README.Rmd

---
output: 
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    variant: gfm
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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  message = FALSE
)
library(mvtnorm)
```

# An R Package for Density Ratio Estimation

#### *Koji MAKIYAMA (@hoxo-m)*

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## 1. Overview

**Density ratio estimation** is described as follows: for given two data samples `x1` and `x2` from unknown distributions `p(x)` and `q(x)` respectively, estimate `w(x) = p(x) / q(x)`, where `x1` and `x2` are d-dimensional real numbers.

The estimated density ratio function `w(x)` can be used in many applications such as **anomaly detection** [Hido et al. 2011], **change-point detection** [Liu et al. 2013], and **covariate shift adaptation** [Sugiyama et al. 2007].
Other useful applications about density ratio estimation were summarized by [Sugiyama et al. 2012].

The package **densratio** provides a function `densratio()` that returns an object with a method to estimate density ratio as `compute_density_ratio()`.

For example, 

```{r result, cache=TRUE}
set.seed(3)
x1 <- rnorm(200, mean = 1, sd = 1/8)
x2 <- rnorm(200, mean = 1, sd = 1/2)

library(densratio)
densratio_obj <- densratio(x1, x2)
```

The densratio object has a function `compute_density_ratio()` that can compute the estimated density ratio `w_hat(x)` for any d-dimensional input `x` (now d=1).

```{r compute-estimated-density-ratio, fig.width=5, fig.height=4}
new_x <- seq(0, 2, by = 0.03)
w_hat <- densratio_obj$compute_density_ratio(new_x)

plot(new_x, w_hat, pch=19)
```

In this case, the true density ratio `w(x) = p(x)/q(y) = Norm(1, 1/8) / Norm(1, 1/2)` is known. 
So we can compare `w(x)` with the estimated density ratio `w-hat(x)`.

```{r compare-true-estimate, fig.width=5, fig.height=4}
true_density_ratio <- function(x) dnorm(x, 1, 1/8) / dnorm(x, 1, 1/2)

plot(true_density_ratio, xlim=c(0, 2), lwd=2, col="red", xlab = "x", ylab = "Density Ratio")
plot(densratio_obj$compute_density_ratio, xlim=c(0, 2), lwd=2, col="green", add=TRUE)
legend("topright", legend=c(expression(w(x)), expression(hat(w)(x))), col=2:3, lty=1, lwd=2, pch=NA)
```

## 2. Installation

You can install the **densratio** package from [CRAN](https://CRAN.R-project.org/package=densratio).

```{r eval=FALSE}
install.packages("densratio")
```

You can also install the package from [GitHub](https://github.com/hoxo-m/densratio).

```{r eval=FALSE}
install.packages("remotes") # if you have not installed "remotes" package
remotes::install_github("hoxo-m/densratio")
```

The source code for **densratio** package is available on GitHub at

- https://github.com/hoxo-m/densratio.

## 3. Details

### 3.1 Basics

The package provides `densratio()`.
The function returns an object that has a function to compute estimated density ratio.

For data samples `x1` and `x2`,

```{r eval=FALSE}
set.seed(3)
x1 <- rnorm(200, mean = 1, sd = 1/8)
x2 <- rnorm(200, mean = 1, sd = 1/2)

library(densratio)
densratio_obj <- densratio(x1, x2)
```

In this case, `densratio_obj$compute_density_ratio()` can compute estimated density ratio.

```{r basics-compute-estimated-density-ratio, fig.width=5, fig.height=4}
new_x <- seq(0, 2, by = 0.03)
w_hat <- densratio_obj$compute_density_ratio(new_x)

