Метод ближайших соседей отрисовка

import numpy as np
import seaborn as sns
import pandas as pd
import warnings
warnings.simplefilter('ignore')
 
# будем отображать графики прямо в jupyter'e
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
#графики в svg выглядят более четкими
%config InlineBackend.figure_format = 'svg' 
 
#увеличим дефолтный размер графиков
from pylab import rcParams
rcParams['figure.figsize'] = 8, 5
 
test=pd.read_csv('Train_network.csv', sep=',')
 
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
 
testovii=test
X_train, X_holdout, y_train, y_holdout = train_test_split(X, y, test_size=0.3,random_state=17)
 
***
knn_1 = KNeighborsClassifier(n_neighbors=1)
np.mean(cross_val_score(knn_1,X_train,y_train,cv=10))

plt.figure()
plt.figure(figsize=(15,10))
plt.title(fontsize=20, fontweight='bold')
sns.set_style("whitegrid")
plt.plot(knn_1)
plt.show()

knn_1

import matplotlib.pyplot as plt
import networkx as nx
 
graph = nx.Graph()
 
for node in knn_1:
    graph.add_node(node)
 
nx.draw_networkx(graph, node_color = 'blue',with_labels = False)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
 
# import some data to play with
iris = datasets.load_iris()
 
# take the first two features
X = iris.data[:, :2]
y = iris.target
h = .02  # step size in the mesh
 
# Calculate min, max and limits
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
 
# Put the result into a color plot
plt.figure()
plt.scatter(X[:, 0], X[:, 1])
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("Data points")
plt.show()