Added model saving for all classifiers.

Author: Olcay Taner YILDIZ

Classification/Classifier/Bagging.py

@@ -33,3 +33,6 @@ def train(self,
             tree = DecisionTree(DecisionNode(InstanceList(bootstrap.getSample())))
             forest.append(tree)
         self.model = TreeEnsembleModel(forest)
+
+    def loadModel(self, fileName: str):
+        self.model = TreeEnsembleModel(fileName)

Classification/Classifier/C45.py

@@ -32,3 +32,6 @@ def train(self,
         else:
             tree = DecisionTree(DecisionNode(trainSet))
         self.model = tree
+
+    def loadModel(self, fileName: str):
+        self.model = DecisionTree(fileName)

Classification/Classifier/C45Stump.py

@@ -22,3 +22,6 @@ def train(self,
         """
         self.model = DecisionTree(DecisionNode(data=trainSet,
                                                isStump=True))
+
+    def loadModel(self, fileName: str):
+        self.model = DecisionTree(fileName)

Classification/Classifier/Classifier.py

@@ -8,7 +8,6 @@
 from Classification.Performance.ConfusionMatrix import ConfusionMatrix
 from Classification.Performance.DetailedClassificationPerformance import DetailedClassificationPerformance
 from Classification.Model.Model import Model
-from DataStructure.CounterHashMap import CounterHashMap
 
 
 class Classifier(object):
@@ -21,6 +20,10 @@ def train(self,
               parameters: Parameter):
         pass
 
+    @abstractmethod
+    def loadModel(self, fileName: str):
+        pass
+
     def discreteCheck(self, instance: Instance) -> bool:
         """
         Checks given instance's attribute and returns true if it is a discrete indexed attribute, false otherwise.

Classification/Classifier/DeepNetwork.py

@@ -27,3 +27,6 @@ def train(self,
         self.model = DeepNetworkModel(trainSet=partition.get(1),
                                       validationSet=partition.get(0),
                                       parameters=parameters)
+
+    def loadModel(self, fileName: str):
+        self.model = DeepNetworkModel(fileName)

Classification/Classifier/Dummy.py

@@ -8,7 +8,7 @@ class Dummy(Classifier):
 
     def train(self,
               trainSet: InstanceList,
-              parameters: Parameter):
+              parameters: Parameter = None):
         """
         Training algorithm for the dummy classifier. Actually dummy classifier returns the maximum occurring class in
         the training data, there is no training.
@@ -21,3 +21,6 @@ def train(self,
             Parameter of the Dummy algorithm.
         """
         self.model = DummyModel(trainSet)
+
+    def loadModel(self, fileName: str):
+        self.model = DummyModel(fileName)

Classification/Classifier/KMeans.py

@@ -18,3 +18,6 @@ def train(self,
         self.model = KMeansModel(priorDistribution=prior_distribution,
                                  classMeans=class_means,
                                  distanceMetric=parameters.getDistanceMetric())
+
+    def loadModel(self, fileName: str):
+        self.model = KMeansModel(fileName)

Classification/Classifier/Knn.py

@@ -22,3 +22,6 @@ def train(self,
         self.model = KnnModel(data=trainSet,
                               k=parameters.getK(),
                               distanceMetric=parameters.getDistanceMetric())
+
+    def loadModel(self, fileName: str):
+        self.model = KnnModel(fileName)

Classification/Classifier/Lda.py

@@ -14,7 +14,7 @@ class Lda(Classifier):
 
     def train(self,
               trainSet: InstanceList,
-              parameters: Parameter):
+              parameters: Parameter = None):
         """
         Training algorithm for the linear discriminant analysis classifier (Introduction to Machine Learning, Alpaydin,
         2015).
@@ -46,3 +46,6 @@ def train(self,
             w0i = -0.5 * wi.dotProduct(average_vector) + math.log(prior_distribution.getProbability(Ci))
             w0[Ci] = w0i
         self.model = LdaModel(prior_distribution, w, w0)
+
+    def loadModel(self, fileName: str):
+        self.model = LdaModel(fileName)

Classification/Classifier/LinearPerceptron.py

@@ -29,3 +29,6 @@ def train(self,
         self.model = LinearPerceptronModel(trainSet=partition.get(1),
                                            validationSet=partition.get(0),
                                            parameters=parameters)
+
+    def loadModel(self, fileName: str):
+        self.model = LinearPerceptronModel(fileName)