SVR的代码（python）

项目中一个早期版本的代码，PCA-SVR，参数寻优采用传统的GridsearchCV。

  1 from sklearn.decomposition import PCA
  2 from sklearn.svm import SVR
  3 from sklearn.model_selection import train_test_split
  4 from sklearn.model_selection import GridSearchCV
  5 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
  6 from sklearn.preprocessing import StandardScaler, MinMaxScaler
  7 from numpy import *
  8 import numpy as np
  9 import matplotlib.pyplot as plt
 10 import xlrd
 11 from svmutil import *
 12 import pandas as pd
 13 
 14 '''前言'''
 15 # pca - svr
 16 # CG测试
 17 
 18 '''预设参数'''
 19 fname = "all01.xlsx"  # 训练数据文件读取 26hao
 20 random_1 = 34  # 样本集选取随机种子
 21 random_2 = 4  # 训练集选取随机种子
 22 newpca = 6  # 降维
 23 yuzhi = 50  # 异常点阈值
 24 rate_1 = 0.8  # 样本集验证集
 25 rate_2 = 0.8  # 训练集测试集
 26 bestc = 384  # c
 27 bestg = 9  # gamma
 28 
 29 '''数据读取'''
 30 # xlrd生成对excel表进行操作的对象
 31 ...
 32 
 33 # 输入输出分割
 34 data_x = data[:, 1:11]
 35 data_y = data[:, 0:1]
 36 
 37 '''PCA'''
 38 pca = PCA(n_components=newpca)  # 加载PCA算法，设置降维后主成分数目为
 39 data_x = pca.fit_transform(data_x)  # 对样本进行降维
 40 print(pca.components_)  # 输出主成分，即行数为降维后的维数，列数为原始特征向量转换为新特征的系数
 41 print(pca.explained_variance_ratio_)  # 新特征 每维所能解释的方差大小在全方差中所占比例
 42 
 43 '''数据划分'''
 44 # 样本数据分割
 45 train_data_x, predict_data_x, train_data_y, predict_data_y = train_test_split(data_x, data_y, test_size=rate_1,
 46                                                                               random_state=random_1)
 47 
 48 # 训练数据分割
 49 train_x, test_x, train_y, test_y = train_test_split(train_data_x, train_data_y, test_size=rate_2, random_state=random_2)
 50 predict_x = predict_data_x
 51 predict_y = predict_data_y
 52 
 53 # reshape y
 54 test_y = np.reshape(test_y, -1)
 55 train_y = np.reshape(train_y, -1)
 56 predict_y = np.reshape(predict_y, (-1, 1))
 57 
 58 # StandardScaler x
 59 ss_X = StandardScaler()
 60 ss_X.fit(train_data_x)  # 20%
 61 train_x = ss_X.transform(train_x)
 62 test_x = ss_X.transform(test_x)
 63 predict_x = ss_X.transform(predict_x)
 64 
 65 '''参数优化与SVR'''
 66 # 网格搜索交叉验证（GridSearchCV）：以穷举的方式遍历所有可能的参数组合
 67 # 测试用
 68 # param_grid = {'gamma': [bestg], 'C': [bestc]}
 69 # rbf_svr_cg = GridSearchCV(SVR(kernel='rbf'), param_grid, cv=5)
 70 # rbf_svr_cg.fit(train_x,train_y)
 71 # bestc = rbf_svr_cg.best_params_.get('C')
 72 # bestg = rbf_svr_cg.best_params_.get('gamma')
 73 
 74 # 最优参数
 75 print(bestc, bestg)
 76 param_grid = {'gamma': [bestg], 'C': [bestc]}
 77 rbf_svr = SVR(kernel='rbf',param_grid) # 需要修改
 78 
 79 # 训练
 80 rbf_svr.fit(train_x, train_y)
 81 
 82 # 预测
 83 test_y_predict = rbf_svr.predict(test_x)
 84 test_y_predict = np.reshape(test_y_predict, (-1, 1))
 85 predict_y_predict = rbf_svr.predict(predict_x)
 86 predict_y_predict = np.reshape(predict_y_predict, (-1, 1))
 87 
 88 '''去异常点'''
 89 print('样本集：', len(train_data_y))
 90 print('验证集：', len(predict_data_y))
 91 size = len(test_y_predict)
 92 count = 0
 93 for i in range(size):
