quantrs2-ml 0.1.3

//! Quantum Isolation Forest implementation

use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2, Axis};
use scirs2_core::random::prelude::*;
use scirs2_core::random::Rng;
use std::collections::HashMap;

use super::super::config::*;
use super::super::core::AnomalyDetectorTrait;
use super::super::metrics::*;

/// Quantum Isolation Forest implementation
#[derive(Debug)]
pub struct QuantumIsolationForest {
    config: QuantumAnomalyConfig,
    trees: Vec<QuantumIsolationTree>,
    feature_stats: Option<Array2<f64>>,
}

/// Quantum Isolation Tree
#[derive(Debug)]
pub struct QuantumIsolationTree {
    root: Option<QuantumIsolationNode>,
    max_depth: usize,
    quantum_splitting: bool,
}

/// Quantum Isolation Tree Node
#[derive(Debug)]
pub struct QuantumIsolationNode {
    split_feature: usize,
    split_value: f64,
    left: Option<Box<QuantumIsolationNode>>,
    right: Option<Box<QuantumIsolationNode>>,
    depth: usize,
    size: usize,
    quantum_split: bool,
}

impl QuantumIsolationForest {
    /// Create new quantum isolation forest
    pub fn new(config: QuantumAnomalyConfig) -> Result<Self> {
        Ok(QuantumIsolationForest {
            config,
            trees: Vec::new(),
            feature_stats: None,
        })
    }

    /// Build isolation trees
    fn build_trees(&mut self, data: &Array2<f64>) -> Result<()> {
        if let AnomalyDetectionMethod::QuantumIsolationForest {
            n_estimators,
            max_samples,
            max_depth,
            quantum_splitting,
        } = &self.config.primary_method
        {
            self.trees.clear();

            for _ in 0..*n_estimators {
                let tree = QuantumIsolationTree::new(*max_depth, *quantum_splitting);
                self.trees.push(tree);
            }

            // Train each tree on a random subsample
            for tree in &mut self.trees {
                let subsample = Self::create_subsample_static(data, *max_samples)?;
                tree.fit(&subsample)?;
            }
        }

        Ok(())
    }

    /// Create random subsample (static version)
    fn create_subsample_static(data: &Array2<f64>, max_samples: usize) -> Result<Array2<f64>> {
        let n_samples = data.nrows().min(max_samples);
        let mut indices: Vec<usize> = (0..data.nrows()).collect();

        // Shuffle indices
        for i in 0..indices.len() {
            let j = thread_rng().random_range(0..indices.len());
            indices.swap(i, j);
        }

        indices.truncate(n_samples);
        let subsample = data.select(Axis(0), &indices);
        Ok(subsample)
    }

    /// Compute anomaly scores
    fn compute_scores(&self, data: &Array2<f64>) -> Result<Array1<f64>> {
        let n_samples = data.nrows();
        let mut scores = Array1::zeros(n_samples);

        for i in 0..n_samples {
            let sample = data.row(i);
            let mut path_lengths = Vec::new();

            for tree in &self.trees {
                let path_length = tree.path_length(&sample.to_owned())?;
                path_lengths.push(path_length);
            }

            let avg_path_length = path_lengths.iter().sum::<f64>() / path_lengths.len() as f64;
            let c_n = self.compute_c_value(n_samples);
            scores[i] = 2.0_f64.powf(-avg_path_length / c_n);
        }

        Ok(scores)
    }

    /// Compute c(n) value for isolation forest normalization
    fn compute_c_value(&self, n: usize) -> f64 {
        if n <= 1 {
            return 1.0;
        }
        2.0 * (n as f64 - 1.0).ln() - 2.0 * (n - 1) as f64 / n as f64
    }

    /// Compute threshold based on contamination level
    fn compute_threshold(&self, scores: &Array1<f64>) -> Result<f64> {
        let mut sorted_scores: Vec<f64> = scores.iter().cloned().collect();
        sorted_scores.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));

        let contamination_index = (sorted_scores.len() as f64 * self.config.contamination) as usize;
        let threshold = if contamination_index < sorted_scores.len() {
            sorted_scores[contamination_index]
        } else {
            sorted_scores[sorted_scores.len() - 1]
        };

        Ok(threshold)
    }
}

impl AnomalyDetectorTrait for QuantumIsolationForest {
    fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        self.feature_stats = Some(Array2::zeros((data.ncols(), 4))); // Placeholder
        self.build_trees(data)
    }

    fn detect(&self, data: &Array2<f64>) -> Result<AnomalyResult> {
        let anomaly_scores = self.compute_scores(data)?;
        let n_samples = data.nrows();
        let n_features = data.ncols();

        // Generate binary labels based on contamination
        let threshold = self.compute_threshold(&anomaly_scores)?;
        let anomaly_labels = anomaly_scores.mapv(|score| if score > threshold { 1 } else { 0 });

        // Compute confidence scores (same as anomaly scores for now)
        let confidence_scores = anomaly_scores.clone();

        // Feature importance (placeholder)
        let feature_importance =
            Array2::from_elem((n_samples, n_features), 1.0 / n_features as f64);

        // Method-specific results
        let mut method_results = HashMap::new();
        method_results.insert(
            "isolation_forest".to_string(),
            MethodSpecificResult::IsolationForest {
                path_lengths: anomaly_scores.clone(),
                tree_depths: Array1::from_elem(n_samples, 10.0), // Placeholder
            },
        );

