I know that scikit-learn models can be persisted in files, by using joblib (as described here: http://scikit-learn.org/stable/modules/model_persistence.html). However, since i have machine learning procedure inside postgresql plpythonu function, I would rather persist the model inside the Postgresql database.
What is recommended, the most convinient way to store scikit-learn model inside a Postgresql database?
Here is a sample code in Python for sending the trained model to a Postgres table. Note that you first need to create a table that has a column with the "bytea" type to store the pickled sklearn model in binary format.
from sklearn import svm
import psycopg2
import pickle
#### # Connect to Postgres
connection = psycopg2.connect(user, password, host, port, database)
cur = connection.cursor()
model = svm.OneClassSVM()
model.fit(features) # features are some training data
data = pickle.dumps(model) # first we should pickle the model
#### # Assuming you have a Postgres table with columns epoch and file
sql = "INSERT INTO sampletable (epoch, file) VALUES(%s)"
cur.execute(sql, (epoch, psycopg2.Binary(data)))
connection.commit()