Python SDML_Supervised.fit_transform Examples

Example #1

    def test_sdml_supervised(self):
        seed = np.random.RandomState(1234)
        sdml = SDML_Supervised(num_constraints=1500)
        sdml.fit(self.X, self.y, random_state=seed)
        res_1 = sdml.transform()

        seed = np.random.RandomState(1234)
        sdml = SDML_Supervised(num_constraints=1500)
        res_2 = sdml.fit_transform(self.X, self.y, random_state=seed)

        assert_array_almost_equal(res_1, res_2)

Example #2

File: test_fit_transform.py Project: scikit-learn-contrib/metric-learn

  def test_sdml_supervised(self):
    seed = np.random.RandomState(1234)
    sdml = SDML_Supervised(n_constraints=1500, balance_param=1e-5,
                           prior='identity', random_state=seed)
    sdml.fit(self.X, self.y)
    res_1 = sdml.transform(self.X)

    seed = np.random.RandomState(1234)
    sdml = SDML_Supervised(n_constraints=1500, balance_param=1e-5,
                           prior='identity', random_state=seed)
    res_2 = sdml.fit_transform(self.X, self.y)

    assert_array_almost_equal(res_1, res_2)

Example #3

  def test_sdml_supervised(self):
    seed = np.random.RandomState(1234)
    sdml = SDML_Supervised(num_constraints=1500, balance_param=1e-5,
                           use_cov=False)
    sdml.fit(self.X, self.y, random_state=seed)
    res_1 = sdml.transform(self.X)

    seed = np.random.RandomState(1234)
    sdml = SDML_Supervised(num_constraints=1500, balance_param=1e-5,
                           use_cov=False)
    res_2 = sdml.fit_transform(self.X, self.y, random_state=seed)

    assert_array_almost_equal(res_1, res_2)