def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.same_input_output_domain = False
cls.signal_name = "Data"
cls.signal_data = cls.data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
cls.housing = Table("housing")
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
cls.scorename = "Silhouette ({})".format(cls.data.domain.class_var.name)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.same_input_output_domain = False
cls.signal_name = "Data"
cls.signal_data = cls.data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Distances"
cls.signal_data = Euclidean(cls.data)
cls.same_input_output_domain = False
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
cls.scorename = "Silhouette ({})".format(cls.data.domain.class_var.name)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Distances"
cls.signal_data = Euclidean(cls.data)
cls.same_input_output_domain = False
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
cls.titanic = Table("titanic")
cls.iris = Table("iris")
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
cls.titanic = Table("titanic")
cls.iris = Table("iris")
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
cls.titanic = Table("titanic")
cls.housing = Table("housing")
cls.heart = Table("heart_disease")
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.distances = Euclidean(cls.data)
cls.signal_name = "距离(Distances)"
cls.signal_data = cls.distances
cls.same_input_output_domain = False
cls.distances_cols = Euclidean(cls.data, axis=0)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.housing = Table("housing")
cls.titanic = Table("titanic")
cls.brown_selected = Table("brown-selected")
cls.signal_name = "Data"
cls.signal_data = cls.data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Distances"
cls.signal_data = Euclidean(cls.data)
cls.same_input_output_domain = False
my_dir = os.path.dirname(__file__)
datasets_dir = os.path.join(my_dir, '..', '..', '..', 'datasets')
cls.datasets_dir = os.path.realpath(datasets_dir)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.iris = Table("iris")
cls.zoo = Table("zoo")
cls.housing = Table("housing")
cls.titanic = Table("titanic")
cls.heart = Table("heart_disease")
cls.data = cls.iris
cls.signal_name = "Data"
cls.signal_data = cls.data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls._init_data()
cls.signal_name = "Reference Data"
cls.signal_data = cls.data
cls.same_input_output_domain = False
genes_path = serverfiles.localpath_download(
"marker_genes", "panglao_gene_markers.tab")
filter_ = FilterString("Organism", FilterString.Equal, "Human")
cls.genes = Values([filter_])(Table(genes_path))
cls.genes.attributes[TAX_ID] = "9606"
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.titanic = Table('titanic')
cls.learner = CN2Learner()
cls.classifier = cls.learner(cls.titanic)
# CN2Learner does not add `instances` attribute to the model, but
# the Rules widget does. We simulate the model we get from the widget.
cls.classifier.instances = cls.titanic
cls.signal_name = "Classifier"
cls.signal_data = cls.classifier
cls.data = cls.titanic
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.titanic = Table('titanic')
cls.learner = CN2Learner()
cls.classifier = cls.learner(cls.titanic)
