Machine learning with Gradio: cheat sheet

Machine learning with Gradio · In any playground, open Reference to copy these.

Models by hand

running total

Start the total at 0 once, then add to it on every pass.

total = 0
while total < 10:
    total += int(input("Add: "))

variable

Give a value a name, so you can reuse and change it.

size = 50

Example

length = 120
forward(length)
left(90)
forward(length / 2)

What it draws — length = 120, then half of it

list

Keep many values in order under one name. The first item is place 0.

colours = ["red", "blue", "green"]

for each item

Work through a list, one item at a time.

for c in colours:
    pass

Example

pensize(6)
for c in ["red", "blue", "green"]:
    pencolor(c)
    forward(40)

print

Show text or a value in the output box.

print("Hello")

random number

Pick a random whole number between two values.

import random

n = random.randint(1, 10)

join text

Join pieces of text into one with +. Spaces only appear where they are inside the quotes.

"Hello, " + name

str

Turn a number into text, so it can be joined: "Score: " + str(score).

str(score)

dictionary value

The value stored under that key. The key goes in quotes. Assign to it to change it: star["speed"] = 8.

star["speed"]

read a file

Opens the file to read and gives you all of it as one piece of text.

with open("save.txt") as f:
    text = f.read()

test a model

Shuffle, then keep some examples back. Train on one part; test only on the other.

random.seed(1)
random.shuffle(penguins)
train = penguins[:250]
test = penguins[250:]

if / else

Do one thing or the other, depending on a condition.

if x > 0:
    pass
else:
    pass

Example

for x in [-90, -30, 30, 90]:
    penup()
    goto(x, 0)
    if x > 0:
        dot(30, "red")
    else:
        dot(30, "blue")
hideturtle()

grow value

Change a value every pass: read the old one, store a new one.

size = 5
for i in range(20):
    forward(size)
    right(90)
    size = size + 5

Example

size = 5
for i in range(30):
    forward(size)
    right(90)
    size = size + 5

def (function)

Name a group of steps, then run it by calling its name.

def shape():
    pass

shape()

Example

def zigzag():
    left(45)
    forward(30)
    right(90)
    forward(30)
    left(45)

zigzag()
zigzag()
zigzag()

dictionary

Values stored under names (keys). One dictionary can hold everything about one thing.

star = {"speed": 5, "points": 1}

in

True if the item is in the list (or the key is in the dictionary, or the letters are in the text).

if item in bag:
    pass

read a CSV

Each row of the file becomes a dictionary, keyed by the column names in the first line. Every value is text.

import csv

with open("music.csv") as f:
    rows = list(csv.DictReader(f))

Real tools

square (loop)

Draw a square by repeating forward and right four times.

for i in range(4):
    forward(100)
    right(90)

every other

i % 2 == 0 is true on every second pass.

for i in range(8):
    if i % 2 == 0:
        pass
    else:
        pass

Example

for i in range(8):
    if i % 2 == 0:
        pencolor("red")
    else:
        pencolor("blue")
    pensize(6)
    forward(30)
    right(45)

Red and blue sides, taking turns

an app with Gradio

Gradio calls your function with what was chosen, and shows what it returns.

import gradio as gr

def answer(choice):
    return "You chose " + choice

gr.Interface(fn=answer, inputs=gr.Dropdown(["pop", "rock"]), outputs="text").launch()

predict with a model

The new example needs the same columns, in the same order, as the training data.

mystery = pd.DataFrame([[47.0, 215.0]], columns=X.columns)
print(model.predict(mystery)[0])

for loop

Repeat the indented lines this many times.

for i in range(4):
    pass

Example

for i in range(5):
    forward(15)
    penup()
    forward(10)
    pendown()

counter step

Use the counter i to make each pass different.

for i in range(20):
    forward(i * 5)
    right(90)

Example

for i in range(30):
    forward(i * 5)
    right(90)

read a CSV with pandas

Reads the whole file into a table (a DataFrame). Numbers stay numbers.

import pandas as pd

songs = pd.read_csv("music.csv")

train and score a model

fit learns from the training examples; score tests on the held-back ones (1.0 is 100%).

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
model = KNeighborsClassifier(n_neighbors=1)
model.fit(X_train, y_train)
print(model.score(X_test, y_test))