Modified the data path of svm-python3.6.py and add the data visualization module

src/python/getting-started/digit-recognizer/svm-python3.6.py

@@ -14,11 +14,13 @@ import csv
 import time
 import numpy as np
 import pandas as pd
+import matplotlib.pyplot as plt
 from sklearn.decomposition import PCA
 from sklearn.svm import SVC
 from sklearn.metrics import classification_report
 from sklearn.model_selection import train_test_split
 
+# 数据路径
 data_dir = '/Users/wuyanxue/Documents/GitHub/datasets/getting-started/digit-recognizer/'
 
 # 加载数据
@@ -59,6 +61,7 @@ def dRCsv(x_train, x_test, preData, COMPONENT_NUM):
     return pcaTrainData,  pcaTestData, pcaPreData
 
 
+
 # 训练模型
 def trainModel(trainData, trainLabel):
     print('Train SVM...')
@@ -181,7 +184,6 @@ def getModel(filename):
     fr = open(filename, 'rb')
     return pickle.load(fr)
 
-
 def trainDRSVM():
     startTime = time.time()
 
@@ -190,8 +192,8 @@ def trainDRSVM():
     # 模型训练 (数据预处理-降维)
     optimalSVMClf, pcaPreData = getOptimalAccuracy(trainData, trainLabel, preData)
 
-    storeModel(optimalSVMClf, os.path.join(data_dir, '/ouput/Result_sklearn_SVM.model'))
-    storeModel(pcaPreData, os.path.join(data_dir, '/ouput/Result_sklearn_SVM.pcaPreData'))
+    storeModel(optimalSVMClf, os.path.join(data_dir, 'output/Result_sklearn_SVM.model'))
+    storeModel(pcaPreData, os.path.join(data_dir, 'output/Result_sklearn_SVM.pcaPreData'))
 
     print("finish!")
     stopTime = time.time()
@@ -201,8 +203,8 @@ def trainDRSVM():
 def preDRSVM():
     startTime = time.time()
     # 加载模型和数据
-    optimalSVMClf = getModel(os.path.join(data_dir, '/ouput/Result_sklearn_SVM.model'))
-    pcaPreData = getModel(os.path.join(data_dir, '/ouput/Result_sklearn_SVM.pcaPreData'))
+    optimalSVMClf = getModel(os.path.join(data_dir, 'output/Result_sklearn_SVM.model'))
+    pcaPreData = getModel(os.path.join(data_dir, 'output/Result_sklearn_SVM.pcaPreData'))
 
     # 结果预测
     testLabel = optimalSVMClf.predict(pcaPreData)
@@ -213,13 +215,30 @@ def preDRSVM():
     stopTime = time.time()
     print('PreModel load time used:%f s' % (stopTime - startTime))
 
+# 数据可视化
+def dataVisulization(data, labels):
+    pca = PCA(n_components=2, whiten=True) # 使用PCA方法降到2维
+    pca.fit(data)
+    pcaData = pca.transform(data)
+    uniqueClasses = set(labels)
+    fig = plt.figure()
+    ax = fig.add_subplot(1, 1, 1)
+    for cClass in uniqueClasses:
+        plt.scatter(pcaData[labels==cClass, 0], pcaData[labels==cClass, 1])
+    plt.xlabel('$x_1$')
+    plt.ylabel('$x_2$')
+    plt.title('MNIST visualization')
+    plt.show()
 
 if __name__ == '__main__':
     trainData, trainLabel, preData = opencsv()
+    dataVisulization(trainData, trainLabel)
+
+
     # 训练并保存模型
-    trainDRSVM()
+    #trainDRSVM()
 
     # 分析数据
-    analyse_data(trainData)
+    #analyse_data(trainData)
     # 加载预测数据集
-    preDRSVM()
+    #preDRSVM()