Project: s41467-024-48107-5 | Function: SVM_Classifier (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Support Vector Machine classifier for automated assessment of nucleus segmentation quality, trained to distinguish successful from failed segmentations using point spread function features

**KEY TERMS:** {
  "clf": "SVM classifier object",
  "gamma": "RBF kernel parameter (0.01)",
  "X_train": "training data features from segmentation images",
  "y_train": "training labels for segmentation quality",
  "predicted": "SVM predictions on test data"
}

**CONTEXT CODE:**         self.classification = []
        
        """the folders that serve as classification"""
        for i in range(len(self.folderpath)):
            subpath = os.listdir(self.folderpath[i])
            for j in subpath:
                array = numpy.load(self.folderpath[i] + "\\" + j)
                if array.shape == (49,):
                    self.images.append(array)
                    self.classification.append(int(self.folders[i]))
                
        """flatten and create the dataset"""
        self.flattened_images = numpy.array(self.images).reshape((len(self.images), -1))
        self.classification   = numpy.array(self.classification)

        """create the dataset test, train etc."""
        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.flattened_images, self.classification, test_size=0.5, shuffle=True)
        
    def manualPSFData(self,
                           psf
                      ):
        """function applies if you want to create test and training data on the fly
        you give it a tuple with lists containing the pointspread functions
        INPUT
            psf: (False:[ARR1,ARR2....ARRn],(True:[ARR1,ARR2....ARRn])"""
        """play around with the datasets"""
        from sklearn.model_selection import train_test_split
        
        """get the pointspreadfunctions of th data"""
        psf_0,psf_1 = psf

        """create thest and trainingdata"""
        self.images         = []
        self.classification = []

        """The psf_0 and the Psf_1 for a specific colorchannel"""        
        for i in psf_0:
            if i.shape == (7,7):
                self.images.append(numpy.array(i.reshape(len(i),-1)))
                self.classification.append(0)
            
        for i in psf_1:
            if i.shape == (7,7):
                self.images.append(numpy.array(i.reshape(len(i),-1)))
                self.classification.append(1)   
                
        """create the dataset test, train etc."""
        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.images, self.classification, test_size=0.5, shuffle=True)
        

    def SVM_Classifier(self,
                       plot = False
                       ):
        """Support Vector Machine classifier for automated assessment of nucleus segmentation quality, trained to distinguish successful from failed segmentations using point spread function features"""
        raise NotImplementedError("Function has been masked for testing purposes")
        
        
    def ANN_PSF(self,):
        import warnings
        import matplotlib.pyplot as plt
        from sklearn.neural_network import MLPClassifier
        from sklearn.preprocessing import MinMaxScaler
        from sklearn import datasets
        from sklearn.exceptions import ConvergenceWarning
        from sklearn import datasets, svm, metrics
        from sklearn.model_selection import train_test_split
            
        # different learning rate schedules and momentum parameters
        params = [{'solver': 'sgd', 'learning_rate': 'constant', 'momentum': 0,
                  'learning_rate_init': 0.2},
                  {'solver': 'sgd', 'learning_rate': 'constant', 'momentum': .9,
                  'nesterovs_momentum': False, 'learning_rate_init': 0.2},
                  {'solver': 'sgd', 'learning_rate': 'constant', 'momentum': .9,
                  'nesterovs_momentum': True, 'learning_rate_init': 0.2},
                  {'solver': 'sgd', 'learning_rate': 'invscaling', 'momentum': 0,
                  'learning_rate_init': 0.2},
                  {'solver': 'sgd', 'learning_rate': 'invscaling', 'momentum': .9,
                  'nesterovs_momentum': True, 'learning_rate_init': 0.2},
                  {'solver': 'sgd', 'learning_rate': 'invscaling', 'momentum': .9,
                  'nesterovs_momentum': False, 'learning_rate_init': 0.2},
                  {'solver': 'adam', 'learning_rate_init': 0.01}]
        
        labels = ["constant learning-rate", "constant with momentum",
                  "constant with Nesterov's momentum",
                  "inv-scaling learning-rate", "inv-scaling with momentum",
                  "inv-scaling with Nesterov's momentum", "adam"]
        
        plot_args = [{'c': 'red', 'linestyle': '-'},
                    {'c': 'green', 'linestyle': '-'},
                    {'c': 'blue', 'linestyle': '-'},
                    {'c': 'red', 'linestyle': '--'},
                    {'c': 'green', 'linestyle': '--'},
                    {'c': 'blue', 'linestyle': '--'},
                    {'c': 'black', 'linestyle': '-'}]
        
        def plot_on_dataset(
                            X, y, name
                            ):
            # for each dataset, plot learning for each learning strategy
            print("\nlearning on dataset %s" % name)
            plt.title(name)
        
            X = MinMaxScaler().fit_transform(self.X_train)
            mlps = []
        
            max_iter = 400
            for label, param in zip(labels, params):
                print("training: %s" % label)
                self.mlp = MLPClassifier(random_state=0,max_iter=max_iter, **param)
        
                # some parameter combinations will not converge as can be seen on the
                # plots so they are ignored here
                with warnings.catch_warnings():
                    warnings.filterwarnings("ignore", category=ConvergenceWarning,
                                            module="sklearn")
                    self.mlp.fit(X, self.y_train)
        
                mlps.append(self.mlp)
                print("Training set score: %f" % self.mlp.score(X, self.y_train))
                print("Training set loss: %f" % self.mlp.loss_)
            for mlp, label, args in zip(mlps, labels, plot_args):
                plt.plot(mlp.loss_curve_, label=label, **args)
            return mlps

        mlps = plot_on_dataset(self.images,self.classification,name='ANN training regimes')
        
        
        for mlp in mlps:
            predicted = mlp.predict(self.X_test)
            disp = metrics.confusion_matrix(self.y_test, predicted)

            
    def SVM_predict(self,
                    image
                    ):
        """resize the image and predict of the
        Nucleus as segemented is a false positive (wrong segementation) or true positive (correct segmentatation)"""
        from skimage.transform import resize
        
        """images are resized to 25x25 pixel objects"""
        image = numpy.array(resize(image, (25, 25)))
        
        """flatten the matrix"""
        data = image.reshape((1, -1))
        
        """return the prediction of the clf model prediction"""
        return self.clf.predict(data/numpy.max(data))
        
    def ANN_Classifier(self,):
        import warnings


Complete the function implementation replacing "raise NotImplementedError".