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SNOW-1805854: Reformat interoperability docs page (#2951)
Signed-off-by: Labanya Mukhopadhyay <labanya.mukhopadhyay@snowflake.com> Co-authored-by: Mahesh Vashishtha <mahesh.vashishtha@snowflake.com>
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‎docs/source/modin/interoperability.rst‎

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@@ -12,53 +12,47 @@ some libraries use to interoperate with Snowpark pandas to the same level of sup
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plotly.express
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==============
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The following table is structured as follows: The first column contains the name of a method in the ``plotly.express`` module.
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The second column is a flag for whether or not interoperability is guaranteed with Snowpark pandas. For each of these
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operations, we validate that passing in Snowpark pandas dataframes or series as the data inputs behaves equivalently
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to passing in pandas dataframes or series.
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.. note::
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``Y`` stands for yes, i.e., interoperability is guaranteed with this method, and ``N`` stands for no.
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For each of the following methods in the ``plotly.express`` module, we validate that passing in Snowpark pandas
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dataframes or series as the data inputs behaves equivalently to passing in pandas dataframes or series.
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.. note::
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Currently only plotly versions <6.0.0 are supported through the dataframe interchange protocol.
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| Method name | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``scatter`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``line`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``area`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``timeline`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``violin`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``bar`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``histogram`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``pie`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``treemap`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``sunburst`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``icicle`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``scatter_matrix`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``funnel`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``density_heatmap`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``boxplot`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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| ``imshow`` | Y | |
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+-------------------------+---------------------------------------------+--------------------------------------------+
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+-------------------------+
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| Method name |
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+-------------------------+
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| ``scatter`` |
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+-------------------------+
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| ``line`` |
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+-------------------------+
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| ``area`` |
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+-------------------------+
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| ``timeline`` |
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+-------------------------+
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| ``violin`` |
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+-------------------------+
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| ``bar`` |
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+-------------------------+
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| ``histogram`` |
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+-------------------------+
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| ``pie`` |
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+-------------------------+
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| ``treemap`` |
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+-------------------------+
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| ``sunburst`` |
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+-------------------------+
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| ``icicle`` |
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+-------------------------+
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| ``scatter_matrix`` |
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+-------------------------+
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| ``funnel`` |
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+-------------------------+
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| ``density_heatmap`` |
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+-------------------------+
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| ``boxplot`` |
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+-------------------------+
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| ``imshow`` |
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+-------------------------+
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scikit-learn
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We break down scikit-learn interoperability by categories of scikit-learn
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operations.
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For each category, we provide a table of interoperability with the following
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structure: The first column describes a scikit-learn operation that may include
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multiple method calls. The second column is a flag for whether or not
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interoperability is guaranteed with Snowpark pandas. For each of these methods,
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we validate that passing in Snowpark pandas objects behaves equivalently to
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passing in pandas objects.
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For each category, we provide scikit-learn operations that may include
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multiple method calls. For each of these methods, we validate that passing in Snowpark pandas objects behaves
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equivalently to passing in pandas objects.
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.. note::
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``Y`` stands for yes, i.e., interoperability is guaranteed with this method, and ``N`` stands for no.
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.. note::
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While some scikit-learn methods accept Snowpark pandas inputs, their
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Classification
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--------------
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| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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| Fitting a ``LinearDiscriminantAnalysis`` | Y | |
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| classifier with the ``fit()`` method and | | |
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| classifying data with the ``predict()`` | | |
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| method. | | |
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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+--------------------------------------------+
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| Operation |
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+--------------------------------------------+
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| Fitting a ``LinearDiscriminantAnalysis`` |
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| classifier with the ``fit()`` method and |
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| classifying data with the ``predict()`` |
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| method |
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+--------------------------------------------+
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Regression
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----------
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| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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| Fitting a ``LogisticRegression`` model | Y | |
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| with the ``fit()`` method and predicting | | |
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| results with the ``predict()`` method. | | |
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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+--------------------------------------------+
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| Operation |
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| Fitting a ``LogisticRegression`` model |
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| with the ``fit()`` method and predicting |
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| results with the ``predict()`` method |
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+--------------------------------------------+
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Clustering
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| Clustering method | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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| ``KMeans.fit()`` | Y | |
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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+--------------------------------------------+
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| Clustering method |
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+--------------------------------------------+
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| ``KMeans.fit()`` |
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+--------------------------------------------+
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Dimensionality reduction
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------------------------
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| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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| Getting the principal components of a | Y | |
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| numerical dataset with ``PCA.fit()``. | | |
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+--------------------------------------------+---------------------------------------------+---------------------------------+
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+--------------------------------------------+
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| Operation |
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+--------------------------------------------+
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| Getting the principal components of a |
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| numerical dataset with ``PCA.fit()`` |
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+--------------------------------------------+
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Model selection
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------------------------
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| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation |
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+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
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| Choosing parameters for a | Y | ``RandomizedSearchCV`` causes Snowpark pandas |
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| ``LogisticRegression`` model with | | to issue many queries. We strongly recommend |
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| ``RandomizedSearchCV.fit()``. | | converting Snowpark pandas inputs to pandas |
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| | | before using ``RandomizedSearchCV`` |
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+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
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+-------------------------------------------------+
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| Operation |
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+-------------------------------------------------+
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| Choosing parameters for a |
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| ``LogisticRegression`` model with |
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| ``RandomizedSearchCV.fit()`` |
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+-------------------------------------------------+
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.. note::
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``RandomizedSearchCV`` causes Snowpark pandas to issue many queries. We strongly
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recommend converting Snowpark pandas inputs to pandas before using ``RandomizedSearchCV``.
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Preprocessing
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-------------
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| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation |
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+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
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| Scaling training data with | Y | |
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| ``MaxAbsScaler.fit_transform()``. | | |
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+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
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+--------------------------------------------+
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| Operation |
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+--------------------------------------------+
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| Scaling training data with |
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| ``MaxAbsScaler.fit_transform()`` |
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+--------------------------------------------+

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