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\title{Metric Learning for Kernel Ridge Regression: Assessment of Molecular Similarity}
\author{Raimon Fabregat\textsuperscript{1,\ddag}, Puck van Gerwen\textsuperscript{1,2,\ddag}, Matthieu Haeberle\textsuperscript{1,3,\ddag}, Friedrich Eisenbrand\textsuperscript{3} and Cl\'emence Corminboeuf\textsuperscript{1,2}}
\address{\textsuperscript{1}Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, \'Ecole Polytechnique F\'ed\'erale de Lausanne, 1015 Lausanne, Switzerland\\
\textsuperscript{2}National Center for Competence in Research-Catalysis (NCCR-Catalysis), Zurich, Switzerland \\
\textsuperscript{3}Chair of Discrete optimisation, \'Ecole Polytechnique F\'ed\'erale de Lausanne, 1015 Lausanne, Switzerland\\
\textsuperscript{\ddag}These authors contributed equally to this work.
The supervised and unsupervised machine learning kernel-based algorithms that dominate in physical sciences rely upon the notion of \textit{similarity}. Pre-defined distance metrics, \textit{e.g.}, the Euclidean or Manhattan distance, especially in combination with high-dimensional feature vectors, result in a polluted notion of similarity.
are widely used to estimate an unknown real-valued function from a high-dimensional feature space. Typically, these methods are based on a distance-based kernel. The standard Euclidean distance is often not reflecting the
differences in the observed function value as signals in the data might be
less relevant. An elegant approach to surmount this shortcoming is \emph{metric learning} to find an appropriate transformation of the training set, consisting
of rotation and scaling. The state-of-the-art of metric learning is based on the \emph{Nadaraya-Watson} estimator and the approximation of an optimal corresponding
transformation. The Nadaraya-Watson estimator is often observed to be inferior to
\emph{Kernel Ridge Regression}, especially for prediction tasks in the area of computational chemistry. In this paper, we propose a method for metric learning that is based on Kernel Ridge Regression instead. While the loss-gradient is considerably more involved, this method shows superior performance for important regression tasks in computational chemistry such as the prediction of atomisation energies of molecules in the QM9 set. This is also visible in standard 2D-projections of the transformed feature
%The supervised and unsupervised machine learning kernel-based algorithms that are broadly used in physical sciences rely upon the notion of \textit{similarity}. Pre-defined distance metrics, \textit{e.g.}, the Euclidean or Manhattan distance, especially in combination with high-dimensional feature vectors, result in a polluted notion of similarity. An elegant solution is provided by metric learning approaches, which allow for a supervised re-definition of the distance metric adapted to the targeted property. Despite promising performance, metric learning has been primarily applied to clustering problems. The only algorithm for regression relies upon the Nadaraya–Watson estimator, which is not suited for regression tasks. Here, we propose an algorithm - Metric Learning for Kernel Ridge Regression (MLKRR) - for prediction based on \emph{regression} and \emph{metric learning}. We demonstrate improved performance for both supervised and unsupervised prototypical molecular regression tasks such as the prediction of atomisation energies of molecules in the QM9 set and the 2D-projection of their feature space.
Supervised and unsupervised machine learning methods have demonstrated tremendous capabilities in the physical sciences in the last decade \cite{von2020introducing, pyzer2020welcome, huang2021review, musil2021physics, butler2018machine, unke2021machine, aspuru2018matter, kitchin2018machine, carleo_review_2019, ceriotti_unsupervised_2019, glielmo2021unsupervised, cheng2020mapping}. Supervised models aim to find a mathematical relationship between input data and target properties. Kernel-based regression methods, \textit{e.g.}, Kernel Ridge Regression (KRR) and Gaussian Process Regression (GPR), in particular have dominated the field \cite{huang2021review, musil2021physics, deringer2021gaussian, kamath2018neural, faber_prediction_2017} due to their data efficiency and interpretability. Unsupervised machine learning algorithms instead provide a means to find underlying structure in unlabelled data. Dimensionality reduction methods like \textit{t}-Distributed Stochastic Neighbor Embedding (\textit{t}-SNE) \cite{van2008visualizing}, PCA, Multidimensional scaling or Isomap \cite{tenenbaum2000global} are omnipotent in the physical sciences \cite{ceriotti_unsupervised_2019, glielmo2021unsupervised, cheng2020mapping} as they enable the visualisation of an otherwise uninterpretable high-dimensional feature space. A central concept to many algorithms belonging to both supervised and unsupervised approaches is that of \textit{similarity}: the underlying assumption being that points close in the feature space should be close in property space. Similarity between two elements in a feature space is often constructed using a kernel, a function that outputs a scalar value between 1 (identical) or 0 (entirely dissimilar). The most widely used kernel functions contain a decreasing exponential of a metric, such as the Euclidean distance (Gaussian kernel) or the Manhattan distance (Laplacian kernel). Alternative measures such as the linear kernel (the dot product between the feature vectors), or the cosine similarity also exist.\\
