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Dev v1.5 (#53)
* include python 3.8 * fix KeyError * fix KeyError * remove lightgbm restriction * disable checks for Py3.8 * versionadded * custom wrapper function name * wrappers for sklearn methods * drop class inheritance from object to avoid @wraps error in 2.7 * Revert "drop class inheritance from object to avoid @wraps error in 2.7" This reverts commit 0259e46 * updated target code * updated predict_proba output * moved pymas setup to package init() * fix for py27 * multiple wrapped functions * remove double counted vars * updated target * no return code/message by default * prep for release * content-type during file uploads * specify response format * cleanup * cleanup * allow rc and msg in output variables * added tests * fixed binary file content * added relationships service * added cassette * determine algorithm * improved metadata from astore * added neural net * added svm * binary gradboost model * added bayes net * cleanup * improved astore registration * added reports and report_images service * added reports and report_images service * enable testing against live server * moved dataset fixtures * explicitly load table * explicitly load table * cleanup * fixed case * cleanup for live tests * change score() to predict() * cleanup * assorted bugfixes & refreshed cassettes * added pagingiterator * require futures for py27 * updated requirements in docs * test PagingIterator * test param combinations * update doc * fixed performance task * refactoring for PagedList implementation * fixes for py27 * add iterator next() methods for py27 * py27 compatibility * debug * debug * debug * implement __getslice__ * cleanup * py27 compatibility * PagedList changes * cleanup & assorted fixes * added example data * enable passing dataframes and arrays directly to module steps * bug fix * pass pd.Series instance * added dataset readme * drop .sas file extensions * test JSON output from CLI * add --format option * fixed error formatting * improved parsing of target level * improved parsing of target level * CLI improvements * handle single-row dataframe input * recoded astore pipeline * sklearn pipeline * verify unpickle * render individual elements * activate deepsource * release file handles * add tests * cleanup * cleanup * cleanup * cleanup * code qual updates * remove list comprehension * code quality * code quality * code quality * fix len() comparisons * code qual * code qual * perf improvements * cleanup * cleanup * refresh cassettes * ignore private files * ds2 package constructor * bug fix * skip mas publish * add cassette * code qual improvements * code qual improvements * code qual improvements * fix for py27 * check swat build * fix py27 error * check pandas compatibility * code qual improvements * code qual improvements * code qual improvements * consistent project creation in register_model * fix KeyErrors * fixed delete_destination * cleanup * cleanup * include basic REST functions * add iris dataset * use local dataset * fix astore model inputs * cleanup * updated pymas wrapper * convert numpy values to scalar * do not wrap predict_proba method by default. * handle string and numeric outputs * handle predict_proba methods. deprecated return_msg and return_code. * updated targets * fixed tests * fixed formatting * fixed formatting * code quality * fixed astore example * fixed full lifecycle example * test sklearn classification model * any valid classification * code quality * code quality * fix for py 2.7 * added SGF examples * updated for py27 * initial draft * record cassettes for scenarios * initial draft * trim spaces * suppress pickle warnings * renamed files * updated list_folders example * HATEOAS section * skipcq * cleanup * enabled additional tests * enable tests on non-master branches * prep for release
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‎.deepsource.toml

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version = 1
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test_patterns = [
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"tests/**"
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]
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exclude_patterns = [
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"tests/**"
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]
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[[analyzers]]
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name = "python"
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enabled = true

‎.gitignore

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## Assorted files that shouldn't be uploaded
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.private/*
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_private/*
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## Editor temporary/working/backup files
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.#*
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*\#*\#

‎.travis.yml

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env: TOXENV=py36-tests-codecov TOX_SKIP_MISSING_INTERPRETERS=False
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- python: 3.7
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env: TOXENV=py37-tests-codecov TOX_SKIP_MISSING_INTERPRETERS=False
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# TODO: Enable when numpy, Pandas, and other dependencies are released on 3.8
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# - python: 3.8
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# env: TOXENV=py38-tests-codecov TOX_SKIP_MISSING_INTERPRETERS=False
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- stage: flake8

‎CHANGELOG.md

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Unreleased
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----------
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-
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-
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v1.5 (2020-2-23)
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----------------
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**Improvements**
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- Registered Python models will now include both `predict` and `predict_proba` methods.
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- Added a new Relationships service for managing links between objects.
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- Added a new Reports service for retrieving SAS Visual Analytics reports.
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- Added a new Report_Images service for rendering content from reports.
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- Additional metadata fields are set when registering an ASTORE model.
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- Collections of items should now return an instance of `PagedList` for lazy loading of results.
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- Module steps can now be called using `module.step(df)` where `df` is the row of a DataFrame or Numpy array.
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- `register_model` sets additional project properties when registering an ASTORE model.
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**Changes**
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- Replaced the `raw` parameter of the `request` methods with a `format` parameter, allowing more control over the
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returned value.
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- The `get_file_content` method of the Files service now returns the actual content instead of the file metadata.
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- JSON output when using `sasctl` from the command line is now formatted correctly.
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**Bugfixes**
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- `model_publish.delete_destination` now works correctly.
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v1.4.6 (2020-1-24)
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------------------

