From 32cb7b747beaf6f18e9bd79e5a6af52b19c004f3 Mon Sep 17 00:00:00 2001
From: zStupan <ziga.stupan@gmail.com>
Date: Fri, 26 Nov 2021 14:28:52 +0100
Subject: [PATCH 1/3] Bumped minimum python version to 3.7

---
 poetry.lock    | 406 +++++++++++++++++++++++++++++--------------------
 pyproject.toml |   6 +-
 2 files changed, 246 insertions(+), 166 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 9ee6cd9..6364134 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -44,6 +44,27 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "fonttools"
+version = "4.28.2"
+description = "Tools to manipulate font files"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=13.0.0)", "xattr"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["scipy", "munkres"]
+lxml = ["lxml (>=4.0,<5)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=13.0.0)"]
+woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
+
 [[package]]
 name = "importlib-metadata"
 version = "4.8.2"
@@ -61,37 +82,40 @@ docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
 perf = ["ipython"]
 testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
 
+[[package]]
+name = "iniconfig"
+version = "1.1.1"
+description = "iniconfig: brain-dead simple config-ini parsing"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "kiwisolver"
-version = "1.3.1"
+version = "1.3.2"
 description = "A fast implementation of the Cassowary constraint solver"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "matplotlib"
-version = "3.3.4"
+version = "3.5.0"
 description = "Python plotting package"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 cycler = ">=0.10"
+fonttools = ">=4.22.0"
 kiwisolver = ">=1.0.1"
-numpy = ">=1.15"
+numpy = ">=1.17"
+packaging = ">=20.0"
 pillow = ">=6.2.0"
-pyparsing = ">=2.0.3,<2.0.4 || >2.0.4,<2.1.2 || >2.1.2,<2.1.6 || >2.1.6"
-python-dateutil = ">=2.1"
-
-[[package]]
-name = "more-itertools"
-version = "8.11.0"
-description = "More routines for operating on iterables, beyond itertools"
-category = "dev"
-optional = false
-python-versions = ">=3.5"
+pyparsing = ">=2.2.1"
+python-dateutil = ">=2.7"
+setuptools_scm = ">=4"
 
 [[package]]
 name = "niapy"
@@ -109,11 +133,11 @@ pandas = ">=0.24.2"
 
 [[package]]
 name = "numpy"
-version = "1.19.5"
+version = "1.21.1"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "openpyxl"
@@ -130,7 +154,7 @@ et-xmlfile = "*"
 name = "packaging"
 version = "21.3"
 description = "Core utilities for Python packages"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6"
 
@@ -139,15 +163,15 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
 [[package]]
 name = "pandas"
-version = "0.25.3"
+version = "1.1.5"
 description = "Powerful data structures for data analysis, time series, and statistics"
 category = "main"
 optional = false
-python-versions = ">=3.5.3"
+python-versions = ">=3.6.1"
 
 [package.dependencies]
-numpy = ">=1.13.3"
-python-dateutil = ">=2.6.1"
+numpy = ">=1.15.4"
+python-dateutil = ">=2.7.3"
 pytz = ">=2017.2"
 
 [package.extras]
@@ -163,17 +187,18 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "pluggy"
-version = "0.13.1"
+version = "1.0.0"
 description = "plugin and hook calling mechanisms for python"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
 importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
 
 [package.extras]
 dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "py"
@@ -196,25 +221,24 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "5.4.3"
+version = "6.2.5"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [package.dependencies]
 atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""}
-attrs = ">=17.4.0"
+attrs = ">=19.2.0"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
-more-itertools = ">=4.0.0"
+iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<1.0"
-py = ">=1.5.0"
-wcwidth = "*"
+pluggy = ">=0.12,<2.0"
+py = ">=1.8.2"
+toml = "*"
 
 [package.extras]
-checkqa-mypy = ["mypy (==v0.761)"]
 testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
 
 [[package]]
@@ -236,6 +260,21 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "setuptools-scm"
+version = "6.3.2"
+description = "the blessed package to manage your versions by scm tags"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+packaging = ">=20.0"
+tomli = ">=1.0.0"
+
+[package.extras]
+toml = ["setuptools (>=42)", "tomli (>=1.0.0)"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -245,20 +284,28 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
-name = "typing-extensions"
-version = "4.0.0"
-description = "Backported and Experimental Type Hints for Python 3.6+"
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
 category = "dev"
 optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+
+[[package]]
+name = "tomli"
+version = "1.2.2"
+description = "A lil' TOML parser"
+category = "main"
+optional = false
 python-versions = ">=3.6"
 
