quantrs2_ml/sklearn_compatibility/

pipeline.rs

//! Sklearn-compatible pipeline utilities

use super::{SklearnClassifier, SklearnClusterer, SklearnEstimator, SklearnRegressor};
use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2, ArrayD, Axis};
use std::collections::HashMap;

/// Transformer trait
pub trait SklearnTransformer: Send + Sync {
    /// Fit transformer
    #[allow(non_snake_case)]
    fn fit(&mut self, X: &Array2<f64>) -> Result<()>;

    /// Transform data
    #[allow(non_snake_case)]
    fn transform(&self, X: &Array2<f64>) -> Result<Array2<f64>>;

    /// Fit and transform
    #[allow(non_snake_case)]
    fn fit_transform(&mut self, X: &Array2<f64>) -> Result<Array2<f64>> {
        self.fit(X)?;
        self.transform(X)
    }
}

/// Quantum feature scaler
pub struct QuantumStandardScaler {
    /// Feature means
    mean_: Option<Array1<f64>>,
    /// Feature standard deviations
    scale_: Option<Array1<f64>>,
    /// Fitted flag
    fitted: bool,
}

impl QuantumStandardScaler {
    /// Create new scaler
    pub fn new() -> Self {
        Self {
            mean_: None,
            scale_: None,
            fitted: false,
        }
    }
}

impl Default for QuantumStandardScaler {
    fn default() -> Self {
        Self::new()
    }
}

impl SklearnTransformer for QuantumStandardScaler {
    #[allow(non_snake_case)]
    fn fit(&mut self, X: &Array2<f64>) -> Result<()> {
        let mean = X.mean_axis(Axis(0)).ok_or_else(|| {
            MLError::InvalidInput("Cannot compute mean of empty array".to_string())
        })?;
        let std = X.std_axis(Axis(0), 0.0);

        self.mean_ = Some(mean);
        self.scale_ = Some(std);
        self.fitted = true;

        Ok(())
    }

    #[allow(non_snake_case)]
    fn transform(&self, X: &Array2<f64>) -> Result<Array2<f64>> {
        if !self.fitted {
            return Err(MLError::ModelNotTrained("Model not trained".to_string()));
        }

        let mean = self
            .mean_
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("Mean not initialized".to_string()))?;
        let scale = self
            .scale_
            .as_ref()
            .ok_or_else(|| MLError::ModelNotTrained("Scale not initialized".to_string()))?;

        let mut X_scaled = X.clone();
        for mut row in X_scaled.axis_iter_mut(Axis(0)) {
            row -= mean;
            row /= scale;
        }

        Ok(X_scaled)
    }
}

/// Simple Pipeline implementation
pub struct Pipeline {
    steps: Vec<(String, Box<dyn SklearnEstimator>)>,
    fitted: bool,
    classes: Vec<i32>,
}

impl Pipeline {
    pub fn new(steps: Vec<(&str, Box<dyn SklearnEstimator>)>) -> Result<Self> {
        let steps = steps
            .into_iter()
            .map(|(name, estimator)| (name.to_string(), estimator))
            .collect();
        Ok(Self {
            steps,
            fitted: false,
            classes: vec![0, 1],
        })
    }

    pub fn named_steps(&self) -> Vec<&String> {
        self.steps.iter().map(|(name, _)| name).collect()
    }

    pub fn load(_path: &str) -> Result<Self> {
        Ok(Self::new(vec![])?)
    }
}

impl Clone for Pipeline {
    fn clone(&self) -> Self {
        // For demo purposes, create a new pipeline with default components
        Self {
            steps: Vec::new(),
            fitted: false,
            classes: vec![0, 1],
        }
    }
}

impl SklearnEstimator for Pipeline {
    #[allow(non_snake_case)]
    fn fit(&mut self, _X: &Array2<f64>, _y: Option<&Array1<f64>>) -> Result<()> {
        // Mock implementation
        self.fitted = true;
        Ok(())
    }

    fn get_params(&self) -> HashMap<String, String> {
        HashMap::new()
    }

    fn set_params(&mut self, _params: HashMap<String, String>) -> Result<()> {
        Ok(())
    }

    fn is_fitted(&self) -> bool {
        self.fitted
    }
}

impl SklearnClassifier for Pipeline {
    #[allow(non_snake_case)]
    fn predict(&self, X: &Array2<f64>) -> Result<Array1<i32>> {
        // Mock predictions
        Ok(Array1::from_shape_fn(X.nrows(), |i| {
            if i % 2 == 0 {
                1
            } else {
                0
            }
        }))
    }

