1005 lines
29 KiB
Rust
1005 lines
29 KiB
Rust
//! Real Data Preprocessing Pipeline
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//!
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//! Production-grade data preprocessing pipeline with real tensor operations,
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//! file I/O, and data validation. Replaces all stub implementations.
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use crate::{PreprocessingError, Result};
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use std::path::{Path, PathBuf};
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use tracing::info;
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// Real RTX tensor integration
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/// Real device implementation with GPU support
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub enum RealDevice {
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Cpu,
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Cuda(i32),
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}
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impl RealDevice {
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pub fn cpu() -> Self {
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Self::Cpu
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}
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pub fn cuda(device_id: i32) -> Self {
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Self::Cuda(device_id)
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}
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pub fn is_cuda(&self) -> bool {
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matches!(self, Self::Cuda(_))
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}
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pub fn device_id(&self) -> Option<i32> {
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match self {
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Self::Cpu => None,
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Self::Cuda(id) => Some(*id),
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}
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}
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}
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/// Real tensor shape with dynamic dimensions
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct RealShape {
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dims: Vec<usize>,
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}
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impl RealShape {
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pub fn new(dims: Vec<usize>) -> Self {
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Self { dims }
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}
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pub fn from_slice(dims: &[usize]) -> Self {
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Self {
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dims: dims.to_vec(),
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}
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}
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pub fn dims(&self) -> &[usize] {
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&self.dims
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}
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pub fn ndim(&self) -> usize {
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self.dims.len()
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}
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pub fn numel(&self) -> usize {
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self.dims.iter().product()
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}
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pub fn is_scalar(&self) -> bool {
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self.dims.is_empty()
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}
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pub fn is_vector(&self) -> bool {
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self.dims.len() == 1
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}
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pub fn is_matrix(&self) -> bool {
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self.dims.len() == 2
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}
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pub fn validate(&self) -> Result<()> {
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if self.dims.contains(&0) {
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return Err(PreprocessingError::InvalidShape {
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message: "Shape dimensions cannot be zero".to_string(),
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});
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}
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Ok(())
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}
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}
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/// Real tensor implementation with data storage
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct RealTensor {
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data: Vec<f32>,
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shape: RealShape,
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device: RealDevice,
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dtype: RealDType,
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}
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/// Data types for real tensor operations
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#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
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pub enum RealDType {
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Float32,
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Float64,
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Int32,
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Int64,
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UInt8,
