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Fix TAP and Kokoro tests caused by NumPy v2 migration.
1. To ensure test compatibility between NumPy v1 and v2 environments, we've adjusted the comparison tolerance to 1e-4. This accommodates slight variations (around 1e-4) in floating-point outcomes between the two NumPy versions. Additionally, we've modified the expected proto float to align with NumPy v2 results. 2. For mutual_information, NumPy v2 is able to handle values > 2**53 if the min and max of the examples are the same. However, since we need to be compatible with NumPy v1 and v2, for related unit tests, we check for the NumPy version before running the associated unit tests. PiperOrigin-RevId: 681598675
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‎.github/workflows/build.yml

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‎README.md

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### 1. Install Docker
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Please first install `docker` and `docker compose` by following the directions:
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Please first install `docker` and `docker-compose` by following the directions:
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[docker](https://docs.docker.com/install/);
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[docker compose](https://docs.docker.com/compose/install/).
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[docker-compose](https://docs.docker.com/compose/install/).
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### 2. Clone the TFDV repository
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Then, run the following at the project root:
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```bash
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sudo docker compose build manylinux2010
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sudo docker compose run -e PYTHON_VERSION=${PYTHON_VERSION} manylinux2010
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sudo docker-compose build manylinux2010
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sudo docker-compose run -e PYTHON_VERSION=${PYTHON_VERSION} manylinux2010
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```
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where `PYTHON_VERSION` is one of `{39, 310, 311}`.
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‎tensorflow_data_validation/integration_tests/sequence_example_e2e_test.py

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histograms {
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buckets {
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low_value: 5.0
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high_value: 6.666666666666667
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sample_count: 10.0220751
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high_value: 6.6666665
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sample_count: 10.0220747
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}
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buckets {
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low_value: 6.666666666666667
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high_value: 8.333333333333334
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low_value: 6.6666665
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high_value: 8.333333
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sample_count: 0.0220751
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buckets {
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low_value: 8.333333333333334
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low_value: 8.333333
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high_value: 10.0
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sample_count: 9.9558499
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sample_count: 9.9558363
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}
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}
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histograms {
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histograms {
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buckets {
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low_value: 1.0
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high_value: 1.3333333333333333
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sample_count: 10.0220751
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high_value: 1.3333334
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sample_count: 10.0220747
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}
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buckets {
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low_value: 1.3333334
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high_value: 1.6666666666666665
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sample_count: 0.0220751
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}
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buckets {
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low_value: 1.6666666666666665
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high_value: 2.0
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sample_count: 9.9558499
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sample_count: 9.9558363
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histograms {
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histograms {
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buckets {
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low_value: 5.0
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high_value: 6.666666666666667
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sample_count: 10.0220751
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high_value: 6.6666665
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sample_count: 10.0220747
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}
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buckets {
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low_value: 6.666666666666667
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high_value: 8.333333333333334
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low_value: 6.6666665
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high_value: 8.333333
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sample_count: 0.0220751
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buckets {
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low_value: 8.333333333333334
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low_value: 8.333333
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sample_count: 9.9558363
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sample_count: 50.1658375
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high_value: 6.6666665
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sample_count: 50.1658363
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}
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low_value: 6.666666666666667
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low_value: 6.6666665
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high_value: 8.333333
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sample_count: 0.1658375
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low_value: 8.333333
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sample_count: 99.668325
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sample_count: 99.6683884
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histograms {
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histograms {
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buckets {
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high_value: 1.3333333333333333
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sample_count: 10.0220751
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high_value: 1.3333334
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sample_count: 10.0220747
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}
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buckets {
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histograms {

‎tensorflow_data_validation/statistics/generators/basic_stats_generator_test.py

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""", statistics_pb2.FeatureNameStatistics()),
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""",
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statistics_pb2.FeatureNameStatistics(),
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),
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types.FeaturePath(['b']): text_format.Parse(
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""", statistics_pb2.FeatureNameStatistics()),
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""",
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),
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‎tensorflow_data_validation/statistics/generators/mutual_information.py

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# np.histogram cannot handle values > 2**53 if the min and max of the
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# examples are the same. https://github.com/numpy/numpy/issues/8627
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# For NumPy version 1.x.x, np.histogram cannot handle values > 2**53 if the
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# min and max of the examples are the same.
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# https://github.com/numpy/numpy/issues/8627
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logging.exception("Unable to encode examples: %s with error: %s",
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flattened_feature_values, e)
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return None

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