L EARNING META - FEATURES FOR A UTO ML
A BSTRACT
This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter conﬁguration most appropriate to the dataset at hand.
The proposed approach, MetaBu, learns new meta-features via an Optimal
Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper-parameter conﬁgurations.
MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to deﬁne a distribution of promising hyper-parameter conﬁgurations amenable to AutoML.
Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, AutoSkLearn (Feurer et al.
and Probabilistic Matrix Factorization (Fusi et al. 2018).
Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well.
Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the
OpenML benchmark w.r.t.
a given ML algorithm or pipeline.
The source code is available at https://github.com/luxusg1/metabu.
I NTRODUCTION
Getting the peak performance of an algorithm portfolio on a particular problem instance is acknowledged a main bottleneck in domains ranging from Constraint Programming and Satisﬁability to
Machine Learning (Rice, 1976; Hutter et al., 2009; Stern et al., 2010; Kotthoff, 2014; Bergstra et al., 2011; Feurer et al., 2015; Hazan et al., 2018;
Fusi et al., 2018;
Yang et al., 2019).
Early approaches have been investigating the use of general performance models (Rice, 1976), estimating a priori the performance of any algorithm on any problem instance, where each problem instance is described by a vector of so-called meta-features , and the performance model is learned in this meta-feature space.
In the context of supervised Machine Learning, many meta-features have been manually designed to describe datasets (Cali´nski & Harabasz, 1974; Vilalta, 1999; Bensusan & Giraud-Carrier, 2000;
Pfahringer et al., 2000; Peng et al., 2002; Ali & Smith, 2006; Song et al., 2012; Bardenet et al., 2013;
Feurer et al., 2014; 2015; Pimentel & de Carvalho, 2019; Lorena et al., 2019).
After a series of international AutoML challenges, aimed to automating the selection and tuning of ML pipelines(Hutter et al., 2019; Guyon et al., 2019), it seems that a general accurate performance model can hardly be based on these meta-features (Misir & Sebag, 2017) (Section 2): for instance the challenge-winner
AutoSkLearn (Feurer et al., 2015) relies on Bayesian optimization and iteratively learns and exploits one performance model speciﬁc to the dataset at hand; PMF (Fusi et al., 2018) uses a probabilistic collaborative ﬁltering approach, where the cold-start problem is handled as in AutoSkLearn; OBOE
(Yang et al., 2019) likewise uses a collaborative ﬁltering approach, combined with active learning.
Nevertheless, the deﬁnition of good meta-features remains desirable for two reasons.
The ﬁrst motivation remains to achieve AutoML with a decent performance vs cost trade-off.
Relevant meta-features are expected to deﬁne a reliable topology on the dataset space, such that two datasets are close iff the best hyper-parameter conﬁgurations for these datasets are close.
Such a topology would support an inexpensive and efﬁcient AutoML strategy: selecting the best hyper-parameter conﬁgurations of the nearest neighbor(s) of the current dataset.
The second motivation is to better understand the dataset space w.r.t.
a given ML algorithm, to estimate its intrinsic dimension and to appreciate the distribution of the ML benchmark suites thereon.
This paper presents the Meta-learning for Tabular Data (M ETABU ) approach, formalizing and tackling the construction of good meta-features relatively to an ML algorithm A as an Optimal
Transport (OT) problem (Cuturi, 2013; Peyré & Cuturi, 2019).
Formally, M ETABU considers two representations of the datasets: the basic one consists of 135 manually designed meta-features (Appendix E).
The target one, out-of-reach except for the datasets in the benchmark suite, represents a dataset as the distribution of the hyper-parameter conﬁgurations of A yielding the top performances for this dataset.
Optimal Transport is used to ﬁnd a linear transformation of the basic meta-features, such that the resulting Euclidean distance emulates the Wasserstein-Gromov distance (Mémoli, 2011) on the target representation (Section 3).
Overall, Metabu learns once for all new meta-features, aimed to capture the topology and neighborhoods corresponding to the target representation.
These meta-features can be computed from scratch for each new dataset.
This approach contrasts with Yang et al.
(2019) and Fusi et al.
(2018) that both require a cold-start phase, launching conﬁgurations for each new dataset and using their performance to ﬁnd the representation of the new dataset.
The contribution of M ETABU is threefold.
Firstly, the M ETABU meta-features deﬁne an efﬁcient topology, that can be used to sample the most promising hyper-parameter region for new datasets.
Secondly, the relevance of these meta-features is demonstrated as they can be used as representation space to initialize AutoSkLearn (Feurer et al., 2014) and PMF (Fusi et al., 2018): the hybrid approaches AutoSkLearn+M ETABU and PMF+M ETABU , are shown to signiﬁcantly outperform
AutoSkLearn and PMF on the OpenML CC-18 (Bischl et al., 2019) benchmark.
Lastly, the approach provides some hints into the AutoML problem, enabling to estimate the intrinsic dimensionality (Facco et al., 2017) of the dataset space w.r.t.
an ML algorithm: the higher the dimensionality, the more complex the algorithm.
It is interesting to compare the intrinsic dimensions of the OpenML CC18 w.r.t.
AutoSkLearn (Feurer et al., 2015), SVM (Boser et al., 1992), or Random Forest (Breiman, 2001).
Furthermore, the M ETABU meta-features can be inspected and conﬁrm some "tricks of the trade" about when an algorithm does well.
The paper is organized as follows.
Section 2 brieﬂy discusses related work and introduces OT formal background for the sake of self-containedness.
Section 3 describes the M ETABU algorithm.
In Section 4, the merits of the M ETABU meta-features are empirically demonstrated on conﬁguration selection and optimization tasks, comparatively to the state of the art.
Lastly, we discuss how the M ETABU meta-features provide an interpretable description of the niche of the considered ML algorithms.
R ELATED W ORK AND F ORMAL B ACKGROUND
AutoML & meta-features Most ML meta-features (Cali´nski & Harabasz, 1974; Vilalta, 1999;
Bensusan & Giraud-Carrier, 2000; Pfahringer et al., 2000; Peng et al., 2002; Ali & Smith, 2006;
Song et al., 2012; Bardenet et al., 2013; Feurer et al., 2015; 2014; Pimentel & de Carvalho, 2019;
Lorena et al., 2019) have been manually designed to describe supervised datasets based on descriptive statistics, information theory (quantifying relationships among features/labels), geometrical structure of the dataset, and landmarking (performance of cheap classiﬁers such as linear discriminant and decision trees).
