v0.4.0 (#20)

.github/workflows/build.yml

@@ -27,15 +27,9 @@ jobs:
           - python-version: 3.7
             args: ""
             os: ubuntu-latest
-          - python-version: 3.6
-            args: ""
-            os: ubuntu-latest
           - python-version: pypy-3.7
             args: ""
             os: ubuntu-latest
-          - python-version: pypy-3.6
-            args: ""
-            os: ubuntu-latest
     steps:
       - name: Checkout repo
         uses: actions/checkout@v2

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2021 Tomáš Mikula
+Copyright (c) 2022 Tomáš Mikula
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -38,6 +38,6 @@ https://doi.org/10.1016/j.ijar.2021.12.001
 
 fcapsy requires:
 
-* Python (>= 3.6)
+* Python (>= 3.7)
 * [concepts](https://github.com/xflr6/concepts)
 * [binsdpy](https://github.com/mikulatomas/binsdpy)

fcapsy/__init__.py

@@ -1,4 +1 @@
-__version__ = "0.3.3"
-__author__ = "Tomáš Mikula"
-__email__ = "mail@tomasmikula.cz"
-__license__ = "MIT license"
+from .category import Category

fcapsy/category.py

@@ -0,0 +1,19 @@
+import typing
+import concepts
+
+from .utils import get_vectors
+
+
+class Category:
+    """Category defined as group of exemplars (extent)."""
+
+    def __init__(self, context: "concepts.Context", extent) -> None:
+        self.context = context
+        self._extent = extent
+
+    @property
+    def extent(self) -> typing.Tuple[str, ...]:
+        return self._extent.members()
+
+    def vectors(self, item: str) -> typing.Dict:
+        return get_vectors(self, item)

fcapsy/cohesion.py

@@ -9,14 +9,18 @@
 import typing
 
 import concepts.lattices
+import fcapsy.category
 
 from itertools import combinations, starmap
 
+from .utils import get_vectors
+
 __all__ = ["cohesion_min", "cohesion_avg"]
 
 
 def cohesion_min(
-    concept: "concepts.lattices.Concept", similarity: typing.Callable
+    concept: typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"],
+    similarity: typing.Callable,
 ) -> float:
     """Calculate cohesion of given concept as worst case (minimal) similarity of its objects.
 
@@ -31,16 +35,14 @@ def cohesion_min(
         >>> cohesion_min(lattice.infimum, similarity.jaccard)
         0
     """
-    extent_size = len(concept.extent)
+    extent_size = concept._extent.count()
 
     if extent_size == 0:
         return 0
     elif extent_size == 1:
         return 1.0
 
-    vectors = map(
-        concept.lattice._context._intents.__getitem__, concept._extent.iter_set()
-    )
+    vectors = get_vectors(concept, concept.extent[0]).values()
 
     combs = combinations(vectors, 2)
 
@@ -64,16 +66,14 @@ def cohesion_avg(
         0
 
     """
-    extent_size = len(concept.extent)
+    extent_size = concept._extent.count()
 
     if extent_size == 0:
         return 0
     elif extent_size == 1:
         return 1.0
 
-    vectors = map(
-        concept.lattice._context._intents.__getitem__, concept._extent.iter_set()
-    )
+    vectors = get_vectors(concept, concept.extent[0]).values()
 
     combs = combinations(vectors, 2)
 

