def test_iris_accuracy():
dbn = DBN([25], pretrain_epochs=0, finetune_epochs=10, finetune_batch_size=10, random_state=1)
X, y = get_iris()
dbn.fit(X, y)
acc = (dbn.predict(X) == y).mean()
eq_(acc, 0.95333, 5)
def test_coef_eq_layers_1():
dbn = DBN([5], pretrain_epochs=0, finetune_epochs=1, random_state=1234)
X, y = get_iris()
dbn.fit(X, y)
eq_(dbn.coef_[:5], dbn.layers[0].b)
eq_(dbn.coef_[5:25], dbn.layers[0].W.reshape(-1))
eq_(dbn.coef_[25:28], dbn.layers[1].b)
eq_(dbn.coef_[28:], dbn.layers[1].W.reshape(-1))
def test_coef_eq_layers_1():
dbn = DBN([5], pretrain_epochs=0, finetune_epochs=1, random_state=1234)
X, y = get_iris()
dbn.fit(X, y)
eq_(dbn.coef_[:5], dbn.layers[0].b)
eq_(dbn.coef_[5:25], dbn.layers[0].W.reshape(-1))
eq_(dbn.coef_[25:28], dbn.layers[1].b)
eq_(dbn.coef_[28:], dbn.layers[1].W.reshape(-1))
def test_iris_accuracy():
dbn = DBN([25],
pretrain_epochs=0,
finetune_epochs=10,
finetune_batch_size=10,
random_state=1)
X, y = get_iris()
dbn.fit(X, y)
acc = (dbn.predict(X) == y).mean()
eq_(acc, 0.95333, 5)
def test_sklearn_api():
''' sklearn API: not functionality
'''
dbn = DBN([5])
X, y = get_iris()
dbn.fit(X, y)
dbn.predict_proba(X)
dbn.predict(X)
def test_reproducible():
X, y = get_iris()
dbn1 = DBN([5], random_state=123)
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn2 = DBN([5], random_state=123)
dbn2.fit(X, y)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
eq_(pred1, pred2)
eq_(prob1, prob2)
def test_sklearn_api():
''' sklearn API: not functionality
'''
dbn = DBN([5])
X, y = get_iris()
dbn.fit(X, y)
dbn.predict_proba(X)
dbn.predict(X)
def test_coef_eq_layers_change():
# Test that the coef_ and layers weights are connected in memory
dbn = DBN([5], pretrain_epochs=0, finetune_epochs=0, random_state=1234)
X, y = get_iris()
dbn.fit(X, y)
eq_(dbn.layers[0].b, np.zeros(5))
eq_(dbn.layers[1].b, np.zeros(3))
dbn.coef_[:] = np.ones(len(dbn.coef_))
eq_(dbn.layers[0].b, np.ones(5))
eq_(dbn.layers[0].W, np.ones((4, 5)))
eq_(dbn.layers[1].b, np.ones(3))
eq_(dbn.layers[1].W, np.ones((5, 3)))
eq_(dbn.layers[0].b[0], 1)
dbn.coef_[0] = 2
eq_(dbn.layers[0].b[0], 2)
eq_(dbn.coef_[1], 1)
dbn.layers[0].b[1] = 3
eq_(dbn.coef_[1], 3)
def test_coef_eq_layers_change():
# Test that the coef_ and layers weights are connected in memory
dbn = DBN([5], pretrain_epochs=0, finetune_epochs=0, random_state=1234)
X, y = get_iris()
dbn.fit(X, y)
eq_(dbn.layers[0].b, np.zeros(5))
eq_(dbn.layers[1].b, np.zeros(3))
dbn.coef_[:] = np.ones(len(dbn.coef_))
eq_(dbn.layers[0].b, np.ones(5))
eq_(dbn.layers[0].W, np.ones((4, 5)))
eq_(dbn.layers[1].b, np.ones(3))
eq_(dbn.layers[1].W, np.ones((5, 3)))
eq_(dbn.layers[0].b[0], 1)
dbn.coef_[0] = 2
eq_(dbn.layers[0].b[0], 2)
eq_(dbn.coef_[1], 1)
dbn.layers[0].b[1] = 3
eq_(dbn.coef_[1], 3)
def test_reproducible():
X, y = get_iris()
dbn1 = DBN([5], random_state=123)
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn2 = DBN([5], random_state=123)
dbn2.fit(X, y)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
eq_(pred1, pred2)
eq_(prob1, prob2)
def test_instance():
from sklearn.base import BaseEstimator
dbn = DBN([5])
assert isinstance(dbn, BaseEstimator)
def test_save_load_weights():
import tempfile
tempdir = tempfile.gettempdir()
tempfile = os.path.join(tempdir, 'w.json')
# tempfile = os.path.join('', 'w.json')
dbn1 = DBN([5], random_state=1234)
X, y = get_iris()
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn1.save(tempfile)
dbn2 = DBN([5])
dbn2.load(tempfile)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
for i, layer in enumerate(dbn1.layers):
eq_(dbn1.layers[i].W, dbn2.layers[i].W)
eq_(pred1, pred2)
eq_(prob1, prob2)
def test_save_load_weights():
import tempfile
tempdir = tempfile.gettempdir()
tempfile = os.path.join(tempdir, 'w.json')
# tempfile = os.path.join('', 'w.json')
dbn1 = DBN([5], random_state=1234)
X, y = get_iris()
dbn1.fit(X, y)
pred1 = dbn1.predict(X)
prob1 = dbn1.predict_proba(X)
dbn1.save(tempfile)
dbn2 = DBN([5])
dbn2.load(tempfile)
pred2 = dbn2.predict(X)
prob2 = dbn2.predict_proba(X)
eq_(dbn1.coef_, dbn2.coef_)
for i, layer in enumerate(dbn1.layers):
eq_(dbn1.layers[i].W, dbn2.layers[i].W)
eq_(pred1, pred2)
eq_(prob1, prob2)
