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Category: Python

Comparing Keras In Python Versus R

Published 2018-06-20 by Kevin Feasel

Dmitry Kisler performs image classification using Keras in both Python and R:

From the plots above, one can see that:

  • the accuracy of your model doesn’t depend on the language you use to build and train it (the plot shows only train accuracy, but the model doesn’t have high variance and the bias accuracy is around 99% as well).

  • even though 10 measurements may be not convincing, but Python would reduce (by up to 15%) the time required to train your CNN model. This is somewhat expected because R uses Python under the hood when executes Keras functions.

This is just one example, but the results are about what I’d expect.

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Auto-Encoders And KernelML

Published 2018-06-20 by Kevin Feasel

Rohan Kotwani gives us an example where KernelML might be better than TensorFlow or PyTorch:

So what’s the point of using KernelML?

1. The parameters in each layer can be non-linear
2. Each parameter can be sampled from a different random distribution
3. The parameters can be transformed to meet certain constraints
4. Network combinations are defined in terms of numpy operations
5. Parameters are probabilistically updated
6. Each parameter update samples the loss function around a local or global minima

KerneML Specs

KernelMLis brute force optimizer that can be used to train machine learning algorithms. The package uses a combination of a machine learning and monte carlo simulations to optimize a parameter vector with a user defined loss function. Using kernelml creates a high computational cost for large complex networks because it samples the loss function using a subspace for each parameter in the parameter vector which requires many random simulations. The computational cost was reduced by enabling parallel computations with the ipyparallel. The decision to use this package was made because it effectively utilizes the cores on a machine.

It’s an interesting use case, though I would have liked to have seen a direct comparison to other frameworks.

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Visualizing Data In Real Time With SQL Server And Dash

Published 2018-06-19 by Kevin Feasel

Tomaz Kastrun shows how to use Python Dash to visualize data living in SQL Server in real time:

The need for visualizing the real-time data (or near-real time) has been and still is a very important daily driver for many businesses. Microsoft SQL Server has many capabilities to visualize streaming data and this time, I will tackle this issue using Python. And python Dash package  for building web applications and visualizations. Dash is build on top of the Flask, React and Plotly and give the wide range of capabilities to create a interactive web applications, interfaces and visualizations.

Tomaz’s example hit SQL Server every half-second to grab the latest changes and gives us an example of roll-your-own streaming.

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Flattening JSON Data With Databricks

Published 2018-06-11 by Kevin Feasel

Ivan Vazharov gives us a Databricks notebook to parse and flatten JSON using PySpark:

With Databricks you get:

  • An easy way to infer the JSON schema and avoid creating it manually
  • Subtle changes in the JSON schema won’t break things
  • The ability to explode nested lists into rows in a very easy way (see the Notebook below)
  • Speed!

Following is an example Databricks Notebook (Python) demonstrating the above claims. The JSON sample consists of an imaginary JSON result set, which contains a list of car models within a list of car vendors within a list of people. We want to flatten this result into a dataframe.

Click through for the notebook.

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Scatterplot Matrices

Published 2018-06-08 by Kevin Feasel

The Plotly folks show off scatterplot matrices in Python:

The scatterplot matrix, known acronymically as SPLOM, is a relatively uncommon graphical tool that uses multiple scatterplots to determine the correlation (if any) between a series of variables.

These scatterplots are then organized into a matrix, making it easy to look at all the potential correlations in one place.

SPLOMs, invented by John Hartigan in 1975, allow data aficionados to quickly realize any interesting correlations between parameters in the data set.

In this post, we’ll go over how to make SPLOMs in Plotly with Python. For extra insights, check out our SPLOM tutorial in Python and R.

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Python And The Tidyverse

Published 2018-06-04 by Kevin Feasel

Leo at Locke Data looks at a couple Python packages which implement Tidyverse concepts:

The Dplython README provides some clear examples of how the package can be used. Below is an summary of the common functions:

  • select() – used to get specific columns of the data-frame.

  • sift() – used to filter out rows based on the value of a variable in that row.

  • sample_n() and sample_frac() – used to provide a random sample of rows from the data-frame.

  • arrange() – used to sort results.

  • mutate() – used to create new columns based on existing columns.

I think the Tidyverse is immediately accessible for data platform professionals, so it’s good to see these concepts making their way to Python as well as R.

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Combining Keras With Apache MXNet

Published 2018-05-23 by Kevin Feasel

Lai Wei, et al, show how to build a neural network in Keras 2 using MXNet as the engine:

Distributed training with Keras 2 and MXNet

This article shows how to install Keras-MXNet and demonstrates how to train a CNN and an RNN. If you tried distributed training with other deep learning engines before, you know that it can be tedious and difficult. Let us show you what it’s like now, with Keras-MXNet.

Installation is only a few steps

  1. Deploy an AWS Deep Learning AMI

  2. Install Keras-MXNet

  3. Configure Keras-MXNet

The Deep Learning AMI is already set up for trial, so it should be easy to follow along.

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The Elitist Shuffle And Recommenders

Published 2018-05-15 by Kevin Feasel

Rodrigo Agundez shows us a way of displaying fresh recommendations without retraining the recommender system:

Suppose you have 10,000 items in total that can be recommended to your user, you run the recommendation system over all the items and those 10,000 items get ranked in order of relevance of the content.

The application shows 5 items on the entry screen. The first time the user opens the application after the re-scoring process the top 5 ranked items are shown. It is decided that from now on (based on user control groups, investigation, AB testing, etc.) until the next re-scoring process the entry screen should not be the same every time and remain relevant for the user.

Based on an investigation from the data scientist it turns out that somewhat relevant items appear until item 100. Then the idea is to somehow shuffle those 100 items such that the top 5 items shown are still relevant but not the same.

Click through for an example in Python and how it compares favorably to a couple other shuffling algorithms.

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Creating Seaborn Plots With R

Published 2018-04-26 by Kevin Feasel

Abdul Majed Raja shows how to call Python from R and build plots using the Seaborn Python package:

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

  • Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
  • Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
  • Flexible binding to different versions of Python including virtual environments and Conda environments.

Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability.

The more common use of reticulate I’ve seen is running TensorFlow neural networks from R.

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Natural Language Generation With Markov Chains

Published 2018-04-24 by Kevin Feasel

Abdul Majed Raja shows off Markovify, a Python package which builds sentences using Markov chains:

Markov chains, named after Andrey Markov, are mathematical systems that hop from one “state” (a situation or set of values) to another. For example, if you made a Markov chain model of a baby’s behavior, you might include “playing,” “eating”, “sleeping,” and “crying” as states, which together with other behaviors could form a ‘state space’: a list of all possible states. In addition, on top of the state space, a Markov chain tells you the probability of hopping, or “transitioning,” from one state to any other state — -e.g., the chance that a baby currently playing will fall asleep in the next five minutes without crying first. Read more about how Markov Chain works in this interactive article by Victor Powell.

Click through for a fun example of headline generation.

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