return train_cv_metric, val_cv_metric, test_metric, all_train_metric
if __name__ == '__main__':
from FAE.DataContainer.DataContainer import DataContainer
from FAE.FeatureAnalysis.Normalizer import NormalizerZeroCenter
from FAE.FeatureAnalysis.Classifier import SVM, LR, LDA, LRLasso, GaussianProcess, NaiveBayes, DecisionTree, RandomForest, AE, AdaBoost
import numpy as np
train_data_container = DataContainer()
train_data_container.Load(
r'C:\MyCode\FAEGitHub\FAE\Example\withoutshape\non_balance_features.csv'
)
normalizer = NormalizerZeroCenter()
train_data_container = normalizer.Run(train_data_container)
data = train_data_container.GetArray()
label = np.asarray(train_data_container.GetLabel())
# param_list = [
# {"hidden_layer_sizes": [(30,), (100,)],
# "solver": ["adam"],
# "alpha": [0.0001, 0.001],
# "learning_rate_init": [0.001, 0.01]}
# ]
# from sklearn.model_selection import ParameterGrid
# pl = ParameterGrid(param_list)
cv = CrossValidation5Folder()
input_data_container = output
return output
def SaveInfo(self, store_folder, all_features):
for fs in self.__selector_list:
fs.SaveInfo(store_folder, all_features)
def SaveDataContainer(self, data_container, store_folder, store_key):
for fs in self.__selector_list:
fs.SaveDataContainer(data_container, store_folder, store_key)
################################################################
if __name__ == '__main__':
from FAE.DataContainer.DataContainer import DataContainer
from FAE.FeatureAnalysis.Normalizer import NormalizerZeroCenter
from FAE.FeatureAnalysis.DimensionReduction import DimensionReductionByPCC
dc = DataContainer()
pcc = DimensionReductionByPCC()
fs = FeatureSelectByKruskalWallis(selected_feature_number=5)
dc.Load(r'..\..\Demo\train_numeric_feature.csv')
dc = NormalizerZeroCenter.Run(dc)
dc = pcc.Run(dc)
print(dc.GetArray().shape)
dc = fs.Run(dc)
print(dc.GetArray().shape)