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Category: Python

Comparing Koalas to PySpark

Published 2020-07-07 by Kevin Feasel

Tori Tompkins gives us an understanding of where Koalas fits in the Spark world:

One significant difference between Spark’s implementation of Dataframes and pandas is its immutability.

With Spark dataframes, you are unable to make changes to the existing object but rather create a brand new dataframe based on the old one. Pandas dataframes, however, allow you to edit the object in place. With Koalas, whilst still spark Dataframes under the hood, have kept the mutable syntax of pandas.

It does this by introducing this concept of an ‘Internal Frame’. This holds the spark immutable dataframe and manages the mapping between the Koalas column names and Spark column names. It also manages the Koalas index names to spark column name to replicate the index functionality in pandas (covered below). It acts as a bridge between Spark and Koalas by mimicking the pandas API with Spark. This Internal Frame replicates the mutable functionality of pandas by creating copies of the internal frame but appearing to be mutable.

Read the whole thing.

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Delaying Loops in Python

Published 2020-07-07 by Kevin Feasel

Jon Fletcher gives us three methods to delay a Python loop:

In this blog post, I will run through 3 different ways to execute a delayed Python loop. In these examples, we will aim to run the loop once every minute.
To show the delay, we will print out the current datetime using the datetime module.

I think I’d be a bit concerned about all three potentially failing—option 1 can get out of sync if it takes more than 1 second to process inside the loop, option 2 might miss a minute, and option 3 can also sometimes miss. But it’s interesting to think about.

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Stock Price Predictions with LSTM Models

Published 2020-06-16 by Kevin Feasel

Thenuja Shanthacumaran walks us through training a Long Short-Term Memory neural network model for predicting stock prices:

LSTM could not process a single data point. it needs a sequence of data for processing and able to store historical information. LSTM is an appropriate algorithm to make prediction and process based-on time-series data. It’s better to work on the regression problem.

The stock market has enormously historical data that varies with trade date, which is time-series data, but the LSTM model predicts future price of stock within a short-time period with higher accuracy when the dataset has a huge amount of data.

Click through for the process and a demo.

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Installing TensorFlow and Keras for R on SQL Server 2019 ML Services

Published 2020-06-09 by Kevin Feasel

I have a post on using TensorFlow and Keras in R on SQL Server 2019 Machine Learning Services:

What I’m doing is building a new virtual environment named r-reticulate, which is what the reticulate package in R desires. Inside that virtual environment, I’m installing the latest versions of tensorflow-probability, tensorflow , and keras. I had DLL loading problems with TensorFlow 2.1 on Windows, so if you run into those, the proper solution is to ensure that you have the appropriate Visual C++ redistributables installed on your server.

Then, I switched back to the base virtual environment and installed the same packages. My thinking here is that I’ll probably need them for other stuff as well (and don’t tell anybody, but I’m not very good with Python environments).

Please continue not to tell anybody that I’m not very good with Python environments. I tend to dump things in the base environment, forget which one I’m in, and all kinds of other bad practices. I think I’m secretly undermining myself in Python, but I don’t have enough proof yet.

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Portfolio Optimization with SAS and Python

Published 2020-06-04 by Kevin Feasel

Sophia Rowland shows off the sastopypackage:

I started by declaring my parameters and sets, including my risk threshold, my stock portfolio, the expected return of my stock portfolio, and covariance matrix estimated using the shrinkage estimator of Ledoit and Wolf(2003). I will use these pieces of information in my objective function and constraints. Now I will need SWAT, sasoptpy, and my optimization model object.

Read on for a demo.

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Avoiding Overfitting and Underfitting in Neural Networks

Published 2020-06-03 by Kevin Feasel

Manas Narkar provides some advice on optimizing neural network models:

Adding Dropout

Dropout is considered as one of the most effective regularization methods. Dropout is basically randomly zero-ing or dropping out features from your layer during the training process, or introducing some noise in the samples. The key thing to note is that this is only applied at training time. At test time, no values are dropped out. Instead, they are scaled. The typical dropout rate is between 0.2 to 0.5.

