forked from moreo/QuaPy
151 lines
6.7 KiB
Python
151 lines
6.7 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 distribution_matching.method_kdey_closed import KDEyclosed
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from distribution_matching.method_kdey_closed_efficient_correct import KDEyclosed_efficient_corr
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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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from method_kdey_closed_efficient import KDEyclosed_efficient
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METHODS = ['EMQ', 'EMQ-C', 'DM', 'DM-T', 'DM-HD', 'KDEy-DMhd3', 'DM-CS', 'KDEy-closed++', 'KDEy-ML'] #['ACC', 'PACC', 'HDy-OvA', 'DIR', 'DM', 'KDEy-DMhd3', 'KDEy-closed++', 'EMQ', 'KDEy-ML'] #, 'KDEy-DMhd2'] #, 'KDEy-DMhd2', 'DM-HD'] 'KDEy-DMjs', 'KDEy-DM', 'KDEy-ML+', 'KDEy-DMhd3+',
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BIN_METHODS = [x.replace('-OvA', '') for x in METHODS]
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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 == 'KDEy-closed':
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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 = KDEyclosed(lr, val_split=10)
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elif method == 'KDEy-closed+':
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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 = KDEyclosed_efficient(lr, val_split=10)
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elif method == 'KDEy-closed++':
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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 = KDEyclosed_efficient_corr(lr, 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 == 'EMQ-C':
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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-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 == 'DM-T':
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method_params = {
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'nbins': [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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'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': [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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'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': [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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'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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# experimental
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elif method in ['KDEy-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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# elif method in ['KDEy-DMhd']:
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# The code to reproduce this run is commented in the min_divergence target, I think it was incorrect...
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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='HD', montecarlo_trials=5000, val_split=10)
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elif method in ['KDEy-DMhd2']:
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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_uniform', divergence='HD', montecarlo_trials=5000, val_split=10)
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elif method in ['KDEy-DMjs']:
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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_uniform', divergence='JS', montecarlo_trials=5000, val_split=10)
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elif method in ['KDEy-DMhd3']:
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# I have realized that there was an error. I am sampling from the validation distribution (V) and not from the
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# test distribution (T) just because the validation can be sampled in fit only once and pre-computed densities
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# can be stored. This means that the reference distribution is V and not T. Then I have found that an
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# f-divergence is defined as D(p||q) \int_{R^n}q(x)f(p(x)/q(x))dx = E_{x~q}[f(p(x)/q(x))], so if I am sampling
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# V then I am computing D(T||V) (and not D(V||T) as I thought).
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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='HD', montecarlo_trials=5000, val_split=10)
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elif method == 'DM-HD':
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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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}
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param_grid = {**method_params, **hyper_LR}
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quantifier = DistributionMatching(lr, divergence='HD')
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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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