Random thoughts about random subjects… From science to literature and between manga and watercolours, passing by data science and rugby; including film, physics and fiction, programming, pictures and puns.
It is great to see all the support that Apple Developers get in terms of tools, ecosystem, community and more.
For starters the Developer Support portal has a ton of information for the new comer as well as for the more expert of experts. Including guides and documentation for tools such as Xcode as well as information for developing software for MacOS and iOS.
Information about Design is available in the same place, including Human Interface Guidelines, Fonts (including downloads for San Francisco!) and information about accessibility and localisation.
Information about new tools and updates such as the latest about Swift, and SwiftUI can be easily found. And testing your apps with the help of tools such as TestFlight makes things so much easier.
Hello everyone! I am very pleased to take a question from John who got in touch with Quantum Tunnel using the form here. John’s favourite scientist is Einstein and his question is as follows:
In Mac mail I cannot delete unwanted email addresses. I have done the routine of deleting all addresses from the previous receiptant list, but when starting a new email unwanted addresses appear.. Any help is appreciated. Thanks, John
John is referring to the solution I provided in this earlier post. Sadly, the list of his lucky friends/colleagues/family (delete as appropriate) he has email recently persists even after clearing the “Previous Recipients” as explained in the post before.
There may be a way to force the clearing of these persistent email address:
Quit Mail and Address Book (in case the latter is open)
Let us start by adding the .mlmodel we created earlier on so that it is an available resource in our project. Open your Xcode project and locate your PriceBoston.mlmodel file. From the menu on the left-hand side select the “BostonPricer” folder. At the bottom of the window you will see a + sign, click on it and select “New Groups”. This will create a sub-folder within “BostonPricer”. Select the new folder and hit the return key, this will let you rename the folder to something more useful. In this case I am going to call this folder “Resources”.
Open Finder and navigate to the location of your BostonPricer.mlmodel. Click and drag the file inside the “Resources” folder we just created. This will open a dialogue box asking for some options for adding this file to your project. I selected the “Create Folder References” and left the rest as it was shown by default. After hitting “Finish” you will see your model now being part of your project. Let’s now go the code in ViewController and make some needed changes. The first one is to tell our project that we are going to need the powers of the CoreML framework. At the top of the file, locate a line of code that imports UIKit, right below it type the following:
Inside the definition of the ViewController class, let us define a constant to reference the model. Look for the definitions of the crimeData and roomData constants and nearby them type the following:
let model = PriceBoston()
You will see that when you start typing the name of the model, Xcode will suggest the right name as it knows about the existence of the model as part of its resources, neat!
We need to make some changes to the getPrediction()function we created in the last post. Go to the function and look for place where we pick the values of crime and rooms and right after that write the following:
You may get a warning telling you that the constant priceBostonOutput was defined but not used. Don’t worry, we will indeed use it in a little while. Just a couple of words about this piece of code, you will see that we are using the prediction method defined in the model and that we are passing the two input parameters that the model expects, namely crime and rooms. We are wrapping this call to the prediction method around a try statement so that we can catch any exceptions. This is where we are implementing our CoreML mode!!! Isn’t that cool‽
We are not done yet though; remember that we have that warning from Xcode about using the model. Looking at the properties of the model, we can see that we also have an output attribute called price. This is the prediction we are looking for and the one we would like to display. Out of the box it may have a lot of decimal figures, and it is never a good practice to display those to the user (although they are important in precision terms…). Also, with Swift’s strong typing we would have to typecast the double returned by the model into a string that can be printed. So, let us prepare some code to format the predicted price. At the top of the ViewController class, find the place where we defined the constants crimeData and roomData. Below them type the following code:
We are defining a format that will show a number as currency in US dollars with two decimal figures. We can now pass our predicted price to this formatter and assign it to a new constant for future reference. Below the code where the getPrediction function was defined, write the following:
let priceText = priceFormat.string(from: NSNumber(value:
Now we have a nicely formatted string that can be used in the display. Let us change the message that we are asking our app to show when pressing the button:
let message = "The predicted price (in $1,000s) is " + priceText!
We are done! Launch your app simulator, select a couple of values from the picker and hit the “Calculate Prediction” button… Et voilà, we have completed our first implementation of a CoreML model in a working app.
There are many more things that we can do to improve the app. For instance, we can impose some constraints on the position of the different elements shown in the screen so that we can deploy the application in the various screen sizes offered by Apple devices. Improve the design and usability of the app and designing appropriate icons for the app (in various sizes). For the time being, I will leave some of those tasks for later. In the meantime you can take a look at the final code in my github site here.
Enjoy and do keep in touch, I would love to hear if you have found this series useful.