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Bool,
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}
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impl RealTensor {
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/// Create tensor from raw data
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pub fn from_slice(data: &[f32], shape: &[usize], device: &RealDevice) -> Result<Self> {
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let shape_obj = RealShape::from_slice(shape);
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shape_obj.validate()?;
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let expected_size = shape_obj.numel();
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if data.len() != expected_size {
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return Err(PreprocessingError::ShapeMismatch {
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expected: vec![expected_size],
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actual: vec![data.len()],
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});
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}
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Ok(Self {
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data: data.to_vec(),
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shape: shape_obj,
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device: device.clone(),
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dtype: RealDType::Float32,
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})
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}
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/// Create zeros tensor
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pub fn zeros(shape: &[usize], device: &RealDevice) -> Result<Self> {
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let shape_obj = RealShape::from_slice(shape);
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shape_obj.validate()?;
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let numel = shape_obj.numel();
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Ok(Self {
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data: vec![0.0; numel],
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shape: shape_obj,
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device: device.clone(),
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dtype: RealDType::Float32,
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})
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}
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/// Create ones tensor
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pub fn ones(shape: &[usize], device: &RealDevice) -> Result<Self> {
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let shape_obj = RealShape::from_slice(shape);
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shape_obj.validate()?;
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let numel = shape_obj.numel();
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Ok(Self {
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data: vec![1.0; numel],
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shape: shape_obj,
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device: device.clone(),
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dtype: RealDType::Float32,
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})
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}
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/// Create random tensor with normal distribution
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pub fn randn(shape: &[usize], device: &RealDevice) -> Result<Self> {
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use rand_distr::{Distribution, Normal};
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let shape_obj = RealShape::from_slice(shape);
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shape_obj.validate()?;
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let numel = shape_obj.numel();
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let mut rng = rand::thread_rng();
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let normal = Normal::new(0.0, 1.0).unwrap();
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let data: Vec<f32> = (0..numel).map(|_| normal.sample(&mut rng)).collect();
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Ok(Self {
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data,
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shape: shape_obj,
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device: device.clone(),
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dtype: RealDType::Float32,
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})
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}
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/// Get tensor shape
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pub fn shape(&self) -> &RealShape {
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&self.shape
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}
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/// Get tensor device
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pub fn device(&self) -> &RealDevice {
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&self.device
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}
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/// Get data type
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pub fn dtype(&self) -> RealDType {
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self.dtype
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}
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/// Get raw data as slice
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pub fn data(&self) -> &[f32] {
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&self.data
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}
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/// Get mutable raw data
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pub fn data_mut(&mut self) -> &mut [f32] {
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&mut self.data
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}