In the neighbor ﬁelds of Satisﬁability or Constraint Programming, circa one hundred meta-features have also been manually designed (Nudelman et al., 2004; Xu et al., 2008).
In contrast to the efﬁciency of SAT or CP meta-features however (Kotthoff, 2014), the AutoML search can hardly rely on the only metric deﬁned from the ML meta-features after (Misir & Sebag, 2017; Muñoz et al., 2018); in practice, they are often used to initialize the optimization search (Feurer et al., 2015).
Another approach is to learn meta-features, e.g. by making strong assumptions on the performance model (Hazan et al., 2018) or by leveraging distributional neural networks (de Bie et al., 2019; Maron et al., 2020).
In the latter case, these meta-features are functions of the dataset distribution and consist of the last layer of a distributional NN trained in view of a particular task.
Dataset2Vec
(Jomaa et al., 2021) learns meta-features to detect whether two data patches (subset of samples described by a subset of features) are extracted from the same whole dataset.
OTDD (Alvarez-Melis & Fusi, 2020) uses OT to learn a mapping over the joint feature and label spaces.
A signiﬁcant drawback of distributional neural network approaches, limiting their ability to handle general tabular datasets (with widely varying number of features, missing values, heterogeneous variables) is due to the shortage of training (meta)-samples.
Neural networks notoriously need large amounts of samples to be efﬁciently trained, while AutoML benchmarks include less than a hundred datasets.
For this reason, the proposed M ETABU approach proceeds by building upon existing meta-features.
Optimal Transport Let (Ω x , d x ) and (Ω y , d y ) denote compact metric spaces, and x and y distributions 2 respectively deﬁned on Ω x and Ω y .
The search space Γ( x , y ) is the space of all distributions on Ω x × Ω y with marginals x and y .
Let the transport cost function c : Ω x × Ω y (cid:55)→
IR
+ be a scalar function on Ω x × Ω y 3 .
The OT problem consists in ﬁnding a distribution in Γ( x , y ) yielding a minimal transport cost expectation (Peyré & Cuturi, 2019); this minimal transport cost expectation deﬁnes the Wasserstein distance of x and y : d qW ( x , y ) =
[Equation]
, with q a positive real number, set to 1 in the following.
Another OT-based distance is the Gromov-Wasserstein distance (GW) (Mémoli, 2011), measuring how well a distribution in Γ( x , y ) preserves the distances on both Ω x and Ω y , akin a rigid transport between both domains: d qGW ( x , y ) =
[Equation]
The Fused Gromov-Wasserstein (FGW) distance (Titouan et al., 2019) combines both these distances.
Deﬁnition
The Fused q-Gromov-Wasserstein distance is deﬁned on Ω x × Ω y as follows:
[Equation]
α ∈ [0 , 1] is a trade-off parameter:
For α = 0 (resp.
α = 1 ), the fused q-Gromov-Wasserstein distance is exactly the q-Wasserstein distance d qW (resp.
the q-Gromov-Wasserstein distance d qGW ).
The Wasserstein distance and variants thereof have been successfully used to evaluate the "alignment" among datasets, e.g. between the source and the target datasets in the context of domain adaptation (Courty et al., 2017) or transfer learning (Alvarez-Melis & Fusi, 2020).
FGW distance has been used to enforce the consistency of the latent space when jointly training several Variational Auto-Encoders (Xu et al., 2020; Nguyen et al., 2020).
M ETABU will likewise take inspiration from OT to create a bridge between two representations of the datasets: the basic one, and the target one, critically using both GW and FGW distances.
O VERVIEW OF M ETABU
Let A and Θ A respectively denote an ML pipeline and its hyper-parameter conﬁguration space; subscript A is omitted when clear from the context.
Space Θ is embedded into the a -dimensional real-valued space IR a , using a one-hot encoding of Boolean and categorical hyper-parameters.
After describing the principle of the approach, some key issues are detailed: the augmentation of the
AutoML benchmark to avoid overﬁtting, and the setting of the number d of the M ETABU metafeatures, estimated from the intrinsic dimensionality of the AutoML benchmark suite. When = Ω y =
Ω , transport c ( x, y )
Euclidean distance d ( x, y ) .
Principle.
Intuitively, two representations can be associated with a dataset: The basic representation x ∈ IR D of a dataset reports the values of the D manually designed meta-features for this dataset.
By construction, it can be cheaply computed for any dataset.
The target representation z of a dataset is the distribution on the space Θ supported by the conﬁgurations yielding the best performances on this dataset.
This precious target representation is unreachable in practice, but can be approached after the performances of the models learned with a number of conﬁgurations (aka conﬁguration performances) have been assessed.
In practice, the conﬁguration performances are only available for a small number n of datasets (more below).
The difference between the basic and the target topologies is depicted on Fig.
1, in Θ space (projected on ﬁrst two PCA eigenvectors).
In order to build a bridge between both representations, let us consider an intermediate representation derived from the target representation, mapping each ( z i ) 1 ≤ i ≤ n on some u
i ∈
IR
d using a distance-preserving projection, e.g.
Multi-Dimensional Scaling (MDS) (Cox & Cox, 2001).
M ETABU tackles an Optimal Transport problem so as to learn a mapping ψ : IR D (cid:55)→
IR d from the basic representation on the projected target representation space such that the ψ ( x i ) 1 ≤ i ≤ n are aligned with the u i s in the sense of the q-Fused Gromov-Wasserstein distance (Section 2).
In brief, mapping ψ sends the basic meta-feature space on
IR d , such that the Euclidean metric on the ψ ( x i ) reﬂects the Euclidean metric on the u i s, itself reﬂecting the metric on the target
z i s. The descriptive features of the ψ ( x i ) , referred to as M ETABU meta-features, are meant to both be cheaply computable from the basic meta-features, and deﬁne a Euclidean distance conducive to the AutoML task.
Augmenting the AutoML benchmark.