Such pre-defined distance metrics pose problems, especially when a feature space is high dimensional and possibly polluted with irrelevant or redundant features. Physics-based molecular representations typically used in chemistry, materials science and condensed matter physics \cite{musil2021physics, huang2021review} illustrate these limitations. These representations are designed to encode all the necessary information to unequivocally describe a molecular structure and generally take the form of non-linear functions of atom types and positions that are directly injected into the ML architecture. Popular examples of such representations are SLATM \cite{huang2020quantum}, SOAP \cite{bartok_2013_soap} and FCHL \cite{faber_alchemical_2018, christensen2020fchl}, among many others \cite{rupp2012fast, huang2016communication, musil2021physics}. They are used to develop predictive models for a variety of properties \cite{rupp2012fast, von2018quantum,christensen2020fchl,li2015molecular, chmiela_machine_2017, chmiela_towards_2018,bereau_transferable_2015,grisafi2018symmetry, wilkins_accurate_nodate,grisafi2018transferable,bartok2010gaussian,fabrizio2019electron,westermayr_machine_2019} or to facilitate the visualisation and interpretation of the chemical space \cite{ceriotti_unsupervised_2019, glielmo2021unsupervised, cheng2020mapping}. Yet, these representations do not provide a universally meaningful measure of molecular similarity, resulting in sub-optimal performance when more challenging properties are targeted \cite{gallarati2021reaction}. This deficiency is intrinsically connected to the use of pre-defined metrics that treats all the features on a equal footing regardless of their redundancy or relevance to a particular task. For instance, the Euclidean distance used in the Gaussian kernel ($d_E(\textbf{a},\textbf{b}) = \sqrt{\sum_i(a_i - b_i)^2} = ||\textbf{a} - \textbf{b}||_2$) is dominated by features with high variance, which we found \cite{gallarati2021reaction} to not necessarily correlate with predictive capabilities. While redundancy in a feature space is easily eliminated by using unsupervised linear dimensionality reduction algorithms such as PCA and CUR decomposition \cite{mahoney2009cur}, adapting the relevance of descriptive features in a metric for a particular target requires a supervised approach \cite{kuhn2013applied}.\\
A family of methods called metric or similarity learning exists specifically for this purpose. Metric learning (or distance metric learning, or similarity learning) \cite{kulis2012metric, yang2006distance} provides an elegant solution to adapt the notion of similarity (and distance) according to the target property. Thus, similarity can be transformed from a global concept (two molecules are similar based on their structure) to an application-specific concept (two molecules are similar, in the context of their properties). This conceptual shift enables a more specific definition of similarity, which naturally circumvents many of the pitfalls of pre-defined similarity notions, as well as enabling more accurate machine learning models. Despite its enormous promise, metric learning algorithms have remained largely absent in the chemical domain, except for a few examples on graph-structured data \cite{coupry2022application}. This is likely because most frameworks are designed for clustering or classification \cite{Kilian2007, koch2015siamese, hoffer2015deep} tasks, while chemistry is dominated by regression tasks. We note that alternatives to metric learning exist with the same goal of optimising representations for a specific task, for example in the GPR framework \cite{gpr_rasmussen}, in (supervised) contrastive learning \cite{chopra2005learning, oh2016deep, khosla2020supervised, stark20213d} or using deep belief networks \cite{larochelle2007empirical, nasser2020improved}.\\
One of the few examples of metric learning algorithms for regression is the Metric Learning for Kernel Regression (MLKR) developed by Weinberger and Tesauro \cite{Kilian2007}. MLKR learns a linear transformation matrix that transforms the distance metric between points in order to optimise the prediction of a particular target in a kernel regression. However, the kernel regression used in MLKR employs the non-parametric Nadaraya–Watson (NW) estimator, which, as demonstrated in this work, is less suited than the KRR estimator for regression tasks. The NW estimator, which is essentially a Nearest-Neighbours estimator, evaluates similarity between points in a relative manner using a notion that is dependent on the distribution of points within a particular dataset. In KRR, the notion of similarity is instead absolute, \textit{i.e.}, independent of the distribution of data. This is an important distinction if the aim is to accurately predict molecular properties. Within this context, identifying the trial instance/molecules that are \textit{close} (in the absolute sense) to the molecular system of interest is more relevant than identifying the \textit{closest} points that potentially correspond to fairly dissimilar molecules. \\
In order to enable metric learning in the KRR framework, we propose a new algorithm - Metric Learning for Kernel Ridge Regresion (MLKRR) - which modifies the MLKR formalism for the KRR estimator. While this is not the first metric learning algorithm for KRR \cite{zhu2018beyond, kulis2012metric}, our implementation is different, and importantly, showcased for and made readily accessible to the physical science community. As such, MLKRR could enable the wide-spread adoption of metric learning for kernel-based machine learning tasks in the physical sciences, thereby re-defining the fundamental notion of similarity upon which they depend. We demonstrate the performance of the MLKRR algorithm on the prototypical task of regressing atomisation energies of small molecules in the QM9 dataset \cite{ramakrishnan_quantum_2014}.
