End-to-end Symbolic Regression with Transformers
Abstract
Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difﬁcult task which usually involves a two-step procedure: predicting the "skeleton" of the expression up to the choice of numerical constants, then ﬁtting the constants by optimizing a non-convex loss function.
The dominant approach is genetic programming, which evolves candidates by iterating this subroutine a large number of times.
Neural networks have recently been tasked to predict the correct skeleton in a single try, but remain much less powerful.
In this paper, we challenge this two-step procedure, and task a Transformer to directly predict the full mathematical expression, constants included.
One can subsequently reﬁne the predicted constants by feeding them to the non-convex optimizer as an informed initialization.
We present ablations to show that this end-to-end approach yields better results, sometimes even without the reﬁnement step.
We evaluate our model on problems from the SRBench benchmark and show that our model approaches the performance of state-of-the-art genetic programming with several orders of magnitude faster inference.
Introduction
Inferring mathematical laws from experimental data is a central problem in natural science; having observed a variable y at n points { x i } i ∈ N n , it implies ﬁnding a function f such that y i
≈ f
( x i ) for all i ∈ N n .
Two types of approaches exist to solve this problem.
In parametric statistics (PS), the function f is deﬁned by a small number of parameters that can directly be estimated from the data.
On the other hand, machine learning (ML) techniques such as decision trees and neural networks select f from large families of non-linear functions by minimizing a loss over the data.
The latter relax the assumptions about the underlying law but their solutions are more difﬁcult to interpret and tend to overﬁt small experimental data sets, yielding poor extrapolation performance.
Symbolic regression (SR) stands as a middle ground between PS and ML approaches: f is selected from a large family of functions, but is required to be deﬁned by an interpretable analytical expression.
It has already proved extremely useful in a variety of tasks such as inferring physical laws [1, 2].
SR is usually performed in two steps.
First, predicting a “skeleton”, a parametric function using a pre-deﬁned list of operators – typically, the basic operations ( + , × , ÷ ) and functions ( sqrt , exp , sin , etc.).
It determines the general shape of the law up to a choice of constants, e.g. f ( x ) = cos( ax + b ) .
Then, the constants in the skeleton ( a, b ) are estimated using optimization techniques, typically the
Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS). [Figure] i [Figure]
s
[Figure] c o n d s ) [Figure] The leading algorithms for SR rely on genetic programming (GP).
At each generation, a population of candidates is predicted [Figure] and the ﬁttest ones are selected based on the data, and mutated to build the next generation.
The algorithm iterates this procedure until a satisfactory accuracy level is achieved.
While GP algorithms achieve good prediction accuracy, they are notably slow (see the Pareto plot of Fig. 1).
Indeed, the manually predeﬁned function space to search is generally vast, and each generation involves a costly call to the BFGS routine.
Also, GP does not leverage past experience: every new problem is learned from scratch.
Inference time, i.e. time required to output a satisfactory expression, for most GP algorithms is both long and unbounded (the longer, the better the results), therefore this property has been neglected by the SR community, however fast inference can be useful to improve search-based SR algorithms, e.g. by reducing search space or providing initial guesses, as well as to tackle practical applications with time constraints, e.g. control or reinforcement learning [3, 4].
Supervised training neural networks built for language modelling on large datasets of synthetic examples has recently been proposed for SR [5, 6].
These references follow the two-step procedure (predicting the skeleton then ﬁtting the constants) inherited from GP.
Once the model is trained, at inference, the skeleton is predicted via a simple forward pass, and a single call to BFGS is needed, thus resulting in a signiﬁcant speed-up compared to GP.
However, these methods are not as accurate as state-of-the-art GP, and have so far been limited to low-dimensional functions ( D ≤ 3 ).
We argue that two reasons underlie their shortcomings.
First, skeleton prediction is an ill-posed problem that does not provide sufﬁcient supervision: different instances of the same skeleton can have very different shapes, and instances of very different skeletons can be very close.
