ajayarunachalam / msda Goto Github PK

AttributeError Traceback (most recent call last)
in
----> 1 FeatureSelection.plot(df, logging_interval=2)

c:\users\sitra\anaconda3\envs\pythondata\lib\site-packages\msda\msda.py in plot(df, logging_interval)
410 cols = list(df.columns)[2:]
411 #columns = df.iloc[:,2:].values.tolist()
--> 412 columns = df.iloc[:,2:].aggregate(lambda x: [x.tolist()], axis=0).map(lambda x:x[0])
413 time_min = []
414 initial=0

c:\users\sitra\anaconda3\envs\pythondata\lib\site-packages\pandas\core\generic.py in getattr(self, name)
5137 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5138 return self[name]
-> 5139 return object.getattribute(self, name)
5140
5141 def setattr(self, name: str, value) -> None:

AttributeError: 'DataFrame' object has no attribute 'map'

I have python 3.6.2 and pandas 1.1.4

I've been studying this project recently and have doubts about model interpretation. In MSDA / blog_ Example / unsupervised-deep-analog-detector-with-interpretable-results.ipynb, which uses the deepcnn or lstmaenn model to train and find exceptions, but the interpretable results of the exception detector use a new model built by keras. This model seems to have nothing to do with the previous exception detection model.
Can you give me some explanations or references? Thank you very much for any reply.

while executing print Ratios of Variations of Values in each column
my df had initially zeros and removed it, still it shows the same error below