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Merge pull request #116 from mlondschien/issue-115

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Add OOB predictions to random forests
This commit is contained in:
VolodymyrOrlov
2021-10-28 08:10:09 -07:00
committed by GitHub
parent 7830946ecb 3bf8813946
commit 521dab49ef
3 changed files with 218 additions and 3 deletions
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src/ensemble/random_forest_classifier.rs
+107 -1
View File
@@ -53,7 +53,7 @@ use rand::Rng;
use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::error::{Failed, FailedError};
use crate::linalg::Matrix;
use crate::math::num::RealNumber;
use crate::tree::decision_tree_classifier::{
@@ -77,6 +77,8 @@ pub struct RandomForestClassifierParameters {
pub n_trees: u16,
/// Number of random sample of predictors to use as split candidates.
pub m: Option<usize>,
/// Whether to keep samples used for tree generation. This is required for OOB prediction.
pub keep_samples: bool,
}
/// Random Forest Classifier
@@ -86,6 +88,7 @@ pub struct RandomForestClassifier<T: RealNumber> {
parameters: RandomForestClassifierParameters,
trees: Vec<DecisionTreeClassifier<T>>,
classes: Vec<T>,
samples: Option<Vec<Vec<bool>>>,
}
impl RandomForestClassifierParameters {
@@ -119,6 +122,12 @@ impl RandomForestClassifierParameters {
self.m = Some(m);
self
}
/// Whether to keep samples used for tree generation. This is required for OOB prediction.
pub fn with_keep_samples(mut self, keep_samples: bool) -> Self {
self.keep_samples = keep_samples;
self
}
}
impl<T: RealNumber> PartialEq for RandomForestClassifier<T> {
@@ -150,6 +159,7 @@ impl Default for RandomForestClassifierParameters {
min_samples_split: 2,
n_trees: 100,
m: Option::None,
keep_samples: false,
}
}
}
@@ -205,8 +215,17 @@ impl<T: RealNumber> RandomForestClassifier<T> {
let k = classes.len();
let mut trees: Vec<DecisionTreeClassifier<T>> = Vec::new();
let mut maybe_all_samples: Option<Vec<Vec<bool>>> = Option::None;
if parameters.keep_samples {
maybe_all_samples = Some(Vec::new());
}
for _ in 0..parameters.n_trees {
let samples = RandomForestClassifier::<T>::sample_with_replacement(&yi, k);
if let Some(ref mut all_samples) = maybe_all_samples {
all_samples.push(samples.iter().map(|x| *x != 0).collect())
}
let params = DecisionTreeClassifierParameters {
criterion: parameters.criterion.clone(),
max_depth: parameters.max_depth,
@@ -221,6 +240,7 @@ impl<T: RealNumber> RandomForestClassifier<T> {
parameters,
trees,
classes,
samples: maybe_all_samples,
})
}
@@ -248,6 +268,42 @@ impl<T: RealNumber> RandomForestClassifier<T> {
which_max(&result)
}
/// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training.