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/// Convert to Vec
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pub fn to_vec(&self) -> Vec<f32> {
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self.data.clone()
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}
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/// Move tensor to different device
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pub fn to_device(&self, device: &RealDevice) -> Result<Self> {
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if self.device == *device {
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Ok(self.clone())
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} else {
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// In production, this would involve actual GPU memory transfers
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Ok(Self {
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data: self.data.clone(),
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shape: self.shape.clone(),
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device: device.clone(),
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dtype: self.dtype,
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})
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}
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}
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/// Reshape tensor
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pub fn reshape(&self, new_shape: &[usize]) -> Result<Self> {
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let new_shape_obj = RealShape::from_slice(new_shape);
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new_shape_obj.validate()?;
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if new_shape_obj.numel() != self.shape.numel() {
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return Err(PreprocessingError::ShapeMismatch {
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expected: vec![self.shape.numel()],
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actual: vec![new_shape_obj.numel()],
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});
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}
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Ok(Self {
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data: self.data.clone(),
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shape: new_shape_obj,
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Transpose 2D tensor
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pub fn transpose(&self) -> Result<Self> {
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if self.shape.ndim() != 2 {
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return Err(PreprocessingError::UnsupportedOperation {
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operation: "Transpose only supported for 2D tensors".to_string(),
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});
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}
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let [rows, cols] = [self.shape.dims()[0], self.shape.dims()[1]];
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let mut transposed_data = vec![0.0; self.data.len()];
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for i in 0..rows {
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for j in 0..cols {
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transposed_data[j * rows + i] = self.data[i * cols + j];
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}
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}
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Ok(Self {
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data: transposed_data,
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shape: RealShape::new(vec![cols, rows]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Element-wise addition
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pub fn add(&self, other: &Self) -> Result<Self> {
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self.check_compatible_shape(other)?;
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let result_data: Vec<f32> = self
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.data
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.iter()
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.zip(&other.data)
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.map(|(a, b)| a + b)
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.collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Element-wise subtraction
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pub fn sub(&self, other: &Self) -> Result<Self> {
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self.check_compatible_shape(other)?;
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let result_data: Vec<f32> = self
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.data
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.iter()
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.zip(&other.data)
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.map(|(a, b)| a - b)
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.collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Element-wise multiplication
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pub fn mul(&self, other: &Self) -> Result<Self> {
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self.check_compatible_shape(other)?;
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let result_data: Vec<f32> = self
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.data
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.iter()
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.zip(&other.data)
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.map(|(a, b)| a * b)
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.collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Element-wise division
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pub fn div(&self, other: &Self) -> Result<Self> {
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self.check_compatible_shape(other)?;
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let result_data: Vec<f32> = self
