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)
def test_prim_init_select(self):
self.results = test_utilities.load_flu_data()
self.classify = flu_classify
experiments, outcomes = self.results
unc = recfunctions.get_names(experiments.dtype)
# test initialization, including t_coi calculation in case of searching
# for results equal to or higher than the threshold
outcomes['death toll'] = outcomes['deceased population region 1'][:, -1]
results = experiments, outcomes
threshold = 10000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.ABOVE, threshold=threshold,
incl_unc=unc)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] >= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
# test initialization, including t_coi calculation in case of searching
# for results equal to or lower than the threshold
threshold = 1000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.BELOW,
threshold=threshold)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] <= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
prim.setup_prim(self.results, self.classify, threshold=prim.ABOVE)
def test_get_univariate_feature_scores(self):
results = test_utilities.load_flu_data()
def classify(data):
#get the output for deceased population
result = data['deceased population region 1']
#make an empty array of length equal to number of cases
classes = np.zeros(result.shape[0])
#if deceased population is higher then 1.000.000 people, classify as 1
classes[result[:, -1] > 1000000] = 1
return classes
# f classify
scores = fs.get_univariate_feature_scores(results, classify)
self.assertEqual(len(scores), len(results[0].dtype.fields))
# chi2
scores = fs.get_univariate_feature_scores(results, classify,
score_func='chi2')
self.assertEqual(len(scores), len(results[0].dtype.fields))
# f regression
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:,-1]
scores = fs.get_univariate_feature_scores(results, ooi)
self.assertEqual(len(scores), len(results[0].dtype.fields))
def test_prim_init_select(self):
self.results = test_utilities.load_flu_data()
self.classify = flu_classify
experiments, outcomes = self.results
unc = recfunctions.get_names(experiments.dtype)
# test initialization, including t_coi calculation in case of searching
# for results equal to or higher than the threshold
outcomes['death toll'] = outcomes['deceased population region 1'][:, -1]
results = experiments, outcomes
threshold = 10000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.ABOVE, threshold=threshold,
incl_unc=unc)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] >= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
# test initialization, including t_coi calculation in case of searching
# for results equal to or lower than the threshold
threshold = 1000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.BELOW,
threshold=threshold)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] <= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
prim.setup_prim(self.results, self.classify, threshold=prim.ABOVE)
def test_get_rf_feature_scores(self):
results = test_utilities.load_flu_data()
def classify(data):
#get the output for deceased population
result = data['deceased population region 1']
#make an empty array of length equal to number of cases
classes = np.zeros(result.shape[0])
#if deceased population is higher then 1.000.000 people, classify as 1
classes[result[:, -1] > 1000000] = 1
return classes
scores, forest = fs.get_rf_feature_scores(results, classify,
random_state=10)
self.assertEqual(len(scores), len(results[0].dtype.fields))
self.assertTrue(isinstance(forest, RandomForestClassifier))
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:,-1]
scores, forest = fs.get_rf_feature_scores(results, ooi,
random_state=10)
self.assertEqual(len(scores), len(results[0].dtype.fields))
self.assertTrue(isinstance(forest, RandomForestRegressor))
def test_setup_prim_exceptions(self):
results = test_utilities.load_flu_data()
self.assertRaises(prim.PrimException,
prim.setup_prim,
results,
'deceased population region 1',
threshold=0.8)
def faulty_classify(outcomes):
return outcomes['deceased population region 1'][:, 0:10]
self.assertRaises(prim.PrimException, prim.setup_prim, results,
faulty_classify, threshold=0.8)
def test_setup_prim_exceptions(self):
results = test_utilities.load_flu_data()
self.assertRaises(prim.PrimException,
prim.setup_prim,
results,
'deceased population region 1',
threshold=0.8)
def faulty_classify(outcomes):
return outcomes['deceased population region 1'][:, 0:10]
self.assertRaises(prim.PrimException, prim.setup_prim, results,
faulty_classify, threshold=0.8)
def test_find_box(self):
results = test_utilities.load_flu_data()
classify = flu_classify
prim_obj = prim.setup_prim(results, classify, threshold=0.8)
box_1 = prim_obj.find_box()
prim_obj._update_yi_remaining()
after_find = box_1.yi.shape[0] + prim_obj.yi_remaining.shape[0]
self.assertEqual(after_find, prim_obj.y.shape[0])
box_2 = prim_obj.find_box()
prim_obj._update_yi_remaining()
after_find = box_1.yi.shape[0] +\
box_2.yi.shape[0] +\
prim_obj.yi_remaining.shape[0]
self.assertEqual(after_find, prim_obj.y.shape[0])
def test_boxes(self):
x = np.array([(0, 1, 2), (2, 5, 6), (3, 2, 1)],
dtype=[('a', np.float), ('b', np.float), ('c', np.float)])
y = {'y': np.array([0, 1, 2])}
results = (x, y)
prim_obj = prim.setup_prim(results, 'y', threshold=0.8)
boxes = prim_obj.boxes
self.assertEqual(len(boxes), 1, 'box length not correct')
# real data test case
prim_obj = prim.setup_prim(test_utilities.load_flu_data(),
flu_classify,
threshold=0.8)
prim_obj.find_box()
boxes = prim_obj.boxes
self.assertEqual(len(boxes), 2, 'box length not correct')
def test_find_box(self):
results = test_utilities.load_flu_data()
classify = flu_classify
prim_obj = prim.setup_prim(results, classify,
threshold=0.8)
box_1 = prim_obj.find_box()
prim_obj._update_yi_remaining()
after_find = box_1.yi.shape[0] + prim_obj.yi_remaining.shape[0]
self.assertEqual(after_find, prim_obj.y.shape[0])
box_2 = prim_obj.find_box()
prim_obj._update_yi_remaining()
after_find = box_1.yi.shape[0] +\
box_2.yi.shape[0] +\
prim_obj.yi_remaining.shape[0]
self.assertEqual(after_find, prim_obj.y.shape[0])
def test_boxes(self):
x = np.array([(0,1,2),
(2,5,6),
(3,2,1)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
y = {'y':np.array([0,1,2])}
results = (x,y)
prim_obj = prim.setup_prim(results, 'y', threshold=0.8)
boxes = prim_obj.boxes
self.assertEqual(len(boxes), 1, 'box length not correct')
# real data test case
prim_obj = prim.setup_prim(test_utilities.load_flu_data(), flu_classify,
threshold=0.8)
prim_obj.find_box()
boxes = prim_obj.boxes
self.assertEqual(len(boxes), 2, 'box length not correct')
def test_setup_cart(self):
results = test_utilities.load_flu_data()
cart_algorithm = cart.setup_cart(results, flu_classify, mass_min=0.05)
def test_setup_cart(self):
results = test_utilities.load_flu_data()
cart_algorithm = cart.setup_cart(results, flu_classify, mass_min=0.05)
