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Add support for XGBoosteval_metrics andobjective#1103

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Merged
montanalow merged 5 commits intomasterfrommontana/pre
Oct 20, 2023
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3 changes: 2 additions & 1 deletionpgml-extension/examples/regression.sql
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Original file line numberDiff line numberDiff line change
Expand Up@@ -106,7 +106,8 @@ SELECT * FROM pgml.deployed_models ORDER BY deployed_at DESC LIMIT 5;
-- do a hyperparam search on your favorite algorithm
SELECT pgml.train(
'Diabetes Progression',
algorithm => 'xgboost',
algorithm => 'xgboost',
hyperparams => '{"eval_metric": "rmse"}'::JSONB,
search => 'grid',
search_params => '{
"max_depth": [1, 2],
Expand Down
101 changes: 92 additions & 9 deletionspgml-extension/src/bindings/xgboost.rs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -128,7 +128,9 @@ fn get_tree_params(hyperparams: &Hyperparams) -> tree::TreeBoosterParameters {
},
"max_leaves" => params.max_leaves(value.as_u64().unwrap() as u32),
"max_bin" => params.max_bin(value.as_u64().unwrap() as u32),
"booster" | "n_estimators" | "boost_rounds" => &mut params, // Valid but not relevant to this section
"booster" | "n_estimators" | "boost_rounds" | "eval_metric" | "objective" => {
&mut params
} // Valid but not relevant to this section
"nthread" => &mut params,
"random_state" => &mut params,
_ => panic!("Unknown hyperparameter {:?}: {:?}", key, value),
Expand All@@ -152,6 +154,52 @@ pub fn fit_classification(
)
}

fn eval_metric_from_string(name: &str) -> learning::EvaluationMetric {
match name {
"rmse" => learning::EvaluationMetric::RMSE,
"mae" => learning::EvaluationMetric::MAE,
"logloss" => learning::EvaluationMetric::LogLoss,
"merror" => learning::EvaluationMetric::MultiClassErrorRate,
"mlogloss" => learning::EvaluationMetric::MultiClassLogLoss,
"auc" => learning::EvaluationMetric::AUC,
"ndcg" => learning::EvaluationMetric::NDCG,
"ndcg-" => learning::EvaluationMetric::NDCGNegative,
"map" => learning::EvaluationMetric::MAP,
"map-" => learning::EvaluationMetric::MAPNegative,
"poisson-nloglik" => learning::EvaluationMetric::PoissonLogLoss,
"gamma-nloglik" => learning::EvaluationMetric::GammaLogLoss,
"cox-nloglik" => learning::EvaluationMetric::CoxLogLoss,
"gamma-deviance" => learning::EvaluationMetric::GammaDeviance,
"tweedie-nloglik" => learning::EvaluationMetric::TweedieLogLoss,
_ => error!("Unknown eval_metric: {:?}", name),
}
}

fn objective_from_string(name: &str, dataset: &Dataset) -> learning::Objective {
match name {
"reg:linear" => learning::Objective::RegLinear,
"reg:logistic" => learning::Objective::RegLogistic,
"binary:logistic" => learning::Objective::BinaryLogistic,
"binary:logitraw" => learning::Objective::BinaryLogisticRaw,
"gpu:reg:linear" => learning::Objective::GpuRegLinear,
"gpu:reg:logistic" => learning::Objective::GpuRegLogistic,
"gpu:binary:logistic" => learning::Objective::GpuBinaryLogistic,
"gpu:binary:logitraw" => learning::Objective::GpuBinaryLogisticRaw,
"count:poisson" => learning::Objective::CountPoisson,
"survival:cox" => learning::Objective::SurvivalCox,
"multi:softmax" => {
learning::Objective::MultiSoftmax(dataset.num_distinct_labels.try_into().unwrap())
}
"multi:softprob" => {
learning::Objective::MultiSoftprob(dataset.num_distinct_labels.try_into().unwrap())
}
"rank:pairwise" => learning::Objective::RankPairwise,
"reg:gamma" => learning::Objective::RegGamma,
"reg:tweedie" => learning::Objective::RegTweedie(Some(dataset.num_distinct_labels as f32)),
_ => error!("Unknown objective: {:?}", name),
}
}

