wax-ml 0.0.4

A Python library for machine-learning and feedback loops on streaming data

WAX-ML: A Python library for machine-learning and feedback loops on streaming data

Quickstart | Install guide | Change logs | Reference docs

🌊 Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing ... 🌊

WAX-ML is a research-oriented Python library providing tools to design powerful machine learning algorithms and feedback loops working on streaming data.

It strives to complement JAX with tools dedicated to time series.

WAX-ML makes JAX-based programs easy to use for end-users working with pandas and xarray for data manipulation.

WAX-ML provides a simple mechanism for implementing feedback loops, allows the implementation of reinforcement learning algorithms with functions, and makes them easy to integrate by end-users working with an object-oriented reinforcement learning framework from Gym.

It is released with an Apache open-source license and available at \footnote{https://github.com/eserie/wax-ml}.

To learn more, you can read our article here or simply access the code in this repository.

Goal

WAX-ML's goal is to expose "traditional" algorithms that are often difficult to find in standard python ecosystem and are related to time-series and more generally to streaming data.

WAX-ML wants to make it easy to work with algorithms from very various computational domains such as machine learning, online learning, reinforcement learning, optimal control, time-series analysis, optimization, statistical modeling.

For now, WAX-ML focuses on time series algorithms as this is one of the areas of machine learning that lacks the most dedicated tools. Working with time series is notoriously known to be difficult and often requires very specific algorithms (statistical modeling, filtering, optimal control).

Even though some of the modern machine learning methods such as RNN, LSTM, or reinforcement learning can do an excellent job on some specific time series problems, most of the problems require to keep using more traditional algorithms such as linear and non-linear filters, FFT, the eigendecomposition of matrices, Riccati solvers for optimal control and filtering...

By adopting a "no-framework" approach WAX-ML aims to be an efficient tool to combine modern machine-learning approaches with more traditional ones.

Some work has been done in this direction, for example, see [2] in References where transformer encoder architectures are massively accelerated, with limited accuracy costs, by replacing the self-attention sublayers with a standard, non-parameterized Fast Fourier Transform (FFT). Their implementation, not yet published, is based on Flax, a tool from the JAX ecosystem.

WAX-ML may also be useful for developing research ideas in areas such as online machine learning (see [1] in References) and development of control, reinforcement learning, and online-optimization methods.

What WAX-ML does?

Well, WAX-ML has some pretty ambitious design and implementation goals.

To do things right, we decided to start it small and in an open-source design from the beginning.

For now, WAX-ML contains:

• transformation tools that we call "unroll" transformations allowing us to apply any transformation, possibly stateful, on sequential data. It generalizes the RNN architecture to any stateful transformation allowing to implement any kind of "filter".

• A "stream" module permitting to synchronize data streams with different time resolutions (see 🌊 Streaming Data 🌊)

• pandas and xarray "accessors" permitting to apply any function implemented with Haiku module on DataFrame, Series, Dataset, DataArray data containers.

• Ready-to-use exponential moving average, variance, and covariance filters.

  • for JAX users: as Haiku modules (EWMA, ... see the complete list in our API documentation ),
  • for pandas/xarray users: with drop-in replacement of pandas ewm accessor.

• a simple module OnlineSupervisedLearner to implement online learning algorithms for supervised machine learning problems.

• Building blocks for designing feedback loops in reinforcement learning. We provide a module called GymFeedback that allows the implementation of a Gym feedback loops (see Gym documentation).

• universal functions: we use EagerPy to implement "universal" modules that can work with TensorFlow, PyTorch, JAX, and NumPy tensors. At the moment, we only implement a demonstration module: EagerEWMA.

What is JAX?

JAX is a research-oriented computational system implemented in python that leverages the XLA optimization framework for machine learning computations. It makes XLA usable with the NumPy API and some functional primitives for just-in-time compilation, differentiation, vectorization, and parallelization. It allows building higher-level transformations or "programs" in a functional programming approach.

Why to use WAX-ML?

If you already deal with time series and are a pandas or xarray user, but you want to use the impressive tools of the JAX ecosystem, then WAX-ML might be the right tool for you, as it implements pandas and xarray accessors to apply JAX functions.

If you are already a user of JAX, you may be interested in adding WAX-ML to your toolbox to address time series problems.

Design

WAX-ML is a research-oriented library. It relies on JAX and Haiku functional programming paradigm to ease the development of research ideas.

WAX-ML is a bit like Flux in Julia programming language.

WAX-ML is not a framework but either a set of tools that aim to complement JAX Ecosystem.

Functional API

In WAX-ML, we pursue a functional programming approach inherited from JAX.

