Create python-package.yml (#141)

* Create python-package.yml

* Switch from setup.py to pyproject.toml

* Add caspailleur to dependencies

* Update requirements.txt and pyproject.toml

* Fixing python versions nuances

* Update python version

* Update requirements

* Update README shields

* Skip plotly tests

---------

Co-authored-by: Egor Dudyrev <egor.dudyrev@loria.fr>

.github/workflows/python-package.yml

@@ -0,0 +1,41 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pip install pytest pytest-cov
+        pytest --doctest-modules --cov=com --cov-report=xml --cov-report=html

.readthedocs.yml

@@ -19,7 +19,7 @@ formats:
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.7
+  version: 3.8
   install:
     - method: pip
       path: .

.travis.yml

@@ -1,6 +1,6 @@
 language: python
 python:
-  - 3.6
+  - 3.8
 before_install:
   - python --version
   - pip install -U pip

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## [0.1.4.2] - 2022-06-01
+
+Elaborate Pattern Structures.
+Rewrite Sofia algorithm to mine hundreds of most stable concepts on big data.
+Add IntervalPS and SetPS to basic pattern structures.
+
 ## [0.1.4.1] - 2022-12-03
 
 OSDA toolkit edition.

README.md

@@ -1,6 +1,7 @@
 # FCApy
 
-[![Travis (.com)](https://img.shields.io/travis/com/EgorDudyrev/FCApy)](https://travis-ci.com/github/EgorDudyrev/FCApy)
+[![PyPi](https://img.shields.io/pypi/v/fcapy)](https://pypi.org/project/fcapy)
+[![GitHub Workflow](https://img.shields.io/github/actions/workflow/status/EgorDudyrev/caspailleur/python-package.yml?logo=github)](https://github.com/EgorDudyrev/fcapy/actions/workflows/python-package.yml)
 [![Read the Docs (version)](https://img.shields.io/readthedocs/fcapy/latest)](https://fcapy.readthedocs.io/en/latest/)
 [![Codecov](https://img.shields.io/codecov/c/github/EgorDudyrev/FCApy)](https://codecov.io/gh/EgorDudyrev/FCApy)
 [![GitHub](https://img.shields.io/github/license/EgorDudyrev/FCApy)](https://github.com/EgorDudyrev/FCApy/blob/main/LICENSE)

docs/source/conf.py

@@ -24,7 +24,7 @@
 author = 'Egor Dudyrev'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1.4.1'
+release = '0.1.4.2'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/index.rst

@@ -14,7 +14,7 @@ FCApy can be installed from `PyPI <https://pypi.org/project/fcapy>`_::
 
 The library has no strict dependencies. However one would better install it with all the additional packages::
 
-    pip install fcapy[all]
+    pip install "fcapy[all]"
 
 
 Contents

fcapy/__init__.py

@@ -22,3 +22,6 @@ def check_installed_packages(package_descriptions):
     'networkx': "The package to convert POSets to Graphs and to visualize them as graphs",
 }
 LIB_INSTALLED = check_installed_packages(PACKAGE_DESCRIPTION)
+
+
+__version__ = '0.1.4.2'

fcapy/algorithms/concept_construction.py

@@ -5,7 +5,7 @@
 
 """
 from collections import deque
-from typing import List, Tuple, Iterator, Iterable
+from typing import List, Tuple, Iterator, Iterable, Union
 
 import numpy as np
 from bitarray import frozenbitarray as fbarray
@@ -20,8 +20,8 @@
 from fcapy.utils import utils
 
 
-def close_by_one(context: FormalContext | MVContext, n_projections_to_binarize: int = 1000)\
-        -> Iterator[FormalConcept | PatternConcept]:
+def close_by_one(context: Union[FormalContext, MVContext], n_projections_to_binarize: int = 1000)\
+        -> Iterator[Union[FormalConcept, PatternConcept]]:
     """Return a list of concepts generated by CloseByOne (CbO) algorithm
 
