Metadata-Version: 2.1
Name: pyadlml
Version: 0.0.3a0
Summary: Sklearn like library supporting numerous Activity of Daily Livings datasets
Home-page: https://github.com/tcsvn/pyadlml
Author: Christian Meier
Author-email: account@meier-lossburg.de
License: MIT
Description: # Activities of Daily Living - Machine Learning
        > Contains data preprocessing and visualization methods for ADL datasets.
        
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        <p align="center"><img width=95% src="https://github.com/tcsvn/pyadlml/blob/master/media/pyadlml_banner.png"></p>
        Activities of Daily living (ADLs) e.g cooking, sleeping, and devices readings are recorded by smart home inhabitants. The goal is to predict inhabitants activities using device readings. Pyadlml offers an easy way to fetch, visualize and preprocess common datasets. A further goal is to replicate prominent work in this domain.
        
        
        
        ## Last Stable Release
        ```sh 
        $ pip install pyadlml
        ```
        ## Latest Development Changes
        ```sh
        $ git clone https://github.com/tcsvn/pyadlml
        $ cd pyadlml
        ```
        
        ## Usage example
        ```python
        from pyadlml.dataset import fetch_amsterdam
        
        # Fetch dataset
        data = fetch_amsterdam(cache=True)
        
        # plot the persons activity density distribution over one day
        from pyadlml.dataset.plot.activities import ridge_line
        ridge_line(data.df_activities)
        
        # plot the signal cross correlation between devices
        from pyadlml.dataset.plot.devices import heatmap_cross_correlation
        heatmap_cross_correlation(data.df_devices)
        
        # create a raw representation with 20 second timeslices
        from pyadlml.preprocessing import DiscreteEncoder, LabelEncoder
        enc_dat = DiscreteEncoder(rep='raw', t_res='20s')
        X = enc_dat.fit_transform(data.df_devices).values
        
        # label the datapoints with the corresponding activity
        y = LabelEncoder(X).fit_transform(data.df_activities)
        
        # from here on do all the other fancy machine learning stuff you already know
        from sklearn import SVM 
        SVM().fit(X).score(X,y)
        ...
        ```
        
        _For more examples and usage, please refer to the Documentation or Notebooks _
        
        ## Features
          - 8 Datasets
          - A bunch of plots visualizing devices, activities and their interaction
          - Different data representations
            - Discrete timeseries
              - raw
              - changepoint
              - lastfired
            - Timeseries as images 
         - Methods for importing data from Home Assistant/Activity Assistant
         
        ### Supported Datasets
          - [x] Amsterdam [1]
          - [x] Aras
          - [x] Casas Aruba (2011)
          - [ ] Casas Milan (2009)
          - [ ] Kasteren House A,B,C
          - [x] MitLab
          - [x] Tuebingen 2019
          - [x] UCI Adl Binary 
          
          
        ### Models
        #### iid data
          - [x] SVM
          - [ ] winnow algorithm
          - [ ] Naive bayes
          - [x] Decision Trees
        #### sequential discretized 
          - [ ] RNNs
          - [ ] LSTMs
          - [ ] HMMs
          - [ ] TCNs
        #### images  
          - [ ] CNN
          - [ ] Transformer
        #### temporal points
          - [ ] THP
          
        ## Replication list  
        Here are papers I plan to replicate
        
        
        ## Contributing 
        1. Fork it (<https://github.com/tcsvn/pyadlml/fork>)
        2. Create your feature branch (`git checkout -b feature/fooBar`)
        3. Commit your changes (`git commit -am 'Add some fooBar'`)
        4. Push to the branch (`git push origin feature/fooBar`)
        5. Create a new Pull Request
        
        ## Related projects
          - [activity-assistant](https://github.com/tcsvn/activity-assistant) - Recording, predicting ADLs within Home assistant.
          
        ## Support 
          - Todo buy me a coffee batch
          
        ## Sources
          - Datasets
            [1]:  
            [2]: Ordonez, F.J.; de Toledo, P.; Sanchis, A. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors. Sensors 2013, 13, 5460-5477.
          - TODO cite every dataset
          - TODO cite every algorithm package that is used
          
        ## License
        MIT  © [tcsvn](http://deadlink)
        
        
        sdf
Keywords: Activity of Daily Living
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Description-Content-Type: text/markdown
