refactoring elasticsearch names to opensearch

CHANGELOG.rst

@@ -25,7 +25,7 @@ Added
 Added
 ^^^^^
 
-* Added support for ``eland.Series.unique()`` (`#448`_, contributed by `@V1NAY8`_)
+* Added support for ``opensearch_py_ml.Series.unique()`` (`#448`_, contributed by `@V1NAY8`_)
 * Added ``--ca-certs`` and ``--insecure`` options to ``eland_import_hub_model`` for configuring TLS (`#441`_)
 
 .. _#448: https://github.com/elastic/eland/pull/448
@@ -95,7 +95,7 @@ Added
 * Added support for Pandas 1.3.x (`#362`_, contributed by `@V1NAY8`_)
 * Added support for LightGBM 3.x (`#362`_, contributed by `@V1NAY8`_)
 * Added ``DataFrame.idxmax()`` and ``DataFrame.idxmin()`` methods (`#353`_, contributed by `@V1NAY8`_)
-* Added type hints to ``eland.ndframe`` and ``eland.operations`` (`#366`_, contributed by `@V1NAY8`_)
+* Added type hints to ``opensearch_py_ml.ndframe`` and ``opensearch_py_ml.operations`` (`#366`_, contributed by `@V1NAY8`_)
 
 Removed
 ^^^^^^^
@@ -350,8 +350,8 @@ Deprecated
 ^^^^^^^^^^
 
 * Deprecated ``info_es()`` in favor of ``es_info()`` (`#208`_)
-* Deprecated ``eland.read_csv()`` in favor of ``eland.csv_to_eland()`` (`#208`_)
-* Deprecated ``eland.read_es()`` in favor of ``eland.DataFrame()`` (`#208`_)
+* Deprecated ``opensearch_py_ml.read_csv()`` in favor of ``opensearch_py_ml.csv_to_eland()`` (`#208`_)
+* Deprecated ``opensearch_py_ml.read_es()`` in favor of ``opensearch_py_ml.DataFrame()`` (`#208`_)
 
 Changed
 ^^^^^^^
@@ -373,7 +373,7 @@ Fixed
   in the index if a sized operation like ``.head(X)`` was applied to the data
   frame (`#205`_, contributed by `@mesejo`_)
 * Fixed issue where both ``scikit-learn`` and ``xgboost`` libraries were
-  required to use ``eland.ml.ImportedMLModel``, now only one library is
+  required to use ``opensearch_py_ml.ml.ImportedMLModel``, now only one library is
   required to use this feature (`#206`_)
 
  .. _#200: https://github.com/elastic/eland/pull/200
@@ -402,13 +402,13 @@ Added
 * Added ``es_type_overrides`` parameter to ``pandas_to_eland()`` (`#181`_)
 * Added ``NDFrame.var()``, ``.std()`` and ``.median()`` aggregations (`#175`_, `#176`_, contributed by `@mesejo`_)
 * Added ``DataFrame.es_query()`` to allow modifying ES queries directly (`#156`_)
-* Added ``eland.__version__`` (`#153`_, contributed by `@mesejo`_)
+* Added ``opensearch_py_ml.__version__`` (`#153`_, contributed by `@mesejo`_)
 
 Removed
 ^^^^^^^
 
 * Removed support for Python 3.5 (`#150`_)
-* Removed ``eland.Client()`` interface, use
+* Removed ``opensearch_py_ml.Client()`` interface, use
   ``elasticsearch.Elasticsearch()`` client instead (`#166`_)
 * Removed all private objects from top-level ``eland`` namespace (`#170`_)
 * Removed ``geo_points`` from ``pandas_to_eland()`` in favor of ``es_type_overrides`` (`#181`_)

CONTRIBUTING.md

@@ -143,7 +143,7 @@ currently using a minimum version of PyCharm 2019.2.4.
 
 * Enter the URL to your fork of eland
 
-    (e.g.  `git@github.com:stevedodson/eland.git` )
+    (e.g.  `git@github.com:stevedodson/opensearch_py_ml.git` )
 
 * Click \'Yes\' for \'Checkout from Version Control\'
 * Configure PyCharm environment:
@@ -190,7 +190,7 @@ currently using a minimum version of PyCharm 2019.2.4.
 * To validate installation, open python console and run
 
     ``` bash
-    > import eland as ed
+    > import opensearch_py_ml as ed
     > ed_df = ed.DataFrame('localhost', 'flights')
     ```
 

