Python中支持向量机的可视化（2D）

我有一个作业，在下面。我已经完成了前5项任务，但最后一项任务有问题。来策划它。请说明怎么做。先谢谢你

*（我几天前刚开始学习SVM和ML，请考虑）

**（我认为对于所有类型的内核，操作顺序应该是相同的。如果您为其中一个内核显示操作顺序，那就太好了。我将尝试为其他内核调整您的代码）

应遵循的程序：

从这张地图上随机抽取样本。（#100）并将其转化为Python for SVC。此数据集包括东距、北距和岩石信息

使用这100个随机选择的样本，再次随机分割以训练和测试数据集

尝试使用线性、多项式、径向基函数和切线的核运行SVC

例如，如果您使用的是径向基函数，那么根据您从精度分数中获得的精度，“C”和“gamma”可以是最佳的

获得拟合模型并计算准确度分数（从测试数据集获得）后，将整个数据集导入获得的拟合模型，并预测reference.csv中所有90000个样本点的输出

向我展示获得的地图以及从每个拟合模型获得的精度分数

数据集如下所示：

同一风格的90000分

代码如下：

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

### Importing Info

df = pd.read_csv("C:/Users/Admin/Desktop/RA/step 1/reference.csv", header=0)
df_model = df.sample(n = 100)
df_model.shape

## X-y split

X = df_model.loc[:,df_model.columns!="Rock"]
y = df_model["Rock"]
y_initial = df["Rock"]

### for whole dataset

X_wd = df.loc[:, df_model.columns!="Rock"]
y_wd = df["Rock"]

## Test-train split

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

## Standardizing the Data

from sklearn.preprocessing import StandardScaler

sc = StandardScaler().fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

## Linear
### Grid Search

from sklearn.model_selection import GridSearchCV
from sklearn import svm
from sklearn.metrics import accuracy_score, confusion_matrix

params_linear = {'C' : (0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500,1000)}
clf_svm_l = svm.SVC(kernel = 'linear')
svm_grid_linear = GridSearchCV(clf_svm_l, params_linear, n_jobs=-1,
                              cv = 3, verbose = 1, scoring = 'accuracy')

svm_grid_linear.fit(X_train_std, y_train)
svm_grid_linear.best_params_
linsvm_clf = svm_grid_linear.best_estimator_
accuracy_score(y_test, linsvm_clf.predict(X_test_std))

### training svm

clf_svm_l = svm.SVC(kernel = 'linear', C = 0.1)
clf_svm_l.fit(X_train_std, y_train)

### predicting model

y_train_pred_linear = clf_svm_l.predict(X_train_std)
y_test_pred_linear = clf_svm_l.predict(X_test_std)
y_test_pred_linear
clf_svm_l.n_support_

### whole dataset

y_pred_linear_wd = clf_svm_l.predict(X_wd)

### map
        


## Poly
### grid search for poly

params_poly = {'C' : (0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500,1000),
         'degree' : (1,2,3,4,5,6)}
clf_svm_poly = svm.SVC(kernel = 'poly')
svm_grid_poly = GridSearchCV(clf_svm_poly, params_poly, n_jobs = -1,
                            cv = 3, verbose = 1, scoring = 'accuracy')
svm_grid_poly.fit(X_train_std, y_train)
svm_grid_poly.best_params_
polysvm_clf = svm_grid_poly.best_estimator_
accuracy_score(y_test, polysvm_clf.predict(X_test_std))

### training svm

clf_svm_poly = svm.SVC(kernel = 'poly', C = 50, degree = 2)
clf_svm_poly.fit(X_train_std, y_train)

### predicting model

y_train_pred_poly = clf_svm_poly.predict(X_train_std)
y_test_pred_poly = clf_svm_poly.predict(X_test_std)

clf_svm_poly.n_support_

### whole dataset

y_pred_poly_wd = clf_svm_poly.predict(X_wd)

### map            


## RBF

### grid search rbf

params_rbf = {'C' : (0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500,1000),
         'gamma' : (0.001, 0.01, 0.1, 0.5, 1)}
clf_svm_r = svm.SVC(kernel = 'rbf')
svm_grid_r = GridSearchCV(clf_svm_r, params_rbf, n_jobs = -1,
                         cv = 10, verbose = 1, scoring = 'accuracy')
svm_grid_r.fit(X_train_std, y_train)
svm_grid_r.best_params_
rsvm_clf = svm_grid_r.best_estimator_
accuracy_score(y_test, rsvm_clf.predict(X_test_std))

### training svm

clf_svm_r = svm.SVC(kernel = 'rbf', C = 500, gamma = 0.5)
clf_svm_r.fit(X_train_std, y_train)

### predicting model

y_train_pred_r = clf_svm_r.predict(X_train_std)
y_test_pred_r = clf_svm_r.predict(X_test_std)

### whole dataset

y_pred_r_wd = clf_svm_r.predict(X_wd)

### map            


## Tangent

### grid search

params_tangent = {'C' : (0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50),
         'gamma' : (0.001, 0.01, 0.1, 0.5, 1)}
clf_svm_tangent = svm.SVC(kernel = 'sigmoid')
svm_grid_tangent = GridSearchCV(clf_svm_tangent, params_tangent, n_jobs = -1,
                            cv = 10, verbose = 1, scoring = 'accuracy')
svm_grid_tangent.fit(X_train_std, y_train)
svm_grid_tangent.best_params_
tangentsvm_clf = svm_grid_tangent.best_estimator_
accuracy_score(y_test, tangentsvm_clf.predict(X_test_std))

### training svm

clf_svm_tangent = svm.SVC(kernel = 'sigmoid', C = 1, gamma = 0.1)
clf_svm_tangent.fit(X_train_std, y_train)

### predicting model

y_train_pred_tangent = clf_svm_tangent.predict(X_train_std)
y_test_pred_tangent = clf_svm_tangent.predict(X_test_std)

### whole dataset

y_pred_tangent_wd = clf_svm_tangent.predict(X_wd)

### map

---

从示例数据来看，您似乎正在处理规则间隔的数据，并且以单调递增的方式迭代行/列。 以下是将此数据集重塑为二维数组（通过将数组重塑为行）并相应打印的一种方法：

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

# create sample data
data = {
    'Easting': [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3],
    'Northing': [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
    'Rocks': [0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0],
}
df = pd.DataFrame(data)

# reshape data into 2d matrix (assuming easting / northing steps from 0 to max value)
max_easting = np.max(df['Easting'])
img_data = np.reshape(data['Rocks'], (max_easting, -1))

# plot as image
plt.imshow(img_data)
plt.show()

---

如果您处理的是间距不规则的数据，即不是每个东距/北距组合都有一个值，您可以查看。

对于那些遇到与我相同问题的人，这里是绘制线性可视化的答案。将这些代码改编成其他内核是很容易的

# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train_std, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, clf_svm_l.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('darkblue', 'yellow')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('blue', 'gold'))(i), label = j)
plt.title('SVM (Training set)')
plt.xlabel('Easting')
plt.ylabel('Northing')
plt.legend()
plt.show()

---

您希望如何绘制数据？作为图像，即东距是列，北距是行，岩石是值？是的，你作为图像是对的，x轴代表东距，y轴代表北距。感谢您对输出的澄清。