 [[package]]
-name = "wcwidth"
-version = "0.2.5"
-description = "Measures the displayed width of unicode strings in a terminal"
+name = "typing-extensions"
+version = "4.0.0"
+description = "Backported and Experimental Type Hints for Python 3.6+"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 
 [[package]]
 name = "zipp"
@@ -274,8 +321,8 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
 
 [metadata]
 lock-version = "1.1"
-python-versions = "^3.6"
-content-hash = "00b009e3e515ed5f24b152e6be02781e03fdf92d166512588e9cade3522870da"
+python-versions = "^3.7"
+content-hash = "5d2491588311828fe71bc4acd196838f96df70043cc39e1c24ce262e7b2c673d"
 
 [metadata.files]
 atomicwrites = [
@@ -298,114 +345,134 @@ et-xmlfile = [
     {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"},
     {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"},
 ]
+fonttools = [
+    {file = "fonttools-4.28.2-py3-none-any.whl", hash = "sha256:eff1da7ea274c54cb8842853005a139f711646cbf6f1bcfb6c9b86a627f35ff0"},
+    {file = "fonttools-4.28.2.zip", hash = "sha256:dca694331af74c8ad47acc5171e57f6b78fac5692bf050f2ab572964577ac0dd"},
+]
 importlib-metadata = [
     {file = "importlib_metadata-4.8.2-py3-none-any.whl", hash = "sha256:53ccfd5c134223e497627b9815d5030edf77d2ed573922f7a0b8f8bb81a1c100"},
     {file = "importlib_metadata-4.8.2.tar.gz", hash = "sha256:75bdec14c397f528724c1bfd9709d660b33a4d2e77387a3358f20b848bb5e5fb"},
 ]
+iniconfig = [
+    {file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
+    {file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
+]
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+    {file = "pandas-1.1.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:731568be71fba1e13cae212c362f3d2ca8932e83cb1b85e3f1b4dd77d019254a"},
+    {file = "pandas-1.1.5-cp39-cp39-manylinux1_i686.whl", hash = "sha256:c61c043aafb69329d0f961b19faa30b1dab709dd34c9388143fc55680059e55a"},
+    {file = "pandas-1.1.5-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:2b1c6cd28a0dfda75c7b5957363333f01d370936e4c6276b7b8e696dd500582a"},
+    {file = "pandas-1.1.5-cp39-cp39-win32.whl", hash = "sha256:c94ff2780a1fd89f190390130d6d36173ca59fcfb3fe0ff596f9a56518191ccb"},
+    {file = "pandas-1.1.5-cp39-cp39-win_amd64.whl", hash = "sha256:edda9bacc3843dfbeebaf7a701763e68e741b08fccb889c003b0a52f0ee95782"},
+    {file = "pandas-1.1.5.tar.gz", hash = "sha256:f10fc41ee3c75a474d3bdf68d396f10782d013d7f67db99c0efbfd0acb99701b"},
 ]
 pillow = [
     {file = "Pillow-8.4.0-cp310-cp310-macosx_10_10_universal2.whl", hash = "sha256:81f8d5c81e483a9442d72d182e1fb6dcb9723f289a57e8030811bac9ea3fef8d"},
@@ -480,8 +552,8 @@ pillow = [
     {file = "Pillow-8.4.0.tar.gz", hash = "sha256:b8e2f83c56e141920c39464b852de3719dfbfb6e3c99a2d8da0edf4fb33176ed"},
 ]
 pluggy = [
-    {file = "pluggy-0.13.1-py2.py3-none-any.whl", hash = "sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"},
-    {file = "pluggy-0.13.1.tar.gz", hash = "sha256:15b2acde666561e1298d71b523007ed7364de07029219b604cf808bfa1c765b0"},
+    {file = "pluggy-1.0.0-py2.py3-none-any.whl", hash = "sha256:74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3"},
+    {file = "pluggy-1.0.0.tar.gz", hash = "sha256:4224373bacce55f955a878bf9cfa763c1e360858e330072059e10bad68531159"},
 ]
 py = [
     {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
@@ -492,8 +564,8 @@ pyparsing = [
     {file = "pyparsing-3.0.6.tar.gz", hash = "sha256:d9bdec0013ef1eb5a84ab39a3b3868911598afa494f5faa038647101504e2b81"},
 ]
 pytest = [
-    {file = "pytest-5.4.3-py3-none-any.whl", hash = "sha256:5c0db86b698e8f170ba4582a492248919255fcd4c79b1ee64ace34301fb589a1"},
-    {file = "pytest-5.4.3.tar.gz", hash = "sha256:7979331bfcba207414f5e1263b5a0f8f521d0f457318836a7355531ed1a4c7d8"},
+    {file = "pytest-6.2.5-py3-none-any.whl", hash = "sha256:7310f8d27bc79ced999e760ca304d69f6ba6c6649c0b60fb0e04a4a77cacc134"},
+    {file = "pytest-6.2.5.tar.gz", hash = "sha256:131b36680866a76e6781d13f101efb86cf674ebb9762eb70d3082b6f29889e89"},
 ]
 python-dateutil = [
     {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
@@ -503,18 +575,26 @@ pytz = [
     {file = "pytz-2021.3-py2.py3-none-any.whl", hash = "sha256:3672058bc3453457b622aab7a1c3bfd5ab0bdae451512f6cf25f64ed37f5b87c"},
     {file = "pytz-2021.3.tar.gz", hash = "sha256:acad2d8b20a1af07d4e4c9d2e9285c5ed9104354062f275f3fcd88dcef4f1326"},
 ]
+setuptools-scm = [
+    {file = "setuptools_scm-6.3.2-py3-none-any.whl", hash = "sha256:4c64444b1d49c4063ae60bfe1680f611c8b13833d556fd1d6050c0023162a119"},
+    {file = "setuptools_scm-6.3.2.tar.gz", hash = "sha256:a49aa8081eeb3514eb9728fa5040f2eaa962d6c6f4ec9c32f6c1fba88f88a0f2"},
+]
 six = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
+toml = [
+    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
+    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
+]
+tomli = [
+    {file = "tomli-1.2.2-py3-none-any.whl", hash = "sha256:f04066f68f5554911363063a30b108d2b5a5b1a010aa8b6132af78489fe3aade"},
+    {file = "tomli-1.2.2.tar.gz", hash = "sha256:c6ce0015eb38820eaf32b5db832dbc26deb3dd427bd5f6556cf0acac2c214fee"},
+]
 typing-extensions = [
     {file = "typing_extensions-4.0.0-py3-none-any.whl", hash = "sha256:829704698b22e13ec9eaf959122315eabb370b0884400e9818334d8b677023d9"},
     {file = "typing_extensions-4.0.0.tar.gz", hash = "sha256:2cdf80e4e04866a9b3689a51869016d36db0814d84b8d8a568d22781d45d27ed"},
 ]
-wcwidth = [
-    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
-    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
-]
 zipp = [
     {file = "zipp-3.6.0-py3-none-any.whl", hash = "sha256:9fe5ea21568a0a70e50f273397638d39b03353731e6cbbb3fd8502a33fec40bc"},
     {file = "zipp-3.6.0.tar.gz", hash = "sha256:71c644c5369f4a6e07636f0aa966270449561fcea2e3d6747b8d23efaa9d7832"},
diff --git a/pyproject.toml b/pyproject.toml
index 4a4d49c..86ef912 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,14 +2,14 @@
 name = "niaarm"
 version = "0.1.0"
 description = ""
-authors = ["Iztok Fister Jr. <iztok.fister1@um.si"]
+authors = ["Iztok Fister Jr. <iztok.fister1@um.si>"]
 