fcapsy/item_weights.py

@@ -0,0 +1,173 @@
+import typing
+
+import concepts.lattices
+import fcapsy.category
+
+from .utils import get_context
+
+
+__all__ = ["presence", "absence", "characteristic", "characteristic_r"]
+
+
+def _get_vectors(item, concept):
+    context = get_context(concept)
+
+    try:
+        instances_with_item = context.intension([item], raw=True)
+        Vector = type(concept._intent)
+        universum = Vector.supremum
+        concept_core = concept._intent
+    except KeyError:
+        instances_with_item = context.extension([item], raw=True)
+        Vector = type(concept._extent)
+        universum = Vector.supremum
+        concept_core = concept._extent
+
+    return instances_with_item, concept_core, Vector, universum
+
+
+def presence(
+    item, concept: typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"]
+):
+    """_summary_
+
+    Args:
+        item (str): object or attribute name
+        concept (typing.Union[concepts.lattices.Concept, fcapsy.category.Category])
+
+    Returns:
+        _type_: _description_
+
+    Example:
+        >>> from concepts import Context
+        >>> context = Context.fromstring('''
+        ...          |2 legs |nests  |flies  |raptor |
+        ... sparrow  |   X   |   X   |   X   |       |
+        ... lark     |   X   |   X   |   X   |       |
+        ... penguin  |   X   |       |       |       |
+        ... chicken  |   X   |   X   |   X   |       |
+        ... vulture  |   X   |       |   X   |   X   |
+        ... ''')
+        >>> birds = context.lattice.supremum
+        >>> presence("2 legs", birds)
+        5
+        >>> presence("nests", birds)
+        3
+        >>> flies = birds.lower_neighbors[0]
+        >>> presence("2 legs", flies)
+        4
+    """
+    instances_with_item, concept_core, Vector, _ = _get_vectors(item, concept)
+
+    occurence_in_concept = Vector.fromint(instances_with_item & concept_core).count()
+
+    return occurence_in_concept
+
+
+def absence(
+    item, concept: typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"]
+):
+    """_summary_
+
+    Args:
+        item (str): object or attribute name
+        concept (typing.Union[concepts.lattices.Concept, fcapsy.category.Category])
+
+    Returns:
+        _type_: _description_
+
+    Example:
+        >>> from concepts import Context
+        >>> context = Context.fromstring('''
+        ...          |2 legs |nests  |flies  |raptor |
+        ... sparrow  |   X   |   X   |   X   |       |
+        ... lark     |   X   |   X   |   X   |       |
+        ... penguin  |   X   |       |       |       |
+        ... chicken  |   X   |   X   |   X   |       |
+        ... vulture  |   X   |       |   X   |   X   |
+        ... ''')
+        >>> birds = context.lattice.supremum
+        >>> absence("2 legs", birds)
+        0
+        >>> absence("nests", birds)
+        2
+        >>> flies = birds.lower_neighbors[0]
+        >>> absence("raptor", flies)
+        3
+    """
+    instances_with_item, concept_core, Vector, universum = _get_vectors(item, concept)
+
+    absence_in_concept = Vector.fromint(
+        (universum ^ instances_with_item) & concept_core
+    ).count()
+
+    return absence_in_concept
+
+
+def characteristic(
+    item: str,
+    concept: typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"],
+) -> float:
+    """Calculates characteristic of object/attribute in the given concept.
+    The object/attribute does not has to be from the extent/intent.
+
+    Args:
+        item (str): object or attribute name
+        concept (typing.Union[concepts.lattices.Concept, fcapsy.category.Category])
+
+    Returns:
+        float: characteristic
+
+    Example:
+        >>> from concepts import Context
+        >>> context = Context.fromstring('''
+        ...          |2 legs |nests  |flies  |raptor |
+        ... sparrow  |   X   |   X   |   X   |       |
+        ... lark     |   X   |   X   |   X   |       |
+        ... penguin  |   X   |       |       |       |
+        ... chicken  |   X   |   X   |   X   |       |
+        ... vulture  |   X   |       |   X   |   X   |
+        ... ''')
+        >>> birds = context.lattice.supremum
+        >>> birds.extent
+        ('sparrow', 'lark', 'penguin', 'chicken', 'vulture')
+        >>> characteristic("raptor", birds)
+        0.2
+        >>> characteristic("flies", birds)
+        0.8
+        >>> characteristic("2 legs", birds)
+        1.0
+    """
+    instances_with_item, concept_core, Vector, _ = _get_vectors(item, concept)
+
+    occurence_in_concept = Vector.fromint(instances_with_item & concept_core).count()
+
+    if not instances_with_item.count():
+        return 0.0
+
+    return (occurence_in_concept / instances_with_item.count()) * (
+        occurence_in_concept / concept_core.count()
+    )
+
+
+def characteristic_r(
+    item: str,
+    concept: typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"],
+) -> float:
+    instances_with_item, concept_core, Vector, _ = _get_vectors(item, concept)
+
+    occurence_in_concept = Vector.fromint(instances_with_item & concept_core).count()
+
+    if not instances_with_item.count():
+        return 0.0
+
+    universum = Vector.supremum
+    universum_minus_core = Vector.fromint(universum & (universum ^ concept_core))
+    universum_minus_core_minus_instances_with_item = Vector.fromint(
+        universum_minus_core & (universum ^ instances_with_item)
+    )
+
+    return (occurence_in_concept / concept_core.count()) * (
+        universum_minus_core_minus_instances_with_item.count()
+        / universum_minus_core.count()
+    )

fcapsy/prototype.py

@@ -0,0 +1,38 @@
+import typing
+
+import concepts.lattices
+import fcapsy.category
+import numpy as np
+
+from .utils import get_vectors
+
+
+def prototype(
+    concept: typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"],
+) -> np.ndarray:
+    """Calcualtes prototype of given concept.
+
+    Args:
+        concept (typing.Union["concepts.lattices.Concept", "fcapsy.category.Category"]): concept
+
+    Returns:
+        np.ndarray: prototype
+
+    Example:
+        >>> from concepts import Context
+        >>> from binsdpy.similarity import jaccard
+        >>> context = Context.fromstring('''
+        ...          |2 legs |nests  |flies  |raptor |engine |
+        ... sparrow  |   X   |   X   |   X   |       |       |
+        ... lark     |   X   |   X   |   X   |       |       |
+        ... penguin  |   X   |       |       |       |       |
+        ... chicken  |   X   |   X   |   X   |       |       |
+        ... vulture  |   X   |       |   X   |   X   |       |
+        ... ''')
+        >>> birds = context.lattice.supremum
+        >>> prototype(birds)
+        array([1. , 0.6, 0.8, 0.2, 0. ])
+    """
+    vectors = get_vectors(concept, concept.extent[0]).values()
+
+    return np.array([a.bools() for a in vectors]).astype(int).mean(axis=0)