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merged

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Alejandro Moreo Fernandez 2025-11-15 18:54:04 +01:00
parent 6388d9b549
commit 047cb9e533
1 changed files with 19 additions and 8 deletions
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27
quapy/method/confidence.py
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@ -565,10 +565,12 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
return np.asarray(samples.mean(axis=0), dtype=float) return np.asarray(samples.mean(axis=0), dtype=float)
def predict_conf(self, instances, confidence_level=None) -> (np.ndarray, ConfidenceRegionABC): def predict_conf(self, instances, confidence_level=None) -> (np.ndarray, ConfidenceRegionABC):
if confidence_level is None:
confidence_level = self.confidence_level
classif_predictions = self.classify(instances) classif_predictions = self.classify(instances)
point_estimate = self.aggregate(classif_predictions) point_estimate = self.aggregate(classif_predictions)
samples = self.get_prevalence_samples() # available after calling "aggregate" function samples = self.get_prevalence_samples() # available after calling "aggregate" function
region = WithConfidenceABC.construct_region(samples, confidence_level=self.confidence_level, method=self.region) region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region)
return point_estimate, region return point_estimate, region
@ -606,6 +608,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
num_warmup: int = 500, num_warmup: int = 500,
num_samples: int = 1_000, num_samples: int = 1_000,
stan_seed: int = 0, stan_seed: int = 0,
confidence_level: float = 0.95,
region: str = 'intervals'): region: str = 'intervals'):
if num_warmup <= 0: if num_warmup <= 0:
@ -622,9 +625,10 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
self.fixed_bins = fixed_bins self.fixed_bins = fixed_bins
self.num_warmup = num_warmup self.num_warmup = num_warmup
self.num_samples = num_samples self.num_samples = num_samples
self.region = region
self.stan_seed = stan_seed self.stan_seed = stan_seed
self.stan_code = _bayesian.load_stan_file() self.stan_code = _bayesian.load_stan_file()
self.confidence_level = confidence_level
self.region = region
def aggregation_fit(self, classif_predictions, labels): def aggregation_fit(self, classif_predictions, labels):
y_pred = classif_predictions[:, self.pos_label] y_pred = classif_predictions[:, self.pos_label]
@ -651,16 +655,23 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
def aggregate(self, classif_predictions): def aggregate(self, classif_predictions):
Px_test = classif_predictions[:, self.pos_label] Px_test = classif_predictions[:, self.pos_label]
test_hist, _ = np.histogram(Px_test, bins=self.bin_limits) test_hist, _ = np.histogram(Px_test, bins=self.bin_limits)
self.prev_distribution = _bayesian.pq_stan( prevs = _bayesian.pq_stan(
self.stan_code, self.n_bins, self.pos_hist, self.neg_hist, test_hist, self.stan_code, self.n_bins, self.pos_hist, self.neg_hist, test_hist,
self.num_samples, self.num_warmup, self.stan_seed self.num_samples, self.num_warmup, self.stan_seed
) ).flatten()
return F.as_binary_prevalence(self.prev_distribution.mean()) self.prev_distribution = np.vstack([1-prevs, prevs]).T
return self.prev_distribution.mean(axis=0)
def predict_conf(self, instances, confidence_level=None) -> (np.ndarray, ConfidenceRegionABC): def aggregate_conf(self, predictions, confidence_level=None):
classif_predictions = self.classify(instances) if confidence_level is None:
point_estimate = self.aggregate(classif_predictions) confidence_level = self.confidence_level
point_estimate = self.aggregate(predictions)
samples = self.prev_distribution samples = self.prev_distribution
region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region) region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region)
return point_estimate, region return point_estimate, region
def predict_conf(self, instances, confidence_level=None) -> (np.ndarray, ConfidenceRegionABC):
predictions = self.classify(instances)
return self.aggregate_conf(predictions, confidence_level=confidence_level)