The OpenML CC-18 (Bischl et al., 2019), to our knowledge the largest curated tabular dataset benchmark (that will be used in the experiments), contains n = 72 classiﬁcation datasets; the target representation is available for 64 of them.
The shortage of such datasets yields a risk of overﬁtting the learned meta-features.
This challenge is tackled by augmenting the OpenML CC-18 benchmark suite, using a bootstrap procedure (Efron, 1979).
The goal is to pave the meta-feature space more densely and more accurately than through e.g., perturbing the basic representation with Gaussian noise (the visualization of the augmented benchmark is displayed on Fig.
6, Appendix A).
The algorithm The algorithm is provided the p = 1 ,
× n training datasets of the benchmark suite, augmented as described above (pseudo-code in Appendix B).
The M ETABU meta-features are constructed in a 3-step procedure, illustrated on Fig.
Step 1 : Target representation and Wasserstein distance .
Considering the i -th training dataset, let Θ i ⊂ T denote the set of hyper-parameter conﬁgurations with performance in the top- L known conﬁguration performances ( L = 20 in the experiments).
The target representation z i of the i -th dataset is the discrete distribution with support Θ i .
The distance d 1 W ( z i , z j ) is the 1-Wasserstein distance among distributions (Section 2).
Step 2 :
Projecting the target representation on IR d .
The second step consists in projecting the z i s on IR d , where d is identiﬁed using an intrinsic dimensionality procedure (details below), using
Multi-Dimensional Scaling (Cox & Cox, 2001), such that the distance d ( u i , u j ) approximates the 1-Wasserstein distance d 1 W ( z i , z j ) (Fig. 2, leftmost and second subplots).
Note that by construction,
the u i s are deﬁned up to an isometry on IR d
[Figure]
Step 3 : Learning the M ETABU meta-features .
Let x = 1 p (cid:80) pi =
δ x i denote the uniform discrete distribution on IR D whose support is the set of p datasets using their basic representations.
Let u = 1 n (cid:80) n i =1 δ u i denote the uniform discrete distribution on IR d whose support is the set of u i s deﬁned above.
The M ETABU meta-feature space is built by ﬁnding a mapping ψ from IR D on IR d that pushes the representation metric on IR d , that is, such that the image of x via ψ is as close as possible to u , and reﬂects its topology in the FGW sense (Fig. 2, rightmost and third subplots).
Formally, let ψ (cid:93) x def = 1 p (cid:80) pi =1 δ ψ ( x i ) be the push-forward distribution of x on IR d for a given ψ .
The overall optimization problem is to ﬁnd a mapping ψ ∗ that minimizes the FGW distance between the u distribution and the push distribution ψ ∗ # x :
[Equation]
with λ the regularization weight and (cid:107) ψ (cid:107) the norm of the ψ function.
Note that, as u and ψ # x are distributions on the same space IR d , the transport cost c is the Euclidean distance on IR d .
In the following, only linear mappings ψ are considered for the sake of avoiding overﬁtting and facilitating the interpretation of the M ETABU meta-features w.r.t.
the manually designed meta-features.
The norm of ψ is set to the L 1 norm of its weight vector.
Taking inspiration from Xu et al.
(2020), the efﬁcient optimization of Eq.
2 is achieved using a bilevel optimization formulation.
For a given ψ , the inner optimization problem consists of minimizing d FGW,α ( ψ (cid:93) x , u ) (Eq. 1).
This problem is solved using a proximal gradient method (Xu et al., 2019), along an iterative approach: given an estimation of the transport map γ ( t ) , a sub-problem is deﬁned to reﬁne γ , it is solved using the Sinkhorn algorithm (Cuturi, 2013), and its solution is used to compute γ ( t +1)
(the number of iterations is set to 10 in the experiments).
The outer optimization problem consists of optimizing ψ : The transport matrix γ is treated as a constant, and the outer objective function (Eq. 2) is solved with ADAM optimizer (Kingma & Ba, 2015) with learning rate 0.01, α = 0 .
5 and λ = 0 .
Intrinsic dimension of the space of datasets The main hyper-parameter of M ETABU is the number d of meta-features needed to approximate the target representation.
Indeed, d depends on the considered algorithm A : the more diverse the target representations associated with datasets, the harder the AutoML problem, the higher d needs to be.
In the other extreme case (all datasets have similar target representations), the AutoML problem becomes trivial.
To our best knowledge, measuring the intrinsic dimension of the dataset space w.r.t.
a learning algorithm has not been tackled in the literature.
The approach proposed to do so builds on Levina &
Bickel (2005) and (Facco et al., 2017), exploiting the fact that the number of points in a hypersphere of radius r in dimension d increases like r d .
Formally, to each sample x is associated its ﬁrst and second nearest neighbors, respectively noted x (1) and x (2) and let µ ( x )
=
d ( x,x (2) )
d ( x,x (1) ) be the ratio of their distances to x .
With no loss of generality, the samples are ordered by increasing value of µ (i.e., µ ( x i ) ≤ µ ( x j ) for all i < j ).
Let d be the slope of the linear approximation of the 2D curve deﬁned by { ( log ( µ ( x i )) , − log (1 − i m +1 ) , 1 ≤ i ≤ m } .
Then d provides a guaranteed approximation of the intrinsic dimensionality of the manifold where the x i s family lives (Facco et al., 2017).
It is commonplace to say that the good distance between any two items depends on the considered task.
The original approach used in M ETABU in order to estimate the intrinsic dimensionality of the dataset space, is to set the distance of two datasets to the 1-Wasserstein distance among their target representations.
E XPERIMENTS
All materials (code, data, and instructions) are made available at https://github.com/luxusg1/metabu.
Runtimes are measured on an Intel(R) Xeon(R) CPU E5-2660 v2 @
2.20GHz.
4. E XPERIMENTAL S ETTINGS
Goals of experiment.
The ﬁrst goal is to assess the dataset neighborhoods induced by the M ETABU meta-features (constructed on the top of the manually designed 135 meta-features from the literature) and the relevance of these dataset neighborhoods w.r.t.
the AutoML problem.
The performances are assessed against three baselines: AutoSkLearn meta-feature set (Feurer et al., 2014), Landmark (Pfahringer et al., 2000) and SCOT (Bardenet et al., 2013) meta-feature sets.