pub fn predict_oob<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
let (n, _) = x.shape();
if self.samples.is_none() {
Err(Failed::because(
FailedError::PredictFailed,
"Need samples=true for OOB predictions.",
))
} else if self.samples.as_ref().unwrap()[0].len() != n {
Err(Failed::because(
FailedError::PredictFailed,
"Prediction matrix must match matrix used in training for OOB predictions.",
))
} else {
let mut result = M::zeros(1, n);
for i in 0..n {
result.set(0, i, self.classes[self.predict_for_row_oob(x, i)]);
}
Ok(result.to_row_vector())
}
}
fn predict_for_row_oob<M: Matrix<T>>(&self, x: &M, row: usize) -> usize {
let mut result = vec![0; self.classes.len()];
for (tree, samples) in self.trees.iter().zip(self.samples.as_ref().unwrap()) {
if !samples[row] {
result[tree.predict_for_row(x, row)] += 1;
}
}
which_max(&result)
}
fn sample_with_replacement(y: &[usize], num_classes: usize) -> Vec<usize> {
let mut rng = rand::thread_rng();
let class_weight = vec![1.; num_classes];
@@ -318,6 +374,7 @@ mod tests {
min_samples_split: 2,
n_trees: 100,
m: Option::None,
keep_samples: false,
},
)
.unwrap();
@@ -325,6 +382,55 @@ mod tests {
assert!(accuracy(&y, &classifier.predict(&x).unwrap()) >= 0.95);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fit_predict_iris_oob() {
let x = DenseMatrix::from_2d_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
&[4.7, 3.2, 1.3, 0.2],
&[4.6, 3.1, 1.5, 0.2],
&[5.0, 3.6, 1.4, 0.2],
&[5.4, 3.9, 1.7, 0.4],
&[4.6, 3.4, 1.4, 0.3],
&[5.0, 3.4, 1.5, 0.2],
&[4.4, 2.9, 1.4, 0.2],
&[4.9, 3.1, 1.5, 0.1],
&[7.0, 3.2, 4.7, 1.4],
&[6.4, 3.2, 4.5, 1.5],
&[6.9, 3.1, 4.9, 1.5],
&[5.5, 2.3, 4.0, 1.3],
&[6.5, 2.8, 4.6, 1.5],
&[5.7, 2.8, 4.5, 1.3],
&[6.3, 3.3, 4.7, 1.6],
&[4.9, 2.4, 3.3, 1.0],
&[6.6, 2.9, 4.6, 1.3],
&[5.2, 2.7, 3.9, 1.4],
]);
let y = vec![
0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
let classifier = RandomForestClassifier::fit(
&x,
&y,
RandomForestClassifierParameters {
criterion: SplitCriterion::Gini,
max_depth: None,
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 100,
m: Option::None,
keep_samples: true,
},
)
.unwrap();
assert!(
accuracy(&y, &classifier.predict_oob(&x).unwrap())
< accuracy(&y, &classifier.predict(&x).unwrap())
);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "serde")]
src/ensemble/random_forest_regressor.rs
+110 -2
View File
@@ -51,7 +51,7 @@ use rand::Rng;
use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::error::{Failed, FailedError};
use crate::linalg::Matrix;
use crate::math::num::RealNumber;
use crate::tree::decision_tree_regressor::{
@@ -73,6 +73,8 @@ pub struct RandomForestRegressorParameters {
pub n_trees: usize,
/// Number of random sample of predictors to use as split candidates.
pub m: Option<usize>,
/// Whether to keep samples used for tree generation. This is required for OOB prediction.
pub keep_samples: bool,
}
/// Random Forest Regressor
@@ -81,6 +83,7 @@ pub struct RandomForestRegressorParameters {
pub struct RandomForestRegressor<T: RealNumber> {
parameters: RandomForestRegressorParameters,
trees: Vec<DecisionTreeRegressor<T>>,
samples: Option<Vec<Vec<bool>>>,
}
impl RandomForestRegressorParameters {
@@ -109,6 +112,12 @@ impl RandomForestRegressorParameters {
self.m = Some(m);
self
}
/// Whether to keep samples used for tree generation. This is required for OOB prediction.