 [tool.poetry.dependencies]
-python = "^3.6"
+python = "^3.7"
 niapy = "2.0.0rc18"
 
 [tool.poetry.dev-dependencies]
-pytest = "^5.2"
+pytest = "^6.2.5"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

From 110d8f583a7bc7794f4a0f3c302a7e0d6ec38b18 Mon Sep 17 00:00:00 2001
From: zStupan <ziga.stupan@gmail.com>
Date: Fri, 26 Nov 2021 14:48:20 +0100
Subject: [PATCH 2/3] Code cleanup

---
 basic_run.py                            |  8 +--
 niaarm/__init__.py                      |  2 -
 niaarm/association_rule.py              | 32 +++-------
 niaarm/dataset.py                       | 78 +++++-------------------
 niaarm/feature.py                       | 50 +++++----------
 niaarm/niaarm.py                        | 38 ++++--------
 niaarm/rule.py                          | 81 ++++++-------------------
 niaarm/tests/test_read_csv.py           | 21 +++----
 niaarm/tests/test_rule_building.py      | 19 ++----
 niaarm/tests/test_support_confidence.py | 20 +++---
 10 files changed, 95 insertions(+), 254 deletions(-)

diff --git a/basic_run.py b/basic_run.py
index 43fb181..630409a 100644
--- a/basic_run.py
+++ b/basic_run.py
@@ -1,10 +1,10 @@
 from niaarm import NiaARM
-from niaarm.dataset import _Dataset
+from niaarm.dataset import Dataset
 from niapy.algorithms.basic import ParticleSwarmAlgorithm, DifferentialEvolution
 from niapy.task import Task, OptimizationType
 