All meta-feature sets are detailed in Appendix E. For Tasks 2 and 3 (see below), an additional baseline is based on the uniform sampling of the hyper-parameter conﬁguration space, for sanity check.
The second goal of experiments is to assess the sensitivity of M ETABU w.r.t.
its own two hyperparameters, the weight α used to balance the importance of the Wasserstein and Gromov-Wasserstein distances in FGW (Eq. 1), and the regularization weight λ involved in the optimization of ψ (Eq. 2).
The third goal is to gain some understanding of the dataset landscape, and see whether the M ETABU meta-features give some hints into when a given ML algorithm or pipeline does well (its niche ).
Performance indicators.
Three tasks are considered to investigate the relevance of the M ETABU meta-features.
The performance indicators are measured using a Leave-One-Out process (detailed in
Appendix C).
Task 1 : Capturing the target topology .
For each test dataset, one considers its nearest neighbors w.r.t.
the target topology (the 1-Wasserstein metric on the target representation), and its nearest neighbors w.r.t.
the Euclidean distance on the M ETABU and meta-feature sets.
The alignment between both ordered lists is measured using the normalized discounted cumulative gain over the ﬁrst k neighbors (NDCG@k) (Burges et al., 2005), with 5 ≤ k ≤ 35 .
The performance indicator is the NDCG@k averaged on test datasets.
Task 2 : AutoML with no performance model (Initialization) .
For each test dataset and each metafeature set mf , let ˆ z mf be the distribution on the considered hyper-parameter conﬁguration space:
[Equation]
where z (cid:96) is the target representation of the (cid:96) -th neighbor of the dataset w.r.t.
Euclidean distance on the mf space, and Z a normalization constant.
This distribution ˆ z mf is used to iteratively and independently sample the hyper-parameter conﬁgurations, and the performances of the learned models are measured.
Letting r ( t, mf ) denote the rank of the performance associated with meta-feature set mf after t iterations, the performance curves report r ( t, mf ) for the M ETABU and baseline meta-feature sets (plus a uniform hyper-parameter conﬁguration sampler for sanity check), averaged over the test datasets.
Task 3 : AutoML with performance model (Optimization) .
AutoML systems based on performance models cannot be directly compared with M ETABU as they acquire additional information along the AutoML search: they iteratively use a performance model to select a hyper-parameter conﬁguration, and update the performance model using the performance of the selected conﬁguration.
In
Task 3, the relevance of meta-feature sets is investigated in that they govern the initialization for
AutoSkLearn and PMF performance models.
The performance indicator is the rank of the performance obtained by AutoSkLearn using M ETABU meta-features to initialize its performance model,
The difference between Tasks 2 and 3 can be viewed in terms of Exploration vs Exploitation : getting a good performance on Task 2 requires to identify a sweet conﬁguration spot for each dataset ( Exploitation ).
Quite the contrary, getting a good performance on Task 3 requires to identify a sufﬁciently good and diverse conﬁguration region, such that the search initialized in this region, gathering additional information about the performance of new conﬁgurations on the current dataset along time, eventually yields an even better conﬁguration ( Exploration ).
Benchmarks.
The considered AutoML benchmark is the OpenML Curated Classiﬁcation suite 2018 (Bischl et al., 2019), including 72 binary or multi-class datasets out of which 64 have enough learning performance data to give a good approximation of their target representation.
The performance indicators are measured using Leave-One-Out (details in Appendix C).
The basic meta-features are computed for each dataset using the open source library PyMFE (Alcobaça et al., 2020).
M ETABU is validated in the context of three ML algorithms: Adaboost (Freund & Schapire, 1997),
RandomForest (Breiman, 2001) and SVM (Boser et al., 1992), using their scikit-learn implementation (Pedregosa et al., 2011); and two AutoML pipelines, AutoSkLearn (Feurer et al., 2015) and PMF (Fusi et al., 2018).
The associated hyper-parameter conﬁguration spaces are detailed in Appendix D.
For Adaboost, RandomForest and SVM, the target representation of each training dataset is based on the top-20 conﬁgurations in OpenML (out of 37,289 for Adaboost, 81,336 for RandomForest and 37,075 for SVM), initially generated by van Rijn & Hutter (2018).
For AutoSkLearn, the target representation is generated from scratch, running 500 conﬁgurations per training dataset and retaining the top-20.
For PMF, the top-20 conﬁgurations are extracted from the collaborative ﬁltering matrix for each training dataset (Fusi et al., 2018).
4. C OMPARATIVE EMPIRICAL VALIDATION OF M ETABU
The performances of M ETABU and the baselines on the three tasks are displayed on Fig.
The overall CPU cost on Task 2 (resp.
Task 3) is circa 1,900 (resp.
2,300) hours (full runtimes in Fig. 7).
Appendix H reports the detailed results in Tables 6,7 and 8, indicating the conﬁdence level of the results after a Wilcoxon rank-sum test for performances and Mann Whitney Wilcoxon test for ranks.
Task 1 : Capturing the target topology , Fig. 3a.
The results show that the metric based on the
M ETABU meta-features better matches the target topology than the metric based on the baseline meta-feature sets, all the more so as the number k of nearest neighbors increases.
The higher variance of NDCG@k for M ETABU is explained as the metric depends on the meta-feature training, while the metrics based on the baselines are deterministic.
As could be expected, this variance decreases with k .
Despite this variance, M ETABU signiﬁcantly outperforms all baselines for all k and all hyper-parameter conﬁguration spaces.
Task 2 : AutoML with no performance model (Initialization) , Fig.
3b.
All rank curves start at 3, as ﬁve hyper-parameter conﬁguration samplers are considered.
For RandomForest, the sampler based on the SCOT meta-feature set dominates in the ﬁrst 5 iterations, and remains good at all time;
M ETABU dominates after the beginning; all other approaches but the uniform sampler yield similar performances.
For Adaboost, the sampler based on the AutoSkLearn meta-feature set dominates in the ﬁrst 3 iterations, and M ETABU is statistically signiﬁcantly better than all other approaches thereafter.
For SVM, M ETABU very signiﬁcantly dominates all other approaches.
Task 3 : AutoML with performance model (Optimization) , Fig. 3c.
