# densityratio

#### Carlos Gonzalez Poses & Thom Benjamin Volker

# Introduction

Density ratio estimation is a powerful tool in many applications, such as two-sample testing, classification, importance weighting and evaluation of synthetic data utility. Each of these tasks can be formulated as a problem of distribution comparison, where the goal is to estimate where and how two distributions differ. The `densityratio` package provides a collection of methods for estimating the density ratio between two distributions. In this vignette, we will provide an overview of the package and demonstrate how to use it for distribution comparison tasks.

# Features

- Fast: Computationally intensive code is executed in `C++` using `Rcpp` and `RcppArmadillo`.
- Automatic: Good default hyperparameters that can be optimized in cross-validation (we do recommend understanding those parameters before using `densityratio` in practice).
- Complete: Several density ratio estimation methods, such as unconstrained least-squares importance fitting (`ulsif()`), Kullback-Leibler importance estimation procedure (`kliep()`), ratio of estimated densities (`naive()`), and extensions for high dimensional data (least-squares heterodistributional subspace search, `lhss()` and spectral density ratio estimation, `spectral()`).
- User-friendly: Simple user interface, default `predict()`, `print()` and `summary()` functions for all density ratio estimation methods; built-in data sets for quick testing.

# Installation

Currently, the package is available through GitHub and R universe. You can download the package like so:

    install.packages('densityratio', repos = 'https://thomvolker.r-universe.dev')

Subsequently, we can easily load the package in the standard way.

    library(densityratio)

# Data

We apply the methods in the `densityratio` package to the `kidiq` data that is introduced in Data analysis using regression and multilevel/hierarchical models by Gelman and Hill (2007) and also included in the package. This data set contains 434 observations measured on five variables:

- `kid_score` Child’s IQ score (continuous)
- `mom_hs` Whether the mother obtained a high school degree (binary)
- `mom_iq` Mother’s IQ score (continuous)
- `mom_work` Whether the mother worked in the first three years of the child’s life (1: not in the first three years; 2: in the second or third year; 3: parttime in the first year; 4: fulltime in the first year)
- `mom_age` Mother’s age (continuous)

For this example, we split the data according to the mother’s education level, and evaluate how the distributions of the two groups differ.

    high_school <- kidiq |> subset(mom_hs == "yes", select = -mom_hs)
    no_high_school <- kidiq |> subset(mom_hs == "no", select = -mom_hs)

# Estimating the density ratio

Now we have the two groups we want to compare, we can evaluate how the distributions of the two groups differ using the density ratio. That is, we express differences between the two groups in terms of the ratio of their densities: \[ r(\mathbf{x}) = \frac{p_{\text{hs}}(\mathbf{x})}{p_{\text{nhs}}(\mathbf{x})}, \] where the subscript refers to the sample of observations from mothers with a high school degree (hs) or without (nhs). The density ratio \(r(\mathbf{x})\) directly shows how the two distributions differ at each point \(\mathbf{x}\) in the support of the data. If the density ratio is larger than 1, there are relatively more observations in the high school group than in the no high school group at that region, and vice versa if the density ratio is smaller than one. Key to almost all estimation methods in the `densityratio` package, is that we estimate \(r(\mathbf{x})\) directly as \(\hat r(\mathbf{x})\), without estimating the densities \(p_{\text{hs}}(\mathbf{x})\) and \(p_{\text{nhs}}(\mathbf{x})\) separately.

To estimate the density ratio, several estimation functions are included.

- `kmm()`: Kernel Mean Matching estimates the density ratio by matching the means of the two distributions in feature space (Huang et al., 2007).
- `kliep()`: Kullback-Leibler Importance Estimation Procedure estimates the density ratio such that the Kullback-Leibler divergence to the true density ratio function is minimized (Sugiyama et al., 2007).
- `ulsif()`: Unconstrained Least-Squares Importance Fitting provides an analytical solution for the density ratio by minimizing the least-squares error (Pearson divergence) between the true density ratio and the estimated density ratio (Kanamori et al., 2009).
- `lhss()`: Least-Squares Heterodistributional Subspace Search extends `ulsif()` to high-dimensional settings by searching for the subspace where the numerator and denominator densities are most different, and estimating the density ratio in that subspace (Sugiyama et al., 2011).
- `spectral()`: Spectral density ratio estimation is another high-dimensional extension to classical density ratio estimation approaches that performs dimension reduction through a spectral decomposition in feature space (Izbicki et al., 2014).
- `naive()`: Naive density ratio estimation through estimating the ratio of the two densities by separate density estimation methods for the two groups.

