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add seed param to search params (#168)

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This commit is contained in:
Montana Low
2022-09-21 16:15:26 -07:00
committed by GitHub
parent 3a44161406
commit 403d3f2348
4 changed files with 61 additions and 0 deletions
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src/cluster/kmeans.rs
+13
View File
@@ -145,6 +145,9 @@ pub struct KMeansSearchParameters {
pub k: Vec<usize>,
/// Maximum number of iterations of the k-means algorithm for a single run.
pub max_iter: Vec<usize>,
/// Determines random number generation for centroid initialization.
/// Use an int to make the randomness deterministic
pub seed: Vec<Option<u64>>,
}
/// KMeans grid search iterator
@@ -152,6 +155,7 @@ pub struct KMeansSearchParametersIterator {
kmeans_search_parameters: KMeansSearchParameters,
current_k: usize,
current_max_iter: usize,
current_seed: usize,
}
impl IntoIterator for KMeansSearchParameters {
@@ -163,6 +167,7 @@ impl IntoIterator for KMeansSearchParameters {
kmeans_search_parameters: self,
current_k: 0,
current_max_iter: 0,
current_seed: 0,
}
}
}
@@ -173,6 +178,7 @@ impl Iterator for KMeansSearchParametersIterator {
fn next(&mut self) -> Option<Self::Item> {
if self.current_k == self.kmeans_search_parameters.k.len()
&& self.current_max_iter == self.kmeans_search_parameters.max_iter.len()
&& self.current_seed == self.kmeans_search_parameters.seed.len()
{
return None;
}
@@ -180,6 +186,7 @@ impl Iterator for KMeansSearchParametersIterator {
let next = KMeansParameters {
k: self.kmeans_search_parameters.k[self.current_k],
max_iter: self.kmeans_search_parameters.max_iter[self.current_max_iter],
seed: self.kmeans_search_parameters.seed[self.current_seed],
};
if self.current_k + 1 < self.kmeans_search_parameters.k.len() {
@@ -187,9 +194,14 @@ impl Iterator for KMeansSearchParametersIterator {
} else if self.current_max_iter + 1 < self.kmeans_search_parameters.max_iter.len() {
self.current_k = 0;
self.current_max_iter += 1;
} else if self.current_seed + 1 < self.kmeans_search_parameters.seed.len() {
self.current_k = 0;
self.current_max_iter = 0;
self.current_seed += 1;
} else {
self.current_k += 1;
self.current_max_iter += 1;
self.current_seed += 1;
}
Some(next)
@@ -203,6 +215,7 @@ impl Default for KMeansSearchParameters {
KMeansSearchParameters {
k: vec![default_params.k],
max_iter: vec![default_params.max_iter],
seed: vec![default_params.seed],
}
}
}
src/svm/svc.rs
+14
View File
@@ -119,6 +119,8 @@ pub struct SVCSearchParameters<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowV
pub kernel: Vec<K>,
/// Unused parameter.
m: PhantomData<M>,
/// Controls the pseudo random number generation for shuffling the data for probability estimates
seed: Vec<Option<u64>>,
}
/// SVC grid search iterator
@@ -128,6 +130,7 @@ pub struct SVCSearchParametersIterator<T: RealNumber, M: Matrix<T>, K: Kernel<T,
current_c: usize,
current_tol: usize,
current_kernel: usize,
current_seed: usize,
}
impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> IntoIterator
@@ -143,6 +146,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> IntoIterator
current_c: 0,
current_tol: 0,
current_kernel: 0,
current_seed: 0,
}
}
}
@@ -157,6 +161,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Iterator
&& self.current_c == self.svc_search_parameters.c.len()
&& self.current_tol == self.svc_search_parameters.tol.len()
&& self.current_kernel == self.svc_search_parameters.kernel.len()
&& self.current_seed == self.svc_search_parameters.kernel.len()
{
return None;
}
@@ -167,6 +172,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Iterator
tol: self.svc_search_parameters.tol[self.current_tol],
kernel: self.svc_search_parameters.kernel[self.current_kernel].clone(),
m: PhantomData,
seed: self.svc_search_parameters.seed[self.current_seed],
};
if self.current_epoch + 1 < self.svc_search_parameters.epoch.len() {
@@ -183,11 +189,18 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Iterator
self.current_c = 0;
self.current_tol = 0;
self.current_kernel += 1;
} else if self.current_kernel + 1 < self.svc_search_parameters.kernel.len() {
self.current_epoch = 0;
self.current_c = 0;
self.current_tol = 0;
self.current_kernel = 0;
self.current_seed += 1;
} else {
self.current_epoch += 1;
self.current_c += 1;
self.current_tol += 1;
self.current_kernel += 1;
self.current_seed += 1;
}
Some(next)
@@ -204,6 +217,7 @@ impl<T: RealNumber, M: Matrix<T>> Default for SVCSearchParameters<T, M, LinearKe
tol: vec![default_params.tol],
kernel: vec![default_params.kernel],
m: PhantomData,
seed: vec![default_params.seed],
}
}
}
src/tree/decision_tree_classifier.rs
+20
View File
@@ -209,14 +209,21 @@ impl Default for DecisionTreeClassifierParameters {
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct DecisionTreeClassifierSearchParameters {
#[cfg_attr(feature = "serde", serde(default))]