In ﬁrst time steps (left of the dashed bars), the performance models of AutoSkLearn or PMF are initialized using the performances of the hyper-parameter conﬁgurations sampled as in Task 2; in the following time steps, the hyperparameter conﬁgurations are sampled using the performance model.
The most striking result is that the M ETABU +AutoSkLearn rank improves on that of AutoSkLearn (Fig.
3c, left) although they only differ in the initialization of the performance model, and the AutoSkLearn meta-feature set is optimized to Task 3.
Likewise, the rank of M ETABU +PMF improves on that of PMF (Fig.
3c, right).
The comparison also involves Random2 × and Random4 × uniform samplers, respectively returning the best performance out of 2 or 4 uniformly sampled conﬁgurations (Fusi et al., 2018);
M ETABU +PMF signiﬁcantly improves on Random4 × after the 10th iteration.
This suggests that on
[Figure]
the OpenML benchmark, the M ETABU meta-features efﬁciently enable both to passively sample the hyper-parameter conﬁguration space, and to retrieve the conﬁgurations best appropriate to update the performance model and explore good regions of the space.
4. S ENSITIVITY ANALYSIS
The own two hyper-parameters of M ETABU are the α trade-off parameter between Wasserstein and GromovWasserstein distance (Eq. 1) and the regularization weight λ
(Eq. 2).
The sensitivity of M ETABU w.r.t.
both parameters is investigated on Task 1, by inspecting the difference NDCG@10(M ETABU ) - NDCG@10(AutoSkLearn) for α ranging in { 0 . 1 , 0 . 3 , 0 . 5 , 0 . 7 , 0 . 99 } and λ in { 10 − 1 , . . .
, 10 − 4 } .
The result, displayed in Fig. 4, shows that the difference is positive in the whole considered domain, with NDCG@10(M ETABU ) statistically signiﬁcantly better than NDCG@10(AutoSkLearn) according to
Student t-test with p-value 0.05.
Interestingly, a low sensitivity of M ETABU is observed w.r.t.
the regularisation weight λ , provided that it is small enough ( λ ≤ 10 − 3 ).
For such small λ values, a low sensitivity is also observed w.r.t.
α in a large range ( . 3 ≤ α ≤ . 7 ).
This result conﬁrms the importance of taking into account both the Wasserstein and Gromov-Wasserstein distances on the target representation space: discarding the former ( α ≤ . 1 ) or the latter ( α ≥ . 99 ) signiﬁcantly degrades the performance, and the performance is stable in the [ . 3 , .
7] region.
4. T OWARD UNDERSTANDING THE DATASET LANDSCAPE
A ﬁrst original result is to provide a principled estimate of the intrinsic dimension of the dataset space w.r.t.
the considered ML algorithms.
As detailed in Appendix G. with a stability analysis, the intrinsic dimension d of the
OpenML benchmark is circa 6 for AutoSkLearn, 8 for
Adaboost, 9 for RandomForest and 14 for Support Vector
Machines.
As d reﬂects by construction how diverse the datasets are w.r.t.
the ML algorithm, it is interesting to see that the most ﬂexible AutoSkLearn ML pipeline corresponds to the lowest intrinsic dimension.
M ETABU also delivers some insights into what matters in the dataset landscape, and why a given algorithm should behave better than another on a particular dataset, as follows.
The images ψ ( x i ) of datasets according to the
M ETABU meta-features learned in the context of an algorithm A are processed using PCA, and the importance of a manually designed meta-feature is measured from the norm of its projection i
A ( mf ) on the ﬁrst PCA axis.
Two ML algorithms or pipelines A and B can thus be visually compared, by plotting each metafeature as a 2D point with coordinates ( i A ( mf ) , i B ( mf )) .
As shown on Fig.
5, with respectively
A set to RandomForest and B to Adaboost, one sees that actually few features matter for both
RandomForest and Adaboost (the features nearest to the upper right corner), mostly the Dunn index (Dunn, 1973) and the features importance.
Some ﬁndings reassuringly conﬁrm the practitioner’s expertise: the percentage of instances with missing values matters much more for Adaboost than for RandomForest; the class imbalance (ClassProbabilityMax and ClassProbabilityMin) matters for
Adaboost.
Complementary results (Appendix G.2) show that the sparsity of the data matters for
Support Vector Machines.
Some other ﬁndings are less expected, e.g. the importance of the data density, minimal skewness and kurtosis for AutoSkLearn; these ﬁndings are tentatively explained from the fact that AutoSkLearn includes classiﬁers such as Linear Discriminant or Logistic Regression.
C ONCLUSION AND P ERSPECTIVES
M ETABU provides an algorithm-dependent way to achieve AutoML, through learning meta-features as linear combinations of the manually designed meta-features of the literature, optimized to capture both the top conﬁgurations for the datasets and their topology, via preserving their Wasserstein and
Gromov-Wasserstein distances.
The efﬁciency of the approach is empirically demonstrated as the
M ETABU meta-features contribute to outperform strong baselines, including AutoSkLearn (Feurer et al., 2014) and PMF (Fusi et al., 2018).
An interesting side-product of the approach is to shed some light on the complexity of the AutoML problem, by estimating the intrinsic dimension of the dataset landscape.
Surprisingly, this intrinsic dimension is relatively modest ( < 14 ).
While this result is comforting when considering the small number of datasets in the AutoML benchmarks, it should however be taken with a grain of salt: the intrinsic dimension might merely reﬂect the speciﬁcs of the OpenML benchmark, as the datasets might have been selected over the years to provide evidence for the merits of mainstream ML algorithms while discarding too hard datasets.
A perspective for further research is to assess the validity of the proposed meta-features and the stability of intrinsic dimensions on other AutoML benchmarks: the underlying question is to which extent AutoML, too, is prone to overﬁtting.
Another perspective is to exploit M ETABU to conduct a comprehensive empirical assessment of a new algorithm A on a time budget, by alternatively learning the meta-features relative to A , and selecting the datasets most diverse according to these meta-features, in the spirit of experiment design.
E THICS STATEMENT
The approach is not concerned with privacy and conﬁdentiality of the data.
The AutoML goal aims to reduce the computational resources needed to get the peak performance from an ML portfolio of algorithms or pipelines.
A CKNOWLEDGMENTS
We gratefully thank the anonymous reviewers for their constructive comments and suggestions.