Which of these functions is most appropriate depends on the problem at hand, but computational efficiency and relative robustness make `ulsif()` a good starting point. For high-dimensional data, `spectral()` and `lhss()` are presumably more appropriate. All these estimation functions, except `naive()`, are based on the same principle of minimizing a divergence between the true density ratio and the estimated density ratio, but they differ in the divergence that is minimized and the constraints that are imposed on the estimated density ratio. These estimation functions are kernel-based and thereby non-parametric. Specifically, we use the following linear model for the density ratio: \[ \hat{r}(\mathbf{x}) = K(\mathbf{x}, \mathbf{x'})\hat{\mathbf{\theta}}, \] with the kernel function defined as \[ K(\mathbf{x}, \mathbf{x'}) = \exp \Bigg(-\frac{||\mathbf{x}-\mathbf{x'}||}{2\sigma^2} \Bigg), \] where \(\sigma\) is the bandwidth of the kernel and \(\hat{\mathbf{\theta}}\) is a vector of parameters that we estimate from the data. While the model is linear in the parameters, the estimate of the density ratio is non-linear through the use of the kernel function. Currently, we use Gaussian kernels for all methods (but we plan to allow for different kernels in a future version of the package), which uses the following transformation of the data. Note that the functions `lhss()` and `spectral()` use a slightly more involved transformation of the data to perform dimension reduction. All estimation functions typically require extensive tuning of hyperparameters, such as the bandwidth of the kernel and potentially a regularization parameter. This tuning is taken care of through in-built cross-validation, such that very little user-specification is required. Below, we illustrate the package by estimating the density ratio using `ulsif()`.

## Input data

The density ratio package expects two data.frames as input, one for the numerator observations, and one for the denominator observations. These data sets must have the same variables: if this assumption is violated, the function returns an error.

    set.seed(123) # for reproducibility
    dr <- ulsif(high_school, no_high_school)

All estimation functions return an object of their respective class (“ulsif”, “kmm”, “kliep”, and so on). Each method has a separate `S3` class, because of differences in models, hyperparameters and important information that is included in the object. Still, all classes have a common interface with `print()`, `summary()` and `predict()` methods. Printing the output of the object `dr` provides some important information of the fitted model:

    dr
    #>
    #> Call:
    #> ulsif(df_numerator = high_school, df_denominator = no_high_school)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>   sigma: num [1:10] 0.913 1.216 1.393 1.551 1.71 ...
    #>
    #> Regularization parameter (lambda): num [1:20] 1000 483.3 233.6 112.9 54.6 ...
    #>
    #> Optimal sigma (loocv): 1.216132
    #> Optimal lambda (loocv): 0.3359818
    #> Optimal kernel weights (loocv): num [1:201] 0.3256 -0.054 0.0098 0.1639 0.2047 ...
    #>

The output shows the number of kernels, the candidate values for the bandwidth parameter `sigma`, the candidates for the regularization parameter `lambda` and the optimal values of these parameters, including the weights that can be used to compute the density ratio for new samples. It is also possible to use different candidate parameters for the bandwidth and regularization parameter, which can be done in different ways.

## Hyperparameter selection

### Specifying the inducing points in the kernel

Kernel-based algorithms are computationally expensive: in the original form, the distance from observations to every other observation is used to calculate the density ratio. This means that the computational cost scales cubically with the number of observations in the data. To alleviate this issue, we make use of inducing points, which are a subset of the observations that are used to calculate the distance matrix. The idea hereof is that we do not use all observations, because most are relatively close to each other and share similar information. By default, the estimation functions use 200 inducing points, randomly sampled from the numerator and denominator data, but this number can be altered by changing the `ncenters` parameter. It is also possible to set the inducing points manually, by providing a `data.frame` with the same variables as the input data, containing the inducing points in the rows, to the `centers` argument. If the inducing points are set manually, the `ncenters` parameter is ignored.

    dr <- ulsif(high_school, no_high_school, ncenters = 100)
    dr <- ulsif(high_school, no_high_school, centers = high_school)