Click through for a demo on dropout, as well as coverage of several other techniques.

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Genetic Algorithms in Python

Published 2020-06-03 by Kevin Feasel

Abhinav Choudhary walks us through building a genetic algorithm library in Python:

Here are quick steps for how the genetic algorithm works:

1. Initial Population– Initialize the population randomly based on the data.
2. Fitness function– Find the fitness value of the each of the chromosomes(a chromosome is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve)
3. Selection– Select the best fitted chromosomes as parents to pass the genes for the next generation and create a new population
4. Cross-over– Create new set of chromosome by combining the parents and add them to new population set
5. Mutation– Perfrom mutation which alters one or more gene values in a chromosome in the new population set generated. Mutation helps in getting more diverse oppourtinity.Obtained population will be used in the next generation

I’m a sucker for genetic algorithms (and even more so its cousin, genetic programming). And there are still good use cases for genetic algorithms, especially in creating scoring functions for neural networks.

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Pandas UDFs and Python Type Hints in Spark 3.0

Published 2020-05-29 by Kevin Feasel

Hyukjin Kwon announces some updates forthcoming in Apache Spark 3.0:

The Pandas UDFs work with Pandas APIs inside the function and Apache Arrow for exchanging data. It allows vectorized operations that can increase performance up to 100x, compared to row-at-a-time Python UDFs.

The example below shows a Pandas UDF to simply add one to each value, in which it is defined with the function called pandas_plus_one decorated by pandas_udf with the Pandas UDF type specified as PandasUDFType.SCALAR.

Click through for explanations and demos for each.

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Distributed Model Training with Cloudera ML

Published 2020-05-21 by Kevin Feasel

Zuling Kang and Anand Patil show us how to train models across several nodes using Cloudera Machine Learning:

Deep learning models are generally trained using the stochastic gradient descendent (SGD) algorithm. For each iteration of SGD, we will sample a mini-batch from the training set, feed it into the training model, calculate the gradient of the loss function of the observed values and the real values, and update the model parameters (or weights). As it is well known that the SGD iterations have to be executed sequentially, it is not possible to speed up the training process by parallelizing iterations. However, as processing one single iteration for a number of commonly used models like CIFAR10 or IMAGENET takes a long time, even using the most sophisticated GPU, we can still try to parallelize the feedforward computation as well as the gradient calculation within each iteration to speed up the model training process.

In practice, we will split the mini-batch of the training data into several parts, like 4, 8, 16, etc. (in this article, we will use the term sub-batch to refer to these split parts), and each training worker takes one sub-batch. Then the training workers do feedforward, gradient computation, and model updating using the sub-batches, respectively, just as in the monolithic training mode. After these steps, a process called model average is invoked, averaging the model parameters of all the workers participating in the training, so as to make the model parameters exactly the same when a new training iteration begins. Then the new round of the training iteration starts again from the data sampling and splitting step.

Read on for the high-level explanation, followed by some Python code working in TensorFlow.

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Counting Table Tennis Ball Bounces

Published 2020-05-13 by Kevin Feasel

Evgeni Chasnovski has some fun counting:

On May 7th 2020 Dan made a successful attempt to beat a world record for the longest duration to control a table tennis ball with a bat. He surpassed current record duration of 5h2m37s by 18 minutes and 27 seconds for a total of 5h21m4s. He also live streamed the event on his “TableTennisDaily” YouTube channel, which later was uploaded (important note for the future: this video is a result of live stream and not a “shot and uploaded” one). During cheering for Dan in real time I got curious about actual number of bounces he made.

And thus the quest begins.

As counting manually is error-prone and extremely boring, I decided to do this programmatically. The idea of solution is straightforward: somehow extract audio from the world record video, detect bounces (as they have distinctive sound) and count them.

Click through for the process as well as a link to a Git repo with the Python code.

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