DeepPower

Dependencies

• Python 3.8
  • matplotlib
  • numpy
  • pandas
  • plotly
  • scikit-learn
  • Tensorflow 2.0
  • pyyaml
  • scipy
  • dvc
• graphviz, for creating a plot of the model architecture.

Experiment pipeline

Stages:

1. Restructure: Restructure raw data into dataframes.
2. Featurize: Add features to data set.
3. Split: Split data set into training and test data.
4. Scale: Scale input data.
5. Sequentialize: Split data into input/output sequences.
6. Train: Train model.
7. Evaluate: Evaluate model.

Restructure

Restructure raw data into dataframes.

Featurize

This stage consists of these steps:

1. Remove data that should not be a part of the model:
   • Time (the time of the workout do not matter).
   • Calories. Currently the calories are not a part of the model, but might be interesting as target values later on.
2. Optionally scale input features.
3. Feature engineering. Examples:
   • Rolling range of breathing data.
   • Gradient of breathing data.
   • Slope angle of the breathing pattern.
4. Optionally delete raw inputs that may be less important, since we prefer a simpler model. This might for example be raw breathing data, if we have engineered features that work better.

Split

This stage splits the data set into a training and test set.

Scale

In this stage the data is scaled.

Sequentialize

In this stage the data is divided into sequences based on a chosen history size.

Usage

Run experiment

All stages are defined in the file dvc.yaml, and the parameters to be used are saved in params.yaml.

To run/reproduce an experiment with any given parameters specified in params.yaml, run:

dvc repro

Evaluate

If a model already exists and you want to test it on a test set, run:

python3 src/evaluate.py

This requires that the test data already is present in the correct folder. Because of this, it is usually better to use the command dvc repro when evaluating models.

Check previous experiments

N/A

Change dataset

To run experiments with another dataset, just change the content of assets/data/raw/ to the files you want to use.

Visualize data set

Data set can be visualized by running

python3 visualize.py