# CN2Learner does not add `instances` attribute to the model, but
# the Rules widget does. We simulate the model we get from the widget.
cls.classifier.instances = cls.titanic
cls.signal_name = "Classifier"
cls.signal_data = cls.classifier
cls.data = cls.titanic
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Load a dataset that contains two variables with the same entropy
data_same_entropy = Table(
path.join(path.dirname(path.dirname(path.dirname(__file__))),
"tests", "datasets", "same_entropy.tab"))
cls.data_same_entropy = tree(data_same_entropy)
cls.data_same_entropy.instances = data_same_entropy
vara = DiscreteVariable("aaa", values=("e", "f", "g"))
root = DiscreteNode(vara, 0, np.array([42, 8]))
root.subset = np.arange(50)
varb = DiscreteVariable("bbb", values=tuple("ijkl"))
child0 = MappedDiscreteNode(varb, 1, np.array([0, 1, 0, 0]), (38, 5))
child0.subset = np.arange(16)
child1 = Node(None, 0, (13, 3))
child1.subset = np.arange(16, 30)
varc = ContinuousVariable("ccc")
child2 = NumericNode(varc, 2, 42, (78, 12))
child2.subset = np.arange(30, 50)
root.children = (child0, child1, child2)
child00 = Node(None, 0, (15, 4))
child00.subset = np.arange(10)
child01 = Node(None, 0, (10, 5))
child01.subset = np.arange(10, 16)
child0.children = (child00, child01)
child20 = Node(None, 0, (90, 4))
child20.subset = np.arange(30, 35)
child21 = Node(None, 0, (70, 9))
child21.subset = np.arange(35, 50)
child2.children = (child20, child21)
domain = Domain([vara, varb, varc], ContinuousVariable("y"))
t = [[i, j, k] for i in range(3) for j in range(4) for k in (40, 44)]
x = np.array((t * 3)[:50])
data = Table.from_numpy(domain, x, np.arange(len(x)))
cls.tree = TreeModel(data, root)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
bayes = NaiveBayesLearner()
tree = TreeLearner()
iris = cls.data
titanic = Table("titanic")
common = dict(k=3, store_data=True)
cls.results_1_iris = CrossValidation(iris, [bayes], **common)
cls.results_2_iris = CrossValidation(iris, [bayes, tree], **common)
cls.results_2_titanic = CrossValidation(titanic, [bayes, tree],
**common)
cls.signal_name = "Evaluation Results"
cls.signal_data = cls.results_1_iris
cls.same_input_output_domain = False
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Load a dataset that contains two variables with the same entropy
data_same_entropy = Table(path.join(
path.dirname(path.dirname(path.dirname(__file__))), "tests",
"datasets", "same_entropy.tab"))
cls.data_same_entropy = tree(data_same_entropy)
cls.data_same_entropy.instances = data_same_entropy
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Load a dataset that contains two variables with the same entropy
data_same_entropy = Table(
path.join(path.dirname(path.dirname(path.dirname(__file__))),
"tests", "datasets", "same_entropy.tab"))
cls.data_same_entropy = tree(data_same_entropy)
cls.data_same_entropy.instances = data_same_entropy
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
bayes = NaiveBayesLearner()
tree = TreeLearner()
# `data` is defined in WidgetOutputsTestMixin, pylint: disable=no-member
cls.iris = cls.data
titanic = Table("titanic")
cv = CrossValidation(k=3, store_data=True)
cls.results_1_iris = cv(cls.iris, [bayes])
cls.results_2_iris = cv(cls.iris, [bayes, tree])
cls.results_2_titanic = cv(titanic, [bayes, tree])
cls.signal_name = "Evaluation Results"
cls.signal_data = cls.results_1_iris
cls.same_input_output_domain = False
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
bayes = NaiveBayesLearner()
tree = TreeLearner()
iris = cls.data
titanic = Table("titanic")
common = dict(k=3, store_data=True)
cls.results_1_iris = CrossValidation(iris, [bayes], **common)
cls.results_2_iris = CrossValidation(iris, [bayes, tree], **common)
cls.results_2_titanic = CrossValidation(titanic, [bayes, tree],
**common)
cls.signal_name = "Evaluation Results"
cls.signal_data = cls.results_1_iris
cls.same_input_output_domain = False
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
# Set up for output tests
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Set up for widget tests
titanic_data = Table('titanic')[::50]
cls.titanic = TreeLearner(max_depth=1)(titanic_data)
cls.titanic.instances = titanic_data
housing_data = Table('housing')[:10]
cls.housing = TreeLearner(max_depth=1)(housing_data)
cls.housing.instances = housing_data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
# Set up for output tests
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Set up for widget tests
titanic_data = Table('titanic')[::50]
cls.titanic = TreeLearner(max_depth=1)(titanic_data)
cls.titanic.instances = titanic_data
housing_data = Table('housing')[:10]
cls.housing = TreeLearner(max_depth=1)(housing_data)
cls.housing.instances = housing_data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data # pylint: disable=no-member
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "距离(Distances)"
cls.signal_data = Euclidean(cls.data)
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls, output_all_on_no_selection=True)
cls.signal_name = "Data"
cls.signal_data = cls.data # pylint: disable=no-member
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data # pylint: disable=no-member
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_name = "Data"
cls.signal_data = cls.data
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.signal_data = cls.data[:25]