     Parameters
@@ -77,7 +77,7 @@ def pattern_concept_factory(extent_i, extent):
     return concepts_iterator
 
 
-def close_by_one_objectwise(context: FormalContext | MVContext) -> Iterator[FormalConcept | PatternConcept]:
+def close_by_one_objectwise(context: Union[FormalContext, MVContext]) -> Iterator[Union[FormalConcept, PatternConcept]]:
     """Return a list of concepts generated by CloseByOne (CbO) algorithm
 
     Parameters
@@ -135,7 +135,8 @@ def create_concept(extent_idxs, intent_idxs):
         combinations_to_check.extend(new_combs)
 
 
-def close_by_one_objectwise_fbarray(context: FormalContext | MVContext) -> Iterator[FormalConcept | PatternConcept]:
+def close_by_one_objectwise_fbarray(context: Union[FormalContext, MVContext])\
+        -> Iterator[Union[FormalConcept, PatternConcept]]:
     """Return a list of concepts generated by CloseByOne (CbO) algorithm
 
     Parameters
@@ -154,7 +155,7 @@ def close_by_one_objectwise_fbarray(context: FormalContext | MVContext) -> Itera
     object_names, attribute_names = context.object_names, context.attribute_names
     context_hash = context.hash_fixed()
 
-    def create_concept(extent_idxs: list[int], intent_ba: fbarray):
+    def create_concept(extent_idxs: List[int], intent_ba: fbarray):
         extent_idxs = sorted(extent_idxs)
         extent = [object_names[g_i] for g_i in extent_idxs]
         if type(context) == FormalContext:
@@ -215,13 +216,13 @@ def extension_iter(intent_ba: fbarray, base_objects: Iterable[int] = range(n_obj
 
 
 def sofia(
-        K: FormalContext | MVContext, L_max: int = 100, min_supp: float = 0,
+        K: Union[FormalContext, MVContext], L_max: int = 100, min_supp: float = 0,
         use_tqdm: bool = False,use_log_stability_bound=True
-) -> list[FormalConcept | PatternConcept]:
+) -> List[Union[FormalConcept, PatternConcept]]:
     min_supp = min_supp * len(K) if min_supp < 1 else min_supp
 
     if use_log_stability_bound:
-        def stability_lbounds(extents: list[fbarray]) -> list[float]:
+        def stability_lbounds(extents: List[fbarray]) -> List[float]:
             #assert all(a.count() <= b.count() for a, b in zip(extents, extents[1:]))
             bounds = []
             for i, extent in enumerate(extents):
@@ -233,7 +234,7 @@ def stability_lbounds(extents: list[fbarray]) -> list[float]:
                 bounds.append(bound)
             return bounds
     else:
-        def stability_lbounds(extents: list[fbarray]) -> list[float]:
+        def stability_lbounds(extents: List[fbarray]) -> List[float]:
             children_ordering = inverse_order(sort_intents_inclusion(extents))
             children_intersections = (
                 ((extent & (~extents[child])).count() for child in children.itersearch(True))
@@ -243,7 +244,7 @@ def stability_lbounds(extents: list[fbarray]) -> list[float]:
             bounds = [1-sum(2**(-v) for v in intersections) for intersections in children_intersections]
             return bounds
 
-    extents_proj: list[fbarray] = [fbarray(~bazeros(K.n_objects))]
+    extents_proj: List[fbarray] = [fbarray(~bazeros(K.n_objects))]
 
     n_projs = K.n_bin_attrs
     proj_iterator = utils.safe_tqdm(enumerate(K.to_bin_attr_extents()), total=n_projs,
@@ -475,8 +476,8 @@ def direct_super_concepts(concept):
     return lattice
 