In this sense, WAX-ML is not a framework, as most object-oriented libraries offer. Instead, we implement "functions" that must be pure to exploit the JAX ecosystem.

We use the "module" mechanism proposed by the Haiku library to easily generate pure function pairs, called init and apply in Haiku, to implement programs that require the management of parameters and/or state variables. In this way, we can recover all the advantages of object-oriented programming but exposed in the functional programming approach.

This approach gives a lot of freedom in the type of ideas that can be implemented.

For instance, JAX has been used recently to accelerate by two orders of magnitude fluid dynamics simulations (see [6] ).

WAX-ML does not want to reinvent the wheel by reimplementing every algorithm. We want existing machine learning libraries to work well together while trying to leverage their strength, which is easy to do with a functional programming approach.

To demonstrate this, in the current version of WAX-ML, we have constructed various examples, such as an exponential moving average, the implementation and calibration of an LSTM architecture with a standard supervised machine learning workflow, or the implementation of online learning and reinforcement learning architectures. They are treated equally and laid out with flat organization in our sub-package wax.modules.

Contents

• 🚀 Quickstart: Colab in the Cloud 🚀
• 🌊 Streaming Data 🌊
• ♻ Feedbacks ♻
• ⚒ Implementation ⚒
• Future plans
• Installation
• Disclaimer
• Development
• References
• License
• Citing WAX-ML
• Reference documentation

🚀 Quickstart 🚀

Jump right in using a notebook in your browser, connected to a Google Cloud GPU or simply read our notebook in the documentation.

Here are some starter notebooks:

• 〰 Compute exponential moving averages with xarray and pandas accessors 〰 : , Open in Documentation
• ⏱ Synchronize data streams ⏱ : , Open in Documentation
• 🌡 Binning temperatures 🌡 : , Open in Documentation
• 🎛 The three steps workflow 🎛 : , Open in Documentation
• 🔭 Reconstructing the light curve of stars with LSTM 🔭: , Open in Documentation
• 🦎 Online linear regression with a non-stationary environment 🦎: , Open in Documentation

🌊 Streaming Data 🌊

WAX-ML may complement JAX ecosystem by adding support for streaming data.

To do this, WAX-ML implements a unique data tracing mechanism that prepares for fast access to in-memory data and allows the execution of JAX tractable functions such as jit, grad, vmap, or pmap.

This mechanism is somewhat special in that it works with time-series data.

The wax.stream.Stream object implements this idea. It uses python generators to synchronize multiple streaming data streams with potentially different temporal resolutions.

The wax.stream.Stream object works on in-memory data stored in xarray.Dataset.

To work with "real" streaming data, it should be possible to implement a buffer mechanism running on any python generator and to use the synchronization and data tracing mechanisms implemented in WAX-ML to apply JAX transformations on batches of data stored in memory. (See our WEP4 enhancement proposal)

⌛ Adding support for time dtypes in JAX ⌛

At the moment datetime64 and string_ dtypes are not supported in JAX.

WAX-ML add support for datetime64 and string_ NumPy dtypes in JAX. To do so, WAX-ML implements:

• an encoding scheme for datetime64 relying on pairs of 32-bit integers similar to PRNGKey in JAX.
• an encoding scheme for string_ relying on LabelEncoder of Scikit-learn.

By providing these two encoding schemes, WAX-ML makes it easy to use JAX algorithms on data of these types.

Currently, the types of time offsets supported by WAX-ML are quite limited and we collaborate with the pandas, xarray, and Astropy teams to further develop the time manipulation tools in WAX-ML (see "WEP1" in WEP.md).

pandas and xarray accessors

WAX-ML implements pandas and xarray accessors to ease the usage of machine-learning algorithms implemented with functions implemented with Haiku modules on high-level data APIs :

• pandas's DataFrame and Series
• xarray's Dataset and DataArray.

To load the accessors, run:

from wax.accessors import register_wax_accessors
register_wax_accessors()

Then run the "one-liner" syntax:

<data-container>.stream(…).apply(…)

Already implemented modules

We have some modules (inherited from Haiku modules) ready to be used in wax.modules (see our api documentation).

They can be considered as "building blocks" that can be reused to build more advanced programs to run on streaming data. We have some "fundamental" modules that are specific to time series management,

• the Buffer module which implements the buffering mechanism
• the UpdateOnEvent module which allows to "freeze" the computation of a program and to update it on some events in the "local flow". To illustrate the use of this module we show how it can be used to compute the opening, high and closing quantities of temperatures recorded during a day, the binning process being reset at each day change. We show an illustrative graph of the final result:

We have a few more specific modules that aim to reproduce some of the logic that pandas users may be familiar with, such as:

• Lag to implement a delay on the input data
• Diff to compute differences between values over time
• PctChange to compute the relative difference between values over time.
• RollingMean to compute the moving average over time.
• EWMA, EWMVar, EWMCov, to compute the exponential moving average, variance, covariance of the input data.

