This problem aims to find the accuracy of an appliance detection algorithm

Setup:

We are given whole home energy data and a truth set of appliances defined by their wattage in Kilo Watts (kW)

Example:

A home has a fridge (200W), an electric vehicle (1500W) and a dryer (5000W) -->

truth set: [ 0.2, 5.0, 1.5 ]

After this data is analyzed a "detected" appliance set is created -->

detected set: [ 0.19, 1.46, 1.8, 1.12 ]

Problem:

Find the True Positives (Detected correctly), False Positives (Detected incorrectly) and False Negatives (Did not detect)

A detected appliance is considered correct if it is within 10% of the truth.

In the above example

True Positives = [ 0.2 (0.19), 1.5 (1.46) ]   ---> Detected Fridge and Electric Vehicle Correctly
False Positives = [  _ (1.12), _ (1.8) ]      ---> Detected a 1.12 kW and 1.8 kW appliance that were not present
False Negatives = [ 5.0 (_) ]                 ---> Failed to detect a 5.0 kW dryer

Write a simple library for algorithm developers to use when measuring the performance of their appliance detection algorithms.

Notes:

• Be sure to consider edge cases. Use your best judgement and the provided context to decide on how to deal with them.
• Use a public version control system such as github or bitbucket and check in with enough frequency that we can get a rough idea of the time spent working on the solution.