From a00224015c7b1f3438e7afcdebee5532b73a4ac4 Mon Sep 17 00:00:00 2001
From: Lorenzo Volpi <lorenzo.volpi@outlook.com>
Date: Wed, 22 Nov 2023 19:25:12 +0100
Subject: [PATCH] main_test updated

---
 quacc/main_test.py | 181 ++++++++++++++++++++++-----------------------
 1 file changed, 89 insertions(+), 92 deletions(-)

diff --git a/quacc/main_test.py b/quacc/main_test.py
index 56aa3a0..1a78815 100644
--- a/quacc/main_test.py
+++ b/quacc/main_test.py
@@ -1,49 +1,95 @@
-from copy import deepcopy
 from time import time
 
 import numpy as np
-from quapy.method.aggregative import SLD
-from quapy.protocol import APP, UPP
-from sklearn.linear_model import LogisticRegression
+import scipy.sparse as sp
+from quapy.protocol import APP
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.metrics import accuracy_score
 
-import quacc as qc
+from baselines.mandoline import estimate_performance
 from quacc.dataset import Dataset
-from quacc.error import acc
-from quacc.evaluation.baseline import ref
-from quacc.evaluation.method import mulmc_sld
-from quacc.evaluation.report import CompReport, EvaluationReport
-from quacc.method.base import MCAE, BinaryQuantifierAccuracyEstimator
-from quacc.method.model_selection import GridSearchAE
 
 
-def test_gs():
+def test_lr():
     d = Dataset(name="rcv1", target="CCAT", n_prevalences=1).get_raw()
 
     classifier = LogisticRegression()
     classifier.fit(*d.train.Xy)
 
-    quantifier = SLD(LogisticRegression())
-    # estimator = MultiClassAccuracyEstimator(classifier, quantifier)
-    estimator = BinaryQuantifierAccuracyEstimator(classifier, quantifier)
+    val, _ = d.validation.split_stratified(0.5, random_state=0)
+    val_X, val_y = val.X, val.y
+    val_probs = classifier.predict_proba(val_X)
 
-    v_train, v_val = d.validation.split_stratified(0.6, random_state=0)
-    gs_protocol = UPP(v_val, sample_size=1000, repeats=100)
-    gs_estimator = GridSearchAE(
-        model=deepcopy(estimator),
-        param_grid={
-            "q__classifier__C": np.logspace(-3, 3, 7),
-            "q__classifier__class_weight": [None, "balanced"],
-            "q__recalib": [None, "bcts", "ts"],
-        },
-        refit=False,
-        protocol=gs_protocol,
-        verbose=True,
-    ).fit(v_train)
+    reg_X = sp.hstack([val_X, val_probs])
+    reg_y = val_probs[np.arange(val_probs.shape[0]), val_y]
+    reg = LinearRegression()
+    reg.fit(reg_X, reg_y)
 
-    estimator.fit(d.validation)
+    _test_num = 10000
+    test_X = d.test.X[:_test_num, :]
+    test_probs = classifier.predict_proba(test_X)
+    test_reg_X = sp.hstack([test_X, test_probs])
+    reg_pred = reg.predict(test_reg_X)
+
+    def threshold(pred):
+        # return np.mean(
+        #     (reg.predict(test_reg_X) >= pred)
+        #     == (
+        #         test_probs[np.arange(_test_num), d.test.y[:_test_num]] == np.max(test_probs, axis=1)
+        #     )
+        # )
+        return np.mean(
+            (reg.predict(test_reg_X) >= pred)
+            == (np.argmax(test_probs, axis=1) == d.test.y[:_test_num])
+        )
+
+    max_p, max_acc = 0, 0
+    for p in reg_pred:
+        acc = threshold(p)
+        if acc > max_acc:
+            max_acc = acc
+            max_p = p
+
+    print(f"{max_p = }, {max_acc = }")
+    reg_pred = reg_pred - max_p + 0.5
+    print(reg_pred)
+    print(np.mean(reg_pred >= 0.5))
+    print(np.mean(np.argmax(test_probs, axis=1) == d.test.y[:_test_num]))
+
+
+def entropy(probas):
+    return -np.sum(np.multiply(probas, np.log(probas + 1e-20)), axis=1)
+
+
+def get_slices(probas):
+    ln, ncl = probas.shape
+    preds = np.argmax(probas, axis=1)
+    pred_slices = np.full((ln, ncl), fill_value=-1, dtype="<i8")
+    pred_slices[np.arange(ln), preds] = 1
+
+    ent = entropy(probas)
+    n_bins = 10
+    range_top = entropy(np.array([np.ones(ncl) / ncl]))[0]
+    bins = np.linspace(0, range_top, n_bins + 1)
+    bin_map = np.digitize(ent, bins=bins, right=True) - 1
+    ent_slices = np.full((ln, n_bins), fill_value=-1, dtype="<i8")
+    ent_slices[np.arange(ln), bin_map] = 1
+
+    return np.concatenate([pred_slices, ent_slices], axis=1)
+
+
+def test_mandoline():
+    d = Dataset(name="cifar10", target="dog", n_prevalences=1).get_raw()
 