\section{Metric learning}
% either same distance, new space or new distance, same space
Metric learning algorithms transform a feature space in order to construct a distance function that minimises the prediction error of a specific property. They learn a linear transformation matrix $A$ that generates a \emph{Mahalanobis distance} \cite{mahalanobis1936generalized} ($d_M$) on the original space
% d_E(\textbf{a}',\textbf{b}') = ||\textbf{a}' - \textbf{b}'||_2 =
d_M(\textbf{a},\textbf{b}) = ||\mathbf{A}\textbf{a} - \mathbf{A}\textbf{b}||_2 = ||\mathbf{A} (\textbf{a} - \textbf{b})||_2.
% rotation and scaling
Since $A^TA$ is positive semidefinite, there exists an orthogonal
matrix $Q$ as well as non-negative diagonal matrix $D$ such that
$A^TA = Q^T D Q$, see \cite{Kilian2007}. Thus, the transformation of
the feature space with $A$ corresponds to a rotation and a scaling of
the components. In this way, distances of points can be increased or
decreased, depending on their relevance for the target property.
\subsection{Metric Learning for Kernel Regression}\label{subsec:mlkr}
Weinberger and Tesauro \cite{Kilian2007} propose the following method to apply the transformation matrix $A$ in the context of prediction.
Suppose that the data points are $x_i \in \mathbb{R}^d$ and the predictions are $y_i \in \mathbb{R}$, $i=1,\dots,n$. The transformation is used in the \emph{Gaussian kernel} as follows
k(x_i, x_j) = %\frac{1}{\sqrt{2\pi} \sigma} e^{-\frac{d_M(x_{i}, x_{j})^2}{\sigma^2}} =
\frac{1}{\sqrt{2\pi} \sigma} e^{-\frac{||\mathbf{A} (x_i - x_j)||_2^2}{\sigma^2}}
% move 1/Z to when it is needed
The authors rely on the \emph{Nadaraya–Watson estimator (NW)}
\widehat{y}_i = \frac{\sum_{j \neq i} y_j k_{ij}}{\sum_{j \neq i} k_{ij}}. %= \frac{1}{Z_i}\sum_{j \neq i} y_j k_{ij},
The prediction of a new data point $x$ is then
f(x) = \widehat{y} = \frac{\sum_{j} y_j k(x,x_j)}{\sum_{j}k(x,x_j)}
The learning of the matrix $A$ is expressed as an optimisation problem. The goal is to minimise the residual sum of squares $\mathcal{L} := \sum_{i}(y_i - \widehat{y_i})^2$. The optimisation then relies on the gradient of the loss with respect to the transformation matrix $A$
\frac{\partial \mathcal{L}}{\partial A} = \frac{4}{\sigma^2}A \sum_i (\widehat{y}_i - y_i) \frac{1}{Z_i} \sum_{j \neq i} (\widehat{y}_i - y_j) k_{ij} x_{ij}x_{ij}^T,
where $x_{ij} := x_i - x_j$ and $Z_i := \sum_{j \neq i} k_{ij}$.
\subsection{Metric Learning for Kernel Ridge Regression}
To enable metric learning in kernel ridge regression tasks omnipresent in the physical sciences, we introduce Metric Learning for Kernel Ridge Regression (MLKRR), which uses the standard parametric \emph{KRR estimator} instead:
\widehat{y}_i = \sum_j \alpha_j k(x_i, x_j)
The prediction of a new data point $x$ is then:
f(x) = \widehat{y} = \sum_j \alpha_j k(x, x_j)
The coefficients $α_j$ that minimise the sum of squares $\mathcal{L}_\alpha := \sum_{i}(y_i - \widehat{y}_i)^2$ are obtained by solving the linear equation \cite{welling_kernelridge}:
\boldsymbol{\alpha} = (K + \lambda I)^{-1} \boldsymbol{y}
where $K ∈ ℝ^{n ×n}$ is the matrix defined my the Gaussian kernel $k(x_i,x_j)$, $\lambda$ is a small regularisation parameter and $I$ is the identity matrix. In classical KRR, the $\boldsymbol{\alpha}$ coefficients are optimised on fixed kernel. In the MLKR approach described above on the other hand, only the matrix $A$ is optimised. The novelty of our method \cite{kulis2012metric} lies in the specific \emph{combination} of both principles. We now explain the two-step procedure that optimises the estimator and then the metric.\\
We first sample $n_\alpha$ data points $\{ \xai{i} \}_{i=1}^{n_\alpha}$ and their corresponding labels $\{ \yai{i} \}_{i=1}^{n_\alpha}$ from our dataset in order to compute the estimator from the coefficients $\boldsymbol{\alpha}$. It follows from the expression \eqref{eq:krr} above that $\boldsymbol{\alpha}$ may be written in terms of the matrix $A$, hidden in the kernel matrix $K_{ij} := k(\xai{i}, \xai{j})$. Once the estimator is found, we use its predictions \eqref{eq:pred-krr} in order to optimise the matrix $A$. For this, a new loss function is considered on new data points. We sample $n_A$ new data points $\{ \xAi{i} \}_{i=1}^{n_A}$ with their corresponding labels $\{ \yAi{i} \}_{i=1}^{n_A}$. The predictions of these points are hence given by
\widehat{y}^{(A)}_i := \sum_j \alpha_j k(x^{(A)}_i, x^{(\alpha)}_j),
which in turn yield the second loss function to minimise $\mathcal{L}_A := \sum_{i}(y^{(A)}_i - \widehat{y}^{(A)}_i)^2$. The computation of the matrix $A$ is done by gradient descent using the gradient $\nabla_A\mathcal{L}_A$. To express the gradient in a closed form, we introduce the following notation.\\