README.md

@@ -1,22 +1,22 @@
 <div align="center">
   <a href="https://github.com/elastic/eland">
-    <img src="https://raw.githubusercontent.com/elastic/eland/main/docs/sphinx/logo/eland.png" width="30%"
+    <img src="https://raw.githubusercontent.com/elastic/eland/main/docs/sphinx/logo/opensearch_py_ml.png" width="30%"
       alt="Eland" />
   </a>
 </div>
 <br />
 <div align="center">
-  <a href="https://pypi.org/project/eland"><img src="https://img.shields.io/pypi/v/eland.svg" alt="PyPI Version"></a>
+  <a href="https://pypi.org/project/eland"><img src="https://img.shields.io/pypi/v/opensearch_py_ml.svg" alt="PyPI Version"></a>
   <a href="https://anaconda.org/conda-forge/eland"><img src="https://img.shields.io/conda/vn/conda-forge/eland"
       alt="Conda Version"></a>
   <a href="https://pepy.tech/project/eland"><img src="https://pepy.tech/badge/eland" alt="Downloads"></a>
-  <a href="https://pypi.org/project/eland"><img src="https://img.shields.io/pypi/status/eland.svg"
+  <a href="https://pypi.org/project/eland"><img src="https://img.shields.io/pypi/status/opensearch_py_ml.svg"
       alt="Package Status"></a>
   <a href="https://clients-ci.elastic.co/job/elastic+eland+main"><img
       src="https://clients-ci.elastic.co/buildStatus/icon?job=elastic%2Beland%2Bmain" alt="Build Status"></a>
-  <a href="https://github.com/elastic/eland/blob/main/LICENSE.txt"><img src="https://img.shields.io/pypi/l/eland.svg"
+  <a href="https://github.com/elastic/eland/blob/main/LICENSE.txt"><img src="https://img.shields.io/pypi/l/opensearch_py_ml.svg"
       alt="License"></a>
-  <a href="https://eland.readthedocs.io"><img
+  <a href="https://opensearch_py_ml.readthedocs.io"><img
       src="https://readthedocs.org/projects/eland/badge/?version=latest" alt="Documentation Status"></a>
 </div>
 
@@ -38,13 +38,13 @@ Eland also provides tools to upload trained machine learning models from common
 Eland can be installed from [PyPI](https://pypi.org/project/eland) with Pip:
 
 ```bash
-$ python -m pip install eland
+$ python -m pip install opensearch_py_ml
 ```
 
 Eland can also be installed from [Conda Forge](https://anaconda.org/conda-forge/eland) with Conda:
 
 ```bash
-$ conda install -c conda-forge eland
+$ conda install -c conda-forge opensearch_py_ml
 ```
 
 ### Compatibility
@@ -73,20 +73,20 @@ Users wishing to use Eland without installing it, in order to just run the avail
 container:
 
 ```bash
-$ docker build -t elastic/eland .
+$ docker build -t elastic/opensearch_py_ml .
 ```
 
 The container can now be used interactively:
 
 ```bash
-$ docker run -it --rm --network host elastic/eland
+$ docker run -it --rm --network host elastic/opensearch_py_ml
 ```
 
 Running installed scripts is also possible without an interactive shell, e.g.:
 
 ```bash
 $ docker run -it --rm --network host \
-    elastic/eland \
+    elastic/opensearch_py_ml \
     eland_import_hub_model \
       --url http://host.docker.internal:9200/ \
       --hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
@@ -103,7 +103,7 @@ You can pass either an instance of `elasticsearch.Elasticsearch` to Eland APIs
 or a string containing the host to connect to:
 
 ```python
-import eland as ed
+import opensearch_py_ml as ed
 
 # Connecting to an Elasticsearch instance running on 'localhost:9200'
 df = ed.DataFrame("localhost:9200", es_index_pattern="flights")
@@ -120,23 +120,23 @@ df = ed.DataFrame(es, es_index_pattern="flights")
 
 ## DataFrames in Eland
 
-`eland.DataFrame` wraps an Elasticsearch index in a Pandas-like API
+`opensearch_py_ml.DataFrame` wraps an Elasticsearch index in a Pandas-like API
 and defers all processing and filtering of data to Elasticsearch
 instead of your local machine. This means you can process large
 amounts of data within Elasticsearch from a Jupyter Notebook
 without overloading your machine.
 
