def test_setup_cart(self):
results = utilities.load_flu_data()
alg = cart.setup_cart(results, flu_classify,
mass_min=0.05)
self.assertTrue(alg.mode==BINARY)
x, outcomes = results
y = {k:v[:, -1] for k,v in outcomes.items()}
temp_results = (x,y)
alg = cart.setup_cart(temp_results,
'deceased population region 1',
mass_min=0.05)
self.assertTrue(alg.mode==REGRESSION)
n_cols = 5
unc = x.columns.values[0:n_cols]
alg = cart.setup_cart(results,
flu_classify,
mass_min=0.05,
incl_unc=unc)
self.assertTrue(alg.mode==BINARY)
self.assertTrue(alg.x.shape[1]==n_cols)
with self.assertRaises(TypeError):
alg = cart.setup_cart(results, 10,
mass_min=0.05)
def test_setup_cart(self):
results = utilities.load_flu_data()
alg = cart.setup_cart(results, flu_classify,
mass_min=0.05)
self.assertTrue(alg.mode==RuleInductionType.BINARY)
x, outcomes = results
y = {k:v[:, -1] for k,v in outcomes.items()}
temp_results = (x,y)
alg = cart.setup_cart(temp_results,
'deceased population region 1',
mass_min=0.05)
self.assertTrue(alg.mode==RuleInductionType.REGRESSION)
n_cols = 5
unc = x.columns.values[0:n_cols]
alg = cart.setup_cart(results,
flu_classify,
mass_min=0.05,
incl_unc=unc)
self.assertTrue(alg.mode==RuleInductionType.BINARY)
self.assertTrue(alg.x.shape[1]==n_cols)
with self.assertRaises(TypeError):
alg = cart.setup_cart(results, 10,
mass_min=0.05)
def test_build_tree(self):
results = utilities.load_flu_data()
alg = cart.setup_cart(results, flu_classify, mass_min=0.05)
alg.build_tree()
self.assertTrue(isinstance(alg.clf, cart.tree.DecisionTreeClassifier))
x, outcomes = results
y = {k: v[:, -1] for k, v in outcomes.items()}
temp_results = (x, y)
alg = cart.setup_cart(temp_results,
'deceased population region 1',
mass_min=0.05)
alg.build_tree()
self.assertTrue(isinstance(alg.clf, cart.tree.DecisionTreeRegressor))
def test_build_tree(self):
results = utilities.load_flu_data()
alg = cart.setup_cart(results, flu_classify,
mass_min=0.05)
alg.build_tree()
self.assertTrue(isinstance(alg.clf,
cart.tree.DecisionTreeClassifier))
x, outcomes = results
y = {k:v[:, -1] for k,v in outcomes.items()}
temp_results = (x,y)
alg = cart.setup_cart(temp_results,
'deceased population region 1',
mass_min=0.05)
alg.build_tree()
self.assertTrue(isinstance(alg.clf,
cart.tree.DecisionTreeRegressor))
def test_show_tree(self):
results = utilities.load_flu_data()
alg = cart.setup_cart(results, flu_classify,
mass_min=0.05)
alg.build_tree()
fig = alg.show_tree(mplfig=True)
bytestream = alg.show_tree(mplfig=False)
self.assertTrue(isinstance(fig, mpl.figure.Figure))
self.assertTrue(isinstance(bytestream, bytes))
return classes
# load data
fn = './data/1000 flu cases with policies.tar.gz'
results = load_results(fn)
experiments, results = results
# extract results for 1 policy
logical = experiments['policy'] == 'no policy'
new_experiments = experiments[logical]
new_results = {}
for key, value in results.items():
new_results[key] = value[logical]
results = (new_experiments, new_results)
# perform cart on modified results tuple
cart_alg = cart.setup_cart(results, classify, mass_min=0.05)
cart_alg.build_tree()
# print cart to std_out
print(cart_alg.stats_to_dataframe())
print(cart_alg.boxes_to_dataframe())
# visualize
cart_alg.display_boxes(together=True)
plt.show()
def test_setup_cart(self):
results = test_utilities.load_flu_data()
cart_algorithm = cart.setup_cart(results, flu_classify, mass_min=0.05)
return classes
# load data
fn = r'./data/1000 flu cases.tar.gz'
results = load_results(fn)
experiments, results = results
# extract results for 1 policy
logical = experiments['policy'] == 'no policy'
new_experiments = experiments[logical]
new_results = {}
for key, value in results.items():
new_results[key] = value[logical]
results = (new_experiments, new_results)
# perform cart on modified results tuple
cart_alg = cart.setup_cart(results, classify, mass_min=0.05)
cart_alg.build_tree()
# print cart to std_out
print cart_alg.stats_to_dataframe()
print cart_alg.boxes_to_dataframe()
# visualize
cart_alg.display_boxes(together=True)
plt.show()
def test_setup_cart(self):
results = test_utilities.load_flu_data()
cart_algorithm = cart.setup_cart(results, flu_classify, mass_min=0.05)
