Add pre-commit hook for isort

.pre-commit-config.yaml

@@ -18,8 +18,14 @@ repos:
     rev: 0.6.1
     hooks:
       - id: nbstripout
-  # Unused imports:
+  # Unused imports
   - repo: https://github.com/hadialqattan/pycln
     rev: "v2.2.2"
     hooks:
       - id: pycln
+  # Sorted imports
+  - repo: https://github.com/PyCQA/isort
+    rev: "5.12.0"
+    hooks:
+      - id: isort
+        additional_dependencies: [toml]

benchmarks/hyperparamopt.py

@@ -1,13 +1,15 @@
 """Start a hyperoptimization from a single node"""
-import sys
-import numpy as np
 import pickle as pkl
-from pysr import PySRRegressor
+import sys
+
 import hyperopt
-from hyperopt import hp, fmin, tpe, Trials
+import numpy as np
+from hyperopt import Trials, fmin, hp, tpe
 from hyperopt.fmin import generate_trials_to_calculate
 from space import *
 
+from pysr import PySRRegressor
+
 # Change the following code to your file
 ################################################################################
 TRIALS_FOLDER = "trials2"

benchmarks/print_best_model.py

@@ -1,11 +1,11 @@
 """Print the best model parameters and loss"""
-import numpy as np
 import pickle as pkl
-import hyperopt
-from hyperopt import hp, fmin, tpe, Trials
-from space import space
 from pprint import PrettyPrinter
 
+import hyperopt
+import numpy as np
+from hyperopt import Trials, fmin, hp, tpe
+from space import space
 
 # Change the following code to your file
 ################################################################################

benchmarks/space.py

@@ -1,5 +1,5 @@
 import numpy as np
-from hyperopt import hp, fmin, tpe, Trials
+from hyperopt import Trials, fmin, hp, tpe
 
 binary_operators = ["*", "/", "+", "-"]
 unary_operators = ["sin", "cos", "exp", "log"]

docs/gen_param_docs.py

@@ -1,12 +1,14 @@
 # Load YAML file param_groupings.yml:
-from yaml import safe_load
+import re
 import sys
 
+from docstring_parser import parse
+from yaml import safe_load
+
 sys.path.append("..")
+
+
 from pysr import PySRRegressor
-import pysr
-import re
-from docstring_parser import parse
 
 found_params = []
 

docs/generate_papers.py

@@ -1,7 +1,8 @@
 """This script generates the papers.md file from the papers.yml file."""
-import yaml
 from pathlib import Path
 
+import yaml
+
 data_file = "papers.yml"
 papers_header = Path("stylesheets") / "papers_header.txt"
 output_file = "papers.md"

pyproject.toml

@@ -0,0 +1,2 @@
+[tool.isort]
+profile = "black"

pysr/__init__.py

@@ -1,30 +1,23 @@
 from . import sklearn_monkeypatch
-from .version import __version__
-from .sr import (
-    pysr,
-    PySRRegressor,
-    best,
-    best_tex,
-    best_callable,
-    best_row,
-)
-from .julia_helpers import install
-from .feynman_problems import Problem, FeynmanProblem
 from .export_jax import sympy2jax
 from .export_torch import sympy2torch
+from .feynman_problems import FeynmanProblem, Problem
+from .julia_helpers import install
+from .sr import PySRRegressor, best, best_callable, best_row, best_tex, pysr
+from .version import __version__
 
 __all__ = [
     "sklearn_monkeypatch",
-    "__version__",
-    "pysr",
+    "sympy2jax",
+    "sympy2torch",
+    "FeynmanProblem",
+    "Problem",
+    "install",
     "PySRRegressor",
     "best",
-    "best_tex",
     "best_callable",
     "best_row",
-    "install",
-    "Problem",
-    "FeynmanProblem",
-    "sympy2jax",
-    "sympy2torch",
+    "best_tex",
+    "pysr",
+    "__version__",
 ]

pysr/_cli/main.py

@@ -1,4 +1,5 @@
 import click
+
 from ..julia_helpers import install
 
 

pysr/export_latex.py

@@ -1,8 +1,9 @@
 """Functions to help export PySR equations to LaTeX."""
+from typing import List
+
+import pandas as pd
 import sympy
 from sympy.printing.latex import LatexPrinter
-import pandas as pd
-from typing import List
 
 
 class PreciseLatexPrinter(LatexPrinter):

pysr/export_numpy.py

@@ -1,8 +1,9 @@
 """Code for exporting discovered expressions to numpy"""
+import warnings
+
 import numpy as np
 import pandas as pd
 from sympy import lambdify
-import warnings
 
 
 class CallableEquation:

pysr/export_torch.py

@@ -5,6 +5,7 @@
 
 import collections as co
 import functools as ft
+
 import sympy
 
 

pysr/feynman_problems.py

@@ -1,8 +1,10 @@
-import numpy as np
 import csv
-from .sr import pysr, best
-from pathlib import Path
 from functools import partial
+from pathlib import Path
+
+import numpy as np
+
+from .sr import best, pysr
 
 PKG_DIR = Path(__file__).parents[1]
 FEYNMAN_DATASET = PKG_DIR / "datasets" / "FeynmanEquations.csv"
@@ -118,6 +120,7 @@ def do_feynman_experiments_parallel(
     data_dir=FEYNMAN_DATASET,
 ):
     import multiprocessing as mp
+
     from tqdm import tqdm
 
     problems = mk_problems(first=first, gen=True, dp=dp, data_dir=data_dir)

pysr/julia_helpers.py

@@ -1,12 +1,13 @@
 """Functions for initializing the Julia environment and installing deps."""
-import sys
+import os
 import subprocess
+import sys
 import warnings
 from pathlib import Path
-import os
+
 from julia.api import JuliaError
 
