2.20	1.28	0.26	1.56	7.100000	0.61	1.33	425
153	3	13.23	3.30	2.28	18.5	98	1.80	0.83	0.61	1.87	10.520000	0.56	1.51	675
154	3	12.58	1.29	2.10	20.0	103	1.48	0.58	0.53	1.40	7.600000	0.58	1.55	640
155	3	13.17	5.19	2.32	22.0	93	1.74	0.63	0.61	1.55	7.900000	0.60	1.48	725
156	3	13.84	4.12	2.38	19.5	89	1.80	0.83	0.48	1.56	9.010000	0.57	1.64	480
157	3	12.45	3.03	2.64	27.0	97	1.90	0.58	0.63	1.14	7.500000	0.67	1.73	880
158	3	14.34	1.68	2.70	25.0	98	2.80	1.31	0.53	2.70	13.000000	0.57	1.96	660
159	3	13.48	1.67	2.64	22.5	89	2.60	1.10	0.52	2.29	11.750000	0.57	1.78	620
160	3	12.36	3.83	2.38	21.0	88	2.30	0.92	0.50	1.04	7.650000	0.56	1.58	520
161	3	13.69	3.26	2.54	20.0	107	1.83	0.56	0.50	0.80	5.880000	0.96	1.82	680
162	3	12.85	3.27	2.58	22.0	106	1.65	0.60	0.60	0.96	5.580000	0.87	2.11	570
163	3	12.96	3.45	2.35	18.5	106	1.39	0.70	0.40	0.94	5.280000	0.68	1.75	675
164	3	13.78	2.76	2.30	22.0	90	1.35	0.68	0.41	1.03	9.580000	0.70	1.68	615
165	3	13.73	4.36	2.26	22.5	88	1.28	0.47	0.52	1.15	6.620000	0.78	1.75	520
166	3	13.45	3.70	2.60	23.0	111	1.70	0.92	0.43	1.46	10.680000	0.85	1.56	695
167	3	12.82	3.37	2.30	19.5	88	1.48	0.66	0.40	0.97	10.260000	0.72	1.75	685
168	3	13.58	2.58	2.69	24.5	105	1.55	0.84	0.39	1.54	8.660000	0.74	1.80	750
169	3	13.40	4.60	2.86	25.0	112	1.98	0.96	0.27	1.11	8.500000	0.67	1.92	630
170	3	12.20	3.03	2.32	19.0	96	1.25	0.49	0.40	0.73	5.500000	0.66	1.83	510
171	3	12.77	2.39	2.28	19.5	86	1.39	0.51	0.48	0.64	9.899999	0.57	1.63	470
172	3	14.16	2.51	2.48	20.0	91	1.68	0.70	0.44	1.24	9.700000	0.62	1.71	660
173	3	13.71	5.65	2.45	20.5	95	1.68	0.61	0.52	1.06	7.700000	0.64	1.74	740
174	3	13.40	3.91	2.48	23.0	102	1.80	0.75	0.43	1.41	7.300000	0.70	1.56	750
175	3	13.27	4.28	2.26	20.0	120	1.59	0.69	0.43	1.35	10.200000	0.59	1.56	835
176	3	13.17	2.59	2.37	20.0	120	1.65	0.68	0.53	1.46	9.300000	0.60	1.62	840
177	3	14.13	4.10	2.74	24.5	96	2.05	0.76	0.56	1.35	9.200000	0.61	1.60	560