class PrepareConnection(QWidget, Ui_Prepare):
close_signal = pyqtSignal(bool)
def __init__(self, parent=None):
super(PrepareConnection, self).__init__(parent)
self.setupUi(self)
self.data_container = DataContainer()
self._filename = os.path.split(__file__)[-1]
self.buttonLoad.clicked.connect(self.LoadData)
self.buttonRemoveAndExport.clicked.connect(self.RemoveInvalidValue)
self.__testing_ref_data_container = DataContainer()
self.__clinical_ref = pd.DataFrame()
self.radioSplitRandom.clicked.connect(self.ChangeSeparateMethod)
self.radioSplitRef.clicked.connect(self.ChangeSeparateMethod)
self.checkUseClinicRef.clicked.connect(
self.RandomSeparateButtonUpdates)
self.loadTestingReference.clicked.connect(
self.LoadTestingReferenceDataContainer)
self.clearTestingReference.clicked.connect(
self.ClearTestingReferenceDataContainer)
self.loadClinicRef.clicked.connect(self.LoadClinicalRef)
self.clearClinicRef.clicked.connect(self.ClearClinicalRef)
self.buttonSave.clicked.connect(self.CheckAndSave)
def closeEvent(self, QCloseEvent):
self.close_signal.emit(True)
QCloseEvent.accept()
def UpdateTable(self):
self.tableFeature.setRowCount(self.data_container.GetFrame().shape[0])
header_name = deepcopy(list(self.data_container.GetFrame().columns))
min_col = np.min([len(header_name), 100])
if min_col == 100:
header_name = header_name[:100]
header_name[-1] = '...'
self.tableFeature.setColumnCount(min_col)
self.tableFeature.setHorizontalHeaderLabels(header_name)
self.tableFeature.setVerticalHeaderLabels(
list(map(str,
self.data_container.GetFrame().index)))
for row_index in range(self.data_container.GetFrame().shape[0]):
for col_index in range(min_col):
if col_index < 99:
self.tableFeature.setItem(
row_index, col_index,
QTableWidgetItem(
str(self.data_container.GetFrame().iloc[
row_index, col_index])))
else:
self.tableFeature.setItem(row_index, col_index,
QTableWidgetItem('...'))
text = "The number of cases: {:d}\n".format(
self.data_container.GetFrame().shape[0])
# To process Label temporally
if 'label' in self.data_container.GetFrame().columns:
label_name = 'label'
text += "The number of features: {:d}\n".format(
self.data_container.GetFrame().shape[1] - 1)
elif 'Label' in self.data_container.GetFrame().columns:
label_name = 'Label'
text += "The number of features: {:d}\n".format(
self.data_container.GetFrame().shape[1] - 1)
else:
label_name = ''
text += "The number of features: {:d}\n".format(
self.data_container.GetFrame().shape[1])
if label_name:
labels = np.asarray(
self.data_container.GetFrame()[label_name].values,
dtype=np.int)
if len(np.unique(labels)) == 2:
positive_number = len(np.where(labels == np.max(labels))[0])
negative_number = len(labels) - positive_number
assert (positive_number + negative_number == len(labels))
text += "The number of positive samples: {:d}\n".format(
positive_number)
text += "The number of negative samples: {:d}\n".format(
negative_number)
self.textInformation.setText(text)
def SetButtonsState(self, state):
self.buttonRemoveAndExport.setEnabled(state)
self.buttonSave.setEnabled(state)
self.checkExport.setEnabled(state)
self.radioRemoveNone.setEnabled(state)
self.radioRemoveNonvalidCases.setEnabled(state)
self.radioRemoveNonvalidFeatures.setEnabled(state)
self.radioSplitRandom.setEnabled(state)
self.radioSplitRef.setEnabled(state)
def LoadData(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self,
'Open SCV file',
filter="csv files (*.csv)")
if file_name:
try:
if self.data_container.Load(file_name, is_update=False):
self.UpdateTable()
self.SetButtonsState(True)
except OSError as reason:
eclog(self._filename).GetLogger().error(
'Load CSV Error: {}'.format(reason))
QMessageBox.about(self, 'Load data Error', reason.__str__())
print('Error！' + str(reason))
except ValueError:
eclog(self._filename).GetLogger().error(
'Open CSV Error: {}'.format(file_name))
QMessageBox.information(self, 'Error',
'The selected data file mismatch.')