Second, the loss function minimized by BFGS can be highly non-nonconvex: even when the skeleton is perfectly predicted, the correct constants are not guaranteed to be found.
For these reasons, we believe, and will show, that doing away with skeleton estimation as a intermediary step can greatly facilitate the task of SR for language models.
Contributions In this paper, we train Transformers over synthetic datasets to perform end-to-end (E2E) symbolic regression: solutions are predicted directly, without resorting to skeletons.
To this effect, we leverage a hybrid symbolic-numeric vocabulary , that uses both symbolic tokens for the operators and variables and numeric tokens for the constants.
One can then perform a reﬁnement of the predicted constants by feeding them as informed guess to BFGS, mitigating nonlinear optimization issues.
Finally, we introduce generation and inference techniques that allow our models to scale to larger problems: up to 10 input features against 3 in concurrent works.
Evaluated over the SRBench benchmark [7], our model signiﬁcantly narrows the accuracy gap with state-of-the-art GP techniques, while providing several orders of magnitude of inference time speedup (see Fig. 1).
We also demonstrate strong robustness to noise and extrapolation capabilities.
Related work SR is a challenging task that traces back from a few decades ago, with a large number of open-source and commercial softwares, and has already been used to accelerate scientiﬁc discoveries [8, 9, 10].
Most popular frameworks for symbolic regression use GP [11, 12, 13, 14, 15, 16, 17, 18, 19] (see [7] for a recent review), but SR has also seen growing interest from the
Deep Learning (DL) community, motivated by the fact that neural networks are good at identifying qualitative patterns.
Neural networks have been combined with GP algorithms, e.g. to simplify the original dataset [1], or to propose a good starting distribution over mathematical expressions[20].
[21, 22] propose modiﬁcations to feed-forward networks to include interpretable components, i.e. replacing usual activation functions by operators such as cos , sin , however these are hard to optimize and prone to numerical issues.
Language models, and especially Transformers [23], have been trained over synthetic datasets to solve various mathematical problems: integration [24], dynamical systems [25], linear algebra [26], formal logic [27] and theorem proving [28].
A few papers apply these techniques to symbolic regression: the aforementioned references [6, 5] train Transformers to predict function skeletons, while [29] infers one-dimensional recurrence relations in sequences of numbers.
[30] trains fully-connected networks to predict simple formulas from tabular data.
The recently introduced SRBench [7] provides a benchmark for rigorous evaluation of SR methods, in addition to 14 SR methods and 7 ML baselines which we will compare to in this work.
Data generation
Our approach consists in training language models on vast synthetic datasets.
Each training example is a pair: a set of N points ( x
, y ) ∈ R D × R as the input, and a function f such that
y =
f ( x ) as the target 2 Examples are generated by ﬁrst sampling a random function f , then a set of N input values ( x i ) i ∈ N N in R D , and computing y i
=
f
( x i ) .
1.1 Generating functions
To sample functions f , we follow the seminal approach of Lample and Charton [24], and generate random trees with mathematical operators as internal nodes and variables or constants as leaves.
The procedure is detailed below (see Table 3 in the Appendix for the values of parameters):
Sample the desired input dimension D of the function f from U{ 1 , D max } . 2.
Sample the number of binary operators b from U{ D − 1 , D + b max } then sample b operators from U{ + , − , ×} 3 . 3.
Build a binary tree with those b nodes, using the sampling procedure of [24].
For each leaf in the tree, sample one of the variables x d , d ∈ N D . 5.
Sample the number of unary operators u from U{ 0 , u max } then sample u operators from the list O u in Table 3, and insert them at random positions in the tree.
For each variable x d and unary operator u , apply a random afﬁne transformation , i
e. replace x d by ax d + b , and u by au + b , with ( a, b ) sampled from D aff .
Note that since we require independent control on the number of unary operators (which is independent of D ) and binary operators (which depends on D ), we cannot directly sample a unary-binary tree as in [24].
Note also that the ﬁrst D variables are sampled in ascending order to obtain the desired input dimension, which means functions with missing variables such as x 1 + x 3 are never encountered; this is not an issue as our model can always set the prefactor of x 2 to zero.
