def VisualizePartsVariance(self,
dc: DataContainer,
max_k=None,
method='SSE',
store_folder=None,
is_show=True):
# method must be one of SSE or SC. SSE denotes xxxx, SC denotes Silhouette Coefficient
# TODO: Normalize the train_data
data = dc.GetArray().transpose()
if max_k is None:
max_k = min(data.shape[0], 50)
assert (method in ['SSE', 'SC'])
#TODO: plot
score = []
for k in range(2, max_k):
if method == 'SSE':
pass
elif method == 'SC':
pass
if store_folder and os.path.isdir(store_folder):
plt.savefig(os.path.join(store_folder, 'ClusteringPlot.jpg'))
if is_show:
plt.show()
def VisualizePartsVariance(self, dc: DataContainer, max_k=None, method='SSE',
store_folder=None, is_show=True):
# method must be one of SSE or SC. SSE denotes xxxx, SC denotes Silhouette Coefficient
data = dc.GetArray() # get train data
processed_data = self._DataPreProcess(dc)
if max_k is None:
max_k = min(data.shape[0], 50)
assert(method in ['SSE', 'SC'])
score = []
for k in range(2, max_k):
print('make cluster k=', k)
estimator = KMeans(n_clusters=k)
estimator.fit(processed_data)
if method == 'SSE':
score.append(estimator.inertia_)
elif method == 'SC':
score.append(silhouette_score(processed_data, estimator.labels_, metric='euclidean'))
X = range(2, max_k)
plt.xlabel('k')
plt.ylabel(method)
plt.plot(X, score, 'o-')
if store_folder and os.path.isdir(store_folder):
plt.savefig(os.path.join(store_folder, 'ClusteringParameterPlot.jpg'))
if is_show:
plt.show()
def TestNewData(NewDataCsv, model_folder, result_save_path=''):
'''
:param NewDataCsv: New radiomics feature matrix csv file path
:param model_folder:The trained model path
:return:classification result
'''
train_info = LoadTrainInfo(model_folder)
new_data_container = DataContainer()
#Normlization
new_data_container.Load(NewDataCsv)
# feature_selector = FeatureSelector()
# feature_selector.SelectFeatureByName(new_data_container, train_info['selected_features'], is_replace=True)
new_data_container = train_info['normalizer'].Transform(new_data_container)
# data_frame = new_data_container.GetFrame()
# data_frame = data_frame[train_info['selected_features']]
# new_data_container.SetFrame(data_frame)
# new_data_container.UpdateDataByFrame()
##Model
train_info['classifier'].SetDataContainer(new_data_container)
model = train_info['classifier'].GetModel()
predict = model.predict_proba(new_data_container.GetArray())[:, 1]
label = new_data_container.GetLabel()
case_name = new_data_container.GetCaseName()
test_result_info = [['CaseName', 'Pred', 'Label']]
for index in range(len(label)):
test_result_info.append(
[case_name[index], predict[index], label[index]])
metric = EstimateMetirc(predict, label)
info = {}
info.update(metric)
cv = CrossValidation()
print(metric)
print('\t')
if result_save_path:
cv.SaveResult(info, result_save_path)
np.save(os.path.join(result_save_path, 'test_predict.npy'), predict)
np.save(os.path.join(result_save_path, 'test_label.npy'), label)
with open(os.path.join(result_save_path, 'test_info.csv'),
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(test_result_info)
return metric
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 BC.DataContainer.DataContainer import DataContainer
from BC.FeatureAnalysis.Normalizer import NormalizerZeroCenter
from BC.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)
vif_dict[exog] = vif
# calculate tolerance
tolerance = 1 - r_squared
tolerance_dict[exog] = tolerance
# return VIF DataFrame
df_vif = pd.DataFrame({'VIF': vif_dict, 'Tolerance': tolerance_dict})
return df_vif
if __name__ == '__main__':
data_path = r'..\..\Demo\train_numeric_feature.csv'
from BC.DataContainer.DataContainer import DataContainer
from BC.FeatureAnalysis.Normalizer import NormalizerZeroCenter
pca = DimensionReductionByPCA()
dc = DataContainer()
dc.Load(data_path)
dc = NormalizerZeroCenter.Run(dc)
# dc = pca.Run(dc)
df = pd.DataFrame(dc.GetArray(),
index=dc.GetCaseName(),
columns=dc.GetFeatureName())
dr = DimensionReductionByVIF()
new_df = dr.CalculateVIF(df)
print(dc.GetArray().shape, new_df.shape)
return "To Remove the unbalance of the training data set, we applied an Tomek link after the " \
"Synthetic Minority Oversampling TEchnique (SMOTE) to make positive/negative samples balance. "
def Run(self, data_container, store_path=''):
data, label, feature_name, label_name = data_container.GetData()
data_resampled, label_resampled = self._model.fit_sample(data, label)
new_case_name = [
'Generate' + str(index) for index in range(data_resampled.shape[0])
]
new_data_container = DataContainer(data_resampled, label_resampled,
data_container.GetFeatureName(),
new_case_name)
if store_path != '':
if os.path.isdir(store_path):
new_data_container.Save(
os.path.join(store_path,
'{}_features.csv'.format(self._name)))
else:
new_data_container.Save(store_path)
return new_data_container
if __name__ == '__main__':
dc = DataContainer()
dc.Load(r'..\..\Example\numeric_feature.csv')
print(dc.GetArray().shape, np.sum(dc.GetLabel()))
b = SmoteTomekSampling()
new = b.Run(dc)
print(new.GetArray().shape, np.sum(new.GetLabel()))