-➤ [Eland DataFrame API documentation](https://eland.readthedocs.io/en/latest/reference/dataframe.html)
+➤ [Eland DataFrame API documentation](https://opensearch_py_ml.readthedocs.io/en/latest/reference/dataframe.html)
 
-➤ [Advanced examples in a Jupyter Notebook](https://eland.readthedocs.io/en/latest/examples/demo_notebook.html)
+➤ [Advanced examples in a Jupyter Notebook](https://opensearch_py_ml.readthedocs.io/en/latest/examples/demo_notebook.html)
 
 ```python
->>> import eland as ed
+>>> import opensearch_py_ml as ed
 
 >>> # Connect to 'flights' index via localhost Elasticsearch node
 >>> df = ed.DataFrame('localhost:9200', 'flights')
 
-# eland.DataFrame instance has the same API as pandas.DataFrame
+# opensearch_py_ml.DataFrame instance has the same API as pandas.DataFrame
 # except all data is in Elasticsearch. See .info() memory usage.
 >>> df.head()
    AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
@@ -149,7 +149,7 @@ without overloading your machine.
 [5 rows x 27 columns]
 
 >>> df.info()
-<class 'eland.dataframe.DataFrame'>
+<class 'opensearch_py_ml.dataframe.DataFrame'>
 Index: 13059 entries, 0 to 13058
 Data columns (total 27 columns):
  #   Column              Non-Null Count  Dtype         
@@ -191,13 +191,13 @@ std        4.578263e+03    2.663867e+02
 Eland allows transforming trained regression and classification models from scikit-learn, XGBoost, and LightGBM
 libraries to be serialized and used as an inference model in Elasticsearch.
 
-➤ [Eland Machine Learning API documentation](https://eland.readthedocs.io/en/latest/reference/ml.html)
+➤ [Eland Machine Learning API documentation](https://opensearch_py_ml.readthedocs.io/en/latest/reference/ml.html)
 
 ➤ [Read more about Machine Learning in Elasticsearch](https://www.elastic.co/guide/en/machine-learning/current/ml-getting-started.html)
 
 ```python
 >>> from xgboost import XGBClassifier
->>> from eland.ml import MLModel
+>>> from opensearch_py_ml.ml import MLModel
 
