80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
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from sklearn.svm import LinearSVC
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from tqdm import tqdm
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import quapy as qp
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import EMQ, PACC, CC, PCC, MS2, MS, ACC
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from quapy.data import LabelledCollection
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from sklearn.preprocessing import StandardScaler
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from commons import *
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np.set_printoptions(linewidth=np.inf)
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cens_y = './data/cens_y.csv'
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survey_y = './data/survey_y.csv'
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# Ate, Xte = load_csv(cens_y)
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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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# Xtr_proc = preprocessor.fit_transform(Xtr)
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# big_train = LabelledCollection(Xtr_proc, ytr)
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# q.fit(big_train)
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trains = get_dataset_by_area(Atr, Xtr, ytr)
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# tests = get_dataset_by_area(Ate, Xte)
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n_area = len(trains)
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# cls = LinearSVC()
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cls = LogisticRegression()
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# cls = LogisticRegressionCV(class_weight='balanced', Cs=10)
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# q = CC(cls)
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# q = PCC(cls)
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# q = PACC(cls)
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q = EMQ(cls)
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# q = MS(cls)
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#q = MaximumLikelihoodPrevalenceEstimation()
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for q in [CC(cls), PCC(cls), ACC(cls), PACC(cls), EMQ(cls), MLPE()]:
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results = np.zeros(shape=(n_area, n_area))
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for i, (Ai, Xi, yi) in tqdm(enumerate(trains), total=n_area):
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# Xi = preprocessor.fit_transform(Xi)
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tr = LabelledCollection(Xi, yi)
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q.fit(tr)
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len_tr = len(tr)
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# len_tr = len(big_train)
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for j, (Aj, Xj, yj) in enumerate(trains):
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if i==j: continue
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# Xj = preprocessor.transform(Xj)
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te = LabelledCollection(Xj, yj)
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pred_prev = q.quantify(te.X)
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true_prev = te.prevalence()
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# qp.environ["SAMPLE_SIZE"] = len(te)
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# err = qp.error.mrae(true_prev, pred_prev)
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err = qp.error.mae(true_prev, pred_prev)
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print(f'{i=} {j=} [#train={len_tr}] true_prev={true_prev[1]:.3f} pred_prev={pred_prev[1]:.3f} {err=:.4f}')
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results[i,j] = err
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import sys; sys.exit()
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q_name = q.__class__.__name__
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# print(results)
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print(f'{q_name} mean results = {results.mean():.4f}')
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results += np.eye(results.shape[0])
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print(results.min(axis=0).mean())
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