def test_fail_minibatches():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=13,
random_seed=1)
mlp.fit(X, y)
assert (y == mlp.predict(X)).all()
def test_binary_sgd():
mlp = MLP(epochs=10,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=len(y_bin),
random_seed=1)
mlp.fit(X_bin, y_bin)
assert (y_bin == mlp.predict(X_bin)).all()
def test_valid_acc():
mlp = MLP(epochs=3,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y, X_valid=X[:100], y_valid=y[:100])
assert len(mlp.valid_acc_) == 3
def test_train_acc():
mlp = MLP(epochs=3,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
assert len(mlp.train_acc_) == 3
def test_score_function_adagrad():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='adagrad',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
acc = mlp.score(X, y)
assert acc == 1.0, acc
def test_multiclass_gd_learningdecay():
mlp = MLP(epochs=5,
eta=0.5,
hidden_layers=[15],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
decay=[0.5, 1.0],
random_seed=1)
mlp.fit(X, y)
expect = [3.11, 2.12, 1.79, 1.65, 1.59]
np.testing.assert_almost_equal(expect, mlp.cost_, decimal=2)
def test_multiclass_gd_dropout():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1,
dropout=0.05)
mlp.fit(X, y)
acc = round(mlp.score(X, y), 2)
assert acc == 0.67, acc
def test_continue_learning():
mlp = MLP(epochs=25,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
assert np.sum(y == mlp.predict(X)) == 144, np.sum(y == mlp.predict(X))
mlp.fit(X, y, init_params=False)
assert np.sum(y == mlp.predict(X)) == 150, np.sum(y == mlp.predict(X))
def test_multiclass_probas():
mlp = MLP(epochs=500,
eta=0.5,
hidden_layers=[10],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
idx = [0, 50, 149] # sample labels: 0, 1, 2
y_pred = mlp.predict_proba(X[idx])
exp = np.array([[1.0, 0.0, 0.0], [0.0, 0.9, 0.1], [0.0, 0.1, 0.9]])
np.testing.assert_almost_equal(y_pred, exp, 1)
def test_mapping():
mlp = MLP()
w, b = mlp._layermapping(n_features=10,
n_classes=11,
hidden_layers=[8, 7, 6])
expect_b = {1: [[8], 'n_hidden_1'],
2: [[7], 'n_hidden_2'],
3: [[6], 'n_hidden_3'],
'out': [[11], 'n_classes']}
expect_w = {1: [[10, 8], 'n_features, n_hidden_1'],
2: [[8, 7], 'n_hidden_1, n_hidden_2'],
3: [[7, 6], 'n_hidden_2, n_hidden_3'],
'out': [[6, 11], 'n_hidden_3, n_classes']}
assert expect_b == b, b
assert expect_w == w, w
def test_activations_invalid_ele_2():
MLP(hidden_layers=[10, 10], activations=['logistic'])
def test_activations_invalid_ele_1():
MLP(hidden_layers=[1], activations=['logistic', 'logistic'])
def test_activations_init_typo():
MLP(hidden_layers=[1, 2], activations=['logistic', 'invalid'])
def test_optimizer_init():
MLP(optimizer='no-optimizer')