Skip to content

SNOW-1805854: Reformat interoperability docs page #2951

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
sfc-gh-lmukhopadhyay merged 8 commits into from
Jan 30, 2025
Merged

SNOW-1805854: Reformat interoperability docs page #2951

Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
f454564
SNOW-1805854: edit interoperability docs page
sfc-gh-lmukhopadhyay Jan 28, 2025
6266b62
Merge branch 'main' into lmukhopadhyay-SNOW-1805854-update-3pl-docs
sfc-gh-lmukhopadhyay Jan 28, 2025
670a523
Merge branch 'main' into lmukhopadhyay-SNOW-1805854-update-3pl-docs
sfc-gh-lmukhopadhyay Jan 29, 2025
47e357e
Merge branch 'main' into lmukhopadhyay-SNOW-1805854-update-3pl-docs
sfc-gh-lmukhopadhyay Jan 29, 2025
27fe6ee
Merge branch 'main' into lmukhopadhyay-SNOW-1805854-update-3pl-docs
sfc-gh-lmukhopadhyay Jan 29, 2025
6f0ec23
Apply suggestions from code review
sfc-gh-lmukhopadhyay Jan 29, 2025
3d51e93
Merge branch 'main' into lmukhopadhyay-SNOW-1805854-update-3pl-docs
sfc-gh-lmukhopadhyay Jan 29, 2025
2abfd40
Merge branch 'main' into lmukhopadhyay-SNOW-1805854-update-3pl-docs
sfc-gh-lmukhopadhyay Jan 30, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
174 changes: 83 additions & 91 deletions docs/source/modin/interoperability.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,53 +12,47 @@ some libraries use to interoperate with Snowpark pandas to the same level of sup
plotly.express
==============

The following table is structured as follows: The first column contains the name of a method in the ``plotly.express`` module.
The second column is a flag for whether or not interoperability is guaranteed with Snowpark pandas. For each of these
operations, we validate that passing in Snowpark pandas dataframes or series as the data inputs behaves equivalently
to passing in pandas dataframes or series.

.. note::
``Y`` stands for yes, i.e., interoperability is guaranteed with this method, and ``N`` stands for no.

For each of the following methods in the ``plotly.express`` module, we validate that passing in Snowpark pandas
dataframes or series as the data inputs behaves equivalently to passing in pandas dataframes or series.

.. note::
Currently only plotly versions <6.0.0 are supported through the dataframe interchange protocol.

+-------------------------+---------------------------------------------+--------------------------------------------+
| Method name | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``scatter`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``line`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``area`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``timeline`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``violin`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``bar`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``histogram`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``pie`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``treemap`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``sunburst`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``icicle`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``scatter_matrix`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``funnel`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``density_heatmap`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``boxplot`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
| ``imshow`` | Y | |
+-------------------------+---------------------------------------------+--------------------------------------------+
+-------------------------+
| Method name |
+-------------------------+
| ``scatter`` |
+-------------------------+
| ``line`` |
+-------------------------+
| ``area`` |
+-------------------------+
| ``timeline`` |
+-------------------------+
| ``violin`` |
+-------------------------+
| ``bar`` |
+-------------------------+
| ``histogram`` |
+-------------------------+
| ``pie`` |
+-------------------------+
| ``treemap`` |
+-------------------------+
| ``sunburst`` |
+-------------------------+
| ``icicle`` |
+-------------------------+
| ``scatter_matrix`` |
+-------------------------+
| ``funnel`` |
+-------------------------+
| ``density_heatmap`` |
+-------------------------+
| ``boxplot`` |
+-------------------------+
| ``imshow`` |
+-------------------------+


scikit-learn
Expand All @@ -67,15 +61,10 @@ scikit-learn
We break down scikit-learn interoperability by categories of scikit-learn
operations.

For each category, we provide a table of interoperability with the following
structure: The first column describes a scikit-learn operation that may include
multiple method calls. The second column is a flag for whether or not
interoperability is guaranteed with Snowpark pandas. For each of these methods,
we validate that passing in Snowpark pandas objects behaves equivalently to
passing in pandas objects.
For each category, we provide scikit-learn operations that may include
multiple method calls. For each of these methods, we validate that passing in Snowpark pandas objects behaves
equivalently to passing in pandas objects.