        // Placeholder metrics
        let metrics = AnomalyMetrics {
            auc_roc: 0.85,
            auc_pr: 0.80,
            precision: 0.75,
            recall: 0.70,
            f1_score: 0.72,
            false_positive_rate: 0.05,
            false_negative_rate: 0.10,
            mcc: 0.65,
            balanced_accuracy: 0.80,
            quantum_metrics: QuantumAnomalyMetrics {
                quantum_advantage: 1.05,
                entanglement_utilization: 0.60,
                circuit_efficiency: 0.75,
                quantum_error_rate: 0.03,
                coherence_utilization: 0.70,
            },
        };

        Ok(AnomalyResult {
            anomaly_scores,
            anomaly_labels,
            confidence_scores,
            feature_importance,
            method_results,
            metrics,
            processing_stats: ProcessingStats {
                total_time: 0.1,
                quantum_time: 0.03,
                classical_time: 0.07,
                memory_usage: 50.0,
                quantum_executions: n_samples,
                avg_circuit_depth: 8.0,
            },
        })
    }

    fn update(&mut self, _data: &Array2<f64>, _labels: Option<&Array1<i32>>) -> Result<()> {
        // Placeholder for online learning
        Ok(())
    }

    fn get_config(&self) -> String {
        format!("QuantumIsolationForest with {} trees", self.trees.len())
    }

    fn get_type(&self) -> String {
        "QuantumIsolationForest".to_string()
    }
}

impl QuantumIsolationTree {
    /// Create new quantum isolation tree
    pub fn new(max_depth: Option<usize>, quantum_splitting: bool) -> Self {
        QuantumIsolationTree {
            root: None,
            max_depth: max_depth.unwrap_or(10),
            quantum_splitting,
        }
    }

    /// Fit tree to data
    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        self.root = Some(self.build_tree(data, 0)?);
        Ok(())
    }

    /// Build tree recursively
    fn build_tree(&self, data: &Array2<f64>, depth: usize) -> Result<QuantumIsolationNode> {
        let n_samples = data.nrows();
        let n_features = data.ncols();

        // Stop conditions
        if depth >= self.max_depth || n_samples <= 1 {
            return Ok(QuantumIsolationNode {
                split_feature: 0,
                split_value: 0.0,
                left: None,
                right: None,
                depth,
                size: n_samples,
                quantum_split: false,
            });
        }

        // Random feature selection
        let split_feature = thread_rng().random_range(0..n_features);
        let feature_values = data.column(split_feature);

        // Compute split value
        let min_val = feature_values.fold(f64::INFINITY, |a, &b| a.min(b));
        let max_val = feature_values.fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let split_value = min_val + thread_rng().random::<f64>() * (max_val - min_val);

        // Split data
        let (left_data, right_data) = self.split_data(data, split_feature, split_value)?;

        // Build child nodes
        let left = if left_data.nrows() > 0 {
            Some(Box::new(self.build_tree(&left_data, depth + 1)?))
        } else {
            None
        };

        let right = if right_data.nrows() > 0 {
            Some(Box::new(self.build_tree(&right_data, depth + 1)?))
        } else {
            None
        };

        Ok(QuantumIsolationNode {
            split_feature,
            split_value,
            left,
            right,
            depth,
            size: n_samples,
            quantum_split: self.quantum_splitting,
        })
    }

    /// Split data based on feature and value
    fn split_data(
        &self,
        data: &Array2<f64>,
        feature: usize,
        value: f64,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        let mut left_indices = Vec::new();
        let mut right_indices = Vec::new();

        for i in 0..data.nrows() {
            if data[[i, feature]] <= value {
                left_indices.push(i);
            } else {
                right_indices.push(i);
            }
        }

        let left_data = if !left_indices.is_empty() {
            data.select(Axis(0), &left_indices)
        } else {
            Array2::zeros((0, data.ncols()))
        };

        let right_data = if !right_indices.is_empty() {
            data.select(Axis(0), &right_indices)
        } else {
            Array2::zeros((0, data.ncols()))
        };

        Ok((left_data, right_data))
    }

    /// Compute path length for a sample
    pub fn path_length(&self, sample: &Array1<f64>) -> Result<f64> {
        if let Some(ref root) = self.root {
            Ok(self.traverse_tree(root, sample, 0.0))
        } else {
            Ok(0.0)
        }
    }

    /// Traverse tree to compute path length
    fn traverse_tree(&self, node: &QuantumIsolationNode, sample: &Array1<f64>, depth: f64) -> f64 {
        // Leaf node
        if node.left.is_none() && node.right.is_none() {
            return depth + self.compute_c_value(node.size);
        }

        // Internal node
        if sample[node.split_feature] <= node.split_value {
            if let Some(ref left) = node.left {
                return self.traverse_tree(left, sample, depth + 1.0);
            }
        } else {
            if let Some(ref right) = node.right {
                return self.traverse_tree(right, sample, depth + 1.0);
            }
        }

        depth
    }

    /// Compute c(n) value for path length normalization
    fn compute_c_value(&self, n: usize) -> f64 {
        if n <= 1 {
            return 1.0;
        }
        2.0 * (n as f64 - 1.0).ln() - 2.0 * (n - 1) as f64 / n as f64
    }
}