(self)
| 100 | cls.booster = xgb.train({"tree_method": "hist"}, dtrain, num_boost_round=10) |
| 101 | |
| 102 | def test_predict(self): |
| 103 | booster = self.booster |
| 104 | X = self.X |
| 105 | test = self.test |
| 106 | |
| 107 | predt_from_array = booster.inplace_predict(X[:10, ...], missing=self.missing) |
| 108 | predt_from_dmatrix = booster.predict(test) |
| 109 | |
| 110 | X_obj = X.copy().astype(object) |
| 111 | |
| 112 | assert X_obj.dtype.hasobject is True |
| 113 | assert X.dtype.hasobject is False |
| 114 | np.testing.assert_allclose( |
| 115 | booster.inplace_predict(X_obj), booster.inplace_predict(X) |
| 116 | ) |
| 117 | |
| 118 | np.testing.assert_allclose(predt_from_dmatrix, predt_from_array) |
| 119 | |
| 120 | predt_from_array = booster.inplace_predict( |
| 121 | X[:10, ...], iteration_range=(0, 4), missing=self.missing |
| 122 | ) |
| 123 | predt_from_dmatrix = booster.predict(test, iteration_range=(0, 4)) |
| 124 | |
| 125 | np.testing.assert_allclose(predt_from_dmatrix, predt_from_array) |
| 126 | |
| 127 | with pytest.raises(ValueError): |
| 128 | booster.predict(test, iteration_range=(0, booster.num_boosted_rounds() + 2)) |
| 129 | |
| 130 | default = booster.predict(test) |
| 131 | |
| 132 | range_full = booster.predict(test, iteration_range=(0, self.num_boost_round)) |
| 133 | np.testing.assert_allclose(range_full, default) |
| 134 | |
| 135 | range_full = booster.predict( |
| 136 | test, iteration_range=(0, booster.num_boosted_rounds()) |
| 137 | ) |
| 138 | np.testing.assert_allclose(range_full, default) |
| 139 | |
| 140 | def predict_dense(x): |
| 141 | inplace_predt = booster.inplace_predict(x) |
| 142 | d = xgb.DMatrix(x) |
| 143 | copied_predt = booster.predict(d) |
| 144 | return np.all(copied_predt == inplace_predt) |
| 145 | |
| 146 | for i in range(10): |
| 147 | run_threaded_predict(X, self.rows, predict_dense) |
| 148 | |
| 149 | def predict_csr(x): |
| 150 | inplace_predt = booster.inplace_predict(sparse.csr_matrix(x)) |
| 151 | d = xgb.DMatrix(x) |
| 152 | copied_predt = booster.predict(d) |
| 153 | return np.all(copied_predt == inplace_predt) |
| 154 | |
| 155 | for i in range(10): |
| 156 | run_threaded_predict(X, self.rows, predict_csr) |
| 157 | |
| 158 | @pytest.mark.skipif(**tm.no_pandas()) |
| 159 | def test_predict_pd(self): |
nothing calls this directly
no test coverage detected