uniform sampling added if *prevs is empty

quapy/data/base.py

@@ -40,6 +40,8 @@ class LabelledCollection:
         return self.n_classes == 2
 
     def sampling_index(self, size, *prevs, shuffle=True):
+        if len(prevs) == 0:  # no prevalence was indicated; returns an index for uniform sampling
+            return np.random.choice(len(self), size, replace=False)
         if len(prevs) == self.n_classes-1:
             prevs = prevs + (1-sum(prevs),)
         assert len(prevs) == self.n_classes, 'unexpected number of prevalences'
@@ -68,9 +70,16 @@ class LabelledCollection:
 
         return indexes_sample
 
+    # def uniform_sampling_index(self, size):
+    #     return np.random.choice(len(self), size, replace=False)
+
+    # def uniform_sampling(self, size):
+    #     unif_index = self.uniform_sampling_index(size)
+    #     return self.sampling_from_index(unif_index)
+
     def sampling(self, size, *prevs, shuffle=True):
-        index = self.sampling_index(size, *prevs, shuffle=shuffle)
-        return self.sampling_from_index(index)
+        prev_index = self.sampling_index(size, *prevs, shuffle=shuffle)
+        return self.sampling_from_index(prev_index)
 
     def sampling_from_index(self, index):
         documents = self.instances[index]
@@ -92,6 +101,14 @@ class LabelledCollection:
         for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
             yield self.sampling_index(sample_size, *prevs)
 
+    def natural_sampling_generator(self, sample_size, repeats=100):
+        for _ in range(repeats):
+            yield self.uniform_sampling(sample_size)
+
+    def natural_sampling_index_generator(self, sample_size, repeats=100):
+        for _ in range(repeats):
+            yield self.uniform_sampling_index(sample_size)
+
     def __add__(self, other):
         if issparse(self.instances) and issparse(other.documents):
             docs = vstack([self.instances, other.documents])

quapy/data/datasets.py

@@ -1,8 +1,8 @@
 import zipfile
-from utils.util import download_file_if_not_exists, download_file, get_quapy_home
+from util import download_file_if_not_exists, download_file, get_quapy_home
 import os
 from os.path import join
-from data.base import Dataset, LabelledCollection
+from data.base import Dataset
 from data.reader import from_text, from_sparse
 from data.preprocessing import text2tfidf, reduce_columns
 

quapy/data/preprocessing.py

@@ -2,7 +2,7 @@ import numpy as np
 from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
 from data.base import Dataset
 from scipy.sparse import spmatrix
-from utils.util import parallelize
+from util import parallelize
 from .base import LabelledCollection
 from tqdm import tqdm
 

quapy/evaluation.py

@@ -1,7 +1,7 @@
 from data import LabelledCollection
 from quapy.method.aggregative import AggregativeQuantifier, AggregativeProbabilisticQuantifier
 from method.base import BaseQuantifier
-from utils.util import temp_seed
+from util import temp_seed
 import numpy as np
 from joblib import Parallel, delayed
 from tqdm import tqdm

quapy/functional.py

@@ -94,11 +94,11 @@ def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1
     """
     __cache={}
     def __f(nc,np):
-        if (nc,np) in __cache:
+        if (nc,np) in __cache:  # cached result
             return __cache[(nc,np)]
-        if nc==1:
+        if nc==1:  # stop condition
             return 1
-        else:
+        else:  # recursive call
             x = sum([__f(nc-1, np-i) for i in range(np)])
             __cache[(nc,np)] = x
             return x

quapy/util.py

@@ -8,8 +8,6 @@ import os
 from pathlib import Path
 
 
-
-
 def get_parallel_slices(n_tasks, n_jobs=-1):
     if n_jobs == -1:
         n_jobs = multiprocessing.cpu_count()

quapy/utils/__init__.py

@@ -1 +0,0 @@
-from . import util