add classical methods

Author: shinshiner

traditional_methods/DecisionTree.py

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+from sklearn.tree import DecisionTreeClassifier
+
+import sys
+sys.path.append('../')
+
+from data_loader import MnistLoader
+
+def DT():
+ loader = MnistLoader(flatten=True, data_path='../data', var_per=None)
+ model = DecisionTreeClassifier(criterion='gini')
+
+ model.fit(loader.data_train, loader.label_train)
+ print('model trained')
+ res = model.score(loader.data_test, loader.label_test)
+ print(res)
+
+ return res
+
+if __name__ == '__main__':
+ DT()

traditional_methods/KNN.py

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+from sklearn.neighbors import KNeighborsClassifier
+
+import sys
+sys.path.append('../')
+
+from data_loader import MnistLoader
+
+def KNN():
+ loader = MnistLoader(flatten=True, data_path='../data', var_per=None)
+ model = KNeighborsClassifier(n_neighbors=5)
+
+ model.fit(loader.data_train, loader.label_train)
+ print('model trained')
+ res = model.score(loader.data_test, loader.label_test)
+ print(res)
+
+ return res
+
+if __name__ == '__main__':
+ KNN()

traditional_methods/LR.py

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+from sklearn.linear_model import LogisticRegression
+
+import sys
+sys.path.append('../')
+
+from data_loader import MnistLoader
+
+def LR():
+ loader = MnistLoader(flatten=True, data_path='../data', var_per=None)
+ model = LogisticRegression(penalty='l2')
+
+ model.fit(loader.data_train, loader.label_train)
+ print('model trained')
+ res = model.score(loader.data_test, loader.label_test)
+ print(res)
+
+ return res
+
+if __name__ == '__main__':
+ LR()

traditional_methods/MLP.py

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+from sklearn.neural_network import MLPClassifier
+
+import sys
+sys.path.append('../')
+
+from data_loader import MnistLoader
+
+def MLP():
+ loader = MnistLoader(flatten=True, data_path='../data', var_per=None)
+ model = MLPClassifier(hidden_layer_sizes=(500, 500, 500))
+
+ model.fit(loader.data_train, loader.label_train)
+ print('model trained')
+ res = model.score(loader.data_test, loader.label_test)
+ print(res)
+
+ return res
+
+if __name__ == '__main__':
+ MLP()

traditional_methods/SGD.py

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+from sklearn.svm import SVC
+
+import sys
+sys.path.append('../')
+
+from data_loader import MnistLoader
+
+def SVM():
+ loader = MnistLoader(flatten=True, data_path='../data', var_per=None)
+ model = SVC(kernel='rbf')
+
+ model.fit(loader.data_train, loader.label_train)
+ print('model trained')
+ res = model.score(loader.data_test, loader.label_test)
+ print(res)
+
+ return res
+
+if __name__ == '__main__':
+ SVM()