87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
import pickle
|
|
import os
|
|
import pandas as pd
|
|
from distribution_matching.commons import METHODS, new_method, show_results
|
|
|
|
import quapy as qp
|
|
from quapy.model_selection import GridSearchQ
|
|
from quapy.protocol import UPP
|
|
|
|
SEED=1
|
|
|
|
if __name__ == '__main__':
|
|
|
|
qp.environ['SAMPLE_SIZE'] = 100
|
|
qp.environ['N_JOBS'] = -1
|
|
n_bags_val = 250
|
|
n_bags_test = 1000
|
|
for optim in ['mae', 'mrae']:
|
|
|
|
result_dir = f'results/tweet/{optim}'
|
|
|
|
os.makedirs(result_dir, exist_ok=True)
|
|
|
|
for method in METHODS:
|
|
|
|
print('Init method', method)
|
|
if method == 'EMQ-C': continue
|
|
|
|
global_result_path = f'{result_dir}/{method}'
|
|
|
|
if not os.path.exists(global_result_path+'.csv'):
|
|
with open(global_result_path+'.csv', 'wt') as csv:
|
|
csv.write(f'Method\tDataset\tMAE\tMRAE\tKLD\n')
|
|
|
|
with open(global_result_path+'.csv', 'at') as csv:
|
|
# four semeval dataset share the training, so it is useless to optimize hyperparameters four times;
|
|
# this variable controls that the mod sel has already been done, and skip this otherwise
|
|
semeval_trained = False
|
|
|
|
for dataset in qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST:
|
|
print('init', dataset)
|
|
|
|
local_result_path = global_result_path + '_' + dataset
|
|
if os.path.exists(local_result_path+'.dataframe'):
|
|
print(f'result file {local_result_path}.dataframe already exist; skipping')
|
|
continue
|
|
|
|
with qp.util.temp_seed(SEED):
|
|
|
|
is_semeval = dataset.startswith('semeval')
|
|
|
|
if not is_semeval or not semeval_trained:
|
|
|
|
param_grid, quantifier = new_method(method)
|
|
|
|
# model selection
|
|
data = qp.datasets.fetch_twitter(dataset, min_df=3, pickle=True, for_model_selection=True)
|
|
|
|
protocol = UPP(data.test, repeats=n_bags_val)
|
|
modsel = GridSearchQ(quantifier, param_grid, protocol, refit=False, n_jobs=-1, verbose=1, error=optim)
|
|
|
|
modsel.fit(data.training)
|
|
print(f'best params {modsel.best_params_}')
|
|
print(f'best score {modsel.best_score_}')
|
|
pickle.dump(
|
|
(modsel.best_params_, modsel.best_score_,),
|
|
open(f'{local_result_path}.hyper.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
|
|
|
|
quantifier = modsel.best_model()
|
|
|
|
if is_semeval:
|
|
semeval_trained = True
|
|
|
|
else:
|
|
print(f'model selection for {dataset} already done; skipping')
|
|
|
|
data = qp.datasets.fetch_twitter(dataset, min_df=3, pickle=True, for_model_selection=False)
|
|
quantifier.fit(data.training)
|
|
protocol = UPP(data.test, repeats=n_bags_test)
|
|
report = qp.evaluation.evaluation_report(quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'], verbose=True)
|
|
report.to_csv(f'{local_result_path}.dataframe')
|
|
means = report.mean()
|
|
csv.write(f'{method}\t{data.name}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\n')
|
|
csv.flush()
|
|
|
|
show_results(global_result_path)
|