new dataset not working

Census/simulated/main.py

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+import numpy as np
+import pandas as pd
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
+
+from quapy.data import LabelledCollection
+from quapy.model_selection import GridSearchQ
+from quapy.protocol import APP
+from quapy.method.aggregative import PACC, PCC, EMQ, DMy, ACC, KDEyML, CC
+import quapy.functional as F
+
+def show_data(X, y=None, nbins=50):
+    import matplotlib.pyplot as plt
+    if y is None:
+        plt.hist(X, bins=nbins, edgecolor='black')
+    else:
+        pos = X[y==1]
+        neg = X[y==0]
+        bins = np.histogram_bin_edges(X, bins=nbins)
+        plt.hist(pos, bins=bins, edgecolor='black', label='positive', alpha=0.5)
+        plt.hist(neg, bins=bins, edgecolor='black', label='negative', alpha=0.5)
+    plt.xlabel('value')
+    plt.ylabel('frequency')
+    plt.show()
+
+df = pd.read_csv('./data/Simulated_PopulationData.csv', index_col=0)
+
+X = df.X.values.reshape(-1,1)
+y = df.Y.values
+A = df.area.values
+
+# X[y==1] += 2
+
+# show_data(X, y, nbins=50)
+
+areas = sorted(np.unique(A))
+n_areas = len(areas)
+
+N_EXPERIMENTS=1
+
+# print(list(df.columns))
+
+for experiment_id in range(1, N_EXPERIMENTS+1):
+    in_sample = df[f'InSample_{experiment_id}'].values.astype(dtype=bool)
+
+    Xtr = X[in_sample]
+    ytr = y[in_sample]
+    Atr = A[in_sample]
+
+    # show_data(Xtr, ytr)
+
+    # Xte = X[~in_sample]
+    # yte = y[~in_sample]
+    # Ate = A[~in_sample]
+    # baseline_soft = df[f'PrCens_{experiment_id}'].values[~in_sample]
+    # baseline_hard = df[f'YCens_{experiment_id}'].values[~in_sample]
+
+    Xte = X
+    yte = y
+    Ate = A
+    baseline_soft = df[f'PrCens_{experiment_id}'].values
+    baseline_hard = df[f'YCens_{experiment_id}'].values
+
+    train = LabelledCollection(Xtr, ytr, classes=[0, 1])
+
+    # print(f'Experiment {experiment_id}: training prevalence = {train.prevalence()[1]:.3f}')
+
+    q = CC(classifier=LogisticRegression())
+    # q = PACC(classifier=LogisticRegression())
+    # q = EMQ(classifier=LogisticRegression())
+    # q = KDEyML(classifier=LogisticRegression(), bandwidth=0.001)
+    q = PCC(classifier=LogisticRegression(C=1))
+    # q = DMy(classifier=LogisticRegression(), nbins=16)
+    q.fit(train)
+
+    # tr, val = train.split_stratified(random_state=0)
+    # mod_sel = GridSearchQ(
+    #     model=q,
+    #     param_grid={
+    #         'classifier__C':np.logspace(-3,3,7),
+    #         'classifier__class_weight':['balance', None],
+    #         'bandwidth': np.linspace(0.02, 0.20, 19)
+        # },
+        # protocol=APP(data=val, sample_size=100, n_prevalences=21, repeats=10, random_state=0),
+        # refit=True,
+        # n_jobs=-1
+    # ).fit(tr)
+    # q = mod_sel.best_model_
+
+    mae = []
+    mae_baseline_soft = []
+    mae_baseline_hard = []
+
+    for area in areas:
+
+        # sel_tr_a = Atr == area
+        sel_te_a = Ate == area
+
+        # train_A = LabelledCollection(Xtr[sel_tr_a], ytr[sel_tr_a], classes=[0,1])
+        test_A = LabelledCollection(Xte[sel_te_a], yte[sel_te_a], classes=[0,1])
+
+        # if np.prod(train_A.prevalence())==0: continue
+
+        # print(f'train-prev A = {train_A.prevalence()} n_instances={len(train_A)}')
+
+        # q = DMy(classifier=LogisticRegression())
+        # q.fit(train_A)
+
+        pred_prev = q.quantify(test_A.X)[1]
+        true_prev = test_A.prevalence()[1]
+        ae = abs(pred_prev-true_prev)
+        mae.append(ae)
+
+        baseline_soft_estim = np.mean(baseline_soft[sel_te_a])
+        ae_baseline_soft = abs(baseline_soft_estim-true_prev)
+        mae_baseline_soft.append(ae_baseline_soft)
+
+        baseline_hard_estim = np.mean(baseline_hard[sel_te_a])
+        ae_baseline_hard = abs(baseline_hard_estim - true_prev)
+        mae_baseline_hard.append(ae_baseline_hard)
+
+        print(f'Area {area} true={true_prev:.2f} '
+              f'baseline-soft={baseline_soft_estim:.3f} (AE={ae_baseline_soft:.3f}) '
+              f'baseline-hard={baseline_hard_estim:.3f} (AE={ae_baseline_hard:.3f}) '
+              f'predicted={pred_prev:.3f} (AE={ae:.3f})')
+
+    mae = np.mean(mae)
+    mae_baseline_soft = np.mean(mae_baseline_soft)
+    mae_baseline_hard = np.mean(mae_baseline_hard)
+    print(f'Experiment {experiment_id} Baseline(soft)={mae_baseline_soft:.3f} Baseline(hard)={mae_baseline_hard:.3f} MAE={mae:.3f}')
+
+
+