 
-def lcm_skmine(context: FormalContext | MVContext, min_supp: float = 1, n_jobs: int = 1)\
-        -> list[FormalConcept | PatternConcept]:
+def lcm_skmine(context: Union[FormalContext, MVContext], min_supp: float = 1, n_jobs: int = 1)\
+        -> List[Union[FormalConcept, PatternConcept]]:
     from skmine.itemsets import LCM
 
     context_bin = context if isinstance(context, FormalContext) else context.binarize()

fcapy/algorithms/lattice_construction.py

@@ -8,17 +8,17 @@
 
 """
 from copy import deepcopy
-from typing import Collection
+from typing import Collection, Union, Tuple, List, Dict, Set
 
 from fcapy.lattice.formal_concept import FormalConcept
 from fcapy.lattice.pattern_concept import PatternConcept
 from fcapy.utils import utils
 
 
 def complete_comparison(
-        concepts: Collection[FormalConcept or PatternConcept],
+        concepts: Collection[Union[FormalConcept, PatternConcept]],
         is_concepts_sorted: bool = False, n_jobs: int = 1, use_tqdm: bool = False
-) -> dict[int, set[int]]:
+) -> Dict[int, Set[int]]:
     """Return a dict with subconcepts relation on given ``concepts``. A slow but accurate bruteforce method
 
     Parameters
@@ -76,7 +76,7 @@ def get_subconcepts(a_i, a, concepts):
     return subconcepts_dict
 
 
-def construct_spanning_tree(concepts, is_concepts_sorted=False, use_tqdm=False) -> dict[int, int]:
+def construct_spanning_tree(concepts, is_concepts_sorted=False, use_tqdm=False) -> Dict[int, int]:
     """Return a spanning tree of subconcepts relation on given ``concepts``.
 
     A spanning tree means that for each concept from ``concepts`` we look for one parent concept only
@@ -145,7 +145,7 @@ def construct_spanning_tree(concepts, is_concepts_sorted=False, use_tqdm=False)
 
 
 def construct_lattice_from_spanning_tree(concepts, sptree_chains, is_concepts_sorted=False, use_tqdm=False)\
-        -> dict[int, set[int]]:
+        -> Dict[int, Set[int]]:
     """Return a dict with subconcepts relation on given concepts from given spanning tree of the relation.
 
     Parameters
@@ -419,7 +419,7 @@ def sort_key(sc_i):
 
 
 def construct_lattice_by_spanning_tree(concepts, is_concepts_sorted=False, n_jobs=1, use_tqdm=False)\
-        -> dict[int, set[int]]:
+        -> Dict[int, Set[int]]:
     """Return a dict with subconcepts relation on given ``concepts``. Uses spanning tree approach to fasten the computation
 
     Parameters
@@ -453,9 +453,9 @@ def construct_lattice_by_spanning_tree(concepts, is_concepts_sorted=False, n_job
     return subconcepts_dict
 
 
-def order_extents_comparison(concepts: list[FormalConcept | PatternConcept]) -> dict[int, set[int]]:
+def order_extents_comparison(concepts: List[Union[FormalConcept, PatternConcept]]) -> Dict[int, Set[int]]:
     from caspailleur.order import inverse_order, sort_intents_inclusion, topological_sorting, test_topologically_sorted
-    from caspailleur.base_functions import isets2bas, bas2isets
+    from caspailleur.base_functions import isets2bas
 
     n_objects = max(len(c.extent_i) for c in concepts)
     extents_ba = list(isets2bas([c.extent_i for c in concepts], n_objects))
@@ -477,7 +477,7 @@ def add_concept(
         new_concept, concepts, subconcepts_dict, superconcepts_dict,
         top_concept_i=None, bottom_concept_i=None,
         inplace=True
-) -> tuple[list[FormalConcept | PatternConcept], dict[int, set[int]], dict[int, set[int]], int, int]:
+) -> Tuple[List[Union[FormalConcept, PatternConcept]], Dict[int, Set[int]], Dict[int, Set[int]], int, int]:
     """Add ``new_concept`` into a set of ``concepts`` regarding its subconcept relation
 
