easy_model

A tiny, dependency-free, sklearn-style neural network classifier in pure Python, backed by the fast C++ engine neuralnetwork-cpp, plus pandas-like CSV reading, train_test_split, and six bundled classic datasets.

from easy_model import NeuralNetworkCPPClassifier, train_test_split, load_iris

Features

• NeuralNetworkCPPClassifier - a scikit-learn compatible classifier (fit / predict / predict_proba / score, get_params / set_params).
• read_csv - read CSV files (including .gz) with pandas-like behavior.
• train_test_split - split data into train/test, with shuffle, random seeding, and stratification, just like sklearn.
• Bundled datasets - iris, wine, breast cancer, diabetes, digits, and linnerud, loaded the sklearn way with return_X_y support.
• No pandas or numpy required - everything is plain Python lists.

Installation

Install from PyPI (this pulls in the only dependency automatically):

pip install nn-easy-model

Or clone the repository and run it directly:

git clone https://github.com/Mohamedboukerche22/easy_model.git
cd easy_model
pip install .            # optional: install the package
python main.py           # runs the demos

Quickstart

from easy_model import NeuralNetworkCPPClassifier, train_test_split, load_iris

# 1. Load a dataset
X, y = load_iris(return_X_y=True)

# 2. Split into train / test
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y,
)

# 3. Train
clf = NeuralNetworkCPPClassifier(
    hidden_layer_sizes=(16, 8),
    learning_rate=0.01,
    max_iter=200,
    batch_size=8,
    random_state=42,
)
clf.fit(X_train, y_train)

# 4. Evaluate and predict
print('train accuracy:', clf.score(X_train, y_train))
print('test  accuracy:', clf.score(X_test, y_test))
print('predicted:', clf.predict(X_test[:3]))

API

NeuralNetworkCPPClassifier

Main parameters:

Methods: fit(X, y), predict(X), predict_proba(X), score(X, y), get_params(), set_params(**params).

Fitted attributes: classes_, n_features_in_, n_classes_, loss_curve_, history_, fit_time_seconds_.

read_csv

from easy_model import read_csv, DATA_DIR

header, rows = read_csv(f'{DATA_DIR}/iris.csv')
# header -> [150, 4, 'setosa', 'versicolor', 'virginica']
# rows   -> [[5.1, 3.5, 1.4, 0.2, 0], ...]

read_csv(path, sep=',', header=0) auto-decompresses .gz files, converts numeric cells to int/float, and keeps strings as strings. Pass header=None to treat every line as data.

train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.3,       # fraction, or an int number of samples
    train_size=None,     # mutually exclusive with test_size
    random_state=42,     # reproducibility
    shuffle=True,
    stratify=y,          # keep class proportions in both splits
)

Bundled datasets

Each loader returns a Bunch (sklearn-style attributes) or, with return_X_y=True, a (X, y) tuple:

bunch = load_wine()
bunch.data, bunch.target, bunch.target_names, bunch.feature_names

X, y = load_wine(return_X_y=True)

Full example

from easy_model import NeuralNetworkCPPClassifier, train_test_split, load_digits

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=0, stratify=y,
)

clf = NeuralNetworkCPPClassifier(
    hidden_layer_sizes=(128, 64), learning_rate=0.003,
    max_iter=10, batch_size=64, random_state=123,
)
clf.fit(X_train, y_train)

print('accuracy:', clf.score(X_test, y_test))

Requirements

• Python 3.8+
• neuralnetwork-cpp (the C++ backend; the only dependency)

License

MIT