python - 我如何让支持向量回归来绘制多项式图

Question

我已经编译了多项式图的代码，但它没有绘图。我正在使用 scikit learn 的 SVR(支持向量回归)，我的代码如下。它没有显示任何错误消息，只是显示我的数据。我不知道发生了什么事。有没有人？它甚至没有在变量控制台上显示任何描述我的数据的内容。

import pandas as pd
import numpy as np
from sklearn.svm import SVR
from sklearn import cross_validation
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt



df = pd.read_csv('coffee.csv')
print(df)

df = df[['Date','Amount_prod','Beverage_index']]

x = np.array(df.Amount_prod)
y = np.array(df.Beverage_index)

x_train, x_test, y_train, y_test = cross_validation.train_test_split(
x, y, test_size=0.2)

x_train = np.pad(x, [(0,0)], mode='constant')
x_train.reshape((26,1))

y_train = np.pad(y, [(0,0)], mode='constant')
y_train.reshape((26,1))

x_train = np.arange(26).reshape((26, 1))
x_train = x.reshape((26, 1))
c = x.T
np.all(x_train == c)

x_test = np.arange(6).reshape((-1,1))
x_test = x.reshape((-1,1))
c2 = x.T
np.all(x_test == c2)

y_test = np.arange(6).reshape((-1,1))
y_test = y.reshape((-1,1))
c2 = y.T
np.all(y_test ==c2)

svr_poly = SVR(kernel='poly', C=1e3, degree=2)
y_poly = svr_poly.fit(x_train,y_train).predict(x_train)




plt.scatter(x_train, y_train, color='black')
plt.plot(x_train,  y_poly)

plt.show()

数据样本:

 Date   Amount_prod Beverage_index
    1990    83000         78
    1991    102000        78
    1992    94567         86
    1993    101340        88
    1994    96909         123
    1995    92987         101
    1996    103489        99
    1997    99650         109
    1998    107849        110
    1999    123467        90
    2000    112586        67
    2001    113485        67
    2002    108765        90

Answer 1

尝试下面的代码。支持向量机期望其输入具有零均值和单位方差。这不是情节，而是阻碍。这是对 fit 的调用。

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

svr_poly = make_pipeline(StandardScaler(), SVR(kernel='poly', C=1e3, degree=2))
y_poly = svr_poly.fit(x_train,y_train).predict(x_train)