pub fn with_keep_samples(mut self, keep_samples: bool) -> Self {
self.keep_samples = keep_samples;
self
}
}
impl Default for RandomForestRegressorParameters {
@@ -119,6 +128,7 @@ impl Default for RandomForestRegressorParameters {
min_samples_split: 2,
n_trees: 10,
m: Option::None,
keep_samples: false,
}
}
}
@@ -174,8 +184,16 @@ impl<T: RealNumber> RandomForestRegressor<T> {
let mut trees: Vec<DecisionTreeRegressor<T>> = Vec::new();
let mut maybe_all_samples: Option<Vec<Vec<bool>>> = Option::None;
if parameters.keep_samples {
maybe_all_samples = Some(Vec::new());
}
for _ in 0..parameters.n_trees {
let samples = RandomForestRegressor::<T>::sample_with_replacement(n_rows);
if let Some(ref mut all_samples) = maybe_all_samples {
all_samples.push(samples.iter().map(|x| *x != 0).collect())
}
let params = DecisionTreeRegressorParameters {
max_depth: parameters.max_depth,
min_samples_leaf: parameters.min_samples_leaf,
@@ -185,7 +203,11 @@ impl<T: RealNumber> RandomForestRegressor<T> {
trees.push(tree);
}
Ok(RandomForestRegressor { parameters, trees })
Ok(RandomForestRegressor {
parameters,
trees,
samples: maybe_all_samples,
})
}
/// Predict class for `x`
@@ -214,6 +236,45 @@ impl<T: RealNumber> RandomForestRegressor<T> {
result / T::from(n_trees).unwrap()
}
/// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training.
pub fn predict_oob<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
let (n, _) = x.shape();
if self.samples.is_none() {
Err(Failed::because(
FailedError::PredictFailed,
"Need samples=true for OOB predictions.",
))
} else if self.samples.as_ref().unwrap()[0].len() != n {
Err(Failed::because(
FailedError::PredictFailed,
"Prediction matrix must match matrix used in training for OOB predictions.",
))
} else {
let mut result = M::zeros(1, n);
for i in 0..n {
result.set(0, i, self.predict_for_row_oob(x, i));
}
Ok(result.to_row_vector())
}
}
fn predict_for_row_oob<M: Matrix<T>>(&self, x: &M, row: usize) -> T {
let mut n_trees = 0;
let mut result = T::zero();
for (tree, samples) in self.trees.iter().zip(self.samples.as_ref().unwrap()) {
if !samples[row] {
result += tree.predict_for_row(x, row);
n_trees += 1;
}
}
// TODO: What to do if there are no oob trees?
result / T::from(n_trees).unwrap()
}
fn sample_with_replacement(nrows: usize) -> Vec<usize> {
let mut rng = rand::thread_rng();
let mut samples = vec![0; nrows];
@@ -266,6 +327,7 @@ mod tests {
min_samples_split: 2,
n_trees: 1000,
m: Option::None,
keep_samples: false,
},
)
.and_then(|rf| rf.predict(&x))
@@ -274,6 +336,52 @@ mod tests {
assert!(mean_absolute_error(&y, &y_hat) < 1.0);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fit_predict_longley_oob() {
let x = DenseMatrix::from_2d_array(&[
&[234.289, 235.6, 159., 107.608, 1947., 60.323],
&[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
&[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
&[284.599, 335.1, 165., 110.929, 1950., 61.187],
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[346.999, 193.2, 359.4, 113.27, 1952., 63.639],
&[365.385, 187., 354.7, 115.094, 1953., 64.989],
&[363.112, 357.8, 335., 116.219, 1954., 63.761],
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[419.18, 282.2, 285.7, 118.734, 1956., 67.857],
&[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
&[444.546, 468.1, 263.7, 121.95, 1958., 66.513],
&[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
&[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
&[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
&[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
]);
let y = vec![
83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
114.2, 115.7, 116.9,
];
let regressor = RandomForestRegressor::fit(
&x,
&y,
RandomForestRegressorParameters {
max_depth: None,
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 1000,
m: Option::None,
keep_samples: true,
},
)
.unwrap();
let y_hat = regressor.predict(&x).unwrap();
let y_hat_oob = regressor.predict_oob(&x).unwrap();
assert!(mean_absolute_error(&y, &y_hat) < mean_absolute_error(&y, &y_hat_oob));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "serde")]
src/linalg/ndarray_bindings.rs
+1
View File
@@ -1007,6 +1007,7 @@ mod tests {
min_samples_split: 2,
n_trees: 1000,
m: Option::None,
keep_samples: false,
},
)
.unwrap()