sklearn中的nearest neighbor

KNN介紹

基礎原理沒什麼介紹的，可以參考我的KNN原理和實現，裏面介紹了KNN的原理同時使用KNN來進行mnist分類

KNN in sklearn

sklearn是這麼說KNN的：

The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. The number of samples can be a user-defined constant (k-nearest neighbor learning), or vary based on the local density of points (radius-based neighbor learning). The distance can, in general, be any metric measure: standard Euclidean distance is the most common choice. Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data (possibly transformed into a fast indexing structure such as a Ball Tree or KD Tree.).

接口介紹

sklearn.neighbors

主要有兩個：

• KNeighborsClassifier（RadiusNeighborsClassifier)
• kNeighborsRegressor (RadiusNeighborsRefressor)

其它的還有一些，不多說，上圖：


classifier

接口定義

KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)

參數介紹

需要注意的點就是：

1. weights（各個neighbor的權重分配）
2. metric（距離的度量）

例子

這次就不寫mnist分類了，其實也很簡單，官網的教程就可以說明問題了

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets

n_neighbors = 15

# 導入iris數據集
iris = datasets.load_iris()  
# iris特徵有四個，這裏只使用前兩個特徵來做分類
X = iris.data[:, :2] 
# iris的label 
y = iris.target  

h = .02  # step size in the mesh

# colormap
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

for weights in ['uniform', 'distance']:
    # KNN分類器
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    # fit
    clf.fit(X, y)

    # Plot the decision boundary.     
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    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))
    # predict
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title("3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights))

plt.show()

其實非常簡單，如果去除畫圖的代碼其實就三行：

1. clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
2. clf.fit(X, y)
3. clf.predict(Z)

如果你的數據不是uniformaly sampled的，你會需要用到RadiusNeighrborsClassifier，使用方法保持一致

regressor

大部分說KNN其實是說的是分類器，其實KNN還可以做迴歸，官網教程是這麼說的：

Neighbors-based regression can be used in cases where the data labels are continuous rather than discrete variables. The label assigned to a query point is computed based the mean of the labels of its nearest neighbors.
例子

同樣是官網的例子

import numpy as np
import matplotlib.pyplot as plt
from sklearn import neighbors

np.random.seed(0)
X = np.sort(5 * np.random.rand(40, 1), axis=0)
T = np.linspace(0, 5, 500)[:, np.newaxis]
y = np.sin(X).ravel()

# Add noise to targets
y[::5] += 1 * (0.5 - np.random.rand(8))

n_neighbors = 5

for i, weights in enumerate(['uniform', 'distance']):
    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
    y_ = knn.fit(X, y).predict(T)

    plt.subplot(2, 1, i + 1)
    plt.scatter(X, y, c='k', label='data')
    plt.plot(T, y_, c='g', label='prediction')
    plt.axis('tight')
    plt.legend()
    plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights)  

    plt.show()

簡單易懂，就不解釋了

與classifier一樣，如果你的數據不是uniformly sampled的，使用RadiusNeighborsRegressor更加合適

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