### Bandwidth and regularization parameter

The bandwidth of the kernel and the regularization parameter are tuned automatically using leave-one-out-cross-validation, which can be calculated analytically. By default, the candidate bandwidth values are chosen by taking quantiles of the interpoint distance matrix corresponding to 10 equally spaced probabilities between 0.05 and 0.95. Changing the `nsigma` parameter results in a different number of quantiles used. The default candidate values for the regularization parameter `lambda` are set to `10^{seq(-3, 3, length.out = 20)}`. By changing `nlambda`, the number of candidate values in this sequence can be altered, to estimate the optimal hyperparameters with higher precision.

    dr <- ulsif(high_school, no_high_school, nsigma = 20, nlambda = 50)
    dr
    #>
    #> Call:
    #> ulsif(df_numerator = high_school, df_denominator = no_high_school,     nsigma = 20, nlambda = 50)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>   sigma: num [1:20] 0.864 1.082 1.181 1.267 1.346 ...
    #>
    #> Regularization parameter (lambda): num [1:50] 1000 754 569 429 324 ...
    #>
    #> Optimal sigma (loocv): 1.345547
    #> Optimal lambda (loocv): 0.4941713
    #> Optimal kernel weights (loocv): num [1:201] 0.2213 -0.0349 -0.0107 0.0222 -0.0175 ...
    #>

It is also possible to specify the candidate parameter values manually. For the bandwidth, this can be done by specifying the probabilities used in calculation of the quantiles, using `sigma_quantile`, or by specifying the bandwidth values directly, using `sigma`. For the regularization parameter, this can be done by specifying the candidate values directly using `lambda`.

    dr <- ulsif(
      high_school,
      no_high_school,
      sigma = c(1, 1.1, 1.2),
      lambda = c(0.01, 0.1, 1)
    )

Other estimation functions have similar arguments, although only `ulsif()` and `lhss()` accept a regularization parameter.

#### Intermezzo: Bandwidth & regularization parameter

> Bandwidth parameter: The bandwidth parameter controls how flexible the density ratio model is. Smaller values place relatively more weight on observations that are very close to the observation at hand, whereas larger values also borrow information from observations that are further away in feature space. A smaller smaller bandwidth allows to model sudden shifts in the density ratio, but might also be prone to overfitting, while a larger bandwidth results in a smoother estimate. Below, we illustrate this by plotting the estimated density ratio for a single variable, `kid_score`, estimated with both a large and a small bandwidth parameter. While both estimates show that the density ratio is larger for larger values of `kid_score`, the estimate with the smaller bandwidth parameter is much more flexible and less smooth.

> Density ratio estimates for a large and small bandwidth parameter.

> Regularization parameter: The regularization parameter controls the amount of regularization applied to the model. A larger value of the regularization parameter results in a smoother estimate, while a smaller value allows for more flexibility in the model. This is shown below, where we plot the estimated density ratio for `kid_score` estimated with both a large and a small regularization parameter. Again, the estimate with the smaller regularization parameter is much more flexible and less smooth. Note, here, that regularization shrinks the kernel weights towards zero, such that the estimated density ratio tends to zero (and not to one, as one might expect). We plan to investigate this further in the future.

> Density ratio estimates for a large and small regularization parameter.

## Scaling of the data

By default, all data is scaled before estimating the density ratio. Because the density ratio is distance-based, variables with larger variance may contribute more to the distance, and are thus implicitly deemed more important in the model, than variables with smaller variance. By default, the data is scaled such that continuous variables in the numerator data has mean 0 and variance 1, and categorical variables (factors and character strings) are turned into dummy variables (i.e., one-hot encoded). The denominator data is scaled accordingly, using the means and variances of the numerator data. If desired, this can be changed by setting the `scale` argument to `scale = "denominator"` to use the denominator means and variances, or to `scale = NULL`, to apply no scaling at all. Additionally, an intercept is added to the model matrix by default. This can be turned off by setting `intercept = FALSE`.

