test_nnet.py

"""
Testing all the library
"""
from __future__ import division, print_function
from sklearn.linear_model.logistic import LogisticRegression
from cmsuml import nnet
from sklearn.datasets import make_blobs
from sklearn.metrics import roc_auc_score

__author__ = 'Alex Rogozhnikov'

import numpy


def test_nnet(n_samples=200, n_features=5, distance=0.5):
    X, y = make_blobs(n_samples=n_samples, n_features=5,
                      centers=[numpy.ones(n_features) * distance, - numpy.ones(n_features) * distance])

    nn_types = [
        nnet.SimpleNeuralNetwork,
        nnet.MultiLayerNetwork,
        nnet.SoftmaxNeuralNetwork,
        nnet.RBFNeuralNetwork,
        nnet.PairwiseNeuralNetwork,
        nnet.PairwiseSoftplusNeuralNetwork,
    ]

    for loss in nnet.losses:
        for NNType in nn_types:
            for trainer in nnet.trainers:
                nn = NNType(layers=[5], loss=loss, trainer=trainer, random_state=42)
                nn.fit(X, y, stages=100, verbose=nnet.SILENT)
                print(roc_auc_score(y, nn.predict_proba(X)[:, 1]), nn)

    lr = LogisticRegression().fit(X, y)
    print(lr, roc_auc_score(y, lr.predict_proba(X)[:, 1]))

    assert 0 == 1



def test_oblivious(n_samples=200, n_features=5, distance=0.5):
    # Since oblivious NN is very slow, it is tested separately
    nn = nnet.ObliviousNeuralNetwork(layers=[3], trainer='irprop-')
    X, y = make_blobs(n_samples=n_samples, n_features=5,
                      centers=[numpy.ones(n_features) * distance, - numpy.ones(n_features) * distance])
    nn.fit(X, y, batch=100, verbose=10)
    print(roc_auc_score(y, nn.predict_proba(X)[:, 1]), nn)


test_oblivious()
test_nnet()