def test_kmeans_breastcancer(self):
# python -m unittest tests_clustering.Tests_Clustering.test_kmeans_breastcancer
from discomll.clustering import kmeans
from sklearn.cluster import KMeans
max_iter = 10
clusters = 2
random_seed = 2
x_train, _, x_test, _ = datasets.breastcancer_disc()
train_data, test_data = datasets.breastcancer_disc_discomll()
kmeans2 = KMeans(n_clusters=clusters,
max_iter=max_iter,
n_init=1,
random_state=random_seed).fit(x_train)
centroids1 = kmeans2.cluster_centers_
predictions1 = kmeans2.predict(x_test)
centroids_url = kmeans.fit(train_data,
n_clusters=clusters,
max_iterations=max_iter,
random_state=random_seed)
predictions_url = kmeans.predict(test_data, centroids_url)
predictions2 = [v[0] for k, v in result_iterator(predictions_url)]
centroids2 = [
v["x"]
for k, v in result_iterator(centroids_url["kmeans_fitmodel"])
]
centroids2[0], centroids2[1] = centroids2[1], centroids2[0]
self.assertTrue(np.allclose(centroids1, centroids2))
def test_kmeans_breastcancer(self):
# python -m unittest tests_clustering.Tests_Clustering.test_kmeans_breastcancer
from discomll.clustering import kmeans
from sklearn.cluster import KMeans
max_iter = 10
clusters = 2
random_seed = 2
x_train, _, x_test, _ = datasets.breastcancer_disc()
train_data, test_data = datasets.breastcancer_disc_discomll()
kmeans2 = KMeans(n_clusters=clusters, max_iter=max_iter, n_init=1, random_state=random_seed).fit(x_train)
centroids1 = kmeans2.cluster_centers_
predictions1 = kmeans2.predict(x_test)
centroids_url = kmeans.fit(train_data,
n_clusters=clusters,
max_iterations=max_iter,
random_state=random_seed)
predictions_url = kmeans.predict(test_data, centroids_url)
predictions2 = [v[0] for k, v in result_iterator(predictions_url)]
centroids2 = [v["x"] for k, v in result_iterator(centroids_url["kmeans_fitmodel"])]
centroids2[0], centroids2[1] = centroids2[1], centroids2[0]
self.assertTrue(np.allclose(centroids1, centroids2))
def test_naivebayes_breastcancer(self):
# python -m unittest tests_classification.Tests_Classification.test_naivebayes_breastcancer
from discomll.classification import naivebayes
train_data1, test_data1 = datasets.breastcancer_disc_orange()
train_data2, test_data2 = datasets.breastcancer_disc_discomll()
for m in range(3):
learner = Orange.classification.bayes.NaiveLearner(m=m)
classifier = learner(train_data1)
predictions1 = [classifier(inst, Orange.classification.Classifier.GetBoth) for inst in test_data1]
predictions1_target = [v[0].value for v in predictions1]
predictions1_probs = [v[1].values() for v in predictions1]
fitmodel_url = naivebayes.fit(train_data2)
predictions_url = naivebayes.predict(test_data2, fitmodel_url, m=m)
predictions2_target = []
predictions2_probs = []
for k, v in result_iterator(predictions_url):
predictions2_target.append(v[0])
predictions2_probs.append(v[1])
self.assertListEqual(predictions1_target, predictions2_target)
self.assertTrue(np.allclose(predictions1_probs, predictions2_probs))
def test_naivebayes_breastcancer(self):
# python -m unittest tests_classification.Tests_Classification.test_naivebayes_breastcancer
from discomll.classification import naivebayes
train_data1, test_data1 = datasets.breastcancer_disc_orange()
train_data2, test_data2 = datasets.breastcancer_disc_discomll()
for m in range(3):
learner = Orange.classification.bayes.NaiveLearner(m=m)
classifier = learner(train_data1)
predictions1 = [classifier(inst, Orange.classification.Classifier.GetBoth) for inst in test_data1]
predictions1_target = [v[0].value for v in predictions1]
predictions1_probs = [v[1].values() for v in predictions1]
fitmodel_url = naivebayes.fit(train_data2)
predictions_url = naivebayes.predict(test_data2, fitmodel_url, m=m)
predictions2_target = []
predictions2_probs = []
for k, v in result_iterator(predictions_url):
predictions2_target.append(v[0])
predictions2_probs.append(v[1])
self.assertListEqual(predictions1_target, predictions2_target)
self.assertTrue(np.allclose(predictions1_probs, predictions2_probs))
