OchreML (≧ω≦)
Classical Machine Learning Library in Rust with PyO3 Bindings
Welcome to OchreML! (＾▽＾)
OchreML is a classical Machine Learning library (Linear Regression & Decision Trees) combining Rust's blazingly fast computational performance with Python's intuitive, scikit-learn-compatible API.
Key Features (^▽^)
-
Linear Regression (OLS / Normal Equation)
- Exact analytical solution using the Normal Equation $(X^T X)^{-1} X^T y$.
- Automated Tikhonov (Ridge) regularization fallback when design matrices are ill-conditioned or singular.
- Configurable
fit_interceptparameter for calculating bias.
-
Decision Tree Classifier
- CART classification trees supporting both Gini Impurity and Entropy criteria.
- Flexible hyperparameters:
max_depth,min_samples_split, andmin_samples_leaf.
-
Decision Tree Regressor
- Non-linear regression trees based on Mean Squared Error (MSE / Variance Reduction) minimization.
- Robust continuous target estimation.
-
Multi-Format Data Interoperability (o´∀｀o)
- Seamlessly accepts and processes:
- Python Nested Lists (
list[list[float]]) - NumPy ndarrays (
numpy.ndarray) - Pandas DataFrames & Series (
pandas.DataFrame,pandas.Series) - Polars DataFrames & Series (
polars.DataFrame,polars.Series)
- Python Nested Lists (
- Seamlessly accepts and processes:
-
Scikit-Learn Compatible API (＾▽＾)
- Standard estimator interface:
fit(X, y),predict(X), andscore(X, y). - Inspection attributes:
coef_,intercept_,classes_, andn_features_in_.
- Standard estimator interface:
Build & Installation (o´∀｀o)
Ensure Rust (cargo) and your Python virtual environment are activated:
# Activate your virtual environment
source .venv/bin/activate
# Compile and install locally with Maturin
maturin develop --release
Quickstart Examples (＾▽＾)
1. Linear Regression with NumPy & Pandas
import numpy as np
import pandas as pd
from ochreml import LinearRegression
# Prepare dataset with Pandas DataFrame
df = pd.DataFrame({
"x1": [1.0, 2.0, 3.0, 4.0, 5.0],
"x2": [2.0, 1.0, 4.0, 3.0, 5.0],
})
# Target relationship: y = 2*x1 + 3*x2 + 5
y = np.array([13.0, 12.0, 23.0, 22.0, 30.0])
model = LinearRegression(fit_intercept=True)
model.fit(df, y)
print("Coefficients:", model.coef_) # approx [2.0, 3.0]
print("Intercept:", model.intercept_) # approx 5.0
print("R^2 Score:", model.score(df, y)) # 1.0
# Predict on new samples
X_new = pd.DataFrame({"x1": [6.0], "x2": [2.0]})
print("Prediction:", model.predict(X_new)) # [23.0]
2. Decision Tree Classifier with Polars
import polars as pl
from ochreml import DecisionTreeClassifier
# Prepare dataset with Polars
df_polars = pl.DataFrame({
"study_hours": [1.0, 2.0, 2.5, 5.0, 6.0, 7.5, 8.0, 9.0],
"attendance": [50.0, 60.0, 40.0, 80.0, 85.0, 90.0, 95.0, 100.0],
})
y_polars = pl.Series("passed", [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
clf = DecisionTreeClassifier(criterion="gini", max_depth=3)
clf.fit(df_polars, y_polars)
print("Classes discovered:", clf.classes_)
print("Training accuracy:", clf.score(df_polars, y_polars))
# Predict on new samples
test_data = pl.DataFrame({"study_hours": [1.5, 7.0], "attendance": [55.0, 88.0]})
print("Predictions:", clf.predict(test_data)) # [0.0, 1.0]
3. Decision Tree Regressor with Standard Python Lists
from ochreml import DecisionTreeRegressor
# Dataset using standard Python nested lists
X = [[100.0], [300.0], [500.0], [1500.0], [1800.0], [2200.0]]
y = [31.0, 30.0, 29.5, 17.0, 16.0, 15.0]
reg = DecisionTreeRegressor(criterion="squared_error", max_depth=2)
reg.fit(X, y)
print("R^2 Score:", reg.score(X, y))
print("Prediction [200.0m]:", reg.predict([[200.0]])) # [31.0]
print("Prediction [2000.0m]:", reg.predict([[2000.0]])) # [15.5]
Testing Suite (★ω★)
# Run internal Rust unit tests
cargo test
# Run Python integration tests (Lists, NumPy, Pandas, Polars)
pytest
1 maintainer
Metadata
1 maintainer
Release files for ochreml 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| ochreml-0.1.1.tar.gz | 21.4 kB | With provenance | Details |
Built distributions (wheels)
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