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Original file line number | Diff line number | Diff line change |
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.. _ludwig: | ||
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Model Training with Ludwig | ||
========================== | ||
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1. Installation | ||
--------------- | ||
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To use the `Ludwig framework <https://ludwig.ai/latest/>`_, we need to install the extra ludwig dependency in your EvaDB virtual environment. | ||
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.. code-block:: bash | ||
pip install evadb[ludwig] | ||
2. Example Query | ||
---------------- | ||
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.. code-block:: sql | ||
CREATE OR REPLACE FUNCTION PredictHouseRent FROM | ||
( SELECT sqft, location, rental_price FROM HomeRentals ) | ||
TYPE Ludwig | ||
PREDICT 'rental_price' | ||
TIME_LIMIT 120; | ||
In the above query, you are creating a new customized function by automatically training a model from the ``HomeRentals`` table. | ||
The ``rental_price`` column will be the target column for predication, while ``sqft`` and ``location`` are the inputs. | ||
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You can also simply give all other columns in ``HomeRentals`` as inputs and let the underlying AutoML framework to figure it out. Below is an example query: | ||
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.. code-block:: sql | ||
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM | ||
( SELECT * FROM HomeRentals ) | ||
TYPE Ludwig | ||
PREDICT 'rental_price' | ||
TIME_LIMIT 120; | ||
.. note:: | ||
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Check out our :ref:`homerental-predict` for working example. | ||
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3. Model Training Parameters | ||
---------------------------- | ||
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.. list-table:: Available Parameters | ||
:widths: 25 75 | ||
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* - PREDICT (**required**) | ||
- The name of the column we wish to predict. | ||
* - TIME_LIMIT | ||
- Time limit to train the model in seconds. Default: 120. | ||
* - TUNE_FOR_MEMORY | ||
- Whether to refine hyperopt search space for available host / GPU memory. Default: False. | ||
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Below is an example query specifying the above parameters: | ||
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.. code-block:: sql | ||
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM | ||
( SELECT * FROM HomeRentals ) | ||
TYPE Ludwig | ||
PREDICT 'rental_price' | ||
TIME_LIMIT 3600 | ||
TUNE_FOR_MEMORY True; |
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.. _sklearn: | ||
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Model Training with Sklearn | ||
============================ | ||
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1. Installation | ||
--------------- | ||
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To use the `Sklearn framework <https://scikit-learn.org/stable/>`_, we need to install the extra sklearn dependency in your EvaDB virtual environment. | ||
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.. code-block:: bash | ||
pip install evadb[sklearn] | ||
2. Example Query | ||
---------------- | ||
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.. code-block:: sql | ||
CREATE OR REPLACE FUNCTION PredictHouseRent FROM | ||
( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals ) | ||
TYPE Sklearn | ||
PREDICT 'rental_price'; | ||
In the above query, you are creating a new customized function by training a model from the ``HomeRentals`` table using the ``Sklearn`` framework. | ||
The ``rental_price`` column will be the target column for predication, while the rest columns from the ``SELET`` query are the inputs. |
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