The predictions \eqref{eq:pred-A} impose the definition of the additional kernel matrix $Q_{ij}:=k(\xAi{i}, \xai{j})$. Further, we set $\Xa$ and $\XA$ the matrices whose rows are the data points of corresponding superscript. Let also $\ya$ and $\yA$ be the vectors whose entries are the property labels of corresponding superscript. The gradient $\nabla_A\mathcal{L}_A$ is now given by
\dA{\mathcal{L}_A} = &-\frac{4}{\sigma^2} A \left(-\XA^T W \Xa - \Xa^T W^T \XA + \XA^T R \XA + \Xa^T S \Xa\right) \\
&+ \frac{4}{\sigma^2} A \Xa^T \left(-\tilde{W} - \tilde{W}^T + \tilde{R} + \tilde{S}\right)\Xa,
where $W_{ij} := (\yh^{(A)}_i - \yAi{i}) \alpha_j Q_{ij}$, $\tilde{W}_{ab} := K_{ab} \alpha_b \left[(K+\lambda I)^{-T} Q^T (\yh^{(A)} - \yA) \right]_{a}$. The diagonal matrices $R, S, \tilde{R}, \tilde{S}$ are the vertical and horizontal sums of $W$ and $\tilde{W}$, that is $R_{ii} := \sum_j W_{ij}$, and $S_{jj} := \sum_i W_{ij}$. More on the definitions of these matrices, together with the proof of correctness of the expression is given in the appendix. \\
Figure \ref{fig:mlkrr} illustrates the MLKRR algorithm at work. Initially, there is no correlation between the two principal components (\textit{i.e.} high-variance components) of a high dimensional dataset and the target property. The MLKRR algorithm learns a transformation matrix $A$ by minimising the squared distance between the predicted target property and the property labels of training data. The matrix $A$ redefines the distance metric on the original feature space. As illustrated in the right panel of Figure \ref{fig:mlkrr}, the high-variance components of the feature space now correlate with the target property. We note that here the matrix $A$ is square such that the transformation rotates and scales the components, but it could be non-square to reduce the dimensionality of the feature space \cite{kulis2012metric}.\\
We implemented MLKR and MLKRR in a python module found at \texttt{} based on the python library \texttt{metric-learn} \cite{de2020metric}.
\caption{This figure shows the result of the transformation of the points, carried out by the MLKRR algorithm on a training set that is divided into two sets labeled with $α$ and $Α$. The colors of the points represent the corresponding function values. The projection of the points with standard PCA does not show a correlation of the value with the first principal components, while the PCA projection of the points (PCA') that have been transformed with $A$ illustrate such a correlation. The transformed point-set features are less noisy and the relevant signals are amplified.
\subsection{Conceptual comparison of MLKR and MLKRR}
The different nature of the similarity used by the KRR and NW estimators affects their respective regression performance. Given a datapoint $x$ in two different data distributions, the KRR definition of similarity between $x$ and a neighbour $a$ is independent of any other points in the distribution. On the other hand, the NW definition of similarity adapts to the distribution of data, so that a point $x$ is similar to $a$ if $a$ is the closest point to $x$ (see Figure \ref{fig:krr_vs_nw}a). Effectively, functions regressed with NW result in piece-wise surfaces resembling Voronoi diagrams, where each region of the feature space is dominated by the nearest data point. Alternatively, KRR generates surfaces that transition smoothly, which generally results in superior prediction performance. This is clearly observed in Figure \ref{fig:krr_vs_nw}b, which shows the result of a simple regression on a 2D feature space.
\caption{a) Depiction of the different approaches to define similarity between KRR and NW. b) Target function depending on two features and the corresponding regressions based on the KRR and the NW estimators. The two models were trained on a limited sample of data points marked as crosses in the first plot. }
\section{Computational details}
All models were built using a random subset of 24,000 molecules from the QM9 dataset \cite{ramakrishnan_quantum_2014} of 134,000 three-dimensional structures and corresponding atomisation energies of small drug-like molecules (up to 9 heavy atoms). The initial set was then split into 20,000 for training, 2,000 for testing (fitting hyperparameters for the KRR, and tracking the accuracy of the models throughout optimisation) and 2,000 for validation (used for the out-of-sample error in the learning curves).