As discussed quantitatively
= ⎡ ⎣ ⎢⎢ 𝑥 1 𝑥 2 𝑦 ⎤ ⎦ ⎥⎥ ⎡ ⎣ ⎢⎢ 237 ⎤ ⎦ ⎥⎥
Scale
Embedder
Transformer
Reﬁne
Input
Output = ⎡ ⎣ ⎢⎢ 𝑥
̃ 1 𝑥 ̃ 2 𝑦 ⎤ ⎦ ⎥⎥ ⎡ ⎣ ⎢⎢ −10.57 ⎤ ⎦ ⎥⎥
Scaledinput
Scaledoutput
Encode Decode Tokenize
FFN ( 𝑁 ,3( 𝐷 +1) )
𝑑 𝑒𝑚𝑏 ⎡ ⎣ ⎢⎢ −,1000, 𝐸 −3 +,5000, 𝐸 −4 +,2500, 𝐸 −3 ⎤ ⎦ ⎥⎥
Embedder
Transformer ( 𝑁 , ) 𝑑 𝑒𝑚𝑏 𝑁
Output
Target 𝑦
= + 𝑥 21 𝑥𝑦 =
+ 𝑥 21 𝑥
Cross-entropy = ⎡ ⎣ ⎢⎢ 𝑥 1 𝑥 2 𝑦 ⎤ ⎦ ⎥⎥ ⎡ ⎣ ⎢⎢ −10.52.5 ⎤ ⎦ ⎥⎥ ( 𝑁 , 𝐷 +1)
Input
Training
Inference
Unscale 𝑦 =( +3.1 +0.9( +2.6) 𝑥 ̃ 1 )
𝑥 ̃𝑦 = + 𝑥 21 𝑥𝑦 =(
+
+
( +2.5) 𝑥 ̃ 1 ) 2 𝑥 ̃ in App.
C, the number of possible skeletons as well as the random sampling of numerical constants guarantees that our model almost never sees the same function twice, and cannot simply perform memorization.
See App.
B for examples of the skeleton of generated expressions
1.2 Generating inputs
For each function f : R D → R , we sample N ∈ U{ 10 D, N max } input values x i ∈ R D from the distribution D x described below, and compute the corresponding output values
y i = f ( x i ) . If any x i is outside the domain of deﬁnition of f or if any y i is larger 10 100 , the process is aborted, and we start again by generating a new function.
Note that rejecting and resampling out-of-domain values of x i , the obvious and cheaper alternative, would provide the model with additional information about f , by allowing it to learn its domain of deﬁnition.
To maximize the diversity of input distributions seen during training, we sample our inputs from a mixture of distributions (uniform or gaussian), centered around k random centroids 4 , see App.
A for some illustrations at D = 2 .
Input samples are generated as follows:
Sample a number of clusters k ∼ U{ 1 , k max } and k weights w i ∼ U (0 , 1) , which are then normalized so that (cid:80) i
w i = 1 .
For each cluster i ∈ N k , sample a centroid µ i ∼ N (0 , 1) D , a vector of variances σ i ∼ U (0 , 1) D and a distribution shape (gaussian or uniform) D i ∈
{N , U} .
For each cluster i ∈ N k , sample (cid:98) w i N (cid:99) input points from D i ( µ i , σ i ) then apply a random rotation sampled from the Haar distribution.
Finally, concatenate all the points obtained and whiten them by substracting the mean and dividing by the standard deviation along each dimension.
1.3 Tokenization
Following [26], we represent numbers in base 10 ﬂoating-point notation, round them to four signiﬁcant digits, and encode them as sequences of 3 tokens: their sign, mantissa (between 0 and 9999 ), and exponent (from E-100 to E100 ).
To represent mathematical functions as sequences, we enumerate the trees in preﬁx order, i.e. direct
Polish notation, as in [24]: operators and variables and integers are represented as single autonomous tokens, and constants are encoded as explained above.
