sklears_impute/dimensionality/
sparse.rs

1//! Sparse Imputation methods
2//!
3//! This module provides sparse imputation methods for high-dimensional data.
4
5use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
6use sklears_core::{
7    error::{Result as SklResult, SklearsError},
8    traits::{Estimator, Fit, Transform, Untrained},
9    types::Float,
10};
11
12/// Sparse Imputation methods for high-dimensional data
13///
14/// Imputation designed for high-dimensional sparse data where most values are zero
15/// or missing. Uses compressed sensing and sparse coding techniques.
16#[derive(Debug, Clone)]
17pub struct SparseImputer<S = Untrained> {
18    state: S,
19    sparsity_level: f64,
20    regularization: f64,
21    max_iter: usize,
22    tol: f64,
23    missing_values: f64,
24    random_state: Option<u64>,
25}
26
27/// Trained state for SparseImputer
28#[derive(Debug, Clone)]
29pub struct SparseImputerTrained {
30    dictionary_: Array2<f64>,
31    sparse_codes_: Array2<f64>,
32    mean_: Array1<f64>,
33    n_features_in_: usize,
34    n_components_: usize,
35}
36
37impl SparseImputer<Untrained> {
38    /// Create a new SparseImputer instance
39    pub fn new() -> Self {
40        Self {
41            state: Untrained,
42            sparsity_level: 0.5,
43            regularization: 0.1,
44            max_iter: 100,
45            tol: 1e-6,
46            missing_values: f64::NAN,
47            random_state: None,
48        }
49    }
50
51    /// Set the expected sparsity level
52    pub fn sparsity_level(mut self, sparsity_level: f64) -> Self {
53        self.sparsity_level = sparsity_level.clamp(0.0, 1.0);
54        self
55    }
56
57    /// Set the L1 regularization parameter
58    pub fn regularization(mut self, regularization: f64) -> Self {
59        self.regularization = regularization;
60        self
61    }
62
63    /// Set the maximum number of iterations
64    pub fn max_iter(mut self, max_iter: usize) -> Self {
65        self.max_iter = max_iter;
66        self
67    }
68
69    /// Set the tolerance for convergence
70    pub fn tol(mut self, tol: f64) -> Self {
71        self.tol = tol;
72        self
73    }
74
75    /// Set the missing values placeholder
76    pub fn missing_values(mut self, missing_values: f64) -> Self {
77        self.missing_values = missing_values;
78        self
79    }
80
81    /// Set the random state
82    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
83        self.random_state = random_state;
84        self
85    }
86
87    fn is_missing(&self, value: f64) -> bool {
88        if self.missing_values.is_nan() {
89            value.is_nan()
90        } else {
91            (value - self.missing_values).abs() < f64::EPSILON
92        }
93    }
94}
95
96impl Default for SparseImputer<Untrained> {
97    fn default() -> Self {
98        Self::new()
99    }
100}
101
102impl Estimator for SparseImputer<Untrained> {
103    type Config = ();
104    type Error = SklearsError;
105    type Float = Float;
106
107    fn config(&self) -> &Self::Config {
108        &()
109    }
110}
111
112impl Fit<ArrayView2<'_, Float>, ()> for SparseImputer<Untrained> {
113    type Fitted = SparseImputer<SparseImputerTrained>;
114
115    #[allow(non_snake_case)]
116    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
117        let X = X.mapv(|x| x);
118        let (n_samples, n_features) = X.dim();
119
120        // Simplified implementation - in practice would implement sparse coding
121        let mean = Array1::zeros(n_features);
122        let dictionary = Array2::zeros((n_features, n_features.min(100)));
123        let sparse_codes = Array2::zeros((n_samples, n_features.min(100)));
124
125        Ok(SparseImputer {
126            state: SparseImputerTrained {
127                dictionary_: dictionary,
128                sparse_codes_: sparse_codes,
129                mean_: mean,
130                n_features_in_: n_features,
131                n_components_: n_features.min(100),
132            },
133            sparsity_level: self.sparsity_level,
134            regularization: self.regularization,
135            max_iter: self.max_iter,
136            tol: self.tol,
137            missing_values: self.missing_values,
138            random_state: self.random_state,
139        })
140    }
141}
142
143impl Transform<ArrayView2<'_, Float>, Array2<Float>> for SparseImputer<SparseImputerTrained> {
144    #[allow(non_snake_case)]
145    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
146        let X = X.mapv(|x| x);
147        let (n_samples, n_features) = X.dim();
148
149        if n_features != self.state.n_features_in_ {
150            return Err(SklearsError::InvalidInput(format!(
151                "Number of features {} does not match training features {}",
152                n_features, self.state.n_features_in_
153            )));
154        }
155
156        // Simplified implementation - just fill missing values with zeros
157        let mut X_imputed = X.clone();
158        for i in 0..n_samples {
159            for j in 0..n_features {
160                if self.is_missing(X[[i, j]]) {
161                    X_imputed[[i, j]] = 0.0; // Sparse assumption
162                }
163            }
164        }
165
166        Ok(X_imputed.mapv(|x| x as Float))
167    }
168}
169
170impl SparseImputer<SparseImputerTrained> {
171    fn is_missing(&self, value: f64) -> bool {
172        if self.missing_values.is_nan() {
173            value.is_nan()
174        } else {
175            (value - self.missing_values).abs() < f64::EPSILON
176        }
177    }
178}
179
180/// Compressed Sensing Imputer for high-dimensional sparse data
181///
182/// Uses compressed sensing principles to reconstruct missing values
183/// in high-dimensional sparse datasets.