 94     if abs(test_y_predict[size - i - 1] - test_y[size - i - 1]) > yuzhi:
 95         test_y_predict = np.delete(test_y_predict, size - i - 1)
 96         test_y = np.delete(test_y, size - i - 1)
 97         count = count + 1
 98 print('测试集异常点', count)
 99 size = len(predict_y_predict)
100 count = 0
101 for i in range(size):
102     if abs(predict_y_predict[size - i - 1] - predict_y[size - i - 1]) > yuzhi:
103         predict_y_predict = np.delete(predict_y_predict, size - i - 1)
104         predict_y = np.delete(predict_y, size - i - 1)
105         count = count + 1
106 print('验证集异常点', count)
107 
108 '''评估'''
109 # # 使用r2__score模块，并输出评估结果，拟合程度，R2决定系数，衡量模型预测能力好坏（真实与预测的相关程度百分比）
110 # print('The value of R-squared of kernal=rbf is',r2_score(test_y,test_y_predict))
111 # # 使用mean_squared_error模块，输出评估结果，均方误差
112 # print('The mean squared error of kernal=rbf is',mean_squared_error(test_y,test_y_predict))
113 # # 使用mean_absolute_error模块，输出评估结果，平均绝对误差
114 # print('The mean absolute error of kernal=rbf is',mean_absolute_error(test_y,test_y_predict))
115 
116 # 使用r2__score模块，并输出评估结果，拟合程度，R2决定系数，衡量模型预测能力好坏（真实与预测的相关程度百分比）
117 print('The value of R-squared of kernal=rbf is', r2_score(predict_y, predict_y_predict))
118 # 使用mean_squared_error模块，输出评估结果，均方误差
119 print('The mean squared error of kernal=rbf is', mean_squared_error(predict_y, predict_y_predict))
120 # 使用mean_absolute_error模块，输出评估结果，平均绝对误差
121 print('The mean absolute error of kernal=rbf is', mean_absolute_error(predict_y, predict_y_predict))
122 # r
123 X1 = pd.Series(np.reshape(predict_y,-1))
124 Y1 = pd.Series(np.reshape(predict_y_predict,-1))
125 print('The r is', X1.corr(Y1, method="pearson"))
126 print('The r is', sqrt(r2_score(predict_y, predict_y_predict)))
127 
128 '''作图'''
129 # PRN
130 print('PRN:', fname)
131 
132 # PCA
133 print()
134 
135 # 残差
136 diff_predict = predict_y_predict - predict_y
137 plt.plot(diff_predict, color='black', label='error')
138 plt.xlabel("no.")
139 plt.ylabel("error(m)")
140 plt.title('xxx')
141 plt.grid()
142 plt.legend()
143 plt.show()
144 
145 # 真实/模型_1
146 plt.plot(predict_y, color='g', label='dtu15mss')
147 plt.plot(predict_y_predict, color='b', label='pre')
148 plt.xlabel("xxx")
149 plt.ylabel("error(m)")
150 plt.title('xxx')
151 plt.grid()
152 plt.legend()
153 plt.show()
154 
155 # 真实/模型_2
156 fig = plt.figure(3)
157 ax1 = fig.add_subplot(2, 1, 1)
158 ax1.plot(predict_y, color='g', label='dtu15mss')
159 ax2 = fig.add_subplot(2, 1, 2)
160 ax2.plot(predict_y_predict, color='b', label='pre')
161 plt.show()
162 
163 # 真实/模型_3
164 p_x = [x for x in range(int(min(predict_y)) - 5, int(max(predict_y)) + 5)]
165 p_y = p_x
166 plt.plot(p_x, p_y, color='black', label='1')
167 plt.scatter(predict_y_predict, predict_y, s=10, color='b', marker='x',
168             label='0')  # https://www.cnblogs.com/shanlizi/p/6850318.html
169 plt.xlabel('PRE')
170 plt.ylabel('DTU')
171 plt.show()

标签：plt,predict,data,代码,print,train,test,SVR,向量
来源： https://www.cnblogs.com/ltkekeli1229/p/15683225.html