\subsection{FCHL representation}
Molecules are represented using the Faber-Christensen-Huang-Lilienfeld representation \cite{christensen2020fchl} (the 2019 version) as implemented in the \texttt{qml} python package \cite{qml_code}, but we note that any other representation could have been used. The default settings were used for all of the FCHL19 parameters. Local representations were converted to global ones by summing all of the atomic contributions, such that the eventual representations were a vector consisting of 720 features.
\subsection{Optimisation details}
The MLKR and MLKRR algorithms were optimised for enough time to reach convergence, which was around 2,000 steps in both cases (see Figure \ref{fig:optimisation}). The function \texttt{minimise} of the SciPy\cite{jones2001scipy} library was used to optimise the matrix $A$, which itself uses the L-BFGS-S\cite{boyd2004convex} algorithm. $A$ was initialised as the identity matrix. For the MLKRR, we used $n_\alpha$ = $n_A$, although some of our tests suggest that $n_\alpha < n_A$ could be superior. In order to reduce overfitting for the MLKRR, the training data was reshuffled into $A$ and $\alpha$ sets every 30 optimisation steps. The starting kernel width $\sigma$ for the MLKRR was $\sigma = 55$, and the regularisation parameter was fixed at $\lambda = 1e^-9$. These parameters are the optimal values for the standard KRR, which were optimised using a grid search on the test set.
\section{Results and Discussion}
\subsection{Learning transformation matrices}
Training the MLKR and MLKRR algorithms consists of optimizing the relevant transformation matrices $A$. Figure \ref{fig:optimisation} illustrates the evolution of the optimisation process for the two algorithm. Despite the noisier optimisation of the MLKRR, which is a result of the periodic reshuffling of the datasets to optimise both $A$ and $\alpha$, both algorithms converge after approximately 2000 steps. \\
\caption{Learning curves showing the evolution of the Mean Absolute Error (MAE) throughout the optimisation process for our implementations of Metric Learning for Kernel Regression (MLKR) and Metric Learning for Kernel Ridge Regression (MLKRR). Both converge after around 2000 steps.}
After the optimisation process, we obtain the optimised transformation matrices $A$, which are visualised in Figure \ref{fig:amats}. Both matrices $A_{\mathrm{MLKR}}$ and $A_{\mathrm{MLKRR}}$ contain many non-zero values, both along the diagonal and in off-diagonal terms. Modifications to diagonal terms are effectively a re-weighting of the original features, akin to the application-based re-weighting of terms by Ceriotti and co-workers \cite{willatt_feature_2018}. Here however, the re-weighting is entirely learned by the algorithm. Diagonal terms $A_{ii}$ where $|A_{ii}|>1$ indicate that a heigher weight is applied to specific features, whereas $|A_{ii}|<1$ indicate that a lower weight is applied. $A_{ii}=0$ indicates that the feature is dropped entirely, reducing the dimensionality of the feature space. Off-diagonal terms $A_{ij}, i\neq j$, are linear combinations of the original features. The highly correlated nature of the FCHL features is illustrated by the fact that the transformation matrices are rather smooth (shown in the panels on the right of Figure \ref{fig:amats}). After convergence, the largest terms in the transformation matrices are the diagonal values. This is partially due to the fact that the initial guess for the optimisation process is the identity matrix. It also indicates that the original features are useful to predict the target property, just with an appropriate re-weighting (\textit{i.e.} a selection and amplification of relevant features for the target property). The selection of off-diagonal terms acts to combine features that are originally correlated, \textit{i.e.} generating new features. Thus, the metric learning process naturally applies a feature selection and extraction protocol for the target property.
