PostgreSQL COO results

[R3gardless]

sql-algorithms/case_study/experiments/main.py

Lines 580 to 583 in 357cb16

	runtime, wp, ap = postgres_experiment(X, y, iterations=args.iterations, COO=True, parameter=parameter)
	results[features][samples]['p'] = runtime
	runtime, wpc, apc = postgres_experiment(X, y, iterations=args.iterations, COO=False, parameter=parameter)
	results[features][samples]['pc'] = runtime

I am currently reading your paper and trying to reproduce the experiment that is mentioned in the paper.

I have a question about the code.

The line of code results[features][samples]['pc'] is used to store the results of the experiment. I am wondering if this is the result of the Postgres COO result. However, the code also has the line COO=False. I am not sure if this means that the results are not stored in the COO format.

Could you please clarify if results[features][samples]['pc'] is the result of the Postgres COO result? If not, could you please explain what format the results are stored in?

[julien-klaus]

Hello, thank you very much for the interest in the experiments. It looks like something was copied incorrectly during the cleanup of the repository. Your intuition is correct, COO=False indicates that the COO format is not used.

A snipped of the dictionary can be the following:

results = 
  { '20': 
    {'10': 
      {'pc': 30s},
    ...
  }

indicating for 20 features and 10 samples for the 'pc' algorithm we have measured 30.1s (no real data, just an example).

[R3gardless]

Thanks. I am working on creating a Softmax regression model using only SQL, as you did in this paper. I noticed a floating-point difference of approximately 10^-7 when comparing results obtained using NumPy and Hyper(using np.allclose()). I am wondering if a similar value difference exists in Logistic Regression.

[julien-klaus]

These are simply rounding errors. They can occur naturally.