knn算法分类准确度(五)
【摘要】 分类准确度 准确度用accuracy表示: 代码实现准确率计算:import numpy as npimport matplotlibimport matplotlib.pyplot as pltfrom sklearn import datasets#加载手写数字集图片数据digits = datasets.load_digits()#查看该数据集的描述信息digits.DES...
分类准确度
准确度用accuracy表示:
代码实现准确率计算:
import numpy as np import matplotlib import matplotlib.pyplot as plt from sklearn import datasets #加载手写数字集图片数据 digits = datasets.load_digits() #查看该数据集的描述信息 digits.DESCR #查看数据集的shape X = digits.data X.shape y = digits.target y.shape
取出某个数据集绘制图像:
some_digit = X[666] y[666] #将数据集变为(8,8)的二维数据 some_digit_image = some_digit.reshape(8,8) #绘制二维图片 plt.imshow(some_digit_image,cmap=matplotlib.cm.binary) plt.show()
利用knn算法进行分类预测:
from knn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(X,y,test_ratio=0.2) from knn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(X,y) from knn.kNN import KNNClassifier knn_clf = KNNClassifier(k=6) knn_clf.fit(X_train,y_train) y_predict = knn_clf.predict(X_test)
计算准确率:
sum(y_predict==y_test)/y_test.shape[0]
封装为代码metrics.py:
import numpy as np def accuracy_score(y_true, y_predict): """计算y_true和y_predict之间的准确率""" assert len(y_true) == len(y_predict), \ "the size of y_true must be equal to the size of y_predict" return np.sum(y_true == y_predict) / y_test.shape[0]
应用算法进行计算:
from knn.metrics import accuracy_score accuracy_score(y_test,y_predict)
将准确率代码整合封装到kNN.py中
上述计算准确率的方式,必须用到y_predict,也就是必须进行预测
在实际开发的某些情况下,开发者可能不想去进行预测,直接得到准确率
修改kNN.py,代码如下:
import numpy as np from math import sqrt from collections import Counter #新添加的代码 from .metrics import accuracy_score class KNNClassifier: def __init__(self, k): """初始化kNN分类器""" assert k >= 1, "k must be valid" self.k = k self._X_train = None self._y_train = None def fit(self, X_train, y_train): """根据训练数据集X_train和y_train训练kNN分类器""" assert X_train.shape[0] == y_train.shape[0], \ "the size of X_train must be equal to the size of y_train" assert self.k <= X_train.shape[0], \ "the size of X_train must be at least k." self._X_train = X_train self._y_train = y_train return self def predict(self, X_predict): """给定待预测数据集X_predict,返回表示X_predict的结果向量""" assert self._X_train is not None and self._y_train is not None, \ "must fit before predict!" assert X_predict.shape[1] == self._X_train.shape[1], \ "the feature number of X_predict must be equal to X_train" y_predict = [self._predict(x) for x in X_predict] return np.array(y_predict) def _predict(self, x): """给定单个待预测数据x,返回x的预测结果值""" assert x.shape[0] == self._X_train.shape[1], \ "the feature number of x must be equal to X_train" distances = [sqrt(np.sum((x_train - x) ** 2)) for x_train in self._X_train] nearest = np.argsort(distances) topK_y = [self._y_train[i] for i in nearest[:self.k]] votes = Counter(topK_y) return votes.most_common(1)[0][0] #新添加的代码 def score(self, X_test, y_test): """根据测试数据集 X_test 和 y_test 确定当前模型的准确度""" y_predict = self.predict(X_test) return accuracy_score(y_test, y_predict) def __repr__(self): return "KNN(k=%d)" % self.k
测试使用:
knn_clf.score(X_test,y_test)
sklearn中的accuracy_score
代码:
from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier #划分数据集 X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2) #训练knn模型 knn_classifier = KNeighborsClassifier(n_neighbors=6) knn_classifier.fit(X_train,y_train) #预测测试集 y_predict = knn_classifier.predict(X_test) #计算准确率 from sklearn.metrics import accuracy_score #方式1: accuracy_score(y_test,y_predict) #方式2: knn_classifier.score(X_test,y_test)
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