forked from moreo/QuaPy
committing last changes before creating a branch
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@ -23,12 +23,19 @@ import os
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import pickle
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models = [#'MLPE',
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'NaiveCC', 'NaivePCC', 'NaiveACC', 'NaivePACC', #'NaiveHDy', 'NaiveSLD',
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'StackCC', 'StackPCC', 'StackACC', 'StackPACC',
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'MRQ-CC', 'MRQ-PCC', 'MRQ-ACC', 'MRQ-PACC',
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'MRQ-StackCC', 'MRQ-StackPCC', 'MRQ-StackACC', 'MRQ-StackPACC',
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'MRQ-StackCC-app', 'MRQ-StackPCC-app', 'MRQ-StackACC-app', 'MRQ-StackPACC-app',
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'LSP-CC', 'LSP-ACC'
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'NaiveCC', 'NaivePCC', 'NaivePCCcal', 'NaiveACC', 'NaivePACC', 'NaivePACCcal', 'NaiveACCit', 'NaivePACCit',
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#'NaiveHDy', 'NaiveSLD',
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'ChainCC', 'ChainPCC', 'ChainACC', 'ChainPACC',
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'StackCC', 'StackPCC', 'StackPCCcal', 'StackACC', 'StackPACC', 'StackPACCcal', 'StackACCit', 'StackP'
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'ACCit',
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'MRQ-CC', 'MRQ-PCC', 'MRQ-ACC', 'MRQ-PACC', 'MRQ-ACCit', 'MRQ-PACCit',
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'StackMRQ-CC', 'StackMRQ-PCC', 'StackMRQ-ACC', 'StackMRQ-PACC',
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'MRQ-StackCC', 'MRQ-StackPCC', 'MRQ-StackACC', 'MRQ-StackPACC',
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'StackMRQ-StackCC', 'StackMRQ-StackPCC', 'StackMRQ-StackACC', 'StackMRQ-StackPACC',
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'MRQ-StackCC-app', 'MRQ-StackPCC-app', 'MRQ-StackACC-app', 'MRQ-StackPACC-app',
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'StackMRQ-StackCC-app', 'StackMRQ-StackPCC-app', 'StackMRQ-StackACC-app', 'StackMRQ-StackPACC-app',
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'LSP-CC', 'LSP-ACC', 'MLKNN-CC', 'MLKNN-ACC',
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'MLAdjustedC', 'MLStackAdjustedC', 'MLprobAdjustedC', 'MLStackProbAdjustedC'
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]
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# datasets = sorted(set([x[0] for x in available_data_sets().keys()]))
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@ -64,6 +71,12 @@ def generate_table(path, protocol, error):
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dataset, model, scores = r
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table.add(dataset, model, scores)
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save_table(table, path)
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save_table(table.getRankTable(), path.replace('.tex','.rank.tex'))
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def save_table(table, path):
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tabular = """
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\\resizebox{\\textwidth}{!}{%
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\\begin{tabular}{|c||""" + ('c|' * len(models)) + """} \hline
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@ -79,7 +92,6 @@ def generate_table(path, protocol, error):
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with open(path, 'wt') as foo:
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foo.write(tabular)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Experiments for multi-label quantification')
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parser.add_argument('--results', type=str, default='./results', metavar='str',
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@ -2,6 +2,8 @@ import argparse
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.linear_model import LogisticRegression
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import itertools
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.multioutput import ClassifierChain
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from tqdm import tqdm
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from skmultilearn.dataset import load_dataset, available_data_sets
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@ -11,7 +13,7 @@ from MultiLabel.mlclassification import MLStackedClassifier, LabelSpacePartion,
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from MultiLabel.mldata import MultilabelledCollection
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from MultiLabel.mlquantification import MLNaiveQuantifier, MLCC, MLPCC, MLRegressionQuantification, \
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MLACC, \
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MLPACC, MLNaiveAggregativeQuantifier, MLMLPE
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MLPACC, MLNaiveAggregativeQuantifier, MLMLPE, StackMLRQuantifier, MLadjustedCount, MLprobAdjustedCount
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from method.aggregative import PACC, CC, EMQ, PCC, ACC, HDy
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import numpy as np
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from data.dataset import Dataset
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@ -49,23 +51,33 @@ def models():
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yield 'MLPE', MLMLPE()
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yield 'NaiveCC', MLNaiveAggregativeQuantifier(CC(cls()))
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yield 'NaivePCC', MLNaiveAggregativeQuantifier(PCC(cls()))
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yield 'NaivePCCcal', MLNaiveAggregativeQuantifier(PCC(calibratedCls()))
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yield 'NaiveACC', MLNaiveAggregativeQuantifier(ACC(cls()))
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yield 'NaivePACC', MLNaiveAggregativeQuantifier(PACC(cls()))