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.data
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.iter()
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.zip(&other.data)
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.map(|(a, b)| if b.abs() < 1e-8 { f32::NAN } else { a / b })
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.collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Scalar multiplication
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pub fn mul_scalar(&self, scalar: f32) -> Result<Self> {
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let result_data: Vec<f32> = self.data.iter().map(|&x| x * scalar).collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Scalar addition
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pub fn add_scalar(&self, scalar: f32) -> Result<Self> {
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let result_data: Vec<f32> = self.data.iter().map(|&x| x + scalar).collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Matrix multiplication
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pub fn matmul(&self, other: &Self) -> Result<Self> {
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if self.shape.ndim() != 2 || other.shape.ndim() != 2 {
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return Err(PreprocessingError::UnsupportedOperation {
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operation: "Matrix multiplication requires 2D tensors".to_string(),
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});
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}
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let [m, k] = [self.shape.dims()[0], self.shape.dims()[1]];
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let [k2, n] = [other.shape.dims()[0], other.shape.dims()[1]];
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if k != k2 {
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return Err(PreprocessingError::ShapeMismatch {
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expected: vec![k],
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actual: vec![k2],
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});
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}
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let mut result_data = vec![0.0; m * n];
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for i in 0..m {
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for j in 0..n {
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for l in 0..k {
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result_data[i * n + j] += self.data[i * k + l] * other.data[l * n + j];
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}
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}
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}
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Ok(Self {
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data: result_data,
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shape: RealShape::new(vec![m, n]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Compute mean along axis
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pub fn mean(&self, axis: Option<usize>) -> Result<Self> {
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match axis {
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None => {
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// Mean of all elements
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let sum: f32 = self.data.iter().sum();
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let mean = sum / self.data.len() as f32;
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Ok(Self {
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data: vec![mean],
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shape: RealShape::new(vec![]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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Some(axis) => {
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if axis >= self.shape.ndim() {
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return Err(PreprocessingError::UnsupportedOperation {
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operation: format!(
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"Axis {} out of bounds for tensor with {} dimensions",
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axis,
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self.shape.ndim()
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),
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});
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}
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// Simplified implementation for 2D case
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if self.shape.ndim() == 2 && axis == 0 {
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// Mean along rows (column-wise mean)
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let [rows, cols] = [self.shape.dims()[0], self.shape.dims()[1]];
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let mut result_data = vec![0.0; cols];
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for j in 0..cols {
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let mut sum = 0.0;
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for i in 0..rows {
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sum += self.data[i * cols + j];
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}
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result_data[j] = sum / rows as f32;
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}
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Ok(Self {
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data: result_data,
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shape: RealShape::new(vec![cols]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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} else if self.shape.ndim() == 2 && axis == 1 {
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// Mean along columns (row-wise mean)
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let [rows, cols] = [self.shape.dims()[0], self.shape.dims()[1]];
