def test_prepare_outcomes(self):
results = utilities.load_flu_data()
# string type correct
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:, -1]
y, categorical = fs._prepare_outcomes(results[1], ooi)
self.assertFalse(categorical)
self.assertTrue(len(y.shape) == 1)
# string type not correct --> KeyError
with self.assertRaises(KeyError):
fs._prepare_outcomes(results[1], "non existing key")
# classify function correct
def classify(data):
result = data['deceased population region 1']
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 1000000] = 1
return classes
y, categorical = fs._prepare_outcomes(results[1], classify)
self.assertTrue(categorical)
self.assertTrue(len(y.shape) == 1)
# neither string nor classify function --> TypeError
with self.assertRaises(TypeError):
fs._prepare_outcomes(results[1], 1)
def test_prepare_outcomes(self):
results = test_utilities.load_flu_data()
# string type correct
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:,-1]
y, categorical = fs._prepare_outcomes(results[1], ooi)
self.assertFalse(categorical)
self.assertTrue(len(y.shape)==1)
# string type not correct --> KeyError
with self.assertRaises(KeyError):
fs._prepare_outcomes(results[1], "non existing key")
# classify function correct
def classify(data):
result = data['deceased population region 1']
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 1000000] = 1
return classes
y, categorical = fs._prepare_outcomes(results[1], classify)
self.assertTrue(categorical)
self.assertTrue(len(y.shape)==1)
# neither string nor classify function --> TypeError
with self.assertRaises(TypeError):
fs._prepare_outcomes(results[1], 1)