 # load dataset from csv
-data = _Dataset("datasets/wiki_test_case.csv")
+data = Dataset("datasets/wiki_test_case.csv")
 
 # preprocess dataset and obtain features
 features = data.get_features()
@@ -13,7 +13,7 @@
 dimension = data.calculate_dimension_of_individual()
 
 # obtain transaction database
-transactions = data.get_transaction_data()
+transactions = data.transaction_data
 
 # create a problem
 problem = NiaARM(dimension, 0, 1, features, transactions, dimension)
@@ -24,7 +24,7 @@
     max_iters=100,
     optimization_type=OptimizationType.MAXIMIZATION)
 
-#use DE
+# use DE
 algo = DifferentialEvolution(population_size=50, differential_weight=0.5, crossover_probability=0.9)
 
 # use PSO algorithm from niapy library
diff --git a/niaarm/__init__.py b/niaarm/__init__.py
index f50d811..98eb3c5 100644
--- a/niaarm/__init__.py
+++ b/niaarm/__init__.py
@@ -1,5 +1,3 @@
 from niaarm.niaarm import NiaARM
 
-__all__ = ["NiaARM"]
-
 __version__ = "0.1.0"
diff --git a/niaarm/association_rule.py b/niaarm/association_rule.py
index 641d9ee..987e24b 100644
--- a/niaarm/association_rule.py
+++ b/niaarm/association_rule.py
@@ -1,26 +1,16 @@
-import sys
 import math
-from niaarm.feature import _Feature
 
 
-class AssociationRule(object):
+class AssociationRule:
     r"""Class for main operations and quality measures.
 
-    Date:
-        2021
-
-    License:
-        MIT
-
     Attributes:
-        value (Optional(any)): Categorical feature value.
-        min (Optional(float)): Maximum numerical feature's value.
-        max (Optional(float)): Minimum numerical feature's value.
+        features (Iterable[Feature]): List of features.
+        permutation (Iterable[])
     """
 
     def __init__(self, features):
         self.features = features
-
         self.permutation = []
 
     def build_rule(self, vector):
@@ -29,7 +19,6 @@ def build_rule(self, vector):
         permutation = self.map_permutation(vector)
         self.permutation = self.get_permutation(permutation)
 
-        vector_position = 0
         for i in range(len(self.features)):
             current_feature = self.permutation[i]
 
@@ -47,7 +36,6 @@ def build_rule(self, vector):
                     vector_position = vector_position + 1
                     border2 = (vector[vector_position] * (self.features[current_feature].max_val -
                                self.features[current_feature].min_val)) + self.features[current_feature].min_val
-                    vector_position = vector_position + 1
 
                     if border1 > border2:
                         inter = border1
@@ -73,8 +61,6 @@ def build_rule(self, vector):
                                 self.features[current_feature].min_val))) +
                         self.features[current_feature].min_val)
 
-                    vector_position = vector_position + 1
-
                     if border1 > border2:
                         inter = border1
                         border1 = border2
@@ -86,7 +72,6 @@ def build_rule(self, vector):
                     categories = self.features[current_feature].categories
                     selected = int(vector[vector_position]
                                    * (len(categories) - 1))
-                    vector_position = vector_position + 1
                     rule.append(
                         [self.features[current_feature].categories[selected]])
             else:
@@ -99,7 +84,7 @@ def map_permutation(self, vector):
     def is_rule_feasible(self, ant, con):
         ant_count = ant.count("NO")
         con_count = con.count("NO")
-        if ((ant_count == len(ant)) or (con_count == len(con))):
+        if (ant_count == len(ant)) or (con_count == len(con)):
             return False
         else:
             return True
@@ -112,14 +97,13 @@ def calculate_threshold_move(self, current_feature):
         return move
 
     def get_current_position_of_feature(self, feature):
-        position = feature * 3
-        return position
+        return feature * 3
 
     def return_permutation(self):
         return self.permutation
 
     def get_cut_point(self, sol, num_attr):
-        cut = int(float(sol) * num_attr)
+        cut = int(sol * num_attr)
         if cut == 0:
             cut = 1
         if cut > num_attr - 1:
@@ -210,11 +194,11 @@ def calculate_support_confidence(
             if (missing_ant + missing_con) == len(self.features):
                 supp = 0.0
 