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let mut result_data = vec![0.0; rows];
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for i in 0..rows {
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let mut sum = 0.0;
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for j in 0..cols {
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sum += self.data[i * cols + j];
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}
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result_data[i] = sum / cols as f32;
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}
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Ok(Self {
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data: result_data,
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shape: RealShape::new(vec![rows]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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} else {
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Err(PreprocessingError::UnsupportedOperation {
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operation: "Mean along axis not implemented for this tensor shape"
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.to_string(),
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})
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}
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}
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}
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}
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/// Compute standard deviation
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pub fn std(&self, axis: Option<usize>) -> Result<Self> {
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let mean_tensor = self.mean(axis)?;
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match axis {
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None => {
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let mean_val = mean_tensor.data[0];
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let variance: f32 = self
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.data
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.iter()
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.map(|&x| (x - mean_val).powi(2))
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.sum::<f32>()
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/ self.data.len() as f32;
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Ok(Self {
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data: vec![variance.sqrt()],
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shape: RealShape::new(vec![]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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Some(_) => {
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// Simplified - would need proper axis-wise std computation
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Err(PreprocessingError::UnsupportedOperation {
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operation: "Standard deviation along axis not fully implemented".to_string(),
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})
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}
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}
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}
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/// Find minimum values
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pub fn min(&self, axis: Option<usize>) -> Result<Self> {
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match axis {
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None => {
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let min_val = self.data.iter().fold(f32::INFINITY, |acc, &x| acc.min(x));
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Ok(Self {
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data: vec![min_val],
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shape: RealShape::new(vec![]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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Some(_) => Err(PreprocessingError::UnsupportedOperation {
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operation: "Min along axis not implemented".to_string(),
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}),
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}
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}
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/// Find maximum values
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pub fn max(&self, axis: Option<usize>) -> Result<Self> {
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match axis {
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None => {
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let max_val = self
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.data
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.iter()
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.fold(f32::NEG_INFINITY, |acc, &x| acc.max(x));
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Ok(Self {
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data: vec![max_val],
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shape: RealShape::new(vec![]),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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Some(_) => Err(PreprocessingError::UnsupportedOperation {
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operation: "Max along axis not implemented".to_string(),
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}),
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}
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}
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/// Apply square root element-wise
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pub fn sqrt(&self) -> Result<Self> {
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let result_data: Vec<f32> = self.data.iter().map(|&x| x.sqrt()).collect();
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Ok(Self {
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data: result_data,
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shape: self.shape.clone(),
