Задача Используем набор данных Wine. Используем первые две главные компоненты в качестве признаков.

Обучить алгоритмы классификации по методу SVC для каждого типа ядер (linear,rbf,poly).
Вычислить точность (относительное число правильных ответов) на обучающем множестве .
Изобразить классы для каждого классификатора в плоскости, образованной двумя главными компонентами.

import numpy as np
import pandas as pd
from sklearn import svm, metrics
import matplotlib.pyplot as plt
%matplotlib inline

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

cols = tuple([wine_df[attr].values for attr in attrs[1:]])
x=np.c_[cols]
y = wine_df['class_id']
print(x, y)

[[  1.42300000e+01   1.71000000e+00   2.43000000e+00 ...,   1.04000000e+00
    3.92000000e+00   1.06500000e+03]
 [  1.32000000e+01   1.78000000e+00   2.14000000e+00 ...,   1.05000000e+00
    3.40000000e+00   1.05000000e+03]
 [  1.31600000e+01   2.36000000e+00   2.67000000e+00 ...,   1.03000000e+00
    3.17000000e+00   1.18500000e+03]
 ..., 
 [  1.32700000e+01   4.28000000e+00   2.26000000e+00 ...,   5.90000000e-01
    1.56000000e+00   8.35000000e+02]
 [  1.31700000e+01   2.59000000e+00   2.37000000e+00 ...,   6.00000000e-01
    1.62000000e+00   8.40000000e+02]
 [  1.41300000e+01   4.10000000e+00   2.74000000e+00 ...,   6.10000000e-01
    1.60000000e+00   5.60000000e+02]] 0      1
1      1
2      1
3      1
4      1
5      1
6      1
7      1
8      1
9      1
10     1
11     1
12     1
13     1
14     1
15     1
16     1
17     1
18     1
19     1
20     1
21     1
22     1
23     1
24     1
25     1
26     1
27     1
28     1
29     1
      ..
148    3
149    3
150    3
151    3
152    3
153    3
154    3
155    3
156    3
157    3
158    3
159    3
160    3
161    3
162    3
163    3
164    3
165    3
166    3
167    3
168    3
169    3
170    3
171    3
172    3
173    3
174    3
175    3
176    3
177    3
Name: class_id, dtype: int64

import sklearn.decomposition as decomposition

pca = decomposition.PCA()

pca.fit(x)

PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)

pca.explained_variance_ratio_

array([  9.98091230e-01,   1.73591562e-03,   9.49589576e-05,
         5.02173562e-05,   1.23636847e-05,   8.46213034e-06,
         2.80681456e-06,   1.52308053e-06,   1.12783044e-06,
         7.21415811e-07,   3.78060267e-07,   2.12013755e-07,
         8.25392788e-08])

plt.figure(figsize=(15,10))
plt.bar(range(1,14),pca.explained_variance_ratio_)
plt.grid(1)
plt.minorticks_on()
plt.show()

pca.n_components = 2
X12 = pca.fit_transform(x)
print(pca.noise_variance_)

1.55306269038

plt.figure(figsize=(8.0,10.0))
plt.scatter(X12[:, 0], X12[:,1], s=25, c=y)
plt.xlabel("Компонент 1")
plt.ylabel("Компонент 2")
plt.grid(1)
plt.show()

def plot_map2d(clf, XX):
    x_min, x_max = XX[:,0].min(), XX[:,0].max()
    y_min, y_max = XX[:,1].min(), XX[:,1].max()
    x_range = np.linspace(x_min, x_max, 200)
    y_range = np.linspace(y_min, y_max, 200)
    xx, yy = np.meshgrid(x_range,y_range)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    plt.imshow(Z, extent=(x_min, x_max,y_min, y_max), aspect="auto", interpolation="bilinear", origin="lower")

def myPlot(kname, X, **kw):
    plt.figure(figsize=(8.0,10.0))
    clf = svm.SVC(kernel=kname, **kw)
    clf.fit(X,y)    
    print('Точность', kname, metrics.accuracy_score(y, clf.predict(X12)))
    plt.title(kname)
    plot_map2d(clf, X)
    plt.scatter(clf.support_vectors_[:,0], clf.support_vectors_[:,1], s=144, c='w')
    plt.scatter(X[:,0], X[:,1], s=25, c=y)
    plt.xlabel('komp1')
    plt.ylabel('komp2')
    plt.show()

myPlot('linear',X12)

Точность linear 0.73595505618

myPlot('rbf',X12, gamma=0.001)

Точность rbf 0.842696629213

myPlot('poly',X12)

	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