Finally, we implement domain-specific modules for online learning and reinforcement learning such as OnlineSupervisedLearner and GymFeedback (see dedicated sections).

For now, WAX-ML offers direct access to some modules through specific accessors for pandas and xarray users. For instance, we have an implementation of the "exponential moving average" directly accessible through the accessor <data-container>.ewm(...).mean() which provides a drop-in replacement for the exponential moving average of pandas.

For now, WAX-ML offers direct access to some modules through specific accessors for pandas and xarray users.

For instance, you can see our implementation of the "exponential moving average". This is a drop-in replacement for the exponential moving average of pandas.

Let's show how it works on the "air temperature" dataset from xarray.tutorials:

import xarray as xr
da = xr.tutorial.open_dataset("air_temperature")
dataframe = da.air.to_series().unstack(["lon", "lat"])

Pandas ewma:

air_temp_ewma = dataframe.ewm(alpha=1.0 / 10.0).mean()

WAX-ML ewma:

air_temp_ewma = dataframe.wax.ewm(alpha=1.0 / 10.0).mean()

Apply a custom function to a Dataset

Now let's illustrate how WAX-ML accessors work on xarray datasets.

from wax.modules import EWMA


def my_custom_function(dataset):
    return {
        "air_10": EWMA(1.0 / 10.0)(dataset["air"]),
        "air_100": EWMA(1.0 / 100.0)(dataset["air"]),
    }


dataset = xr.tutorial.open_dataset("air_temperature")
output, state = dataset.wax.stream().apply(
    my_custom_function, format_dims=dataset.air.dims
)

_ = output.isel(lat=0, lon=0).drop(["lat", "lon"]).to_pandas().plot(figsize=(12, 8))

You can see our Documentation for examples with EWMA or Binning on the air temperature dataset.

⏱ Synchronize streams ⏱

Physicists have brought a solution to the synchronization problem called the Poincaré–Einstein synchronization (See Poincaré-Einstein synchronization Wikipedia page for more details). In WAX-ML we implement a similar mechanism by defining a "local time", borrowing Henri Poincaré terminology, to denominate the timestamps of the stream (the "local stream") in which the user wants to apply transformations and unravel all other streams. The other streams, which we call "secondary streams", are pushed back in the local stream using embedding maps which specify how to convert timestamps from a secondary stream into timestamps in the local stream.

This synchronization mechanism permits to work with secondary streams having timestamps at frequencies that can be lower or higher than the local stream. The data from these secondary streams will be represented in the "local stream" either with the use of a forward filling mechanism for lower frequencies or a buffering mechanism for higher frequencies.

We implement a "data tracing" mechanism to optimize access to out-of-sync streams. This mechanism works on in-memory data. We perform the first pass on the data, without actually accessing it, and determine the indices necessary to later access the data. Doing so we are vigilant to not let any "future" information pass through and thus guaranty a data processing that respects causality.

The buffering mechanism used in the case of higher frequencies works with a fixed buffer size (see the WAX-ML module wax.modules.Buffer which allows us to use JAX / XLA optimizations and have efficient processing).

We give simple usage examples in our documentation.

Let's illustrate with a small example how wax.stream.Stream synchronizes data streams.

Let's use the dataset "air temperature" with :

• An air temperature is defined with hourly resolution.
• A "fake" ground temperature is defined with a daily resolution as the air temperature minus 10 degrees.

from wax.accessors import register_wax_accessors

register_wax_accessors()

from wax.modules import EWMA


def my_custom_function(dataset):
    return {
        "air_10": EWMA(1.0 / 10.0)(dataset["air"]),
        "air_100": EWMA(1.0 / 100.0)(dataset["air"]),
        "ground_100": EWMA(1.0 / 100.0)(dataset["ground"]),
    }

results, state = dataset.wax.stream(
    local_time="time", ffills={"day": 1}, pbar=True
).apply(my_custom_function, format_dims=dataset.air.dims)

_ = results.isel(lat=0, lon=0).drop(["lat", "lon"]).to_pandas().plot(figsize=(12, 8))

⚡ Performance on big dataframes ⚡

Check out our Documentation to see how you can use our "3-step workflow" to speed things up!

🔥 Speed 🔥

WAX-ML algorithms are implemented in JAX, so they are fast!