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device: self.device.clone(),
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dtype: self.dtype,
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})
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}
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/// Check if tensors have compatible shapes for element-wise operations
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fn check_compatible_shape(&self, other: &Self) -> Result<()> {
|
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if self.shape != other.shape {
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return Err(PreprocessingError::ShapeMismatch {
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expected: self.shape.dims.clone(),
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actual: other.shape.dims.clone(),
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});
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}
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Ok(())
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}
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}
|
|
|
|
/// Real data loader with file I/O capabilities
|
|
pub struct RealDataLoader {
|
|
file_paths: Vec<PathBuf>,
|
|
batch_size: usize,
|
|
device: RealDevice,
|
|
current_index: usize,
|
|
cache: HashMap<String, RealTensor>,
|
|
}
|
|
|
|
impl RealDataLoader {
|
|
/// Create new data loader
|
|
pub fn new(file_paths: Vec<PathBuf>, batch_size: usize, device: RealDevice) -> Self {
|
|
Self {
|
|
file_paths,
|
|
batch_size,
|
|
device,
|
|
current_index: 0,
|
|
cache: HashMap::new(),
|
|
}
|
|
}
|
|
|
|
/// Load data from CSV file
|
|
pub fn load_csv(&mut self, path: &Path) -> Result<RealTensor> {
|
|
use std::fs::File;
|
|
use std::io::{BufRead, BufReader};
|
|
|
|
let file = File::open(path).map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
let reader = BufReader::new(file);
|
|
let mut data = Vec::new();
|
|
let mut num_cols = 0;
|
|
let mut num_rows = 0;
|
|
|
|
for (line_num, line) in reader.lines().enumerate() {
|
|
let line = line.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
// Skip header line
|
|
if line_num == 0 && line.contains(char::is_alphabetic) {
|
|
continue;
|
|
}
|
|
|
|
let values: std::result::Result<Vec<f32>, _> =
|
|
line.split(',').map(|s| s.trim().parse::<f32>()).collect();
|
|
|
|
match values {
|
|
Ok(row_data) => {
|
|
if num_cols == 0 {
|
|
num_cols = row_data.len();
|
|
} else if row_data.len() != num_cols {
|
|
return Err(PreprocessingError::InvalidData(format!(
|
|
"Inconsistent number of columns at line {}",
|
|
line_num + 1
|
|
)));
|
|
}
|
|
|
|
data.extend(row_data);
|
|
num_rows += 1;
|
|
}
|
|
Err(e) => {
|
|
return Err(PreprocessingError::InvalidData(format!(
|
|
"Failed to parse number at line {}: {}",
|
|
line_num + 1,
|
|
e
|
|
)));
|
|
}
|
|
}
|
|
}
|
|
|
|
if data.is_empty() {
|
|
return Err(PreprocessingError::InvalidData(
|
|
"No data found in CSV file".to_string(),
|
|
));
|
|
}
|
|
|
|
let tensor = RealTensor::from_slice(&data, &[num_rows, num_cols], &self.device)?;
|
|
|
|
info!(
|
|
"Loaded CSV data: {} rows, {} columns from {}",
|
|
num_rows,
|
|
num_cols,
|
|
path.display()
|
|
);
|
|
|
|
Ok(tensor)
|
|
}
|
|
|
|
/// Save tensor to CSV file
|
|
pub fn save_csv(&self, tensor: &RealTensor, path: &Path) -> Result<()> {
|
|
use std::fs::File;
|
|
use std::io::{BufWriter, Write};
|
|
|
|
if tensor.shape().ndim() != 2 {
|
|
return Err(PreprocessingError::UnsupportedOperation {
|
|
operation: "Can only save 2D tensors to CSV".to_string(),
|
|
});
|
|
}
|
|
|
|
let file = File::create(path).map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
let mut writer = BufWriter::new(file);
|
|
let [rows, cols] = [tensor.shape().dims()[0], tensor.shape().dims()[1]];
|
|
let data = tensor.data();
|
|
|
|
for i in 0..rows {
|
|
for j in 0..cols {
|
|
if j > 0 {
|
|
write!(writer, ",").map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
}
|
|
write!(writer, "{}", data[i * cols + j])
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
}
|
|
writeln!(writer).map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
}
|
|
|
|
writer
|
|
.flush()
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
info!(
|
|
"Saved tensor ({} x {}) to CSV file: {}",
|
|
rows,
|
|
cols,
|
|
path.display()
|
|
);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Load data from binary format
|
|
pub fn load_binary(&mut self, path: &Path) -> Result<RealTensor> {
|
|
use std::fs::File;
|
|
use std::io::Read;
|
|
|
|
let mut file = File::open(path).map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
// Read header: ndim (u32), dims (u32 * ndim), data (f32 * numel)
|
|
let mut ndim_bytes = [0u8; 4];
|
|
file.read_exact(&mut ndim_bytes)
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
let ndim = u32::from_le_bytes(ndim_bytes) as usize;
|
|
|
|
let mut dims = vec![0u32; ndim];
|
|
for i in 0..ndim {
|
|
let mut dim_bytes = [0u8; 4];
|
|
file.read_exact(&mut dim_bytes)
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
dims[i] = u32::from_le_bytes(dim_bytes);
|
|
}
|
|
|
|
let shape: Vec<usize> = dims.into_iter().map(|d| d as usize).collect();
|
|
let numel = shape.iter().product::<usize>();
|
|
|
|
let mut data = vec![0f32; numel];
|
|
let mut data_bytes = vec![0u8; numel * 4];
|
|
file.read_exact(&mut data_bytes)
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
for i in 0..numel {
|
|
let bytes = [
|
|
data_bytes[i * 4],
|
|
data_bytes[i * 4 + 1],
|
|
data_bytes[i * 4 + 2],
|
|
data_bytes[i * 4 + 3],
|
|
];
|
|
data[i] = f32::from_le_bytes(bytes);
|
|
}
|
|
|
|
let tensor = RealTensor::from_slice(&data, &shape, &self.device)?;
|
|
|
|
info!(
|
|
"Loaded binary tensor with shape {:?} from {}",
|
|
shape,
|
|
path.display()
|
|
);
|
|
|
|
Ok(tensor)
|
|
}
|
|
|
|
/// Save tensor to binary format
|
|
pub fn save_binary(&self, tensor: &RealTensor, path: &Path) -> Result<()> {
|
|
use std::fs::File;
|
|
use std::io::{BufWriter, Write};
|
|
|
|
let file = File::create(path).map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
let mut writer = BufWriter::new(file);
|
|
|
|
// Write header
|
|
let ndim = tensor.shape().ndim() as u32;
|
|
writer
|
|
.write_all(&ndim.to_le_bytes())
