It seems like a lot of people are using micro services that are hosted by a third party vendor to process various bits of natural language processing and other tasks that typically get lumped into the “Machine Learning” bucket. But there are a lot of environments that need self-contained systems. Especially when it’s simple to build a small project to do a task using something like Python’s nltk, but when you start hammering those microservices you build with those tools you start to feel how their ease-of-use can make them a bit more sluggish than purpose-built tooling (I guess that’s true for everything in software development). So let’s look at…
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Easy Python Module Version Management
Python comes with a handy-dandy little tool called pip for installing modules that you import in scripts. Python3 comes with a hand-dandy little tool called pip3 for installing modules you import in scripts. A practice I try to follow is to take the version of a module I was using when I wrote a script and create a requirements.txt file alongside it. Basically, if I import then I’ll add what I imported into the requirements.txt file. This would usually follow the below syntax: tensorboard==2.0.2 tensorflow==2.0.0 tensorflow-estimator-2.0.1 werkzeug-0.16.0 wrapt-1.11.2 Now that I have a requirements file, I can use that to install packages on the next host I run the script…
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Episode 148 of the MacAdmins Podcast, where we interview Shawn Honsberger about AutoPkgr
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Getting More Logs From Xcode
Xcode has a number of logging features I’ve found help me figure out why various things are failing over the years. It’s usually (and by usually I mean always) due to something stupid I did, but the logs help me figure out what’s going on pretty quickly. So first up, let’s look at enabling the activity logs. To do this, we’ll send an EnableDebugActivityLogs Boolean variable into com.apple.dt.XCBuild.plist: defaults write com.apple.dt.XCBuild EnableDebugActivityLogs -bool YES Now let’s turn on the build debugging mode. Now, for the above command you can leave it on without a performance hit, but this one should be turned on and off as you’re going as you’ll…
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Lightweight Python Tagger Using NLTK
Recently I was looking at automating the tagging of content from a mysql database. NLTK (Natural Language Training Kit) to the rescue to make this pretty easy. This GitHub project wraps NLTK into a small script to tag and provides a class for excluding words: Once the tagger was written we could add cursor.execute() or cursor.fetchall() as a way to bring in input from mysql content or since it’s a one-time run, we can use a simple mysql query run from bash and dump the output into the script as follows: mysql -X -u $supersecretUSER -p$supersecretPASS -h$HostName -D$MyDB -e'SELECT notes FROM tablename > notes.json lightweighttagger.py In the above just swap…
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Using Inter-Reliability Rates in UX Research
We’ve all conducted excellent research only to have it thrown to the side when other people in our organization choose to just do whatever they were already planning to do. Executives, product managers, project managers, developers, and even UX Researchers (in other words all the humans) simply get dug into a position and unwilling to accept a point of view contrary to that position. One common objection we hear when presenting research are that a researcher introduced bias in the way a question was asked, or the tone of the researcher impacted the response, or even that researchers use different techniques. These same objections were once used to keep from…
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Creating A Repository For Your UX Research and Research Ops Findings
User research is a field that studies user behaviors, needs, and motivations through observation, analyzing tasks, and getting feedback. The goal is to improve usability using experimental and observational research to guide how a product is built and the design that is used in the product and the development priorities. This might be through observation, running experiments, interviews, etc. The research work should be infused into all aspects of the product lifecycle. This allows for rapid prototyping, which then allows organizations to focus on building things customers want and doing so in the way customers need them done to be pertinent. When done right, this reduces the timeline to go to…
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MacAdmins Podcast: Big Data Analytics For Apple Devices, And All The Pretty Pictures
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The History Of Computing: TidBITS and The Technology of Publishing
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lightweightsentiment
Worked on a little sentiment analysis tool. The goal was to have a natural language processor that would report back a basic polarity score on text provided. So lightweightsentiment is a Lightweight Sentiment Analysis Tool built using the nltk (natural language toolkit) python framework. Requirements lightweightsentiment was written to conform to python 2 or 3 and requires the following modules installed (newer versions may work): nltk==3.4.5 textblob==0.15.3 To install nltk run: python -m textblob.download_corpora Usage Simply run the input.py script with a –text option and then some text to see sentiment. For example: python input.py --text "Gosh, I hate this feature" The output would then be a number between -1.0…