     tstart = time()
-    erb, ergs = EvaluationReport("base"), EvaluationReport("gs")
+    classifier = LogisticRegression()
+    classifier.fit(*d.train.Xy)
+
+    val_probs = classifier.predict_proba(d.validation.X)
+    val_preds = np.argmax(val_probs, axis=1)
+    D_val = get_slices(val_probs)
+    emprical_mat_list_val = (1.0 * (val_preds == d.validation.y))[:, np.newaxis]
+
     protocol = APP(
         d.test,
         sample_size=1000,
@@ -51,68 +97,19 @@ def test_gs():
         repeats=100,
         return_type="labelled_collection",
     )
-    for sample in protocol():
-        e_sample = gs_estimator.extend(sample)
-        estim_prev_b = estimator.estimate(e_sample.eX)
-        estim_prev_gs = gs_estimator.estimate(e_sample.eX)
-        erb.append_row(
-            sample.prevalence(),
-            acc=abs(acc(e_sample.prevalence()) - acc(estim_prev_b)),
-        )
-        ergs.append_row(
-            sample.prevalence(),
-            acc=abs(acc(e_sample.prevalence()) - acc(estim_prev_gs)),
-        )
-
-    cr = CompReport(
-        [erb, ergs],
-        "test",
-        train_prev=d.train_prev,
-        valid_prev=d.validation_prev,
-    )
-
-    print(cr.table())
-    print(f"[took {time() - tstart:.3f}s]")
-
-
-def test_mc():
-    d = Dataset(name="rcv1", target="CCAT", prevs=[0.9]).get()[0]
-    classifier = LogisticRegression().fit(*d.train.Xy)
-    protocol = APP(
-        d.test,
-        sample_size=1000,
-        repeats=100,
-        n_prevalences=21,
-        return_type="labelled_collection",
-    )
-
-    ref_er = ref(classifier, d.validation, protocol)
-    mulmc_er = mulmc_sld(classifier, d.validation, protocol)
-
-    cr = CompReport(
-        [mulmc_er, ref_er],
-        name="test_mc",
-        train_prev=d.train_prev,
-        valid_prev=d.validation_prev,
-    )
-
-    with open("test_mc.md", "w") as f:
-        f.write(cr.data().to_markdown())
-
-
-def test_et():
-    d = Dataset(name="imdb", prevs=[0.5]).get()[0]
-    classifier = LogisticRegression().fit(*d.train.Xy)
-    estimator = MCAE(
-        classifier,
-        SLD(LogisticRegression(), exact_train_prev=False),
-        confidence="entropy",
-    ).fit(d.validation)
-    e_test = estimator.extend(d.test)
-    ep = estimator.estimate(e_test.eX)
-    print(f"estim prev = {qc.error.acc(ep)}")
-    print(f"true prev {qc.error.acc(e_test.prevalence())}")
+    res = []
+    for test in protocol():
+        test_probs = classifier.predict_proba(test.X)
+        test_preds = np.argmax(test_probs, axis=1)
+        D_test = get_slices(test_probs)
+        wp = estimate_performance(D_val, D_test, None, emprical_mat_list_val)
+        score = wp.all_estimates[0].weighted[0]
+        res.append(abs(score - accuracy_score(test.y, test_preds)))
+        print(score)
+    res = np.array(res).reshape((21, 100))
+    print(res.mean(axis=1))
+    print(f"time: {time() - tstart}s")
 
 
 if __name__ == "__main__":
-    test_et()
+    test_mandoline()