\caption{Colormaps representing the learned transformation matrices $A$ for MLKR ($A_{\mathrm{MLKR}}$) and MLKRR ($A_{\mathrm{MLKRR}}$). The lefthand panels illustrate all matrix elements, whereas the righthand panels illustrate zoomed-in components. Both transformation matrices amplify the diagonal components in order to select relevant features for the target property, as well as choosing off-diagonal components to reduce the number of redundant features. The color scale is capped in the range [-2, 2] to facilitate the visualisation of the off-diagonal elements (the highest absolute values of the matrices are below 5).}
\subsection{Improving predictions of atomisation energies of QM9 molecules}
With our trained transformation matrices in hand, we now proceed to evaluating the new feature space for the prediction of atomisation energies of QM9 molecules. As we showed in our previous work \cite{gallarati2021reaction}, unless modified by feature selection or metric learning protocols, feature variance is not necessarily correlated to predictive capabilities. Here, since we have modified the feature space and corresponding distance metric, we expect the variance to again correlate with the relevance of features for a target property. In Figure \ref{fig:variances}, the variance of the original FCHL features and modified FCHL\textsubscript{MLKR} and FCHL\textsubscript{MLKRR} features is shown. The original and transformed features do not represent exactly the same information, as the transformed features are constructed as a linear combination of the original ones. Nevertheless, they are still the dominant components, as seen in the diagonal from the $A$ matrices in Figure \ref{fig:amats}. As observed, FCHL\textsubscript{MLKRR}, and to a lesser extent, FCHL\textsubscript{MLKR} makes use of almost all of the feature space. This suggests that the metric learning procedure effectively manipulates all of the features to eliminate irrelevance and redundancy. \\
\caption{Feature variances of the original FCHL and the metric-learned alternatives (FCHL$_{\text{MLKR}}$ and FCHL$_{\text{MLKRR}}$) on the QM9 dataset (left), with a zoom on the [0,5] range of the $y$ axis
The goal of the metric learning procedure is to improve predictions on the target property. In Figure \ref{fig:learning}, we compare the relative capabilities of the distance metric learned by MLKR and MLKRR to improve predictions in a KRR model. The learning curves illustrate the evolution of the MAE with increasing training data. In the left panel of Figure \ref{fig:learning}, we observe that the metric obtained with MLKRR offers a significant improvement over the original. The learning curve shows a faster decrease and the MAE of the final model is $\sim$30\% lower. On the other hand, the metric obtained with MLKR in fact performs worse than the original one. The learning curve is shifted upwards and the performance of the final model is $\sim$10\% worse. This suggests that the distance metric learned by the MLKR framework does not necessarily improve its capabilities for KRR. Since the MLKR optimisation procedure relies on the NW estimator, we suspect that the nature of this estimator compared to that of the KRR results in a metric that is less suitable for regression tasks.
% make it more clear that FCHL blue is KRR no metric learning
% can we show that NW is subset of KRR solns ?
\caption{Learning curves with the evolution of the MAE on a validation set of 2k structures obtained using a KRR model (left) and distribution of errors in the same set obtained with a KRR constructed using 10000 data points from the training set (right), generated using the FCHL representation and the altered variations obtained with MLKR (FCHL\textsubscript{MLKR}) and MLKRR (FCHL\textsubscript{MLKRR}).}
\subsection{Dimensionality reduction}
We illustrate the effects of our metric-learned similarity measures using t-SNE and PCA models by constructing 2D projections of the QM9 dataset using the feature spaces spanned by FCHL, FCHL$_{\text{MLKR}}$ (FCHL $\cdot \textbf{A}_{\text{MLKR}}^T$) and FCHL$_{\text{MLKRR}}$ (FCHL $\cdot \textbf{A}_{\text{MLKRR}}^T$). t-SNE projects data points in a $N$-dimensional space ($N$=2 in general) by minimising the difference of the pairwise affinity between points in the original and new spaces. The definition of affinity between two points in t-SNE is:
p_{ij}= e^{- \gamma d(x_i, x_j)^2} / \sum_{k \neq i} e^{- \gamma d(x_i, x_k)^2}
akin to the weights of the kernel average for the predictions in the NW estimator of MLKR (see equation \ref{eq:NW}). PCA instead performs the projection such that the new dimensions are those with the highest variance in the original data. It does not rely on an explicit distance as in the \textit{t}-SNE, but still selects the principal components by evaluating $\mathbf{X}^T \mathbf{X}$ (where $\mathbf{X}$ is the feature vector), which is analogous to a distance. \\
The \textit{t}-SNE and PCA maps obtained with FCHL, FCHL$_{\mathrm{MLKR}}$ and FCHL$_{\mathrm{MLKRR}}$ are shown in Figure \ref{fig:tsnes}, where each of the points representing a molecule are colored by their excess atomisation energy. Distinct differences between the maps are observed. The t-SNE and PCA maps obtained with the FCHL$_{\mathrm{MLKR}}$ features tend to organise local clusters. Yet, there is no global coherence between the organisation of the projected points and the colored target property. Instead, the FCHL$_{\mathrm{MLKRR}}$ maps resemble more closely those of the original FCHL, albeit organised into larger regions offering a better coherence with respect to the target property. Overall, MLKRR improves the organisation of data to uncover the patterns (Fig. \ref{fig:tsnes}) relevant to optimise the prediction of the targeted properties (Fig. \ref{fig:learning}). \\
\caption{2D projections of FCHL, FCHL$_{\text{MLKR}}$ and FCHL$_{\text{MLKRR}}$ constructed using t-SNE and PCA algorithms (first and second row, respectively). The colormap shows the excess atomisation energy for each compound.}
The proposed MLKRR algorithm offers additional functionalities beyond maximising prediction accuracy for specific applications. Careful analysis of the learned transformation matrices provide valuable insights as to why some representations behave better than others \cite{faber_prediction_2017}. In addition, the algorithm offers a mathematical route to construct a hierarchy of molecular representations adaptable and tailored to specific targets as an alternative to building representations encoding the relevant information in absolute terms \cite{musil2021physics}. Comparisons between the similarities and metrics learned for a wide range of applications might also uncover hidden trends useful to develop improved and more general molecular representations. Finally, MLKRR could also be exploited in transfer-learning or meta-learning approaches: a concept which has so far been limited to neural network applications \cite{brazdil2008metalearning}.
