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Commit74bfa5e

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move models dir inside pg\
1 parent4867e50 commit74bfa5e

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5 files changed

+55
-17
lines changed

5 files changed

+55
-17
lines changed

‎README.md‎

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Original file line numberDiff line numberDiff line change
@@ -71,13 +71,6 @@ sudo python3 setup.py install
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cd ../
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```
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74-
Create a directory for PostgresML to store its models (this is currently hardcoded):
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76-
```
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mkdir -p /app/models
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chown postgres:postgres /app/models
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```
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Run the test:
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```

‎pgml/pgml/score.py‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,17 @@
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"""Score"""
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3+
importos
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importpickle
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6+
frompgml.exceptionsimportPgMLException
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8+
9+
defload(name,source):
10+
"""Load a model from file."""
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path=os.path.join(source,name)
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13+
ifnotos.path.exists(path):
14+
raisePgMLException(f"Model source directory `{path}` does not exist.")
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16+
withopen(path,"rb")asf:
17+
returnpickle.load(f)

‎pgml/pgml/sql.py‎

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Original file line numberDiff line numberDiff line change
@@ -1,5 +1,6 @@
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"""Tools to run SQL.
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"""
3+
importos
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45

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defall_rows(cursor):
@@ -11,3 +12,21 @@ def all_rows(cursor):
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1213
forrowinrows:
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yieldrow
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defmodels_directory(plpy):
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"""Get the directory where we store our models."""
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data_directory=plpy.execute(
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"""
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SELECT setting FROM pg_settings WHERE name = 'data_directory'
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""",
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1,
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)[0]["setting"]
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models_dir=os.path.join(data_directory,"pgml_models")
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# TODO: Ideally this happens during extension installation.
29+
ifnotos.path.exists(models_dir):
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os.mkdir(models_dir,0o770)
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32+
returnmodels_dir

‎pgml/pgml/train.py‎

Lines changed: 4 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -8,13 +8,14 @@
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fromsklearn.metricsimportmean_squared_error,r2_score
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importpickle
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importos
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frompgml.sqlimportall_rows
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frompgml.exceptionsimportPgMLException
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frompgml.validateimportcheck_type
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17-
deftrain(cursor,y_column,name,save=True):
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deftrain(cursor,y_column,name,save=True,destination="/tmp/pgml_models"):
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"""Train the model on data on some rows.
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Arguments:
@@ -65,7 +66,8 @@ def train(cursor, y_column, name, save=True):
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msq=mean_squared_error(y_test,y_pred)
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r2=r2_score(y_test,y_pred)
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68-
path=f"/app/models/{name}"
69+
path=os.path.join(destination,name)
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ifsave:
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withopen(path,"wb")asf:
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pickle.dump(lr,f)

‎sql/install.sql‎

Lines changed: 15 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -21,6 +21,7 @@ CREATE TABLE pgml.model_versions(
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nameVARCHAR,
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locationVARCHARNULL,
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data_sourceTEXT,
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y_columnVARCHAR,
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started_atTIMESTAMP WITHOUT TIME ZONE DEFAULTCURRENT_TIMESTAMP,
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ended_atTIMESTAMP WITHOUT TIME ZONENULL,
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mean_squared_errorDOUBLE PRECISION,
@@ -50,29 +51,34 @@ CREATE OR REPLACE FUNCTION pgml_train(table_name TEXT, y TEXT)
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RETURNSTEXT
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AS $$
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frompgml.train import train
54+
frompgml.sql import models_directory
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import os
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data_source= f"SELECT * FROM {table_name}"
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5659
# Start training.
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start=plpy.execute(f"""
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INSERT INTO pgml.model_versions
59-
(name, data_source)
60-
VALUES ('{table_name}', '{data_source}')
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(name, data_source, y_column)
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VALUES
64+
('{table_name}', '{data_source}', '{y}')
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RETURNING *""",1)
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6367
id_= start[0]["id"]
6468
name= f"{table_name}_{id_}"
6569

70+
destination= models_directory(plpy)
71+
6672
# Train!
67-
location, msq, r2= train(plpy.cursor(data_source), y_column=y, name=name)
73+
location, msq, r2= train(plpy.cursor(data_source), y_column=y, name=name, destination=destination)
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6975
plpy.execute(f"""
7076
UPDATE pgml.model_versions
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SET location = '{location}',
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successful = true,
73-
ended_at = NOW(),
7479
mean_squared_error = '{msq}',
75-
r2_score = '{r2}'
80+
r2_score = '{r2}',
81+
ended_at = clock_timestamp()
7682
WHERE id = {id_}""")
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7884
return name
@@ -86,14 +92,15 @@ DROP FUNCTION pgml_score(model_name TEXT, VARIADIC features DOUBLE PRECISION[]);
8692
CREATE OR REPLACEFUNCTIONpgml_score(model_nameTEXT, VARIADIC featuresDOUBLE PRECISION[])
8793
RETURNSDOUBLE PRECISION
8894
AS $$
95+
frompgml.sql import models_directory
96+
frompgml.score import load
8997
import pickle
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9199
if model_namein SD:
92100
model= SD[model_name]
93101
else:
94-
with open(f"/app/models/{model_name}","rb")as f:
95-
model=pickle.load(f)
96-
SD[model_name]= model
102+
SD[model_name]= load(model_name, models_directory(plpy))
103+
model= SD[model_name]
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98105
scores=model.predict([features,])
99106
return scores[0]

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