ENH improve ARFF parser using pandas #21938

glemaitre · 2021-12-10T00:01:08Z

build upon #22026
closes #19774
closes #11821
closes #14855
closes #11817

It also addresses 3 remaining example from #21598 by reducing the computational time:

60s to 30s plot_poisson_regression_non_normal_loss.py
91s to 35s plot_sgd_early_stopping.py
34s to 23s plot_stack_predictors.py

Some other example should benefit from these changes.

Add an option parser that can take "liac-arff" to get the current behaviour, "pandas" to get a C-parser that is much faster, more memory efficient, and infer the proper data type, and "auto" (would default on "pandas" if installed otherwise on "liac-arff").

A couple of benchmark:

glemaitre · 2021-12-10T23:12:35Z

sklearn/datasets/_arff_parser.py

+def _split_sparse_columns(
+    arff_data: ArffSparseDataType, include_columns: List
+) -> ArffSparseDataType:
+    """Obtains several columns from sparse ARFF representation. Additionally,
+    the column indices are re-labelled, given the columns that are not
+    included. (e.g., when including [1, 2, 3], the columns will be relabelled
+    to [0, 1, 2]).
+
+    Parameters
+    ----------
+    arff_data : tuple
+        A tuple of three lists of equal size; first list indicating the value,
+        second the x coordinate and the third the y coordinate.
+
+    include_columns : list
+        A list of columns to include.
+
+    Returns
+    -------
+    arff_data_new : tuple
+        Subset of arff data with only the include columns indicated by the
+        include_columns argument.
+    """
+    arff_data_new: ArffSparseDataType = (list(), list(), list())
+    reindexed_columns = {
+        column_idx: array_idx for array_idx, column_idx in enumerate(include_columns)
+    }
+    for val, row_idx, col_idx in zip(arff_data[0], arff_data[1], arff_data[2]):
+        if col_idx in include_columns:
+            arff_data_new[0].append(val)
+            arff_data_new[1].append(row_idx)
+            arff_data_new[2].append(reindexed_columns[col_idx])
+    return arff_data_new
+
+
+def _sparse_data_to_array(
+    arff_data: ArffSparseDataType, include_columns: List
+) -> np.ndarray:
+    # turns the sparse data back into an array (can't use toarray() function,
+    # as this does only work on numeric data)
+    num_obs = max(arff_data[1]) + 1
+    y_shape = (num_obs, len(include_columns))
+    reindexed_columns = {
+        column_idx: array_idx for array_idx, column_idx in enumerate(include_columns)
+    }
+    # TODO: improve for efficiency
+    y = np.empty(y_shape, dtype=np.float64)
+    for val, row_idx, col_idx in zip(arff_data[0], arff_data[1], arff_data[2]):
+        if col_idx in include_columns:
+            y[row_idx, reindexed_columns[col_idx]] = val
+    return y


Those line are moved from the previous implementation

sklearn/datasets/_arff_parser.py

glemaitre · 2021-12-10T23:14:45Z

sklearn/datasets/_arff_parser.py

+        frame = pd.concat(dfs, ignore_index=True)
+        del dfs, first_df
+
+        frame = _cast_frame(frame, columns_info_openml, infer_casting)


This is the only difference with the previous implementation where we attempt to infer casting if requested.

sklearn/datasets/_arff_parser.py

glemaitre · 2021-12-10T23:17:59Z

sklearn/datasets/_arff_parser.py

+    return y
+
+
+def _cast_frame(frame, columns_info, infer_casting=False):


It is the equivalence of _feature_to_dtype. It makes smarter casting.

thomasjpfan

Thank you for working on this @glemaitre . I left a comment on a possible way to make this PR smaller.

sklearn/datasets/_arff_parser.py

glemaitre · 2022-01-05T09:52:31Z

Still a big diff but this is mainly due to the refactoring of the test file indeed.

ogrisel

Some first comments, I haven't completed my review yet.

sklearn/datasets/_arff_parser.py

sklearn/datasets/_openml.py

sklearn/datasets/_arff_parser.py

ogrisel

More comments about the test and the behavior of parser="auto", I will do a quick final pass once addressed but the PR LGTM otherwise.

sklearn/datasets/tests/test_arff_parser.py

sklearn/datasets/tests/test_openml.py

sklearn/datasets/_openml.py

sklearn/datasets/_arff_parser.py

doc/whats_new/v1.1.rst

ogrisel · 2022-02-01T17:47:47Z

sklearn/datasets/_arff_parser.py

+    )
+    columns_to_select = feature_names_to_select + target_names_to_select
+
+    nominal_attributes = {


Maybe this should be renamed to "categorical" because we do not really know if categorical variables should be treated as nominal or ordinal in ARFF (e.g. for categories encoded with integer values).

sklearn/datasets/_openml.py

thomasjpfan

At a glance, there looks to be refactor of _liac_arff_parser, where _convert_arff_data + _convert_arff_data_dataframe are moved into _liac_arff_parser? There is also a _post_process_frame + tests.

My sense is that the refactor can be done in another PR. How do you feel about splitting the refactor out? (I know we done this once already, but the diff is quite big now)

sklearn/datasets/_arff_parser.py

glemaitre · 2022-02-01T22:32:41Z

At a glance, there looks to be refactor of _liac_arff_parser, where _convert_arff_data + _convert_arff_data_dataframe are moved into _liac_arff_parser?

