ENH: Add incremental algorithms support #160
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samir-nasibli
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/azp run CI |
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Azure Pipelines failed to run 1 pipeline(s). |
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/azp run ml-benchmarks |
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/azp run |
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| @@ -239,6 +239,7 @@ def get_result_tables_as_df( | |||
| bench_cases = pd.DataFrame( | |||
| [flatten_dict(bench_case) for bench_case in results["bench_cases"]] | |||
| ) | |||
| bench_cases = bench_cases.map(lambda x: str(x) if not isinstance(x, Hashable) else x) | |||
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What is non-hashable object you are trying to convert?
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basic statistics result_options parameter is a list
| def create_online_function( | ||
| estimator_instance, method_instance, data_args, num_batches, batch_size | ||
| ): | ||
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| if "y" in list(inspect.signature(method_instance).parameters): | ||
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| def ndarray_function(x, y): | ||
| for i in range(n_batches): | ||
| for i in range(num_batches): |
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Leave old simple logic with batch_size only.
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Why change? It overcomplicates data slicing with extra parameter checks and calculations, also, it is more common to know batch size before partial_fit call in real world cases.
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Why change?
Adding new feature which can be useful.
It overcomplicates data slicing with extra parameter checks and calculations
It costs nothing. And doing calculations in the code is better than doing them in calculator before running benchmarks.
it is more common to know batch size before
partial_fitcall in real world cases.
But while doing benchmarking it is not less common (I'd say even more) when the user wants to specify exact number of partial_fit calls.
| if method == "partial_fit": | ||
| num_batches = get_bench_case_value(bench_case, "data:num_batches") | ||
| batch_size = get_bench_case_value(bench_case, "data:batch_size") |
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Instead of separate branch for partial_fit, extend mechanism of online_inference_mode to partial fitting too.
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could you provide the exact link to implementation of this mechanism? i was not able to find the usage of this parameter, just see its setting in the config.
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Actually, online_inference_mode was removed as unnecessary before merge of refactor branch. This mode is enabled by batch_size != None only.
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You can split batch size into two for training and inference.
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Actually,
online_inference_modewas removed as unnecessary before merge of refactor branch.
what should I extend then?
configs/incremental.json
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| "library": "sklearnex", | ||
| "num_batches": {"training": 2} |
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| "library": "sklearnex", | |
| "num_batches": {"training": 2} | |
| "library": "sklearnex" |
configs/incremental.json
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| "library": "sklearnex", | ||
| "num_batches": {"training": 2} |
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| "library": "sklearnex", | |
| "num_batches": {"training": 2} | |
| "library": "sklearnex" |
configs/incremental.json
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| "library": "sklearnex.preview", | ||
| "num_batches": {"training": 2} |
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| "library": "sklearnex.preview", | |
| "num_batches": {"training": 2} | |
| "library": "sklearnex.preview" |
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