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yield 'NaivePACCcal', MLNaiveAggregativeQuantifier(PACC(calibratedCls()))
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yield 'NaiveACCit', MLNaiveAggregativeQuantifier(ACC(cls()))
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yield 'NaivePACCit', MLNaiveAggregativeQuantifier(PACC(cls()))
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# yield 'NaiveHDy', MLNaiveAggregativeQuantifier(HDy(cls()))
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# yield 'NaiveSLD', MLNaiveAggregativeQuantifier(EMQ(calibratedCls()))
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yield 'StackCC', MLCC(MLStackedClassifier(cls()))
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yield 'StackPCC', MLPCC(MLStackedClassifier(cls()))
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yield 'StackPCCcal', MLPCC(MLStackedClassifier(calibratedCls()))
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yield 'StackACC', MLACC(MLStackedClassifier(cls()))
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yield 'StackPACC', MLPACC(MLStackedClassifier(cls()))
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# yield 'ChainCC', MLCC(ClassifierChain(cls(), cv=None, order='random'))
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# yield 'ChainPCC', MLPCC(ClassifierChain(cls(), cv=None, order='random'))
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# yield 'ChainACC', MLACC(ClassifierChain(cls(), cv=None, order='random'))
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# yield 'ChainPACC', MLPACC(ClassifierChain(cls(), cv=None, order='random'))
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yield 'StackPACCcal', MLPACC(MLStackedClassifier(calibratedCls()))
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yield 'StackACCit', MLACC(MLStackedClassifier(cls()))
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yield 'StackPACCit', MLPACC(MLStackedClassifier(cls()))
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# yield 'ChainCC', MLCC(ClassifierChain(cls(), cv=None))
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# yield 'ChainPCC', MLPCC(ClassifierChain(cls(), cv=None))
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# yield 'ChainACC', MLACC(ClassifierChain(cls(), cv=None))
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# yield 'ChainPACC', MLPACC(ClassifierChain(cls(), cv=None))
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common={'sample_size':sample_size, 'n_samples': n_samples, 'norm': True, 'means':False, 'stds':False, 'regression':'svr'}
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yield 'MRQ-CC', MLRegressionQuantification(MLNaiveQuantifier(CC(cls())), **common)
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yield 'MRQ-PCC', MLRegressionQuantification(MLNaiveQuantifier(PCC(cls())), **common)
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yield 'MRQ-ACC', MLRegressionQuantification(MLNaiveQuantifier(ACC(cls())), **common)
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yield 'MRQ-PACC', MLRegressionQuantification(MLNaiveQuantifier(PACC(cls())), **common)
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yield 'MRQ-ACCit', MLRegressionQuantification(MLNaiveQuantifier(ACC(cls())), **common)
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yield 'MRQ-PACCit', MLRegressionQuantification(MLNaiveQuantifier(PACC(cls())), **common)
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yield 'MRQ-StackCC', MLRegressionQuantification(MLCC(MLStackedClassifier(cls())), **common)
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yield 'MRQ-StackPCC', MLRegressionQuantification(MLPCC(MLStackedClassifier(cls())), **common)
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yield 'MRQ-StackACC', MLRegressionQuantification(MLACC(MLStackedClassifier(cls())), **common)
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@ -74,6 +86,23 @@ def models():
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yield 'MRQ-StackPCC-app', MLRegressionQuantification(MLPCC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'MRQ-StackACC-app', MLRegressionQuantification(MLACC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'MRQ-StackPACC-app', MLRegressionQuantification(MLPACC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'StackMRQ-CC', StackMLRQuantifier(MLNaiveQuantifier(CC(cls())), **common)
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yield 'StackMRQ-PCC', StackMLRQuantifier(MLNaiveQuantifier(PCC(cls())), **common)
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yield 'StackMRQ-ACC', StackMLRQuantifier(MLNaiveQuantifier(ACC(cls())), **common)
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yield 'StackMRQ-PACC', StackMLRQuantifier(MLNaiveQuantifier(PACC(cls())), **common)
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yield 'StackMRQ-StackCC', StackMLRQuantifier(MLCC(MLStackedClassifier(cls())), **common)
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yield 'StackMRQ-StackPCC', StackMLRQuantifier(MLPCC(MLStackedClassifier(cls())), **common)
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yield 'StackMRQ-StackACC', StackMLRQuantifier(MLACC(MLStackedClassifier(cls())), **common)
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yield 'StackMRQ-StackPACC', StackMLRQuantifier(MLPACC(MLStackedClassifier(cls())), **common)
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yield 'StackMRQ-StackCC-app', StackMLRQuantifier(MLCC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'StackMRQ-StackPCC-app', StackMLRQuantifier(MLPCC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'StackMRQ-StackACC-app', StackMLRQuantifier(MLACC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'StackMRQ-StackPACC-app', StackMLRQuantifier(MLPACC(MLStackedClassifier(cls())), protocol='app', **common)
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yield 'MLAdjustedC', MLadjustedCount(OneVsRestClassifier(cls()))
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yield 'MLStackAdjustedC', MLadjustedCount(MLStackedClassifier(cls()))
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# yield 'MLprobAdjustedC', MLprobAdjustedCount(OneVsRestClassifier(calibratedCls()))
