Skip to main content

No project description provided

Project description

TimeSeriesRNNInterpretor

Build Status

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

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

Calling

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)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

TimeSeriesInterpretorRNN-0.0.11.tar.gz (4.6 kB view details)

Uploaded Source

Built Distribution

TimeSeriesInterpretorRNN-0.0.11-py3-none-any.whl (5.4 kB view details)

Uploaded Python 3

File details

Details for the file TimeSeriesInterpretorRNN-0.0.11.tar.gz.

File metadata

  • Download URL: TimeSeriesInterpretorRNN-0.0.11.tar.gz
  • Upload date:
  • Size: 4.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.1 requests/2.23.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.7.10

File hashes

Hashes for TimeSeriesInterpretorRNN-0.0.11.tar.gz
Algorithm Hash digest
SHA256 f0335001e43343bfd0ee24bd44f15e0efe6bfc7c4a122c72557f6062d782efa1
MD5 703b3853bc9a4af6f0849038f1d5fa2c
BLAKE2b-256 2fe636b5c277a4f17cb63ccafb7f69f261646ca778448c4ca79d7a3e73d2fe48

See more details on using hashes here.

File details

Details for the file TimeSeriesInterpretorRNN-0.0.11-py3-none-any.whl.

File metadata

  • Download URL: TimeSeriesInterpretorRNN-0.0.11-py3-none-any.whl
  • Upload date:
  • Size: 5.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.1 requests/2.23.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.7.10

File hashes

Hashes for TimeSeriesInterpretorRNN-0.0.11-py3-none-any.whl
Algorithm Hash digest
SHA256 170bb6db489a8400bb2f8dbff09997f9e6dcb9089da0b56daaf84c8a88f984e2
MD5 3f2a9199fe4d5d8e11a2cfd6bc60af58
BLAKE2b-256 fddba954da0772f734710f772699a4854a3c761d2be37bad7b79ad6f02bce727

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page