Python skillmodels.SkillModel类代码示例

    def test_loadings_intercepts_transparams_anchparams_and_xzeros(self):
        self.nobs = 5000
        self.base_meas_sd = 0.00001
        self.base_trans_sd = 0.00001
        self.anch_sd = 0.1

        self.true_meas_sd = self.true_loadings * self.base_meas_sd
        self.true_meas_var = self.true_meas_sd ** 2
        self.true_trans_sd = self.base_trans_sd * np.arange(
            start=0.2, step=0.1, stop=0.75).reshape(self.nperiods - 1, 2)
        self.true_trans_var = self.true_trans_sd ** 2
        self.true_cov_matrix = np.array([[1.44, 0.05, 0.1],
                                         [0.05, 2.25, 0.0],
                                         [0.1, 0.0, 4.0]])
        self.true_P_zero = self.true_cov_matrix[np.triu_indices(self.nfac)]

        self.y_data = generate_test_data(
            nobs=self.nobs, factors=self.factor_names, periods=self.periods,
            included_positions=self.included_positions,
            meas_names=self.meas_names,
            initial_mean=self.true_X_zero, initial_cov=self.true_cov_matrix,
            intercepts=self.true_intercepts, loadings=self.true_loadings,
            meas_sd=self.true_meas_sd, gammas=self.true_gammas,
            trans_sd=self.true_trans_sd,
            anch_intercept=self.anch_intercept,
            anch_loadings=self.anch_loadings, anch_sd=self.anch_sd)

        wa_model = SkillModel(
            model_name='no_squares_translog', dataset_name='test_data',
            model_dict=model_dict, dataset=self.y_data, estimator='wa')

        calc_storage_df, calc_X_zero, calc_P_zero, calc_gammas, trans_vars, \
            anch_intercept, anch_loadings, anch_variance = \
            wa_model._calculate_wa_quantities()

        calc_loadings = calc_storage_df['loadings']
        calc_intercepts = calc_storage_df['intercepts']

        aaae(calc_loadings.values, self.true_loadings, decimal=3)
        aaae(calc_intercepts.values, self.true_intercepts, decimal=3)
        aaae(calc_X_zero, self.true_X_zero, decimal=1)
        for arr1, arr2 in zip(calc_gammas, self.true_gammas):
            aaae(arr1, arr2, decimal=3)
        assert_almost_equal(anch_intercept, 3.0, places=1)
        aaae(anch_loadings, self.anch_loadings, decimal=2)

 def test_set_bounds_for_Q_robust(self):
     self.robust_bounds = True
     expected = np.zeros(100, dtype=object)
     expected[:] = None
     expected[10: 30] = 0.001
     smo._set_bounds_for_Q(self, slice(10, 30))
     aae(self.lower_bound, expected)

 def test_set_bounds_P_zero_restricted_not_robust(self):
     self.robust_bounds = False
     self.restrict_P_zeros = True
     expected = np.zeros(100, dtype=object)
     expected[:] = None
     expected[self.bound_indices[:4]] = 0.0
     smo._set_bounds_for_P_zero(self, slice(10, 20))
     aae(self.lower_bound, expected)

    def test_predict_ff_mocked_same_result_in_second(self, mock_tsp, mock_pp):
        # this test makes sure that y copy arrays where necessary
        mock_tsp.side_effect = fake_tsp
        self.likelihood_arguments_dict = Mock(return_value=self.lh_args)

        calc1 = smo._predict_final_factors(self, self.change)
        calc2 = smo._predict_final_factors(self, self.change)
        aaae(calc1, calc2)

 def test_params_slice_deltas(self):
     arr0 = np.ones((4, 2), dtype=bool)
     arr1 = np.ones((6, 3), dtype=bool)
     arr1[(0, 1), :] = 0
     self._deltas_bool = Mock(return_value=[arr0, arr1, arr0])
     self._general_params_slice = Mock()
     smo._params_slice_for_deltas(self, 'short')
     self._general_params_slice.assert_has_calls(
         [call(8), call(12), call(8)])

    def test_set_bounds_for_X_zero(self):
        self.lower_bound = np.empty(100, dtype=object)
        self.lower_bound[:] = None

