fix(ci): ultralast

Author: iraedeus

.pre-commit-config.yaml

@@ -16,12 +16,14 @@ repos:
         entry: poetry run ruff check --fix --exit-non-zero-on-fix
         language: system
         types: [python]
+        files: ^(mpest/|experimental_env/|tests/).*
 
     -   id: ruff-format
         name: ruff format
         entry: poetry run ruff format
         language: system
         types: [python]
+        files: ^(mpest/|experimental_env/|tests/).*
 
     -   id: mypy
         name: mypy check

examples/big_mono_tests.py

@@ -3,17 +3,17 @@
 import random
 
 import numpy as np
-
-from examples.config import MAX_WORKERS, TESTS_OPTIMIZERS
-from examples.mono_test_generator import generate_mono_test
-from examples.utils import Clicker, Test, init_solver, run_tests, save_results
 from mpest.models import (
     AModelWithGenerator,
     ExponentialModel,
     GaussianModel,
     WeibullModelExp,
 )
 
+from examples.config import MAX_WORKERS, TESTS_OPTIMIZERS
+from examples.mono_test_generator import generate_mono_test
+from examples.utils import Clicker, Test, init_solver, run_tests, save_results
+
 if __name__ == "__main__":
     random.seed(42)
     np.random.seed(42)

examples/diff_test.py

@@ -3,12 +3,12 @@
 import random
 
 import numpy as np
+from mpest import Distribution, MixtureDistribution, Problem
+from mpest.models import GaussianModel, WeibullModelExp
 
 from examples.config import MAX_WORKERS, TESTS_OPTIMIZERS
 from examples.mono_test_generator import Clicker
 from examples.utils import Test, init_solver, run_tests, save_results
-from mpest import Distribution, MixtureDistribution, Problem
-from mpest.models import GaussianModel, WeibullModelExp
 
 # Gaussian
 

examples/mono_test_generator.py

@@ -4,14 +4,14 @@
 from collections.abc import Iterable
 
 import numpy as np
-
-from examples.utils import Clicker, Test
 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.models import AModel, AModelWithGenerator
 
+from examples.utils import Clicker, Test
+
 
 def generate_mono_test(
     model_t: type[AModelWithGenerator],

examples/prepare_result.py

@@ -4,13 +4,13 @@
 
 import numpy as np
 import pandas as pd
+from mpest.annotations import Samples
+from mpest.core.mixture_distribution import DistributionInMixture, MixtureDistribution
 from tqdm.contrib.concurrent import process_map
 
 from examples.config import MAX_WORKERS
 from examples.mono_test_generator import Clicker
 from examples.utils import SingleSolverResult, TestResult
-from mpest.annotations import Samples
-from mpest.core.mixture_distribution import DistributionInMixture, MixtureDistribution
 
 
 def nll(samples: Samples, mixture: MixtureDistribution) -> float:

examples/quick_test.py

@@ -3,10 +3,6 @@
 import random
 
 import numpy as np
-
-from examples.config import MAX_WORKERS
-from examples.mono_test_generator import generate_mono_test
-from examples.utils import Clicker, Test, init_solver, run_tests, save_results
 from mpest.models import (
     AModelWithGenerator,
     ExponentialModel,
@@ -15,6 +11,10 @@
 )
 from mpest.optimizers import ALL_OPTIMIZERS
 
+from examples.config import MAX_WORKERS
+from examples.mono_test_generator import generate_mono_test
+from examples.utils import Clicker, Test, init_solver, run_tests, save_results
+
 
 def run_test():
     """Runs the mixture distributions of single model quick test"""

examples/readme_example/example.py

@@ -3,7 +3,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
-
 from mpest import Distribution, MixtureDistribution, Problem
 from mpest.em import EM
 from mpest.em.breakpointers import StepCountBreakpointer

examples/readme_example/example_ml.py

@@ -7,11 +7,6 @@
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from scipy.stats import entropy, wasserstein_distance
-from sklearn.cluster import DBSCAN, AgglomerativeClustering, KMeans
-from sklearn.metrics import calinski_harabasz_score, davies_bouldin_score, silhouette_score
-from sklearn.neighbors import NearestNeighbors
-
 from mpest import Distribution, MixtureDistribution, Problem
 from mpest.em import EM
 from mpest.em.breakpointers import StepCountBreakpointer
@@ -20,6 +15,10 @@
 from mpest.em.methods.method import Method
 from mpest.models import GaussianModel, WeibullModelExp
 from mpest.optimizers import ScipyCG
+from scipy.stats import entropy, wasserstein_distance
+from sklearn.cluster import DBSCAN, AgglomerativeClustering, KMeans
+from sklearn.metrics import calinski_harabasz_score, davies_bouldin_score, silhouette_score
+from sklearn.neighbors import NearestNeighbors
 
 os.makedirs("results", exist_ok=True)
 os.makedirs("results/plots", exist_ok=True)

examples/utils.py

@@ -7,9 +7,6 @@
 from typing import ClassVar, NamedTuple
 
 import numpy as np
-from tqdm.contrib.concurrent import process_map
-
-from examples.config import RESULTS_FOLDER
 from mpest.annotations import Samples
 from mpest.core.mixture_distribution import MixtureDistribution
 from mpest.core.problem import Problem, Result
@@ -21,6 +18,9 @@
 )
 from mpest.em.methods.likelihood_method import LikelihoodMethod
 from mpest.optimizers import TOptimizer
+from tqdm.contrib.concurrent import process_map
+
+from examples.config import RESULTS_FOLDER
 
 np.seterr(all="ignore")
 

experimental_env/analysis/metrics.py

@@ -4,10 +4,9 @@
 from itertools import permutations
 
 import numpy as np
-from scipy.integrate import quad
-
 from mpest import MixtureDistribution
 from mpest.utils import ANamed
+from scipy.integrate import quad
 
 
 class AMetric(ANamed, ABC):