Comments (3)
We are working on group-by-key based analyzers. Do you have a specific use case in mind? tf-idf is not implemented with a group-by-key approach, so you could potentially copy its implementation for your own use case. Generally we would expected group-by-key based analyzers to be faster but they may not have any advantage when the data being aggregated is small, as would typically be the case for a numeric analyzer.
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Yes, the use case is to normalize energy use per customer. If the most important metric is absolute error, then this is not necessary, but if it's about predicting the shape of every load curve as accurately as possible this might be a good transformation. We need this mainly to experiment though, and have switched to using Dask for computing statistics and doing preprocessing as it is much more flexible. We're still stealing from TFT the idea of embedding these normalization stats in the TF graph, specifically we're using the normalizer_fn
kwarg of tf.feature_column.numeric_column
.
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This question is better asked on StackOverflow since it is not a bug or feature request. There is also a larger community that reads questions there.
If you think we've misinterpreted a bug, please comment again with a clear explanation, as well as all of the information requested in the issue template. Thanks!
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Related Issues (20)
- Segmentation Fault on tft.compute_and_apply_vocabulary HOT 8
- Release new version that's compatible with `tfx[kfp]==1.10.0` HOT 7
- Can't install due to dependency on numpy HOT 5
- Could tft support embedding transform function? HOT 11
- Python 3.10 support HOT 19
- TypeError: an integer is required [while running 'AnalyzeAndTransformDataset/TransformDataset/InstrumentInputBytes[Transform] HOT 6
- Allow Identity "Transformation" HOT 4
- Beam AnalyzeAndTransformDataset runs expensive transformation _InstanceDictInputToTFXIOInput Twice HOT 3
- Install fails on Apple M2 Ventura HOT 3
- Apple Silicon support for tensorflow-transform not available as tfx-bsl does not support it? HOT 3
- Raise pyarrow upper bound? HOT 12
- apart from the batch dimension, all dimensions must have known size [while running 'AnalyzeAndTransformDataset/AnalyzeDataset/CreateSavedModel[tf_v2_only]/CreateSavedModel'] HOT 2
- Transform component and tf.function HOT 4
- Transform graph returns an empty dictionary at serve_tf_examples_fn function HOT 4
- Graph error when using TFHub Universal-sentence-encoder model HOT 2
- Wheel missing py.typed HOT 2
- universal sentence encoder batch pipeline failing HOT 3
- tfx.components.Transform returns invalid results HOT 1
- TFX Transform layer returns dict with missing keys HOT 2
- Pip fails to install TFT on Python 3.11 on Linux in a clean environment.
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