Use ruff for checking python code

.github/workflows/ruff_action.yml

@@ -0,0 +1,8 @@
+name: Ruff
+on: [ push, pull_request ]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v2

.pre-commit-config.yaml

@@ -14,12 +14,6 @@ repos:
           files: (^(src|tests)/)|(^[^/]*$)
           args: [--in-place, --remove-all-unused-imports, --recursive, --ignore-init-module-imports]
 
-    - repo: https://github.com/pycqa/isort
-      rev: 5.12.0
-      hooks:
-        - id: isort
-          args: ["--profile", "black", "--filter-files"]
-
     - repo: https://github.com/kynan/nbstripout
       rev: 0.6.0
       hooks:
@@ -31,13 +25,17 @@ repos:
       - id: flake8
         args: ["--count", "--select=E9,F63,F7,F82", "--show-source", "--statistics"]
 
-    - repo: https://github.com/psf/black
-      rev: 23.10.1
-      hooks:
-      - id: black-jupyter
-        language_version: python3.11
-        language: python
-        types: [python]
+    - repo: https://github.com/astral-sh/ruff-pre-commit
+      # Ruff version.
+      rev: v0.8.2
+      hooks:
+        # Run the linter.
+        - id: ruff
+          types_or: [ python, pyi, jupyter ]
+          args: [ --fix ]
+        # Run the formatter.
+        - id: ruff-format
+          types_or: [ python, pyi, jupyter ]
 
     - repo: local
       hooks:

docs/examples/example2_student_admission.py

@@ -81,17 +81,13 @@
 
 """
 
-import sys
-
 import gurobipy as gp
 import gurobipy_pandas as gppd
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn.tree import DecisionTreeRegressor
 
 from gurobi_ml import add_predictor_constr
 

docs/examples/example4_price_optimization.py

@@ -75,14 +75,20 @@
 # the data.
 #
 
-import warnings
-
+import gurobipy as gp
+import gurobipy_pandas as gppd
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-import sklearn
-from sklearn import tree
+from sklearn.compose import make_column_transformer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+
+from gurobi_ml import add_predictor_constr
 
 ######################################################################
 # The dataset from HAB contains sales data for the years 2019-2022. This
@@ -310,8 +316,6 @@ def peak_season(row):
 # weights using ``Scikit-learn``.
 #
 
-from sklearn.model_selection import train_test_split
-
 X = df[["region", "price", "year", "peak"]]
 y = df["units_sold"]
 # Split the data for training and testing
@@ -330,9 +334,6 @@ def peak_season(row):
 # ``make_column_transformer``.
 #
 
-from sklearn.compose import make_column_transformer
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-
 feat_transform = make_column_transformer(
     (OneHotEncoder(drop="first"), ["region"]),
     (StandardScaler(), ["price", "year"]),
@@ -349,10 +350,6 @@ def peak_season(row):
 # Define it and train it.
 #
 
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import r2_score
-from sklearn.pipeline import make_pipeline
-
 lin_reg = make_pipeline(feat_transform, LinearRegression())
 lin_reg.fit(X_train, y_train)
 
@@ -497,9 +494,6 @@ def peak_season(row):
 # dataframe.
 #
 
-import gurobipy as gp
-import gurobipy_pandas as gppd
-
 m = gp.Model("Avocado_Price_Allocation")
 
 p = gppd.add_vars(m, data, name="price", lb=a_min, ub=a_max)
@@ -655,8 +649,6 @@ def peak_season(row):
 # to insert the constraints linking the features and the demand.
 #
 
-from gurobi_ml import add_predictor_constr
-
 pred_constr = add_predictor_constr(m, lin_reg, feats, d)
 
 pred_constr.print_stats()

docs/examples_userguide/example_simple.py

@@ -23,7 +23,6 @@
 import gurobipy as gp
 import numpy as np
 from sklearn.datasets import make_regression
-from sklearn.metrics import mean_squared_error
 from sklearn.neural_network import MLPRegressor
 
 from gurobi_ml import add_predictor_constr

notebooks/Functions_approx/2DPeakFunction skorch polynomials.ipynb

@@ -112,6 +112,8 @@
     "# Create a small class to apply polynomial features and convert\n",
     "\n",
     "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "\n",
     "class MyPolynomialFeatures(BaseEstimator, TransformerMixin):\n",
     "    def __init__(self):\n",
     "        self.poly_feat = PolynomialFeatures()\n",
@@ -136,12 +138,12 @@
     "hs = 16\n",
     "nn_regression = NeuralNetRegressor(\n",
     "    torch.nn.Sequential(\n",
-    "    torch.nn.Linear(6, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, 1),\n",
-    "),\n",
+    "        torch.nn.Linear(6, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, 1),\n",
+    "    ),\n",
     "    max_epochs=20,\n",
     "    lr=0.1,\n",
     "    iterator_train__shuffle=True,\n",
@@ -246,7 +248,10 @@
     "#   the function to add a pytorch model on the pytorch model\n",
     "# - Register that function by associating it to the NeuralNetRegressor class\n",
     "def add_skorch_constr(gp_model, skorch_model, input_vars, output_vars=None, **kwargs):\n",
-    "    return add_sequential_constr(gp_model, skorch_model.module, input_vars, output_vars, **kwargs)\n",
+    "    return add_sequential_constr(\n",
+    "        gp_model, skorch_model.module, input_vars, output_vars, **kwargs\n",
+    "    )\n",
+    "\n",
     "\n",
     "register_predictor_constr(NeuralNetRegressor, add_skorch_constr)"
    ]
@@ -260,7 +265,10 @@
    "source": [
     "# Now do the same for the polynomial features\n",
     "def add_my_polynomial_features_constr(gp_model, poly_feat, input_vars, **kwargs):\n",
-    "    return add_polynomial_features_constr(gp_model, poly_feat.poly_feat, input_vars, **kwargs)\n",
+    "    return add_polynomial_features_constr(\n",
+    "        gp_model, poly_feat.poly_feat, input_vars, **kwargs\n",
+    "    )\n",
+    "\n",
     "\n",
     "register_predictor_constr(MyPolynomialFeatures, add_my_polynomial_features_constr)"
    ]

