Loads the IFCB dataset for quantification <https://zenodo.org/records/10036244 >`. For more
-information on this dataset check the zenodo site.
-This dataset is based on the data available publicly at <https://github.com/hsosik/WHOI-Plankton >.
-The scripts for the processing are available at <https://github.com/pglez82/IFCB_Zenodo >
-Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.
+quapy.data.datasets. fetch_IFCB ( single_sample_train = True for_model_selection = False data_home = None ) [source] Loads the IFCB dataset for quantification from Zenodo (for more
+information on this dataset, please follow the zenodo link).
+This dataset is based on the data available publicly at
+WHOI-Plankton repo .
+The scripts for the processing are available at P. González’s repo .
+Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.
The datasets are downloaded only once, and stored for fast reuse.
Parameters:
-single_sample_train – boolean. If True (default), it returns the train dataset as an instance of
+
single_sample_train – a boolean. If true, it will return the train dataset as a
quapy.data.base.LabelledCollection
for_model_selection – if True, then returns a split 30% of the training set (86 out of 286 samples) to be used for model selection;
+if False, then returns the full training set as training set and the test set as the test set
data_home – specify the quapy home directory where collections will be dumped (leave empty to use the default
~/quay_data/ directory)
Returns:
a tuple (train, test_gen) where train is an instance of
-quapy.data.base.LabelledCollection single_sample_train is True or
-quapy.data._ifcb.IFCBTrainSamplesFromDir quapy.data._ifcb.IFCBTestSamples quapy.data.base.LabelledCollection single_sample_train is true or
+quapy.data._ifcb.IFCBTrainSamplesFromDir quapy.data._ifcb.IFCBTestSamples
diff --git a/docs/build/html/quapy.method.html b/docs/build/html/quapy.method.html
index 4600719..e843d2a 100644
--- a/docs/build/html/quapy.method.html
+++ b/docs/build/html/quapy.method.html
@@ -22,7 +22,6 @@
quapy.classification package quapy.data package quapy.method package quapy.tests package Submodules
`. For more
- information on this dataset check the zenodo site.
- This dataset is based on the data available publicly at .
- The scripts for the processing are available at
-
+ Loads the IFCB dataset for quantification from `Zenodo `_ (for more
+ information on this dataset, please follow the zenodo link).
+ This dataset is based on the data available publicly at
+ `WHOI-Plankton repo `_.
+ The scripts for the processing are available at `P. González's repo `_.
Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.
The datasets are downloaded only once, and stored for fast reuse.
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index a831bcb..f49bd3e 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -60,6 +60,19 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
"""
pass
+ def _check_non_empty_classes(self, data: LabelledCollection):
+ """
+ Asserts all classes have positive instances.
+
+ :param data: LabelledCollection
+ :return: Nothing. May raise an exception.
+ """
+ sample_prevs = data.prevalence()
+ empty_classes = np.argwhere(sample_prevs==0).flatten()
+ if len(empty_classes)>0:
+ empty_class_names = data.classes_[empty_classes]
+ raise ValueError(f'classes {empty_class_names} have no training examples')
+
def fit(self, data: LabelledCollection, fit_classifier=True, val_split=None):
"""
Trains the aggregative quantifier. This comes down to training a classifier and an aggregation function.
@@ -93,6 +106,9 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
self._check_classifier(adapt_if_necessary=(self._classifier_method() == 'predict_proba'))
+ if fit_classifier:
+ self._check_non_empty_classes(data)
+
if predict_on is None:
predict_on = self.val_split
@@ -100,7 +116,6 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
if fit_classifier:
self.classifier.fit(*data.Xy)
predictions = None
-
elif isinstance(predict_on, float):
if fit_classifier:
if not (0. < predict_on < 1.):