Migrated to numpy 2.0.0

benchmarks/benchmark.py

@@ -11,36 +11,25 @@
 E-mail: ole.e@di.ku.dk
 """
 import os
+import sys
+
+# Add the parent directory of 'CVMatrix' to sys.path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Set the number of threads to 1. This must be done before importing numpy.
+os.environ["OMP_NUM_THREADS"] = "1"
+
 from itertools import product
 from timeit import timeit
 from typing import Hashable, Iterable, Union
 
 import numpy as np
 
-import cvmatrix
+from cvmatrix.__init__ import __version__
 from cvmatrix.cvmatrix import CVMatrix
 from tests.naive_cvmatrix import NaiveCVMatrix
 
 
-def set_env_vars():
-    value = 1
-    str_value = str(value)
-    os.environ['OMP_NUM_THREADS'] = str_value # Set number of threads to 1
-    os.environ['OPENBLAS_NUM_THREADS'] = str_value # Set number of threads to 1
-    os.environ['MKL_NUM_THREADS'] = str_value # Set number of threads to 1
-    os.environ['VECLIB_MAXIMUM_THREADS'] = str_value # Set number of threads to 1
-    os.environ['NUMEXPR_NUM_THREADS'] = str_value # Set number of threads to 1
-    os.environ['OPENBLAS_MAIN_FREE'] = str_value # Set number of threads to 1
-    print_env_vars()
-
-def print_env_vars():
-    print("OMP_NUM_THREADS:", os.environ['OMP_NUM_THREADS'])
-    print("OPENBLAS_NUM_THREADS:", os.environ['OPENBLAS_NUM_THREADS'])
-    print("MKL_NUM_THREADS:", os.environ['MKL_NUM_THREADS'])
-    print("VECLIB_MAXIMUM_THREADS:", os.environ['VECLIB_MAXIMUM_THREADS'])
-    print("NUMEXPR_NUM_THREADS:", os.environ['NUMEXPR_NUM_THREADS'])
-    print()
-
 def save_result_to_csv(
         model,
         P,
@@ -130,7 +119,6 @@ def execute_algorithm(
         model.training_XTX_XTY(fold)
 
 if __name__ == '__main__':
-    set_env_vars()
     seed = 42 # Seed for reproducibility
     rng = np.random.default_rng(seed=seed)
     N = 100000 # 100k samples
@@ -179,7 +167,7 @@ def execute_algorithm(
             scale_X,
             scale_Y,
             time,
-            cvmatrix.__version__
+            __version__
         )
 
         if (center_X == center_Y == scale_X == scale_Y or
@@ -212,5 +200,5 @@ def execute_algorithm(
                 scale_X,
                 scale_Y,
                 time,
-                cvmatrix.__version__
+                __version__
             )

cvmatrix/__init__.py

@@ -1 +1 @@
-__version__ = '1.0.0.post1'
+__version__ = '1.0.1'

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cvmatrix"
-version = "1.0.0.post1"
+version = "1.0.1"
 description = "Fast computation of possibly centered/scaled training set kernel matrices in a cross-validation setting."
 authors = ["Sm00thix <oleemail@icloud.com>"]
 maintainers = ["Sm00thix <oleemail@icloud.com>"]
@@ -11,7 +11,7 @@ repository = "https://github.com/Sm00thix/CVMatrix"
 
 [tool.poetry.dependencies]
 python = ">=3.9, <3.13"
-numpy = "^1.26.4"
+numpy = "^2.0.0"
 
 [build-system]
 requires = ["poetry-core"]

tests/load_data.py

@@ -64,7 +64,7 @@ def load_spectra():
         resp_byte_array = resp.read()
     byte_contents = io.BytesIO(resp_byte_array)
     npz_arr = np.load(byte_contents)
-    spectra = np.row_stack([npz_arr[k] for k in npz_arr.keys()])
+    spectra = np.vstack([npz_arr[k] for k in npz_arr.keys()])
     spectra = spectra.astype(np.float64)
     spectra = -np.log10(spectra)
     return spectra

tests/test_cvmatrix.py

@@ -45,18 +45,18 @@ class tests for equivalency between the naive, straight-forward algorithms
     csv = load_data.load_csv()
     raw_spectra = load_data.load_spectra()
 
-    def load_X(self) -> npt.NDArray[np.float_]:
+    def load_X(self) -> npt.NDArray[np.float64]:
         """
         Loads the raw spectral data.
 
         Returns
         -------
-        npt.NDArray[np.float_]
+        npt.NDArray[np.float64]
             A copy of the raw spectral data.
         """
         return np.copy(self.raw_spectra)
 
-    def load_Y(self, names: list[str]) -> npt.NDArray[np.float_]:
+    def load_Y(self, names: list[str]) -> npt.NDArray[np.float64]:
         """
         Loads target values based on the specified column names.
 
@@ -67,7 +67,7 @@ def load_Y(self, names: list[str]) -> npt.NDArray[np.float_]:
         
         Returns
         -------
-        npt.NDArray[np.float_]
+        npt.NDArray[np.float64]
             A copy of the target values.
         """
         return self.csv[names].to_numpy()