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# yield 'MLStackProbAdjustedC', MLprobAdjustedCount(MLStackedClassifier(calibratedCls()))
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# yield 'MRQ-ChainCC', MLRegressionQuantification(MLCC(ClassifierChain(cls())), **common)
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# yield 'MRQ-ChainPCC', MLRegressionQuantification(MLPCC(ClassifierChain(cls())), **common)
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# yield 'MRQ-ChainACC', MLRegressionQuantification(MLACC(ClassifierChain(cls())), **common)
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@ -82,10 +111,10 @@ def models():
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# yield 'LSP-ACC', MLACC(LabelSpacePartion(cls()))
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# yield 'TwinSVM-CC', MLCC(MLTwinSVM())
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# yield 'TwinSVM-ACC', MLACC(MLTwinSVM())
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yield 'MLKNN-CC', MLCC(MLknn())
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yield 'MLKNN-PCC', MLPCC(MLknn())
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yield 'MLKNN-ACC', MLACC(MLknn())
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yield 'MLKNN-PACC', MLPACC(MLknn())
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# yield 'MLKNN-CC', MLCC(MLknn())
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#yield 'MLKNN-PCC', MLPCC(MLknn())
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# yield 'MLKNN-ACC', MLACC(MLknn())
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#yield 'MLKNN-PACC', MLPACC(MLknn())
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def get_dataset(dataset_name, dopickle=True):
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@ -1,7 +1,7 @@
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from copy import deepcopy
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.linear_model import LogisticRegression, Ridge
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.preprocessing import StandardScaler
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from skmultilearn.adapt import MLTSVM
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@ -44,6 +44,25 @@ class MLStackedClassifier: # aka Funnelling Monolingual
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return self.meta.predict_proba(P)
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class MLStackedRegressor:
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def __init__(self, base_regressor=Ridge(normalize=True)):
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self.base = deepcopy(base_regressor)
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self.meta = deepcopy(base_regressor)
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def fit(self, X, y):
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assert y.ndim==2, 'the dataset does not seem to be multi-label'
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self.base.fit(X, y)
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R = self.base.predict(X)
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# R = self.norm.fit_transform(R)
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self.meta.fit(R, y)
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return self
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def predict(self, X):
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R = self.base.predict(X)
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# R = self.norm.transform(R)
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return self.meta.predict(R)
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class LabelSpacePartion:
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def __init__(self, base_estimator=LogisticRegression()):
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graph_builder = LabelCooccurrenceGraphBuilder(weighted=True, include_self_edges=False)
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@ -6,6 +6,7 @@ from sklearn.model_selection import train_test_split
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from quapy.data import LabelledCollection
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from quapy.functional import artificial_prevalence_sampling
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from skmultilearn.model_selection import iterative_train_test_split
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class MultilabelledCollection:
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def __init__(self, instances, labels):
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@ -67,10 +68,13 @@ class MultilabelledCollection:
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labels = self.labels[index]
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return MultilabelledCollection(documents, labels)
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def train_test_split(self, train_prop=0.6, random_state=None):
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#raise ValueError('use the scikit-multilearn implementation')
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tr_docs, te_docs, tr_labels, te_labels = \
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train_test_split(self.instances, self.labels, train_size=train_prop, random_state=random_state)
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def train_test_split(self, train_prop=0.6, random_state=None, iterative=False):
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if iterative:
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tr_docs, tr_labels, te_docs, te_labels = \
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iterative_train_test_split(self.instances, self.labels, test_size=1-train_prop)
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else:
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tr_docs, te_docs, tr_labels, te_labels = \
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train_test_split(self.instances, self.labels, train_size=train_prop, random_state=random_state)
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return MultilabelledCollection(tr_docs, tr_labels), MultilabelledCollection(te_docs, te_labels)