-        skupni_supp = float(float(supp) / float(len(transactions)))
+        skupni_supp = supp / transactions
         if conf_counter == 0:
             skupni_conf = 0.0
         else:
-            skupni_conf = float(float(conf) / float(conf_counter))
+            skupni_conf = conf / conf_counter
 
         return skupni_supp, skupni_conf
 
diff --git a/niaarm/dataset.py b/niaarm/dataset.py
index 555a8ea..8df7281 100644
--- a/niaarm/dataset.py
+++ b/niaarm/dataset.py
@@ -1,19 +1,10 @@
-import numpy as np
 import pandas as pd
-from niaarm.feature import _Feature
+from niaarm.feature import Feature
 
-__all__ = ["Dataset"]
 
-
-class _Dataset:
+class Dataset:
     r"""Class for working with dataset.
 
-    Date:
-        2021
-
-    License:
-        MIT
-
     Attributes:
         path (str): Path to the dataset.
         has_header (Optional(str)): Is header present in csv file.
@@ -25,53 +16,33 @@ def __init__(self, path, has_header="Yes", delimiter=","):
         self.has_header = has_header
         self.delimiter = delimiter
 
+        self.data = None
         self.header = []
         self.features = []
 
     def read_file(self):
-        r"""Read dataset from file.
-            Arguments:
-                None
-            Returns:
-                None
-        """
+        r"""Read dataset from file."""
         self.data = pd.read_csv(self.path, sep=self.delimiter)
 
     def print_raw_output(self):
-        r"""Print the whole datatable.
-            Arguments:
-                None
-            Returns:
-                None
-        """
+        r"""Print the whole datatable."""
         print(self.data)
 
     def get_all_column_names(self):
-        r"""Preprocess all column names.
-            Arguments:
-                None
-            Returns:
-                None
-        """
+        r"""Preprocess all column names."""
         for col in self.data.columns:
             self.header.append(col)
 
     def return_header(self):
         r"""Return all column names.
-            Arguments:
-                None
+
             Returns:
                 Iterable[any]: list of columns.
         """
         return self.header
 
     def analyse_types(self):
-        r"""Extract data types for data in dataset..
-            Arguments:
-                None
-            Returns:
-                None
-        """
+        r"""Extract data types for data in dataset."""
         for head in self.header:
             col = self.data[head]
 
@@ -94,7 +65,7 @@ def analyse_types(self):
                 max_value = None
 
             self.features.append(
-                _Feature(
+                Feature(
                     head,
                     dtype,
                     min_value,
@@ -102,35 +73,23 @@ def analyse_types(self):
                     unique_categories))
 
     def get_features(self):
-        r"""Get feature data.
-            Arguments:
-                None
-            Returns:
-                None
-        """
+        r"""Get feature data."""
         self.read_file()
         self.get_all_column_names()
         self.analyse_types()
 
         return self.features
 
-    def get_transaction_data(self):
-        r"""Get all transactions.
-            Arguments:
-                None
-            Returns:
-                None
-        """
+    @property
+    def transaction_data(self):
         return self.data.values
 
     def calculate_dimension_of_individual(self):
         r"""Calculate the dimension of the problem.
             Dimension of the problem is used in optimization task.
 
-            Arguments:
-                None
             Returns:
-                number (int)
+                int: dimension
         """
         dimension = 0
         for feature in self.features:
@@ -144,13 +103,6 @@ def calculate_dimension_of_individual(self):
         return dimension
 
     def get_feature_report(self):
-        r"""Print feature details.
-
-            Arguments:
-                None
-            Returns:
-                None
-        """
+        r"""Print feature details."""
         for feature in self.features:
-            print("Name: ", feature.name, " Type: ", feature.dtype,
-                  " Range: (", feature.min_val, ", ", feature.max_val, ")")
+            print(feature)
diff --git a/niaarm/feature.py b/niaarm/feature.py
index f636bb1..f221af5 100644
--- a/niaarm/feature.py
+++ b/niaarm/feature.py
@@ -1,42 +1,22 @@
-__all__ = ["_Feature"]
+from dataclasses import dataclass
+from typing import Iterable, Optional
 
 
-class _Feature:
-    r"""Class for rule representation of association rule
-
-    Date:
-        2021
-
-    License:
-        MIT
+@dataclass
+class Feature:
+    r"""Class representing a feature.
 
