rustytorch/crates/models/rtx-timeseries/src/error.rs

//! Error types for the time series library

use thiserror::Error;

/// Result type for time series operations
pub type Result<T> = std::result::Result<T, TimeSeriesError>;

/// Comprehensive error types for time series analysis and forecasting
#[derive(Error, Debug)]
pub enum TimeSeriesError {
    /// Input validation errors
    #[error("Validation error: {0}")]
    ValidationError(String),

    /// Model fitting errors
    #[error("Model fitting error: {0}")]
    FittingError(String),

    /// Forecasting errors
    #[error("Forecasting error: {0}")]
    ForecastingError(String),

    /// Data preprocessing errors
    #[error("Data preprocessing error: {0}")]
    PreprocessingError(String),

    /// Configuration errors
    #[error("Configuration error: {0}")]
    ConfigurationError(String),

    /// Serialization/deserialization errors
    #[error("Serialization error: {0}")]
    SerializationError(String),

    /// GPU/device errors
    #[error("Device error: {0}")]
    DeviceError(String),

    /// Optimization convergence errors
    #[error("Optimization error: {0}")]
    OptimizationError(String),

    /// Stationarity test errors
    #[error("Stationarity test error: {0}")]
    StationarityError(String),

    /// Seasonality detection errors
    #[error("Seasonality detection error: {0}")]
    SeasonalityError(String),

    /// Anomaly detection errors
    #[error("Anomaly detection error: {0}")]
    AnomalyDetectionError(String),

    /// Memory allocation errors
    #[error("Memory error: {0}")]
    MemoryError(String),

    /// Numerical computation errors
    #[error("Numerical error: {0}")]
    NumericalError(String),

    /// Missing data handling errors
    #[error("Missing data error: {0}")]
    MissingDataError(String),

    /// Model state errors
    #[error("Model state error: {0}")]
    ModelStateError(String),

    /// Time dimension errors
    #[error("Time dimension error: {0}")]
    TimeDimensionError(String),

    /// Frequency detection errors
    #[error("Frequency detection error: {0}")]
    FrequencyError(String),

    /// Integration with other RTX components
    #[error("Integration error: {0}")]
    IntegrationError(String),

    /// Tensor operations errors
    #[error("Tensor error: {source}")]
    TensorError {
        #[from]
        source: rtx_tensor::TensorError,
    },

    /// Runtime errors
    #[error("Runtime error: {source}")]
    RuntimeError {
        #[from]
        source: rtx_runtime::RuntimeError,
    },

    /// Validation library errors
    #[error("Validation error: {source}")]
    ValidationLibraryError {
        #[from]
        source: rtx_validation::ValidationError,
    },

    /// AutoML errors
    #[error("AutoML error: {source}")]
    AutoMLError {
        #[from]
        source: rtx_automeasure::AutoMLError,
    },

    /// Generic I/O errors
    #[error("I/O error: {source}")]
    IoError {
        #[from]
        source: std::io::Error,
    },

    /// JSON serialization errors
    #[error("JSON error: {source}")]
    JsonError {
        #[from]
        source: serde_json::Error,
    },

    /// Generic errors
    #[error("Internal error: {0}")]
    InternalError(String),
}

impl TimeSeriesError {
    /// Create a validation error
    pub fn validation<S: Into<String>>(msg: S) -> Self {
        Self::ValidationError(msg.into())
    }

    /// Create a fitting error
    pub fn fitting<S: Into<String>>(msg: S) -> Self {
        Self::FittingError(msg.into())
    }

    /// Create a forecasting error
    pub fn forecasting<S: Into<String>>(msg: S) -> Self {
        Self::ForecastingError(msg.into())
    }

    /// Create a configuration error
    pub fn configuration<S: Into<String>>(msg: S) -> Self {
        Self::ConfigurationError(msg.into())
    }

    /// Create a device error
    pub fn device<S: Into<String>>(msg: S) -> Self {
        Self::DeviceError(msg.into())
    }

    /// Create an optimization error
    pub fn optimization<S: Into<String>>(msg: S) -> Self {
        Self::OptimizationError(msg.into())
    }

    /// Create a numerical error
    pub fn numerical<S: Into<String>>(msg: S) -> Self {
        Self::NumericalError(msg.into())
    }

    /// Create a model state error
    pub fn model_state<S: Into<String>>(msg: S) -> Self {
        Self::ModelStateError(msg.into())
    }

    /// Check if error is recoverable
    pub fn is_recoverable(&self) -> bool {
        match self {
            Self::ValidationError(_) => false,
            Self::ConfigurationError(_) => false,
            Self::DeviceError(_) => true,
            Self::OptimizationError(_) => true,
            Self::MemoryError(_) => true,
            Self::NumericalError(_) => true,
            Self::IntegrationError(_) => true,
            _ => false,
        }
    }

    /// Get error category
    pub fn category(&self) -> &'static str {
        match self {
            Self::ValidationError(_) => "validation",
            Self::FittingError(_) => "fitting",
            Self::ForecastingError(_) => "forecasting",
            Self::PreprocessingError(_) => "preprocessing",
            Self::ConfigurationError(_) => "configuration",
            Self::SerializationError(_) => "serialization",
            Self::DeviceError(_) => "device",
            Self::OptimizationError(_) => "optimization",
            Self::StationarityError(_) => "stationarity",
            Self::SeasonalityError(_) => "seasonality",
            Self::AnomalyDetectionError(_) => "anomaly_detection",
            Self::MemoryError(_) => "memory",
            Self::NumericalError(_) => "numerical",
            Self::MissingDataError(_) => "missing_data",
            Self::ModelStateError(_) => "model_state",
            Self::TimeDimensionError(_) => "time_dimension",
            Self::FrequencyError(_) => "frequency",
            Self::IntegrationError(_) => "integration",
            Self::TensorError { .. } => "tensor",
            Self::RuntimeError { .. } => "runtime",
            Self::ValidationLibraryError { .. } => "validation_library",
            Self::AutoMLError { .. } => "automl",
            Self::IoError { .. } => "io",
            Self::JsonError { .. } => "json",
            Self::InternalError(_) => "internal",
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_error_creation() {
        let err = TimeSeriesError::validation("Invalid input");
        assert_eq!(err.category(), "validation");
        assert!(!err.is_recoverable());

        let err = TimeSeriesError::device("GPU out of memory");
        assert_eq!(err.category(), "device");
        assert!(err.is_recoverable());
    }

    #[test]
    fn test_error_display() {
        let err = TimeSeriesError::fitting("Model failed to converge");
        let display = format!("{}", err);
        assert!(display.contains("Model fitting error"));
        assert!(display.contains("Model failed to converge"));
    }

    #[test]
    fn test_error_categorization() {
        let validation_err = TimeSeriesError::ValidationError("test".to_string());
        let device_err = TimeSeriesError::DeviceError("test".to_string());
        let numerical_err = TimeSeriesError::NumericalError("test".to_string());

        assert_eq!(validation_err.category(), "validation");
        assert_eq!(device_err.category(), "device");
        assert_eq!(numerical_err.category(), "numerical");
    }
}