Integrating Technology in the Adult Education Classroom

ISTE Standards Transform learning and teaching.  

Let's replicate our inefficientSquare example, but this time we'll use our memoizer to cache results.

The biggest problem with JSON.stringify is that it doesn't serialize certain inputs, like functions and Symbols (and anything you wouldn't find in JSON).

Let's reason through our memoizer before we write any code.

// Takes a reference to a function
const memoize = func => {
  // Creates a cache of results
  const results = {};
  // Returns a function
  return (...args) => {
    // Create a key for results cache
    const argsKey = JSON.stringify(args);
    // Only execute func if no cached value
    if (!results[argsKey]) {
      // Store function call result in cache
      results[argsKey] = func(...args);
    }
    // Return cached value
    return results[argsKey];
  };
};

Memoization is an optimization technique used in many programming languages to reduce the number of redundant, expensive function calls. This is done by caching the return value of a function based on its inputs.

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Use uppercase only in constants.

export const API_KEY = 'SOMEKEY';

Use PascalCase only when naming constructors or classes.

// good
class User {
  constructor(options) {
    this.name = options.name;
  }
}

const good = new User({
  name: 'yup',
});

Use camelCase when naming objects, functions, and instances.

const thisIsMyFuction() {}

Don’t go to code academy, go to design academy. Be advocates of the user & consumer. It’s not about learning how to code, it’s about translating real-world needs to technological specifications in just ways that give end users agency and equity in design, development and delivery. Be a champion of user-centric design. Learn how to steward data and offer your help.

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They switch to get features like good doc lookup, good syntax highlighting, integration with unit tests, and (critically!) the ability to produce final, distributable source code files, as opposed to notebooks or REPL histories

Development Pros Cons

Exploratory programming is based on the observation that most of us spend most of our time as coders exploring and experimenting

This kind of “exploring” is easiest when you develop on the prompt (or REPL), or using a notebook-oriented development system like Jupyter Notebooks

notebook contains an actual running Python interpreter instance that you’re fully in control of. So Jupyter can provide auto-completions, parameter lists, and context-sensitive documentation based on the actual state of your code

Creating meticulous tests before exploring the data is a big mistake, and will result in a well-crafted garbage-in, garbage-out pipeline. We need an environment flexible enough to encourage experiments, especially in the initial place.

I recommend Airbnb style JavaScript style guide and Airbnb TypeScript)

TSDoc is a way of writing TypeScript comments where they’re linked to a particular function, class or method (like Python docstrings).

I need to specify types of input and output. But then I get speedup due to autocompletion, hints, and linting if for any reason I make a mistake.

I found that the overhead to use types in TypeScript is minimal (if any).

Write a new test and the result. If you want to make it REPL-like, instead of writing console.log(x.toString()) use expect(x.toString()).toBe('') and you will directly get the result.

ESLint does automatic code linting

I would use ESLint in full strength, tests for some (especially end-to-end, to make sure a commit does not make project crash), and add continuous integration.

A few takeaways

With Codecov it is easy to make jest & Travis CI generate one more thing:

I set it with a few clicks at Travis CI, and by creating a .travis.yml file in the repo

language: node_js
node_js: node

before_script:
  - npm install -g typescript
  - npm install codecov -g

script:
  - yarn lint
  - yarn build
  - yarn test
  - yarn build-docs

after_success:
  - codecov

Continuous integration makes it easy to check against cases when the code: does not work (but someone didn’t test it and pushed haphazardly), does work only locally, as it is based on local installations, does work only locally, as not all files were committed.

It is fine to start adding tests gradually, by adding a few tests to things that are the most difficult (ones you need to keep fingers crossed so they work) or most critical (simple but with many other dependent components).

Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.

I set it with a few clicks at Travis CI, and by creating a .travis.yml file in the repo

language: node_js
node_js: node

before_script:
  - npm install -g typescript
  - npm install codecov -g

script:
  - yarn lint
  - yarn build
  - yarn test
  - yarn build-docs

after_success:
  - codecov

interactive notebooks fall short when you want to write bigger, maintainable code

Continuous Delivery for Machine Learning (CD4ML) is a software engineering approach in which a cross-functional team produces machine learning applications based on code, data, and models in small and safe increments that can be reproduced and reliably released at any time, in short adaptation cycles.