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def artificial_sampling_generator(self, sample_size, category, n_prevalences=101, repeats=1):
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@ -98,6 +102,10 @@ class MultilabelledCollection:
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for c in self.classes_:
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yield self.asLabelledCollection(c)
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# @property
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# def label_cardinality(self):
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# return self.labels.sum()/len(self)
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@property
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def Xy(self):
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return self.instances, self.labels
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@ -1,6 +1,8 @@
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import numpy as np
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from copy import deepcopy
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import sklearn.preprocessing
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from sklearn.ensemble import StackingRegressor
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from sklearn.metrics import confusion_matrix
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from sklearn.multioutput import MultiOutputRegressor
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from sklearn.preprocessing import StandardScaler
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@ -9,7 +11,7 @@ from sklearn.linear_model import LogisticRegression, Ridge, Lasso, LassoCV, Mult
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ElasticNet, MultiTaskElasticNetCV, MultiTaskElasticNet, LinearRegression, ARDRegression, BayesianRidge, SGDRegressor
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import quapy as qp
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from MultiLabel.mlclassification import MLStackedClassifier
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from MultiLabel.mlclassification import MLStackedClassifier, MLStackedRegressor
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from MultiLabel.mldata import MultilabelledCollection
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from method.aggregative import CC, ACC, PACC, AggregativeQuantifier
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from method.base import BaseQuantifier
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@ -166,13 +168,17 @@ class MLRegressionQuantification:
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norm=True,
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means=True,
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stds=True):
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assert regression in ['ridge', 'svr'], 'unknown regression model'
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assert protocol in ['npp', 'app'], 'unknown protocol'
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self.estimator = mlquantifier
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if regression == 'ridge':
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self.reg = Ridge(normalize=norm)
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elif regression == 'svr':
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self.reg = MultiOutputRegressor(LinearSVR())
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if isinstance(regression, str):
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assert regression in ['ridge', 'svr'], 'unknown regression model'
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if regression == 'ridge':
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self.reg = Ridge(normalize=norm)
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elif regression == 'svr':
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self.reg = MultiOutputRegressor(LinearSVR())
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else:
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self.reg = regression
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self.protocol = protocol
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# self.reg = MultiTaskLassoCV(normalize=norm)
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# self.reg = KernelRidge(kernel='rbf')
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@ -215,7 +221,7 @@ class MLRegressionQuantification:
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Xs, ys = [], []
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samples_mean, samples_std = [], []
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for sample in val.natural_sampling_generator(sample_size=self.sample_size, repeats=self.n_samples):
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self._extract_features(self, sample, Xs, ys, samples_mean, samples_std)
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self._extract_features(sample, Xs, ys, samples_mean, samples_std)
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return self._prepare_arrays(Xs, ys, samples_mean, samples_std)
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@ -227,7 +233,7 @@ class MLRegressionQuantification:
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repeats = max(self.n_samples // (ncats * nprevs), 1)
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for cat in self.classes_:
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for sample in val.artificial_sampling_generator(sample_size=self.sample_size, category=cat, n_prevalences=nprevs, repeats=repeats):
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self._extract_features(self, sample, Xs, ys, samples_mean, samples_std)
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self._extract_features(sample, Xs, ys, samples_mean, samples_std)
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return self._prepare_arrays(Xs, ys, samples_mean, samples_std)
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def fit(self, data:MultilabelledCollection):
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@ -259,4 +265,97 @@ class MLRegressionQuantification:
