Repository refactoring

.github/workflows/check.yml

@@ -0,0 +1,37 @@
+name: CI
+
+on: [ push, pull_request ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.11", "3.12", "3.13" ]
+
+    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 poetry
+        run: pipx install poetry
+
+      - name: Install dependencies
+        run: poetry install --with dev
+
+      - name: Ruff
+        run: poetry run ruff check
+
+      - name: Install stubs
+        run: poetry run pip install types-PyYAML types-tqdm
+
+      - name: Mypy
+        run: poetry run mypy
+
+      - name: Pytest
+        run:  poetry run python -m pytest tests

.gitignore

@@ -25,6 +25,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+poetry.lock
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
@@ -157,7 +158,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 #vscode
 .vscode

README.md

@@ -1,7 +1,6 @@
-![PyLint](https://github.com/PySATL/MPEst/actions/workflows/pylint.yml/badge.svg)
-![Check code style](https://github.com/PySATL/MPEst/actions/workflows/code_style.yml/badge.svg)
-[![Code style](https://img.shields.io/badge/Code%20style-black-000000.svg)](https://github.com/psf/black)
-![Unit Tests](https://github.com/PySATL/MPEst/actions/workflows/test.yml/badge.svg)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+![CI](https://github.com/PySATL/pysatl-mpest/actions/workflows/check.yml/badge.svg)
+<a href="https://github.com/PySATL/pysatl-mpest/blob/main/LICENSE"><img alt="License: MIT" src="https://black.readthedocs.io/en/stable/_static/license.svg"></a>
 
 ## Installation
 
@@ -98,6 +97,6 @@ plt.show()
 ![plot](https://github.com/toxakaz/EM-algo/raw/main/examples/readme_example/example.png)
 
 ## Requirements
-- python 3.11
+- python >= 3.11
 - numpy
 - scipy

examples/big_mono_tests.py

@@ -22,9 +22,7 @@
 
     counter = Clicker()
 
-    def _generate_test(
-        model: type[AModelWithGenerator], o_borders: list[tuple[float, float]]
-    ) -> list[Test]:
+    def _generate_test(model: type[AModelWithGenerator], o_borders: list[tuple[float, float]]) -> list[Test]:
         return generate_mono_test(
             model_t=model,
             params_borders=o_borders,
@@ -36,10 +34,7 @@ def _generate_test(
             tests_per_size=8,
             tests_per_cond=2,
             runs_per_test=1,
-            solvers=[
-                init_solver(16, 0.1, 0.001, 3, optimizer)
-                for optimizer in TESTS_OPTIMIZERS
-            ],
+            solvers=[init_solver(16, 0.1, 0.001, 3, optimizer) for optimizer in TESTS_OPTIMIZERS],
         )
 
     tests += _generate_test(WeibullModelExp, [(0.25, 25), (0.25, 25)])

examples/config.py

@@ -7,11 +7,7 @@
 
 CPU_COUNT = os.cpu_count()
 MAX_WORKERS_PERCENT = 0.75
-MAX_WORKERS = (
-    min(round(CPU_COUNT * MAX_WORKERS_PERCENT), CPU_COUNT)
-    if CPU_COUNT is not None
-    else 1
-)
+MAX_WORKERS = min(round(CPU_COUNT * MAX_WORKERS_PERCENT), CPU_COUNT) if CPU_COUNT is not None else 1
 # MAX_WORKERS = 4
 
 RESULTS_FOLDER = EXAMPLES / "results"

examples/diff_test.py

@@ -28,11 +28,7 @@
 for sp in gaussian_start_params:
     main_distr = list(gaussian.generate(np.array(sp), BASE_SIZE // 2, normalized=False))
     for second_sp in np.linspace(sp[0] - 5, sp[0] + 5, num=8, endpoint=True):
-        x = main_distr + list(
-            gaussian.generate(
-                np.array((second_sp, 3.0)), BASE_SIZE // 2, normalized=False
-            )
-        )
+        x = main_distr + list(gaussian.generate(np.array((second_sp, 3.0)), BASE_SIZE // 2, normalized=False))
         random.shuffle(x)
 
