chore: replace black with ruff and format all files

Author: iraedeus

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

@@ -1,7 +1,7 @@
 """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
 
@@ -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)
                                         ]
                                     ),
                                 ),

examples/prepare_result.py

@@ -21,9 +21,7 @@ def nll(samples: Samples, mixture: MixtureDistribution) -> float:
     return occur
 
 
-def identity_guessing_chance(
-    dx: MixtureDistribution, dy: MixtureDistribution, sample: Samples
-):
+def identity_guessing_chance(dx: MixtureDistribution, dy: MixtureDistribution, sample: Samples):
     """Identity guessing chance metric"""
 
     dxs = list(dx)
@@ -64,9 +62,7 @@ def result_to_df_diff(result: SingleSolverResult):
                         dct[clicker.click()] = (sp[0], second_sp)
 
     for sp in weibull_start_params:
-        for second_sp in np.linspace(
-            max(sp[0] - 5, 0.1), sp[0] + 5, num=8, endpoint=True
-        ):
+        for second_sp in np.linspace(max(sp[0] - 5, 0.1), sp[0] + 5, num=8, endpoint=True):
             for _ in sizes:
                 for _ in range(tests_per_cond):
                     for _ in range(tests_per_size):
@@ -76,7 +72,7 @@ def result_to_df_diff(result: SingleSolverResult):
         [
             (
                 d
-                if (d.prior_probability is not None) and (d.prior_probability > 0.001)
+                if (d.prior_probability is not None) and (d.prior_probability > 0.001)  # noqa: PLR2004
                 else DistributionInMixture(d.model, d.params, None)
             )
             for d in result.result.content
@@ -100,14 +96,12 @@ def result_to_df_diff(result: SingleSolverResult):
         "time": result.time,
         "model": result.test.true_mixture[0].model.name,
         "size": len(result.test.problem.samples),
-        "success": (result.steps < 128) and not failed,
+        "success": (result.steps < 128) and not failed, # noqa: PLR2004
         "failed": failed,
         "occur": nll(result.test.all_data, mixture_distribution),
         "start": start,
         "diff": diff,
-        "res_err": identity_guessing_chance(
-            result.test.true_mixture, result.result.content, result.test.all_data
-        ),
+        "res_err": identity_guessing_chance(result.test.true_mixture, result.result.content, result.test.all_data),
     }
 
 
@@ -131,7 +125,7 @@ def result_to_df(result: SingleSolverResult):
         [
             (
                 d
-                if (d.prior_probability is not None) and (d.prior_probability > 0.001)
+                if (d.prior_probability is not None) and (d.prior_probability > 0.001) # noqa: PLR2004
                 else DistributionInMixture(d.model, d.params, None)
             )
             for d in result.result.content
@@ -152,7 +146,7 @@ def result_to_df(result: SingleSolverResult):
         "time": result.time,
         "model": result.test.true_mixture[0].model.name,
         "size": len(result.test.problem.samples),
-        "success": (result.steps < 16) and failed,
+        "success": (result.steps < 16) and failed, # noqa: PLR2004
         "failed": failed,
         "occur": nll(result.test.all_data, mixture_distribution),
     }

examples/quick_test.py

@@ -26,9 +26,7 @@ def run_test():
 
     counter = Clicker()
 
-    def _generate_test(
-        model: type[AModelWithGenerator], params_borders: list[tuple[float, float]]
-    ) -> list[Test]:
+    def _generate_test(model: type[AModelWithGenerator], params_borders: list[tuple[float, float]]) -> list[Test]:
         test = generate_mono_test(
             model_t=model,
             clicker=counter,
@@ -40,10 +38,7 @@ def _generate_test(
             tests_per_size=1,
             tests_per_cond=1,
             runs_per_test=1,
-            solvers=[
-                init_solver(16, 0.1, 0.001, 3, optimizer)
-                for optimizer in ALL_OPTIMIZERS
-            ],
+            solvers=[init_solver(16, 0.1, 0.001, 3, optimizer) for optimizer in ALL_OPTIMIZERS],
         )
         return test
 

examples/utils.py

@@ -4,7 +4,7 @@
 import random
 import time
 from functools import partial
-from typing import Callable, NamedTuple
+from typing import ClassVar, NamedTuple
 
 import numpy as np
 from tqdm.contrib.concurrent import process_map
@@ -48,7 +48,7 @@ class SingleSolverResult(NamedTuple):
     steps: int
     time: float
 
