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svm.py
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from sklearn.preprocessing import StandardScaler
from sklearn import svm
import numpy as np
import create_training_test_data as cd
import pandas as pd
def normalize_data(Xtrain, Xtest):
sc = StandardScaler()
X_train_std = sc.fit_transform(Xtrain)
X_test_std = sc.transform(Xtest)
return X_train_std, X_test_std
def doSVM():
# X_train = pd.read_csv('training_test_data_shuffled/Xtrain.csv', header=None).values
# X_test = pd.read_csv('training_test_data_shuffled/Xtest.csv', header=None).values
# y_train = pd.read_csv('training_test_data_shuffled/ytrain.csv', header=None).values
# y_test = pd.read_csv('training_test_data_shuffled/ytest.csv', header=None).values
#
# X_train = X_train.flatten()
# X_test = X_test.flatten()
# y_train = y_train.flatten()
# y_test = y_test.flatten()
dataCreator = cd.TrainingTestDataGenerator()
X_train, X_test, y_train, y_test = dataCreator.concatenate_training_test_data()
detailResults = []
X_train, X_test = normalize_data(X_train, X_test)
predicthome = [0, 0, 0]
predictdraw = [0, 0, 0]
predictaway = [0, 0, 0]
clf = svm.SVC(kernel='linear')
clf.fit(X_train, y_train)
print('the score is')
print(clf.score)
# right = 0
# for temp in enumerate(X_test):
# detailResult = [X_test[temp[0]][2], X_test[temp[0]][3], X_test[temp[0]][6]]
# if (y_test[temp[0]] == 1):
# if (clf.predict(temp[1])[0] == 1):
# detailResult.append('H')
# right += 1
# predicthome[0] += 1
# elif (clf.predict(temp[1])[0] == 0):
# detailResult.append('D')
# predicthome[1] += 1
# else:
# detailResult.append('A')
# predicthome[2] += 1
# elif (y_test[temp[0]] == 0):
# if (clf.predict(temp[1])[0] == 1):
# detailResult.append('H')
# predictdraw[0] += 1
# elif (clf.predict(temp[1])[0] == 0):
# detailResult.append('D')
# right += 1
# predictdraw[1] += 1
# else:
# detailResult.append('A')
# predictdraw[2] += 1
# else:
# if (clf.predict(temp[1])[0] == 1):
# detailResult.append('H')
# predictaway[0] += 1
# elif (clf.predict(temp[1])[0] == 0):
# detailResult.append('D')
# predictaway[1] += 1
# else:
# detailResult.append('A')
# right += 1
# predictaway[2] += 1
# detailResults.append(detailResult)
#
# print(float(right) / len(y_test))
# print(detailResults)
# print(predicthome, predictdraw, predictaway)
def doSVMOnline(self):
x, y = self.get_features_one_season()
print(len(self.train_features))
# scale_features = preprocessing.scale(np.array(self.trainFeatures,dtype = float))
detailResults = []
# scale_features = preprocessing.scale(np.array(x,dtype = float))
# print scale_features
tx = [x[1:] for x in self.test_features]
ty = [x[0] for x in self.test_features]
# print self.trainFeatures
# print x
# print y
predicthome = [0, 0, 0]
predictdraw = [0, 0, 0]
predictaway = [0, 0, 0]
clf = svm.SVC(kernel='rbf')
clf.decision_function_shape = 'ovr'
right = 0
for temp in enumerate(tx):
flag = 0
clf.fit(x, y)
detailResult = [self.test_set[temp[0]][2], self.test_set[temp[0]][3], self.test_set[temp[0]][6]]
if (ty[temp[0]] == 1):
if (clf.predict(temp[1])[0] == 1):
detailResult.append('H')
right += 1
flag = 1
predicthome[0] += 1
elif (clf.predict(temp[1])[0] == 0):
detailResult.append('D')
predicthome[1] += 1
else:
detailResult.append('A')
predicthome[2] += 1
elif (ty[temp[0]] == 0):
if (clf.predict(temp[1])[0] == 1):
detailResult.append('H')
predictdraw[0] += 1
elif (clf.predict(temp[1])[0] == 0):
detailResult.append('D')
right += 1
flag = 1
predictdraw[1] += 1
else:
detailResult.append('A')
predictdraw[2] += 1
else:
if (clf.predict(temp[1])[0] == 1):
detailResult.append('H')
predictaway[0] += 1
elif (clf.predict(temp[1])[0] == 0):
detailResult.append('D')
predictaway[1] += 1
else:
detailResult.append('A')
right += 1
flag = 1
predictaway[2] += 1
detailResults.append(detailResult)
if (flag == 0):
##update training set
newTrainEx = self.combine_label_and_features(self.test_set[temp[0]][2], self.test_set[temp[0]][3],
self.test_set[temp[0]][1], self.test_set[temp[0]][6], 'E2015')
print(newTrainEx)
x.append(newTrainEx[1:])
y.append(newTrainEx[0])
print(float(right) / len(tx))
print(detailResults)
print(predicthome, predictdraw, predictaway)
doSVM()