From b4aeaa97b7ea3709d333ec813b3c84fc57043fc2 Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Mon, 21 Jun 2021 12:55:39 +0200
Subject: [PATCH] fixing issue regarding fit_learner=False in QuaNetTrainer
---
quapy/data/base.py | 6 +++++-
quapy/method/aggregative.py | 13 +++++++------
quapy/method/neural.py | 12 +++++++-----
3 files changed, 19 insertions(+), 12 deletions(-)
diff --git a/quapy/data/base.py b/quapy/data/base.py
index 5234d8d..b3eae11 100644
--- a/quapy/data/base.py
+++ b/quapy/data/base.py
@@ -130,11 +130,15 @@ class LabelledCollection:
yield self.uniform_sampling_index(sample_size)
def __add__(self, other):
- if issparse(self.instances) and issparse(other.instances):
+ if other is None:
+ return self
+ elif issparse(self.instances) and issparse(other.instances):
join_instances = vstack([self.instances, other.instances])
elif isinstance(self.instances, list) and isinstance(other.instances, list):
join_instances = self.instances + other.instances
elif isinstance(self.instances, np.ndarray) and isinstance(other.instances, np.ndarray):
+ print(self.instances.shape)
+ print(other.instances.shape)
join_instances = np.concatenate([self.instances, other.instances])
else:
raise NotImplementedError('unsupported operation for collection types')
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 5997e3a..3df88c1 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -125,7 +125,7 @@ def training_helper(learner,
if not (0 < val_split < 1):
raise ValueError(f'train/val split {val_split} out of range, must be in (0,1)')
train, unused = data.split_stratified(train_prop=1 - val_split)
- elif val_split.__class__.__name__ == LabelledCollection.__name__: # isinstance(val_split, LabelledCollection):
+ elif isinstance(val_split, LabelledCollection):
train = data
unused = val_split
else:
@@ -144,10 +144,8 @@ def training_helper(learner,
if not hasattr(learner, 'predict_proba'):
raise AssertionError('error: the learner cannot be calibrated since fit_learner is set to False')
unused = None
- if val_split.__class__.__name__ == LabelledCollection.__name__:
+ if isinstance(val_split, LabelledCollection):
unused = val_split
- if data is not None:
- unused = unused+data
return learner, unused
@@ -307,19 +305,22 @@ class PACC(AggregativeProbabilisticQuantifier):
# fit the learner on all data
self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True,
val_split=None)
+ classes = data.classes_
else:
self.learner, val_data = training_helper(
self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split)
y_ = self.learner.predict_proba(val_data.instances)
y = val_data.labels
+ classes = val_data.classes_
self.pcc = PCC(self.learner)
# estimate the matrix with entry (i,j) being the estimate of P(yi|yj), that is, the probability that a
# document that belongs to yj ends up being classified as belonging to yi
- confusion = np.empty(shape=(data.n_classes, data.n_classes))
- for i,class_ in enumerate(data.classes_):
+ n_classes = len(classes)
+ confusion = np.empty(shape=(n_classes, n_classes))
+ for i, class_ in enumerate(classes):
confusion[i] = y_[y == class_].mean(axis=0)
self.Pte_cond_estim_ = confusion.T
diff --git a/quapy/method/neural.py b/quapy/method/neural.py
index 1cc7b96..4decc74 100644
--- a/quapy/method/neural.py
+++ b/quapy/method/neural.py
@@ -91,12 +91,14 @@ class QuaNetTrainer(BaseQuantifier):
train_data.instances = self.learner.transform(train_data.instances)
self.quantifiers = {
- 'cc': CC(self.learner).fit(classifier_data, fit_learner=False),
- 'acc': ACC(self.learner).fit(classifier_data, fit_learner=False, val_split=valid_data),
- 'pcc': PCC(self.learner).fit(classifier_data, fit_learner=False),
- 'pacc': PACC(self.learner).fit(classifier_data, fit_learner=False, val_split=valid_data),
- 'emq': EMQ(self.learner).fit(classifier_data, fit_learner=False),
+ 'cc': CC(self.learner).fit(None, fit_learner=False),
+ 'acc': ACC(self.learner).fit(None, fit_learner=False, val_split=valid_data),
+ 'pcc': PCC(self.learner).fit(None, fit_learner=False),
+ 'pacc': PACC(self.learner).fit(None, fit_learner=False, val_split=valid_data),
+ # 'emq': EMQ(self.learner).fit(classifier_data, fit_learner=False),
}
+ if classifier_data is not None:
+ self.quantifiers['emq'] = EMQ(self.learner).fit(classifier_data, fit_learner=False)
self.status = {
'tr-loss': -1,