49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from classification.methods import PCALR
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from method.meta import QuaNet
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from quapy.method.aggregative import *
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from NewMethods.methods import *
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from experiments import run, SAMPLE_SIZE
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import numpy as np
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import itertools
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from joblib import Parallel, delayed
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import settings
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import argparse
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import torch
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parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
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#parser.add_argument('svmperfpath', metavar='SVMPERF_PATH', type=str, help='path to the directory with svmperf')
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args = parser.parse_args()
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def quantification_models():
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def newLR():
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return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
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__C_range = np.logspace(-4, 5, 10)
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lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
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svmperf_params = {'C': __C_range}
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#yield 'paccsld', PACCSLD(newLR()), lr_params
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yield 'hdysld', OneVsAll(HDySLD(newLR())), lr_params # <-- promising!
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#device = 'cuda' if torch.cuda.is_available() else 'cpu'
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#print(f'Running QuaNet in {device}')
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#yield 'quanet', QuaNet(PCALR(**newLR().get_params()), SAMPLE_SIZE, device=device), lr_params
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if __name__ == '__main__':
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print(f'Result folder: {args.results}')
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np.random.seed(0)
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optim_losses = ['mae']
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datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
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models = quantification_models()
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results = Parallel(n_jobs=settings.N_JOBS)(
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delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
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)
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