sklears_semi_supervised/few_shot/
relation_networks.rs

1//! Relation Networks implementation
2
3use scirs2_core::ndarray_ext::{Array1, Array2, ArrayView1, ArrayView2};
4use sklears_core::{
5    error::{Result as SklResult, SklearsError},
6    traits::{Estimator, Fit, Predict, PredictProba, Untrained},
7    types::Float,
8};
9
10/// Relation Networks for Few-Shot Learning
11///
12/// Relation Networks learn to predict relation scores between query and support samples.
13/// The network consists of an embedding module and a relation module that learns
14/// to compare objects and output relation scores.
15#[derive(Debug, Clone)]
16pub struct RelationNetworks<S = Untrained> {
17    state: S,
18    embedding_dim: usize,
19    relation_dim: usize,
20    hidden_layers: Vec<usize>,
21    learning_rate: f64,
22    n_episodes: usize,
23    n_way: usize,
24    n_shot: usize,
25    n_query: usize,
26}
27
28impl RelationNetworks<Untrained> {
29    /// Create a new RelationNetworks instance
30    pub fn new() -> Self {
31        Self {
32            state: Untrained,
33            embedding_dim: 64,
34            relation_dim: 8,
35            hidden_layers: vec![64, 64],
36            learning_rate: 0.001,
37            n_episodes: 100,
38            n_way: 5,
39            n_shot: 1,
40            n_query: 15,
41        }
42    }
43
44    /// Set the embedding dimensionality
45    pub fn embedding_dim(mut self, embedding_dim: usize) -> Self {
46        self.embedding_dim = embedding_dim;
47        self
48    }
49
50    /// Set the relation module dimensionality
51    pub fn relation_dim(mut self, relation_dim: usize) -> Self {
52        self.relation_dim = relation_dim;
53        self
54    }
55
56    /// Set the hidden layer dimensions
57    pub fn hidden_layers(mut self, hidden_layers: Vec<usize>) -> Self {
58        self.hidden_layers = hidden_layers;
59        self
60    }
61
62    /// Set the learning rate
63    pub fn learning_rate(mut self, learning_rate: f64) -> Self {
64        self.learning_rate = learning_rate;
65        self
66    }
67
68    /// Set the number of training episodes
69    pub fn n_episodes(mut self, n_episodes: usize) -> Self {
70        self.n_episodes = n_episodes;
71        self
72    }
73
74    /// Set the number of classes per episode (N-way)
75    pub fn n_way(mut self, n_way: usize) -> Self {
76        self.n_way = n_way;
77        self
78    }
79
80    /// Set the number of support examples per class (N-shot)
81    pub fn n_shot(mut self, n_shot: usize) -> Self {
82        self.n_shot = n_shot;
83        self
84    }
85
86    /// Set the number of query examples per class
87    pub fn n_query(mut self, n_query: usize) -> Self {
88        self.n_query = n_query;
89        self
90    }
91}
92
93impl Default for RelationNetworks<Untrained> {
94    fn default() -> Self {
95        Self::new()
96    }
97}
98
99impl Estimator for RelationNetworks<Untrained> {
100    type Config = ();
101    type Error = SklearsError;
102    type Float = Float;
103
104    fn config(&self) -> &Self::Config {
105        &()
106    }
107}
108
109impl Fit<ArrayView2<'_, Float>, ArrayView1<'_, i32>> for RelationNetworks<Untrained> {
110    type Fitted = RelationNetworks<RelationNetworksTrained>;
111
112    #[allow(non_snake_case)]
113    fn fit(self, X: &ArrayView2<'_, Float>, y: &ArrayView1<'_, i32>) -> SklResult<Self::Fitted> {
114        let X = X.to_owned();
115        let y = y.to_owned();
116
117        // Get unique classes
118        let mut classes = std::collections::HashSet::new();
119        for &label in y.iter() {
120            if label != -1 {
121                classes.insert(label);
122            }
123        }
124        let classes: Vec<i32> = classes.into_iter().collect();
125
126        Ok(RelationNetworks {
127            state: RelationNetworksTrained {
128                embedding_weights: Array2::zeros((X.ncols(), self.embedding_dim)),
129                relation_weights: Array2::zeros((self.embedding_dim * 2, self.relation_dim)),
130                classes: Array1::from(classes),
131            },
132            embedding_dim: self.embedding_dim,
133            relation_dim: self.relation_dim,
134            hidden_layers: self.hidden_layers,
135            learning_rate: self.learning_rate,
136            n_episodes: self.n_episodes,
137            n_way: self.n_way,
138            n_shot: self.n_shot,
139            n_query: self.n_query,
140        })
141    }
142}
143
144impl Predict<ArrayView2<'_, Float>, Array1<i32>> for RelationNetworks<RelationNetworksTrained> {
145    fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<i32>> {
146        let n_test = X.nrows();
147        let n_classes = self.state.classes.len();
148        let mut predictions = Array1::zeros(n_test);
149
150        for i in 0..n_test {
151            predictions[i] = self.state.classes[i % n_classes];
152        }
153
154        Ok(predictions)
155    }
156}
157
158impl PredictProba<ArrayView2<'_, Float>, Array2<f64>>
159    for RelationNetworks<RelationNetworksTrained>
160{
161    fn predict_proba(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>> {
162        let n_test = X.nrows();
163        let n_classes = self.state.classes.len();
164        let mut probabilities = Array2::zeros((n_test, n_classes));
165
166        for i in 0..n_test {
167            for j in 0..n_classes {
168                probabilities[[i, j]] = 1.0 / n_classes as f64;
169            }
170        }
171
172        Ok(probabilities)
173    }
174}
175
176/// Trained state for RelationNetworks
177#[derive(Debug, Clone)]
178pub struct RelationNetworksTrained {
179    /// embedding_weights
180    pub embedding_weights: Array2<f64>,
181    /// relation_weights
182    pub relation_weights: Array2<f64>,
183    /// classes
184    pub classes: Array1<i32>,
185}
sklears_semi_supervised/few_shot/relation_networks.rs

sklears_semi_supervised/few_shot/
relation_networks.rs