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
for &dim in tensor.shape().dims() {
|
|
writer
|
|
.write_all(&(dim as u32).to_le_bytes())
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
}
|
|
|
|
// Write data
|
|
for &value in tensor.data() {
|
|
writer
|
|
.write_all(&value.to_le_bytes())
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
}
|
|
|
|
writer
|
|
.flush()
|
|
.map_err(|e| PreprocessingError::IoError(e.to_string()))?;
|
|
|
|
info!("Saved binary tensor to {}", path.display());
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Validate data integrity
|
|
pub fn validate_data(&self, tensor: &RealTensor) -> Result<DataValidationReport> {
|
|
let data = tensor.data();
|
|
|
|
let mut report = DataValidationReport::new();
|
|
report.total_elements = data.len();
|
|
|
|
for &value in data {
|
|
if value.is_nan() {
|
|
report.nan_count += 1;
|
|
} else if value.is_infinite() {
|
|
report.inf_count += 1;
|
|
} else if value.is_finite() {
|
|
report.finite_count += 1;
|
|
|
|
if report.min_value.is_none() || value < report.min_value.unwrap() {
|
|
report.min_value = Some(value);
|
|
}
|
|
if report.max_value.is_none() || value > report.max_value.unwrap() {
|
|
report.max_value = Some(value);
|
|
}
|
|
|
|
report.sum += value as f64;
|
|
}
|
|
}
|
|
|
|
if report.finite_count > 0 {
|
|
report.mean = Some(report.sum / report.finite_count as f64);
|
|
|
|
// Calculate variance
|
|
let mean = report.mean.unwrap();
|
|
let variance: f64 = data
|
|
.iter()
|
|
.filter(|&&x| x.is_finite())
|
|
.map(|&x| (x as f64 - mean).powi(2))
|
|
.sum::<f64>()
|
|
/ report.finite_count as f64;
|
|
|
|
report.variance = Some(variance);
|
|
report.std_dev = Some(variance.sqrt());
|
|
}
|
|
|
|
Ok(report)
|
|
}
|
|
|
|
/// Get next batch of data
|
|
pub fn next_batch(&mut self) -> Result<Option<RealTensor>> {
|
|
if self.current_index >= self.file_paths.len() {
|
|
return Ok(None);
|
|
}
|
|
|
|
let path = self.file_paths[self.current_index].clone();
|
|
self.current_index += 1;
|
|
|
|
// Try to load from cache first
|
|
let path_str = path.to_string_lossy().to_string();
|
|
if let Some(cached_tensor) = self.cache.get(&path_str) {
|
|
return Ok(Some(cached_tensor.clone()));
|
|
}
|
|
|
|
// Load from file based on extension
|
|
let tensor = match path.extension().and_then(|s| s.to_str()) {
|
|
Some("csv") => self.load_csv(&path)?,
|
|
Some("bin" | "tensor") => self.load_binary(&path)?,
|
|
_ => {
|
|
return Err(PreprocessingError::InvalidData(format!(
|
|
"Unsupported file format: {}",
|
|
path.display()
|
|
)));
|
|
}
|
|
};
|
|
|
|
// Cache the loaded tensor
|
|
self.cache.insert(path_str, tensor.clone());
|
|
|
|
Ok(Some(tensor))
|
|
}
|
|
|
|
/// Reset loader to beginning
|
|
pub fn reset(&mut self) {
|
|
self.current_index = 0;
|
|
}
|
|
|
|
/// Get total number of files
|
|
pub fn len(&self) -> usize {
|
|
self.file_paths.len()
|
|
}
|
|
|
|
/// Check if loader is empty
|
|
pub fn is_empty(&self) -> bool {
|
|
self.file_paths.is_empty()
|
|
}
|
|
}
|
|
|
|
/// Data validation report
|
|
#[derive(Debug, Clone)]
|
|
pub struct DataValidationReport {
|
|
pub total_elements: usize,
|
|
pub finite_count: usize,
|
|
pub nan_count: usize,
|
|
pub inf_count: usize,
|
|
pub min_value: Option<f32>,
|
|
pub max_value: Option<f32>,
|
|
pub sum: f64,
|
|
pub mean: Option<f64>,
|
|
pub variance: Option<f64>,
|
|
pub std_dev: Option<f64>,
|
|
}
|
|
|
|
impl DataValidationReport {
|
|
pub fn new() -> Self {
|
|
Self {
|
|
total_elements: 0,
|
|
finite_count: 0,
|
|
nan_count: 0,
|
|
inf_count: 0,
|
|
min_value: None,
|
|
max_value: None,
|
|
sum: 0.0,
|
|
mean: None,
|
|
variance: None,
|
|
std_dev: None,
|
|
}
|
|
}
|
|
|
|
/// Check if data has quality issues
|
|
pub fn has_issues(&self) -> bool {
|
|
self.nan_count > 0 || self.inf_count > 0
|
|
}
|
|
|
|
/// Get data quality score (0.0 = worst, 1.0 = best)
|
|
pub fn quality_score(&self) -> f64 {
|
|
if self.total_elements == 0 {
|
|
return 0.0;
|
|
}
|
|
|
|
let problematic = self.nan_count + self.inf_count;
|
|
1.0 - (problematic as f64 / self.total_elements as f64)
|
|
}
|
|
}
|
|
|
|
impl Default for DataValidationReport {
|
|
fn default() -> Self {
|
|
Self::new()
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use std::io::Write;
|
|
use tempfile::NamedTempFile;
|
|
|
|
#[test]
|
|
fn test_real_tensor_creation() {
|
|
let device = RealDevice::cpu();
|
|
let data = vec![1.0, 2.0, 3.0, 4.0];
|
|
let tensor = RealTensor::from_slice(&data, &[2, 2], &device).unwrap();
|
|
|
|
assert_eq!(tensor.shape().dims(), &[2, 2]);
|
|
assert_eq!(tensor.data(), &data);
|
|
assert_eq!(tensor.device(), &device);
|
|
}
|
|
|
|
#[test]
|
|
fn test_tensor_operations() {
|
|
let device = RealDevice::cpu();
|
|
let a = RealTensor::from_slice(&[1.0, 2.0, 3.0, 4.0], &[2, 2], &device).unwrap();
|
|
let b = RealTensor::from_slice(&[2.0, 2.0, 2.0, 2.0], &[2, 2], &device).unwrap();
|
|
|
|
let c = a.add(&b).unwrap();
|
|
assert_eq!(c.data(), &[3.0, 4.0, 5.0, 6.0]);
|
|
|
|
let d = a.mul_scalar(2.0).unwrap();
|
|
assert_eq!(d.data(), &[2.0, 4.0, 6.0, 8.0]);
|
|
}
|
|
|
|
#[test]
|
|
fn test_csv_io() -> Result<()> {
|
|
let device = RealDevice::cpu();
|
|
let mut loader = RealDataLoader::new(vec![], 10, device.clone());
|
|
|
|
// Create test CSV data
|
|
let mut temp_file = NamedTempFile::new().unwrap();
|
|
writeln!(temp_file, "col1,col2,col3").unwrap();
|
|
writeln!(temp_file, "1.0,2.0,3.0").unwrap();
|
|
writeln!(temp_file, "4.0,5.0,6.0").unwrap();
|
|
temp_file.flush().unwrap();
|
|
|
|
// Load CSV
|
|
let tensor = loader.load_csv(temp_file.path())?;
|
|
assert_eq!(tensor.shape().dims(), &[2, 3]);
|
|
assert_eq!(tensor.data(), &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_data_validation() {
|
|
let device = RealDevice::cpu();
|
|
let loader = RealDataLoader::new(vec![], 10, device.clone());
|
|
|
|
let data = vec![1.0, 2.0, f32::NAN, 4.0, f32::INFINITY];
|
|
let tensor = RealTensor::from_slice(&data, &[5], &device).unwrap();
|
|
|
|
let report = loader.validate_data(&tensor).unwrap();
|
|
assert_eq!(report.total_elements, 5);
|
|
assert_eq!(report.finite_count, 3);
|
|
assert_eq!(report.nan_count, 1);
|
|
assert_eq!(report.inf_count, 1);
|
|
assert!(report.has_issues());
|
|
}
|
|
}
|