Benchmark Supported Data Types #2206
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gsheni
requested changes
Sep 6, 2024
| np.datetime64('2025-01-01T00:00:00'), | ||
| ]) | ||
| }), | ||
| 'np.timedelta64': pd.DataFrame({ |
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Add a few more numpy dtypes:
import numpy as np
np.dtypes.Float16DType()
np.dtypes.Float32DType()
np.dtypes.Float64DType()
amontanez24
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Sep 9, 2024
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gsheni
reviewed
Sep 10, 2024
| } | ||
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| PYARROW_DTYPES = { | ||
| 'pa.int8': pd.DataFrame({'pa.int8': pd.Series([1, -1, 127], dtype=pd.ArrowDtype(pa.int8()))}), |
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Should there be NaNs for the all the columns? I believe pyarrow supports that
amontanez24
reviewed
Sep 16, 2024
| - main | ||
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| jobs: | ||
| build: |
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minor: can we use more specific names for the jobs? We can actually require certain jobs to pass before allowing merging, so it's helpful if the names are unique
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| - main |
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are we still running the tests every time without updating the sheet?
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Yes, the benchmark is only for message in slack and updating the gdrive.
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Resolves #2200
CU-86b1xxa7d
This pull request introduces a benchmarking suite designed to test all supported data types for our synthesizers and validation processes. Key changes and additions include:
Benchmarking Integration: Added a new benchmarking framework to evaluate the functionality of all supported data types.
Private Spreadsheet Integration: The benchmarking results are compared against data read from a private spreadsheet. This spreadsheet contains the expected outcomes for each data type, ensuring that our tests remain accurate and relevant.
Automated Test Failures: If a data type is no longer supported due to recent changes, the test will automatically fail. This helps in catching unsupported data types and ensures that our system continues to function correctly with all valid data types.