def setUp(self):
self.params = {"num_trees":5,"num_sweeps":2,"num_cutpoints":10,
"max_depth_num":5,"burnin":1}
self.model = xbart.XBART(**self.params)
self.model_2 = xbart.XBART(**self.params)
n = 100
self.x = np.random.rand(n)
def test_z_from_json(self):
model = xbart.XBART()
model.from_json("model.xbart")
n_test = 1000; d = 10
x_test= np.random.rand(n_test,d)
y_pred_json = model.predict(x_test,return_mean=False)
self.assertFalse(np.array_equal(y_pred_json,y_pred_json*0))
def test_dimension_mismatch_x_y(self): with self.assertRaises(AssertionError): x,x_test,y,y_test,d = self.make_data() # Make y not match x y = np.concatenate((y,y)) model = xbart.XBART(model="Probit",**self.params) model.fit(x,y,d-1)
def test_dimension_mismatch_x_x(self): with self.assertRaises(AssertionError): x,x_test,y,y_test,d = self.make_data() model = xbart.XBART(model="Probit",**self.params) model.fit(x,y,d-1) # Make x_test not match x_test = np.concatenate((x_test,x_test),axis=1) model.predict(x_test)
def test_multinomial(self):
x,x_test,y,y_test,d = self.make_data()
y = (y >0)*1 ; y_test = (y_test > 0)*1
model = xbart.XBART(model="Multinomial",**self.params,num_classes=2)
model.fit(x,y,d-1)
y_pred = (model.predict(x_test)[:,1] > 0)*1
y_bin = (y_test > 0 )*1
acc = np.mean(y_pred == y_bin)
print("Accuracy:" + str(acc))
self.assertTrue(acc > 0.1)
def test_probit(self):
x,x_test,y,y_test,d = self.make_data()
y = (y >0)*2-1 ; y_test = (y_test > 0)*2-1
model = xbart.XBART(model="Probit",**self.params)
model.fit(x,y,d-1)
y_pred = (model.predict(x_test) > 0)*1
y_bin = (y_test > 0 )*1
acc = np.mean(y_pred == y_bin)
print("Accuracy:" + str(acc))
self.assertTrue(acc > 0.6)
def test_bool_with_good_float(self): xbart.XBART(parallel = 0.0)
def test_bool_with_good_int(self): xbart.XBART(parallel = 0)
def test_bool_with_bad_string(self): with self.assertRaises(TypeError): xbart.XBART(parallel = "2")
def test_bool_with_bad_float(self): with self.assertRaises(TypeError): xbart.XBART(parallel= 2.2)
def test_bool_with_bad_int(self):
with self.assertRaises(TypeError):
params = {"parallel":2}
xbart.XBART(parallel = 2)
def test_float_bad_string(self): with self.assertRaises(TypeError): xbart.XBART(alpha="5")
def test_float_good_int(self): xbart.XBART(alpha=5)
def test_int_as_good_float(self): xbart.XBART(num_trees = 5.0)
def test_int_as_bad_string(self): with self.assertRaises(TypeError): xbart.XBART(num_trees = 5.1)
def test_missing_values(self): x,x_test,y,y_test,d = self.make_data() x[0,5] = np.NINF model = xbart.XBART(**self.params) model.fit(x,y,d-1)
