diamond mixed effects models in python

Diamond: Mixed Effects Models in Python

Timothy Sweetser

Stitch Fixhttp://github.com/stitchfix/diamond

[email protected]

@hacktuarial

November 27, 2017

Timothy Sweetser (Stitch Fix) Diamond November 27, 2017 1 / 32

Overview

1 context and motivation

2 what is the mixed effects model

3 application to recommender systems

4 computation

5 diamond

6 appendix


context and motivation

Stitch Fix


what is the mixed effects model

Refresher: Linear Model

y ∼ N(Xβ, σ2I )

y is n x 1

X is n x p

β is an unknown vector of length p

σ2 is an unknown, nonnegative constant



Mixed Effects Model

y |b ∼ N(Xβ + Zb, σ2I )

We have a second set of features, Z, n x q

the coefficients on Z are b ∼ N(0,Σ)

Σ is q x q



simple example of a mixed effects model

You think there is some relationship between a woman’s height and theideal length of jeans for her:

length = α + β ∗ height + ε

But, you think the length might need to be shorter or longer, dependingon the silhouette of the jeans. In other words, you want α to vary bysilhouette.



why might silhouette affect length ∼ height?

SkinnyBootcut



linear model: formula

Linear models can be expressed in formula notation, used by patsy,statsmodels, and R

import statsmodels.formula.api as smf

lm = smf.ols(’length ~ 1 + height ’, data=train_df).fit()

in math, this means length = Xβ + ε

Xi = [1.0, 64.0]

β is what we want to learn, using (customer, item) data from jeansthat fit well



linear model: illustration



mixed effects: formula

Now, allow the intercept to vary by silhouette

mix = smf.mixedlm(’length ~ 1 + height ’,

data=train_df ,

re_formula=’1’,

groups=’silhouette ’,

use_sparse=True).fit()



illustration



mixed effects regularization

y |b ∼ N(Xβ + Zb, σ2I )

Sort by silhouette:

Z =

1bootcut 0 0 0

0 1skinny 0 00 0 1straight 00 0 0 1wide

X is n x 2

Z is n x 4



matrices and formulas - mixed effects

Zb =

1bootcut 0 0 0

0 1skinny 0 00 0 1straight 00 0 0 1wide

µbootcutµskinnyµstraightµwide

Each µsilhouette is drawn from N(0, σ2)

This allows for deviations from the average effects, µ and β, bysilhouette, to the extend that the data support it


application to recommender systems

a basic model

rating ∼ 1 + (1|user id) + (1|item id)

In math, this meansrui = µ+ αu + βi + εui

where

µ is an unknown constant

αu ∼ N(0, σ2user )

βi ∼ N(0, σ2item)

some items are more popular than others

some users are more picky than others



add features

rating ∼ 1 + (1 + item feature1 + item feature2|user id)+

(1 + user feature1 + user feature2|item id)

Now,

αu ∼ N(0,Σuser )

βi ∼ N(0,Σitem)

the good: we’re using features! learn individual and shared preferences

helps with new items, new users

the bad: scales as O(p2)



comments

rating ∼ 1 + (1 + item feature1 + item feature2|user id)+

(1 + user feature1 + user feature2|item id)

this is a parametric model, and much less flexible than trees, neuralnetworks, or matrix factorization

but you don’t have to choose!

you can use an ensemble, or use this as a feature in another model


computation

computation

How can you fit models like this? We were using R’s lme4 package

Maximum likelihood computation works like this:

Estimate covariance structure of random effects, Σgiven Σ, estimate coefficients β and bwith these, compute loglikelihoodrepeat until convergence

Doesn’t scale well with number of observations, n

lme4 supports a variety of generalized linear models, but is notoptimized for any one in particular

Is it really necessary to update hyperparameters Σ every time youestimate the coefficients?


computation

diamond

Diamond solves a similar problem using these tricks:

Input Σ. Conditional on Σ, the optimization problem is convex

Use Hessian of L2 penalized loglikelihood function (pencil + paper)

logistic regressioncumulative logistic regression, for ordinal responsesif Y ∈ (1, 2, 3, . . . , J),

log

(Pr(Y ≤ j)

1− Pr(Y ≤ j)

)= αj + βT x

for j = 1, 2, . . . , J − 1

quasi-Newton optimization techniques from Minka 2003


computation

other solvers

How else could you fit mixed effects models?

