PCA&SVM.py

import numpy
from sklearn.decomposition import PCA
from sklearn.svm import SVC

COMPONENT_NUM = 2

print('Read training data...')
with open('Dataset/temphumidity.csv', 'r') as reader:
    reader.readline()
    train_label = []
    train_data = []
    for line in reader.readlines():
        data = list(map(float, line.rstrip().split(',')))
        train_label.append(data[0])
        train_data.append(data[1:])
print('Loaded ' + str(len(train_label)))

print('Reduction...')
train_label = numpy.array(train_label)
train_data = numpy.array(train_data)
pca = PCA(n_components=COMPONENT_NUM, whiten=True)
pca.fit(train_data)
train_data = pca.transform(train_data)

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(train_data,train_label,test_size=0.2,random_state=32)

print('Train SVM...')
svc = SVC(C=10.0,kernel="rbf",gamma=0.1)
svc.fit(X_train, y_train)

#print('Read testing data...')
#with open('../input/test.csv', 'r') as reader:
 #   reader.readline()
  #  test_data = []
   # for line in reader.readlines():
    #    pixels = list(map(int, line.rstrip().split(',')))
     #   test_data.append(pixels)
#print('Loaded ' + str(len(test_data)))

print('Predicting...')
from sklearn.metrics import accuracy_score
predict = svc.predict(X_test)
acc=accuracy_score(predict,y_test)
print ("Accuracy % : ",acc)
#print('Saving...')
#with open('predict.csv', 'w') as writer:
 #   writer.write('"ImageId","Label"\n')
  #  count = 0
   # for p in predict:
    #    count += 1
   #     writer.write(str(count) + ',"' + str(p) + '"\n')#