A deployment pipeline automates the process for getting software from version control into production, including all the stages, approvals, testing, and deployment to different environments

example of how to combine different test pyramids for data, model, and code in CD4ML

Another approach is to use a tool like H2O to export the model as a POJO in a JAR Java library, which you can then add as a dependency in your application. The benefit of this approach is that you can train the models in a language familiar to Data Scientists, such as Python or R, and export the model as a compiled binary that runs in a different target environment (JVM), which can be faster at inference time

In order to formalise the model training process in code, we used an open source tool called DVC (Data Science Version Control). It provides similar semantics to Git, but also solves a few ML-specific problems:

Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications.

common functional silos in large organizations can create barriers, stifling the ability to automate the end-to-end process of deploying ML applications to production

There are different types of testing that can be introduced in the ML workflow.

Continuous Delivery for Machine Learning end-to-end process

We chose to use GoCD as our Continuous Delivery tool, as it was built with the concept of pipelines as a first-class concern

Machine Learning pipeline for our Sales Forecasting problem, and the 3 steps to automate it with DVC

From examining the type hinting, you can immediately tell that the function expects the input name to be of a type str, or string. You can also tell that the expected output of the function will be of a type str, or string, as well.

def hello_name(name: str) -> str:
    return(f"Hello {name}")

class constructor parameters should be documented within the __init__ class method docstring

say_hello.__doc__ = "A simple function that says hello... Richie style"

def say_hello(name):
    print(f"Hello {name}, is it me you're looking for?")

say_hello.__doc__ = "A simple function that says hello... Richie style"

def say_hello(name):
    """A simple function that says hello... Richie style"""
    print(f"Hello {name}, is it me you're looking for?")

>>> help(say_hello)

Help on function say_hello in module __main__:

say_hello(name)
    A simple function that says hello... Richie style

There are specific docstrings formats that can be used to help docstring parsers and users have a familiar and known format.

Docstring conventions are described within PEP 257. Their purpose is to provide your users with a brief overview of the object.

Since everything in Python is an object, you can examine the directory of the object using the dir() command

Daniele Procida gave a wonderful PyCon 2017 talk and subsequent blog post about documenting Python projects. He mentions that all projects should have the following four major sections to help you focus your work:

Class method docstrings should contain the following: A brief description of what the method is and what it’s used for Any arguments (both required and optional) that are passed including keyword arguments Label any arguments that are considered optional or have a default value Any side effects that occur when executing the method Any exceptions that are raised Any restrictions on when the method can be called

In all cases, the docstrings should use the triple-double quote (""") string format.

Along with docstrings, Python also has the built-in function help() that prints out the objects docstring to the console.

If you use argparse, then you can omit parameter-specific documentation, assuming it’s correctly been documented within the help parameter of the argparser.parser.add_argument function. It is recommended to use the __doc__ for the description parameter within argparse.ArgumentParser’s constructor.

Docstrings can be further broken up into three major categories: Class Docstrings: Class and class methods Package and Module Docstrings: Package, modules, and functions Script Docstrings: Script and functions

Documenting your Python code is all centered on docstrings. These are built-in strings that, when configured correctly, can help your users and yourself with your project’s documentation.

Scripts are considered to be single file executables run from the console. Docstrings for scripts are placed at the top of the file and should be documented well enough for users to be able to have a sufficient understanding of how to use the script.

All multi-lined docstrings have the following parts: A one-line summary line A blank line proceeding the summary Any further elaboration for the docstring Another blank line

"""This is the summary line

This is the further elaboration of the docstring. Within this section,
you can elaborate further on details as appropriate for the situation.
Notice that the summary and the elaboration is separated by a blank new
line.

# Notice the blank line above. Code should continue on this line.

The general layout of the project and its documentation should be as follows:

project_root/
│
├── project/  # Project source code
├── docs/
├── README
├── HOW_TO_CONTRIBUTE
├── CODE_OF_CONDUCT
├── examples.py

Documenting your code, especially large projects, can be daunting. Thankfully there are some tools out and references to get you started

Sometimes, the best way to learn is to mimic others. Here are some great examples of projects that use documentation well:

Along with these tools, there are some additional tutorials, videos, and articles that can be useful when you are documenting your project

Since we have much faster CPUs now, numerical calculations are done in Python which is much slower than Fortran. So numerical calculations basically take the same amount of time as they did 20 years ago.

