Remove unused jinja templates
#199
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Signed-off-by: neNasko1 <[email protected]>
Signed-off-by: neNasko1 <[email protected]>
neNasko1
requested review from
adityagoel4512,
cbourjau and
jbachurski
as code owners
adityagoel4512
requested changes
Jan 3, 2025
| @@ -1,33 +0,0 @@ | |||
| def xloop( | |||
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It looks like xloop was made private at some point and then removed. Let's let @jbachurski or @cbourjau weigh in briefly for some historical context first, but to my eye this looks perfectly reasonable.
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I don’t remember this very well, so it may have been an attempt to make the API more convenient. It’s probably fine to remove since it’s unused.
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### Description This change: - Introduces attribute checking for `CategoryMapper` - Includes tests for `CategoryMapper` in the case of wrong attributes - Includes tests for `OneHotEncoder` in the case of wrong attributes - Fix the tests around `TreeEnsemble` ### Motivation and Context There is some similar functionality currently in [spox](https://github.com/Quantco/spox), ([related](Quantco/spox#199)), which will be better lived here. Note: The fix around the `TreeEnsemble` tests is related to the fact that the test parametrization was wrongly implemented. Relevant [documentation](https://pypi.org/project/parameterized/). ``` > xxx(10180)test_tree_ensemble_fails_if_invalid_attributes() -> breakpoint() (Pdb) ll 10133 @parameterized.expand( 10134 [ 10135 { 10136 "nodes_truenodeids": [0] * 6, 10137 "leaf_weights": make_tensor( 10138 "leaf_weights", TensorProto.DOUBLE, (9,), [1] * 9 10139 ), 10140 "nodes_splits": make_tensor( 10141 "nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5 10142 ), 10143 }, 10144 { 10145 "nodes_truenodeids": [0] * 5, 10146 "leaf_weights": make_tensor( 10147 "leaf_weights", TensorProto.FLOAT, (9,), [1] * 9 10148 ), 10149 "nodes_splits": make_tensor( 10150 "nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5 10151 ), 10152 }, 10153 { 10154 "nodes_truenodeids": [0] * 5, 10155 "leaf_weights": make_tensor( 10156 "leaf_weights", TensorProto.DOUBLE, (18,), [1] * 18 10157 ), 10158 "nodes_splits": make_tensor( 10159 "nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5 10160 ), 10161 }, 10162 { 10163 "nodes_truenodeids": [0] * 5, 10164 "leaf_weights": make_tensor( 10165 "leaf_weights", TensorProto.DOUBLE, (9,), [1] * 9 10166 ), 10167 "nodes_splits": make_tensor( 10168 "nodes_splits", TensorProto.FLOAT, (5,), [1] * 5 10169 ), 10170 }, 10171 ] 10172 ) 10173 @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators") 10174 def test_tree_ensemble_fails_if_invalid_attributes( 10175 self, 10176 nodes_truenodeids, 10177 leaf_weights, 10178 nodes_splits, 10179 ) -> None: 10180 -> breakpoint() 10181 interior_nodes = 5 10182 leaves = 9 10183 tree = make_node( 10184 "TreeEnsemble", 10185 ["x"], 10186 ["y"], 10187 domain=ONNX_ML_DOMAIN, 10188 n_targets=5, 10189 nodes_featureids=[0] * interior_nodes, 10190 nodes_splits=nodes_splits, 10191 nodes_modes=make_tensor( 10192 "nodes_modes", 10193 TensorProto.UINT8, 10194 (interior_nodes,), 10195 [0] * interior_nodes, 10196 ), 10197 nodes_truenodeids=nodes_truenodeids, 10198 nodes_falsenodeids=[0] * interior_nodes, 10199 nodes_trueleafs=[0] * interior_nodes, 10200 nodes_falseleafs=[0] * interior_nodes, 10201 leaf_targetids=[0] * leaves, 10202 leaf_weights=leaf_weights, 10203 tree_roots=[0], 10204 ) 10205 10206 graph = self._make_graph( 10207 [("x", TensorProto.DOUBLE, ("Batch Size", "Features"))], 10208 [tree], 10209 [], 10210 ) 10211 self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) (Pdb) nodes_truenodeids 'nodes_truenodeids' (Pdb) leaf_weights 'leaf_weights' (Pdb) nodes_splits 'nodes_splits' (Pdb) ``` --------- Signed-off-by: neNasko1 <[email protected]>
Signed-off-by: Atanas Dimitrov <[email protected]>
adityagoel4512
approved these changes
Jan 6, 2025
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This PR removes some of the old/unused
jinjatemplates.Also based on the following upstreamed changes we can safely remove some of the custom type propagation subroutines:
Since we do some extra checking around the following operators, I am asking for input whether to include them in this PR.
EDIT: We decided to wait for those 3 operators until some later
onnxrelease.Checklist
CHANGELOG.rstentry