fn fit(
dataset: &Dataset,
hyperparams: &Hyperparams,
Expand All@@ -170,14 +218,40 @@ fn fit(
Some(value) => value.as_u64().unwrap(),
None => 0,
};
let learning_params = learning::LearningTaskParametersBuilder::default()
.objective(objective)
let eval_metrics = match hyperparams.get("eval_metric") {
Some(metrics) => {
if metrics.is_array() {
learning::Metrics::Custom(
metrics
.as_array()
.unwrap()
.iter()
.map(|metric| eval_metric_from_string(metric.as_str().unwrap()))
.collect(),
)
} else {
learning::Metrics::Custom(Vec::from([eval_metric_from_string(
metrics.as_str().unwrap(),
)]))
}
}
None => learning::Metrics::Auto,
};
let learning_params = match learning::LearningTaskParametersBuilder::default()
.objective(match hyperparams.get("objective") {
Some(value) => objective_from_string(value.as_str().unwrap(), dataset),
None => objective,
})
.eval_metrics(eval_metrics)
.seed(seed)
.build()
.unwrap();
{
Ok(params) => params,
Err(e) => error!("Failed to parse learning params:\n\n{}", e),
};

// overall configuration for Booster
let booster_params = BoosterParametersBuilder::default()
let booster_params =matchBoosterParametersBuilder::default()
.learning_params(learning_params)
.booster_type(match hyperparams.get("booster") {
Some(value) => match value.as_str().unwrap() {
Expand All@@ -195,7 +269,10 @@ fn fit(
)
.verbose(true)
.build()
.unwrap();
{
Ok(params) => params,
Err(e) => error!("Failed to configure booster:\n\n{}", e),
};

let mut builder = TrainingParametersBuilder::default();
// number of training iterations is aliased
Expand All@@ -207,18 +284,24 @@ fn fit(
},
};

let params = builder
let params =matchbuilder
// dataset to train with
.dtrain(&dtrain)
// optional datasets to evaluate against in each iteration
.evaluation_sets(Some(evaluation_sets))
// model parameters
.booster_params(booster_params)
.build()
.unwrap();
{
Ok(params) => params,
Err(e) => error!("Failed to create training parameters:\n\n{}", e),
};

// train model, and print evaluation data
let booster = Booster::train(&params).unwrap();
let booster = match Booster::train(&params) {
Ok(booster) => booster,
Err(e) => error!("Failed to train model:\n\n{}", e),
};

