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QuaPy
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adding experiments for svmmae and svmmrae

This commit is contained in:
Alejandro Moreo Fernandez 2021-01-15 18:37:37 +01:00
parent 5e64d2588a
commit d510630d3b
1 changed files with 11 additions and 9 deletions
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20
TweetSentQuant/experiments.py
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@ -19,20 +19,19 @@ def quantification_models():
def newLR(): def newLR():
return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1) return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
__C_range = np.logspace(-4, 5, 10) __C_range = np.logspace(-4, 5, 10)
#lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']} lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
svmperf_params = {'C': __C_range} svmperf_params = {'C': __C_range}
lr_params = {'C': [1,10]}
yield 'cc', qp.method.aggregative.CC(newLR()), lr_params yield 'cc', qp.method.aggregative.CC(newLR()), lr_params
#yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
#yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
#yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
#yield 'sld', qp.method.aggregative.EMQ(newLR()), lr_params yield 'sld', qp.method.aggregative.EMQ(newLR()), lr_params
#yield 'svmq', OneVsAll(qp.method.aggregative.SVMQ(settings.SVMPERF_HOME)), svmperf_params #yield 'svmq', OneVsAll(qp.method.aggregative.SVMQ(settings.SVMPERF_HOME)), svmperf_params
#yield 'svmkld', OneVsAll(qp.method.aggregative.SVMKLD(settings.SVMPERF_HOME)), svmperf_params #yield 'svmkld', OneVsAll(qp.method.aggregative.SVMKLD(settings.SVMPERF_HOME)), svmperf_params
#yield 'svmnkld', OneVsAll(qp.method.aggregative.SVMNKLD(settings.SVMPERF_HOME)), svmperf_params #yield 'svmnkld', OneVsAll(qp.method.aggregative.SVMNKLD(settings.SVMPERF_HOME)), svmperf_params
yield 'svmmae', OneVsAll(qp.method.aggregative.SVMAE(settings.SVMPERF_HOME)), svmperf_params
yield 'svmmrae', OneVsAll(qp.method.aggregative.SVMRAE(settings.SVMPERF_HOME)), svmperf_params
# 'svmmae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mae'),
# 'svmmrae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mrae'),
# 'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(), # 'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(),
@ -81,9 +80,12 @@ def run(experiment):
if is_already_computed(dataset_name, model_name, optim_loss=optim_loss): if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.') print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.')
return return
elif (optim_loss=='mae' and model_name=='svmmrae') or (optim_loss=='mrae' and model_name=='svmmae'):
print(f'skipping model={model_name} for optim_loss={optim_loss}')
return
else: else:
print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}') print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}')
benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True) benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
benchmark_devel.stats() benchmark_devel.stats()