pyadlml 0.0.2a0

Sklearn like library supporting numerous Activity of Daily Livings datasets

Activity of Daily Living - Machine Learning

Contains data preprocessing and visualization methods for ADL datasets.

Activity of Daily livings (ADLs) e.g cooking, sleeping, and devices readings are recorded by smart homes 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 works in this domain.

Last Stable Release

$ pip install pyadlml

Latest Development Changes

$ git clone https://github.com/tcsvn/pyadlml
$ cd pyadlml

Usage example

from pyadlml.dataset import fetch_amsterdam

# Fetch dataset
data = fetch_amsterdam(cache=True)

# plot the activity density distribution for the person over one day
from pyadlml.dataset.plot.activities import ridge_line
ridge_line(data.df_activities)

# plot the signal cross correlation between the devices
from pyadlml.dataset.plot.devices import heatmap_cross_correlation
heatmap_cross_correlation(data.df_devices)

# create a raw representation with timeslices of 20 seconds
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)

# do all the other fancy machine learning stuff
from sklearn import SVM 
SVM().fit(X).score(X,y)
...

_For more examples and usage, please refer to the Notebooks _

Features

• 8 Datasets to fetch
• A bunch of plots visualizing devices, activities and their interaction
• Different data representations
  • Discrete timeseries
    • raw
    • changepoint
    • lastfired
  • Timeseries as images

Supported Datasets

• Casas Aruba (2011)
• Casas Milan (2009)
• Aras
• Amsterdam
• MitLab
• Tuebingen 2019
• Kasteren House A,B,C

Models

iid data

• SVM
• winnow algorithm
• Naive bayes
• 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 - Recording, Prediciting ADLs in Home assistant.

Support

• Todo buy me o coffee batch

Sources

• TODO cite every dataset
• TODO cite every algorithm package
• https://github.com/anfederico/Clairvoyant#readme

License

MIT © tcsvn

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