forked from moreo/QuaPy
125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
import numpy as np
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import pandas as pd
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from distribution_matching.method.kdex import KDExML
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from distribution_matching.method.method_kdey import KDEy
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from distribution_matching.method.method_kdey_closed_efficient_correct import KDEyclosed_efficient_corr
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from distribution_matching.method.kdey import KDEyCS, KDEyHD, KDEyML
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from quapy.method.aggregative import EMQ, CC, PCC, DistributionMatching, PACC, HDy, OneVsAllAggregative, ACC
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from distribution_matching.method.dirichlety import DIRy
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from sklearn.linear_model import LogisticRegression
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# set to True to get the full list of methods tested in the paper (reported in the appendix)
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# set to False to get the reduced list (shown in the body of the paper)
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FULL_METHOD_LIST = False
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if FULL_METHOD_LIST:
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ADJUSTMENT_METHODS = ['ACC', 'PACC']
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DISTR_MATCH_METHODS = ['HDy-OvA', 'DM-T', 'DM-HD', 'KDEy-HD', 'DM-CS', 'KDEy-CS']
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MAX_LIKE_METHODS = ['DIR', 'EMQ', 'EMQ-BCTS', 'KDEy-ML']
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else:
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ADJUSTMENT_METHODS = ['PACC']
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DISTR_MATCH_METHODS = ['DM-T', 'DM-HD', 'KDEy-HD', 'DM-CS', 'KDEy-CS']
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MAX_LIKE_METHODS = ['EMQ', 'KDEy-ML']
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# list of methods to consider
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METHODS = ADJUSTMENT_METHODS + DISTR_MATCH_METHODS + MAX_LIKE_METHODS
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BIN_METHODS = [x.replace('-OvA', '') for x in METHODS]
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# common hyperparameterss
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hyper_LR = {
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'classifier__C': np.logspace(-3,3,7),
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'classifier__class_weight': ['balanced', None]
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}
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hyper_kde = {
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'bandwidth': np.linspace(0.01, 0.2, 20)
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}
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nbins_range = [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 64]
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# instances a new quantifier based on a string name
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def new_method(method, **lr_kwargs):
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lr = LogisticRegression(**lr_kwargs)
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if method == 'CC':
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param_grid = hyper_LR
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quantifier = CC(lr)
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elif method == 'PCC':
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param_grid = hyper_LR
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quantifier = PCC(lr)
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elif method == 'ACC':
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param_grid = hyper_LR
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quantifier = ACC(lr)
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elif method == 'PACC':
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param_grid = hyper_LR
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quantifier = PACC(lr)
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elif method in ['KDEy-HD']:
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param_grid = {**hyper_kde, **hyper_LR}
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quantifier = KDEyHD(lr)
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elif method == 'KDEy-CS':
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param_grid = {**hyper_kde, **hyper_LR}
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quantifier = KDEyCS(lr)
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elif method == 'KDEy-ML':
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param_grid = {**hyper_kde, **hyper_LR}
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quantifier = KDEyML(lr)
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elif method == 'KDEx-ML':
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param_grid = {
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'bandwidth': np.linspace(0.001, 2, 501)
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}
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quantifier = KDExML()
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elif method == 'DIR':
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param_grid = hyper_LR
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quantifier = DIRy(lr)
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elif method == 'EMQ':
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param_grid = hyper_LR
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quantifier = EMQ(lr)
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elif method == 'EMQ-BCTS':
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method_params = {'exact_train_prev': [False], 'recalib': ['bcts']}
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param_grid = {**method_params, **hyper_LR}
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quantifier = EMQ(lr)
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elif method == 'HDy':
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param_grid = hyper_LR
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quantifier = HDy(lr)
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elif method == 'HDy-OvA':
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param_grid = {'binary_quantifier__' + key: val for key, val in hyper_LR.items()}
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quantifier = OneVsAllAggregative(HDy(lr))
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elif method == 'DM-T':
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method_params = {
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'nbins': nbins_range,
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'val_split': [10],
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'divergence': ['topsoe']
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}
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param_grid = {**method_params, **hyper_LR}
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quantifier = DistributionMatching(lr)
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elif method == 'DM-HD':
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method_params = {
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'nbins': nbins_range,
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'val_split': [10],
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'divergence': ['HD']
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}
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param_grid = {**method_params, **hyper_LR}
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quantifier = DistributionMatching(lr)
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elif method == 'DM-CS':
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method_params = {
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'nbins': nbins_range,
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'val_split': [10],
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'divergence': ['CS']
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}
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param_grid = {**method_params, **hyper_LR}
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quantifier = DistributionMatching(lr)
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else:
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raise NotImplementedError('unknown method', method)
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return param_grid, quantifier
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def show_results(result_path):
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df = pd.read_csv(result_path+'.csv', sep='\t')
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pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
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pv = df.pivot_table(index='Dataset', columns="Method", values=["MAE", "MRAE"])
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print(pv)
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