For example, the expression f ( x ) = cos(2 .
4242 x ) is encoded as [cos,mul,+,2424,E-3,x] .
Note that the vocabulary of the decoder contains a mix of symbolic tokens (operators and variables) and numeric tokens, whereas that of the encoder contains only numeric tokens 5 . The embeddings of numeric tokens are not L a y e r Head
Head Head Head Head
Head
Head
Head
[Figure] r  Methods Below we describe our approach for end-to-end symbolic regression; please refer to Fig.
2 for an illustration.
2.1 Model
Embedder Our model is provided N input points ( x
, y ) ∈
R D +1 , each of which is [-representedas 
 (-]represented as 3( D + 1) tokens of dimension d emb .
As D and N become large, this results in long input sequences (e.g. 6600 tokens for D = 10 and N = 200 ), which challenge the quadratic complexity of Transformers.
To mitigate this, we introduce an embedder to map each input point to a single embedding.
The embedder pads the empty input dimensions to D max , then feeds the 3( D max +1) d emb -dimensional vector into a 2-layer fully-connected feedforward network (FFN) with ReLU activations, which projects down to dimension d {+emb
+} The resulting N embeddings of dimension d emb are then fed to the
Transformer.
Transformer We use a sequence to sequence Transformer architecture [23] with 16 attention heads and an embedding dimension of 512, containing a total of 86M parameters.
Like [26], we observe that the best architecture for this problem is asymmetric, with a deeper decoder: we use 4 layers in the encoder and 16 in the decoder.
A notable property of this task is the permutation invariance of the
N input points.
To account for this invariance, we remove positional embeddings from the encoder.
As shown in Fig.
3 and detailed in App.
D, the encoder captures the most distinctive features of the functions considered, such as critical points and periodicity, and blends a mix of short-ranged heads focusing on local details with long-ranged heads which capture the global shape of the function.
Training We optimize a cross-entropy loss with the Adam optimizer, warming up the learning rate from 10 − 7 to 2 .
10 − 4 over the ﬁrst 10,000 steps, then decaying it as the inverse square root of the number of steps, following [23].
We hold out a validation set of 10 4 examples from the same generator, and train our models until the accuracy on the validation set saturates (around 50 epochs of 3M examples).
Input sequence lengths vary signiﬁcantly with the number of points N ; to avoid wasteful padding, we batch together examples of similar lengths, ensuring that a full batch contains a minimum of 10, tokens.
On 32 GPU with 32GB memory each, one epoch is processed in about half an hour.
2.2 Inference tricks
In this section, we describe three tricks to improve the performance of our model at inference. [Table]
The skeleton approach recovers an incorrect skeleton. The E2E approach predicts the right skeleton. Reﬁnement worsens original prediction when randomly initialized, and yields the correct result when initialized with predicted constants. Reﬁnement Previous language models for SR, such as [6], follow a skeleton approach: they ﬁrst predict equation skeletons, then ﬁt the constants with a non-linear optimisation solver such as BFGS.
In this paper, we follow an end-to-end (E2E) approach: predicting simultaneously the function and the values of the constants.
However, we improve our results by adding a reﬁnement step: ﬁne-tuning the constants a posteriori with BFGS, initialized with our model predictions 7 .
This results in a large improvement over the skeleton approach, as we show by training a Transformer to predict skeletons in the same experimental setting.
The improvement comes from two reasons: ﬁrst, prediction of the full formula provides better supervision, and helps the model predict the skeleton; second, the BFGS routine strongly beneﬁts from the informed initial guess, which helps the model predict the constants.
This is illustrated qualitatively in Table 1, and quantitatively in Table 2.
Scaling As described in Section 1.2, all input points presented to the model during training are whitened: their distribution is centered around the origin and has unit variance.
To allow accurate prediction for input points with a different mean and variance, we introduce a scaling procedure during inference.
Let f the function to be inferred, x be the input points, and µ = mean( x ) , σ = std( x ) .
As illustrated in Fig.
