Activity for Ansam Akram
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Ansam Akram committed [0b8a4f]
trainnning time for comment
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more results
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all request done
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complete test2
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added login, sign up, getstate and get video requsts
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cleaning up features format, finishing up rate video
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finishing up features
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recommitting
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added text to map severity
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modified auc
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modified style
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organized landing page
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temporary testing phase 1
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video rate page added
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initial commit
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added create
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createtask and view task
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added handleSelect to all and create new view for visual scheduling
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added child select before questionnaire-QCHAT
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Qchat select
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request done QA chat, almost done in AQchat
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added ResultDisplay to Adolescent
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added result shoe for AQ adult and added CorAdult in resultdisplay to differntiate between child or adult
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done AQ for all
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All models with auc in temp.csv
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anything
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Create child account 3l website+ mn l questionnaire ll child
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Upload videos to server
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Questionnaire result stage
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Wesbtie Questionaire
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Est2bl files 3l server
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Time of models
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Main Page For Questionnaire on Website
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Website Login and Sign Up
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added picklization for features used by models, added fromratertoprediction.py given rater's answers to all features gives prediction using pickled model and pickeled features
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Interface to rate new videos In python
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addes docstring to all files, deleted unneeded files
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Tests For Diagnosis Results
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AAC
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Video Rater Portal: (Login, Rating Page)
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Interface to rate new videos In python
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Sync with Nabil
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Main Page For Questionnaire on Website
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Wesbtie Questionaire
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Website Login and Sign Up
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Visual Schedule Start
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Est2bl files 3l server
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Random Forest ANOVA
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Random Forest Forward Selection
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modular code to pickle all best scoring models with their corresponding feature selection method
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All performing model, train and save then how to predict directly
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modular code to work for all models with all classifiers
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added pickelization of all DTs and saved them in solved models
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was able to pickle all DT and de pickle saving results (ik they are useless but why not) as well
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All performing model, train and save then how to predict directly
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Done first bit with DT, working on making code more modular with all DT first
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Check data in paper
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30 questions as multiple choice questions at seventh grade reading level 0 to 3 with scores indicating severity of autism features 8 to indicate feature can not be scored
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30 questions as multiple choice questions at seventh grade reading level 0 to 3 with scores indicating severity of autism features 8 to indicate feature can not be scored
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latest result sgd top 5 performing with all feature selection
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Calc AUC
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All performing model, train and save then how to predict directly
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Check data in paper
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added auc_calc for sgd top 700 with diff feature selectors
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Calc AUC
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Calc AUC
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Calc AUC
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Add decision tree visualization
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Modify text according to Dr Karma's comments
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Add decision tree visualization
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Add random forest results
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Add Feature map for best performing models and add text accordingly
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Given foldername and filename visualize tree
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modular visualization for DT
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added Dtvisual and DTvisualforward
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