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]