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
