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from unittest.mock import Mock
import numpy as np
from Orange import classification, evaluation
from Orange.data import Table, Domain, DiscreteVariable
from Orange.evaluation import Results
from Orange.evaluation.performance_curves import Curves
from Orange.tests import test_filename
from Orange.widgets.tests.base import WidgetTest
class EvaluateTest(WidgetTest):
def setUp(self):
super().setUp()
n, p = (0, 1)
actual, probs = np.array([
(p, .8), (n, .7), (p, .6), (p, .55), (p, .54), (n, .53), (n, .52),
(p, .51), (n, .505), (p, .4), (n, .39), (p, .38), (n, .37),
(n, .36), (n, .35), (p, .34), (n, .33), (p, .30), (n, .1)]).T
self.curves = Curves(actual, probs)
probs2 = (probs + 1) / 2
self.curves2 = Curves(actual, probs2)
pred = probs > 0.5
pred2 = probs2 > 0.5
probs = np.vstack((1 - probs, probs)).T
probs2 = np.vstack((1 - probs2, probs2)).T
domain = Domain([], DiscreteVariable("y", values=("a", "b")))
self.results = Results(
domain=domain,
actual=actual,
folds=np.array([Ellipsis]),
models=np.array([[Mock(), Mock()]]),
row_indices=np.arange(19),
predicted=np.array((pred, pred2)),
probabilities=np.array([probs, probs2]))
self.lenses = data = Table(test_filename("datasets/lenses.tab"))
majority = classification.MajorityLearner()
majority.name = "majority"
knn3 = classification.KNNLearner(n_neighbors=3)
knn3.name = "knn-3"
knn1 = classification.KNNLearner(n_neighbors=1)
knn1.name = "knn-1"
self.lenses_results = evaluation.TestOnTestData(
store_data=True, store_models=True)(
data=data[::2], test_data=data[1::2],
learners=[majority, knn3, knn1])
self.lenses_results.learner_names = ["majority", "knn-3", "knn-1"]
def test_many_evaluation_results(self):
if not hasattr(self, "widget"):
return
data = Table("iris")
learners = [
classification.MajorityLearner(),
classification.LogisticRegressionLearner(),
classification.TreeLearner(),
classification.SVMLearner(),
classification.KNNLearner(),
classification.CN2Learner(),
classification.SGDClassificationLearner(),
classification.RandomForestLearner(),
classification.NaiveBayesLearner(),
classification.SGDClassificationLearner()
]
res = evaluation.CrossValidation(k=2, store_data=True)(data, learners)
# this is a mixin; pylint: disable=no-member
self.send_signal("Evaluation Results", res)
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