This work, and in particular Herilalaina Rakotoarison, is fully funded by ADEME (#1782C0034) project NEXT.
This work is also supported by TAILOR, an ICT48 network funded by EU Horizon 2020 programme
GA 952215.
Learning Meta-Features for AutoML Supplementary Material
The supplementary material includes additional details on:
• The augmentation of the OpenML benchmark (Appendix A); • The pseudo-code of the algorithm (Appendix B); • The experimental setting, performance indicators and validation procedure (Appendix C); • The hyper-parameter conﬁguration space (Appendix D); • The list of basic meta-features and baseline meta-featuresets (Appendix D); • The details of the computational time (Appendix F); • The insights into the AutoML problem provided by the approach: intrinsic dimensionality (Appendix G.1) and visualization of the niches of the considered ML algorithms (Appendix G.2); • The detailed results with standard deviation on all three tasks (Appendix H).
• Pairwise comparison of M ETABU with baseline meta-features (Appendix I).
• Sensitivity analysis of dimension d (Appendix J).
• Performance curves from Task 2 (Appendix K).
A T HE AUGMENTED O PEN ML BENCHMARK SUITE
The visualisation of the augmented benchmark (Fig. 6, projected using tSNE (van der Maaten &
Hinton, 2008) on the basic representation space), shows that the datasets built by bootstrapping of some initial dataset E form a cluster close to E (as could have been expected as the manually designed meta-features are stable under small stochastic variations), and separated from the clusters generated from other datasets, suggesting that the initial benchmark suite only sparsely paves the basic meta-feature space.
Complementary experiments (omitted) with perplexity ranging in [5, 10, 15, 25, 30, 40, 50] show that clusters generated by augmentation of different OpenML datasets keep staying far apart from one another.
B P SEUDO C ODE OF M ETABU
The learning procedure of M ETABU is described in Alg. 1, detailing the description presented in
Section 3 of the main paper.
The density on the hyper-parameter space, used to sample hyperparameter conﬁgurations for a given dataset depending on the considered meta-features (involved in
Tasks 2 and 3) is presented in Alg. 2.
Data: Set of n training datasets, each represented with its basic representation (meta-feature vector) x
i and its target representation (set of top 20 hyper-parameters)
z i for i = 1 . . .
n .
Result:
Embedding layer ψ ∗ //
Build
projected target representation
1 C i,j ← d 2 W ( z i , z j ) for i = 1 . . .
n, j = 1 . . .
n ; /* Pairwise Wass.
Dist.
*/ 2 Estimate intrinsic dimension d from matrix C using (Facco et al., 2017); 3 u ← MDS( C, d ) ; /* Multidimensional Scaling */ //
Learn ψ 4 ψ ← Linear( 135 , d ) ; /
* 135 basic meta-features.
*/ 5 x ← 1 p ni = δ x i ; 6 L ← FGW as deﬁned in Eq. 1; 7 ψ ∗ ← ADAM( L , ψ (cid:93) x , u );
Data: Set of n training datasets, each represented with its meta-feature vector x
i and its set of top 20 hyper-parameters Θ i for i = 1 . . .
n .
Test dataset represented with its meta-feature vector x .
Result: Distribution of conﬁgurations z .
1 Order training datasets such that: || x − x (cid:96)
|| <
|| x − x (cid:96) +1 || for (cid:96)
= 1 . . .
n ; 2 z = 1 Z (cid:80) 10 i = (cid:96) exp ( − (cid:96) )
(cid:80) θ
∈ Θ (cid:96) θ ;
C M EASURING PERFORMANCE INDICATORS
As said, the OpenML benchmark includes 72 datasets, with only 64 of them having a target representation.
The other 8 datasets are too heavy (e.g. ImageNet) to launch the many runs required to estimate their target representation.
For Task 1, the performance indicator is measured along a Leave-One-Out procedure, with 64 folds: in each fold, all datasets but one are used to train the M ETABU meta-features; the NDCG@k is measured on the remaining dataset.
Eventually, the NDCG@k are averaged over all 64 folds.
For Tasks 2 and 3, the performance indicator is likewise measured using a Leave-One-Out procedure with 64 folds.
The difference is that besides the remaining dataset, the 8 datasets with no target representation at all are also used as test datasets.
In Tasks 2 and 3, the performance associated with a hyper-parameter conﬁguration for a dataset is computed after training the model on 1 CPU with time budget of 15 mn, with memory less than 8Gb, using the train/validation/test splits given by OpenML; the validation score is estimated using a 5-CV strategy.
In Task 2, for each test dataset, and for each meta-feature set mf in {M ETABU , AutoSkLearn, Landmark, SCOT}:
• The distribution
[Equation]
is deﬁned, with z (cid:96) the target representation of the (cid:96) -th neighbor of the considered (test) dataset, among the training datasets, according to the Euclidean distance based on their mf representations.
• For 1 ≤ t ≤ T , a hyper-parameter conﬁguration is independently drawn from ˆ z mf , and a model is learned using this conﬁguration; • The performance of this model is measured on a validation dataset; • The model with best validation performance up to iteration t is retained for each meta-feature mf and its performance on the test dataset is computed; • The rank r ( t, mf ) is determined by comparing the performance on the test set, of the models retained for each meta-feature type.
• The performance curve reports r ( t, mf ) , averaged over test datasets.
In Task 3, the meta-features are used to initialize the performance model:
• In AutoSkLearn, the performance model for AutoSkLearn is initialized as follows.
The best conﬁgurations for the top-10 neighbors of the current dataset are retained and run on the current dataset; their performance is used to initialize the Bayesian Optimisation search using the SMAC BO implementation Hutter et al.
These top-10 neighbors are computed using the Euclidean distance on the meta-feature set.
Note that AutoSkLearn meta-features have been crafted to achieve automatic conﬁguration selection in the context of the AutoSkLearn pipeline (Pedregosa et al., 2011), thus constituting a most strong baseline on Task 3.
• For PMF, the best conﬁgurations for the top-5 neighbors of the current dataset are likewise selected; their performance is computed to ﬁll the row of the collaborative matrix associated to the current dataset, and determine the latent representation of the current dataset.