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I am not sure how but one kind soul somehow found the project, forked it, refactored it, "modernized" it, added linting, code sniffing, added CI and opened the pull request.

It is solved when you understand why it occurred and why it no longer does.

This ‘evolutionary theory of socialisation’ proposes that children who experience family stresses, including father absence,should experience early puberty, ‘precocious’sexuality and anxiety (in women) or aggression (men),as such a strategy resultsin higher reproductive success in a stressful adult environment where paternal investment was likely to be low, and interpersonal relationships unreliable.

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We work according to the principles of conversational development.

So Leftley tried again, changing both the product and the way he reached potential customers—offering them free insurance through their mobile phones. People could sign up without paying any premiums; they simply had to buy a certain number of extra minutes. They could keep earning this insurance by renewing the purchase each month. When a customer buys the required minutes, the telecom company pays his or her premium to both MicroEnsure and the partner insurer. Over time customers are offered additional insurance products, such as “double cover” (for a spouse) and “family cover,” which cost extra—from three cents to $1 per month, with payment collected through their phones. Revenue from the supplemental plans is split among MicroEnsure, the partner insurer, and the phone company.

Founded in 2002, MicroEnsure has registered more than 56 million people in emerging economies for insurance (adding 18 million in 2017 alone), paying out $30 million in claims and radically innovating the insurance business model. It has introduced new forms of protection for customers, including microhealth, political-violence, crop, and mobile insurance.

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DevOps has taught us that the software development process can be generalized and reused for dealing with change not just in application code but also in infrastructure, docs and tests. It can all just be code.

As developers we love our own local setup. We spend a lot of time and effort on making sure our favorite code editor and command line shell is as we want it to be, everything else is subpar, a hindrance to our productivity. The local environment is king. It’s where we should be coding our load test scripts and from where we should initiate our load tests.

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Your idea should stem from solving someone’s problem. Ideally, your own problem. It’s important that you choose an idea which interests you. Interest is key to fuelling motivation which is crucial when making a web app. It takes effort building web apps and it’s important you have fun during the process.

Each format makes tradeoffs in encoding, flexibility, and expressiveness to best suit a specific use case.

Your project has no business value today unless it includes blockchain and AI, although a centralized and rule-based version would be much faster and more efficient.

There is StackOverflow which simply didn’t exist back then. Asking a programming question involved talking to your colleagues.

Cross-platform development is now a standard because of wide variety of architectures like mobile devices, cloud servers, embedded IoT systems. It was almost exclusively PCs 20 years ago.

IDEs and the programming languages are getting more and more distant from each other. 20 years ago an IDE was specifically developed for a single language, like Eclipse for Java, Visual Basic, Delphi for Pascal etc. Now, we have text editors like VS Code that can support any programming language with IDE like features.

Language tooling is richer today. A programming language was usually a compiler and perhaps a debugger. Today, they usually come with the linter, source code formatter, template creators, self-update ability and a list of arguments that you can use in a debate against the competing language.

Being a software development team now involves all team members performing a mysterious ritual of standing up together for 15 minutes in the morning and drawing occult symbols with post-its.

A package management ecosystem is essential for programming languages now. People simply don’t want to go through the hassle of finding, downloading and installing libraries anymore. 20 years ago we used to visit web sites, downloaded zip files, copied them to correct locations, added them to the paths in the build configuration and prayed that they worked.

Audiences had strong reactions to the new disturbing themes the horror plays presented. One of the most prevalent themes staged at the Grand-Guignol was the demoralization and corruption of science. The "evil doctor" was a reoccurring trope in the horror shows performed.

“No Code” systems are extremely good for putting together proofs-of-concept which can demonstrate the value of moving forward with development.

With someone else’s platform, you often end up needing to construct elaborate work-arounds for missing functionality, or indeed cannot implement a required feature at all.

the developer doesn’t need to worry about allocating memory, or the character set encoding of the string, or a host of other things.

First, you’ve spread the logic across a variety of different systems, so it becomes more difficult to reason about the application as a whole. Second, more importantly, the logic has been implemented as configuration as opposed to code. The logic is constrained by the ability of the applications which have been wired together, but it’s still there.

