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TimeSeriesRNNInterpretor, Interprets the RNN black box model. It internally uses shap.DeepExplainer. We have tried to interpret only single instance with respect to time. We have additionally included monthly effect of the instance.

  • ✨TimeSeriesRNNInterpretor✨


Parameters required to Pass

Parameter To be passed Format
model RNN model tensorflow V1 behaviour
background training data sample Numpy array
test Instance data which we want to explain Numpy array
features feature_names or column names df.columns
max_output max output value(1) 1 for binary classification
max_display max display features for waterfall plot a number < no of features
decision_range max display features for decision plot a number < no of features
Measure "absolute" or "relative" or "total " for plotting waterfall plot w.r.t basevalue
dependence_feature_3 3rd feature name to plot dependence plot a feature name or index of feature
dependence_feature_2 2nd feature name to plot dependence plot a feature name or index of the feature


from TimeSeriesRNNInterpretor import TimeSeriesRNNInterpretor

temp = TimeSeriesInterpretorRNN(model=model,background=background,test=test,features=features,max_output=1,max_display=15,decision_range=30,Measure="absolute",dependence_feature_3="age.",dependence_feature_2="gender")

Plotting Different Plots

temp.global_patient() # To the know the feature importance of a instance
temp.monthly_chart() # timeframe charts of a instance (day,month, or any time frame)
temp.two_month_compare(17, 18) # Specify two timeframe numbers of a instancee to compare (days,months, or any time frames)
temp.single_month(18) # Specify the required specific timeframe number of instance(day,month, or any time frame)

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