Metadata-Version: 2.1
Name: MlTrackTool
Version: 0.0.2
Summary: Ml tracking tool using jupyter extensions
Home-page: https://github.com/anesh-ml/ML_tracking_tool
Author: Anesh
Author-email: analytics955@gmail.com
License: UNKNOWN
Description: 
        # ML tracking tool 
        
        - This is still a prototype and some features are yet to be developed
        
        - Users can for now,
        
            - DO **data versioning** and **notebook versioning**
            - Open a spreadsheet(not excel spreadsheet) from a **dataframe, file and create a new spreadsheet**
            - **Interpret ML models** using LIME and Shapley. Analyze the model output and then may be curate/edit the data based on the interpretation
            - **Plot metrics** such as accuracy, precision, recall and ROC curve. With a click, users can know these metrics and then maybe jot down these metrics in a spreadsheet and download it.
            - Users can **write down notes, metrics** in the spreadsheet and **download** to a folder.
        
        - All these can be done with a button click. Users just need to type the arguments in a cell and click the appropriate button.
        
        ## Open spreadsheet
        
        - Create a spreadsheet from dataframe,file or a new spreadsheet
        
        
        ### If opening from **dataframe**:
        
           **Command**: $ \textit{DataFrame}$ `rows`
            
                Eg: df 10 or df
                
            - rows is given if you want to return only top n_rows
            
            
        ### If opening a sheet from a **file**
        
         **Command**: `FileName`
         
                 Eg: "sample_data.csv"
                 
        ### Opening a **new spreadsheet**
        
           **Command**: `"new sheet",n_rows,n_cols`
        
        -------------------------------------------------------------------------------------------------------------------------------
        
        ## Edit sheet
        
        - Operations supported are **creating columns** and **filter**
        
        ### Create columns
        
        **Command**: `create_col n_col`
        
                Eg: create_col 3
                
        ### Filter sheet
        
        - Only **==** and multiple **AND** conditions are supported now. 
        
        **Command**: `filter  condition`
        
                Eg: filter "col==question&col==request"
                
        
        
        -------------------------------------------------------------------------------------------------------------------------------
        
        ## Interpret Model
        
        - Currently, interpretation for NLP model is supported. Will be extending it to computer vision model.
        
        **Command**: `class_name(list), sheet/text, inference code`
        
        - **class_name** can be like ["positive","negative"]. 
        
        - input can be a **sheet, list of texts or just a single sentence**.
        
        - **inference code** is the prediction code which take in **raw string** and **output probabilities**.
        
              Eg: ['postive','negative'], ["movie is good", 'waste of money'], inference_code
              
                  ["positive","negative"],sheet,inference_code
                    
                  ["positive","negative"],"movie is good",inference_code
        
        --------------------------------------------------------------------------------------------------------------------------------
        
        ## Download sheet
        
        - Download the filled sheet. It will download the latest created and edited sheet.
        
        **Command**: `"./folder/sheet.xls"`
        
        -------------------------------------------------------------------------------------------------------------------------------
        
        ## Notebook Versioning
        
        - Save the notebook with different to a folder and keep working in the same notebook.
        
        - With a single click, make a version of your notebook.
        
        **Command**: file path
        
            eg: "./experiment1/notebook_v1.ipynb"
        
        --------------------------------------------------------------------------------------------------------------------------------
        
        ## Plot metrics
        
        - Plot ROC curve along the accuracy, F1 score, precision, recall.
        
        - User needs to get the probability of the prediction and true labels.
        
                Eg: y_true,y_pred_proba
        
Keywords: python,Machine learning,MLops,ML tracking system
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.7
Description-Content-Type: text/markdown