2 we pre-process the input data by replacing x by ˜ x
= x − µσ .
The model then predicts ˆ f (˜ x )
=
ˆ f ( σx + µ ) , and we can recover an approximation of f by unscaling the variables in ˆ f .
This gives our model the desirable property to be insensitive to the scale of the input points: DL-based approaches to SR are known to fail when the inputs are outside the range of values seen during training [29, 26].
Note that here, the scale of the inputs translates to the scale of the constants in the function f ; although these coefﬁcients are sampled in D aff during training, coefﬁcients outside D aff can be expressed by multiplication of constants in D aff .
Bagging and decoding Since our model was trained on N ≤ 200 input points, it does not perform satisfactorily at inference when presented with more than 200 input points.
To take advantage of large datasets while accommodating memory constraints, we perform bagging : whenever N is larger than 200 at inference, we randomly split the dataset into B bags of 200 input points 8 .
For each bag, we apply a forward pass and generate C function candidates via random sampling or beam search using the next token distribution.
As shown in App.
F (Fig. 16), the more commonly used beam search [34] strategy leads to much less good results than sampling due to the lack of diversity induced by constant prediction (typical beams will look like sin( x ) , sin(1 .
1 x ) , sin(0 .
9 x ) , . . . ).
This provides us with a set of BC candidate solutions.
Inference time Our model inference speed has two sources: the forward passes described above on one hand (which can be parallelized up to memory limits of the GPU), and the reﬁnements of candidate functions on the other (which are CPU-based and could also be parallelized, although we did not consider this option here). [Table]
Metrics are computed over the 10 , 000 examples of the evaluation set. Since BC can become large, we rank candidate functions (according to their error on all input points), get rid of redundant skeleton functions and keep the best K candidates for the reﬁnement step 9 .
To speed up the reﬁnement, we use a subset of at most 1024 input points for the optimization.
The parameters B , C and K can be used as cursors in the speed-accuracy tradeoff: in the experiments presented in Fig.
1, we selected B = 100 , C = 10 , K = 10 .
Results
In this section, we present the results of our model.
We begin by studying in-domain accuracy, then present results on out-of-domain datasets.
3.1 In-domain performance
We report the in-domain performance of our models by evaluating them on a ﬁxed validation set of 100,000 examples, generated as per Section 1.
Validation functions are uniformly spread out over three difﬁculty factors: number of unary operators, binary operators, and input dimension.
For each function, we evaluate the performance of the model when presented N = [50 , 100 , 150 , 200] input points ( x, y ) , and prediction accuracy is evaluated on N test = 200 points sampled from a fresh instance of the multimodal distribution described in Section 1.2.
We assess the performance of our model using two popular metrics: R 2 -score [7] and accuracy to tolerance τ [6, 29]:
[Equation]
where 1 is the indicator function.
R 2 is classically used in statistics, but it is unbounded, hence a single bad prediction can cause the average R 2 over a set of examples to be extremely bad.
To circumvent this, we set R 2 = 0 upon pathological examples as in [7](such examples occur in less that 1% of cases) 10 .
The accuracy metric provides a better idea of the precision of the predicted expression as it depends on a desired tolerance threshold.
However, due to the presence of the max operator, it is sensitive to outliers, and hence to the number of points considered at test time (more points entails a higher risk of outlier).
To circumvent this, we discard the 5% worst predictions, following [6].
End-to-end outperforms skeleton
In Table 2, we report the average in-domain results of our models.
Without reﬁnement, our E2E model outperforms the skeleton model trained under the same protocol in terms of low precision prediction ( R 2 and Acc 0 . 1 metrics), but small errors in the prediction of the constants lead to lower performance at high precision (Acc 0 .
001 metric).
The reﬁnement procedure alleviates this issue signiﬁcantly, inducing a three-fold increase in Acc 0 .while also boosting other metrics.
Initializing BFGS with the constants estimated in the E2E phase plays a crucial role: with random initialization, the BFGS step actually degrades E2E performance.