def LoadTestingReferenceDataContainer(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self,
'Open SCV file',
filter="csv files (*.csv)")
if file_name:
try:
self.__testing_ref_data_container.Load(file_name)
self.loadTestingReference.setEnabled(False)
self.clearTestingReference.setEnabled(True)
self.spinBoxSeparate.setEnabled(False)
except OSError as reason:
eclog(self._filename).GetLogger().error(
'Load Testing Ref Error: {}'.format(reason))
print('Error！' + str(reason))
except ValueError:
eclog(self._filename).GetLogger().error(
'Open CSV Error: {}'.format(file_name))
QMessageBox.information(self, 'Error',
'The selected data file mismatch.')
def ClearTestingReferenceDataContainer(self):
del self.__testing_ref_data_container
self.__testing_ref_data_container = DataContainer()
self.loadTestingReference.setEnabled(True)
self.clearTestingReference.setEnabled(False)
self.spinBoxSeparate.setEnabled(False)
def LoadClinicalRef(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self,
'Open SCV file',
filter="csv files (*.csv)")
if file_name:
try:
self.__clinical_ref = pd.read_csv(file_name, index_col=0)
if list(self.__clinical_ref.index) != list(
self.data_container.GetFrame().index):
QMessageBox.information(
self, 'Error',
'The index of clinical features is not consistent to the data'
)
return None
self.loadClinicRef.setEnabled(False)
self.clearClinicRef.setEnabled(True)
except OSError as reason:
eclog(self._filename).GetLogger().error(
'Load Clinical Ref Error: {}'.format(reason))
QMessageBox.information(self, 'Error',
'Can not Open the Files')
except ValueError:
eclog(self._filename).GetLogger().error(
'OpenCSV Error: {}'.format(file_name))
QMessageBox.information(self, 'Error',
'The selected data file mismatch.')
return None
def ClearClinicalRef(self):
del self.__clinical_ref
self.__clinical_ref = pd.DataFrame()
self.loadClinicRef.setEnabled(True)
self.clearClinicRef.setEnabled(False)
def RemoveInvalidValue(self):
if not self.data_container.IsEmpty():
if self.checkExport.isChecked():
dlg = QFileDialog()
store_path, _ = dlg.getSaveFileName(self,
'Save CSV feature files',
'features.csv',
filter="CSV files (*.csv)")
# folder_name = QFileDialog.getExistingDirectory(self, "Save Invalid data")
# store_path = os.path.join(folder_name, 'invalid_feature.csv')
else:
store_path = ''
if self.radioRemoveNonvalidCases.isChecked():
self.data_container.RemoveInvalid(store_path=store_path,
remove_index=REMOVE_CASE)
elif self.radioRemoveNonvalidFeatures.isChecked():
self.data_container.RemoveInvalid(store_path=store_path,
remove_index=REMOVE_FEATURE)
self.UpdateTable()
def ChangeSeparateMethod(self):
if self.radioSplitRandom.isChecked():
self.spinBoxSeparate.setEnabled(True)
self.checkUseClinicRef.setEnabled(True)
self.loadTestingReference.setEnabled(False)
self.clearTestingReference.setEnabled(False)
elif self.radioSplitRef.isChecked():
self.spinBoxSeparate.setEnabled(False)
self.checkUseClinicRef.setEnabled(False)
if self.__testing_ref_data_container.IsEmpty():
self.loadTestingReference.setEnabled(True)
self.clearTestingReference.setEnabled(False)
else:
self.loadTestingReference.setEnabled(False)
self.clearTestingReference.setEnabled(True)
self.RandomSeparateButtonUpdates()
def RandomSeparateButtonUpdates(self):
if self.checkUseClinicRef.isChecked():
if self.__clinical_ref.size > 0:
self.loadClinicRef.setEnabled(False)
self.clearClinicRef.setEnabled(True)
else:
self.loadClinicRef.setEnabled(True)
self.clearClinicRef.setEnabled(False)
else:
self.loadClinicRef.setEnabled(False)
self.clearClinicRef.setEnabled(False)
def CheckAndSave(self):
if self.data_container.IsEmpty():
QMessageBox.warning(self, "Warning", "There is no data",
QMessageBox.Ok)
return None
if self.data_container.HasInvalidNumber():
QMessageBox.warning(self, "Warning", "There are nan items",
QMessageBox.Ok)
non_valid_number_index = self.data_container.FindInvalidNumberIndex(
)
old_edit_triggers = self.tableFeature.editTriggers()
self.tableFeature.setEditTriggers(QAbstractItemView.CurrentChanged)
self.tableFeature.setCurrentCell(non_valid_number_index[0],
non_valid_number_index[1])
self.tableFeature.setEditTriggers(old_edit_triggers)
