fix: transform onehot encoder outputs to float32 tensor
#3242
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arnavgarg1
requested changes
Mar 14, 2023
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| def test_onehot_category_encoder(): | ||
| config = { | ||
| "defaults": {"category": {"encoder": {"type": "onehot"}}}, | ||
| "input_features": [ | ||
| {"name": "MSSubClass", "type": "category"}, | ||
| {"name": "MSZoning", "type": "category"}, | ||
| {"name": "Street", "type": "category"}, | ||
| {"name": "Neighborhood", "type": "category"}, | ||
| ], | ||
| "model_type": "ecd", | ||
| "output_features": [{"name": "SalePrice", "type": "number"}], | ||
| "trainer": {"train_steps": 1}, | ||
| "combiner": {"type": "concat", "num_fc_layers": 2}, | ||
| } | ||
| ModelConfig.from_dict(config) |
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@abidwael does just doing .from_dict() repro the error? I would image this would require at least 1-2 steps of training for the error in your PR comment to be reproed
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Agreed, it doesn't. Will remove this test as it will be captured in #2991
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I would probably add a test here as well since these will run on every commit, whereas the tests in #2991 will run on merge to master? or is that not the case?
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It will run on every commit
arnavgarg1
approved these changes
Mar 14, 2023
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When we specify a
onehotencoder for category features and have dense layers down the line, we get the following errorThis PR makes sure to convert the outputs of the
onehotencoder tofloat32