add seed param to search params (#168)
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@@ -209,14 +209,21 @@ impl Default for DecisionTreeClassifierParameters {
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct DecisionTreeClassifierSearchParameters {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Split criteria to use when building a tree. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
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pub criterion: Vec<SplitCriterion>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// Tree max depth. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
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pub max_depth: Vec<Option<u16>>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The minimum number of samples required to be at a leaf node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
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pub min_samples_leaf: Vec<usize>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The minimum number of samples required to split an internal node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
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pub min_samples_split: Vec<usize>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// Controls the randomness of the estimator
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pub seed: Vec<Option<u64>>,
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}
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/// DecisionTreeClassifier grid search iterator
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@@ -226,6 +233,7 @@ pub struct DecisionTreeClassifierSearchParametersIterator {
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current_max_depth: usize,
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current_min_samples_leaf: usize,
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current_min_samples_split: usize,
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current_seed: usize,
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}
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impl IntoIterator for DecisionTreeClassifierSearchParameters {
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@@ -239,6 +247,7 @@ impl IntoIterator for DecisionTreeClassifierSearchParameters {
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current_max_depth: 0,
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current_min_samples_leaf: 0,
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current_min_samples_split: 0,
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current_seed: 0,
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}
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}
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}
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@@ -267,6 +276,7 @@ impl Iterator for DecisionTreeClassifierSearchParametersIterator {
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.decision_tree_classifier_search_parameters
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.min_samples_split
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.len()
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&& self.current_seed == self.decision_tree_classifier_search_parameters.seed.len()
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{
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return None;
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}
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@@ -283,6 +293,7 @@ impl Iterator for DecisionTreeClassifierSearchParametersIterator {
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min_samples_split: self
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.decision_tree_classifier_search_parameters
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.min_samples_split[self.current_min_samples_split],
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seed: self.decision_tree_classifier_search_parameters.seed[self.current_seed],
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};
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if self.current_criterion + 1
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@@ -319,11 +330,19 @@ impl Iterator for DecisionTreeClassifierSearchParametersIterator {
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self.current_max_depth = 0;
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self.current_min_samples_leaf = 0;
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self.current_min_samples_split += 1;
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} else if self.current_seed + 1 < self.decision_tree_classifier_search_parameters.seed.len()
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{
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self.current_criterion = 0;
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self.current_max_depth = 0;
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self.current_min_samples_leaf = 0;
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self.current_min_samples_split = 0;
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self.current_seed += 1;
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} else {
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self.current_criterion += 1;
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self.current_max_depth += 1;
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self.current_min_samples_leaf += 1;
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self.current_min_samples_split += 1;
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self.current_seed += 1;
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}
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Some(next)
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@@ -339,6 +358,7 @@ impl Default for DecisionTreeClassifierSearchParameters {
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max_depth: vec![default_params.max_depth],
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min_samples_leaf: vec![default_params.min_samples_leaf],
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min_samples_split: vec![default_params.min_samples_split],
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seed: vec![default_params.seed],
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}
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}
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}
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