forked from moreo/QuaPy
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
import numpy as np
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import pandas as pd
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from distribution_matching.method_kdey import KDEy
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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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METHODS = ['ACC', 'PACC', 'HDy-OvA', 'DIR', 'DM', 'KDEy-DM', 'EMQ', 'KDEy-ML']
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BIN_METHODS = ['ACC', 'PACC', 'HDy', 'DIR', 'DM', 'KDEy-DM', 'EMQ', 'KDEy-ML']
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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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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 == 'KDEy-ML':
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method_params = {'bandwidth': np.linspace(0.01, 0.2, 20)}
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param_grid = {**method_params, **hyper_LR}
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quantifier = KDEy(lr, target='max_likelihood', val_split=10)
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elif method in ['KDEy-DM']:
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method_params = {'bandwidth': np.linspace(0.01, 0.2, 20)}
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param_grid = {**method_params, **hyper_LR}
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quantifier = KDEy(lr, target='min_divergence', divergence='l2', montecarlo_trials=5000, val_split=10)
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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 == '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':
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method_params = {
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'nbins': [4,8,16,32],
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'val_split': [10, 0.4],
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'divergence': ['HD', 'topsoe', 'l2']
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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 in ['KDE-DMkld']:
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method_params = {'bandwidth': np.linspace(0.01, 0.2, 20)}
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param_grid = {**method_params, **hyper_LR}
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quantifier = KDEy(lr, target='min_divergence', divergence='KLD', montecarlo_trials=5000, val_split=10)
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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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