## Computational efficiency

Parallel computation is natively supported by most functions in the density ratio package (current exceptions are `lhss()` and `kliep()`, for which storage requirements can defeat speedup due to parallelization, we aim to solve this in a future version). Parallel computation can be enabled simply by setting `parallel = TRUE`. By default, the number of available threads minus one is used, but this can be changed by setting the `nthreads` argument. Note that the number specified in `nthreads` is not only limited by the number of cores on the machine, but also by the number of hyperparameters than can be estimated in parallel (such as `nlambda`).

    dr <- ulsif(
      high_school,
      no_high_school,
      parallel = TRUE,
      nthreads = 10
    )

## Other estimation functions

In this section, we briefly skim over the other estimation functions available in the package. These functions are all built on the same principles, but use different algorithms with different hyperparameters. These hyperparameters are still tuned automatically, but use \(k\)-fold cross-validation instead of leave-one-out cross-validation. Although \(k\) can be set to equal the minimum number of observations in the numerator or denominator data, which yields leave-one-out cross-validation, this might significantly affect the computational cost, because no analytical solution to the loss is available.

### `kmm()`

The kernel mean matching algorithm is quite similar to the `ulsif()` algorithm, but starts from a different perspective: we estimate the density ratio such that reweighing the denominator samples with the density ratio results in a distribution that is as similar as possible to the numerator distribution in terms of the L2 norm. This approach does not require tuning of a regularization parameter, but does require tuning of the bandwidth parameter (the same defaults as for `ulsif()` apply). By default, `kmm()` uses unconstrained optimization to estimate the density ratio parameters `constrained = FALSE`, but this can be changed to `constrained = TRUE` to use constrained optimization. The unconstrained optimization is more efficient, but might yield negative estimates for the density ratio. Additionally, cross-validation can be disabled by setting `cv = FALSE`, and the number of cross-validation folds can be altered by changing `nfold` parameter.

    dr_kmm <- kmm(
      high_school,
      no_high_school,
      constrained = TRUE,
      nfold = 10,
      parallel = TRUE
    )

    dr_kmm
    #>
    #> Call:
    #> kmm(df_numerator = high_school, df_denominator = no_high_school,     constrained = TRUE, nfold = 10, parallel = TRUE)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>   sigma: num [1:10] 0.989 1.252 1.444 1.606 1.772 ...
    #>
    #> Optimal sigma (10-fold cv): 1.606
    #> Optimal kernel weights (10-fold cv):  num [1:200, 1] 3.16e-04 9.80e-05 -6.06e-05 5.70e-06 -2.26e-04 ...
    #>
    #> Optimization parameters:
    #>   Optimization method:  Constrained

### `kliep()`

The Kullback-Leibler importance fitting procedure optimizes a somewhat different loss compared to `ulsif()` (the Kullback-Leibler loss, rather than the Pearson loss), and does not include a regularization parameter. Whereas `ulsif()` can be estimated analytically, `kliep()` uses a gradient descent algorithm to estimate the parameters. The `kliep()` function has some additional arguments for the optimization routine, such as the maximum number of iterations `maxit` and the learning rate `epsilon`. Again, hyperparameter values and cross-validation scheme can be altered as described above.

    dr_kliep <- kliep(
      high_school,
      no_high_school,
      nsigma = 20,
      maxit = 10000,
      epsilon = 0.0001
    )

    dr_kliep
    #>
    #> Call:
    #> kliep(df_numerator = high_school, df_denominator = no_high_school,     nsigma = 20, epsilon = 1e-04, maxit = 10000)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>   sigma: num [1:20] 0.914 1.11 1.207 1.298 1.377 ...
    #>
    #> Optimal sigma (5-fold cv): 0.9142
    #> Optimal kernel weights (5-fold cv):  num [1:200, 1] 0.278 0.0659 0.0434 0.1052 0.2286 ...
    #>
    #> Optimization parameters:
    #>   Learning rate (epsilon): 1e-04
    #>   Maximum number of iterations:  10000

