100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
import numpy as np
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from sklearn.linear_model import LogisticRegressionCV
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from Census.methods import AreaQuantifier, AggregationRule
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from quapy.data import LabelledCollection
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
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from commons import *
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from table import Table
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from tqdm import tqdm
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import quapy as qp
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from copy import deepcopy
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np.set_printoptions(linewidth=np.inf)
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def classifier():
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return LogisticRegressionCV()
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def quantifiers():
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cls = classifier()
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yield 'MLPE', MLPE()
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yield 'CC', CC(cls)
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yield 'PCC', PCC(cls)
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yield 'ACC', ACC(cls)
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yield 'PACC', PACC(cls)
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yield 'SLD', EMQ(cls)
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yield 'SModelLR', StatModelLR()
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yield 'SModel', StatModel(mean=prob_mean, scale=prob_std)
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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prob_mean, prob_std = preprocessor.get_mean_std(column=-1) # get the mean and std of the "prob" colum
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data = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(data)
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areas = [Ai for Ai, _, _ in data]
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q_names = [q_name for q_name, _ in quantifiers()]
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tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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for aggr in ['median', 'mean']:
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# areas on which a quantifier is trained, e.g., 'PACC-w/o46' means a PACC quantifier
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# has been trained on all areas but 46
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methods = [f'{q_name}-{aggr}' for q_name in q_names]
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table = Table(name=f'all{aggr}', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.sta = False
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table.format.remove_zero = True
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for q_name, q in quantifiers():
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# pretrain quantifiers per area
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pretrained_area_q = []
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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q_i = deepcopy(q)
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q_i.fit(LabelledCollection(Xi, yi))
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pretrained_area_q.append(AreaQuantifier(Ai, q_i))
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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# compose members of the rule (quantifiers are already fit)
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#training
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area_quantifiers = [qA_j for qA_j in pretrained_area_q if qA_j.area != Ai]
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rule = AggregationRule(area_quantifiers, aggr=aggr)
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#test
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te = LabelledCollection(Xi, yi)
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qp.environ["SAMPLE_SIZE"] = len(te)
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pred_prev = rule.predict(Ai, te.X)
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true_prev = te.prevalence()
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err = qp.error.mae(true_prev, pred_prev)
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method_name = f'{q_name}-{aggr}'
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table.add(benchmark=f'te-{Ai}', method=method_name, v=err)
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# text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
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tables.append(table)
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Table.LatexPDF(f'./results/allaggregation/doc.pdf', tables)
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# with open(f'./results/classifier/output.txt', 'tw') as foo:
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# foo.write('\n'.join(text_outputs))
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