However, reﬁnement with random initialization still achieves better results than the skeleton model: this suggests that the E2E model predicts skeletons better that the skeleton model. [Figure]
Ablation Fig.
4A,B,C presents an ablation over three indicators of formula difﬁculty (from left to right): number of unary operators, number of binary operators and input dimension.
In all cases, increasing the factor of difﬁculty degrades performance, as one could expect.
This may give the impression that our model does not scale well with the input dimension, but we show that our model scales in fact very well on out-of-domain datasets compared to concurrent methods (see Fig.
15 of the Appendix).
We include a qualitative ablation on the improvement caused by the use of mixture of distributions in App.
E.
Fig.
4D shows how performance depends on the number of input points fed to the model, N .
In all cases, performance increases, but much more signicantly for the E2E models than for the skeleton model, demonstrating the importance of having a lot of data to accurately predict the constants in the expression.
Extrapolation and robustness In Fig.
4E, we examine the ability of our models to interpolate/extrapolate by varying the scale of the test points: instead of normalizing the test points to unit variance, we normalize them to a scale σ .
As expected, performance degrades as we increase σ , however the extrapolation performance remains decent even very far away from the inputs ( σ = 32 ).
Finally, in Fig.
4F, we examine the effect of corrupting the targets y with a multiplicative noise of variance σ : y → y (1 + ξ ) , ξ ∼ N (0 , ε ) .
The results reveal something interesting: without reﬁnement, the E2E model is not robust to noise, and actually performs worse than the skeleton model at high noise.
This shows how sensitive the Transformer is to the inputs when predicting constants.
Reﬁnement improves robustness signiﬁcantly, but the initialization of constants to estimated values has less impact, since the prediction of constants is corrupted by the noise.
3.2 Out-of-domain generalization
We evaluate our method on the recently released benchmark SRBench[7].
Its repository contains a set of 252 regression datasets from the Penn Machine Learning Benchmark (PMLB)[35] in addition to 14 open-source SR and ML baselines.
The datasets consist in "ground-truth" problems where the true underlying function is known, as well as "black-box" problems which are more general regression datasets without an underlying ground truth.
We ﬁlter out problems from SRBench to only keep regression problems with D ≤ 10 with continuous features; this results in 190 regression datasets, splitted into 57 black-box problems (combination of real-world and noisy, synthetic datasets),[-
-]
SR datasets from the Feynman [1] and 14 SR datasets from the ODE-Strogatz [36] databases.
Each dataset is split into 75% training data and 25% test data, on which performance is evaluated.
The overall performance of our models is illustrated in the Pareto plot of Fig.
1, where we see that on both types of problems, our model achieves performance close to state-of-the-art GP models such as
Operon with a fraction of the inference time 11 .
Impressively, our model outperforms all classic ML methods (e.g. XGBoost and Random Forests) on real-world problems with a lower inference time, and while outputting an interpretable formula.
We provide more detailed results on Feynman problems in Fig.
5, where we additionally plot the formula complexity, i.e. the number of nodes in the mathematical tree (see App.
F for similar results on black-box and Strogatz problems).
Varying the noise applied to the targets noise, we see that our model displays similar robustness to state-of-the-art GP models.
We additionally include ablation on the use of scaling during inference in App. E. While the average accuracy or our model is only ranked fourth, it outputs formulas with lower complexity than the top 2 models (Operon and SBP-GP), which is an important criteria for SR problems: see App. 11 for complexity-accuracy Pareto plots.
To the best of our knowledge, our model is the ﬁrst non-GP approach to achieve such competitive results for SR.
[Figure]
Conclusion
In this work [Figure] we introduced a competitive deep learning model for SR by using a novel numericsymbolic approach. Through rigorous ablations
[Figure] we showed that predicting the constants in an expression not only improves performance compared to predicting a skeleton, but can also serve as an informed initial condition for a solver to reﬁne the value of the constants.
Our model outperforms previous deep learning approaches by a margin on SR benchmarks, and scales to larger dimensions.
