def generate_data_transformers(self): # Data Transformation (Scaling, Normalization) if self.data_transform: if self.data_transform == 'EXP': transformer = '' transformer.name = '' elif data_transform == 'NORM': pass transformer.params = utils.get_params_string( self.data_transform_params) self.transformer = transformer # Feature Selection (Var, Chi^2) if self.feature_selection: if self.feature_selection == 'VAR': selector = VarianceThreshold(**self.feature_selection_params) selector.name = 'VarianceThreshold' elif self.feature_selection == 'CHI2': pass selector.params = utils.get_params_string( self.feature_selection_params) self.selector = selector # Kernel Approximation (RBF, Chi^2) if self.approximation_kernel: if self.approximation_kernel == 'RBF': approx_kernel_map = RBFSampler( **self.kernel_approximation_params) approx_kernel_map.name = 'RBFSampler' elif self.approximation_kernel == 'CHI2': approx_kernel_map = AdditiveChi2Sampler( **self.kernel_approximation_params) approx_kernel_map.name = 'AdditiveChi2Sampler' approx_kernel_map.params = utils.get_params_string( self.kernelapproximation_params) self.approx_kernel_map = approx_kernel_map