Supervised Learning
Getting Training Data
A large part of the machine learning workflow is acquiring, cleaning, and preparing data for training algorithms. Naturally, we think Postgres is a great place to store your data. For the purpose of this example, we'll load a toy dataset, the classic handwritten digits image collection, from scikit-learn.
SELECT * FROM pgml.load_dataset('digits');
pgml=# SELECT * FROM pgml.load_dataset('digits');
NOTICE: table "digits" does not exist, skipping
table_name | rows
-------------+------
pgml.digits | 1797
(1 row)
This NOTICE can safely be ignored. PostgresML attempts to do a clean reload by dropping the pgml.digits table if it exists. The first time this command is run, the table does not exist.
PostgresML loaded the Digits dataset into the pgml.digits table. You can examine the 2D arrays of image data, as well as the label in the target column:
SELECT
target,
image
FROM pgml.digits LIMIT 5;
target | image
-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------
0 | {{0,0,5,13,9,1,0,0},{0,0,13,15,10,15,5,0},{0,3,15,2,0,11,8,0},{0,4,12,0,0,8,8,0},{0,5,8,0,0,9,8,0},{0,4,11,0,1,12,7,0},{0,2,14,5,10,12,0,0},{0,0,6,13,10,0,0,0}}
1 | {{0,0,0,12,13,5,0,0},{0,0,0,11,16,9,0,0},{0,0,3,15,16,6,0,0},{0,7,15,16,16,2,0,0},{0,0,1,16,16,3,0,0},{0,0,1,16,16,6,0,0},{0,0,1,16,16,6,0,0},{0,0,0,11,16,10,0,0}}
2 | {{0,0,0,4,15,12,0,0},{0,0,3,16,15,14,0,0},{0,0,8,13,8,16,0,0},{0,0,1,6,15,11,0,0},{0,1,8,13,15,1,0,0},{0,9,16,16,5,0,0,0},{0,3,13,16,16,11,5,0},{0,0,0,3,11,16,9,0}}
3 | {{0,0,7,15,13,1,0,0},{0,8,13,6,15,4,0,0},{0,2,1,13,13,0,0,0},{0,0,2,15,11,1,0,0},{0,0,0,1,12,12,1,0},{0,0,0,0,1,10,8,0},{0,0,8,4,5,14,9,0},{0,0,7,13,13,9,0,0}}
4 | {{0,0,0,1,11,0,0,0},{0,0,0,7,8,0,0,0},{0,0,1,13,6,2,2,0},{0,0,7,15,0,9,8,0},{0,5,16,10,0,16,6,0},{0,4,15,16,13,16,1,0},{0,0,0,3,15,10,0,0},{0,0,0,2,16,4,0,0}}
(5 rows)
Training a Model
Now that we've got data, we're ready to train a model using an algorithm. We'll start with the default linear algorithm to demonstrate the basics. See the Algorithms for a complete list of available algorithms.
SELECT * FROM pgml.train(
'Handwritten Digit Image Classifier',
'classification',
'pgml.digits',
'target'
);
INFO: Snapshotting table "pgml.digits", this may take a little while...
INFO: Snapshot of table "pgml.digits" created and saved in "pgml"."snapshot_1"
INFO: Dataset { num_features: 64, num_labels: 1, num_rows: 1797, num_train_rows: 1348, num_test_rows: 449 }
INFO: Training Model { id: 1, algorithm: linear, runtime: python }
INFO: Hyperparameter searches: 1, cross validation folds: 1
INFO: Hyperparams: {}
INFO: Metrics: {
"f1": 0.91903764,
"precision": 0.9175061,
"recall": 0.9205743,
"accuracy": 0.9175947,
"mcc": 0.90866333,
"fit_time": 0.17586434,
"score_time": 0.01282608
}
project | task | algorithm | deployed
------------------------------------+----------------+-----------+----------
Handwritten Digit Image Classifier | classification | linear | t
(1 row)
The output gives us information about the training run, including the deployed status. This is great news indicating training has successfully reached a new high score for the project's key metric and our new model was automatically deployed as the one that will be used to make new predictions for the project. See Deployments for a guide to managing the active model.
Inspecting the results
Now we can inspect some of the artifacts a training run creates.
SELECT * FROM pgml.overview;
pgml=# SELECT * FROM pgml.overview;
name | deployed_at | task | algorithm | runtime | relation_name | y_column_name | test_sampling | test_size
------------------------------------+----------------------------+----------------+-----------+---------+---------------+---------------+---------------+-----------
Handwritten Digit Image Classifier | 2022-10-11 12:43:15.346482 | classification | linear | python | pgml.digits | {target} | last | 0.25
(1 row)
Inference
The pgml.predict() function is the key value proposition of PostgresML. It provides online predictions using the best, automatically deployed model for a project.
API
The API for predictions is very simple and only requires two arguments: the project name and the features used for prediction.
select pgml.predict (
project_name TEXT,
features REAL[]
)
Parameters
| Parameter | Description | Example |
|---|---|---|
project_name |
The project name used to train models in pgml.train(). |
My First PostgresML Project |
features |
The feature vector used to predict a novel data point. | ARRAY[0.1, 0.45, 1.0] |
Example
SELECT pgml.predict(
'My Classification Project',
ARRAY[0.1, 2.0, 5.0]
) AS prediction;
where ARRAY[0.1, 2.0, 5.0] is the same type of features used in training, in the same order as in the training data table or view. This score can be used in other regular queries.
SELECT *,
pgml.predict(
'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;
Example
If you've already been through the Training Overview, you can see the results of those efforts:
SELECT
target,
pgml.predict('Handwritten Digit Image Classifier', image) AS prediction
FROM pgml.digits
LIMIT 10;
target | prediction
--------+------------
0 | 0
1 | 1
2 | 2
3 | 3
4 | 4
5 | 5
6 | 6
7 | 7
8 | 8
9 | 9
(10 rows)
Active Model
Since it's so easy to train multiple algorithms with different hyperparameters, sometimes it's a good idea to know which deployed model is used to make predictions. You can find that out by querying the pgml.deployed_models view:
SELECT * FROM pgml.deployed_models;
id | name | task | algorithm | runtime | deployed_at
----+------------------------------------+----------------+-----------+---------+----------------------------
4 | Handwritten Digit Image Classifier | classification | xgboost | rust | 2022-10-11 13:06:26.473489
(1 row)
PostgresML will automatically deploy a model only if it has better metrics than existing ones, so it's safe to experiment with different algorithms and hyperparameters.
Take a look at Deploying Models documentation for more details.
Specific Models
You may also specify a model_id to predict rather than a project name, to use a particular training run. You can find model ids by querying the pgml.models table.
SELECT models.id, models.algorithm, models.metrics
FROM pgml.models
JOIN pgml.projects
ON projects.id = models.project_id
WHERE projects.name = 'Handwritten Digit Image Classifier';
id | algorithm | metrics
----+-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------
1 | linear | {"f1": 0.9190376400947571, "mcc": 0.9086633324623108, "recall": 0.9205743074417114, "accuracy": 0.9175946712493896, "fit_time": 0.8388963937759399, "p
recision": 0.9175060987472534, "score_time": 0.019625699147582054}
For example, making predictions with model_id = 1:
SELECT
target,
pgml.predict(1, image) AS prediction
FROM pgml.digits
LIMIT 10;
target | prediction
--------+------------
0 | 0
1 | 1
2 | 2
3 | 3
4 | 4
5 | 5
6 | 6
7 | 7
8 | 8
9 | 9
(10 rows)
