description: Decompose an input vector into it's principal components

Chunks are pieces of documents split using some specified splitter. This is typically done before embedding.

pgml.decompose(

project_name TEXT, -- project name

vector REAL[] -- features to decompose

)

| Parameter | Example | Description |

|----------------|---------------------------------|----------------------------------------------------------|

| `project_name` | `'My First PostgresML Project'` | The project name used to train models in `pgml.train()`. |

| `vector` | `ARRAY[0.1, 0.45, 1.0]` | The feature vector that needs decomposition. |

SELECT pgml.decompose('My PCA', ARRAY[0.1, 2.0, 5.0]);

!!! example

SELECT *,

pgml.decompose(

'Buy it Again',

ARRAY[

user.location_id,

NOW() - user.created_at,

user.total_purchases_in_dollars

]

) AS buying_score

FROM users

WHERE tenant_id = 5

ORDER BY buying_score

LIMIT 25;

!!!

SELECT * FROM pgml.train('Handwritten Digit Clusters', 'clustering', 'pgml.digit_vectors', hyperparams => '{"n_clusters": 10}');

All clustering algorithms implemented by PostgresML are online versions. You may use the [pgml.predict](../../../api/sql-extension/pgml.predict/ "mention")function to cluster novel data points after the clustering model has been trained.

Models can be trained using `pgml.train` on unlabeled data to identify important features within the data. To decompose a dataset into it's principal components, we can use the table or a view. Since decomposition is an unsupervised algorithm, we don't need a column that represents a label as one of the inputs to `pgml.train`.

This example trains models on the sklearn digits dataset -- which is a copy of the test set of the [UCI ML hand-written digits datasets](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits). This demonstrates using a table with a single array feature column for principal component analysis. You could do something similar with a vector column.

SELECT pgml.load_dataset('digits');

CREATE VIEW pgml.digit_vectors AS

SELECT image FROM pgml.digits;

SELECT left(image::text, 40) || ',...}' FROM pgml.digit_vectors LIMIT 10;

SELECT * FROM pgml.train('Handwritten Digit Components', 'decomposition', 'pgml.digit_vectors', hyperparams => '{"n_components": 3}');

SELECT target, pgml.decompose('Handwritten Digit Components', image) AS pca

FROM pgml.digits

LIMIT 10;

Note that the input vectors have been reduced from 64 dimensions to 3, which explain nearly half of the variance across all samples.

All decomposition algorithms implemented by PostgresML are online versions. You may use the [pgml.decompose](../../../api/sql-extension/pgml.decompose "mention") function to decompose novel data points after the model has been trained.

| Algorithm | Reference |

|---------------------------|---------------------------------------------------------------------------------------------------------------------|

| `pca` | [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) |

SELECT * FROM pgml.train('Handwritten Digit Clusters', algorithm => 'pca', hyperparams => '{"n_components": 10}');

"clustering" => Ok("silhouette"),

"decomposition" => Ok("cumulative_explained_variance"),

"clustering" => Ok("silhouette"),

"decomposition" => Ok("Cumulative Explained Variance"),

version = "2.8.4"

SELECT * FROM pgml.train('Handwritten Digit Clusters', 'clustering', 'pgml.digit_vectors', hyperparams => '{"n_clusters": 10}');

\timing on

SELECT pgml.load_dataset('digits');

SELECT left(image::text, 40) || ',...}', target FROM pgml.digits LIMIT 10;

CREATE VIEW digit_vectors AS

SELECT image FROM pgml.digits;

SELECT * FROM pgml.train('Handwritten Digits Reduction', 'decomposition', 'digit_vectors');

SELECT target, pgml.decompose('Handwritten Digits Reduction', image) AS pca

FROM pgml.digits

LIMIT 10;

SELECT * FROM pgml.train('Handwritten Digits Reduction', hyperparams => '{"n_components": 3}');

SELECT target, pgml.decompose('Handwritten Digits Reduction', image) AS pca

FROM pgml.digits

LIMIT 10;

SELECT trained_models.* FROM pgml.trained_models

JOIN pgml.models on models.id = trained_models.id

ORDER BY models.metrics->>'cumulative_explained_variance' DESC LIMIT 5;

SELECT * FROM pgml.deploy('Handwritten Digits Reduction', 'most_recent', 'pca');

SELECT * FROM pgml.deployed_models ORDER BY deployed_at DESC LIMIT 5;

SELECT * FROM pgml.deploy('Handwritten Digits Reduction', 'best_score');

SELECT * FROM pgml.deploy('Handwritten Digits Reduction', 'most_recent');

SELECT * FROM pgml.deploy('Handwritten Digits Reduction', 'rollback');

SELECT * FROM pgml.deploy('Handwritten Digits Reduction', 'best_score', 'pca');

SELECT target, pgml.predict('Handwritten Digits Reduction', image) AS prediction

FROM pgml.digits

LIMIT 10;