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Intelligent Machine Reliability

Code written for research during my Master's degree.

Two major contributions are publicly available in this repository:

  1. GVFOD - An outlier detection model for time series databases. Using general value functions (GVFs) and Reinforcement Learning, anomalous or outlying behavior can be found.
  2. A simulator for a robot arm with a single degree of freedom. It includes framework for parallelized (multi-node) data-driven system identification. A PID controller compatible with scipy IVP solvers is also implemented.

Usage Instructions

These instructions are for me to guide development

To use GVFOD

  • To install this package

    git clone https://github.com/andywong36/GVFOD.git
cd GVFOD  # Navigate to the directory of this file
pip install .  # Installs this package

    If the intention is to run the experiments, use the following install instead:

    pip install -e .[exp]

  • To use this package, import the GVFOD class as follows

    from gvfod import GVFOD
train, test = ...  # put your data here

kwargs = {}  # See documentation for GVFOD parameters
model = GVFOD(
    contamination=0.05,
    **kwargs
) 
model.fit(train)
y = model.predict(test)

... # and so on

Replicate GVFOD paper experiments

This requires the robot arm dataset to be provided. Extract the data into the directory data/robotarm. The file containing normal data will be data/robotarm/Normal_adjusted_150.csv

Install the package as above.

Then, in the shell:

  • Make the dataset from the raw data. This caches the cleaned data in data/pickles/robotarm_{type}.pkl where type is one of {normal, loose_l2, loose_l1, tight, sandy, highT}

    cd GVFOD
python exp/make_robotarm_dataset.py data/robotarm data/pickles

  • Parameter tuning / search

    python -m exp.exp_gvfod.tuning gvfod_exp > exp/exp_gvfod/logs/exp_gvfod.log 2>&1

    This will place all the search information in the folder exp/exp_gvfod/tuning_results/. The experiment name gvfod_exp needs to be replaced for every algorithm that is to be tested. The experiments and their names are all defined in exp/exp_gvfod/tuning_settings.py.

    Since search is stochastic, and results will differ between random state, operating system, python and dependency versions, the best parameters are hand-copied into .../GVFOD/exp/exp_gvfod/exp_train_size_settings.py

  • Experiments

    The flags --no-default-param and --default-param refer to optimized (hyperopt) and hand-selected parameters respectively. --tuning-data and --no-tuning-data refer to the half of the data that was used.

    python -m exp.exp_gvfod.train_size --no-default-param --tuning-data exp/exp_gvfod/results 
python -m exp.exp_gvfod.train_size --no-default-param --no-tuning-data exp/exp_gvfod/results
python -m exp.exp_gvfod.train_size --default-param --tuning-data exp/exp_gvfod/results
python -m exp.exp_gvfod.train_size --default-param --no-tuning-data exp/exp_gvfod/results

    Results will be placed into exp/exp_gvfod/results. These commands generate the results in the order of the plots in the order found in the paper.

  • Visualization

    python -m exp.exp_gvfod.vis_train_size exp/exp_gvfod/results

    Figures will be placed into the same folder

  • Runtime estimates

    python -m exp.exp_gvfod.runtime test --lag 0 --no-default-param --no-tuning-data exp/exp_gvfod/runtime_results
python -m exp.exp_gvfod.runtime summary exp/exp_gvfod/runtime_results

  • Statistical tests

    The statistical results are calculated using a paired sample t-test, for one training size, and for one (of the four) experiments.

    python -m exp.exp_gvfod.t_test --train 2000 exp/exp_gvfod/results/train_size_delay_0_default_False_trainloss_True.json

Replicate simulator paper experiments

In progress