/// Split criteria to use when building a tree. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
pub criterion: Vec<SplitCriterion>,
#[cfg_attr(feature = "serde", serde(default))]
/// Tree max depth. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
pub max_depth: Vec<Option<u16>>,
#[cfg_attr(feature = "serde", serde(default))]
/// The minimum number of samples required to be at a leaf node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
pub min_samples_leaf: Vec<usize>,
#[cfg_attr(feature = "serde", serde(default))]
/// The minimum number of samples required to split an internal node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
pub min_samples_split: Vec<usize>,
#[cfg_attr(feature = "serde", serde(default))]
/// Controls the randomness of the estimator
pub seed: Vec<Option<u64>>,
}
/// DecisionTreeClassifier grid search iterator
@@ -226,6 +233,7 @@ pub struct DecisionTreeClassifierSearchParametersIterator {
current_max_depth: usize,
current_min_samples_leaf: usize,
current_min_samples_split: usize,
current_seed: usize,
}
impl IntoIterator for DecisionTreeClassifierSearchParameters {
@@ -239,6 +247,7 @@ impl IntoIterator for DecisionTreeClassifierSearchParameters {
current_max_depth: 0,
current_min_samples_leaf: 0,
current_min_samples_split: 0,
current_seed: 0,
}
}
}
@@ -267,6 +276,7 @@ impl Iterator for DecisionTreeClassifierSearchParametersIterator {
.decision_tree_classifier_search_parameters
.min_samples_split
.len()
&& self.current_seed == self.decision_tree_classifier_search_parameters.seed.len()
{
return None;
}
@@ -283,6 +293,7 @@ impl Iterator for DecisionTreeClassifierSearchParametersIterator {
min_samples_split: self
.decision_tree_classifier_search_parameters
.min_samples_split[self.current_min_samples_split],
seed: self.decision_tree_classifier_search_parameters.seed[self.current_seed],
};
if self.current_criterion + 1
@@ -319,11 +330,19 @@ impl Iterator for DecisionTreeClassifierSearchParametersIterator {
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split += 1;
} else if self.current_seed + 1 < self.decision_tree_classifier_search_parameters.seed.len()
{
self.current_criterion = 0;
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split = 0;
self.current_seed += 1;
} else {
self.current_criterion += 1;
self.current_max_depth += 1;
self.current_min_samples_leaf += 1;
self.current_min_samples_split += 1;
self.current_seed += 1;
}
Some(next)
@@ -339,6 +358,7 @@ impl Default for DecisionTreeClassifierSearchParameters {
max_depth: vec![default_params.max_depth],
min_samples_leaf: vec![default_params.min_samples_leaf],
min_samples_split: vec![default_params.min_samples_split],
seed: vec![default_params.seed],
}
}
}
src/tree/decision_tree_regressor.rs
+14
View File
@@ -148,6 +148,8 @@ pub struct DecisionTreeRegressorSearchParameters {
pub min_samples_leaf: Vec<usize>,
/// The minimum number of samples required to split an internal node. See [Decision Tree Regressor](../../tree/decision_tree_regressor/index.html)
pub min_samples_split: Vec<usize>,
/// Controls the randomness of the estimator
pub seed: Vec<Option<u64>>,
}
/// DecisionTreeRegressor grid search iterator
@@ -156,6 +158,7 @@ pub struct DecisionTreeRegressorSearchParametersIterator {
current_max_depth: usize,
current_min_samples_leaf: usize,
current_min_samples_split: usize,
current_seed: usize,
}
impl IntoIterator for DecisionTreeRegressorSearchParameters {
@@ -168,6 +171,7 @@ impl IntoIterator for DecisionTreeRegressorSearchParameters {
current_max_depth: 0,
current_min_samples_leaf: 0,
current_min_samples_split: 0,
current_seed: 0,
}
}
}
@@ -191,6 +195,7 @@ impl Iterator for DecisionTreeRegressorSearchParametersIterator {
.decision_tree_regressor_search_parameters
.min_samples_split
.len()
&& self.current_seed == self.decision_tree_regressor_search_parameters.seed.len()
{
return None;
}
@@ -204,6 +209,7 @@ impl Iterator for DecisionTreeRegressorSearchParametersIterator {
min_samples_split: self
.decision_tree_regressor_search_parameters
.min_samples_split[self.current_min_samples_split],
seed: self.decision_tree_regressor_search_parameters.seed[self.current_seed],
};
if self.current_max_depth + 1
@@ -230,10 +236,17 @@ impl Iterator for DecisionTreeRegressorSearchParametersIterator {
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split += 1;
} else if self.current_seed + 1 < self.decision_tree_regressor_search_parameters.seed.len()
{
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split = 0;
self.current_seed += 1;
} else {
self.current_max_depth += 1;
self.current_min_samples_leaf += 1;
self.current_min_samples_split += 1;
self.current_seed += 1;
}
Some(next)
@@ -248,6 +261,7 @@ impl Default for DecisionTreeRegressorSearchParameters {
max_depth: vec![default_params.max_depth],
min_samples_leaf: vec![default_params.min_samples_leaf],
min_samples_split: vec![default_params.min_samples_split],
seed: vec![default_params.seed],
}
}
}