         start_params_borders = [
@@ -45,12 +41,7 @@
                 samples = random.sample(x, size)
                 for _ in range(TESTS_PER_SIZE):
                     start_params = [
-                        np.array(
-                            [
-                                random.uniform(border[0], border[1])
-                                for border in start_params_borders
-                            ]
-                        )
+                        np.array([random.uniform(border[0], border[1]) for border in start_params_borders])
                         for _ in range(2)
                     ]
                     tests.append(
@@ -81,22 +72,15 @@
                                     ]
                                 ),
                             ),
-                            [
-                                init_solver(16, 0.1, 0.001, 3, optimizer)
-                                for optimizer in TESTS_OPTIMIZERS
-                            ],
+                            [init_solver(16, 0.1, 0.001, 3, optimizer) for optimizer in TESTS_OPTIMIZERS],
                             1,
                         )
                     )
 
 for sp in weibull_start_params:
     main_distr = list(weibull.generate(np.array(sp), BASE_SIZE // 2, normalized=False))
     for second_sp in np.linspace(max(sp[0] - 5, 0.1), sp[0] + 5, num=8, endpoint=True):
-        x = main_distr + list(
-            weibull.generate(
-                np.array((second_sp, 1.0)), BASE_SIZE // 2, normalized=False
-            )
-        )
+        x = main_distr + list(weibull.generate(np.array((second_sp, 1.0)), BASE_SIZE // 2, normalized=False))
         random.shuffle(x)
 
         start_params_borders = [
@@ -109,12 +93,7 @@
                 samples = random.sample(x, size)
                 for _ in range(TESTS_PER_SIZE):
                     start_params = [
-                        np.array(
-                            [
-                                random.uniform(border[0], border[1])
-                                for border in start_params_borders
-                            ]
-                        )
+                        np.array([random.uniform(border[0], border[1]) for border in start_params_borders])
                         for _ in range(2)
                     ]
                     tests.append(
@@ -145,10 +124,7 @@
                                     ]
                                 ),
                             ),
-                            [
-                                init_solver(16, 0.1, 0.001, 3, optimizer)
-                                for optimizer in TESTS_OPTIMIZERS
-                            ],
+                            [init_solver(16, 0.1, 0.001, 3, optimizer) for optimizer in TESTS_OPTIMIZERS],
                             1,
                         )
                     )

examples/mono_test_generator.py

@@ -1,16 +1,16 @@
 """Module which contains mixture distributions of single model tests generator"""
 
 import random
-from typing import Iterable
+from collections.abc import Iterable
 
 import numpy as np
 
 from examples.utils import Clicker, Test
-from mpest.distribution import Distribution
+from mpest.core.distribution import Distribution
+from mpest.core.mixture_distribution import MixtureDistribution
+from mpest.core.problem import Problem
 from mpest.em import EM
-from mpest.mixture_distribution import MixtureDistribution
 from mpest.models import AModel, AModelWithGenerator
-from mpest.problem import Problem
 
 
 def generate_mono_test(
@@ -42,9 +42,7 @@ def generate_mono_test(
             models: list[AModel] = []
 
             for _ in range(k):
-                params = np.array(
-                    [random.uniform(border[0], border[1]) for border in params_borders]
-                )
+                params = np.array([random.uniform(border[0], border[1]) for border in params_borders])
                 model = model_t()
                 x += list(model.generate(params, per_model, normalized=False))
 
@@ -62,12 +60,7 @@ def generate_mono_test(
                     samples = random.sample(x, size)
                     for _ in range(tests_per_size):
                         start_params = [
-                            np.array(
-                                [
-                                    random.uniform(border[0], border[1])
-                                    for border in start_params_borders
-                                ]
-                            )
+                            np.array([random.uniform(border[0], border[1]) for border in start_params_borders])
                             for _ in range(k)
                         ]
                         tests.append(
@@ -91,9 +84,7 @@ def generate_mono_test(
                                                 model,
                                                 params,
                                             )
-                                            for model, params in zip(
-                                                models, start_params
-                                            )
+                                            for model, params in zip(models, start_params)
                                         ]
                                     ),
                                 ),