136 lines
4.6 KiB
Plaintext
136 lines
4.6 KiB
Plaintext
Tasks:
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1) Glacier training
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1.1) preprocessing disable
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1.2) parameterization
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1.2.1) add a radio button (custom, default) for custom the user would be able to evaluate the parameters
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while for default he would have access to presets for glacier training following zhen dong's presets
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1.3) file management after training
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2) Tabular training
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3) Uploaded files exploring
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14/10/2024
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----------
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1) glacier downloads the respective dataset -> dataset is given as a parameter (done)
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2) IntervalImportance error (when training a 1dCNN model and want to save the importance for future use in plots).
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gc_latentcf_search_1dcnn_function.py
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Commented out for now...
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3) Counterfactual.html looks really bad should be improved (a bit)
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4) after 1dCnn train there is an error with classification_report
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figure out what to plot after train of 1dCnn to improve
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charts.html content
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5) add learning rate as parameter to glacier_compute_counterfactuals.py
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6) check for more datasets
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7) positive negative labels in pipeline json to be part of the dataset information and not of glacier model (done)
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8) load experiments text is still json format, should look prettier
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9) add ford-a dataset (done)
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10) hard coded positive and negative label values during selection of timeseries dataset (done)
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11) If no experiments to load case (done)
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Counterfactuals.html
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1) new experiment, load experiment buttons
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1.1) spacing
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1.2) aHave buttons active all the time unless another is clicked
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2) New experiment run
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2.1) enable loaded experiments
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3) Load experiments
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3.1) when changing timeseries page scrolls up and it is weird
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21/10/2024
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1) counterfactuals for tabular-> features to vary could have a drop down and a drop up to hide when needed (done)
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2) Original Data table does not take up all the card space
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3) Tsne should be appended to the DOM imidiately since it is common plot for all the pre trained models
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23/10/2024
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1) replace dice ml
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2) fix counterfactuals.html for time series (prettyfie)
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25/10/2024
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1) description of extremum
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2) functionalitites->explain
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3) pages depend on (enabled or disabled) the actions the user took (train a model enables pre-trained.html and counterfactuals.html
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otherwise disables)
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4) explain workflow
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5) home page seperate from dataset selection
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6) main buttons
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1) tabular
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2) timeseries
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3) upload -> information
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7) Train.html dataframe summary text
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- Functionality:
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1) after training done: save model as (give a name) with all the cases
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Give message: "Model trained succesfully"
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"See the results: (click)" -> demonstrate what is displayed at charts.html but in a different page
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Goal is to distinguish between pre trained models and newly trained models
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save the model or not
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1) Save (name, message etc, prompt train another model or counterfactuals)
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2) or Retrain (prompt back to the train.html)
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2) delete available pre trained models if needed with the use of a button
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3) check if coutnerfactual has similar class
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- give information about the classifiers
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- merge both tables together
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feature|original value|counterfactual value
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Focus on the web version:
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1) no upload
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2) no train[]
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29/10/2024
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1) choose between uploaded files (done)
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2) add seperate home page that prompts to dataset selection (done)
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3/11/2024
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1) add message if cf on tabular takes too much time (done in backend should fetch in the front)
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2) then should add a second tsne after the comoutation of the counterfactuals that would be further
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down below in the same page where the user can scroll with the use of a button (done)
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3/11/2024
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1) finished the layout for the tabular data
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2) need to fix the layout for the timeseries too (done)
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3) need to train glacier wildboar for all the timeseries datasets
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6/11/2024
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1) fix the layouts (done)
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2) should add fade ins to all the pages (only in counterfactuals.html and only for timeseries datasets now)
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3) should add proper info modal windows for the respective content (all the pages, only for timeseries datasets now)
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6/11/2024
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1) add proper fade in to all the pages
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2) solve some frontend bugs
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3) try docker
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4) apply styling options to opensource version
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Todo:
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1) apply error message if cf takes too long to compute
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2) delete uploaded files
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web extremum:
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accuracy in classification report needs an individual cell
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hide error after successful cf
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move label table
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upload
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glacier wildboar paper |