     Parameters
@@ -590,7 +590,7 @@ def remove_concept(
         concept_i, concepts, subconcepts_dict, superconcepts_dict,
         top_concept_i=None, bottom_concept_i=None,
         inplace=True
-) -> tuple[list[FormalConcept | PatternConcept], dict[int, set[int]], dict[int, set[int]], int, int]:
+) -> Tuple[List[Union[FormalConcept, PatternConcept]], Dict[int, Set[int]], Dict[int, Set[int]], int, int]:
     """Remove a ``concept_i`` from a set of ``concepts`` regarding its subconcept relation
 
     Parameters

fcapy/context/bintable.py

@@ -8,7 +8,7 @@
 from fcapy.context import bintable_errors as berrors
 from fcapy import LIB_INSTALLED
 #if LIB_INSTALLED['bitarray']:
-from bitarray import frozenbitarray as fbarray, bitarray as barray, util as butil
+from bitarray import frozenbitarray as fbarray, util as butil
 
 #if LIB_INSTALLED['numpy']:
 import numpy as np
@@ -232,7 +232,7 @@ def decide_dataclass(data: Collection) -> str:
             return 'BinTableBitarray'
         if isinstance(data, np.ndarray):
             return 'BinTableNumpy'
-        if isinstance(data, tuple) and len(data) == 2 and isinstance(data[0], fbitarray) and isinstance(data[1], int):
+        if isinstance(data, tuple) and len(data) == 2 and isinstance(data[0], fbarray) and isinstance(data[1], int):
             return 'BinTableBitarray'
 
         raise berrors.UnknownDataTypeError(type(data))

fcapy/context/formal_context.py

@@ -713,7 +713,7 @@ def to_numeric(self):
         """
         return self._data.to_list(), self._attribute_names
 
-    def to_bin_attr_extents(self) -> Iterator[tuple[str, fbarray]]:
+    def to_bin_attr_extents(self) -> Iterator[Tuple[str, fbarray]]:
         for i, m in enumerate(self.attribute_names):
             extent = fbarray(self.data[:, i])
             yield m, extent

fcapy/mvcontext/mvcontext.py

@@ -6,7 +6,7 @@
 from frozendict import frozendict
 from itertools import combinations
 import zlib
-from typing import Tuple, Iterator
+from typing import Tuple, Iterator, List
 import json
 from bitarray import frozenbitarray as fbarray
 
@@ -97,7 +97,7 @@ def attribute_names(self, value):
         self._attribute_names = value
 
     @property
-    def pattern_structures(self) -> list[PS.AbstractPS]:
+    def pattern_structures(self) -> List[PS.AbstractPS]:
         """A list of pattern structures kept in a context"""
         return self._pattern_structures
 
@@ -115,7 +115,7 @@ def target(self):
         """A list of target values for Supervised ML scenarios"""
         return self._target
 
-    def assemble_pattern_structures(self, data, pattern_types) -> list[PS.AbstractPS]:
+    def assemble_pattern_structures(self, data, pattern_types) -> List[PS.AbstractPS]:
         """Return pattern_structures based on ``data`` and the ``pattern_types``"""
         if data is None:
             return None
@@ -302,7 +302,7 @@ def read_json(path: str = None, json_data: str = None, pattern_types: Tuple[PS.A
             A path to .json file
         json_data: `str`
             A json encoded data
-        pattern_types: `tuple[AbstractPS]`
+        pattern_types: `Tuple[AbstractPS]`
             Tuple of additional Pattern Structures not defined in fcapy.mvcontext.pattern_structure
 
         Returns
@@ -608,7 +608,7 @@ def describe_pattern(self, data: dict) -> str:
         description = [descr for descr in description if descr]
         return '; '.join(description)
 
-    def to_bin_attr_extents(self) -> Iterator[tuple[str, fbarray]]:
+    def to_bin_attr_extents(self) -> Iterator[Tuple[str, fbarray]]:
         for ps_i, ps in enumerate(self.pattern_structures):
             for m, extent in ps.to_bin_attr_extents():
                 yield m, extent