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unifying n_bins in PQ and DMy

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Alejandro Moreo Fernandez 2025-11-18 10:13:34 +01:00
parent db49cd31be
commit 6db659e3c4
1 changed files with 7 additions and 7 deletions
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14
quapy/method/confidence.py
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@ -624,7 +624,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
classifier: BaseEstimator=None, classifier: BaseEstimator=None,
fit_classifier=True, fit_classifier=True,
val_split: int = 5, val_split: int = 5,
n_bins: int = 4, nbins: int = 4,
fixed_bins: bool = False, fixed_bins: bool = False,
num_warmup: int = 500, num_warmup: int = 500,
num_samples: int = 1_000, num_samples: int = 1_000,
@ -642,7 +642,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
"Run `$ pip install quapy[bayes]` to install them.") "Run `$ pip install quapy[bayes]` to install them.")
super().__init__(classifier, fit_classifier, val_split) super().__init__(classifier, fit_classifier, val_split)
self.n_bins = n_bins self.nbins = nbins
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
@ -657,10 +657,10 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
# Compute bin limits # Compute bin limits
if self.fixed_bins: if self.fixed_bins:
# Uniform bins in [0,1] # Uniform bins in [0,1]
self.bin_limits = np.linspace(0, 1, self.n_bins + 1) self.bin_limits = np.linspace(0, 1, self.nbins + 1)
else: else:
# Quantile bins # Quantile bins
self.bin_limits = np.quantile(y_pred, np.linspace(0, 1, self.n_bins + 1)) self.bin_limits = np.quantile(y_pred, np.linspace(0, 1, self.nbins + 1))
# Assign each prediction to a bin # Assign each prediction to a bin
bin_indices = np.digitize(y_pred, self.bin_limits[1:-1], right=True) bin_indices = np.digitize(y_pred, self.bin_limits[1:-1], right=True)
@ -670,14 +670,14 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
neg_mask = ~pos_mask neg_mask = ~pos_mask
# Count positives and negatives per bin # Count positives and negatives per bin
self.pos_hist = np.bincount(bin_indices[pos_mask], minlength=self.n_bins) self.pos_hist = np.bincount(bin_indices[pos_mask], minlength=self.nbins)
self.neg_hist = np.bincount(bin_indices[neg_mask], minlength=self.n_bins) self.neg_hist = np.bincount(bin_indices[neg_mask], minlength=self.nbins)
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)
prevs = _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.nbins, 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() ).flatten()
self.prev_distribution = np.vstack([1-prevs, prevs]).T self.prev_distribution = np.vstack([1-prevs, prevs]).T