-    log: list[EM.Log.Item] = []
+    log: ClassVar[list[EM.Log.Item]] = []
 
 
 class TestResult(NamedTuple):
@@ -133,7 +133,10 @@ def run_tests(
     )
 
     if shuffled:
-        key: Callable[[TestResult], int] = lambda t: t.test.index
+
+        def key(t):
+            return t.test.index
+
         results.sort(key=key)
 
     return results
@@ -170,8 +173,6 @@ def init_solver(
     return EM(
         breakpointer,
         FiniteChecker()
-        + PriorProbabilityThresholdChecker(
-            prior_probability_threshold, prior_probability_threshold_step
-        ),
+        + PriorProbabilityThresholdChecker(prior_probability_threshold, prior_probability_threshold_step),
         method,
     )

experimental_env/analysis/analysis.py

@@ -1,4 +1,5 @@
-""" A module that provides a class for performing the third stage of the experiment """
+"""A module that provides a class for performing the third stage of the experiment"""
+
 from pathlib import Path
 
 from tqdm import tqdm
@@ -33,7 +34,8 @@ def analyze(self, results: ParserOutput, method: str):
         """
         A function for analyzing the method
 
-        :param results: The result of the method on the second stage of the experiment, which was obtained using a parser.
+        :param results: The result of the method on the second stage of the experiment,
+         which was obtained using a parser.
         :param method: Name of analyzed method
         """
         method_dir: Path = self._out_dir.joinpath(method)
@@ -48,9 +50,7 @@ def analyze(self, results: ParserOutput, method: str):
             with tqdm(total=len(exp_descriptions)) as pbar:
                 for exp_descr in exp_descriptions:
                     pbar.update()
-                    exp_dir: Path = mixture_dir.joinpath(
-                        f"experiment_{exp_descr.exp_num}"
-                    )
+                    exp_dir: Path = mixture_dir.joinpath(f"experiment_{exp_descr.exp_num}")
 
                     for action in self._actions:
                         action.set_path(exp_dir)
@@ -66,21 +66,21 @@ def compare(
         """
         A function for comparing the methods
 
-        :param results_1: The result of the first method on the second stage of the experiment, which was obtained using a parser.
-        :param results_2: The result of the second method on the second stage of the experiment, which was obtained using a parser.
+        :param results_1: The result of the first method on the second stage of the experiment,
+         which was obtained using a parser.
+        :param results_2: The result of the second method on the second stage of the experiment,
+         which was obtained using a parser.
         :param method_1: Name of the first method
         :param method_2: Name of the second method
         """
         method_dir = self._out_dir.joinpath(f"{method_1} + {method_2}")
 
-        for mixture_name in results_1.keys():
+        for mixture_name in results_1:
             mixture_dir = method_dir.joinpath(mixture_name)
 
             for summarizer in self._summarizers:
                 summarizer.set_path(mixture_dir)
-                summarizer.compare_methods(
-                    results_1[mixture_name], results_2[mixture_name], method_1, method_2
-                )
+                summarizer.compare_methods(results_1[mixture_name], results_2[mixture_name], method_1, method_2)
 
             with tqdm(total=len(results_1[mixture_name])) as pbar:
                 for res in zip(results_1[mixture_name], results_2[mixture_name]):
@@ -89,6 +89,4 @@ def compare(
 
                     for action in self._actions:
                         action.set_path(exp_dir)
-                        action.overall_compare_methods(
-                            res[0], res[1], method_1, method_2
-                        )
+                        action.overall_compare_methods(res[0], res[1], method_1, method_2)

experimental_env/analysis/analyze_strategies/analysis_strategy.py

@@ -1,4 +1,4 @@
-""" A module containing an abstract strategy class for analysis methods. """
+"""A module containing an abstract strategy class for analysis methods."""
 # pylint: disable=duplicate-code
 
 from abc import ABC, abstractmethod
@@ -9,7 +9,8 @@
 
 class AnalysisStrategy(ABC):
     """
-    An abstract strategy class that contains methods for analysis and comparison, as well as a method for establishing a directory
+    An abstract strategy class that contains methods for analysis and comparison,
+     as well as a method for establishing a directory
     """
 
     def __init__(self):
@@ -39,7 +40,8 @@ def overall_analyze_method(self, result: ExperimentDescription, method: str):
         """
         Analyze the method result of the experiment
 
-        :param result: The result of the method on the second stage of the experiment, which was obtained using a parser.
+        :param result: The result of the method on the second stage of the experiment,
+         which was obtained using a parser.
         :param method: The name of the method that we are analyzing
         """
 
@@ -56,8 +58,10 @@ def overall_compare_methods(
         """
         A function for comparing the methods by two results, with the same base mixture
 
-        :param result_1: The result of the first method on the second stage of the experiment, which was obtained using a parser.
-        :param result_2: The result of the second method on the second stage of the experiment, which was obtained using a parser.
+        :param result_1: The result of the first method on the second stage of the experiment,
+         which was obtained using a parser.
+        :param result_2: The result of the second method on the second stage of the experiment,
+        which was obtained using a parser.
         :param method_1: Name of the first method
         :param method_2: Name of the second method
         """

experimental_env/analysis/analyze_strategies/density_plot.py

@@ -1,4 +1,4 @@
-""" A module providing a class for saving a distribution density graph. """
+"""A module providing a class for saving a distribution density graph."""
 
 import matplotlib.pyplot as plt
 import numpy as np