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return np.asarray([neg_prevs, adjusted]).T
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# class
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class StackMLRQuantifier:
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def __init__(self,
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mlquantifier=MLNaiveQuantifier(CC(LinearSVC())),
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regression='ridge',
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protocol='npp',
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n_samples=500,
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sample_size=500,
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norm=True,
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means=True,
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stds=True):
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if regression == 'ridge':
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reg = MLStackedRegressor(Ridge(normalize=True))
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elif regression == 'svr':
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reg = MLStackedRegressor(MultiOutputRegressor(LinearSVR()))
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else:
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ValueError(f'unknown regressor {regression}')
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self.base = MLRegressionQuantification(
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mlquantifier=mlquantifier,
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regression=reg,
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protocol=protocol,
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n_samples=n_samples,
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sample_size=sample_size,
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norm=norm,
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means=means,
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stds=stds)
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def fit(self, data:MultilabelledCollection):
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self.classes_ = data.classes_
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self.base.fit(data)
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return self
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def quantify(self, instances):
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return self.base.quantify(instances)
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class MLadjustedCount(MLAggregativeQuantifier):
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def __init__(self, learner):
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self.learner = learner
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def preclassify(self, instances):
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return self.learner.predict(instances)
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def fit(self, data: MultilabelledCollection, train_prop=0.6):
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self.classes_ = data.classes_
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train, val = data.train_test_split(train_prop=train_prop)
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self.learner.fit(*train.Xy)
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val_predictions = self.preclassify(val.instances)
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val_true = val.labels
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N = len(val)
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C = val_predictions.T.dot(val_true) / N # join probabilities [[P(y1,\hat{y}1), P(y2,\hat{y}1)], ... ]
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priorP = val_predictions.mean(axis=0).reshape(-1,1) # priors [P(hat{y}1), P(hat{y}2), ...]
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self.Pte_cond_estim_ = np.true_divide(C, priorP, where=priorP>0) # cond probabilities [[P(y1|\hat{y}1), P(y2|\hat{y}1)], ... ]
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return self
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def aggregate(self, predictions):
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P = sklearn.preprocessing.normalize(predictions, norm='l1')
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correction = P.dot(self.Pte_cond_estim_)
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adjusted = correction.mean(axis=0)
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return np.asarray([1-adjusted, adjusted]).T
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class MLprobAdjustedCount(MLAggregativeQuantifier):
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def __init__(self, learner):
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self.learner = learner
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def preclassify(self, instances):
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return self.learner.predict_proba(instances)
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def fit(self, data: MultilabelledCollection, train_prop=0.6):
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||||
self.classes_ = data.classes_
|
||||
train, val = data.train_test_split(train_prop=train_prop)
|
||||
self.learner.fit(*train.Xy)
|
||||
val_predictions = self.preclassify(val.instances)
|
||||
val_true = val.labels
|
||||
|
||||
N = len(val)
|
||||
|
||||
C = (val_predictions>0.5).T.dot(val_true) / N # join probabilities [[P(y1,\hat{y}1), P(y2,\hat{y}1)], ... ]
|
||||
# not sure...
|
||||
|
||||
|
||||
priorP = val_predictions.mean(axis=0).reshape(-1,1) # priors [P(hat{y}1), P(hat{y}2), ...]
|
||||
self.Pte_cond_estim_ = np.true_divide(C, priorP, where=priorP>0) # cond probabilities [[P(y1|\hat{y}1), P(y2|\hat{y}1)], ... ]
|
||||
|
||||
return self
|
||||
|
||||
def aggregate(self, predictions):
|
||||
P = sklearn.preprocessing.normalize(predictions, norm='l1')
|
||||
correction = P.dot(self.Pte_cond_estim_)
|
||||
adjusted = correction.mean(axis=0)
|
||||
return np.asarray([1-adjusted, adjusted]).T
|
||||
|
|
Loading…
Reference in New Issue