Metadata-Version: 2.1
Name: cycle_prediction
Version: 1.2.0
Summary: A package to train and predict the end of a process from        history logs
Home-page: https://github.com/fazaki/cycle_prediction/tree/master
Author: Fadi Baskharon
Author-email: nzfadi@gmail.com
License: UNKNOWN
Description: [![GitHub Actions](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fatrox%2Fsync-dotenv%2Fbadge&label=build&logo=none)](https://actions-badge.atrox.dev/atrox/sync-dotenv/goto)
        
        # Predicting Remaining Cycle Time from Ongoing Case
        ![alt text](docs/api/overview.png)
        Predicting the remaining cycle time of ongoing cases is one important use case of predictive process monitoring. 
        It is machine learning approach based on survival analysis that can learn from complete/ongoing traces.  
        we train a neural network to predict the probability density function of the remaining cycle time of a running case. 
        
        # Documentation:
        
        https://fazaki.github.io/cycle_prediction/
        
        
        # Getting started:
        
        ## A) pip installation
        
        
        #### 1. Cd to home dir
            cd ~
        
        #### 2. Initialize a virtualenv that uses the Python 3.7 available at home directory
            virtualenv -p ~/python-3.7/bin/python3 PROJECTNAME
        
        #### 3. Activate the virtualenv
        
        Windows:
        
        	source ~/PROJECTNAME/Scripts/activate
        	
        Linux:
        
        	source ~/PROJECTNAME/bin/activate
        
        #### 4. Install below packages
            pip install cycle-prediction
            
        #### 5. Create a new kernel with the same project name
            pip install -U pip ipykernel
            ipython kernel install --user --name=PROJECTNAME
        
        #### 6. Use the example notebook
        
        
        ## B) Source code installation:
        
        #### 1. Cd to home dir
            cd ~
        
        #### 2. Initialize a virtualenv that uses the Python 3.7 available at home directory
        	Virtualenv -p ~/python-3.7/bin/python3 PROJECTNAME
        
        #### 3. Activate the virtualenv
        
        Windows:
        
        	source ~/PROJECTNAME/Scripts/activate
        	
        Linux:
        
        	source ~/PROJECTNAME/bin/activate
        
        #### 4. Install ipykernel
            pip install -U pip ipykernel
        
        #### 5.  Clone the repo
            git clone https://github.com/fazaki/time-to-event/tree/master
            cd time-to-event
        
        #### 6.  Install required dependencies: 
            pip install -e .
        
        #### 7.  Use the example notebook
        
        
        # Theory
        - Paper publication in progress
        
        
        # References
        
        - https://arxiv.org/abs/1612.02130
        - https://ragulpr.github.io/assets/draft_master_thesis_martinsson_egil_wtte_rnn_2016.pdf
        - https://verenich.github.io/ProcessSequencePrediction/
        - https://github.com/ragulpr/wtte-rnn
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