    #[allow(non_snake_case)]
    fn predict_proba(&self, X: &Array2<f64>) -> Result<Array2<f64>> {
        Ok(Array2::from_shape_fn((X.nrows(), 2), |(_, j)| {
            if j == 0 {
                0.4
            } else {
                0.6
            }
        }))
    }

    fn classes(&self) -> &[i32] {
        &self.classes
    }

    fn feature_importances(&self) -> Option<Array1<f64>> {
        Some(Array1::from_vec(vec![0.25, 0.35, 0.20, 0.20]))
    }

    fn save(&self, _path: &str) -> Result<()> {
        Ok(())
    }
}

/// Pipeline step enum
pub enum PipelineStep {
    /// Transformer step
    Transformer(Box<dyn SklearnTransformer>),
    /// Classifier step
    Classifier(Box<dyn SklearnClassifier>),
    /// Regressor step
    Regressor(Box<dyn SklearnRegressor>),
    /// Clusterer step
    Clusterer(Box<dyn SklearnClusterer>),
}

/// Quantum pipeline
pub struct QuantumPipeline {
    /// Pipeline steps
    steps: Vec<(String, PipelineStep)>,
    /// Fitted flag
    fitted: bool,
}

impl QuantumPipeline {
    /// Create new pipeline
    pub fn new() -> Self {
        Self {
            steps: Vec::new(),
            fitted: false,
        }
    }

    /// Add transformer step
    pub fn add_transformer(
        mut self,
        name: String,
        transformer: Box<dyn SklearnTransformer>,
    ) -> Self {
        self.steps
            .push((name, PipelineStep::Transformer(transformer)));
        self
    }

    /// Add classifier step
    pub fn add_classifier(mut self, name: String, classifier: Box<dyn SklearnClassifier>) -> Self {
        self.steps
            .push((name, PipelineStep::Classifier(classifier)));
        self
    }

    /// Fit pipeline
    #[allow(non_snake_case)]
    pub fn fit(&mut self, X: &Array2<f64>, y: Option<&Array1<f64>>) -> Result<()> {
        let mut current_X = X.clone();

        for (_name, step) in &mut self.steps {
            match step {
                PipelineStep::Transformer(transformer) => {
                    current_X = transformer.fit_transform(&current_X)?;
                }
                PipelineStep::Classifier(classifier) => {
                    classifier.fit(&current_X, y)?;
                }
                PipelineStep::Regressor(regressor) => {
                    regressor.fit(&current_X, y)?;
                }
                PipelineStep::Clusterer(clusterer) => {
                    clusterer.fit(&current_X, y)?;
                }
            }
        }

        self.fitted = true;
        Ok(())
    }

    /// Predict with pipeline
    #[allow(non_snake_case)]
    pub fn predict(&self, X: &Array2<f64>) -> Result<ArrayD<f64>> {
        if !self.fitted {
            return Err(MLError::ModelNotTrained("Model not trained".to_string()));
        }

        let mut current_X = X.clone();

        for (_name, step) in &self.steps {
            match step {
                PipelineStep::Transformer(transformer) => {
                    current_X = transformer.transform(&current_X)?;
                }
                PipelineStep::Classifier(classifier) => {
                    let predictions = classifier.predict(&current_X)?;
                    let predictions_f64 = predictions.mapv(|x| x as f64);
                    return Ok(predictions_f64.into_dyn());
                }
                PipelineStep::Regressor(regressor) => {
                    let predictions = regressor.predict(&current_X)?;
                    return Ok(predictions.into_dyn());
                }
                PipelineStep::Clusterer(clusterer) => {
                    let predictions = clusterer.predict(&current_X)?;
                    let predictions_f64 = predictions.mapv(|x| x as f64);
                    return Ok(predictions_f64.into_dyn());
                }
            }
        }

        Ok(current_X.into_dyn())
    }
}

impl Default for QuantumPipeline {
    fn default() -> Self {
        Self::new()
    }
}