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Luiz GUStavo 💉💉🎉

@gusthema

17 Jun

Generating combinations and permutations is usually a tricky task in programming.

In Python 🐍, using the itertools library this becomes much easier, less memory intensive and faster!

Let me tell you what I learned with them

[4.45 min]

1/13🧵

Let's suppose you want to create all possible cards of a regular deck. What you have to do is to join:

• All cards ranks: A, 2 to 10, J, Q and K
• The 4 suits: ♥️♣️♦️♠️

How to generate all cards?

2/13🧵

The operation that can solve this is a cartesian product:

ranks = ['A'] + list(range(2, 11)) + ['J', 'Q', 'K']
suits = ['♥️', '♣️', '♦️', '♠️']

all_cards = it.product(suits, ranks)
>>> ('A', '♥️'),('A', '♣️'),('A', '♦️'),('A', '♠️'),...
len(all_cards)
>>> 52

3/13🧵

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Luiz GUStavo 💉💉🎉

@gusthema

15 Jun

Following up from my previous thread, let's continue taking a look at some additional itertools methods

Some of them, as you will see, have very similar built-in versions but the key here is: itertools works on iterables and generators that are lazy evaluated collections

1/14🧵

One example is the method islice. It does slicing but for iterables (potentially endless collections). The main difference is that it doesn't accept negative indexes like regular slicing.

numbers = range(10)
items = it.islice(numbers, 2, 4)
>>> [2, 3]

2/14🧵

zip_longest vs zip
both do the same thing: aggregates elements from multiple iterators.

The difference is that zip_longest aggregates until the longest iterator ends while zip stops on the shortest one.

It will fill the missing values with any value you want

3/14🧵

Read 14 tweets

Luiz GUStavo 💉💉🎉

@gusthema

9 Jun

Quick⚡️ Python🐍 trick:
How do you merge two dictionaries?

[55 sec]🤯

1/6🧵

For Python 3.5 and up you can do:

d1 = {"a":1, "b":2}
d2 = {"c":3, "d":4}
d3 = {**d1, **d2}
d3 == {"a": 1, "b": 2, "c":3, "d":4}

Why?

2/6🧵

The ** operator expands the collection, so, you can think it as this:

d1 = {"a":1, "b":2}
d2 = {"c":3, "d":4}
d3 = {**d1, **d2} -> {"a":1, "b":2, "c":3, "d":4}
d3 == {"a": 1, "b": 2, "c":3, "d":4}

3/6🧵

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Luiz GUStavo 💉💉🎉

@gusthema

8 Jun

I was telling a friend that one cool feature from Python is list slice notation

So instead of just posting the link I decided to do a brief explanation.

[5 min]

Python's regular array indexing as in multiple languages is: a[index]

a = [0, 1, 2, 3, 4]
a[0] == 0

1/12🧵

Python has negative indexing too:

a = [0, 1, 2, 3, 4]
a[-1] == 4
a[-2] == 3

2/12🧵

Python also enables creating sub-lists from a list, or a slice:

-> a[start_index:last_index]

a = [0, 1, 2, 3, 4]
a[1:3] == [1, 2]

start_index is inclusive
last_index is exclusive

3/12🧵

Read 12 tweets

Luiz GUStavo 💉💉🎉

@gusthema

30 May

I've been trying the new TensorFlow Decision Forest (TF-DF) library today and it's very good!

Not only the ease of use but also all the available metadata, documentation and integrations you get!

Let me show you some of the cool things I've learned so far...

[5 min]

1/11🧵

TensorFlow Decision Forests have implemented 3 algorithms:

• CART
• Random Forest
• Gradient Boosted Trees

You can get this list with tfdf.keras.get_all_models()

All of them enable Classification, Regression and Ranking Tasks

2/11🧵

CART or Classification and Regression Trees is a simple decision tree. 🌳

The process divides the dataset in two parts
The first is used to grow the tree while the second is used to prune the tree

This is a good basic algorithm to learn and understand Decision Trees

3/11🧵

Read 11 tweets

Luiz GUStavo 💉💉🎉

@gusthema

29 May

Machine learning goes beyond Deep Learning and Neural Networks

Sometimes a simpler technique might give you better results and be easier to understand

A very versatile algorithm is the Decision Forest
🌴🌲🌳?

What is it and how does it work?
Let me tell you..

[7 min]

1/10🧵

Before understanding a Forest, let's start by what's a Tree

Imagine you have a table of data of characteristics of Felines. With features like size, weight, color, habitat and a column with the labels like lion, tiger, house cat, lynx and so on.

2/10🧵

With some time, you could write a code based on if/else statements that could, for each a row in the table, decide which feline it is

This is exactly what a Decision Tree does
During its training it creates the if/elses

en.wikipedia.org/wiki/Decision_…

3/10🧵

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