我如何让我的支持向量回归来绘制我的多项式图

Question

我已经为多项式图编译了我的代码，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，它只是显示我的数据。我不知道发生了什么。有没有人？它甚至没有在描述我的数据的变量控制台上显示任何内容。

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

试试下面的代码。支持向量机期望它们的输入具有零均值和单位方差。不是剧情，是堵。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)

Answer 2

只是以马特的回答为基础。你的策划没有任何错误。当你用 'unreasonably' 大数调用 svr_poly.fit 时，不会抛出任何错误（但我仍然不得不杀死我的内核）。通过修改此代码中的指数值，我估计您可以在它中断之前达到 1e5，但不会更多。因此你的问题。正如 Matt 所说，应用 StandardScaler 将解决您的问题。

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



x_train = np.random.rand(10,1)      # between 0 and 1
y_train = np.random.rand(10,)       # between 0 and 1
x_train = np.multiply(x_train,1e5)  #scaling up to 1e5
svr_poly = SVR(kernel='poly', C=1e3, degree=1)
svr_poly.fit(x_train,y_train)#.predict(x_train)
y_poly = svr_poly.predict(x_train)

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

plt.show()

我如何让我的支持向量回归来绘制我的多项式图

How do i get my support vector regression to work to plot my polynomial graph

python

machine-learning

svm

scikit-learn