133, Data and scripts for predicting build-orders in StarCraft: Brood War, HTML Our initial approach was to translate the feature descriptor objects output by Featuretools into Spark SQL code that we could run against our raw input data. Many startups are now hiring data scientists as early employees, but it’s often unclear how someone in this role can have impact at a small organization. Ben Weber Distinguished Data Scientist at Zynga. In this case, the customer attributes are at depth 1 and the transaction attributes are at depth 2. The goal of this UDF is to determine if there is a relationship between goals and hits in the NHL, based on a simple linear model fit. If you enjoy reading this site, you might also want to check out these UBM Tech sites: What happened with Microsoft's Switch publishing experiments? Our driver notebook is responsible for spinning up a cluster for each of our games and then publishes the results to our real-time database. In the second step, we use the resulting dataframe (encodedDF) as input to deep feature synthesis with a depth of 3. We use the Featuretools library to perform feature generation, and the output is a set of feature descriptors that we use to translate our raw tracking data into per-player summaries. San Francisco Bay Area. Distinguished Data Scientist at Zynga. Ben Weber We Help Realtors Book 20-30 Appointments/Week With Tailor-Fitted Prospects Through Systems and Teams.

The slides from my presentation are available on Google Drive. 41 One of the main outcomes of this system is that data scientists are now spending more time with product managers discussing how to improve our games, rather than their models. I mentioned this constraint in slide 15, and our data representation fits this condition. The first phase in our modeling pipeline is extracting data from our data lake and making it accessible as a Spark dataframe. Follow to get new release updates and improved recommendations. Number 8860726. 27 We create an events entity set using our raw input data, using feature synthesis with a depth of 1 to create a set of feature descriptors (defs), and then use encode_features to perform 1-hot encoding. We also use a two-step encoding process that first performs 1-hot encoding on our input table and then performs deep feature synthesis on the encoded table. Ben Weber. Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization.
The input is a table that is deep and narrow, which may contain hundreds of records per player and only a few columns, and the output is a shallow and wide table, containing a record per user with hundreds or thousands of columns. ( We used PySpark to build AutoModel as an end-to-end data product, where the input is tracking events in our data lake and the output is player records in our real-time database.

Distinguished Data Scientist at Zynga @bgweber.

Zynga has been adopting Spark as a tool to scale up our data and modeling pipelines. bgweber has no activity The input and output of a Pandas UDF is a Spark dataframe. We are leveraging UDFs to scale our experimentation capabilities, using functions from the SciPy, NumPy, and StatsModels packages. yet for this period. One of the challenges with using a Pandas UDF is that you can only pass a single object as input. We need to have a generalizable way of translating our raw event data into a feature vector that summarizes a player. It is a python library that uses deep feature synthesis to perform feature generation. One of the constraints that we had was that all of the inputs tables in our entity set need to be stored as a single table, and I’ll describe why we had this constraint later on. ). It also meant that we could not support all of the feature transformations available, such as skew. An output Pandas dataframe is returned, in this case a summary object that describes the coefficients used to fit the linear relationship. Neither of these functions were coded to natively operate in a distributed mode, but Pandas UDFs enable these types of functions to scale as long as you can subdivide your task with a partition key.

I recently self published a book on building data science workflows in Python. These problems resulted from issues with Apache Arrow and data type mismatches, since Spark uses Arrow as an intermediate representation between Spark and Pandas dataframes. We use essential cookies to perform essential website functions, e.g. Registered in England and Wales. However, you are able to use global variables that have been instantiated on the driver node, which are the feature transformations (saved_features) that we generated during our feature engineering phase of our pipeline. However, the takeaway is to show how you can use libraries such as Featuretools, which require Pandas dataframes, and scale them to massive data sets. Learn more. The goal of the book is to provide readers with hands-on experience with cloud computing environments and large-scale machine learning pipelines. repository. 8. While we’ve been known for our analytics prowess for over a decade, we’re now embracing machine learning in new ways. Another recent tool we’ve been leveraging is the Featuretools library, which we use to perform automated feature engineering. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Our games have diverse event taxonomies, since a slots game has different actions to track versus a match-3 game or a card game. While XGBoost is not native to MLlib, we’ve integrated it into our pipeline in addition to gradient boosted trees, random forests, and logistic regression.
To accomplish this task, we used a new feature in Spark that enables distributed calculations on Pandas dataframes, and were able to scale to our full data set. Given the size of our portfolio and number of responses that we predict, the system generates hundreds of propensity models every day. Help us improve our Author Pages by updating your bibliography and submitting a new or current image and biography. 4 We use Pandas UDFs in combination with the Featuretools library to perform feature generation on tens of millions of users. It is intended for analysts and data scientists that want to broaden their responsibilities and grow a data science discipline. Ben Weber is a distinguished data scientist at Zynga with past experience in the gaming industry at Twitch, Sony Online Entertainment, Electronic Arts, and Microsoft Studios. 22 Our approach for testing Pandas UDFs is to first use toPandas() on a small dataframe and write a groupby apply function that runs on the driver node before trying to distribute the operation.

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