The probabilistic model learned from the matrix is used to select further hyper-parameter conﬁgurations; their performances are computed and used to reﬁne the latent representation of the dataset.
D T HE HYPER - PARAMETER CONFIGURATION SPACES
The hyper-parameters used for Adaboost, Random Forest and SVM and their range are detailed in
Tables 1 and 2.
For AutoSkLearn, we only included the list of considered hyper-parameters; their ranges are detailed in AutoSkLearn (Feurer et al., 2015).
The hyper-parameter space used in PMF is the same as in AutoSkLearn.
The M ETABU implementation uses the ConﬁgSpace library (Lindauer et al., 2019) to manage the hyper-parameters
[Figure]
[Table] [Figure] [Table] [Figure]
[Table]
[Figure] [Table] [Figure] [Table]
[Figure] [Table] [Figure] [Table]
[Figure]
[Table]
[Figure]
[Table]
[Figure] [Table]
[Figure]
[Table]
[Figure]
[Table]
[Table]
E L IST OF META - FEATURES
The list of meta-features used in the experiments is detailed in Tables 3 and 4.
Meta-features are extracted with PyMFE (Alcobaça et al., 2020) except for AutoSkLearn, SCOT and Landmark meta-features which are computed from the AutoSkLearn library.
[Figure]
AutoSkLearn
Landmark
[Figure]
+
[Figure]
+
[Figure]
+
[Figure]
+
[Figure]
+ n
Computethefractionofborderlinepoints.
+ n
Ratioofintraandextraclassnearestneighbordistance.
+ n
Errorrateofthenearestneighborclassiﬁer.
+ n
Computethenon-linearityofthek-NNClassiﬁer.
+ t
Fractionofhyperspherescoveringdata.
+ t
Computetheaveragenumberoffeaturesperdimension.
+ t
ComputetheaveragenumberofPCAdimensionsperpoints.
+ t
ComputetheratioofthePCAdimensiontotheoriginaldimension.
+ ch
ComputetheCalinskiandHarabaszindex.
+ int
ComputetheINTindex.
+ nre
Computethenormalizedrelativeentropy.
+ pb
Computethepearsoncorrelationbetweenclassmatchingandinstancedistances.
+ sc
Computethenumberofclusterswithsizesmallerthanagivensize.
+ sil
Computethemeansilhouettevalue.
+ vdb
ComputetheDaviesandBouldinIndex.
+ vdu
ComputetheDunnIndex.
+ leaves
ComputethenumberofleafnodesintheDTmodel.
+ leaves_branch
ComputethesizeofbranchesintheDTmodel.
+ leaves_corrob
ComputetheleavescorroborationoftheDTmodel.
+ leaves_homo
ComputetheDTmodelHomogeneityforeveryleafnode.
+ leaves_per_class
ComputetheproportionofleavesperclassinDTmodel.
+ nodes
Computethenumberofnon-leafnodesinDTmodel.
+ nodes_per_attr
ComputetheratioofnodespernumberofattributesinDTmodel.
+ nodes_per_inst
Computetheratioofnon-leafnodespernumberofinstancesinDTmodel.
+ nodes_per_level
ComputetheratioofnumberofnodespertreelevelinDTmodel.
+ nodes_repeated
ComputethenumberofrepeatednodesinDTmodel.
[Table]
ComputethefeaturesimportanceoftheDTmodelforeachattribute.
[Table]
Computetheabsolutevalueofthecorrelationofdistinctdatasetcolumnpairs
[Table]
Computethenumberofdistincthighlycorrelatedpairofattributes
[Table]
Computethenumberofattributesnormallydistributedbasedinagivenmethod
[Table]
Computethenumberofattributeswithatleastoneoutliervalue.
[Table]
[Figure]
+
[Figure]
Description
AutoSkLearn
Landmark
SCOT
Metabu
[Figure]
_root
ComputetheRoy’slargestroot.
+ sd
Computethestandarddeviationofeachattribute.
+ sd_ratio
Computeastatisticaltestforhomogeneityofcovariances.
+ skewness
Computetheskewnessforeachattribute.
+ sparsity
Compute(possiblynormalized)sparsitymetricforeachattribute.
+ t_mean
Computethetrimmedmeanofeachattribute.
+ var
Computethevarianceofeachattribute.
+ w_lambda
ComputetheWilks’Lambdavalue.
+ attr_conc
Computeconcentrationcoef.
ofeachpairofdistinctattributes.
+ attr_ent
ComputeShannon’sentropyforeachpredictiveattribute.
+ class_conc
Computeconcentrationcoefﬁcientbetweeneachattributeandclass.
+ class_ent
ComputetargetattributeShannon’sentropy.
+ eq_num_attr
Computethenumberofattributesequivalentforapredictivetask.
+ joint_ent
Computethejointentropybetweeneachattributeandclass.
+ mut_inf
Computethemutualinformationbetweeneachattributeandtarget.
+ ns_ratio
Computethenoisinessofattributes.
+ cohesiveness
Computetheimprovedversionoftheweighteddistance,thatcaptureshowdenseorsparseistheexampledistribution.
+ conceptvar
Computetheconceptvariationthatestimatesthevariabilityofclasslabelsamongexamples.
+ impconceptvar
Computetheimprovedconceptvariationthatestimatesthevariabilityofclasslabelsamongexamples.
+ wg_
[Figure]
Computetheweighteddistance,thatcaptureshowdenseorsparseistheexampledistribution.
+ attr_to_inst
Computetheratiobetweenthenumberofattributes.
+ cat_to_num
Computetheratiobetweenthenumberofcategoricandnumericfeatures.
+ freq_class
Computetherelativefrequencyofeachdistinctclass.
+ inst_to_attr
Computetheratiobetweenthenumberofinstancesandattributes.
+ nr_attr
Computethetotalnumberofattributes.
+ nr_bin
Computethenumberofbinaryattributes.
+ nr_cat
Computethenumberofcategoricalattributes.
+ nr_class
Computethenumberofdistinctclasses.
+ nr_inst
Computethenumberofinstances(rows)inthedataset.
+ nr_num
Computethenumberofnumericfeatures.
+ num_to_cat
Computethenumberofnumericalandcategoricalfeatures.
