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fix and test preprocessing examples#1520

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montanalow merged 2 commits intomasterfrommontana/sd
Jun 11, 2024
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2 changes: 1 addition & 1 deletion.github/workflows/ubuntu-packages-and-docker-image.yml
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Expand Up@@ -4,7 +4,7 @@ on:
workflow_dispatch:
inputs:
packageVersion:
default: "2.8.2"
default: "2.9.1"
jobs:
#
# PostgresML extension.
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2 changes: 1 addition & 1 deletionpgml-cms/docs/resources/developer-docs/contributing.md
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Expand Up@@ -127,7 +127,7 @@ SELECT pgml.version();
postgres=# select pgml.version();
version
-------------------
2.7.4
2.9.1
(1 row)
```
{% endtab %}
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2 changes: 1 addition & 1 deletionpgml-cms/docs/resources/developer-docs/installation.md
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Expand Up@@ -132,7 +132,7 @@ CREATE EXTENSION
pgml_test=# SELECT pgml.version();
version
---------
2.7.4
2.9.1
(1 row)
```

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Expand Up@@ -80,7 +80,7 @@ Time: 41.520 ms
postgresml=# SELECT pgml.version();
version
---------
2.7.13
2.9.1
(1 row)
```

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Expand Up@@ -115,6 +115,6 @@ Type "help" for help.
postgresml=> SELECT pgml.version();
version
---------
2.7.9
2.9.1
(1 row)
```
2 changes: 1 addition & 1 deletionpgml-extension/Cargo.lock
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2 changes: 1 addition & 1 deletionpgml-extension/Cargo.toml
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@@ -1,6 +1,6 @@
[package]
name = "pgml"
version = "2.9.0"
version = "2.9.1"
edition = "2021"

[lib]
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33 changes: 33 additions & 0 deletionspgml-extension/examples/preprocessing.sql
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@@ -0,0 +1,33 @@
-- load the diamonds dataset, that contains text categorical variables
SELECT pgml.load_dataset('jdxcosta/diamonds');

-- view the data
SELECT * FROM pgml."jdxcosta/diamonds" LIMIT 10;

-- drop the Unamed column, since it's not useful for training (you could create a view instead)
ALTER TABLE pgml."jdxcosta/diamonds" DROP COLUMN "Unnamed: 0";

-- train a model using preprocessors to scale the numeric variables, and target encode the categoricals
SELECT pgml.train(
project_name => 'Diamond prices',
task => 'regression',
relation_name => 'pgml.jdxcosta/diamonds',
y_column_name => 'price',
algorithm => 'lightgbm',
preprocess => '{
"carat": {"scale": "standard"},
"depth": {"scale": "standard"},
"table": {"scale": "standard"},
"cut": {"encode": "target", "scale": "standard"},
"color": {"encode": "target", "scale": "standard"},
"clarity": {"encode": "target", "scale": "standard"}
}'
);

-- run some predictions, notice we're passing a heterogeneous row (tuple) as input, rather than a homogenous ARRAY[].
SELECT price, pgml.predict('Diamond prices', (carat, cut, color, clarity, depth, "table", x, y, z)) AS prediction
FROM pgml."jdxcosta/diamonds"
LIMIT 10;

-- This is a difficult dataset for more algorithms, which makes it a good challenge for preprocessing, and additional
-- feature engineering. What's next?
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7 changes: 4 additions & 3 deletionspgml-extension/src/bindings/transformers/mod.rs
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Expand Up@@ -380,7 +380,7 @@ pub fn load_dataset(
.ok_or(anyhow!("dataset `data` key is not an object"))?;
let column_names = types
.iter()
.map(|(name, _type)|name.clone())
.map(|(name, _type)|format!("\"{}\"", name))
.collect::<Vec<String>>()
.join(", ");
let column_types = types
Expand All@@ -393,13 +393,14 @@ pub fn load_dataset(
"int64" => "INT8",
"int32" => "INT4",
"int16" => "INT2",
"int8" => "INT2",
"float64" => "FLOAT8",
"float32" => "FLOAT4",
"float16" => "FLOAT4",
"bool" => "BOOLEAN",
_ => bail!("unhandled dataset feature while reading dataset: {type_}"),
};
Ok(format!("{name} {type_}"))
Ok(format!("\"{name}\" {type_}"))
})
.collect::<Result<Vec<String>>>()?
.join(", ");
Expand DownExpand Up@@ -455,7 +456,7 @@ pub fn load_dataset(
.into_datum(),
)),
"dict" | "list" => row.push((PgBuiltInOids::JSONBOID.oid(), JsonB(value.clone()).into_datum())),
"int64" | "int32" | "int16" => row.push((
"int64" | "int32" | "int16"| "int8"=> row.push((
PgBuiltInOids::INT8OID.oid(),
value
.as_i64()
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13 changes: 6 additions & 7 deletionspgml-extension/src/orm/model.rs
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Expand Up@@ -344,13 +344,12 @@ impl Model {
).unwrap().first();

if !result.is_empty() {
let project_id = result.get(2).unwrap().unwrap();
let project = Project::find(project_id).unwrap();
let snapshot_id = result.get(3).unwrap().unwrap();
let snapshot = Snapshot::find(snapshot_id).unwrap();
let algorithm = Algorithm::from_str(result.get(4).unwrap().unwrap()).unwrap();
let runtime = Runtime::from_str(result.get(5).unwrap().unwrap()).unwrap();

