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()