        params_slice = slice(10, 22)

        expected = self.lower_bound.copy()
        expected[[16, 20]] = 0

        smo._set_bounds_for_X_zero(self, params_slice=params_slice)

        aae(self.lower_bound, expected)

 def test_marginal_effect_outcome_anch_outcome(self):
     self.anchoring = True
     self.me_anchor_on = True
     self.me_on = 'anch_outcome'
     exp = np.ones((10)) * 4
     calc = smo._marginal_effect_outcome(self, self.change)
     aaae(calc, exp)

 def test_extended_meas_coeffs_no_constant_factor_and_intercepts_case(self):
     coeff_type = 'intercepts'
     calc_intercepts = smo.extended_meas_coeffs(self, coeff_type, 0)
     expected_intercepts = pd.Series(
         data=[0.8, 1.2, 1.6, 2.0],
         name='intercepts', index=['y01', 'y02', 'y03', 'y04'])
     assert_series_equal(calc_intercepts, expected_intercepts)

 def test_predict_ff_intermediate_false_mocked(self, mock_tsp, mock_pp):
     mock_tsp.side_effect = fake_tsp
     self.likelihood_arguments_dict = Mock(return_value=self.lh_args)
     exp = np.ones((10, 2)) * 4
     exp[:, 0] = 12
     calc = smo._predict_final_factors(self, self.change)
     aaae(calc, exp)

 def test_expand_params(self, mock_pt):
     mock_pt.transform_params_for_X_zero.return_value = np.arange(3)
     mock_pt.transform_params_for_trans_coeffs.return_value = np.ones(9)
     mock_pt.transform_params_for_P_zero.return_value = np.ones(3) * 17
     expected = np.array([0] * 5 + [1] * 9 + [0, 1, 2] + [17] * 3)
     aae(smo._transform_params(self, np.zeros(18), 'short_to_long'),
         expected)

 def test_all_variables_for_iv_equations_constant_factor(self):
     calc_meas_list = smo.all_variables_for_iv_equations(
         self, 1, 'f1', 'test')
     expected_meas_list = [
         ['y11_test', 'y12_test'],
         ['y07_copied_test', 'y08_copied_test']]
     assert_equal(calc_meas_list, expected_meas_list)

 def test_all_bootstrap_params(self):
     calc_params = smo.all_bootstrap_params(self, params=np.ones(3))
     expected_params = pd.DataFrame(
         data=[[0.0] * 3, [1.0] * 3, [2.0] * 3],
         index=['rep_0', 'rep_1', 'rep_2'],
         columns=['p1', 'p2', 'p3'])
     assert_frame_equal(calc_params, expected_params)

 def test_initial_trans_coeffs(self, mock_tf):
     mock_tf.nr_coeffs_first_func.return_value = 3
     mock_tf.nr_coeffs_second_func.return_value = 10
     expected = [np.zeros((2, 3)), np.zeros((2, 10))]
     initials = smo._initial_trans_coeffs(self)
     for i, e in zip(initials, expected):
         aae(i, e)

 def test_residual_measurements(self):
     expected_data = np.array([
         [1.5, 2],
         [0.5, -2]])
     expected_resid = pd.DataFrame(
         expected_data, columns=['m1_resid', 'm2_resid'])
     calc_resid = smo.residual_measurements(self, period=1)
     assert_frame_equal(calc_resid, expected_resid)

    def test_set_bounds_for_trans_coeffs(self, mock_tf):
        lb = np.array([0, None, None], dtype=object)
        ub = np.array([None, None, 1], dtype=object)
        mock_tf.bounds_first_func.return_value = (lb, ub)
        del mock_tf.bounds_second_func
        sl = [[slice(0, 3)] * 2, [slice(3, 13), slice(13, 23)]]

        expected_lb = self.lower_bound.copy()
        expected_lb[0] = 0

        expected_ub = self.upper_bound.copy()
        expected_ub[2] = 1

        smo._set_bounds_for_trans_coeffs(self, sl)

        aae(self.lower_bound, expected_lb)
        aae(self.upper_bound, expected_ub)