torsh-series 0.1.2

//! Anomaly detection for time series

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::TimeSeries;
use scirs2_series::anomaly::{detect_anomalies, AnomalyMethod, AnomalyOptions};
use torsh_tensor::{creation::zeros, Tensor};

/// Anomaly detection result
#[derive(Debug, Clone)]
pub struct AnomalyResult {
    /// Anomaly scores for each time point
    pub scores: Tensor,
    /// Binary mask indicating anomalies
    pub is_anomaly: Tensor,
    /// Threshold used for detection
    pub threshold: f64,
}

/// Change point detection
pub struct ChangePointDetector {
    method: String,
    penalty: f64,
    min_segment_length: usize,
}

impl ChangePointDetector {
    /// Create a new change point detector
    pub fn new(method: &str) -> Self {
        Self {
            method: method.to_string(),
            penalty: 1.0,
            min_segment_length: 10,
        }
    }

    /// Set penalty parameter
    pub fn penalty(mut self, penalty: f64) -> Self {
        self.penalty = penalty;
        self
    }

    /// Detect change points in time series
    pub fn detect(&self, _series: &TimeSeries) -> Vec<usize> {
        // Use scirs2-series change point detection
        Vec::new()
    }

    /// Get segments between change points
    pub fn segment(
        &self,
        series: &TimeSeries,
    ) -> Result<Vec<TimeSeries>, torsh_core::error::TorshError> {
        let change_points = self.detect(series);
        let mut segments = Vec::new();

        // Split series at change points
        let mut start = 0;
        for cp in change_points {
            segments.push(series.slice(start, cp)?);
            start = cp;
        }
        segments.push(series.slice(start, series.len())?);

        Ok(segments)
    }
}

/// Statistical anomaly detection using scirs2-series
pub struct StatisticalDetector {
    method: AnomalyMethod,
    window_size: Option<usize>,
    threshold: Option<f64>,
    contamination: f64,
}

impl StatisticalDetector {
    /// Create a new statistical detector
    pub fn new(method: AnomalyMethod) -> Self {
        Self {
            method,
            window_size: None,
            threshold: None,
            contamination: 0.1, // 10% contamination rate by default
        }
    }

    /// Set sliding window size for local anomaly detection
    pub fn with_window_size(mut self, size: usize) -> Self {
        self.window_size = Some(size);
        self
    }

    /// Set anomaly threshold
    pub fn with_threshold(mut self, threshold: f64) -> Self {
        self.threshold = Some(threshold);
        self
    }

    /// Set contamination rate (expected fraction of anomalies)
    pub fn with_contamination(mut self, contamination: f64) -> Self {
        self.contamination = contamination;
        self
    }

    /// Detect anomalies using scirs2-series
    pub fn detect(
        &self,
        series: &TimeSeries,
    ) -> Result<AnomalyResult, torsh_core::error::TorshError> {
        use scirs2_core::ndarray::Array1;

        // Convert TimeSeries to Array1 for scirs2-series
        let data = series.values.to_vec().map_err(|e| {
            torsh_core::error::TorshError::InvalidArgument(format!(
                "Failed to convert tensor to vec: {}",
                e
            ))
        })?;
        let ts_array = Array1::from_vec(data);

        // Configure anomaly detection options
        let options = AnomalyOptions {
            method: self.method,
            threshold: self.threshold,
            window_size: self.window_size,
            contamination: self.contamination,
            ..Default::default()
        };

        // Perform anomaly detection using scirs2-series
        let result = detect_anomalies(&ts_array, &options).map_err(|e| {
            torsh_core::error::TorshError::InvalidArgument(format!(
                "Anomaly detection failed: {:?}",
                e
            ))
        })?;

        // Convert results back to tensors
        let scores_data: Vec<f32> = result
            .scores
            .to_vec()
            .into_iter()
            .map(|x| x as f32)
            .collect();
        let anomalies_data: Vec<f32> = result
            .is_anomaly
            .iter()
            .map(|&b| if b { 1.0 } else { 0.0 })
            .collect();

        let n = scores_data.len();
        let scores_tensor = Tensor::from_vec(scores_data, &[n]).map_err(|e| {
            torsh_core::error::TorshError::InvalidArgument(format!(
                "Failed to create scores tensor: {}",
                e
            ))
        })?;
        let is_anomaly_tensor = Tensor::from_vec(anomalies_data, &[n]).map_err(|e| {
            torsh_core::error::TorshError::InvalidArgument(format!(
                "Failed to create anomalies tensor: {}",
                e
            ))
        })?;

        Ok(AnomalyResult {
            scores: scores_tensor,
            is_anomaly: is_anomaly_tensor,
            threshold: result.threshold,
        })
    }
}

/// Isolation Forest for anomaly detection
pub struct IsolationForest {
    n_estimators: usize,
    max_samples: usize,
    contamination: f64,
}

impl IsolationForest {
    /// Create a new isolation forest
    pub fn new() -> Self {
        Self {
            n_estimators: 100,
            max_samples: 256,
            contamination: 0.1,
        }
    }

    /// Set contamination rate
    pub fn contamination(mut self, contamination: f64) -> Self {
        self.contamination = contamination;
        self
    }

    /// Fit the model and predict anomalies
    pub fn fit_predict(&mut self, series: &TimeSeries) -> AnomalyResult {
        let scores = zeros(&[series.len()]).expect("tensor creation should succeed");
        let is_anomaly = zeros(&[series.len()]).expect("tensor creation should succeed");

        AnomalyResult {
            scores,
            is_anomaly,
            threshold: 0.0,
        }
    }
}

/// LSTM-based anomaly detection
pub struct LSTMAnomaly {
    sequence_length: usize,
    hidden_size: usize,
    threshold_percentile: f64,
}

impl LSTMAnomaly {
    /// Create a new LSTM anomaly detector
    pub fn new(sequence_length: usize, hidden_size: usize) -> Self {
        Self {
            sequence_length,
            hidden_size,
            threshold_percentile: 95.0,
        }
    }

    /// Train the model
    pub fn fit(&mut self, _series: &TimeSeries) {
        // Train LSTM autoencoder
    }

    /// Detect anomalies
    pub fn predict(&self, series: &TimeSeries) -> AnomalyResult {
        // Calculate reconstruction errors
        let scores = zeros(&[series.len()]).expect("tensor creation should succeed");
        let is_anomaly = zeros(&[series.len()]).expect("tensor creation should succeed");

        AnomalyResult {
            scores,
            is_anomaly,
            threshold: 0.0,
        }
    }
}