I can try to refactor the _liac_arff_parser part only. But at the end over the 1,934 lines of changes, there is already 1,750 lines of test refactoring and a couple of lines of variable renaming only. So after refactoring, I don't expect the diff to be reduced by many lines.

thomasjpfan · 2022-02-02T22:11:31Z

So after refactoring, I don't expect the diff to be reduced by many lines.

Okay, I'll review this PR. My remaining concern for this PR is how parquet support may be coming soon: openml/OpenML#1133 I suspect we will switch to parquet when it comes available on openml. Do we want to maintain our own arff parser using pandas when we switch to parquet?

adrinjalali · 2022-02-04T15:20:41Z

Okay, I'll review this PR. My remaining concern for this PR is how parquet support may be coming soon: openml/OpenML#1133 I suspect we will switch to parquet when it comes available on openml. Do we want to maintain our own arff parser using pandas when we switch to parquet?

We could also make that decision when it happens? :D

glemaitre · 2022-02-08T18:09:01Z

I think that we can add support for parquet when it comes. However, it could be nice to have a reader that is fast enough in the meanwhile. Adding support for parquet will also add a new dependency (pyarrow I assume) that is still optional with pandas. So I think that this is worth adding the parser in the meanwhile.

adrinjalali

I think something's gone wrong with your merge from main. There are all changes from other PRs here.

doc/whats_new/v1.1.rst

adrinjalali · 2022-02-09T10:40:37Z

sklearn/datasets/_openml.py

    if parser == "auto":
        parser = "liac-arff" if return_sparse else "pandas"


shouldn't we use liac-arff if pandas is not installed instead of raising?

You can refer to the discussion with @ogrisel #21938 (comment)

Basically, since the two parsers behave slightly differently, changing depending on the libraries installed will be difficult to debug and it fails. I recall such a bug in pandas with numexpr is installed or not, and this is not a good thing.

A potential fix is given in #22354. But it is ugly, make a drop in performance, and I am not sure that we should spend more time trying to improve the LIAC-ARFF parser.

Also fetch_openml with default arguments on a dense dataset will already raise an exception in scikit-learn 1.0 because as_frame="auto" arlready requires pandas if the dataset is dense.

So better get parser and as_frame follow the same logic by default.

glemaitre · 2022-02-09T17:23:05Z

The remaining failure is linked with casting and handling of np.NA that would provide integer missing values but require pandas >= 1.0. So we would need to bump pandas minimum version to have consistent behaviour.

adrinjalali · 2022-02-10T11:23:30Z

I think that's an old enough pandas, and it's not an essential dependency. We could bump the min version.

thomasjpfan

Gave this a deeper pass. Overall, looks good!

sklearn/datasets/_arff_parser.py

sklearn/datasets/tests/test_arff_parser.py

sklearn/utils/_mocking.py

sklearn/datasets/tests/test_openml.py

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

examples/inspection/plot_permutation_importance.py

thomasjpfan

I went over each example that was changed and the output is the same as main.

I think this is ready to ship. LGTM

ogrisel

LGTM, just a final batch on nitpicks.

doc/datasets/loading_other_datasets.rst

sklearn/datasets/tests/test_openml.py

doc/datasets/loading_other_datasets.rst

sklearn/datasets/_arff_parser.py

sklearn/datasets/_openml.py

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

adrinjalali

Such a PR! It turned out to be a LOT larger than anticipated at first. But very happy with it. It'd be nice if we could merge and have it in the release @jeremiedbb

adrinjalali · 2022-05-12T12:22:39Z

CI fails

ogrisel · 2022-05-12T12:26:21Z

The circle ci arm64 failure might be caused by a dependency upgrade?

https://app.circleci.com/pipelines/github/scikit-learn/scikit-learn/26909/workflows/74388600-25aa-456a-8cd8-7136dca5e1a8/jobs/192262

Indeed, and it's being addressed in #23336.

adrinjalali · 2022-05-12T12:27:50Z

Then shall we merge when CI is green? Not sure where the release is (cc @jeremiedbb )

jeremiedbb · 2022-05-12T12:29:53Z

already generated the wheels (#23321) and tagged. And the PR on conda-forge is already opened (conda-forge/scikit-learn-feedstock#186)

:/

adrinjalali · 2022-05-12T12:33:16Z

Pity, then the versions need to be fixed and then we can merge :)

doc/whats_new/v1.1.rst

doc/datasets/loading_other_datasets.rst

ogrisel · 2022-05-13T16:15:24Z

Congrats on the merge!

glemaitre marked this pull request as draft December 10, 2021 00:01

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fetch_openml with mnist_784 uses excessive memory #19774

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MAINT refactor ARFF parser #22026

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Accelerate slow examples #21598

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FIX downcast nominal features whenever possible in LIAC-ARFF #22354

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Update sklearn/datasets/_openml.py

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Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

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Parameter parser='auto' in fetch_openml. See scikit-learn#21938

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[MRG] ENH speed up plot_poisson_regression_non_normal_loss.py #21787

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ogrisel commented May 13, 2022

		return y


		def _cast_frame(frame, columns_info, infer_casting=False):

		if parser == "auto":
		parser = "liac-arff" if return_sparse else "pandas"

ENH improve ARFF parser using pandas #21938

ENH improve ARFF parser using pandas #21938

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