[FIX] remove unnecessary functions in utils

abl/reasoning/reasoner.py

@@ -11,7 +11,7 @@ from ..utils.utils import confidence_dist, hamming_dist
 
 class Reasoner:
     """
-    Base class for reasoner.
+    Reasoner for minimizing the inconsistency between the knowledge base and learning models.
 
     Parameters
     ----------

abl/utils/__init__.py

@@ -1,7 +1,6 @@
 from .cache import Cache, abl_cache
 from .logger import ABLLogger, print_log
 from .utils import (
-    calculate_revision_num,
     confidence_dist,
     flatten,
     hamming_dist,
@@ -13,7 +12,6 @@ __all__ = [
     "Cache",
     "ABLLogger",
     "print_log",
-    "calculate_revision_num",
     "confidence_dist",
     "flatten",
     "hamming_dist",

abl/utils/utils.py

@@ -112,38 +112,6 @@ def confidence_dist(pred_prob, candidates):
     return 1 - np.prod(pred_prob[cols, candidates], axis=1)
 
 
-def block_sample(X, Z, Y, sample_num, seg_idx):
-    """
-    Extract a block of samples from lists X, Z, and Y.
-
-    Parameters
-    ----------
-    X, Z, Y : list
-        Input lists from which to extract the samples.
-    sample_num : int
-        The number of samples per block.
-    seg_idx : int
-        The block index to extract.
-
-    Returns
-    -------
-    tuple of lists
-        The extracted block samples from X, Z, and Y.
-
-    Example
-    -------
-    >>> X = [1, 2, 3, 4, 5, 6]
-    >>> Z = ['a', 'b', 'c', 'd', 'e', 'f']
-    >>> Y = [10, 11, 12, 13, 14, 15]
-    >>> block_sample(X, Z, Y, 2, 1)
-    ([3, 4], ['c', 'd'], [12, 13])
-    """
-    start_idx = sample_num * seg_idx
-    end_idx = sample_num * (seg_idx + 1)
-
-    return (data[start_idx:end_idx] for data in (X, Z, Y))
-
-
 def to_hashable(x):
     """
     Convert a nested list to a nested tuple so it is hashable.
@@ -184,40 +152,3 @@ def restore_from_hashable(x):
     return x
 
 
-def calculate_revision_num(parameter, total_length):
-    """
-    Convert a float parameter to an integer, based on a total length.
-
-    Parameters
-    ----------
-    parameter : int or float
-        The parameter to convert. If float, it should be between 0 and 1.
-        If int, it should be non-negative. If -1, it will be replaced with total_length.
-    total_length : int
-        The total length to calculate the parameter from if it's a fraction.
-
-    Returns
-    -------
-    int
-        The calculated parameter.
-
-    Raises
-    ------
-    TypeError
-        If parameter is not an int or a float.
-    ValueError
-        If parameter is a float not in [0, 1] or an int below 0.
-    """
-    if not isinstance(parameter, (int, float)):
-        raise TypeError("Parameter must be of type int or float.")
-
-    if parameter == -1:
-        return total_length
-    elif isinstance(parameter, float):
-        if not (0 <= parameter <= 1):
-            raise ValueError("If parameter is a float, it must be between 0 and 1.")
-        return round(total_length * parameter)
-    else:
-        if parameter < 0:
-            raise ValueError("If parameter is an int, it must be non-negative.")
-        return parameter

docs/API/abl.reasoning.rst

@@ -3,25 +3,24 @@ abl.reasoning
 
 .. currentmodule:: abl.reasoning
 
-Reasoning
----------
+Knowledge Base
+--------------
 
 .. autosummary::
    :toctree: generated
    :nosignatures:
    :template: classtemplate.rst
 
-   Reasoner
+   KBBase
+   GroundKB
+   PrologKB
 
-Knowledge Base
---------------
+Reasoner
+---------
 
 .. autosummary::
    :toctree: generated
    :nosignatures:
    :template: classtemplate.rst
 
-   KBBase
-   GroundKB
-   PrologKB
-
+   Reasoner

docs/API/abl.utils.rst

@@ -32,7 +32,6 @@ Utils
    :nosignatures:
    :template: classtemplate.rst
 
-   calculate_revision_num
    confidence_dist
    flatten
    hamming_dist