     Attributes:
        name (str): Name of feature.
-       dtype (int): Data type of feature.
-       min_val (Optional(float)): Minimum value of feature in transaction database.
-       max_val (Optional(float)): Maximum value of feature in transaction database.
-       categories (Optional(Iterable[float])): Possible categorical feature's values.
-    """
+       dtype (str): Datatype of feature.
+       min_val (Optional[float]): Minimum value of feature in transaction database.
+       max_val (Optional[float]): Maximum value of feature in transaction database.
+       categories (Optional[Iterable[float]]): Possible categorical feature's values.
 
-    def __init__(
-            self,
-            name,
-            dtype,
-            min_val=None,
-            max_val=None,
-            categories=None,
-            **kwargs):
-        r"""Initialize instance of _Rule.
+    """
 
-        Arguments:
-            name (str): Name of feature.
-            dtype (int): Data type of a feature.
-            min_val (Optional(float)): Minimum value of feature in transaction database.
-            max_val (Optional(float)): Maximum value of feature in transaction database.
-            categories (Optional(Iterable[float])): Possible categorical feature's values.
-        """
-        self.name = name
-        self.dtype = dtype
-        self.min_val = min_val
-        self.max_val = max_val
-        self.categories = categories
+    name: str
+    dtype: str
+    min_val: Optional[float] = None
+    max_val: Optional[float] = None
+    categories: Optional[Iterable[float]] = None
diff --git a/niaarm/niaarm.py b/niaarm/niaarm.py
index 515e895..d0950bd 100644
--- a/niaarm/niaarm.py
+++ b/niaarm/niaarm.py
@@ -1,6 +1,4 @@
-from pandas.api.types import is_numeric_dtype
-from niaarm.dataset import _Dataset
-from niaarm.rule import _Rule
+from niaarm.rule import Rule
 from niaarm.association_rule import AssociationRule
 from niapy.problems import Problem
 import numpy as np
@@ -16,7 +14,7 @@ class NiaARM(Problem):
         2021
 
     Reference:
-        The implementation is composed from ideas found in the following papers:
+        The implementation is composed of ideas found in the following papers:
 
         I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. [Differential evolution for association rule mining using categorical and numerical attributes](http://www.iztok-jr-fister.eu/static/publications/231.pdf) In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.
 
@@ -38,10 +36,11 @@ def __init__(
             transactions,
             alpha=1.0,
             beta=1.0,
-            gamma=1.0, # TODO (for shrinking)
-            delta=1.0): # TODO (for coverage)
+            gamma=1.0,  # TODO (for shrinking)
+            delta=1.0):  # TODO (for coverage)
         r"""Initialize instance of NiaARM.
-            Arguments:
+
+        Arguments:
 
         """
         self.dim = dimension
@@ -61,6 +60,7 @@ def rule_not_exist(self, antecedent, consequence):
             Arguments:
                 antecedent (array): .
                 consequence (array): .
+
             Returns:
                 None
         """
@@ -81,27 +81,16 @@ def rules_to_csv(self):
                 for rule in self.rules:
                     writer.writerow(
                         [rule.antecedent, rule.consequence, rule.fitness])
-        except BaseException as e:
-            print('BaseException:', output_file)
+        except OSError:
+            print('OSError:', output_file)
         else:
             print("Output successfully")
 
     def sort_rules(self):
         self.rules.sort(key=lambda x: x.fitness, reverse=True)
 
-    def print_improvement(self, fitness, support, confidence):
-        print(
-            "Fitness: ",
-            fitness,
-            " Support: ",
-            support,
-            " Confidence: ",
-            confidence)
-
     def _evaluate(self, sol):
-        r"""Evaluate association rule.
-
-        """
+        r"""Evaluate association rule."""
         arm = AssociationRule(self.features)
 
         cut_value = sol[self.dim - 1]  # get cut point value
@@ -135,18 +124,17 @@ def _evaluate(self, sol):
                 # save feasible rule
                 if self.rule_not_exist(antecedent, consequence):
                     self.rules.append(
-                        _Rule(
+                        Rule(
                             antecedent,
                             consequence,
                             fitness,
                             support,
                             confidence,
-                            None,
-                            None))
+                            ))
 
                 if fitness > self.best_fitness:
                     self.best_fitness = fitness
-                    self.print_improvement(fitness, support, confidence)
+                    print("Fitness:", fitness, "Support:", support, "Confidence:", confidence)
             return fitness
         else:
             return -1.0
diff --git a/niaarm/rule.py b/niaarm/rule.py
index fc61712..2eb5acb 100644
--- a/niaarm/rule.py
+++ b/niaarm/rule.py
@@ -1,71 +1,26 @@
-__all__ = ["_Rule"]
+from dataclasses import dataclass
+from typing import Iterable, Optional
 
 
-class _Rule:
+@dataclass
+class Rule:
     r"""Class for representation of association rule.
 
