CoreMLMath
A multithreaded C++ backend for Machine Learning Math.
Financial Support
Installation
The easiest way to install CoreMLMath is via pip.
pip install coremlmath
Build Instructions
-
Clone the repo locally:
git clone https://github.com/novfensec/coremlmath -b master --single-branch --depth 1 cd coremlmath
-
Linux:
sudo apt update sudo apt install -y build-essential cmake
-
Windows (Powershell):
Install the necessary C++ build tools and CMake using Windows Package Manager (
winget):This has to be done only once.
winget install -e --id Microsoft.VisualStudio.BuildTools --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools --includeRecommended" --source winget winget install -e --id Kitware.CMake --source winget
-
Install pybind11:
pip install "pybind11[global]" uv
-
Build and install using standard pip:
pip install .
Or using
uvfor isolated, fast builds:uv pip install .
-
Build an installable wheel:
uv build --wheel
-
Build Linux wheels locally using cibuildwheel:
pip install cibuildwheel cibuildwheel --platform linux
-
Build Android wheels locally using cibuildwheel (requires Android SDK):
pip install cibuildwheel cibuildwheel --platform android
-
Build Windows wheels locally using cibuildwheel (requires MSVC):
pip install cibuildwheel cibuildwheel --platform windows
The repository also includes GitHub Actions workflows under .github/workflows/ (targeting Python 3.11 through 3.14):
wheels-linux.yml: Compilesmanylinux_2_28wheels forx86_64andaarch64(via QEMU) on Ubuntu runners.wheels-android.yml: Compiles PEP 738 Android wheels forarm64_v8aandx86_64on Ubuntu runners.publish.yml: Triggered upon GitHub Release creation (or manual dispatch); buildssdist, Linux (x86_64,aarch64), Android (arm64_v8a,x86_64), and Windows (x86_64) wheels, then automatically publishes them to PyPI using PyPA Trusted Publishing (OIDC).
Usage
The package expects 2D and 1D NumPy arrays of integers for maximum memory efficiency.
import numpy as np
import cmlmath
import time
print("Generating massive dataset (1,000,000 rows, 500 features)...")
X_raw = np.random.randint(0, 2, size=(1000000, 500), dtype=np.int32)
y_raw = np.random.randint(0, 16, size=(1000000,), dtype=np.int32)
# Enforce C-contiguous (Row-Major) layout in memory.
# (np.random generates C-contiguous arrays by default, but this guarantees
# safety in case of Pandas DataFrame or transposed array).
X = np.ascontiguousarray(X_raw, dtype=np.int32)
y = np.ascontiguousarray(y_raw, dtype=np.int32)
print(f"Starting multithreaded C++ calculation using {cmlmath.get_cpu_cores()} cores...")
start = time.time()
info_gains = cmlmath.information_gain(X, y)
gini_gains = cmlmath.gini_gain(X, y)
calc_time = time.time() - start
print(f"Calculated 500 Information and Gini Gains in {calc_time:.4f} seconds.")
print(f"Best feature index to split on (Information Gain): {np.argmax(info_gains)}")
print(f"Best feature index to split on (Gini Gain): {np.argmax(gini_gains)}")
Decision Tree Classifier
coremlmath includes a full, scikit-learn compatible DecisionTreeClassifier powered directly by the multithreaded OpenMP C++ backend:
Showcase
from cmlmath import DecisionTreeClassifier
# Initialize classifier (supports criterion="entropy" or "gini")
clf = DecisionTreeClassifier(
criterion="entropy",
max_depth=5,
min_samples_split=2,
min_samples_leaf=1,
)
# Fit on training data (supports NumPy arrays and Pandas DataFrames)
clf.fit(X_train, y_train)
# Inference and accuracy evaluation
y_pred = clf.predict(X_test)
accuracy = clf.score(X_test, y_test)
probas = clf.predict_proba(X_test)
# Inspect tree structure & feature contributions
clf.print_tree()
print("Feature importances:", clf.feature_importances_)
# Plot tree using matplotlib (or save directly to image)
clf.plot_tree(save_path="decision_tree.png")
# Or using the functional plot_tree API
from cmlmath import plot_tree
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(16, 9))
plot_tree(clf, ax=ax, filled=True, rounded=True)
plt.show()
1 maintainer
Metadata
1 maintainer
Release files for coremlmath 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|---|
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Built distributions (wheels)
Total release size: 18.2 MB
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