‎README.md

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If not already present, these packages will be downloaded and installed automatically.
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- requests
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- six
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- futures (Python 2.7 only)
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The following additional packages are recommended for full functionality:
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- swat
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A slightly more low-level way to interact with the environment is to use
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the service methods directly:
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```
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>>> from pprint import pprint
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>>> from sasctl import Session
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>>> from sasctl.services import folders
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>>> with Session(host, username, password):
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... folders = folders.list_folders()
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... pprint(folders)
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{'links': [{'href': '/folders/folders',
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'method': 'GET',
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'rel': 'folders',
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'type': 'application/vnd.sas.collection',
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'uri': '/folders/folders'},
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{'href': '/folders/folders',
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'method': 'POST',
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'rel': 'createFolder',
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... for f in folders.list_folders():
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... print(f)
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Public
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Projects
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ESP Projects
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Risk Environments
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... # truncated for clarity
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'rel': 'createSubfolder',
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'type': 'application/vnd.sas.content.folder',
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'uri': '/folders/folders?parentFolderUri=/folders/folders/{parentId}'}],
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'version': 1}
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My Folder
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My History
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My Favorites
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SAS Environment Manager
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```
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‎doc/api/services/relationships.rst

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sasctl.services.relationships
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=============================
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.. automodule:: sasctl._services.relationships
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/services/report_images.rst

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sasctl.services.report_images
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=============================
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.. automodule:: sasctl._services.report_images
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:members:
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:undoc-members:

‎doc/api/services/reports.rst

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sasctl.services.reports
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=======================
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.. automodule:: sasctl._services.reports
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:members:
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:undoc-members:

‎doc/index.rst

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- requests
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- futures (Python 2.7 only)
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The following additional packages are recommended for full functionality:
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>>> with Session('example.com', authinfo=<authinfo file>):
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... model = lm.LogisticRegression()
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... register_model(model, 'Sklearn Model', 'My Project')
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A slightly more low-level way to interact with the environment is to use the service
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methods directly::
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>>> from pprint import pprint
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>>> from sasctl import Session
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>>> from sasctl.services import folders
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>>> with Session(host, username, password):
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... folders = folders.list_folders()
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... pprint(folders)
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... for f in folders.list_folders():
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... print(f)
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{'links': [{'href': '/folders/folders',
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'method': 'GET',
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'rel': 'folders',
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Public
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Projects
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ESP Projects
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Risk Environments
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... # truncated for clarity
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'rel': 'createSubfolder',
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'type': 'application/vnd.sas.content.folder',
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'uri': '/folders/folders?parentFolderUri=/folders/folders/{parentId}'}],
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'version': 1}
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My Folder
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My History
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My Favorites
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SAS Environment Manager
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The most basic way to interact with the server is simply to call REST functions
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directly, though in general, this is not recommended.::
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>>> from pprint import pprint
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>>> from sasctl import Session
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>>> from sasctl import get, Session
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>>> with Session(host, username, password) as s:
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... folders = s.get('/folders')
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>>> with Session(host, username, password):
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... folders = get('/folders')
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... pprint(folders)
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{'links': [{'href': '/folders/folders',
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Common Uses
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++++++++++++
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Registering a model built with SWAT.
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Register a SAS model
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~~~~~~~~~~~~~~~~~~~~~~~
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::
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import swat
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from sasctl import register_model, Session
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with swat.CAS('hostname', 5570, 'username', 'password') as cas:
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astore = cas.CASTable('model_astore_table')
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Session('hostname', 'username', 'password'):
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model = register_model(astore, 'Model Name', 'Project Name')
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.. literalinclude:: ../examples/astore_model.py
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:caption: examples/astore_model.py
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:lines: 7-
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Registering a model built with sci-kit learn.
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Register a scikit-learn model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. literalinclude:: ../examples/sklearn_model.py
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:caption: examples/sklearn_model.py
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::
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from sklearn.linear_model import LogisticRegression
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from sasctl import register_model, Session
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model = LogisticRegression()
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model.fit(X, y)
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# Establish a session with Viya
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with Session('hostname', 'username', 'password'):
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register_model(model, 'Model Name', 'Project Name', input=X)
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Publish a model
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~~~~~~~~~~~~~~~
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::
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from sasctl.services import model_repository as mr
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model = mr.get_model('Model Name')
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publish_model(model, 'Destination Name')
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Execute a model in MAS
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~~~~~~~~~~~~~~~~~~~~~~
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::
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from sasctl import Session
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from sasctl.services import microanalytic_score as mas
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module.predict(model_inputs)
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- publish a model
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- score a model
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See the :file:`examples/` directory in the repository for more complete examples.
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HATEOAS
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Coming soon.
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Many of the SAS microservices follow the `HATEOAS`_ paradigm and the standard is for services to return a links
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collection containing valid operations. Most **sasctl** operations return one or more instances of :class:`~sasctl.core.RestObj`. Any
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links related to that object are accessible via the ['links'] key. Each link is represented as a dictionary containing metadata::
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{'method': 'POST', 'rel':
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'createFolder',
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'href': '/folders/folders',
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'uri': '/folders/folders',
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'type': 'application/vnd.sas.content.folder'}
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However, instead of having to parse this collection to find a link, **sasctl** includes some functions to make this easy: :func:`~sasctl.core.get_link` and :func:`~sasctl.core.request_link`.
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Given an object and a link name (`rel`) :func:`~sasctl.core.get_link` will return the metadata for that link. Similarly,
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:func:`~sasctl.core.request_link` will make the request to the link and return the response object.
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.. _`HATEOAS`: https://restfulapi.net/hateoas/
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API Reference
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-------------

‎examples/astore_model.py

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