If you have been following the posts in this open notebook, you may know that by now we have managed to create a linear regression model for the Boston Price dataset based on two predictors, namely crime rate and average number of rooms. It is by no means the best model out there ad our aim is to explore the creation of a model (in this case with Python) and convert it to a Core ML model that can be deployed in an iOS app.
Before move on to the development of the app, I thought it would be good to take a look at the properties of the converted model. If we open the PriceBoston.mlmodel we saved in the previous post (in Xcode of course) we will see the following information:
We can see the name of the model (PriceBoston) and the fact that it is a “Pipeline Regressor”. The model can be given various attributes such as Author, Description, License, etc. We can also see the listing of the Model Evaluation Parameters in the form of Inputs (crime rate and number of rooms) and Outputs (price). There is also an entry to describe the Model Class (PriceBoston) and without attaching this model to a target the class is actually not present. Once we make this model part of a target inside an app, Xcode will generate the appropriate code
Just to give you a flavour of the code that will be generated when we attach this model to a target, please take a look at the screenshot below:
You can see that the code was generated automatically (see the comment at the beginning of the Swift file). The code defines the input variables and feature names, defines a way to extract values out of the input strings, sets up the model output and other bits and pieces such as defining the class for model loading and prediction (not shown). All this is taken care of by Xcode, making it very easy for us to use the model in our app. We will start building that app in the following posts (bear with me, I promise we will get there).
In a previous post I mentioned that I will be sharing some notes about my journey with doing data science and machine learning by Apple technology. This is the firsts of those posts and here I will go about what Core ML is…
Core ML is a computer framework. So what is a framework? Well, in computer terms is a software abstraction that enables generic functionality to be modified as required by the user to transform it into software for specific purposes to enable the development of a system or even a humble project.
So Core ML is an Apple provided framework to speed apps that use trained machine learning models. Notice that word in bold – trained – is part of the description of the framework. This means that the model has to be developed externally with appropriate training data for the specific project in mind. For instance if you are interested in building a classifier that distinguishes cats from cars, then you need to train the model with lots of cat and car images.
As it stands Core ML supports a variety of machine learning models, from generalised linear models (GLMs for short) to neural nets. Furthermore it helps with the tests of adding the trained machine learning model to your application by automatically creating a custom programmatic interface that supplies an APU to your model. All this within the comfort of Xcode!
There is an important point to remember. The model has to be developed externally from Core ML, in other words you may want to use your favourite machine learning framework (that word again), computer language and environment to cover the different aspects of the data science workflow. You can read more in that in Chapter 3 of my “Data Science and Analytics with Python” book. So whether you use Scikit-learnm, Keras or Caffe, the model you develop has to be trained (tested and evaluated) beforehand. Once you are ready, then Core ML will support you in bringing it to the masses via your app.
As mentioned in the Core ML documentation:
Core ML is optimized for on-device performance, which minimizes memory footprint and power consumption. Running strictly on the device ensures the privacy of user data and guarantees that your app remains functional and responsive when a network connection is unavailable.
OK, so in the next few posts we will be using Python and coreml tools to generate a so-called .mlmodel file that Xcode can use and deploy. Stay tuned!
I am not a big used of Google Drive. It is a good service and it mostly does what one may need from a suite of productivity apps… but for some reason I only use it in very limited cases.
So, no surprise that I had not noticed that the synching between the cloud version of my documents and those in my mac had gone pear shaped. I tried logging out of Drive but that did not help. I attempted forcing the synch by making changed in both the cloud version and the Mac, but same result.
I managed to sort it out in the end and here is what I did:
Exit the Drive application
Navigate to the Application Support folder and look for the Google folderYou may need to find the hidden Library folder
In Finder look for the Go menu and press Option + Cmd to reveal the hidden folder
Once there look for the “Application Support”
Alternatively you can press Cmd + Shift + G and go to “~/Library/Application Support/Google”
I was confronted with an old issue, that had not been an issue for a while: writing to an external hard drive that was formatted with Windows (NTFS) from my mac. I used to have NTFS-3G (together with MacFUSE) installed and that used to be fine. However, I guess something when a bit eerie with Mavericks as I was not able to get my old solution to work.
So, here is what I did (you will need superuser powers, so be prepared to type your password):
Open a Terminal (Terminal.app) and create a file called stab in the /etc folder. For instance you can type:
$ sudo nano /etc/fstab
You can now enter some information in your newly created file telling MacOS information about your device. If your external drive is called mydevice enter the following:
LABEL=mydevice none ntfs rw,auto,nobrowse
Use tabs between the fields listed above. Save your file and you are now ready to plug your device.
There is a small caveat: Once you do this, your hard drive is not going to appear in your Desktop. But do not disappear, you can still use the terminal to access the drives mounted by going to /Volumes folder as follows: