Distributions

.github/workflows/run_tests.yml

@@ -8,6 +8,10 @@ on:
     branches:
       - main
       - development
+    paths:
+      - "tutorials/**"
+      - "dlordinal/**"
+      - ".github/workflows/**"
 
 jobs:
   tests:

dlordinal/distributions/__init__.py

@@ -1,19 +1,19 @@
-from .beta_distribution import get_beta_probabilities
-from .binomial_distribution import get_binomial_probabilities
-from .exponential_distribution import get_exponential_probabilities
+from .beta_distribution import get_beta_softlabels
+from .binomial_distribution import get_binomial_softlabels
+from .exponential_distribution import get_exponential_softlabels
 from .general_triangular_distribution import (
     get_general_triangular_params,
-    get_general_triangular_probabilities,
+    get_general_triangular_softlabels,
 )
 from .poisson_distribution import get_poisson_probabilities
-from .triangular_distribution import get_triangular_probabilities
+from .triangular_distribution import get_triangular_softlabels
 
 __all__ = [
-    "get_beta_probabilities",
-    "get_exponential_probabilities",
-    "get_binomial_probabilities",
+    "get_beta_softlabels",
+    "get_exponential_softlabels",
+    "get_binomial_softlabels",
     "get_poisson_probabilities",
-    "get_triangular_probabilities",
+    "get_triangular_softlabels",
     "get_general_triangular_params",
-    "get_general_triangular_probabilities",
+    "get_general_triangular_softlabels",
 ]

dlordinal/distributions/beta_distribution.py

@@ -57,7 +57,7 @@ def beta_dist(x, p, q, a=1.0):
     return (x ** (a * p)) / (p * beta_func(p, q)) * hyp2f1(p, 1 - q, p + 1, x**a)
 
 
-def get_beta_probabilities(J, p, q, a=1.0):
+def get_beta_softlabels(J, p, q, a=1.0):
     """Get probabilities from a beta distribution :math:`B(p,q,a)` for ``J`` splits.
     The :math:`[0,1]` interval is split into ``J`` intervals and the probability for
     each interval is computed as the difference between the value of the distribution

dlordinal/distributions/binomial_distribution.py

@@ -2,7 +2,7 @@
 from scipy.stats import binom
 
 
-def get_binomial_probabilities(J):
+def get_binomial_softlabels(J):
     """Get probabilities for the binomial distribution for ``J`` classes or splits
     using the approach described in :footcite:t:`liu2020unimodal`.
     The :math:`[0,1]` interval is split into ``J`` intervals and the probability for

dlordinal/distributions/exponential_distribution.py

@@ -2,7 +2,7 @@
 from scipy.special import softmax
 
 
-def get_exponential_probabilities(J, p=1.0, tau=1.0):
+def get_exponential_softlabels(J, p=1.0, tau=1.0):
     """Get probabilities from exponential distribution for ``J`` classes or splits as
     described in :footcite:t:`liu2020unimodal` and :footcite:t:`vargas2023exponential`.
     The :math:`[0,1]` interval is split into ``J`` intervals and the probability for

dlordinal/distributions/general_triangular_distribution.py

@@ -158,7 +158,7 @@ def abcj(J, j, alpha2j_1, alpha2j):
     return params
 
 
-def get_general_triangular_probabilities(J: int, alphas: np.ndarray, verbose: int = 0):
+def get_general_triangular_softlabels(J: int, alphas: np.ndarray, verbose: int = 0):
     """Get probabilities from triangular distributions for ``J`` classes or splits.
     The :math:`[0,1]` interval is split into ``J`` intervals and the probability for
     each interval is computed as the difference between the value of the triangular
@@ -213,8 +213,8 @@ def get_general_triangular_probabilities(J: int, alphas: np.ndarray, verbose: in
 
     if not isinstance(alphas, np.ndarray) or alphas.shape != (2 * J,):
         raise ValueError(
-            f"alphas must be a numpy array or list of shape (2 * n,),"
-            f" but got {{alphas.shape}}"
+            "alphas must be a numpy array or list of shape (2 * n,),"
+            " but got {alphas.shape}"
         )
 
     intervals = get_intervals(J)

dlordinal/distributions/tests/test_beta_distribution.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-from ..beta_distribution import beta_dist, beta_func, get_beta_probabilities
+from ..beta_distribution import beta_dist, beta_func, get_beta_softlabels
 
 
 def test_beta_inc():
@@ -57,7 +57,7 @@ def test_beta_probabilities():
     p = 2.0
     q = 3.0
     a = 1.0
-    result = get_beta_probabilities(n, p, q, a)
+    result = get_beta_softlabels(n, p, q, a)
     expected_result = [
         0.1808000009216,
         0.34399999942400017,
@@ -74,7 +74,7 @@ def test_beta_probabilities():
     p = 1.5
     q = 2.5
     a = 2.0
-    result = get_beta_probabilities(n, p, q, a)
+    result = get_beta_softlabels(n, p, q, a)
     expected_result = [
         0.05010107325697135,
         0.283232260076362,

dlordinal/distributions/tests/test_binomial_distribution.py

@@ -1,13 +1,13 @@
 import numpy as np
 import pytest
 
-from ..binomial_distribution import get_binomial_probabilities
+from ..binomial_distribution import get_binomial_softlabels
 
 
 def test_get_binomial_probabilities():
     # Case 1: n = 5
     n = 5
-    result = get_binomial_probabilities(n)
+    result = get_binomial_softlabels(n)
     expected_result = np.array(
         [
             [6.561e-01, 2.916e-01, 4.860e-02, 3.600e-03, 1.000e-04],

dlordinal/distributions/tests/test_exponential_distribution.py

@@ -1,14 +1,14 @@
 import numpy as np
 import pytest
 
-from ..exponential_distribution import get_exponential_probabilities
+from ..exponential_distribution import get_exponential_softlabels
 
 
 def test_get_exponential_probabilities():
     n = 5
     p = 1.0
     tau = 1.0
-    result = get_exponential_probabilities(n, p, tau)
+    result = get_exponential_softlabels(n, p, tau)
     expected_result = np.array(
         [
             [0.63640865, 0.23412166, 0.08612854, 0.03168492, 0.01165623],
@@ -35,7 +35,7 @@ def test_exponential_probabilities():
     n = 4
     p = 2.0
     tau = 1.0
-    result = get_exponential_probabilities(n, p, tau)
+    result = get_exponential_softlabels(n, p, tau)
     expected_result = np.array(
         [
             [7.21334965e-01, 2.65364304e-01, 1.32117107e-02, 8.90198068e-05],

dlordinal/distributions/tests/test_triangular_distribution.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-from ..triangular_distribution import get_triangular_probabilities
+from ..triangular_distribution import get_triangular_softlabels
 
 
 def test_get_triangular_probabilities():
@@ -10,7 +10,7 @@ def test_get_triangular_probabilities():
     alpha2 = 0.01
     verbose = 0
 
-    result = get_triangular_probabilities(n, alpha2, verbose)
+    result = get_triangular_softlabels(n, alpha2, verbose)
 
     expected_result = [
         [0.98845494, 0.01154505, 0.0, 0.0, 0.0],
@@ -39,7 +39,7 @@ def test_get_triangular_probabilities():
     alpha2 = 0.01
     verbose = 0
 
-    result = get_triangular_probabilities(n, alpha2, verbose)
+    result = get_triangular_softlabels(n, alpha2, verbose)
 
     expected_result = [
         [0.98845494, 0.01154505, 0.0, 0.0, 0.0, 0.0, 0.0],
@@ -72,7 +72,7 @@ def test_get_triangular_probabilities_verbose():
     alpha2 = 0.01
     verbose = 4
 
-    result = get_triangular_probabilities(n, alpha2, verbose)
+    result = get_triangular_softlabels(n, alpha2, verbose)
 
     expected_result = [
         [

dlordinal/distributions/triangular_distribution.py

@@ -5,7 +5,7 @@
 from .utils import get_intervals, triangular_cdf
 
 
-def get_triangular_probabilities(J: int, alpha2: float = 0.01, verbose: int = 0):
+def get_triangular_softlabels(J: int, alpha2: float = 0.01, verbose: int = 0):
     """
     Get probabilities from triangular distributions for ``J`` classes or splits using
     the approach described in :footcite:t:`vargas2023softlabelling`.
@@ -46,7 +46,7 @@ def get_triangular_probabilities(J: int, alpha2: float = 0.01, verbose: int = 0)
             1 & \\text{if } j = J
         \\end{cases}
 
-    The value of :math:`\\alpha_1`, that represents the error for the first class, 
+    The value of :math:`\\alpha_1`, that represents the error for the first class,
     is computed as follows:
 
     .. math::
@@ -57,7 +57,7 @@ def get_triangular_probabilities(J: int, alpha2: float = 0.01, verbose: int = 0)
     is computed as follows:
 
     .. math::
-        \\alpha_3 = \\left(\\frac{1 - 
+        \\alpha_3 = \\left(\\frac{1 -
         \\sqrt{1 - 4\\left(\\frac{J - 1}{J}\\right)^2(1 - 2\\alpha_2)(\\sqrt{2\\alpha_2}
           (-1 + \\sqrt{2\\alpha_2}))}}{2}\\right)^2
 

dlordinal/losses/beta_loss.py

@@ -3,7 +3,7 @@
 import torch
 from torch import Tensor
 
-from ..distributions import get_beta_probabilities
+from ..distributions import get_beta_softlabels
 from .custom_targets_loss import CustomTargetsCrossEntropyLoss
 
 # Params [a,b] for beta distribution
@@ -177,7 +177,7 @@ def __init__(
         # Precompute class probabilities for each label
         cls_probs = torch.tensor(
             [
-                get_beta_probabilities(
+                get_beta_softlabels(
                     num_classes,
                     self.params[num_classes][i][0],
                     self.params[num_classes][i][1],

dlordinal/losses/binomial_loss.py

@@ -3,7 +3,7 @@
 import torch
 from torch import Tensor
 
-from ..distributions import get_binomial_probabilities
+from ..distributions import get_binomial_softlabels
 from .custom_targets_loss import CustomTargetsCrossEntropyLoss
 
 
@@ -59,7 +59,7 @@ def __init__(
         label_smoothing: float = 0.0,
     ):
         # Precompute class probabilities for each label
-        cls_probs = torch.tensor(get_binomial_probabilities(num_classes)).float()
+        cls_probs = torch.tensor(get_binomial_softlabels(num_classes)).float()
 
         super().__init__(
             cls_probs=cls_probs,

dlordinal/losses/exponential_loss.py

@@ -3,7 +3,7 @@
 import torch
 from torch import Tensor
 
-from ..distributions import get_exponential_probabilities
+from ..distributions import get_exponential_softlabels
 from .custom_targets_loss import CustomTargetsCrossEntropyLoss
 
 
@@ -63,7 +63,7 @@ def __init__(
         label_smoothing: float = 0.0,
     ):
         # Precompute class probabilities for each label
-        cls_probs = torch.tensor(get_exponential_probabilities(num_classes, p)).float()
+        cls_probs = torch.tensor(get_exponential_softlabels(num_classes, p)).float()
 
         super().__init__(
             cls_probs=cls_probs,

dlordinal/losses/general_triangular_loss.py

@@ -4,7 +4,7 @@
 import torch
 from torch import Tensor
 
-from ..distributions import get_general_triangular_probabilities
+from ..distributions import get_general_triangular_softlabels
 from .custom_targets_loss import CustomTargetsCrossEntropyLoss
 
 
@@ -63,7 +63,7 @@ def __init__(
         label_smoothing: float = 0.0,
     ):
         # Precompute class probabilities for each label
-        r = get_general_triangular_probabilities(num_classes, alphas, verbose=0)
+        r = get_general_triangular_softlabels(num_classes, alphas, verbose=0)
         cls_probs = torch.tensor(r)
 
         super().__init__(

dlordinal/losses/triangular_loss.py

@@ -3,7 +3,7 @@
 import torch
 from torch import Tensor
 
-from ..distributions import get_triangular_probabilities
+from ..distributions import get_triangular_softlabels
 from .custom_targets_loss import CustomTargetsCrossEntropyLoss
 
 
@@ -62,7 +62,7 @@ def __init__(
         label_smoothing: float = 0.0,
     ):
         # Precompute class probabilities for each label
-        cls_probs = torch.tensor(get_triangular_probabilities(num_classes, alpha2))
+        cls_probs = torch.tensor(get_triangular_softlabels(num_classes, alpha2))
         super().__init__(
             cls_probs=cls_probs,
             eta=eta,