Exemplos de KNearestDatasets.kBestSuggestions em Python

Exemplo n.º 1

    def test_kBestSuggestions(self):
        kND = KNearestDatasets()
        kND.fit(pd.DataFrame([self.krvskp, self.labor]),
                self.runs.loc[:, [233, 234]])
        neighbor = kND.kBestSuggestions(self.anneal, 1)
        self.assertEqual([(233, 1.8229893712531495, 1)], neighbor)
        neighbors = kND.kBestSuggestions(self.anneal, 2)
        self.assertEqual([(233, 1.8229893712531495, 1),
                          (234, 2.2679197196559415, 2)], neighbors)
        neighbors = kND.kBestSuggestions(self.anneal, -1)
        self.assertEqual([(233, 1.8229893712531495, 1),
                          (234, 2.2679197196559415, 2)], neighbors)

        self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, 0)
        self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, -2)

Exemplo n.º 2

 def test_random_metric(self):
     kND = KNearestDatasets(metric=get_random_metric(random_state=1))
     kND.fit(pd.DataFrame([self.krvskp, self.labor]),
             self.runs.loc[:, [233, 234]])
     distances = []
     for i in range(20):
         neighbor = kND.kBestSuggestions(self.anneal, 1)
         distances.append(neighbor[0][1])
     self.assertEqual(len(np.unique(distances)), 20)

Exemplo n.º 3

Arquivo: test_kND.py Projeto: Allen1203/auto-sklearn

    def test_kBestSuggestions(self):
        kND = KNearestDatasets()
        kND.fit(pd.DataFrame([self.krvskp, self.labor]),
                self.runs.loc[:,[233, 234]])
        neighbor = kND.kBestSuggestions(self.anneal, 1)
        self.assertEqual([(233, 1.8229893712531495, 1)],
                         neighbor)
        neighbors = kND.kBestSuggestions(self.anneal, 2)
        self.assertEqual([(233, 1.8229893712531495, 1),
                          (234, 2.2679197196559415, 2)],
                         neighbors)
        neighbors = kND.kBestSuggestions(self.anneal, -1)
        self.assertEqual([(233, 1.8229893712531495, 1),
                          (234, 2.2679197196559415, 2)],
                         neighbors)

        self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, 0)
        self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, -2)

Exemplo n.º 4

Arquivo: test_kND.py Projeto: Allen1203/auto-sklearn

 def test_random_metric(self):
     kND = KNearestDatasets(metric=get_random_metric(random_state=1))
     kND.fit(pd.DataFrame([self.krvskp, self.labor]),
             self.runs.loc[:,[233, 234]])
     distances = []
     for i in range(20):
         neighbor = kND.kBestSuggestions(self.anneal, 1)
         distances.append(neighbor[0][1])
     self.assertEqual(len(np.unique(distances)), 20)

Exemplo n.º 5

Arquivo: test_kND.py Projeto: zrdail/auto-sklearn

    def test_kBestSuggestions(self):
        kND = KNearestDatasets()
        kND.fit(pd.DataFrame([self.krvskp, self.labor]),
                self.runs.loc[:, [233, 234]])
        neighbor = kND.kBestSuggestions(self.anneal, 1)
        np.testing.assert_array_almost_equal(
            [(233, 3.8320802803440586, 1)],
            neighbor,
        )
        neighbors = kND.kBestSuggestions(self.anneal, 2)
        np.testing.assert_array_almost_equal(
            [(233, 3.8320802803440586, 1), (234, 4.367919719655942, 2)],
            neighbors,
        )
        neighbors = kND.kBestSuggestions(self.anneal, -1)
        np.testing.assert_array_almost_equal(
            [(233, 3.8320802803440586, 1), (234, 4.367919719655942, 2)],
            neighbors,
        )

        self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, 0)
        self.assertRaises(ValueError, kND.kBestSuggestions, self.anneal, -2)