ols.py

#pylint: disable=invalid-name

import sys
import statsmodels.formula.api as smf
import pandas as pd

def train_model(cpu_chips, ram, tdp, cpu_threads):

    df = pd.read_csv('./data/spec_data_cleaned.csv')

    formula = 'power ~ utilization'

    if cpu_threads is not None:
        formula = f"{formula} + CPUThreads"

    if cpu_chips is not None:
        formula = f"{formula}*C(CPUChips)"

    if ram is not None:
        formula = f"{formula} + HW_MemAmountGB"

    if tdp is not None:
        formula = f"{formula} + TDP"

    return smf.ols(formula=formula, data=df).fit()

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser()

    parser.add_argument('--cpu-chips', type=float, help='Number of CPUChips')
    parser.add_argument('--cpu-threads', type=float, help='Number of CPU Threads')
    parser.add_argument('--cpu-freq',
        type=float,
        help='CPU frequency. (Not used. Only for compatibility with XGBoost model)'
    )
    parser.add_argument('--tdp', type=float, help='TDP of the CPU')
    parser.add_argument('--ram', type=float, help='Amount of RAM for the bare metal system')
    parser.add_argument('--vhost-ratio',
        type=float,
        help='Virtualization ratio of the system. Input numbers between (0,1].',
        default=1.0
    )
    parser.add_argument('--silent',
        action='store_true',
        help='Will suppress all debug output. Typically used in production.'
    )

    args = parser.parse_args()

    model = train_model(args.cpu_chips, args.ram, args.tdp, args.cpu_threads)
    my_data = pd.DataFrame.from_dict({
        'utilization': 0,
        'CPUChips': [args.cpu_chips],
        'CPUThreads': [args.cpu_threads],
        'HW_MemAmountGB': [args.ram],
        'TDP' : [args.tdp]
    })

    # Drop all arguments that were not supplied
    my_data = my_data.dropna(axis=1)

    if not args.silent:
        print('Sending following dataframe to model', my_data)
        print('vHost ratio is set to ', args.vhost_ratio)


    for line in sys.stdin:
        my_data['utilization'] = float(line.strip())
        print(model.predict(my_data)[0] * args.vhost_ratio)