def test_fit(self):
model = CovidSIRDc()
X, y = model.iterate2array(derivatives=True)
with self.assertRaises(TypeError):
model.fit(X, {})
with self.assertRaises(ValueError):
model.fit(X, numpy.array([4]))
with self.assertRaises(ValueError):
model.fit(X, numpy.array([[4, 5, 6, 7, 8]]))
X2 = X.copy()
X2[0, 0] = -5
with self.assertRaises(ValueError):
model.fit(X2, y, learning_rate_init=0.01)
exp = numpy.array(
[model['beta'], model['nu'], model['mu'], model['b']])
model.fit(X, y, verbose=False, max_iter=10, learning_rate_init=0.01)
coef = numpy.array(
[model['beta'], model['nu'], model['mu'], model['b']])
err = numpy.linalg.norm(exp - coef)
self.assertLess(err, 1e-5)
model['nu'] = model['mu'] = model['beta'] = 0.1
buf = io.StringIO()
with redirect_stdout(buf):
model.fit(X, y, verbose=True, max_iter=20)
out = buf.getvalue()
self.assertIn('20/20', out)
coef = numpy.array(
[model['beta'], model['nu'], model['mu'], model['b']])
err = numpy.linalg.norm(exp - coef)
self.assertLess(err, 1)
loss = model.score(X, y)
self.assertGreater(loss, 0)
loss = model.score_l1(X, y)
self.assertGreater(loss, 0)
def test_noise(self):
model = CovidSIRDc()
X, y = model.iterate2array(derivatives=True)
X2 = model.add_noise(X)
diff = numpy.abs(X - X2).max()
self.assertEqual(X.shape, X.shape)
s1 = numpy.sum(X, axis=1)
s2 = numpy.sum(X2, axis=1)
assert_almost_equal(s1, s2)
self.assertGreater(diff, 1)
def test_fit_sirc_simulate(self):
model = CovidSIRDc()
X, y = model.iterate2array(derivatives=True)
epi = EpidemicRegressor('sirc', max_iter=10)
epi.fit(X, y)
sim = epi.simulate(X[4:6])
self.assertEqual(sim.shape, (2, 7, 4))
self.assertEqual(X[4].tolist(), sim[0, 0].tolist())
self.assertEqual(X[5].tolist(), sim[1, 0].tolist())
sim = epi.simulate(numpy.array([[10000., 0, 0, 0]]))
self.assertEqual(sim[0].tolist(), sim[-1].tolist())
def test_fit_sirc(self):
model = CovidSIRDc()
X, y = model.iterate2array(derivatives=True)
epi = EpidemicRegressor('sirc', max_iter=10)
with self.assertRaises(RuntimeError):
epi.score(X, y)
with self.assertRaises(RuntimeError):
epi.predict(X)
epi.fit(X, y)
loss = epi.score(X, y)
self.assertGreater(loss, 0)
def test_predict(self):
model = CovidSIRDc()
sim = list(model.iterate(derivatives=True))
self.assertIsInstance(sim, list)
X = model.iterate2array(derivatives=False)
self.assertIsInstance(X, numpy.ndarray)
X, y = model.iterate2array(derivatives=True)
self.assertEqual(X.shape, y.shape)
y2 = model.predict(X)
assert_almost_equal(y / 100, y2 / 100, decimal=6)
with self.assertRaises(TypeError):
model.predict({})
with self.assertRaises(ValueError):
model.predict(numpy.array([4]))
with self.assertRaises(ValueError):
model.predict(numpy.array([[4, 5, 6, 7, 8]]))
X2 = X.copy()
X2[0, 0] -= 50
with self.assertRaises(ValueError):
model.predict(X2)
X2 = X.copy()
X2[:, 0] -= 50
with self.assertRaises(ValueError):
model.predict(X2)