import numpy as np

import pytest

from keras.datasets import imdb

from deep_learning4e import *

from learning4e import DataSet, grade_learner, err_ratio

random.seed("aima-python")

def test_neural_net():

iris = DataSet(name='iris')

classes = ['setosa', 'versicolor', 'virginica']

iris.classes_to_numbers(classes)

nnl_adam = NeuralNetLearner(iris, [4], learning_rate=0.001, epochs=200, optimizer=adam)

nnl_gd = NeuralNetLearner(iris, [4], learning_rate=0.15, epochs=100, optimizer=gradient_descent)

tests = [([5.0, 3.1, 0.9, 0.1], 0),

([5.1, 3.5, 1.0, 0.0], 0),

([4.9, 3.3, 1.1, 0.1], 0),

([6.0, 3.0, 4.0, 1.1], 1),

([6.1, 2.2, 3.5, 1.0], 1),

([5.9, 2.5, 3.3, 1.1], 1),

([7.5, 4.1, 6.2, 2.3], 2),

([7.3, 4.0, 6.1, 2.4], 2),

([7.0, 3.3, 6.1, 2.5], 2)]

assert grade_learner(nnl_adam, tests) >= 1 / 3

assert grade_learner(nnl_gd, tests) >= 1 / 3

assert err_ratio(nnl_adam, iris) < 0.21

assert err_ratio(nnl_gd, iris) < 0.21

def test_perceptron():

iris = DataSet(name='iris')

classes = ['setosa', 'versicolor', 'virginica']

iris.classes_to_numbers(classes)

pl = PerceptronLearner(iris, learning_rate=0.01, epochs=100)

tests = [([5, 3, 1, 0.1], 0),

([5, 3.5, 1, 0], 0),

([6, 3, 4, 1.1], 1),

([6, 2, 3.5, 1], 1),

([7.5, 4, 6, 2], 2),

([7, 3, 6, 2.5], 2)]

assert grade_learner(pl, tests) > 1 / 2

assert err_ratio(pl, iris) < 0.4

def test_rnn():

data = imdb.load_data(num_words=5000)

train, val, test = keras_dataset_loader(data)

train = (train[0][:1000], train[1][:1000])

val = (val[0][:200], val[1][:200])

rnn = SimpleRNNLearner(train, val)

score = rnn.evaluate(test[0][:200], test[1][:200], verbose=0)

assert score[1] >= 0.3

def test_autoencoder():

iris = DataSet(name='iris')

classes = ['setosa', 'versicolor', 'virginica']

iris.classes_to_numbers(classes)

inputs = np.asarray(iris.examples)

al = AutoencoderLearner(inputs, 100)

print(inputs[0])

print(al.predict(inputs[:1]))

if __name__ == "__main__":

pytest.main()