-from .version import __version__, __symbolic_regression_jl_version__
+from .version import __symbolic_regression_jl_version__, __version__
 
 juliainfo = None
 julia_initialized = False

pysr/sr.py

@@ -1,40 +1,37 @@
 """Define the PySRRegressor scikit-learn interface."""
 import copy
-from io import StringIO
 import os
-import sys
-import numpy as np
-import pandas as pd
-import sympy
-from sympy import sympify
+import pickle as pkl
 import re
-import tempfile
 import shutil
-from pathlib import Path
-import pickle as pkl
-from datetime import datetime
+import sys
+import tempfile
 import warnings
+from datetime import datetime
+from io import StringIO
 from multiprocessing import cpu_count
-from sklearn.base import BaseEstimator, RegressorMixin, MultiOutputMixin
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import sympy
+from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
 from sklearn.utils import check_array, check_consistent_length, check_random_state
-from sklearn.utils.validation import (
-    _check_feature_names_in,
-    check_is_fitted,
-)
+from sklearn.utils.validation import _check_feature_names_in, check_is_fitted
+from sympy import sympify
 
+from .deprecated import make_deprecated_kwargs_for_pysr_regressor
+from .export_latex import generate_multiple_tables, generate_single_table, to_latex
+from .export_numpy import CallableEquation
 from .julia_helpers import (
-    init_julia,
-    _process_julia_project,
-    is_julia_version_greater_eq,
     _escape_filename,
+    _load_backend,
     _load_cluster_manager,
+    _process_julia_project,
     _update_julia_project,
-    _load_backend,
+    init_julia,
+    is_julia_version_greater_eq,
 )
-from .export_numpy import CallableEquation
-from .export_latex import generate_single_table, generate_multiple_tables, to_latex
-from .deprecated import make_deprecated_kwargs_for_pysr_regressor
-
 
 Main = None  # TODO: Rename to more descriptive name like "julia_runtime"
 
@@ -2454,7 +2451,7 @@ def idx_model_selection(equations: pd.DataFrame, model_selection: str) -> int:
 def _denoise(X, y, Xresampled=None, random_state=None):
     """Denoise the dataset using a Gaussian process."""
     from sklearn.gaussian_process import GaussianProcessRegressor
-    from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel
+    from sklearn.gaussian_process.kernels import RBF, ConstantKernel, WhiteKernel
 
     gp_kernel = RBF(np.ones(X.shape[1])) + WhiteKernel(1e-1) + ConstantKernel()
     gpr = GaussianProcessRegressor(

pysr/test/__init__.py

@@ -1,7 +1,7 @@
 from .test import runtests
+from .test_cli import runtests as runtests_cli
 from .test_env import runtests as runtests_env
 from .test_jax import runtests as runtests_jax
 from .test_torch import runtests as runtests_torch
-from .test_cli import runtests as runtests_cli
 
 __all__ = ["runtests", "runtests_env", "runtests_jax", "runtests_torch", "runtests_cli"]

pysr/test/test.py

@@ -1,28 +1,28 @@
+import inspect
 import os
+import pickle as pkl
+import tempfile
 import traceback
-import inspect
 import unittest
+import warnings
+from pathlib import Path
+
 import numpy as np
+import pandas as pd
+import sympy
 from sklearn import model_selection
 from sklearn.utils.estimator_checks import check_estimator
-import sympy
-import pandas as pd
-import warnings
-import pickle as pkl
-import tempfile
-from pathlib import Path
 
-from .. import julia_helpers
-from .. import PySRRegressor
+from .. import PySRRegressor, julia_helpers
+from ..export_latex import to_latex
 from ..sr import (
-    run_feature_selection,
-    _handle_feature_selection,
-    _csv_filename_to_pkl_filename,
-    idx_model_selection,
     _check_assertions,
+    _csv_filename_to_pkl_filename,
+    _handle_feature_selection,
     _process_constraints,
+    idx_model_selection,
+    run_feature_selection,
 )
-from ..export_latex import to_latex
 
 DEFAULT_PARAMS = inspect.signature(PySRRegressor.__init__).parameters
 DEFAULT_NITERATIONS = DEFAULT_PARAMS["niterations"].default

pysr/test/test_cli.py

@@ -1,5 +1,7 @@
 import unittest
+
 from click import testing as click_testing
+
 from .._cli.main import pysr
 
 

pysr/test/test_env.py

@@ -1,7 +1,7 @@
 """Contains tests for creating and initializing custom Julia projects."""
 
-import unittest
 import os
+import unittest
 from tempfile import TemporaryDirectory
 
 from .. import julia_helpers

pysr/test/test_jax.py

@@ -1,10 +1,11 @@
 import unittest
+from functools import partial
+
 import numpy as np
 import pandas as pd
 import sympy
-from functools import partial
 
-from .. import sympy2jax, PySRRegressor
+from .. import PySRRegressor, sympy2jax
 
 
 class TestJAX(unittest.TestCase):

pysr/test/test_torch.py

@@ -1,9 +1,11 @@
+import platform
 import unittest
+
 import numpy as np
 import pandas as pd
-import platform
 import sympy
-from .. import sympy2torch, PySRRegressor
+
+from .. import PySRRegressor, sympy2torch
 
 # Need to initialize Julia before importing torch...