merging from pull request uci binary

quapy/data/_ifcb.py

@@ -1,6 +1,7 @@
 import os
 import pandas as pd
 import math
+from typing import Optional
 from quapy.data import LabelledCollection
 from quapy.protocol import AbstractProtocol
 from pathlib import Path
@@ -66,7 +67,7 @@ class IFCBTrainSamplesFromDir(AbstractProtocol):
 
 class IFCBTestSamples(AbstractProtocol):
 
-    def __init__(self, path_dir:str, test_prevalences: pd.DataFrame, samples: list = None, classes: list=None):
+    def __init__(self, path_dir:str, test_prevalences: Optional[pd.DataFrame]=None, samples: list=None, classes: list=None):
         self.path_dir = path_dir
         self.test_prevalences = test_prevalences
         self.classes = classes

quapy/data/datasets.py

@@ -271,7 +271,7 @@ def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, verbose=Fals
 
     >>> import quapy as qp
     >>> collection = qp.datasets.fetch_UCIBinaryLabelledCollection("yeast")
-    >>> for data in qp.train.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
+    >>> for data in qp.datasets.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
     >>>     ...
 
     The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`
@@ -647,7 +647,6 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_clas
     :param dataset_name: a dataset name
     :param data_home: specify the quapy home directory where the dataset will be dumped (leave empty to use the default
         ~/quay_data/ directory)
-    :param test_split: proportion of instances to be included in the test set. The rest conforms the training set
     :param min_class_support: minimum number of istances per class. Classes with fewer instances
         are discarded (deafult is 100)
     :param verbose: set to True (default is False) to get information (stats) about the dataset
@@ -736,6 +735,8 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_clas
         return LabelledCollection(X, y)
 
     def filter_classes(data: LabelledCollection, min_ipc):
+        if min_ipc is None:
+            min_ipc = 0
         classes = data.classes_
         # restrict classes to only those with at least min_ipc instances
         classes = classes[data.counts() >= min_ipc]
@@ -763,10 +764,12 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_clas
 def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
     df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False)
 
+
 def _array_replace(arr, repl={"yes": 1, "no": 0}):
     for k, v in repl.items():
         arr[arr == k] = v
 
+
 def fetch_lequa2022(task, data_home=None):
     """
     Loads the official datasets provided for the `LeQua <https://lequa2022.github.io/index>`_ competition.
@@ -784,7 +787,6 @@ def fetch_lequa2022(task, data_home=None):
     See `4.lequa2022_experiments.py` provided in the example folder, that can serve as a guide on how to use these
     datasets.
 
-
     :param task: a string representing the task name; valid ones are T1A, T1B, T2A, and T2B
     :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
         ~/quay_data/ directory)