Yet, the dimensions considered here remain moderate ( D < 10 ): adapting to the truly high-dimensional setup is an interesting future direction, and will likely require qualitative changes in the data generation protocol.
While our model narrows the gap between GP and DL based
SR, closing the gap also remains a challenge for future work.
This work opens up a whole new range of applications for SR in ﬁelds which require real-time inference.
We hope that the methods presented here may also serve as a toolbox for many future applications of Transformers for symbolic tasks. A Details on the training data In Tab.
3 we provide the detailed set of parameters used in our data generator.
The probabilities of the unary operators were selected to match the natural frequencies appearing in the Feynman dataset. In Fig. [Table] we show the statistics of the data generation.
The number of expressions diminishes with the input dimension and number of unary operators because of the higher likelihood of generating out-of-domain inputs. One could easily make the distribution uniform by enforcing to retry as long as a valid example is not found, however we ﬁnd empirically that having more easy examples than hard ones eases learning and provides better out-of-domain generalization, which is our ultimate goal. In Fig. [Table]
we show some examples of the input distributions generated by our multimodal approach.
Notice the diversity of shapes obtained by this procedure.
[Table]
[Figure]
B Examples of expressions generated
Below, we give some typical examples of the expressions generated by our random generator for dimensions between 1 and 3 (Tab. 4), as well as some examples of equations from the Feynman dataset (Tab. 5).
[Equation]
[Table] [Figure] C Does memorization occur? It is natural to ask the following question: due to the large amount of data seen during training, is our model simply memorizing the training set ?
Answering this question involves computing the number of possible functions which can be generated.
To estimate this number, calculating the number of possible skeleton N s is insufﬁcient, since a given skeleton can give rise to very different functions according to the sampling of the constants, and even for a given choice of the constants, the input points { x } can be sampled in many different ways.
Nonetheless, we provide the lower bound N s as a function of the number of nodes in Fig. 8, using the equations provided in [24].
For small expressions (up to four operators), the number of possible expressions is lower or similar to than the number of expressions encountered during training, hence one cannot exclude the possibility that some expressions were seen several times during training, but with different realizations due to the initial conditions.
However, for larger expressions, the number of possibilities is much larger, and one can safely assume that the expressions encountered at test time have not been seen during training.
[Figure]
A natural question is whether self-attention based architectures are optimally suited for symbolic regression tasks. In Fig. 9, we show the attention maps produced by the encoder of our Transformer model, which contains 4 layers avec 16 attention heads (we only keep the ﬁrst 8 for the sake of space).
In order to make the maps readable, we consider one-dimensional inputs and sort them in ascending order. The attention plots demonstrate the complementarity of the attention heads. Some focus on speciﬁc regions of the input, whereas others are more spread out. Some are concentrated along the diagonal (focusing on neighboring points), whereas others are concentrated along the anti-diagonal (focusing on far-away points.
Most strikingly, the particular features of the functions studied clearly stand out in the attention plots.
Focus, for example, on the 7th head of layer 2. For the exponential function, it focuses on the extreme points (near -1 and 1); for the inverse function, it focuses on the singularity around the origin; for the sine function, it reﬂects the periodicity, with evenly spaces vertical lines. The same phenomenology can be acrossed is several other heads.
Fig. 10, we present a similar ablation as Fig.
4 of the main text but using the R 2 score as metric rather than accuracy. F Additional out-of-domain results Complexity-accuracy
In Fig.
11, we display a Pareto plot comparing accuracy and formula complexity on SRBench datasets. Jin benchmark In Fig.
12, we show the predictions of our model on the functions provided in [37]. Our model gets all of them correct except for one.
Black-box datasets In Fig. 13, we display the results of our model on the black-box problems of SRBench. Strogatz datasets Each of the 14 datasets from the ODE-Strogatz benchmark is the trajectory of a 2-state system following a ﬁrst-order ordinary differential equation (ODE).
Therefore, the input data has a very particular, time-ordered distribution, which differs signiﬁcantly from that seen at train time.