”Exact” methods

Full Bayes: MCMC. e.g. PyStan, PyMC3, Edwarddiamond, but you must specify the hyperparameters Σstatsmodels only supports linear regression for Gaussian-distributedoutcomesR/lme4

Approximate methods

Simple, global L2 regularizationFull Bayes: Variational Inferencemoment-based methods


diamond

Speed test

MovieLens, 20M observations like (userId, movieId, rating)

binarize (ordinal!) rating → 1(rating > 3.5)

this is well-balanced

Fit a model like

rating ∼ 1 + (1|user id) + (1|item id)


diamond

diamond

from diamond.glms.logistic import LogisticRegression

import pandas as pd

train_df = ...

priors_df = pd.DataFrame({

’group’: [’userId ’, ’movieId ’],

’var1’: [’intercept ’] * 2,

’var2’: [np.nan , np.nan],

’vcov’: [0.9, 1.0]

})

m = LogisticRegression(train_df=train_df , priors_df=

priors_df)

results = m.fit(’liked ~ 1 + (1 | userId) + (1 | movieId)’,

tol=1e-5, max_its=200 , verbose=True)


diamond

Speed test vs. sklearn

Diamond

estimate covariance on sample of 1M observations in R. 1-time, 60minutes

σ2user = 0.9, σ2

movie = 1.0

Takes 83 minutes on my laptop to fit in diamond

sklearn LogisticRegression

use cross validation to estimate regularization. 1-time, takes 24minutes

grid search would be a fairer comparison

refit takes 1 minute


diamond

diamond vs. sklearn predictions

Global L2 regularization is a good approximation for this problem, but maynot work as well when σ2

user >> σ2item, vice versa, or for more models with

more featuresTimothy Sweetser (Stitch Fix) Diamond November 27, 2017 23 / 32

diamond

diamond vs. R

lme4 takes more than 360 minutes to fit


diamond

diamond vs. moment-based

active area of research by statisticians at Stanford, NYU, elsewhere

very fast to fit simple models using method of moments

e.g. rating ∼ 1 + (1 + x |user id)

or rating ∼ 1 + (1|user id) + (1|item id)

Fitting this to movie lens 20M took 4 minutes

but not rating ∼ 1 + (1 + x |user id) + (1|item id)


diamond

diamond vs. variational inference

I fit this model in under 5 minutes using Edward, and didn’t have toinput Σ.

VI is very promising!


diamond

why use diamond?

http://github.com/stitchfix/diamond

scales well with number of observations (compared to pure R, MCMC)

solves the exact problem (compared to variational, moment-based)

scales ok with P (compared to simple global L2)

supports ordinal logistic regression

if Y ∈ (1, 2, 3, . . . , J),

log

(Pr(Y ≤ j)

1− Pr(Y ≤ j)

)= αj + βT x

for j = 1, 2, . . . , J − 1Reference: Agresti, Categorical Data Analysis


diamond

summary

mixed effects models are useful for recommender systems and otherdata science applications

they can be hard to fit for large datasets

they play well with other kinds of models

diamond, moment-based approaches, and variational inference aregood ways to estimate models quickly


diamond

discussion


diamond

References I

Patrick Perry (2015)

Moment Based Estimation for Hierarchical Models

https://arxiv.org/abs/1504.04941

Alan Agresti (2012)

Categorical Data Analysis, 3rd Ed.

ISBN-13 978-0470463635

Gao + Owen (2016)

Estimation and Inference for Very Large Linear Mixed Effects Models

https://arxiv.org/abs/1610.08088

Edward

A Library for probabilistic modeling, inference, and criticism.

https://github.com/blei-lab/edward


diamond

References II

inka

A comparison of numerical optimizers for logistic regression

https://tminka.github.io/papers/logreg/minka-logreg.pdf

me4

https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf


appendix

regularization

Usual L2 regularization. If each βi ∼ N(0, 1λ)

minimizeβ

loss +1

2βT (λIp)β

Here, the four b coefficient vectors are samples from N(0,Σ). If we knewΣ, the regularization would be

minimizeb

loss +1

2bT

Σ−1 0 0 0

0 Σ−1 0 00 0 Σ−1 00 0 0 Σ−1

b


diamond mixed effects models in python

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