plot(new_x, w_hat, pch=19)
```

### 3.2 Methods

`densratio()` has `method` argument that you can pass `"uLSIF"`, `"RuSLIF"`, or `"KLIEP"`.

- **uLSIF** (unconstrained Least-Squares Importance Fitting) is the default method.
This algorithm estimates density ratio by minimizing the squared loss.
You can find more information in [Kanamori et al. 2009] and [Hido et al. 2011].
- **RuLSIF** (Relative unconstrained Least-Squares Importance Fitting).
This algorithm estimates relative density ratio by minimizing the squared loss.
You can find more information in [Yamada et al. 2011] and [Liu et al. 2013].
- **KLIEP** (Kullback-Leibler Importance Estimation Procedure).
This algorithm estimates density ratio by minimizing Kullback-Leibler divergence.
You can find more information in [Sugiyama et al. 2007].

The methods assume that density ratio are represented by linear model:

- `w(x) = theta_1 * K(x, c_1) + theta_2 * K(x, c_2) + ... + theta_b * K(x, c_b)`

where

- `K(x, c) = exp(-||x - c||^2 / 2 * sigma^2)`

is the Gaussian (RBF) kernel.

`densratio()` performs the following: 

- Decides kernel parameter `sigma` by cross-validation, 
- Optimizes the kernel weights `theta` (in other words, find the optimal coefficients of the linear model), and
- The parameters `sigma` and `theta` are saved into `densratio` object, and are used when to compute density ratio in the call `compute_density_ratio()`.

### 3.3 Result and Arguments

`densratio()` outputs the result like as follows:

```{r echo=FALSE}
densratio_obj
```

- **Kernel type** is fixed as Gaussian.
- **Number of kernels** is the number of kernels in the linear model. 
You can change by setting `kernel_num` argument. 
In default, `kernel_num = 100`.
- **Bandwidth (sigma)** is the Gaussian kernel bandwidth.
In default, `sigma = "auto"`, the algorithm automatically select an optimal value by cross validation. 
If you set `sigma` a number, that will be used. 
If you set `sigma` a numeric vector, the algorithm select an optimal value in them by cross validation.
- **Centers** are centers of Gaussian kernels in the linear model. 
These are selected at random from the data sample `x1` underlying a numerator distribution `p(x)`. 
You can find the whole values in `result$kernel_info$centers`.
- **Kernel Weights** are `theta` parameters in the linear kernel model. 
You can find these values in `result$kernel_weights`.
- **Function to Estimate Density Ratio** is named `compute_density_ratio()`.

## 4. Multi Dimensional Data Samples

So far, the input data samples `x1` and `x2` were one dimensional. 
`densratio()` allows to input multidimensional data samples as `matrix`, as long as their dimensions are the same.

For example,

```{r cache=TRUE}
library(densratio)
library(mvtnorm)

set.seed(3)
x1 <- rmvnorm(300, mean = c(1, 1), sigma = diag(1/8, 2))
x2 <- rmvnorm(300, mean = c(1, 1), sigma = diag(1/2, 2))

densratio_obj_2d <- densratio(x1, x2)
densratio_obj_2d
```

In this case, as well, we can compare the true density ratio with the estimated density ratio.

```{r compare-2d, fig.width=7, fig.height=4}
true_density_ratio <- function(x) {
  dmvnorm(x, mean = c(1, 1), sigma = diag(1/8, 2)) /
    dmvnorm(x, mean = c(1, 1), sigma = diag(1/2, 2))
}

N <- 20
range <- seq(0, 2, length.out = N)
input <- expand.grid(range, range)
w_true <- matrix(true_density_ratio(input), nrow = N)
w_hat <- matrix(densratio_obj_2d$compute_density_ratio(input), nrow = N)

par(mfrow = c(1, 2))
contour(range, range, w_true, main = "True Density Ratio")
contour(range, range, w_hat, main = "Estimated Density Ratio")
```

## 5. Related work

- A Python Package for Density Ratio Estimation
    - https://pypi.org/project/densratio/
- APPEstimation: Adjusted Prediction Model Performance Estimation
    - https://cran.r-project.org/package=APPEstimation

## References

- Hido, S., Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori.
**Statistical outlier detection using direct density ratio estimation.**
Knowledge and Information Systems, 2011.
- Kanamori, T., S. Hido, and M. Sugiyama.
**A least-squares approach to direct importance estimation.**
Journal of Machine Learning Research, 2009.
- Liu, S., M. Yamada, N. Collier, M. Sugiyama.
**Change-point detection in time-series data by relative density-ratio estimation.**
Neural Net, 2013
- Sugiyama, M., S. Nakajima, H. Kashima, P. von Bünau, and M. Kawanabe. 
**Direct importance estimation with model selection and its application to covariate shift adaptation.**
NIPS 2007.
- Sugiyama, M., T. Suzuki, and T. Kanamori. 
**Density ratio estimation in machine learning.**
Cambridge University Press, 2012.
- Yamada, M., T. Suzuki, T. Kanamori, H. Hachiya, and M. Sugiyama.
**Relative density-ratio estimation for robust distribution comparison.**
NIPS 2011.