The use of JAX allows for leveraging hardware accelerators that optimize programs for the CPU, GPU, and TPU.

With WAX-ML, you can already compute an exponential moving average on a dataframe with 1 million rows with a 3x to 100x speedup (depending on the data container you use and speed measurement methodology) compared to pandas implementation. (See our notebook in the Quick Start Documentation or in Colaboratory ).

♻ Feedbacks ♻

Feedback is a fundamental notion in time-series analysis and has a wide history (see Feedback Wikipedia page for instance). So, we believe it is important to be able to implement them well in WAX-ML.

A fundamental piece in the implementation of feedbacks is the delay operator that we implement with the module Lag. This module is itself implemented with a kind of more fundamental module in WAX-ML which implements the buffering mechanism: the Buffer module.

The linear state-space models used to model linear time-invariant systems in signal theory are a well-known place where feedbacks are used to implement for instance infinite impulse response filters. This should be easily implemented with the WAX-ML tools and should be implemented in it at a later time.

Another example is control theory or reinforcement learning. In these fields, feedback is used to make an agent and an environment interacting. This generally results in a non-trivial global dynamic. In WAX-ML, we propose a simple module called GymFeedBack that allows the implementation of reinforcement learning experiences.

This is built from an agent and an environment with an agent being a function with:

• an "observation" input and an "action" output and
• an environment being a function with a pair "(action, raw observation)" as input and a pair "(reward, observation)" as output. We can represent them with block diagrams:

A feedback instance GymFeedback(agent, env) is a module with a "raw observation" input and a "reward" output. We can describe furthermore an instance of the module GymFeedback(agent, env) by representing it with a pair of pure functions init and apply as is the custom in Haiku library (see their documentation for more details).

We can describe how the agent and environment are assembled to build the module with the following block diagram describing these two pure functions:

We also make a concrete usage of this architecture in our 🦎: online learning example , Open in Documentation 🦎

Here is an illustrative plot of the final result of the study:

We see that the regret first converges, then jumps on step 2000, and finally readjusts to a new regime for the linear regression problem. We see that the weights converge to the correct values in both regimes.

Compatibility with other reinforcement learning frameworks

In addition, to ensure compatibility with other tools in the Gym ecosystem, we propose a transformation mechanism to transform functions into standard stateful python objects following the Gym API for agents and environments implemented in deluca. These wrappers are in the wax.gym package.

WAX-ML implements callbacks in the wax.gym package. The callback API was inspired by the one in the one in dask.

WAX-ML should provide tools for reinforcement learning that should complement well those already existing such as RLax or deluca.

⚒ Implementation ⚒

Currently, WAX-ML uses the Haiku module API and Haiku transformation functions to facilitate the development of robust and reusable features.

Haiku's module API integrates well with the functional paradigm of JAX and makes it easy to develop "mini-languages" tailored to specific scientific domains.

** Flax also has a module API. We should consider using it in WAX-ML too!

Universal functions

WAX-ML uses EagerPy to efficiently mix different types of tensors and develop high level APIs to work with e.g. NumPy, pandas, xarray.

The use of EagerPy should allow, if necessary, to propose implementations of algorithms compatible with other tensor libraries such as NumPy, TensorFlow, and PyTorch, with native performance.

We currently have a working example for the EWMA module which is implemented in wax.universal.modules. See the code in :

wax.universal.modules.ewma.py
wax.universal.modules.ewma_test.py

For now, the core algorithms in WAX-ML are only implemented in JAX to "stay focused". But if there is interest in implementing universal algorithms, more work could be done from this simple example.

Currently, WAX-ML uses an internalized version EagerPy implemented in wax.external.eagerpy sub-package.

Future plans

Feedback loops and control theory

We would like to implement other types of feedback loops in WAX-ML. For instance, those of the standard control theory toolboxes, such as those implemented in the SLICOT SLICOT library.

Many algorithms in this space are absent from the python ecosystem and we aim to provide JAX-based implementations and expose them with a simple API.

An idiomatic example in this field is the Kalman filter, a now-standard algorithm that dates back to the 1950s. After 30 years of existence, the Python ecosystem has still not integrated this algorithm into widely adopted libraries! Some implementations can be found in python libraries such as python-control, stats-models, SciPy Cookbook. Also, some machine learning libraries have some closed and non-solved issues on this subject , see Scikit-learn#862 issue or River#355 issue. Why has the Kalman filter not found its place in these libraries? We think it may be because they have an object-oriented API, which makes them very well suited to the specific problems of modern machine learning but, on the other hand, prevents them from accommodating additional features such as Kalman filtering. We think the functional approach of WAX-ML, inherited from JAX, could well help to integrate a Kalman filter implementation in a machine learning ecosystem.