Comments to your code should be kept brief and focused. Avoid using long comments when possible. Additionally, you should use the following four essential rules as suggested by Jeff Atwood:

Commenting your code serves multiple purposes

In general, commenting is describing your code to/for developers. The intended main audience is the maintainers and developers of the Python code. In conjunction with well-written code, comments help to guide the reader to better understand your code and its purpose and design

Documenting code is describing its use and functionality to your users. While it may be helpful in the development process, the main intended audience is the users.

According to PEP 8, comments should have a maximum length of 72 characters.

use `multiple line comment`

“Code is more often read than written.” — Guido van Rossum

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Gujarat often produced growth faster than the national average, fewer regulations, better infrastructure and less corruption

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There is created in the State treasury a special fund, which shall be held separate and apart from all other State moneys, to be known as the Cannabis Business Development Fund. The Cannabis Business Development Fund shall be exclusively used for the following purposes:        (1) to provide low-interest rate loans to Qualified      Social Equity Applicants to pay for ordinary and necessary expenses to start and operate a cannabis business establishment permitted by this Act;        (2) to provide grants to Qualified Social Equity     Applicants to pay for ordinary and necessary expenses to start and operate a cannabis business establishment permitted by this Act;        (3) to compensate the Department of Commerce and     Economic Opportunity for any costs related to the provision of low-interest loans and grants to Qualified Social Equity Applicants;         (4) to pay for outreach that may be provided or     targeted to attract and support Social Equity Applicants and Qualified Social Equity Applicants;        (5) (blank);         (6) to conduct any study or research concerning the     participation of minorities, women, veterans, or people with disabilities in the cannabis industry, including, without limitation, barriers to such individuals entering the industry as equity owners of cannabis business establishments;        (7) (blank); and        (8) to assist with job training and technical     assistance for residents in Disproportionately Impacted Areas.

 Sec. 7-25. Transfer of license awarded to Qualified Social Equity Applicant.     (a) In the event a Qualified Social Equity Applicant seeks to transfer, sell, or grant a cannabis business establishment license within 5 years after it was issued to a person or entity that does not qualify as a Social Equity Applicant, the transfer agreement shall require the new license holder to pay the Cannabis Business Development Fund an amount equal to:         (1) any fees that were waived by any State agency     based on the applicant's status as a Social Equity Applicant, if applicable;        (2) any outstanding amount owed by the Qualified     Social Equity Applicant for a loan through the Cannabis Business Development Fund, if applicable; and        (3) the full amount of any grants that the Qualified     Social Equity Applicant received from the Department of Commerce and Economic Opportunity, if applicable.     (b) Transfers of cannabis business establishment licenses awarded to a Social Equity Applicant are subject to all other provisions of this Act, the Compassionate Use of Medical Cannabis Program Act, and rules regarding transfers. (Source: P.A. 101-27, eff. 6-25-19; 101-593, eff. 12-4-19.)

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Continuous Delivery of Deployment is about running as thorough checks as you can to catch issues on your code. Completeness of the checks is the most important factor. It is usually measured in terms code coverage or functional coverage of your tests. Catching errors early on prevents broken code to get deployed to any environment and saves the precious time of your test team.

Continuous Integration is a trade off between speed of feedback loop to developers and relevance of the checks your perform (build and test). No code that would impede the team progress should make it to the main branch.

A good CD build: Ensures that as many features as possible are working properly The faster the better, but it is not a matter of speed. A 30-60 minutes build is OK

A good CI build: Ensures no code that breaks basic stuff and prevents other team members to work is introduced to the main branch Is fast enough to provide feedback to developers within minutes to prevent context switching between tasks

The idea of Continuous Delivery is to prepare artefacts as close as possible from what you want to run in your environment. These can be jar or war files if you are working with Java, executables if you are working with .NET. These can also be folders of transpiled JS code or even Docker containers, whatever makes deploy shorter (i.e. you have pre built in advance as much as you can).

Continuous Delivery is about being able to deploy any version of your code at all times. In practice it means the last or pre last version of your code.

Continuous Integration is not about tools. It is about working in small chunks and integrating your new code to the main branch and pulling frequently.

The app should build and start Most critical features should be functional at all times (user signup/login journey and key business features) Common layers of the application that all the developers rely on, should be stable. This means unit tests on those parts.

Continuous Integration is all about preventing the main branch of being broken so your team is not stuck. That’s it. It is not about having all your tests green all the time and the main branch deployable to production at every commit.

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