notebooks/Functions_approx/2DPeakFunction skorch.ipynb

@@ -32,12 +32,9 @@
     "from matplotlib import cm\n",
     "from matplotlib import pyplot as plt\n",
     "from sklearn import metrics\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
     "\n",
     "from gurobi_ml import add_predictor_constr, register_predictor_constr\n",
     "\n",
-    "from gurobi_ml.sklearn import add_polynomial_features_constr\n",
     "from gurobi_ml.torch import add_sequential_constr"
    ]
   },
@@ -106,12 +103,12 @@
     "hs = 30\n",
     "nn_regression = NeuralNetRegressor(\n",
     "    torch.nn.Sequential(\n",
-    "    torch.nn.Linear(2, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, 1),\n",
-    "),\n",
+    "        torch.nn.Linear(2, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, 1),\n",
+    "    ),\n",
     "    max_epochs=20,\n",
     "    lr=0.1,\n",
     "    iterator_train__shuffle=True,\n",
@@ -214,7 +211,10 @@
     "#   the function to add a pytorch model on the pytorch model\n",
     "# - Register that function by associating it to the NeuralNetRegressor class\n",
     "def add_skorch_constr(gp_model, skorch_model, input_vars, output_vars=None, **kwargs):\n",
-    "    return add_sequential_constr(gp_model, skorch_model.module, input_vars, output_vars, **kwargs)\n",
+    "    return add_sequential_constr(\n",
+    "        gp_model, skorch_model.module, input_vars, output_vars, **kwargs\n",
+    "    )\n",
+    "\n",
     "\n",
     "register_predictor_constr(NeuralNetRegressor, add_skorch_constr)"
    ]

notebooks/Functions_approx/Parabola.ipynb

@@ -276,9 +276,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\n",
-    "    f\"Maximal error in predicted values in solution {np.max(pconstr.get_error())}\"\n",
-    ")"
+    "print(f\"Maximal error in predicted values in solution {np.max(pconstr.get_error())}\")"
    ]
   },
   {

notebooks/Janos/Decision Tree.ipynb

@@ -135,10 +135,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",

notebooks/Janos/Gradient Boosting Regression.ipynb

@@ -131,10 +131,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",

notebooks/Janos/LightGBM.ipynb

@@ -23,12 +23,10 @@
    "source": [
     "import gurobipy as gp\n",
     "import numpy as np\n",
-    "import math\n",
     "import pandas as pd\n",
     "import gurobipy_pandas as gppd\n",
     "from matplotlib import pyplot as plt\n",
-    "import lightgbm as lgb\n",
-    "from sklearn.metrics import r2_score"
+    "import lightgbm as lgb"
    ]
   },
   {
@@ -41,7 +39,8 @@
     "%load_ext autoreload\n",
     "%autoreload 2\n",
     "import sys\n",
-    "sys.path.append('../../src')"
+    "\n",
+    "sys.path.append(\"../../src\")"
    ]
   },
   {
@@ -52,8 +51,7 @@
    "outputs": [],
    "source": [
     "from gurobi_ml import add_predictor_constr\n",
-    "from gurobi_ml import lightgbm\n",
-    "import gurobi_ml as gml"
+    "from gurobi_ml import lightgbm"
    ]
   },
   {
@@ -167,10 +165,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, lb=-1e100, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, lb=-1e100, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",
@@ -255,7 +253,9 @@
     "pred_constr.remove()\n",
     "\n",
     "# Add new constraint setting epsilon to 1e-5\n",
-    "pred_constr = lightgbm.add_lgbmregressor_constr(m, regression, studentsdata, y, epsilon=1e-4)\n",
+    "pred_constr = lightgbm.add_lgbmregressor_constr(\n",
+    "    m, regression, studentsdata, y, epsilon=1e-4\n",
+    ")\n",
     "\n",
     "pred_constr.print_stats()\n",
     "\n",

notebooks/Janos/Random Forest.ipynb

@@ -130,10 +130,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",

notebooks/Janos/XGBoost.ipynb

@@ -23,12 +23,10 @@
    "source": [
     "import gurobipy as gp\n",
     "import numpy as np\n",
-    "import math\n",
     "import pandas as pd\n",
     "import gurobipy_pandas as gppd\n",
     "from matplotlib import pyplot as plt\n",
     "import xgboost as xgb\n",
-    "from sklearn.metrics import r2_score\n",
     "\n",
     "from gurobi_ml import add_predictor_constr"
    ]
@@ -84,7 +82,9 @@
    "source": [
     "# Run our regression\n",
     "n_estimators = 20\n",
-    "regression = xgb.XGBRegressor(n_estimators=n_estimators, max_depth=5, random_state=1, booster=\"gbtree\")\n",
+    "regression = xgb.XGBRegressor(\n",
+    "    n_estimators=n_estimators, max_depth=5, random_state=1, booster=\"gbtree\"\n",
+    ")\n",
     "\n",
     "regression.fit(X=historical_data.loc[:, features], y=historical_data.loc[:, target])"
    ]
@@ -135,10 +135,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",

notebooks/adversarial/adversarial_pytorch.ipynb

@@ -35,7 +35,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
     "import torch\n",