184#[derive(Debug, Clone)]
185pub struct CompressedSensingImputer<S = Untrained> {
186    state: S,
187    measurement_ratio: f64,
188    regularization: f64,
189    max_iter: usize,
190    tol: f64,
191    missing_values: f64,
192}
193
194/// Trained state for CompressedSensingImputer
195#[derive(Debug, Clone)]
196pub struct CompressedSensingImputerTrained {
197    measurement_matrix_: Array2<f64>,
198    n_features_in_: usize,
199}
200
201impl CompressedSensingImputer<Untrained> {
202    /// Create a new CompressedSensingImputer instance
203    pub fn new() -> Self {
204        Self {
205            state: Untrained,
206            measurement_ratio: 0.3,
207            regularization: 0.1,
208            max_iter: 1000,
209            tol: 1e-4,
210            missing_values: f64::NAN,
211        }
212    }
213}
214
215impl Default for CompressedSensingImputer<Untrained> {
216    fn default() -> Self {
217        Self::new()
218    }
219}
220
221impl Estimator for CompressedSensingImputer<Untrained> {
222    type Config = ();
223    type Error = SklearsError;
224    type Float = Float;
225
226    fn config(&self) -> &Self::Config {
227        &()
228    }
229}
230
231impl Fit<ArrayView2<'_, Float>, ()> for CompressedSensingImputer<Untrained> {
232    type Fitted = CompressedSensingImputer<CompressedSensingImputerTrained>;
233
234    #[allow(non_snake_case)]
235    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
236        let X = X.mapv(|x| x);
237        let (_, n_features) = X.dim();
238
239        // Create a simple measurement matrix (stub implementation)
240        let measurement_matrix = Array2::eye(n_features);
241
242        Ok(CompressedSensingImputer {
243            state: CompressedSensingImputerTrained {
244                measurement_matrix_: measurement_matrix,
245                n_features_in_: n_features,
246            },
247            measurement_ratio: self.measurement_ratio,
248            regularization: self.regularization,
249            max_iter: self.max_iter,
250            tol: self.tol,
251            missing_values: self.missing_values,
252        })
253    }
254}
255
256impl Transform<ArrayView2<'_, Float>, Array2<Float>>
257    for CompressedSensingImputer<CompressedSensingImputerTrained>
258{
259    #[allow(non_snake_case)]
260    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
261        let X = X.mapv(|x| x);
262        let (_n_samples, n_features) = X.dim();
263
264        if n_features != self.state.n_features_in_ {
265            return Err(SklearsError::InvalidInput(format!(
266                "Number of features {} does not match training features {}",
267                n_features, self.state.n_features_in_
268            )));
269        }
270
271        // Stub implementation - just return input with zeros for missing values
272        let X_imputed = X.mapv(|x| if self.is_missing(x) { 0.0 } else { x });
273        Ok(X_imputed.mapv(|x| x as Float))
274    }
275}
276
277impl CompressedSensingImputer<CompressedSensingImputerTrained> {
278    fn is_missing(&self, value: f64) -> bool {
279        if self.missing_values.is_nan() {
280            value.is_nan()
281        } else {
282            (value - self.missing_values).abs() < f64::EPSILON
283        }
284    }
285}
sklears_impute/dimensionality/sparse.rs

sklears_impute/dimensionality/
sparse.rs
