method MS2 (Medium Sweep 2) fixed
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from copy import deepcopy
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import quapy as qp
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.linear_model import LogisticRegression
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from quapy.classification.methods import LowRankLogisticRegression
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from quapy.method.meta import QuaNet
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from quapy.protocol import APP
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from quapy.method.aggregative import CC, ACC, PCC, PACC, MAX, MS, MS2, EMQ, HDy, newSVMAE, T50, X
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from quapy.method.meta import EHDy
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import numpy as np
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import os
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import pickle
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import itertools
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import argparse
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from glob import glob
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import pandas as pd
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from time import time
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N_JOBS = -1
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qp.environ['SAMPLE_SIZE'] = 100
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def newLR():
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return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
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def calibratedLR():
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return CalibratedClassifierCV(LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1))
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__C_range = np.logspace(-3, 3, 7)
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lr_params = {'classifier__C': __C_range, 'classifier__class_weight': [None, 'balanced']}
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svmperf_params = {'classifier__C': __C_range}
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def quantification_models():
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yield 'acc', ACC(newLR()), lr_params
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yield 'T50', T50(newLR()), lr_params
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yield 'X', X(newLR()), lr_params
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yield 'MAX', MAX(newLR()), lr_params
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yield 'MS', MS(newLR()), lr_params
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yield 'MS+', MS(newLR()), lr_params
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# yield 'MS2', MS2(newLR()), lr_params
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def result_path(path, dataset_name, model_name, optim_loss):
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return os.path.join(path, f'{dataset_name}-{model_name}-{optim_loss}.pkl')
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def is_already_computed(dataset_name, model_name, optim_loss):
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return os.path.exists(result_path(args.results, dataset_name, model_name, optim_loss))
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def save_results(dataset_name, model_name, optim_loss, *results):
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rpath = result_path(args.results, dataset_name, model_name, optim_loss)
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qp.util.create_parent_dir(rpath)
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with open(rpath, 'wb') as foo:
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pickle.dump(tuple(results), foo, pickle.HIGHEST_PROTOCOL)
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def run(experiment):
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optim_loss, dataset_name, (model_name, model, hyperparams) = experiment
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if dataset_name in ['acute.a', 'acute.b', 'iris.1']: return
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if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
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print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.')
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return
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dataset = qp.datasets.fetch_UCIDataset(dataset_name)
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print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}')
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# model selection (hyperparameter optimization for a quantification-oriented loss)
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train, test = dataset.train_test
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train, val = train.split_stratified()
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if hyperparams is not None:
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model_selection = qp.model_selection.GridSearchQ(
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deepcopy(model),
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param_grid=hyperparams,
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protocol=APP(val, n_prevalences=21, repeats=25),
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error=optim_loss,
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refit=True,
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timeout=60*60,
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verbose=True
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)
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model_selection.fit(train)
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model = model_selection.best_model()
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else:
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model.fit(dataset.training)
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# model evaluation
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true_prevalences, estim_prevalences = qp.evaluation.prediction(
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model,
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protocol=APP(test, n_prevalences=21, repeats=100)
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)
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mae = qp.error.mae(true_prevalences, estim_prevalences)
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save_results(dataset_name, model_name, optim_loss, mae)
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if __name__ == '__main__':
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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, default='results_tmp',
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help='path to the directory where to store the results')
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parser.add_argument('--svmperfpath', metavar='SVMPERF_PATH', type=str, default='../svm_perf_quantification',
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help='path to the directory with svmperf')
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args = parser.parse_args()
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print(f'Result folder: {args.results}')
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np.random.seed(0)
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qp.environ['SVMPERF_HOME'] = args.svmperfpath
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optim_losses = ['mae']
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datasets = qp.datasets.UCI_DATASETS
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tstart = time()
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models = quantification_models()
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qp.util.parallel(run, itertools.product(optim_losses, datasets, models), n_jobs=N_JOBS)
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tend = time()
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# open all results and show
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df = pd.DataFrame(columns=('method', 'dataset', 'mae'))
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for i, file in enumerate(glob(f'{args.results}/*.pkl')):
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mae = float(pickle.load(open(file, 'rb'))[0])
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*dataset, method, _ = file.split('/')[-1].split('-')
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dataset = '-'.join(dataset)
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df.loc[i] = [method, dataset, mae]
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print(df.pivot_table(index='dataset', columns='method', values='mae', margins=True))
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print(f'took {(tend-tstart)}s')
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@ -1131,21 +1131,15 @@ class ThresholdOptimization(BinaryAggregativeQuantifier):
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if len(candidates) == 0:
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# if no candidate gives rise to a valid combination of tpr and fpr, this method defaults to the standard
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# classify & count; this is akin to assign tpr=1, fpr=0, threshold=0
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tpr, fpr, threshold, score = 1, 0, 0, 0
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candidates.append([tpr, fpr, threshold, score])
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tpr, fpr, threshold = 1, 0, 0
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candidates.append([tpr, fpr, threshold])
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scores.append(0)
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candidates = np.asarray(candidates)
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candidates = candidates[np.argsort(scores)] # sort candidates by candidate_score
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return candidates
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# def aggregate_with_threshold(self, classif_predictions, tpr, fpr, threshold):
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# prevs_estim = np.mean(classif_predictions >= threshold)
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# if tpr - fpr != 0:
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# prevs_estim = (prevs_estim - fpr) / (tpr - fpr)
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# prevs_estim = F.as_binary_prevalence(prevs_estim, clip_if_necessary=True)
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# return prevs_estim
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def aggregate_with_threshold(self, classif_predictions, tprs, fprs, thresholds):
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prevs_estims = np.mean(classif_predictions[:, None] >= thresholds, axis=0)
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prevs_estims = (prevs_estims - fprs) / (tprs - fprs)
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@ -1286,13 +1280,9 @@ class MS(ThresholdOptimization):
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def aggregate(self, classif_predictions: np.ndarray):
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prevalences = self.aggregate_with_threshold(classif_predictions, self.tprs, self.fprs, self.thresholds)
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return np.median(prevalences, axis=0)
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# prevalences = []
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# for tpr, fpr, threshold in self.tprs_fprs_thresholds:
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# pos_prev = self.aggregate_with_threshold(classif_predictions, tpr, fpr, threshold)[1]
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# prevalences.append(pos_prev)
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# median = np.median(prevalences)
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# return F.as_binary_prevalence(median)
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if prevalences.ndim==2:
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prevalences = np.median(prevalences, axis=0)
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return prevalences
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class MS2(MS):
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