     Attributes:
-        antecedent ((Iterable[str])): A list of antecedents of association rule.
-        consequence ((Iterable[str])): A list of consequents of association rule.
+        antecedent (Iterable[str]): A list of antecedents of association rule.
+        consequence (Iterable[str]): A list of consequents of association rule.
         fitness (float): Value of fitness function.
-        support (Optional(float)): Value of support.
-        confidence (Optional(float)): Value of confidence.
-        shrink (Optional(float)): Value of shrink.
-        coverage (Optional(float)): Value of coverage.
-    """
-
-    def __init__(
-            self,
-            antecedent,
-            consequence,
-            fitness,
-            support,
-            confidence,
-            shrink,
-            coverage):
-        r"""Initialize instance of _Rule.
-
-        Arguments:
-            antecedent ((Iterable[str])): A list of antecedents of association rule.
-            consequence ((Iterable[str])): A list of consequents of association rule.
-            fitness (float): Value of fitness function.
-            support (Optional(float)): Value of support.
-            confidence (Optional(float)): Value of confidence.
-            shrink (Optional(float)): Value of shrink.
-            coverage (Optional(float)): Value of coverage.
-        """
-        self.__antecedent = antecedent
-        self.__consequence = consequence
-        self.__fitness = fitness
-        self.__support = support
-        self.__confidence = confidence
-        self.__shrink = shrink
-        self.__coverage = coverage
-
-    @property
-    def antecedent(self):
-        return self.__antecedent
-
-    @property
-    def consequence(self):
-        return self.__consequence
+        support (float): Value of support.
+        confidence (float): Value of confidence.
+        shrink (Optional[float]): Value of shrink.
+        coverage (Optional[float]): Value of coverage.
 
-    @property
-    def fitness(self):
-        return self.__fitness
-
-    @property
-    def support(self):
-        return self.__support
-
-    @property
-    def confidence(self):
-        return self.__confidence
-
-    @property
-    def shrink(self):
-        return self.__shrink
+    """
 
-    @property
-    def coverage(self):
-        return self.__coverage
+    antecedent: Iterable[str]
+    consequence: Iterable[str]
+    fitness: float
+    support: float
+    confidence: float
+    shrink: Optional[float] = None
+    coverage: Optional[float] = None
diff --git a/niaarm/tests/test_read_csv.py b/niaarm/tests/test_read_csv.py
index ccc341d..588270d 100644
--- a/niaarm/tests/test_read_csv.py
+++ b/niaarm/tests/test_read_csv.py
@@ -1,9 +1,8 @@
 from unittest import TestCase
-from niaarm.association_rule import AssociationRule
-from niaarm.feature import _Feature
-from niaarm.dataset import _Dataset
+from niaarm.dataset import Dataset
 
-class TestReadCSV_Abalone(TestCase):
+
+class TestReadCSVAbalone(TestCase):
     def test_read_features(self):
 
         header = ['Sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings']
@@ -11,7 +10,7 @@ def test_read_features(self):
         maxval = [None, 0.815, 0.65, 1.13, 2.8255, 1.488, 0.76, 1.005, 29]
         dtypes_a = ['cat', 'float', 'float', 'float', 'float', 'float', 'float', 'float', 'int']
 
-        data = _Dataset("datasets/Abalone.csv")
+        data = Dataset("datasets/Abalone.csv")
 
         features = data.get_features()
 
@@ -19,8 +18,6 @@ def test_read_features(self):
 
         header_a = data.return_header()
 
-        transactions = data.get_transaction_data()
-
         min_value = []
         max_value = []
         dtypes = []
@@ -37,15 +34,15 @@ def test_read_features(self):
         self.assertEqual(max_value, maxval)
         self.assertEqual(dtypes, dtypes_a)
 
-class TestReadCSV_Wiki(TestCase):
+
+class TestReadCSVWiki(TestCase):
     def test_read_features(self):
         header = ['Antecedent', 'Consequent']
         minval = [None, 0]
         maxval = [None, 1]
         dtypes_a = ['cat', 'int']
-        categories_a = [['A','B'], None]
 
-        data = _Dataset("datasets/wiki_test_case.csv")
+        data = Dataset("datasets/wiki_test_case.csv")
 
         features = data.get_features()
 