let project_id = result.get(2).unwrap().expect("project_id is i64");
let project = Project::find(project_id).expect("project doesn't exist");
let snapshot_id = result.get(3).unwrap().expect("snapshot_id is i64");
let snapshot = Snapshot::find(snapshot_id).expect("snapshot doesn't exist");
let algorithm = Algorithm::from_str(result.get(4).unwrap().unwrap()).expect("algorithm is malformed");
let runtime = Runtime::from_str(result.get(5).unwrap().unwrap()).expect("runtime is malformed");
let data = Spi::get_one_with_args::<Vec<u8>>(
"
SELECT data
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4 changes: 2 additions & 2 deletionspgml-extension/src/orm/sampling.rs
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Expand Up@@ -55,7 +55,7 @@ impl Sampling {
Sampling::stratified => {
format!(
"
SELECT*
SELECT{col_string}
FROM (
SELECT
*,
Expand DownExpand Up@@ -125,7 +125,7 @@ mod tests {
let columns = get_column_fixtures();
let sql = sampling.get_sql("my_table", columns);
let expected_sql = "
SELECT*
SELECT\"col1\", \"col2\"
FROM (
SELECT
*,
Expand Down
24 changes: 17 additions & 7 deletionspgml-extension/src/orm/snapshot.rs
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Expand Up@@ -230,16 +230,24 @@ impl Column {
if self.preprocessor.encode == Encode::target {
let categories = self.statistics.categories.as_mut().unwrap();
let mut sums = vec![0_f32; categories.len() + 1];
let mut total = 0.;
Zip::from(array).and(target).for_each(|&value, &target| {
total += target;
sums[value as usize] += target;
});
let avg_target = total / categories.len() as f32;
for category in categories.values_mut() {
let sum = sums[category.value as usize];
category.value = sum / category.members as f32;
if category.members > 0 {
let sum = sums[category.value as usize];
category.value = sum / category.members as f32;
} else {
// use avg target for categories w/ no members, e.g. __NULL__ category in a complete dataset
category.value = avg_target;
}
}
}

// Data is filtered for NaN because it is not welldefined statistically, and they are counted as separate stat
// Data is filtered for NaN because it is not well-defined statistically, and they are counted as separate stat
let mut data = array
.iter()
.filter_map(|n| if n.is_nan() { None } else { Some(*n) })
Expand DownExpand Up@@ -404,7 +412,8 @@ impl Snapshot {
.first();
if !result.is_empty() {
let jsonb: JsonB = result.get(7).unwrap().unwrap();
let columns: Vec<Column> = serde_json::from_value(jsonb.0).unwrap();
let columns: Vec<Column> =
serde_json::from_value(jsonb.0).expect("invalid json description of columns");
// let jsonb: JsonB = result.get(8).unwrap();
// let analysis: Option<IndexMap<String, f32>> = Some(serde_json::from_value(jsonb.0).unwrap());
let mut s = Snapshot {
Expand DownExpand Up@@ -500,9 +509,10 @@ impl Snapshot {

let preprocessors: HashMap<String, Preprocessor> = serde_json::from_value(preprocess.0).expect("is valid");

let mut position = 0; // Postgres column positions are not updated when other columns are dropped, but we expect consecutive positions when we read the table.
Spi::connect(|mut client| {
let mut columns: Vec<Column> = Vec::new();
client.select("SELECT column_name::TEXT, udt_name::TEXT, is_nullable::BOOLEAN, ordinal_position::INTEGER FROM information_schema.columns WHERE table_schema = $1 AND table_name = $2 ORDER BY ordinal_position ASC",
client.select("SELECT column_name::TEXT, udt_name::TEXT, is_nullable::BOOLEAN FROM information_schema.columns WHERE table_schema = $1 AND table_name = $2 ORDER BY ordinal_position ASC",
None,
Some(vec![
(PgBuiltInOids::TEXTOID.oid(), schema_name.into_datum()),
Expand All@@ -520,7 +530,7 @@ impl Snapshot {
pg_type = pg_type[1..].to_string() + "[]";
}
let nullable = row[3].value::<bool>().unwrap().unwrap();
letposition= row[4].value::<i32>().unwrap().unwrap() as usize;
position+= 1;
let label = match y_column_name {
Some(ref y_column_name) => y_column_name.contains(&name),
None => false,
Expand DownExpand Up@@ -1158,7 +1168,7 @@ impl Snapshot {
pub fn numeric_encoded_dataset(&mut self) -> Dataset {
let mut data = None;
Spi::connect(|client| {
// PostgresArraysarrays are 1 indexed and so are SPI tuples...
// Postgres arrays are 1 indexed and so are SPI tuples...
let result = client.select(&self.select_sql(), None, None).unwrap();
let num_rows = result.len();
let (num_train_rows, num_test_rows) = self.train_test_split(num_rows);
Expand Down
1 change: 1 addition & 0 deletionspgml-extension/tests/test.sql
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Expand Up@@ -30,5 +30,6 @@ SELECT pgml.load_dataset('wine');
\i examples/regression.sql
\i examples/vectors.sql
\i examples/chunking.sql
\i examples/preprocessing.sql
-- transformers are generally too slow to run in the test suite
--\i examples/transformers.sql
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