Similarity-based machine learning methods dominate in the physical sciences. Their dependence on a pre-defined distance metric is problematic in combination with high-dimensional feature vectors often containing irrelevant or redundant features. To address this shortcoming, we introduce an algorithm, Metric Learning for Kernel Ridge Regression (MLKRR), which re-defines the notion of similarity between points to optimise the prediction of specific target properties in kernel ridge regression tasks. MLKRR was shown to offer improved performance (30\%) on the prototypical regression task of atomisation energies of the QM9 dataset \cite{ramakrishnan_quantum_2014}, as well as generating more meaningful low-dimensional projections of transformed feature vectors. In addition, we illustrate why our MLKRR algorithm is more suited to the prediction of continuous target properties, as is typical in the physical sciences, than the related MLKR algorithm based on the Nadaraya–Watson (NW) estimator.
R.F. and C.C. acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant 817977). P.v.G. and C.C. acknowledge the National Centre of Competence in Research (NCCR) “Sustainable chemical process through catalysis (Catalysis)” of the Swiss National Science Foundation (SNSF, Grant No. 180544) for financial support. M.N.H., F.E. and C.C acknowledge the FSB Intrafaculty Seed Funding. We thank Alberto Fabrizio for helpful discussions and feedback on the manuscript.
\section{Appendix: Derivation of MLKR and MLKRR gradients}
\subsection{Derivation of the MLKR gradient in equation \eqref{eq:grad_mlkr}}
%Details on how to of the MLKR gradient are given hereafter.
Starting from the top, we find
\dA{\mathcal{L}} = 2 \sum_i (\yh_i - y_i) \dA{\yh_i},
where $\yh_i = \frac{1}{Z_i} \sum_{j\neq i} k_{ij} y_j$ and $Z_i = \sum_{j \neq i} k_{ij}$.
This naturally leads us to compute $\dA{\yh_i}$, and hence $\dA{k_{ij}}$.
Firstly, the kernel derivative is
\dA{k_{ij}} &= -\frac{1}{\sigma^2} k_{ij} \dA{}\left(d(x_i,x_j)^2\right),\\
&= -\frac{2k_{ij}}{\sigma^2} A x_{ij}x_{ij}^T,
where $x_{ij} := x_i - x_j$.
Consequently, one has
\dA{\yh_i} &= \frac{1}{Z_i} \sum_{j\neq i} \dA{k_{ij}} y_j - \frac{1}{Z_i} \yh_i \sum_{j \neq i} \dA{k_{ij}}, \\
&= - \frac{2}{\sigma^2 Z_i} A \sum_{j\neq i} k_{ij} y_j x_{ij} x_{ij}^T + \frac{2}{\sigma^2 Z_i} A \yh_i \sum_{j\neq i} k_{ij} x_{ij} x_{ij}^T, \\
&= \frac{2}{\sigma^2 Z_i} A \sum_{j\neq i} k_{ij} (\yh_i - y_j) x_{ij} x_{ij}^T.
From which the conclusion stems naturally.
\subsection{Derivation of the MLKRR gradient of equation \eqref{eq:grad}}
In the following, the derivation of the gradient \eqref{eq:grad} and its precise definition are given.
The notations defined in section \ref{subsec:mlkr} will be reused along with the subsequent new ones.
We consider two different kernel matrices: one between $\Xa$ and itself and one between $\XA$ and $\Xa$ (seen as $K$ and $Q$ respectively in equation \eqref{eq:grad}). We also denote by $H$ the regularised kernel defining the coefficients $\alpha$ for kernel ridge regression.
To simplify notations, we let the index $i$ range over $\{1, \dots, n_A \}$, while the indices $j, a$, and $b$ range over $\{1, \dots, n_\alpha\}$. We further lose the superscripts on $x$ and $y$ when the indices suffice to determine them, and use the same letter $K$ for both kernels.
k_{i,j} &:= \kernel{i}{j}, \qquad\qquad &&k_{j,j'} := \kernel{j}{j'}, \\
H &:= (K+\lambda I), &&\quad \ \alpha :=H^{-1} \ya.
% To further simplify notations, we let the index $i$ range over $\{1, \dots, n_A \}$, while the indices $j, a$, and $b$ range over $\{1, \dots, n_\alpha\}$. We also lose the superscripts on $x$ and $y$ when the indices suffice to determine them.
