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ENH: Add incremental algorithms support #160

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535c1e4
Add incremental algorithms support
olegkkruglov Sep 23, 2024
d6952ac
Fix win yml
olegkkruglov Sep 23, 2024
03a152a
Remove samples/ms info
olegkkruglov Sep 24, 2024
3ac5c23
Remove BS from config (need to add after pip version update)
olegkkruglov Sep 24, 2024
9461fad
Add condition for finalize
olegkkruglov Sep 25, 2024
b82d772
Fix num_batches usage
olegkkruglov Sep 25, 2024
b5ad233
Reduce config
olegkkruglov Sep 27, 2024
fc4ad2b
Add covariance module to incremental config
olegkkruglov Sep 27, 2024
040802d
Rename example config
olegkkruglov Oct 4, 2024
69cc4c1
Remove bs mentioning in config (need to be added later)
olegkkruglov Oct 4, 2024
f275062
Fix num_batches and batch_size reading from config
olegkkruglov Oct 4, 2024
5a9be80
Revert accidentally pushed changes
olegkkruglov Oct 4, 2024
66d977d
Merge branch 'main' into inc-support
ethanglaser Mar 7, 2025
1d48f3a
remove batch_size logic from incremental benchmarking for num_batches
ethanglaser Mar 18, 2025
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Add condition for finalize
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@olegkkruglov
olegkkruglov committed Sep 25, 2024
commit 9461fad69a00ecbf69a3e5fcef662fb1bafd4253
12 changes: 8 additions & 4 deletions sklbench/benchmarks/sklearn_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -347,27 +347,31 @@ def ndarray_function(x, y):
x[i * batch_size : (i + 1) * batch_size],
y[i * batch_size : (i + 1) * batch_size],
)
estimator_instance._onedal_finalize_fit()
if hasattr(estimator_instance, "_onedal_finalize_fit"):
estimator_instance._onedal_finalize_fit()

def dataframe_function(x, y):
for i in range(n_batches):
method_instance(
x.iloc[i * batch_size : (i + 1) * batch_size],
y.iloc[i * batch_size : (i + 1) * batch_size],
)
estimator_instance._onedal_finalize_fit()
if hasattr(estimator_instance, "_onedal_finalize_fit"):
estimator_instance._onedal_finalize_fit()

else:

def ndarray_function(x):
for i in range(n_batches):
method_instance(x[i * batch_size : (i + 1) * batch_size])
estimator_instance._onedal_finalize_fit()
if hasattr(estimator_instance, "_onedal_finalize_fit"):
estimator_instance._onedal_finalize_fit()

def dataframe_function(x):
for i in range(n_batches):
method_instance(x.iloc[i * batch_size : (i + 1) * batch_size])
estimator_instance._onedal_finalize_fit()
if hasattr(estimator_instance, "_onedal_finalize_fit"):
estimator_instance._onedal_finalize_fit()
Comment on lines +364 to +365
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is it necessary to call finalize_fit? wouldn't this happen automatically? we specifically have flexibly logic here (ie use of method_instance variable) so let's avoid specific calls if possible

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finalize_fit is called if any result attribute has been called. only partial_fit would be measured here without this call


if "ndarray" in str(type(data_args[0])):
return ndarray_function
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