### `lhss()`

The `lhss()` function implements the least-squares heterodistributional subspace search, which is a high-dimensional extension to `ulsif()`. Rather than applying `ulsif()` to the original input data, `lhss()` first finds a lower-dimensional subspace where the numerator and denominator samples are maximally different, and then applies `ulsif()` to the data projected to this subspace. Note that this subspace is a linear projection of the original data. This method works well if such a subspace indeed exists, but might not be optimal if such a linear projection cannot capture the differences between the two distributions. Parameters for `lhss()` are similar to those for `ulsif()`, although the bandwidth values are by default obtained after the projection (that is, `nsigma` and `sigma_quantile` are applied to the projected data, but `sigma` is considered as is). One parameter that is specific to `lhss()` concerns the dimensionality of the subspace, which can be set by changing `m`, which defaults to the square root of the number of features in the data. Because the algorithm is quite expensive computationally, no optimization with respect to the subspace is carried out. Different choices can be considered by running the algorithm with different values of `m`, but this requires multiple calls to the `lhss` function. Currently, no parallel computation is supported for `lhss()`, but this is planned for a future version, together with native support for optimizing the dimension of the subspace. Furthermore, the function uses a gradient descent algorithm to find the subspace: the number of iterations used defaults to `maxit = 200` but can be adapted by the user.

    dr_lhss <- lhss(
      high_school,
      no_high_school,
      m = 1
    )

    dr_lhss
    #>
    #> Call:
    #> lhss(df_numerator = high_school, df_denominator = no_high_school,     m = 1)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>   sigma: num [1:10, 1:10] 0.00514 0.04845 0.13661 0.27564 0.4775 ...
    #>
    #> Regularization parameter (lambda): num [1:10] 1000 215.44 46.42 10 2.15 ...
    #>
    #> Subspace dimension (m): 1
    #> Optimal sigma: 0.1366127
    #> Optimal lambda: 0.1
    #> Optimal kernel weights (loocv): num [1:201] 1.3467 0 0.1011 0.0874 0 ...
    #>

### `spectral()`

The spectral() method offers an alternative way to estimate the density ratio in high-dimensional settings. Instead of reducing the dimensionality before applying a kernel, it first applies a kernel to the denominator data and performs an eigen decomposition to find a low-dimensional representation. As computing this eigen decomposition is computationally expensive, it is also possible to use only a subset of the denominator data to compute this eigen decomposition (by setting the parameter `ncenters`, similarly to the other functions). The density ratio is then estimated in this reduced space by matching the numerator data to the denominator data. The goal is to find weights such that, when applied to the denominator data in kernel space, the resulting distribution closely resembles the numerator distribution. Similarly to `lhss()`, the `spectral()` function has a parameter `m` that determines the dimensionality of the subspace, which defaults to an evenly-spaced sequence of length 50 between 1 and the number of centers in the denominator data (or the size of the subset used). Again, cross-validation is used to find the optimal value of `m` and the optimal bandwidth parameter. The number of folds can be altered using the `nfold` parameter, and cross-validation can be disabled by setting `cv = FALSE`. Also, parallel computation is supported, and can be enabled by setting `parallel = TRUE`, potentially with a different number of threads using the `nthreads` parameter.

    dr_spectral <- spectral(
      high_school,
      no_high_school
    )

    dr_spectral
    #>
    #> Call:
    #> spectral(df_numerator = high_school, df_denominator = no_high_school)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 93
    #>   sigma: num [1:10] 1.01 1.28 1.47 1.65 1.82 ...
    #>
    #>
    #> Subspace dimension (J): num [1:50] 1 2 4 6 7 9 11 12 14 16 ...
    #>
    #> Optimal sigma: 3.215855
    #> Optimal subspace: 16
    #> Optimal kernel weights (cv): num [1:16] 0.952 -0.461 -0.755 -0.559 -0.342 ...
    #>

### `naive()`

Naive density ratio estimation estimates the density ratio by estimating the numerator and denominator densities separately. We use a singular value decomposition to account for relationships between the features, estimated on the numerator data and project the denominator data onto the same subspace. As such, dimension reduction is readily implemented. Subsequently, we make use of the naive Bayes assumption, implying independence of the features, to estimate the density ratio on the latent space. Hence, the `naive()` function has a parameter `m` that determines the dimensionality of the subspace, which defaults to the number of variables in the data (no dimension reduction). Additionally parameters are inherited from the `density()` function, which is used to estimate the numerator and denominator densities. Note that we do not advice this method, it is merely included as a benchmark.

