python - 如何打印基于SVM分类器的分类点

Question

我使用“svm”分类器来分类它是自行车还是汽车。所以，我的特征是 0,1,2 列，依赖项是第三列。我可以清楚地看到分类，但我不知道如何根据图表中的分类打印所有点。

    import numpy as np

    import operator
    from matplotlib import pyplot as plt
    from sklearn import svm
    from matplotlib.colors import ListedColormap
    from sklearn.model_selection import train_test_split
    from sklearn import preprocessing
    from sklearn.svm import SVC
    dataframe=pd.read_csv(DATASET_PATH)
    dataframe = dataframe.dropna(how='any',axis=0)
    SVM_Trained_Model = preprocessing.LabelEncoder()

    train_data=dataframe[0:len(dataframe)]
    le=preprocessing.LabelEncoder()
    col=dataframe.columns[START_TRAIN_COLUMN:].astype('U') 
    col_name=["no_of_wheels","dimensions","windows","vehicle_type"]
    for i in range(0,len(col_name)):
     self.train_data[col_name[i]]=le.fit_transform(self.train_data[col_name[i]])
    train_column=np.array(train_data[col]).astype('U')

    data=train_data.iloc[:,[0,1,2]].values

    target=train_data.iloc[:,3].values

    data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.30, 
    random_state = 0) `split test and test train`

    svc_model=SVC(kernel='rbf', probability=True))'classifier model'

    svc_model.fit(data_train, target_train)

    all_labels =svc_model.predict(data_test)

    X_set, y_set = data_train, target_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))

    Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(1)]).T

    pred = svc_model.predict(Xpred).reshape(X1.shape)

    plt.contourf(X1, X2, pred,alpha = 0.75, cmap = ListedColormap(('white','orange','pink')))

    plt.xlim(X1.min(),X1.max())

    plt.ylim(X2.min(), X2.max())


    colors=['red','yellow','cyan','blue']
    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((colors[i]))(i), label 
       = j)

    plt.title('Multiclass Classifier ')
    plt.xlabel('Features')
    plt.ylabel('Dependents')
    plt.legend()
    plt.show()

Image

这是我的图表，我需要根据图表中的粉色和白色区域使用 python print() 打印点。请帮助我获取这些点。

Answer 1

您只需选择并使用 2 个特征即可绘制 2D 曲面图。

from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

def make_meshgrid(x, y, h=.02):
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    return xx, yy

def plot_contours(ax, clf, xx, yy, **params):
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out

model = svm.SVC(kernel='linear')
clf = model.fit(X, y)

fig, ax = plt.subplots()
# title for the plots
title = ('Decision surface of linear SVC ')
# Set-up grid for plotting.
X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrid(X0, X1)

plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_ylabel('y label here')
ax.set_xlabel('x label here')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(title)
ax.legend()
plt.show()