Ok(Box::new(Estimator { estimator: booster }))
}
Expand Down
48 changes: 23 additions & 25 deletionspgml-extension/src/orm/model.rs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,6 +2,7 @@ use anyhow::{anyhow, bail, Result};
use parking_lot::Mutex;
use std::collections::HashMap;
use std::fmt::{Display, Error, Formatter};
use std::num::NonZeroUsize;
use std::str::FromStr;
use std::sync::Arc;
use std::time::Instant;
Expand DownExpand Up@@ -962,16 +963,13 @@ impl Model {
pub fn numeric_encode_features(&self, rows: &[pgrx::datum::AnyElement]) -> Vec<f32> {
// TODO handle FLOAT4[] as if it were pgrx::datum::AnyElement, skipping all this, and going straight to predict
let mut features = Vec::new(); // TODO pre-allocate space
let columns = &self.snapshot.columns;
for row in rows {
match row.oid() {
pgrx_pg_sys::RECORDOID => {
let tuple = unsafe { PgHeapTuple::from_composite_datum(row.datum()) };
for index in 1..tuple.len() + 1 {
let column = &columns[index - 1];
let attribute = tuple
.get_attribute_by_index(index.try_into().unwrap())
.unwrap();
for (i, column) in self.snapshot.features().enumerate() {
let index = NonZeroUsize::new(i + 1).unwrap();
let attribute = tuple.get_attribute_by_index(index).unwrap();
match &column.statistics.categories {
Some(_categories) => {
let key = match attribute.atttypid {
Expand All@@ -982,14 +980,14 @@ impl Model {
| pgrx_pg_sys::VARCHAROID
| pgrx_pg_sys::BPCHAROID => {
let element: Result<Option<String>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.unwrap_or(snapshot::NULL_CATEGORY_KEY.to_string())
}
pgrx_pg_sys::BOOLOID => {
let element: Result<Option<bool>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.map_or(snapshot::NULL_CATEGORY_KEY.to_string(), |k| {
Expand All@@ -998,7 +996,7 @@ impl Model {
}
pgrx_pg_sys::INT2OID => {
let element: Result<Option<i16>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.map_or(snapshot::NULL_CATEGORY_KEY.to_string(), |k| {
Expand All@@ -1007,7 +1005,7 @@ impl Model {
}
pgrx_pg_sys::INT4OID => {
let element: Result<Option<i32>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.map_or(snapshot::NULL_CATEGORY_KEY.to_string(), |k| {
Expand All@@ -1016,7 +1014,7 @@ impl Model {
}
pgrx_pg_sys::INT8OID => {
let element: Result<Option<i64>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.map_or(snapshot::NULL_CATEGORY_KEY.to_string(), |k| {
Expand All@@ -1025,7 +1023,7 @@ impl Model {
}
pgrx_pg_sys::FLOAT4OID => {
let element: Result<Option<f32>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.map_or(snapshot::NULL_CATEGORY_KEY.to_string(), |k| {
Expand All@@ -1034,7 +1032,7 @@ impl Model {
}
pgrx_pg_sys::FLOAT8OID => {
let element: Result<Option<f64>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
element
.unwrap()
.map_or(snapshot::NULL_CATEGORY_KEY.to_string(), |k| {
Expand All@@ -1056,79 +1054,79 @@ impl Model {
}
pgrx_pg_sys::BOOLOID => {
let element: Result<Option<bool>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
features.push(
element.unwrap().map_or(f32::NAN, |v| v as u8 as f32),
);
}
pgrx_pg_sys::INT2OID => {
let element: Result<Option<i16>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
features
.push(element.unwrap().map_or(f32::NAN, |v| v as f32));
}
pgrx_pg_sys::INT4OID => {
let element: Result<Option<i32>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
features
.push(element.unwrap().map_or(f32::NAN, |v| v as f32));
}
pgrx_pg_sys::INT8OID => {
let element: Result<Option<i64>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
features
.push(element.unwrap().map_or(f32::NAN, |v| v as f32));
}
pgrx_pg_sys::FLOAT4OID => {
let element: Result<Option<f32>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
features.push(element.unwrap().map_or(f32::NAN, |v| v));
}
pgrx_pg_sys::FLOAT8OID => {
let element: Result<Option<f64>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
features
.push(element.unwrap().map_or(f32::NAN, |v| v as f32));
}
// TODO handle NULL to NaN for arrays
pgrx_pg_sys::BOOLARRAYOID => {
let element: Result<Option<Vec<bool>>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
for j in element.as_ref().unwrap().as_ref().unwrap() {
features.push(*j as i8 as f32);
}
}
pgrx_pg_sys::INT2ARRAYOID => {
let element: Result<Option<Vec<i16>>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
for j in element.as_ref().unwrap().as_ref().unwrap() {
features.push(*j as f32);
}
}
pgrx_pg_sys::INT4ARRAYOID => {
let element: Result<Option<Vec<i32>>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
for j in element.as_ref().unwrap().as_ref().unwrap() {
features.push(*j as f32);
}
}
pgrx_pg_sys::INT8ARRAYOID => {
let element: Result<Option<Vec<i64>>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
for j in element.as_ref().unwrap().as_ref().unwrap() {
features.push(*j as f32);
}
}
pgrx_pg_sys::FLOAT4ARRAYOID => {
let element: Result<Option<Vec<f32>>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
for j in element.as_ref().unwrap().as_ref().unwrap() {
features.push(*j);
}
}
pgrx_pg_sys::FLOAT8ARRAYOID => {
let element: Result<Option<Vec<f64>>, TryFromDatumError> =
tuple.get_by_index(index.try_into().unwrap());
tuple.get_by_index(index);
for j in element.as_ref().unwrap().as_ref().unwrap() {
features.push(*j as f32);
}
Expand Down
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