pyrus-nn 0.2.1

Micro Neural Network framework
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use ndarray::{ArrayView2, Axis};
use serde_derive::{Deserialize, Serialize};
use std::f32::MIN;

/// Cost function selection `enum`
#[derive(Serialize, Deserialize)]
pub enum CostFunc {
    MSE,
    MAE,
    Accuracy,
    CrossEntropy,
}


impl From<String> for CostFunc {
    fn from(name: String) -> Self {
        match name.to_lowercase().as_str() {
            "mse" => CostFunc::MSE,
            "mae" => CostFunc::MAE,
            "accuracy" => CostFunc::Accuracy,
            "crossentropy" => CostFunc::CrossEntropy,
            _ => panic!("Unknown cost function: {}", &name)
        }
    }
}


impl std::default::Default for CostFunc {
    fn default() -> Self {
        CostFunc::MSE
    }
}

/// Cross entropy; aka logistic loss / log loss
pub fn cross_entropy(y_true: ArrayView2<f32>, y_hat: ArrayView2<f32>) -> f32 {
    let elipson = 1e-15; // TODO: Make this a parameter

    // Clip
    let mut y_hat = y_hat
        .mapv(|v| if v > 1.0 { v - elipson } else { v + elipson })
        .to_owned();

    // Normalize
    y_hat = y_hat.to_owned()
        / y_hat
            .sum_axis(Axis(1))
            .into_shape((y_hat.shape()[0], 1))
            .unwrap()
            .to_owned();

    // Loss
    -(y_true.to_owned() * y_hat.mapv(|v| v.ln()))
        .sum_axis(Axis(1))
        .sum()
        / y_hat.rows() as f32
}

/// Cross entropy score of single element
pub fn single_cross_entropy(y_true: f32, y_hat: f32) -> f32 {
    let y_hat = if y_hat > 1.0 {
        1.0 - 1e-15
    } else if y_hat < 0.0 {
        0.0 + 1e-15
    } else {
        y_hat
    };
    -(y_hat.ln() + (1. - y_true) * (1. - y_hat).ln())
}

/// Measure accuracy score
pub fn accuracy_score(y_true: ArrayView2<f32>, y_hat: ArrayView2<f32>) -> f32 {
    y_true
        .outer_iter()
        .zip(y_hat.outer_iter())
        .map(|(yt, yh)| {
            if yt.len() > 1 {
                let (ytrue_argmax, _max) =
                    yt.iter()
                        .enumerate()
                        .fold((None, MIN), |(idx, acc), (i, x)| {
                            if x > &acc {
                                (Some(i), *x)
                            } else {
                                (idx, acc)
                            }
                        });

                let (yhat_argmax, _max) =
                    yh.iter()
                        .enumerate()
                        .fold((None, MIN), |(idx, acc), (i, x)| {
                            if x > &acc {
                                (Some(i), *x)
                            } else {
                                (idx, acc)
                            }
                        });

                accuracy(
                    ytrue_argmax.unwrap_or(0) as f32,
                    yhat_argmax.unwrap_or(0) as f32,
                )
            } else {
                accuracy(yt[0], yh[0])
            }
        })
        .sum::<f32>()
        / y_true.rows() as f32
}

/// Measure if two `f32` elements are equal; here for consistency.
pub fn accuracy(y_true: f32, y_hat: f32) -> f32 {
    if y_hat == y_true {
        1.
    } else {
        0.
    }
}

/// Determine the Mean Squared Error
pub fn mean_squared_error(y_true: ArrayView2<f32>, y_hat: ArrayView2<f32>) -> f32 {
    y_true
        .iter()
        .zip(y_hat.iter())
        .map(|(yt, yh)| squared_error(*yt, *yh))
        .sum::<f32>()
        / y_true.rows() as f32
}

/// Squared error between two `f32` values
pub fn squared_error(y_true: f32, y_hat: f32) -> f32 {
    (y_true - y_hat).powf(2.0)
}

/// Determin the Mean Absolute Error
pub fn mean_absolute_error(y_true: ArrayView2<f32>, y_hat: ArrayView2<f32>) -> f32 {
    y_true
        .iter()
        .zip(y_hat.iter())
        .map(|(yt, yh)| absolute_error(*yt, *yh))
        .sum::<f32>()
        / y_true.rows() as f32
}

/// Absolute error between two `f32` values
pub fn absolute_error(y_true: f32, y_hat: f32) -> f32 {
    (y_true - y_hat).abs()
}