As above, we start from the following expression
\dA{\mathcal{L}} = 2 \sum_i (\yh_i - y_i) \dA{\yh_i},
where $\dA{\yh_i} = \sum_j \dA{}(\alpha_j k_{ij})$. Remembering that both $\alpha$ and $K$ depend on $A$, we therefore aim to compute $\dA{k_{ij}}$ and $\dA{\alpha_j}$.
Remember that the kernel derivative is already computed in \eqref{eq:kerngrad}.
\dA{k_{ij}} = -\frac{2k_{ij}}{\sigma^2} A x_{ij}x_{ij}^T,
where $x_{ij} := x_i - x_j$.
The second term requires treating the derivative of $\Hmo$, the details of which are spared. In fact, routine computation yields that
\dA{}(\Hmo)_{jj'} &= \frac{2}{\sigma^2} A \sum_{a,b} (\Hmo)_{ja} (\Hmo)_{bj'} k_{ab} x_{ab}x_{ab}^T,
and hence that
\dA{\alpha_j} &= \frac{2}{\sigma^2} A \sum_{a,b} (\Hmo)_{ja} k_{ab} \alpha_b x_{ab}x_{ab}^T.
Combining both derivatives gives the following expression for the gradient.
\dA{\mathcal{L}} &= 2 \sum_{i,j} (\yh_i - y_i) \left[\dA{k_{ij}} \alpha_j + \dA{\alpha_j} k_{ij}\right], \nonumber \\
&= -\frac{4}{\sigma^2} A \sum_{i,j} (\yh_i - y_i) \alpha_j k_{ij} x_{ij}x_{ij}^T \\
& \quad + \frac{4}{\sigma^2} A \sum_{a,b} k_{ab} \alpha_b \left[H^{-T} K^T (\yh - \yA)\right]_{a} x_{ab}x_{ab}^T.
We recall that $K$ has two different definitions depending on its indexing. To clarify, we set $K \in \R^{n_\alpha \times n_\alpha}$ the kernel between $\Xa$ and itself; and we set $Q \in \R^{n_A \times n_\alpha}$ the kernel between $\XA$ and $\Xa$.
The lemma below allows us to turn this expression into matrix form. Indeed, note that both sums are of the form $\sum_{i,j} W_{ij} (x_i - x_j)(x_i-x_j)^T$, where $x_i$ and $x_j$ are lines of $\XA$ or $\Xa$ depending on their index.
Consider two matrices $A$ and $B$ with lines $\{a_i\}$, $\{b_j\}$ of matching dimensions, and let $x_{ij} = a_i - b_j$. Set further the matrix
\Sigma = \sum_{i,j} W_{ij} x_{ij}x_{ij}^T,
with some coefficients $W_{ij}$ making up a matrix $W$. Then
\Sigma = -A^T W B - B^T W^T A + A^T R A + B^T S B,
where $R$ and $S$ are both diagonal matrices with $R_{ii} = \sum_j W_{ij}$, and $S_{jj} = \sum_i W_{ij}$.
Applying the lemma to both expressions in \eqref{eq:grad0} leads us to construct the matrices $W \in \R^{n_A \times n_\alpha}$ and $\tilde{W} \in \R^{n_\alpha \times n_\alpha}$ in the following way.
W_{ij} := (\yh_i - y_i) \alpha_j Q_{ij}, \\
\tilde{W}_{ab} := K_{ab} \alpha_b \left[H^{-T} Q^T (\yh - \yA)\right]_{a}.
Finally, the diagonal matrices $R, S, \tilde{R}, \tilde{S}$ given by the lemma conclude.
\begin{proof}[Proof of lemma \ref{lem:2}]
The right-hand side of \eqref{eq:1} splits into four sums which make up each term of the result.
\Sigma &= \sum_{i,j} W_{ij} (a_i - b_j)(a_i-b_j)^T, \\
&= \sum_{i} \left(\sum_j W_{ij}\right) a_i a_i^T + \sum_{j} \left(\sum_i W_{ij}\right) b_j b_j^T, \\
& \quad - \sum_{i,j} W_{ij} a_i b_j^T - \sum_{i,j} W_{ij} b_j a_i^T.
Note that the last two terms are transpose of each others so that only one has to be treated. Additionally, for a general matrix $M$ of appropriate dimensions, one can easily verify that
A^T M B = \sum_{i,j} M_{ij}a_i b_j^T.
Taking $M=W, R$, and $S$ yields the desired result.
% \subsection*{optimisation of MLKRR}
% Figure \ref{fig:shuffle} illustrates the training of the MLKRR with and without shuffling the training data.
% \begin{figure}[h!]
% \centering
% \includegraphics[width=1\textwidth]{Figures/optimisation_smooth.eps}
% \caption{Evolution of the MAE during the training of a MLKRR with shuffling (right) and without shuffling(left). As can be seen, without the shuffling the train MAE decreases monotonically while the test MAE eventually starts going up, indicating a possible overfitting. The shuffling prevents this, and at the same time generate a metric with a smaller gap between train and test MAE, which implies better generalisation.}
% \label{fig:shuffle}
% \end{figure}
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