Unsurprisingly, Fig.
14 shows that our model performs somewhat less well to this kind of data in comparison with GP-based methods. Ablation on input dimension In Fig. 15, we show how the performance of our model depends on the dimensionality of the inputs on Feynamn and black-box datasets. Ablation on decoding strategy In Fig.
16, we display the difference in performance using two decoding strategies. Ablation on the use of i) mixture of distributions during training, ii) scaling during inference
It is generally observed that Transformers struggle to generalize out-of-distribution, especially in mathematical tasks [39].
We demonstrate that both i) and ii) are necessary to handle datasets involving input distributions that are (i) neither gaussian nor uniform, and (ii) vary across wide ranges of scales.
For (i), we provide in Fig.
17 a qualitative example on a model trained with Gaussian and uniform distributions that a distribution-shift at test time can cause failure. Consider the function y = x 1 cos( x 0 + x 1 ) .
Recall the model was trained on datapoints sampled from distributions either N (0 , 1) or U ([ − 1 , 1]) . As we sample 100 datapoints from U ([0 , 6]) , we see the E2E model makes good predictions, whereas, adding 100 datapoints, sampled uniformly between U ([ − 7 , 5]) , degrades the model prediction.
For (ii), we also provide in Fig.
18 a qualitative example of failure on the same function y = x 1 cos( x 0 + x 1 ) and datapoints when scaling is not used.
We additionally report results on SRBench evaluation for our E2E model without scaling in Table 6 and show that changes in scales during inference put transformers outside their comfort zone.
[Table]
Results on SRBench show that scaling is necessary to achieve competitive results. Note that reﬁnement of constants can improve the performance of the unscaled prediction, however it is not enough to even catch up with the E2E without reﬁnement model.
SymbolicGPT [5] and NSRS [6] both train Transformers to predict function skeletons with other tokenization strategies. SymbolicGPT is prone to training instabilities when considering functions with high value variations and NSRS’ architecture is not able to scale to high dimensions because the tokenized input grows linearly with the input dimension. [29] also predicts skeletons but focus on the problem of inferring one-dimensional recurrence relations from small sets of points in case only, while we estimate functions of many variables over larger sets of points.
Only NSRS [6] provides a pre-trained model, but it was only trained on problems with dimensions ≤ 3 , corresponding to a very small subset of SRBench.
Note however that even at these low dimensions, NSRS seems to perform less well than our model: the authors report an accuracy (deﬁned at R2 > 0 . 95 ) on the Feynman datasets of ≈ 0 .
75 in their appendix (Fig. 9), whereas we get ≈ 0 .
84 on R2 > 0 .
99 on all dimensions.
The benchmark we used for our comparison, SRBench, is currently the most extensive and up-to-date benchmark for SR, and provides comparisons with other DL-based methods such as DSR [20].
Note also that the ablation of Tables 1 and 2 and Fig.
4 (in-domain), as well as Figures 5 and(out-of-domain) are provided to show the beneﬁt of the E2E approach over skeleton approaches.
Note that an other end-to-end approach[40], very similar to ours, was released two months after us.
H Extension of our model to dimension > 10 .
Our method still remains improvable in scaling to larger dimensions.
The reason we restricted our model to dimension ≤ 10 is that the input sequence length becomes prohibitively long beyond, and that generating high-dimensional functions in an unbiased way becomes increasingly tricky.
Nonetheless, since the objective of SR is to output interpretable formulas, we argue that SR is most useful for moderately low dimensional problems.
For example, 1 − 10 dimensional problems already cover a large class of physical systems : for instance, point objects can be represented by their position, speed and mass, 7 parameters.
Additionally, in many real world problems where more than 10 features are available, some of the features are often irrelevant or heavily correlated.
To mitigate this, one typically carries out feature selection before modeling the data.
We tested our model on the high-dimensional problems of SRBench (up to 1000 input dimensions), by feeding to our model only the 10 features most correlated with the output.
This naive strategy already obtained encouraging results
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