It turns out that python code written with JAX is not very far from Fortran, a mathematical FORmula TRANslating system. It should therefore be quite easy and natural to reimplement standard algorithms implemented in Fortran, such as those in the SLICOT library with JAX. It seems that some questions about the integration of Fortran into JAX has already been raised. As noted in this discussion on JAX's Github page, it might even be possible to simply wrap Fortran code in JAX. This would avoid a painful rewriting process!

Along with the implementation of good old algorithms, we would like to implement more recent ones from the online learning literature that somehow revisits the filtering and control problems. In particular, we would like to implement the online learning version of the ARMA model developed in [3] and some online-learning versions of control theory algorithms, an approach called "the non-stochastic control problem", such as the linear quadratic regulator (see [4]).

Optimization

The JAX ecosystem already has a library dedicated to optimization: Optax, which we use in WAX-ML. We could complete it by offering other first-order algorithms such as the Alternating Direction Multiplier Method (ADMM). One can find "functional" implementations of proximal algorithms in libraries such as proxmin), ProximalOperators, or COSMO, which could give good reference implementations to start the work.

Another type of work took place around automatic differentiation and optimization. In [5] the authors implement differentiable layers based on convex optimization in the library cvxpylayers. They have implemented a JAX API but, at the moment, they cannot use the jit compilation of JAX yet (see this issue). We would be interested to help to solve this issue.

Other algorithms

The machine learning libraries Scikit-learn, River, ml-numpy implement many "traditional" machine learning algorithms that should provide an excellent basis for linking or reimplementing in JAX. WAX-ML could help to build a repository for JAX versions of these algorithms.

Other APIS

As it did for the Gym API, WAX-ML could add support for other high-level object-oriented APIs like Keras, Scikit-learn, River ...

Collaborations

The WAX-ML team is open to discussion and collaboration with contributors from any field who interested in using WAX-ML for their problems on streaming data. We are looking for use cases around data streaming in audio processing, natural language processing, astrophysics, biology, finance, engineering ...

We believe that good software design, especially in the scientific domain, requires practical use cases and that the more diversified these use cases are, the more the developed functionalities will be guaranteed to be well implemented.

We expect interactions with the projects cited in this README. Do not hesitate to contact us if you think your project can have fruitful interactions with WAX-ML.

By making this software public, we hope to find enthusiasts who aim to develop WAX-ML further!

Installation

For now, WAX-ML can only be installed from sources:

pip install "wax-ml[dev,complete] @ git+https://github.com/eserie/wax-ml.git"

Disclaimer

WAX-ML is in its early stages of development and its features and API are very likely to evolve.

Development

You can contribute to WAX-ML by asking questions, proposing practical use cases, or by contributing to the code or the documentation. You can have a look at our Contributing Guidelines and Developer Documentation .

We maintain a "WAX-ML Enhancement Proposals" in WEP.md file.

References

[1] Google Princeton AI and Hazan Lab @ Princeton University

[2] FNet: Mixing Tokens with Fourier Transforms, James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon

[3] Online Learning for Time Series Prediction, Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir

[4] The Nonstochastic Control Problem, Elad Hazan, Sham M. Kakade, Karan Singh

[5] Differentiable Convex Optimization Layers, Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter

[6] Machine learning accelerated computational fluid dynamics, Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, Stephan Hoyer

License

Copyright 2021 The WAX-ML Authors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

WAX-ML bundles portions of astropy, dask, deluca, eagerpy, haiku, jax, xarray.

astropy, dask are available under a "3-clause BSD" license:

• dask: wax/gym/callbacks/callbacks.py
• astropy: CONTRIBUTING.md

deluca, haiku, jax and xarray are available under a "Apache" license:

• deluca: wax/gym/entity.py
• haiku: docs/notebooks/05_reconstructing_the_light_curve_of_stars.*
• jax: docs/conf.py, docs/developer.md
• xarray: wax/datasets/generate_temperature_data.py

eagerpy is available under an "MIT" license:

• eagerpy: wax.external.eagerpy, wax.external.eagerpy_tests

The full text of these licenses is included in the licenses directory.

Citing WAX-ML

To cite this repository:

@software{wax-ml2021github,
  author = {Emmanuel Sérié},
  title = {{WAX-ML}: A {P}ython library for machine-learning and feedbacks on streaming data},
  url = {http://github.com/eserie/wax-ml},
  version = {0.0.2},
  year = {2021},
}

Reference documentation

For details about the WAX-ML API, see the reference documentation.