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| # -*- coding: utf-8 -*-
# @Time : 19-7-15 上午11:33
# @Author : zj
from builtins import range
from classifier.knn_classifier import KNN
import pandas as pd
import numpy as np
from sklearn import utils
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
def load_iris(iris_path, shuffle=True, tsize=0.8):
"""
加载iris数据
"""
data = pd.read_csv(iris_path, header=0, delimiter=',')
if shuffle:
data = utils.shuffle(data)
species_dict = {
'Iris-setosa': 0,
'Iris-versicolor': 1,
'Iris-virginica': 2
}
data['Species'] = data['Species'].map(species_dict)
data_x = np.array(
[data['SepalLengthCm'], data['SepalWidthCm'], data['PetalLengthCm'], data['PetalWidthCm']]).T
data_y = data['Species']
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, train_size=tsize, test_size=(1 - tsize),
shuffle=False)
return x_train, x_test, y_train, y_test
def load_german_data(data_path, shuffle=True, tsize=0.8):
data_list = pd.read_csv(data_path, header=None, sep='\s+')
data_array = data_list.values
height, width = data_array.shape[:2]
data_x = data_array[:, :(width - 1)]
data_y = data_array[:, (width - 1)]
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, train_size=tsize, test_size=(1 - tsize),
shuffle=shuffle)
y_train = np.array(list(map(lambda x: 1 if x == 2 else 0, y_train)))
y_test = np.array(list(map(lambda x: 1 if x == 2 else 0, y_test)))
return x_train, x_test, y_train, y_test
def compute_accuracy(y, y_pred):
num = y.shape[0]
num_correct = np.sum(y_pred == y)
acc = float(num_correct) / num
return acc
def cross_validation(x_train, y_train, k_choices, num_folds=5, Classifier=KNN):
X_train_folds = np.array_split(x_train, num_folds)
y_train_folds = np.array_split(y_train, num_folds)
# 计算预测标签和验证集标签的精度
k_to_accuracies = {}
for k in k_choices:
k_accuracies = []
# 随机选取其中一份为验证集，其余为测试集
for i in range(num_folds):
x_folds = X_train_folds.copy()
y_folds = y_train_folds.copy()
x_vals = x_folds.pop(i)
x_trains = np.vstack(x_folds)
y_vals = y_folds.pop(i)
y_trains = np.hstack(y_folds)
classifier = Classifier()
classifier.train(x_trains, y_trains)
y_val_pred = classifier.predict(x_vals, k=k)
k_accuracies.append(compute_accuracy(y_vals, y_val_pred))
k_to_accuracies[k] = k_accuracies
return k_to_accuracies
def plot(k_choices, k_to_accuracies):
# plot the raw observations
for k in k_choices:
accuracies = k_to_accuracies[k]
plt.scatter([k] * len(accuracies), accuracies)
# plot the trend line with error bars that correspond to standard deviation
accuracies_mean = np.array([np.mean(v) for k, v in sorted(k_to_accuracies.items())])
accuracies_std = np.array([np.std(v) for k, v in sorted(k_to_accuracies.items())])
plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)
plt.title('Cross-validation on k')
plt.xlabel('k')
plt.ylabel('Cross-validation accuracy')
plt.show()
if __name__ == '__main__':
# iris_path = '/home/zj/data/iris-species/Iris.csv'
# x_train, x_test, y_train, y_test = load_iris(iris_path, shuffle=True, tsize=0.8)
data_path = '/home/zj/data/german/german.data-numeric'
x_train, x_test, y_train, y_test = load_german_data(data_path, shuffle=True, tsize=0.8)
x_train = x_train.astype(np.double)
x_test = x_test.astype(np.double)
mu = np.mean(x_train, axis=0)
var = np.var(x_train, axis=0)
eps = 1e-8
x_train = (x_train - mu) / np.sqrt(var + eps)
x_test = (x_test - mu) / np.sqrt(var + eps)
k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 30, 50, 100]
k_to_accuracies = cross_validation(x_train, y_train, k_choices)
# print(k_to_accuracies)
# Print out the computed accuracies
for k in sorted(k_to_accuracies):
for accuracy in k_to_accuracies[k]:
print('k = %d, accuracy = %f' % (k, accuracy))
plot(k_choices, k_to_accuracies)
accuracies_mean = np.array([np.mean(v) for k, v in sorted(k_to_accuracies.items())])
k = k_choices[np.argmax(accuracies_mean)]
print('最好的k值是：%d' % k)
# 测试集测试
classifier = KNN()
classifier.train(x_train, y_train)
y_test_pred = classifier.predict(x_test, k=k)
y_test_acc = compute_accuracy(y_test, y_test_pred)
print('测试集精度为：%f' % y_test_acc)
|