.. note::
``Y`` stands for yes, i.e., interoperability is guaranteed with this method, and ``N`` stands for no.

.. note::
While some scikit-learn methods accept Snowpark pandas inputs, their
Expand All @@ -88,66 +77,69 @@ passing in pandas objects.
Classification
--------------

+--------------------------------------------+---------------------------------------------+---------------------------------+
| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
+--------------------------------------------+---------------------------------------------+---------------------------------+
| Fitting a ``LinearDiscriminantAnalysis`` | Y | |
| classifier with the ``fit()`` method and | | |
| classifying data with the ``predict()`` | | |
| method. | | |
+--------------------------------------------+---------------------------------------------+---------------------------------+
+--------------------------------------------+
| Operation |
+--------------------------------------------+
| Fitting a ``LinearDiscriminantAnalysis`` |
| classifier with the ``fit()`` method and |
| classifying data with the ``predict()`` |
| method |
+--------------------------------------------+


Regression
----------

+--------------------------------------------+---------------------------------------------+---------------------------------+
| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
+--------------------------------------------+---------------------------------------------+---------------------------------+
| Fitting a ``LogisticRegression`` model | Y | |
| with the ``fit()`` method and predicting | | |
| results with the ``predict()`` method. | | |
+--------------------------------------------+---------------------------------------------+---------------------------------+
+--------------------------------------------+
| Operation |
+--------------------------------------------+
| Fitting a ``LogisticRegression`` model |
| with the ``fit()`` method and predicting |
| results with the ``predict()`` method |
+--------------------------------------------+

Clustering
----------

+--------------------------------------------+---------------------------------------------+---------------------------------+
| Clustering method | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
+--------------------------------------------+---------------------------------------------+---------------------------------+
| ``KMeans.fit()`` | Y | |
+--------------------------------------------+---------------------------------------------+---------------------------------+
+--------------------------------------------+
| Clustering method |
+--------------------------------------------+
| ``KMeans.fit()`` |
+--------------------------------------------+


Dimensionality reduction
------------------------

+--------------------------------------------+---------------------------------------------+---------------------------------+
| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation|
+--------------------------------------------+---------------------------------------------+---------------------------------+
| Getting the principal components of a | Y | |
| numerical dataset with ``PCA.fit()``. | | |
+--------------------------------------------+---------------------------------------------+---------------------------------+
+--------------------------------------------+
| Operation |
+--------------------------------------------+
| Getting the principal components of a |
| numerical dataset with ``PCA.fit()`` |
+--------------------------------------------+


Model selection
------------------------

+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation |
+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
| Choosing parameters for a | Y | ``RandomizedSearchCV`` causes Snowpark pandas |
| ``LogisticRegression`` model with | | to issue many queries. We strongly recommend |
| ``RandomizedSearchCV.fit()``. | | converting Snowpark pandas inputs to pandas |
| | | before using ``RandomizedSearchCV`` |
+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
+-------------------------------------------------+
| Operation |
+-------------------------------------------------+
| Choosing parameters for a |
| ``LogisticRegression`` model with |
| ``RandomizedSearchCV.fit()`` |
+-------------------------------------------------+

.. note::
``RandomizedSearchCV`` causes Snowpark pandas to issue many queries. We strongly
recommend converting Snowpark pandas inputs to pandas before using ``RandomizedSearchCV``.

Preprocessing
-------------

+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
| Operation | Interoperable with Snowpark pandas? (Y/N) | Notes for current implementation |
+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
| Scaling training data with | Y | |
| ``MaxAbsScaler.fit_transform()``. | | |
+--------------------------------------------+---------------------------------------------+-----------------------------------------------+
+--------------------------------------------+
| Operation |
+--------------------------------------------+
| Scaling training data with |
| ``MaxAbsScaler.fit_transform()`` |
+--------------------------------------------+
Loading