    dr_naive <- naive(
      high_school,
      no_high_school,
      m = 2
    )

    dr_naive
    #>
    #> Call:
    #> naive(df_numerator = high_school, df_denominator = no_high_school,     m = 2)
    #>
    #> Naive density ratio
    #>   Number of variables: 4
    #>   Number of numerator samples: 341
    #>   Number of denominator samples: 93
    #>   Numerator density: num [1:341] 3.82 2.43 5.32 2.09 4.13 ...
    #>   Denominator density: num [1:93] 0.459 0.417 0.629 1.823 0.434 ...
    #>

# Summarizing the density ratio objects

The `densityratio` package contains several functions to help interpreting the output of each estimation method. The `summary()` function provides a brief summary of the estimated object, including the optimal hyperparameter values, the number of inducing points and the divergence between the numerator and denominator samples.

    summary(dr)
    #>
    #> Call:
    #> ulsif(df_numerator = high_school, df_denominator = no_high_school,     parallel = TRUE, nthreads = 10)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>
    #> Optimal sigma: 1.387557
    #> Optimal lambda: 0.6951928
    #> Optimal kernel weights: num [1:201] 0.16916 0.00301 0.05082 -0.00791 0.03594 ...
    #>
    #> Pearson divergence between P(nu) and P(de): 0.4149
    #> For a two-sample homogeneity test, use 'summary(x, test = TRUE)'.

Which divergence measure is reported depends on the method used. The `ulsif()`, `kmm()`, `lhss()` and `spectral()` methods report the Pearson divergence, while the `kliep()` method reports the Kullback-Leibler divergence. The `naive()` method reports the squared difference between the average log density ratio of the numerator samples and the average log density ratio of the denominator samples. As these divergences are hard to interpret in isolation and provide relatively little information on where the two distributions differ, we recommend to use the plotting functionality that is described in the next section. Alternatively, if the goal is to evaluate the distributions more formally, the divergence measures can be used for two-sample homogeneity testing.

### Two-sample homogeneity testing

Two sampling testing functionality is readily implemented in the `summary()` function, and can be used by setting `test = TRUE`. For each estimation function, homogeneity testing is performed using a permutation test on the divergence statistic, and the corresponding \(p\)-value is reported.

    summary(dr, test = TRUE)
    #>
    #> Call:
    #> ulsif(df_numerator = high_school, df_denominator = no_high_school,     parallel = TRUE, nthreads = 10)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>
    #> Optimal sigma: 1.387557
    #> Optimal lambda: 0.6951928
    #> Optimal kernel weights: num [1:201] 0.16916 0.00301 0.05082 -0.00791 0.03594 ...
    #>
    #> Pearson divergence between P(nu) and P(de): 0.4149
    #> Pr(P(nu)=P(de)) < .001
    #> Bonferroni-corrected for testing with r(x) = P(nu)/P(de) AND r*(x) = P(de)/P(nu).

This permutation test shows that it is highly unlikely that the high school and no high school samples are drawn from the same distribution. Intuitively, this is no surprise, as it is quite likely that the two samples have different marginal distributions for the features.

The permutation test can also be executed in parallel, by setting `parallel = TRUE`. Parallel computation is performed using the `pbreplicate` function from the `pbapply` package. To specify the cluster, users can directly create and supply a cluster using the `parallel::makeCluster()` functionality (see the documentation in the `pbapply` and `parallel` packages). If no cluster is supplied, a default cluster is created using the number of available cores minus one.

    summary(dr, test = TRUE, parallel = TRUE, cluster = 12)
    #>
    #> Call:
    #> ulsif(df_numerator = high_school, df_denominator = no_high_school,     parallel = TRUE, nthreads = 10)
    #>
    #> Kernel Information:
    #>   Kernel type: Gaussian with L2 norm distances
    #>   Number of kernels: 200
    #>
    #> Optimal sigma: 1.387557
    #> Optimal lambda: 0.6951928
    #> Optimal kernel weights: num [1:201] 0.16916 0.00301 0.05082 -0.00791 0.03594 ...
    #>
    #> Pearson divergence between P(nu) and P(de): 0.4149
    #> Pr(P(nu)=P(de)) < .001
    #> Bonferroni-corrected for testing with r(x) = P(nu)/P(de) AND r*(x) = P(de)/P(nu).

# Visualizing the density ratio

When mere testing is not sufficient, visualizing the density ratio can provide more tangible information on the differences between the two distributions. The `plot()` function summarizes the model output by displaying the estimated density ratio values.

    plot(dr)
    #> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Density ratio values for the high school and no high school samples.