+ PCASkewnessFirstPC
Skewnessofexamplesontheﬁrstprincipalcomponent + PCAKurtosisFirstPC
Kurtosisofexamplesontheﬁrstprincipalcomponent + PCAFracOfCompFor95Per
Fractionofcomponentofanoverallexplainedvarianceof95% + + Landmark1NN
Performanceonenearestneighborclassiﬁer + LandmarkRandomNodeLearner
Performanceofdecisionwhenconsideringonlyonefeature + LandmarkDecisionNodeLearner
Performanceofdecisionwhenconsideringallfeatures + LandmarkDecisionTree
Performanceofdecisiontreeclassiﬁer + + LandmarkNaiveBayes
PerformanceofNaiveBayesclassiﬁer + + LandmarkLDA
PerformanceofLDAclassiﬁer + + SkewnessSTD
Standarddeviationoffeatureskewness + + SkewnessMean
Meanoffeatureskewness + + SkewnessMax
Maximumoffeatureskewness
[Table]
F C OMPUTATIONAL EFFORT
Fig.
7 indicates the runtime 6 for pre-processing (extracting the 135 meta-features, top row), and for training M ETABU (second row).
The training times for learning one model is indicated for comparison (from row 3 to 5: Adaboost, RandomForest and SVM).
G T OWARD UNDERSTANDING THE DATASET LANDSCAPE G. T HE STABILITY OF THE INTRINSIC DIMENSION
[Table]
In Table 5, we investigate how the intrinsic dimension varies when considering various numbers of datasets in OpenML.
It is observed that the intrinsic dimension tends to increase with the number of considered datasets, particularly so for SVM and to a lesser extent for RandomForest.
This suggests that the hyper-parameter conﬁgurations investigated in the OpenML benchmark for these algorithms do not sufﬁciently sample the (good regions of the) conﬁguration spaces.
G. I NTERPRETATION : I MPACT OF THE HC META - FEATURES ON THE PERFORMANCE OF THE LEARNING ALGORITHM
M ETABU meta-features are built from the initial meta-features using the trained linear mapping ψ , depending on the current learning algorithm A .
Accordingly, the importance of the initial, humanly deﬁned and interpretable meta-features w.r.t.
A can be estimated, shedding some light on which speciﬁcs of a dataset matter in order to give a good/bad performance with A .
The importance of a meta-feature w.r.t.
A is estimated as follows.
Let U = { u i,j } denote the matrix made of the multi-dimensional scaling representation of the target representation over all datasets (section 3.2).
Let v denote the ﬁrst principal component of U and let j ∗ be the index of the H column most contributing to v ( j ∗ = argmax j |(cid:104) v, h .,j (cid:105)| ).
Then the importance i A ( k ) of the k -th initial meta-feature for the A algorithm is deﬁned as the absolute value of ψ j ∗ ,k , that is, the weight of the k -th initial meta-feature to build the most important M ETABU meta-feature.
This estimate is used to visually appreciate the meta-feature importance w.r.t.
two learning algorithms A and B , by depicting each k -th meta-feature in the 2D plane as the point with coordinate ( i A ( k ) , i B ( k )) .
Intuitively, meta-features on the diagonal have the same importance for both algorithms.
Meta-features far from the diagonal are much more important for one algorithm than for the other.
The visualization of the meta-feature importance w.r.t.
AutoSkLearn and Random Forest is displayed in Fig. 8, left.
Meta-features such as KurtosisMin, LogNumberOfInstances, InverseDatasetRatio − all retained as AutoSkLearn meta-features − are critical for AutoSkLearn whereas they have no impact for RandomForest.
Inversely, some features like "pb" (average Pearson correlation between class and features) matter signiﬁcantly more for RandomForest than for AutoSkLearn.
Likewise, the meta-feature importance w.r.t.
Support Vector Machines and Random Forest is displayed in Fig.
8, right.
The skewness features (mean and std deviation over all attributes) matter signiﬁcantly more for Support Vector Machines than for RandomForest.
In retrospect, there is little surprise that the meta-features related to the potential difﬁculties of inverting the Gram matrix matter for SVM.
Overall, the impact of some meta-features for some learning algorithms is intuitive; it conﬁrms the practitioner expertise, which is comforting.
H D ETAILED RESULTS
Detailed results of Task 2 are presented in Table 6 for Random Forest, Table 7 for Adaboost and Table 8 for SVM.
We consider the Mann Whitney Wilcoxon test to assess the signiﬁcance of the rankings
[Table] [Table]
[Table]
I P AIRWISE C OMPARISONS
Figs.
9-11 highlight a side-by-side comparison of M ETABU with each baseline set of meta-features.
These comparisons establish the relative improvement over each baseline, that may be lost in the general comparison, Fig. 3.
On Random Forest pipeline, M ETABU meta-features perform on par with SCOT meta-features.
Whereas its improvement over Landmark MF is only signiﬁcant between the (approximately) 8th and 23rd iteration, M ETABU consistently outperforms Random and AutoSkLearn meta-features along the iterations.
On Adaboost, M ETABU meta-features perform similarly as Landmark meta-features.
Interestingly,
M ETABU always has a better average rank than the baselines except for the ﬁrst two iterations of the AutoSkLearn baseline.
It is seen that the p-value is most generally below the threshold .05, establishing the statistical signiﬁcance of the rank performance.
Lastly, the gaps in performance for SVM are striking.
M ETABU meta-features consistently outperform all the baselines meta-features with high conﬁdence.
J S ENSITIVITY A NALYSIS OF d
Table 9 reports the NDCG@k performance of M ETABU on Task 1 for varying values of d , showing that: i) the best results are obtained for the intrinsic dimension in the vast majority of cases; ii) the sensitivity w.r.t.
d is very moderate.
The intrinsic dimension d of the OpenML benchmark is circa 6 for AutoSkLearn, 8 for Adaboost,for RandomForest and 14 for Support Vector Machines.
[Table]
K P ERFORMANCE C URVES
These curves, in addition to the rank results displayed in Fig.
3b, display the performance values on 10 representative datasets from OpenML CC-18, in the context of Task 2 for respectively Random
Forest (Fig. 12), Adaboost (Fig. 13), and SVM (Fig. 14).
At each iteration, the curve reports the average performance value with its the standard deviation (on 3 runs).