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levkklevkk
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Merge pull request #18 from postgresml/circleci-project-setup
Add .circleci/config.yml
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‎.circleci/config.yml

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# Use the latest 2.1 version of CircleCI pipeline process engine.
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# See: https://circleci.com/docs/2.0/configuration-reference
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version: 2.1
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# Define a job to be invoked later in a workflow.
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# See: https://circleci.com/docs/2.0/configuration-reference/#jobs
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jobs:
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red-wine-quality:
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# Specify the execution environment. You can specify an image from Dockerhub or use one of our Convenience Images from CircleCI's Developer Hub.
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# See: https://circleci.com/docs/2.0/configuration-reference/#docker-machine-macos-windows-executor
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docker:
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- image: levkk/postgresml:latest
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# Add steps to the job
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# See: https://circleci.com/docs/2.0/configuration-reference/#steps
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steps:
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- checkout
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- run:
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name: "Train and predict"
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command: "bash docker/entrypoint.sh psql -U root -p 5432 -h 127.0.0.1 -f sql/test.sql"
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# Invoke jobs via workflows
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# See: https://circleci.com/docs/2.0/configuration-reference/#workflows
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workflows:
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end-to-end-tests:
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jobs:
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- red-wine-quality

‎README.md

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![PostgresML](./logo-small.png)
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[![PostgresML](https://circleci.com/gh/postgresml/postgresml/tree/master.svg?style=svg)](https://circleci.com/gh/postgresml/postgresml/tree/master)
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PostgresML is an end-to-end machine learning system. Using only SQL, it allows to train models and run online predictions, alongside normal queries, directly using the data in your databases.
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## Why

‎sql/install.sql

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---
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--- Create schema for models.
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---
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DROP SCHEMA pgml CASCADE;
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DROP SCHEMA IF EXISTS pgml CASCADE;
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CREATE SCHEMA IF NOT EXISTS pgml;
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CREATE OR REPLACE FUNCTION pgml.auto_updated_at(tbl regclass)

‎sql/test.sql

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\timing
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SELECT pgml.train('Red Wine Scores', 'regression', 'wine_quality_red', 'quality');
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SELECT * FROM pgml.train('Red Wine Scores', 'regression', 'wine_quality_red', 'quality');
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SELECT pgml.predict('Red Wine Scores', '{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
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SELECT pgml.predict('Red Wine Scores', '{6.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
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SELECT pgml.predict('Red Wine Scores', '{5.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
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SELECT pgml.predict('Red Wine Scores', '{3.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
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SELECT pgml.train('Red Wine Categories', 'classification', 'wine_quality_red', 'quality', 'svm');
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SELECT * FROM pgml.train('Red Wine Categories', 'classification', 'wine_quality_red', 'quality', 'svm');
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SELECT pgml.predict('Red Wine Categories', '{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
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