@@ -53,8 +50,6 @@ def test_read_features(self):
 
         header_a = data.return_header()
 
-        transactions = data.get_transaction_data()
-
         min_value = []
         max_value = []
         dtypes = []
@@ -72,4 +67,4 @@ def test_read_features(self):
         self.assertEqual(min_value, minval)
         self.assertEqual(max_value, maxval)
         self.assertEqual(dtypes, dtypes_a)
-        #TODO: Sort
+        # TODO: Sort
diff --git a/niaarm/tests/test_rule_building.py b/niaarm/tests/test_rule_building.py
index 0c51e36..faa21f5 100644
--- a/niaarm/tests/test_rule_building.py
+++ b/niaarm/tests/test_rule_building.py
@@ -1,26 +1,17 @@
 from unittest import TestCase
 from niaarm.association_rule import AssociationRule
-from niaarm.feature import _Feature
-from niaarm.dataset import _Dataset
+from niaarm.dataset import Dataset
+
 
 class TestSupportConfidence(TestCase):
     # let's borrow test case from wikipedia: https://en.wikipedia.org/wiki/Lift_(data_mining)
     def test_numerical_categorical(self):
-        data = _Dataset("datasets/wiki_test_case.csv")
+        data = Dataset("datasets/wiki_test_case.csv")
 
         features = data.get_features()
 
-        transactions = data.get_transaction_data()
-
-        antecedent = ['A']
-
-        consequence = [0]
-
-        Oper = AssociationRule(features)
-
-        permutation = Oper.map_permutation([0.98328107, 0.93655004, 0.6860223,  0.78527931, 0.96291945, 0.18117294, 0.50567635])
+        oper = AssociationRule(features)
 
+        permutation = oper.map_permutation([0.98328107, 0.93655004, 0.6860223,  0.78527931, 0.96291945, 0.18117294, 0.50567635])
 
         self.assertEqual(permutation, [0.18117294, 0.50567635])
-
-
diff --git a/niaarm/tests/test_support_confidence.py b/niaarm/tests/test_support_confidence.py
index 4e02d7d..ab2c57c 100644
--- a/niaarm/tests/test_support_confidence.py
+++ b/niaarm/tests/test_support_confidence.py
@@ -1,16 +1,16 @@
 from unittest import TestCase
 from niaarm.association_rule import AssociationRule
-from niaarm.feature import _Feature
-from niaarm.dataset import _Dataset
+from niaarm.dataset import Dataset
+
 
 class TestSupportConfidence(TestCase):
     # let's borrow test case from wikipedia: https://en.wikipedia.org/wiki/Lift_(data_mining)
     def test_numerical_categorical(self):
-        data = _Dataset("datasets/wiki_test_case.csv")
+        data = Dataset("datasets/wiki_test_case.csv")
 
         features = data.get_features()
 
-        transactions = data.get_transaction_data()
+        transactions = data.transaction_data
 
         antecedent_a = [['A']]
 
@@ -23,17 +23,15 @@ def test_numerical_categorical(self):
 
         vector = [0.83393188, 0.66680227, 0.67480834, 0.13308981, 0.55182048, 0.04805541, 0.51910747]
 
-        Oper = AssociationRule(features)
-
-        rule = Oper.build_rule(vector)
+        oper = AssociationRule(features)
 
-        cut = Oper.get_cut_point(0, len(features))
+        cut = oper.get_cut_point(0, len(features))
 
-        rule = Oper.build_rule(vector)
+        rule = oper.build_rule(vector)
 
-        antecedent, consequence = Oper.get_ant_con(rule, cut)
+        antecedent, consequence = oper.get_ant_con(rule, cut)
 
-        support, confidence = Oper.calculate_support_confidence(
+        support, confidence = oper.calculate_support_confidence(
                 antecedent, consequence, transactions)
 
         self.assertEqual(antecedent, antecedent_a)

From 9cf489c8b427afdb63808a418c3d20462ed21a3b Mon Sep 17 00:00:00 2001
From: zStupan <ziga.stupan@gmail.com>
Date: Fri, 26 Nov 2021 14:57:05 +0100
Subject: [PATCH 3/3] Removed unnecessary __all__

---
 niaarm/niaarm.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/niaarm/niaarm.py b/niaarm/niaarm.py
index d0950bd..c926a39 100644
--- a/niaarm/niaarm.py
+++ b/niaarm/niaarm.py
@@ -4,8 +4,6 @@
 import numpy as np
 import csv
 
-__all__ = ["NiaARM"]
-
 
 class NiaARM(Problem):
     r"""Implementation of NiaARM.