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#!/usr/bin/python
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# coding: utf-8
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'''
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Created on 2018-05-16
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Update on 2018-05-16
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Author: ccyf00
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Github: https://github.com/ccyf00/kaggle-1
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'''
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.model_selection import train_test_split
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from sklearn.decomposition import PCA
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from sklearn.neighbors import KNeighborsClassifier
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train=pd.read_csv('datasets/getting-started/digit-recognizer/input/train.csv')
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test=pd.read_csv('datasets/getting-started/digit-recognizer/input/test.csv')
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Y_train=train["label"]
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X_train=train.drop(['label'],axis=1)
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del train
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pca = PCA(n_components=45)
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X_train_transformed=pca.fit_transform(X_train)
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X_test_transformed=pca.transform(test)
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X_train_pca, X_test_pca, Y_train_pca, Y_test_pca = train_test_split(
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X_train_transformed, Y_train, test_size=0.1, random_state=13)
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components = [10,15,20,25,30,35,40,45]
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neighbors = [2,3,4,5,6,7]
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scores = np.zeros((components[len(components)-1]+1,neighbors[len(neighbors)-1]+1))
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for component in components:
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for n in neighbors:
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knn=KNeighborsClassifier(n_neighbors=n)
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knn.fit(X_train_pca[:,:component],Y_train_pca)
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score = knn.score(X_test_pca[:,:component],Y_test_pca)
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scores[component][n]=score
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print('Components=',component,'neighbors = ',n,'Score = ',score)
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knn=KNeighborsClassifier(n_neighbors=5)
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knn.fit(X_train_pca[:,:35],Y_train_pca)
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predictLabel=knn.predict(X_test_transformed[:,:35])
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Submission = pd.DataFrame({"ImageId":range(1,predictLabel.shape[0]+1),
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"Label":predictLabel})
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Submission.to_csv("datasets/getting-started/digit-recognizer/ouput/KnnMnistSubmission.csv", index=False)
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#分数：0.97385
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@ -2,53 +2,79 @@
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# coding: utf-8
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'''
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Created on 2017-10-26
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Update on 2017-10-26
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Author: 片刻
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Update on 2018-05-16
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Author: 片刻/ccyf00
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Github: https://github.com/apachecn/kaggle
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'''
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import os.path
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import csv
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import time
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import numpy as np
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import pandas as pd
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from numpy import shape, ravel
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from sklearn.decomposition import PCA
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from sklearn.neighbors import KNeighborsClassifier
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data_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'
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# 加载数据
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def opencsv():
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# 使用 pandas 打开
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data = pd.read_csv(
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'datasets/getting-started/digit-recognizer/input/train.csv')
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data1 = pd.read_csv(
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'datasets/getting-started/digit-recognizer/input/test.csv')
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data = pd.read_csv(os.path.join(data_dir, 'train.csv'))
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data1 = pd.read_csv(os.path.join(data_dir, 'test.csv'))
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train_data = data.values[0:, 1:] # 读入全部训练数据, [行，列]
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train_label = data.values[0:, 0] # 读取列表的第一列
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train_label = data.values[0:, 0] # 读取列表的第一列
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test_data = data1.values[0:, 0:] # 测试全部测试个数据
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return train_data, train_label, test_data
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def saveResult(result, csvName):
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with open(csvName, 'w',newline='') as myFile: # 创建记录输出结果的文件（w 和 wb 使用的时候有问题）
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#python3里面对 str和bytes类型做了严格的区分，不像python2里面某些函数里可以混用。所以用python3来写wirterow时，打开文件不要用wb模式，只需要使用w模式，然后带上newline=''
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myWriter = csv.writer(myFile) # 对文件执行写入
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myWriter.writerow(["ImageId", "Label"]) # 设置表格的列名
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with open(csvName, 'w', newline='') as myFile: # 创建记录输出结果的文件（w 和 wb 使用的时候有问题）
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# python3里面对 str和bytes类型做了严格的区分，不像python2里面某些函数里可以混用。所以用python3来写wirterow时，打开文件不要用wb模式，只需要使用w模式，然后带上newline=''
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myWriter = csv.writer(myFile) # 对文件执行写入
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myWriter.writerow(["ImageId", "Label"]) # 设置表格的列名
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index = 0
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for i in result:
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tmp = []
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index = index + 1
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tmp.append(index)
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# tmp.append(i)
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tmp.append(int(i)) # 测试集的标签值
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tmp.append(int(i)) # 测试集的标签值
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myWriter.writerow(tmp)
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def knnClassify(trainData, trainLabel):
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knnClf = KNeighborsClassifier() # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
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knnClf.fit(trainData, ravel(trainLabel)) # ravel Return a contiguous flattened array.
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knnClf = KNeighborsClassifier() # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
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knnClf.fit(trainData, np.ravel(trainLabel)) # ravel Return a contiguous flattened array.
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return knnClf
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# 数据预处理-降维 PCA主成成分分析
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def dRPCA(x_train, x_test, COMPONENT_NUM):
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print('dimensionality reduction...')
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trainData = np.array(x_train)
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testData = np.array(x_test)
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'''
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使用说明：https://www.cnblogs.com/pinard/p/6243025.html
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n_components>=1
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n_components=NUM 设置占特征数量比
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0 < n_components < 1
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n_components=0.99 设置阈值总方差占比
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'''
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pca = PCA(n_components=COMPONENT_NUM, whiten=True)
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pca.fit(trainData) # Fit the model with X
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pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维.
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pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维.
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# pca 方差大小、方差占比、特征数量
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print(pca.explained_variance_, '\n', pca.explained_variance_ratio_, '\n',
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pca.n_components_)
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print(sum(pca.explained_variance_ratio_))
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return pcaTrainData, pcaTestData
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def dRecognition_knn():
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start_time = time.time()
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@ -61,6 +87,9 @@ def dRecognition_knn():
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stop_time_l = time.time()
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print('load data time used:%f' % (stop_time_l - start_time))
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# 降维处理
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trainData, testData = dRPCA(trainData, testData, 35)
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# 模型训练
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knnClf = knnClassify(trainData, trainLabel)
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testLabel = knnClf.predict(testData)
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# 结果的输出
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saveResult(
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testLabel,
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'datasets/getting-started/digit-recognizer/output/Result_sklearn_knn.csv'
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)
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saveResult(testLabel, os.path.join(data_dir, 'Result_sklearn_knn.csv'))
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print("finish!")
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stop_time_r = time.time()
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print('classify time used:%f' % (stop_time_r - start_time))
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@ -79,11 +79,11 @@ def saveResult(result, csvName):
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# 分析数据,看数据是否满足要求（通过这些来检测数据的相关性，考虑在分类的时候提取出重要的特征）
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def analyse_data(dataMat):
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meanVals = np.mean(dataMat, axis=0) # np.mean 求出每列的平均值meanVals
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meanVals = np.mean(dataMat, axis=0) # np.mean 求出每列的平均值meanVals
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meanRemoved = dataMat-meanVals # 每一列特征值减去该列的特征值均值
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#计算协方差矩阵，除数n-1是为了得到协方差的 无偏估计
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#cov(X,0) = cov(X) 除数是n-1(n为样本个数)
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#cov(X,1) 除数是n
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# 计算协方差矩阵，除数n-1是为了得到协方差的 无偏估计
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# cov(X,0) = cov(X) 除数是n-1(n为样本个数)
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# cov(X,1) 除数是n
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covMat = np.cov(meanRemoved, rowvar=0) # cov 计算协方差的值,
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# np.mat 是用来生成一个矩阵的
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# 保存特征值(eigvals)和对应的特征向量(eigVects)
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