Esempi in Python per LPMC_Full

Esempio n. 1

File: LS-ABS_hybrid-inv.py Progetto: glederrey/HAMABS

import json

from algos import OptAlg

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full_L with LS-ABS and hybrid-inv")

    model = LPMC_Full(data_folder, file='12_13_14.csv')

    ioa = OptAlg(alg_type='LS-ABS', direction='hybrid-inv')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(
            ioa, **{
                'verbose': False,
                'max_epochs': 1000,
                'batch': 1000
            })

        res['time'].append(tmp['opti_time'])

Esempio n. 2

from algos import OptAlg

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    parameters = {''}

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Testing parameters for LPMC_Full_L")

    model = LPMC_Full(data_folder, file='12_13_14.csv')

    ioa = OptAlg(alg_type='LS-ABS', direction='hybrid-inv')

    base_param = {
        'perc_hybrid': 30,
        'thresh_upd': 1,
        'count_upd': 2,
        'window': 10,
        'factor_upd': 2
    }

    main_params = {'verbose': False, 'nbr_epochs': 1000, 'batch': 1000}
    main_params.update(base_param)

    draws = 20

Esempio n. 3

File: TR-ABS_hess.py Progetto: glederrey/HAMABS

import json

from algos import OptAlg

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full_M with TR-ABS and hess")

    model = LPMC_Full(data_folder)

    ioa = OptAlg(alg_type='TR-ABS', direction='hess')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(
            ioa, **{
                'verbose': False,
                'max_epochs': 1000,
                'batch': 1000
            })

        res['time'].append(tmp['opti_time'])

Esempio n. 4

import os
import time
import json

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full with current Biogeme optimization algorithm.")

    model = LPMC_Full(data_folder)

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        results = model.biogeme.estimate()

        res['time'].append(model.biogeme.optimizationTime.total_seconds())
        res['LL'].append(results.data.logLike)
        res['epochs'].append(model.biogeme.numberOfIterations)

        with open('results/biogeme_bfgs.json', 'w') as outfile:
            json.dump(res, outfile)

        # Delete pickle files

Esempio n. 5

File: biogeme_bfgs.py Progetto: glederrey/HAMABS

import os
import time
import json

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full with current Biogeme optimization algorithm.")

    model = LPMC_Full(data_folder, file='12.csv')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        results = model.biogeme.estimate()

        res['time'].append(model.biogeme.optimizationTime.total_seconds())
        res['LL'].append(results.data.logLike)
        res['epochs'].append(model.biogeme.numberOfIterations)

        with open('results/biogeme_bfgs.json', 'w') as outfile:
            json.dump(res, outfile)

        # Delete pickle files

Esempio n. 6

File: biogeme_bfgs.py Progetto: glederrey/HAMABS

import os
import time
import json

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full with current Biogeme optimization algorithm.")

    model = LPMC_Full(data_folder, file='12_13_14.csv')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        results = model.biogeme.estimate()

        res['time'].append(model.biogeme.optimizationTime.total_seconds())
        res['LL'].append(results.data.logLike)
        res['epochs'].append(model.biogeme.numberOfIterations)

        with open('results/biogeme_bfgs.json', 'w') as outfile:
            json.dump(res, outfile)

        # Delete pickle files