return None
self.data_container.UpdateDataByFrame()
if not self.data_container.IsBinaryLabel():
QMessageBox.warning(self, "Warning", "There are not 2 Labels",
QMessageBox.Ok)
return None
remove_features_with_same_value = RemoveSameFeatures()
self.data_container = remove_features_with_same_value.Run(
self.data_container)
if self.radioSplitRandom.isChecked() or self.radioSplitRef.isChecked():
folder_name = QFileDialog.getExistingDirectory(self, "Save data")
if folder_name != '':
data_separate = DataSeparate.DataSeparate()
try:
if self.__testing_ref_data_container.IsEmpty():
testing_data_percentage = self.spinBoxSeparate.value()
if self.__clinical_ref.size == 0:
training_data_container, _, = \
data_separate.RunByTestingPercentage(self.data_container,
testing_data_percentage,
store_folder=folder_name)
else:
training_data_container, _, = \
data_separate.RunByTestingPercentage(self.data_container,
testing_data_percentage,
clinic_df=self.__clinical_ref,
store_folder=folder_name)
else:
training_data_container, _, = \
data_separate.RunByTestingReference(self.data_container,
self.__testing_ref_data_container,
folder_name)
if training_data_container.IsEmpty():
QMessageBox.information(
self, 'Error',
'The testing data does not mismatch, please check the testing data '
'really exists in current data')
return None
os.system("explorer.exe {:s}".format(
os.path.normpath(folder_name)))
except Exception as e:
content = 'PrepareConnection, splitting failed: '
eclog(self._filename).GetLogger().error('Split Error: ' +
e.__str__())
QMessageBox.about(self, content, e.__str__())
else:
file_name, _ = QFileDialog.getSaveFileName(
self, "Save data", filter="csv files (*.csv)")
if file_name:
self.data_container.Save(file_name)
class DataSplitterByFeatureCluster(object):
def __init__(self,
parts=30,
repeat_times=100,
test_ratio=0.3,
random_seed=10):
self.parts = parts
self.repeat_times = repeat_times
self.test_ratio = test_ratio
self.random_seed = random_seed
self.feature_labels = []
self.current_dc = DataContainer()
#################################################
def _DataPreProcess(self, dc):
data = dc.GetArray() # get train data
# min_max, Process the features of each column
min_max_scaler = preprocessing.MinMaxScaler()
processed_data = min_max_scaler.fit_transform(data).T
return processed_data
def _Cluster(self, dc):
# According Cluster to selecte features and combine them into a DataContainer
processed_data = self._DataPreProcess(dc)
feature_name_list = dc.GetFeatureName()
k_means = KMeans(n_clusters=self.parts,
random_state=self.random_seed,
init='k-means++')
k_means.fit(processed_data) # training
count_label = [0 for _ in range(self.parts)]
count_feature = [[] for _ in range(self.parts)]
count_distance = [[] for _ in range(self.parts)]
feature_predict = k_means.labels_
cluster_centers = k_means.cluster_centers_
for j in range(len(feature_name_list)):
count_label[feature_predict[j]] += 1
count_feature[feature_predict[j]].append(feature_name_list[j])
cluster_center = cluster_centers[feature_predict[j]]
distance = np.square(processed_data[j] - cluster_center).sum()
count_distance[feature_predict[j]].append(distance)
print('The number of feature in each class \n', count_label)
min_distance_feature = []
for k in range(self.parts):
k_feature = count_feature[k]
k_distance = count_distance[k]
idx = k_distance.index(min(k_distance))
selected_feature = k_feature[idx]
min_distance_feature.append(selected_feature)
print('min distance feature in this class {} is {}'.format(
k, selected_feature))
print('its distance is', min(k_distance), 'while max distance is',
max(k_distance))
return min_distance_feature, feature_predict
def _MergeClinical(self, dc, cli_df):
# Merge DataContainer and a dataframe of clinical
if 'label' in cli_df.columns.tolist():
del cli_df['label']
elif 'Label' in cli_df.columns.tolist():
del cli_df['Label']
df = pd.merge(dc.GetFrame(),
cli_df,
how='left',
left_index=True,
right_index=True)
merge_dc = DataContainer()
merge_dc.SetFrame(df)
merge_dc.UpdateFrameByData()
return merge_dc
def _EstimateAllFeatureDistribution(self, dc):
feature_name_list = dc.GetFeatureName()
