sklears_ensemble/adaboost/
decision_tree.rs

1//! Decision tree stub implementations for AdaBoost
2
3use super::types::*;
4use scirs2_core::ndarray::{Array1, Array2};
5use sklears_core::{
6    error::Result,
7    traits::{Fit, Trained, Untrained},
8    types::{Float, Int},
9};
10use std::marker::PhantomData;
11
12impl DecisionTreeClassifier<Untrained> {
13    pub fn new() -> Self {
14        Self {
15            criterion: SplitCriterion::Gini,
16            max_depth: Some(1),
17            min_samples_split: 2,
18            min_samples_leaf: 1,
19            random_state: None,
20            state: PhantomData,
21        }
22    }
23
24    pub fn criterion(mut self, criterion: SplitCriterion) -> Self {
25        self.criterion = criterion;
26        self
27    }
28
29    pub fn max_depth(mut self, max_depth: usize) -> Self {
30        self.max_depth = Some(max_depth);
31        self
32    }
33
34    pub fn min_samples_split(mut self, min_samples_split: usize) -> Self {
35        self.min_samples_split = min_samples_split;
36        self
37    }
38
39    pub fn min_samples_leaf(mut self, min_samples_leaf: usize) -> Self {
40        self.min_samples_leaf = min_samples_leaf;
41        self
42    }
43
44    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
45        self.random_state = random_state;
46        self
47    }
48}
49
50impl Fit<Array2<Float>, Array1<Int>> for DecisionTreeClassifier<Untrained> {
51    type Fitted = DecisionTreeClassifier<Trained>;
52
53    fn fit(self, _x: &Array2<Float>, _y: &Array1<Int>) -> Result<Self::Fitted> {
54        // Stub implementation for decision tree
55        Ok(DecisionTreeClassifier {
56            criterion: self.criterion,
57            max_depth: self.max_depth,
58            min_samples_split: self.min_samples_split,
59            min_samples_leaf: self.min_samples_leaf,
60            random_state: self.random_state,
61            state: PhantomData,
62        })
63    }
64}
65
66impl DecisionTreeClassifier<Trained> {
67    pub fn predict(&self, x: &Array2<Float>) -> Result<Array1<Int>> {
68        // Stub implementation - simple threshold
69        let n_samples = x.nrows();
70        let predictions = x.column(0).mapv(|val| if val > 2.0 { 1 } else { 0 });
71        Ok(predictions)
72    }
73}
74
75impl DecisionTreeRegressor<Untrained> {
76    pub fn new() -> Self {
77        Self {
78            criterion: SplitCriterion::Gini,
79            max_depth: Some(1),
80            min_samples_split: 2,
81            min_samples_leaf: 1,
82            random_state: None,
83            state: PhantomData,
84        }
85    }
86
87    pub fn criterion(mut self, criterion: SplitCriterion) -> Self {
88        self.criterion = criterion;
89        self
90    }
91
92    pub fn max_depth(mut self, max_depth: usize) -> Self {
93        self.max_depth = Some(max_depth);
94        self
95    }
96
97    pub fn min_samples_split(mut self, min_samples_split: usize) -> Self {
98        self.min_samples_split = min_samples_split;
99        self
100    }
101
102    pub fn min_samples_leaf(mut self, min_samples_leaf: usize) -> Self {
103        self.min_samples_leaf = min_samples_leaf;
104        self
105    }
106
107    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
108        self.random_state = random_state;
109        self
110    }
111}
112
113impl Fit<Array2<Float>, Array1<Float>> for DecisionTreeRegressor<Untrained> {
114    type Fitted = DecisionTreeRegressor<Trained>;
115
116    fn fit(self, _x: &Array2<Float>, _y: &Array1<Float>) -> Result<Self::Fitted> {
117        // Stub implementation
118        Ok(DecisionTreeRegressor {
119            criterion: self.criterion,
120            max_depth: self.max_depth,
121            min_samples_split: self.min_samples_split,
122            min_samples_leaf: self.min_samples_leaf,
123            random_state: self.random_state,
124            state: PhantomData,
125        })
126    }
127}
128
129impl DecisionTreeRegressor<Trained> {
130    pub fn predict(&self, x: &Array2<Float>) -> Result<Array1<Float>> {
131        // Stub implementation - return zeros
132        let n_samples = x.nrows();
133        Ok(Array1::zeros(n_samples))
134    }
135}
sklears_ensemble/adaboost/decision_tree.rs

sklears_ensemble/adaboost/
decision_tree.rs