 # Train and exercise an XGBoost ML model locally
 >>> xgb_model = XGBClassifier(booster="gbtree")
@@ -236,8 +236,8 @@ $ eland_import_hub_model \
 ```python
 >>> import elasticsearch
 >>> from pathlib import Path
->>> from eland.ml.pytorch import PyTorchModel
->>> from eland.ml.pytorch.transformers import TransformerModel
+>>> from opensearch_py_ml.ml.pytorch import PyTorchModel
+>>> from opensearch_py_ml.ml.pytorch.transformers import TransformerModel
 
 # Load a Hugging Face transformers model directly from the model hub
 >>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")

bin/eland_import_hub_model

@@ -33,11 +33,13 @@ import textwrap
 
 from elastic_transport.client_utils import DEFAULT
 from elasticsearch import AuthenticationException, Elasticsearch
+from warnings import warn
 
 MODEL_HUB_URL = "https://huggingface.co"
 
 
 def get_arg_parser():
+    warn('function has been deprecated - only works for ElasticSearch', DeprecationWarning, stacklevel=2)
     parser = argparse.ArgumentParser()
     location_args = parser.add_mutually_exclusive_group(required=True)
     location_args.add_argument(
@@ -166,8 +168,8 @@ if __name__ == "__main__":
     logger.setLevel(logging.INFO)
 
     try:
-        from eland.ml.pytorch import PyTorchModel
-        from eland.ml.pytorch.transformers import (
+        from opensearch_py_ml.ml.pytorch import PyTorchModel
+        from opensearch_py_ml.ml.pytorch.transformers import (
             SUPPORTED_TASK_TYPES,
             TaskTypeError,
             TransformerModel,

docs/guide/dataframes.asciidoc

@@ -1,19 +1,19 @@
 [[dataframes]]
 == Data Frames
 
-`eland.DataFrame` wraps an Elasticsearch index in a Pandas-like API
+`opensearch_py_ml.DataFrame` wraps an Elasticsearch index in a Pandas-like API
 and defers all processing and filtering of data to Elasticsearch
 instead of your local machine. This means you can process large
 amounts of data within Elasticsearch from a Jupyter Notebook
 without overloading your machine.
 
 [source,python]
 -------------------------------------
->>> import eland as ed
+>>> import opensearch_py_ml as ed
 >>> # Connect to 'flights' index via localhost Elasticsearch node
 >>> df = ed.DataFrame('http://localhost:9200', 'flights')
 
-# eland.DataFrame instance has the same API as pandas.DataFrame
+# opensearch_py_ml.DataFrame instance has the same API as pandas.DataFrame
 # except all data is in Elasticsearch. See .info() memory usage.
 >>> df.head()
    AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
@@ -26,7 +26,7 @@ without overloading your machine.
 [5 rows x 27 columns]
 
 >>> df.info()
-<class 'eland.dataframe.DataFrame'>
+<class 'opensearch_py_ml.dataframe.DataFrame'>
 Index: 13059 entries, 0 to 13058
 Data columns (total 27 columns):
  #   Column              Non-Null Count  Dtype         

docs/guide/machine-learning.asciidoc

@@ -12,7 +12,7 @@ model in {es}.
 [source,python]
 ------------------------
 >>> from xgboost import XGBClassifier
->>> from eland.ml import MLModel
+>>> from opensearch_py_ml.ml import MLModel
 
 # Train and exercise an XGBoost ML model locally
 >>> xgb_model = XGBClassifier(booster="gbtree")
@@ -61,8 +61,8 @@ $ eland_import_hub_model <authentication> \ <1>
 ------------------------
 >>> import elasticsearch
 >>> from pathlib import Path
->>> from eland.ml.pytorch import PyTorchModel
->>> from eland.ml.pytorch.transformers import TransformerModel
+>>> from opensearch_py_ml.ml.pytorch import PyTorchModel
+>>> from opensearch_py_ml.ml.pytorch.transformers import TransformerModel
 
 # Load a Hugging Face transformers model directly from the model hub
 >>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")

docs/guide/overview.asciidoc

@@ -2,7 +2,7 @@
 == Overview
 
 Eland is a Python client and toolkit for DataFrames and {ml} in {es}.
-Full documentation is available on https://eland.readthedocs.io[Read the Docs].
+Full documentation is available on https://opensearch_py_ml.readthedocs.io[Read the Docs].
 Source code is available on https://github.com/elastic/eland[GitHub].
 
 [discrete]
@@ -28,7 +28,7 @@ Create a `DataFrame` object connected to an {es} cluster running on `http://loca
 
 [source,python]
 ------------------------------------
->>> import eland as ed
+>>> import opensearch_py_ml as ed
 >>> df = ed.DataFrame(
 ...    es_client="http://localhost:9200",
 ...    es_index_pattern="flights",
@@ -57,7 +57,7 @@ You can also connect Eland to an Elasticsearch instance in Elastic Cloud:
 
 [source,python]
 ------------------------------------
->>> import eland as ed
+>>> import opensearch_py_ml as ed
 >>> from elasticsearch import Elasticsearch
 
 # First instantiate an 'Elasticsearch' instance connected to Elastic Cloud

docs/sphinx/conf.py

@@ -41,13 +41,13 @@
 
 # -- Project information -----------------------------------------------------
 
-project = "eland"
+project = "opensearch_py_ml"
 copyright = f"{datetime.date.today().year}, Elasticsearch BV"
 
 # The full version, including alpha/beta/rc tags
-import eland
+import opensearch_py_ml
 
-version = str(eland._version.__version__)
+version = str(opensearch_py_ml._version.__version__)
 
 release = version
 
@@ -67,7 +67,7 @@
 
 doctest_global_setup = """
 try:
-    import eland as ed
+    import opensearch_py_ml as ed
 except ImportError:
     ed = None
 try:
@@ -100,7 +100,7 @@
 plot_html_show_formats = False
 plot_html_show_source_link = False
 plot_pre_code = """import numpy as np
-import eland as ed"""
+import opensearch_py_ml as ed"""
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
@@ -127,7 +127,7 @@
 # so a file named "default.css" will overwrite the builtin "default.css".
 # html_static_path = ['_static']
 
-html_logo = "logo/eland.png"
+html_logo = "logo/opensearch_py_ml.png"
 html_favicon = "logo/eland_favicon.png"
 
 master_doc = "index"

docs/sphinx/development/contributing.rst

@@ -150,7 +150,7 @@ Configuring PyCharm And Running Tests
     Control\'-\>\'Git\' on the \"Welcome to PyCharm\" page  <or other>
 
 - Enter the URL to your fork of eland
-     <e.g. `git@github.com:stevedodson/eland.git`>
+     <e.g. `git@github.com:stevedodson/opensearch_py_ml.git`>
 
 - Click \'Yes\' for \'Checkout from Version Control\'
 
@@ -189,7 +189,7 @@ Configuring PyCharm And Running Tests
 - To validate installation, open python console and run
    .. code-block:: bash
 
-      import eland as ed
+      import opensearch_py_ml as ed
       ed_df = ed.DataFrame('localhost', 'flights')
 
 - To run the automatic formatter and check for lint issues