The figure shows the distribution of density ratio values for the numerator and denominator samples (on a log-scale, to improve symmetry). This allows to evaluate how dissimilar the two distributions are: the more the density ratio values are separated, the more the two groups stand apart. That is, if the two distributions are very similar, the density ratio will be close to zero on the log-scale (one on the original scale) for all samples, regardless of the group, but more importantly, the height of the bars will be similar for both groups at all density ratio values.

Additionally, it is possible to plot the estimated density ratio values against the features, both univariate and bivariate. This may help to identify features, or combinations of features, on which the two groups differ. For example, below we plot the density ratio values against `kid_score` and `mom_iq` in a grid (by specifying `grid = TRUE`, the default `grid = FALSE` produces a list with separate figures for each feature).

    plot_univariate(dr, c("kid_score", "mom_iq"), grid = TRUE)

Univariate plots of `kid_score` and `mom_iq` against the estimated density ratio values for the two groups of samples.

This figure shows that the density ratio values do not seem to vary so much with `kid_score`, but do differ substantially with `mom_iq`. That is, for `kid_score`, the samples of the two groups mix quite well, although there might be slightly more `no high school` samples with a lower `kid_score`, resulting in slightly lower density ratio values in this region. For `mom_iq` this difference is much more pronounced: lower values of `mom_iq` are typically associated with lower density ratio values, indicating that there are typically more `no high school` samples with lower `mom_iq` values.

Similar figures can be created for bivariate features, by using the `plot_bivariate()` function, which allows to visualize the density ratio values against two features at the same time. Now, the density ratio values are mapped to the color scale.

    plot_bivariate(dr, c("kid_score", "mom_iq", "mom_age"), grid = TRUE)

Bivariate scatter plots for `kid_score`, `mom_iq` and `mom_age`, with density ratio values mapped to the color scale.

This figure shows that density ratio values are especially small for observations with low values for both `mom_iq` and `kid_score`. The values do not differ so much in the direction of `mom_age`.

# Predicting the density ratio for new cases

Under the hood, the plotting functionality makes extensive use of the `predict()` function, which takes a fitted density ratio object and a new data set as input, and returns the predicted density ratio values for these new cases. By default, the `predict()` function predicts the density ratio values for the numerator samples, but this can be changed by providing another data set in the `newdata` argument.

    predict(dr) |> head()
    #> , , 1
    #>
    #>           [,1]
    #> [1,] 1.4928033
    #> [2,] 2.2715889
    #> [3,] 2.5855672
    #> [4,] 1.9345356
    #> [5,] 1.9679638
    #> [6,] 0.7939163

By default, the `predict()` function uses the optimal hyperparameter values as determined by cross-validation (if cross-validation is performed, otherwise it returns the predicted density ratio values for all parameters). By changing the parameters `sigma` (and, if applicable, `lamdba` or `m`), it is also possible to predict the density ratio using different parameters. These values can alternatively be set to “all”, which returns the predicted density ratio values for all parameter values, or a specific value, which returns the predicted density ratio values for that specific hyperparameter value.

# Conclusion

This vignette showed how the `densityratio` package can be used to compare samples from two distributions. This allows for two sample homogeneity testing, and to visualize the differences between the two distribution.

# References

Huang, J., Smola, A., Gretton, K., Borgwardt, K. M., & Schölkopf, B. (2007). Correcting sample selection bias by unlabeled data. Advances in Neural Information Processing Systems, 19, 601-608.

Izbicki, R., Lee, A., & Schafer, C. (2014). High-dimensional density ratio estimation with extensions to approximate likelihood computation. Proceedings of Machine Learning Research, 33, 420-429.

Kanamori, T., Hido, S., & Sugiyama, M. (2009). A least-squares approach to direct importance estimation. Journal of Machine Learning Research, 10, 1391-1445.

Sugiyama, M., Nakajima, S., Kashima, H., Von Bünau, P., & Kawanabe, M. (2007). Direct importance estimation with model selection and its application to covariate shift adaptation. Advances in Neural Information Processing Systems, 20.

Sugiyama, M., Yamada, M., Von Bünau, P., Suzuki, T., Kanamori, T., & Kawanabe, M. (2011). Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. Neural Networks, 24(2), 183-198.