distribution = dict()
for i in range(len(feature_name_list)):
feature = feature_name_list[i]
feature_data = dc.GetFrame()[feature]
_, normal_p = normaltest(feature_data, axis=0)
if len(
set(feature_data)
) < 10: # TODO: a better way to distinguish discrete numeric values
distribution[feature] = 'Category'
elif normal_p > 0.05:
distribution[feature] = 'Normal'
else:
distribution[feature] = 'Non-normal'
# return a dict {"AGE": 'Normal', 'Gender': 'Category', ... }
return distribution
def _EstimateDcFeaturePvalue(self, dc1, dc2, feature_type):
array1, array2 = dc1.GetArray(), dc2.GetArray()
p_values = {}
for index, feature in enumerate(dc1.GetFeatureName()):
p_values[feature] = GetPvalue(array1[:, index], array2[:, index],
feature_type[feature])
return p_values
#################################################
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 VisualizeCluster(self,
dimension='2d',
select_feature=None,
store_folder=None,
is_show=True):
if len(self.feature_labels) != 0 and self.current_dc.GetFrame(
).size != 0:
processed_data = self._DataPreProcess(self.current_dc)
if select_feature is None:
select_feature = [0, 1, 2]
assert dimension in ['2d', '3d']
if dimension == '2d':
plt.scatter(processed_data[:, select_feature[0]],
processed_data[:, select_feature[1]],
s=5,
c=self.feature_labels)
elif dimension == '3d':
ax = plt.figure().add_subplot(111, projection='3d')
ax.scatter(processed_data[:, select_feature[0]],
processed_data[:, select_feature[1]],
processed_data[:, select_feature[2]],
s=10,
c=self.feature_labels,
marker='^')
ax.set_title('Cluster Result 3D')
if store_folder and os.path.isdir(store_folder):
plt.savefig(
os.path.join(
store_folder,
'ClusteringProcessPlot{}.jpg'.format(dimension)))
if is_show:
plt.show()
def Run(self,
dc: DataContainer,
output_folder: str,
clinical_feature=None):
self.current_dc = dc
selected_feature_names, self.feature_labels = self._Cluster(dc)
fs = FeatureSelector()
selected_dc = fs.SelectFeatureByName(dc, selected_feature_names)
if clinical_feature is not None:
if isinstance(clinical_feature, str):
clinical_feature = pd.read_csv(clinical_feature, index_col=0)
assert (isinstance(clinical_feature, pd.DataFrame))
merge_dc = self._MergeClinical(selected_dc, clinical_feature)
else:
merge_dc = selected_dc
feature_distribution_type = self._EstimateAllFeatureDistribution(
merge_dc) # a dict
splitter = DataSeparate()
output_train_dc, output_test_dc = DataContainer(), DataContainer()
output_p_value = []
mean_p_value = -1
for _ in range(self.repeat_times):
train_dc, test_dc = splitter.RunByTestingPercentage(
merge_dc, testing_data_percentage=self.test_ratio)
feature_p_value = self._EstimateDcFeaturePvalue(
train_dc, test_dc, feature_distribution_type)
if np.mean(list(feature_p_value.values())) > mean_p_value:
mean_p_value = np.mean(list(feature_p_value.values()))
output_train_dc, output_test_dc = train_dc, test_dc
output_p_value = feature_p_value
if output_folder is not None and os.path.isdir(output_folder):
output_train_dc.Save(os.path.join(output_folder, 'train.csv'))
output_test_dc.Save(os.path.join(output_folder, 'test.csv'))
p_value_df = pd.DataFrame(output_p_value, index=['P Value'])
distribute_df = pd.DataFrame(feature_distribution_type,
index=['Distribution'])
store_df = pd.concat((p_value_df, distribute_df), axis=0)
store_df.to_csv(os.path.join(output_folder, 'split_info.csv'))
'''
import pandas as pd
from copy import deepcopy
from BC.DataContainer.DataContainer import DataContainer
class FeatureEncodingOneHot():
def __init__(self):
pass
def OneHotOneColumn(self, data_container, feature_list):
info = data_container.GetFrame()
feature_name = data_container.GetFeatureName()
for feature in feature_list:
assert (feature in feature_name)
new_info = pd.get_dummies(info, columns=feature_list)
new_data = DataContainer()
new_data.SetFrame(new_info)
return new_data
if __name__ == '__main__':
data = DataContainer()
data.Load(r'c:\Users\yangs\Desktop\test.csv')
info = data.GetFrame()
new_info = pd.get_dummies(
info, columns=['bGs', 'PIRADS', 't2score', 'DWIscore', 'MR_stage'])
new_info.to_csv(r'c:\Users\yangs\Desktop\test_onehot.csv')
