Clustering

Models can be trained using pgml.train on unlabeled data to identify groups within the data. To build clusters on a given dataset, we can use the table or a view. Since clustering is an unsupervised algorithm, we don't need a column that represents a label as one of the inputs to pgml.train.

Example

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. This demonstrates using a table with a single array feature column for clustering. You could do something similar with a vector column.

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SELECT pgml.load_dataset('digits');

-- create an unlabeled table of the images for unsupervised learning
CREATE VIEW pgml.digit_vectors AS
SELECT image FROM pgml.digits;

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

-- train a simple model to classify the data
SELECT * FROM pgml.train('Handwritten Digit Clusters', 'cluster', 'pgml.digit_vectors', hyperparams => '{"n_clusters": 10}');

-- check out the predictions
SELECT target, pgml.predict('Handwritten Digit Clusters', image) AS prediction
FROM pgml.digits
LIMIT 10;

Algorithms

All clustering algorithms implemented by PostgresML are online versions. You may use the pgml.predictfunction to cluster novel datapoints after the clustering model has been trained.

Examples

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SELECT * FROM pgml.train('Handwritten Digit Clusters', algorithm => 'affinity_propagation');
SELECT * FROM pgml.train('Handwritten Digit Clusters', algorithm => 'birch', hyperparams => '{"n_clusters": 10}');
SELECT * FROM pgml.train('Handwritten Digit Clusters', algorithm => 'kmeans', hyperparams => '{"n_clusters": 10}');
SELECT * FROM pgml.train('Handwritten Digit Clusters', algorithm => 'mini_batch_kmeans', hyperparams => '{