Add support to python 3.13 (#1486)

.github/workflows/python-package.yml

@@ -40,7 +40,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -74,6 +74,11 @@ jobs:
         run: |
           pip install pyspark==4.0.1
           pip list | grep "pyspark"
+      - name: On Ubuntu python 3.13, install pyspark 4.1.0
+        if: matrix.python-version == '3.13' && matrix.os == 'ubuntu-latest'
+        run: |
+          pip install pyspark==4.1.0
+          pip list | grep "pyspark"
       # # TODO: support ray
       # - name: If linux and python<3.11, install ray 2
       #   if: matrix.os == 'ubuntu-latest' && matrix.python-version < '3.11'

README.md

@@ -34,7 +34,7 @@ FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source,
 
 ## Installation
 
-The latest version of FLAML requires **Python >= 3.10 and < 3.13**. While other Python versions may work for core components, full model support is not guaranteed. FLAML can be installed via `pip`:
+The latest version of FLAML requires **Python >= 3.10 and < 3.14**. While other Python versions may work for core components, full model support is not guaranteed. FLAML can be installed via `pip`:
 
 ```bash
 pip install flaml

flaml/version.py

@@ -1 +1 @@
-__version__ = "2.4.1"
+__version__ = "2.4.2"

setup.py

@@ -52,8 +52,8 @@ setuptools.setup(
         ],
         "test": [
             "numpy>=1.17,<2.0.0; python_version<'3.13'",
-            "numpy>2.0.0; python_version>='3.13'",
-            "jupyter; python_version<'3.13'",
+            "numpy>=1.17; python_version>='3.13'",
+            "jupyter",
             "lightgbm>=2.3.1",
             "xgboost>=0.90,<2.0.0; python_version<'3.11'",
             "xgboost>=2.0.0; python_version>='3.11'",
@@ -68,10 +68,10 @@ setuptools.setup(
             "pre-commit",
             "torch",
             "torchvision",
-            "catboost>=0.26; python_version<'3.13'",
+            "catboost>=0.26",
             "rgf-python",
             "optuna>=2.8.0,<=3.6.1",
-            "openml; python_version<'3.13'",
+            "openml",
             "statsmodels>=0.12.2",
             "psutil",
             "dataclasses",
@@ -82,7 +82,7 @@ setuptools.setup(
             "rouge_score",
             "hcrystalball",
             "seqeval",
-            "pytorch-forecasting; python_version<'3.13'",
+            "pytorch-forecasting",
             "mlflow-skinny<=2.22.1",  # Refer to https://mvnrepository.com/artifact/org.mlflow/mlflow-spark
             "joblibspark>=0.5.0",
             "joblib<=1.3.2",
@@ -140,7 +140,7 @@ setuptools.setup(
             "prophet>=1.1.5",
             "statsmodels>=0.12.2",
             "hcrystalball>=0.1.10",
-            "pytorch-forecasting>=0.10.4; python_version<'3.13'",
+            "pytorch-forecasting>=0.10.4",
             "pytorch-lightning>=1.9.0",
             "tensorboardX>=2.6",
         ],

test/automl/test_extra_models.py

@@ -188,7 +188,11 @@ def _test_sparse_matrix_classification(estimator):
         "n_jobs": 1,
         "model_history": True,
     }
-    X_train = scipy.sparse.random(1554, 21, dtype=int)
+    # NOTE: Avoid `dtype=int` here. On some NumPy/SciPy combinations (notably
+    # Windows + Python 3.13), `scipy.sparse.random(..., dtype=int)` may trigger
+    # integer sampling paths which raise "low is out of bounds for int32".
+    # A float sparse matrix is sufficient to validate sparse-input support.
+    X_train = scipy.sparse.random(1554, 21, dtype=np.float32)
     y_train = np.random.randint(3, size=1554)
     automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
 

test/automl/test_multiclass.py

@@ -368,7 +368,11 @@ class TestMultiClass(unittest.TestCase):
             "n_jobs": 1,
             "model_history": True,
         }
-        X_train = scipy.sparse.random(1554, 21, dtype=int)
+        # NOTE: Avoid `dtype=int` here. On some NumPy/SciPy combinations (notably
+        # Windows + Python 3.13), `scipy.sparse.random(..., dtype=int)` may trigger
+        # integer sampling paths which raise "low is out of bounds for int32".
+        # A float sparse matrix is sufficient to validate sparse-input support.
+        X_train = scipy.sparse.random(1554, 21, dtype=np.float32)
         y_train = np.random.randint(3, size=1554)
         automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
         print(automl_experiment.classes_)

test/spark/test_multiclass.py

@@ -262,7 +262,11 @@ class TestMultiClass(unittest.TestCase):
             "n_concurrent_trials": 2,
             "use_spark": True,
         }
-        X_train = scipy.sparse.random(1554, 21, dtype=int)
+        # NOTE: Avoid `dtype=int` here. On some NumPy/SciPy combinations (notably
+        # Windows + Python 3.13), `scipy.sparse.random(..., dtype=int)` may trigger
+        # integer sampling paths which raise "low is out of bounds for int32".
+        # A float sparse matrix is sufficient to validate sparse-input support.
+        X_train = scipy.sparse.random(1554, 21, dtype=np.float32)
         y_train = np.random.randint(3, size=1554)
         automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
         print(automl_experiment.classes_)