pyrus-nn 0.2.1

Micro Neural Network framework
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# -*- coding: utf-8 -*-

import pytest
import numpy as np

from pyrus_nn.models import Sequential
from pyrus_nn import layers
from pyrus_nn.rust.pyrus_nn import PyrusSequential


@pytest.fixture
def model():
    model = Sequential(lr=0.01, n_epochs=2, batch_size=32, cost_func='mse')
    model.add(layers.Dense(10, 8, 'linear'))
    model.add(layers.Dense(8, 1, 'sigmoid'))
    return model


def test_serialization(model: Sequential):
    conf = model.to_dict()
    clone = Sequential.from_dict(conf)
    assert model == clone


def test_rust_raw_init():
    """Basic init test"""
    _nn = PyrusSequential(lr=0.05, n_epoch=2, batch_size=32, cost_func='mae')


@pytest.mark.parametrize('cost_func', ('mse', 'mae', 'accuracy', 'crossentropy'))
def test_py_interface_init(cost_func: str):
    """
    Basic init test for py wrapper
    """
    model = Sequential(lr=0.01, n_epochs=2, cost_func=cost_func)
    assert model.lr == 0.01
    assert model.n_epochs == 2
    assert model.cost_func == cost_func


@pytest.mark.parametrize("layer", [
    (layers.Dense(2, 3, 'linear'),),
    (layers.Dense(2, 3, 'sigmoid'),),
    (layers.Dense(128, 256, 'tanh'),)
])
def test_py_interface_add_layer(layer):
    model = Sequential(lr=0.01, n_epochs=2)
    model.add(layer)


@pytest.mark.parametrize("n_features", list(range(1, 500, 91)))
@pytest.mark.parametrize("use_lists", (True, False))
def test_fit_predict_numpy(n_features: int, use_lists: bool):
    model = Sequential(lr=0.01, n_epochs=2)
    model.add(layers.Dense(n_features, 4, 'linear'))
    model.add(layers.Dense(4, 1, 'sigmoid'))

    X = np.random.random(size=n_features * 50).reshape(-1, n_features)
    y = np.random.randint(0, 10, size=50).reshape(-1, 1)

    X = X.tolist() if use_lists else X
    y = y.tolist() if use_lists else y

    model.fit(X, y)

    out = model.predict(X)
    assert len(out) == 50