Positional-only arguments in Python

Positional-only arguments in Python

An introduction to the new `/` syntax in Python 3.8.

Originally published by Sanket at https://deepsource.io

The ability to specify positional-only arguments using the / marker in function definitions is among the many new improvements to the Python language coming in the upcoming 3.8 release. This addition to syntax has performance benefits and enables better API design. Let's look at the motivation behind positional-only arguments and how to use it, with examples. 

Background

Keyword-only arguments have been available in Python with the * marker, and addition of / marker for positional-only arguments improves the language’s consistency. With positional-or-keyword parameters, the mix of calling conventions is not always desirable. Consider these examples:

  • Some function parameters already have semantic meaning: namedtuple(typenames, field_names, …)
  • The parameter name has no true external meaning: arg1, arg2, …, etc for min()

If the users start using a keyword argument, the library author cannot rename the parameter because it would be a breaking change. In case of min(), the name of the parameter provides no intrinsic value and forces the author to maintain its name forever since callers might pass arguments as a keywords. This problem is solved by positional-only arguments. In addition, the parsing and handling of positional-only arguments is faster.

How to use positional-only arguments

To specify arguments as positional-only, a / marker should be added after all those arguments in the function definition. Take this example:

def pow(x, y, /, mod=None):
   r = x ** y
   if mod is not None:
       r %= mod
   return r

The following would apply:

  • All parameters to the left of / (in this case, x and y) can only be passed positionally.
  • mod can be passed positionally or with a keyword.
>>> pow(2, 10)  # valid

>>> pow(2, 10, 17)  # valid

>>> pow(2, 10, mod=17)  # valid

>>> pow(x=2, y=10)  # invalid, will raise a TypeError

>>> pow(2, y=10)  # invalid, will raise a TypeError

Another example:

def table_format(items, separator=',', /, prettify=False):

    pass

  • Once a positional-only parameter is specified with a default, the following positional-only and positional-or-keyword parameters need to have defaults as well (in this case, prettify).
  • Positional-only parameters which do not have default values are required positional-only parameters (in this case, items).

The generic case

A function definition, with all flavors of argument-passing variants, would look like this:

def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):

      -----------    ----------     ----------

        |             |                  |

        |        Positional or keyword   |

        |                                - Keyword only

         -- Positional only

When to use positional-only arguments
  • Use positional-only if names do not matter or have no meaning, and there are only a few arguments which will always be passed in the same order.
  • Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names.

Positional-only arguments were proposed in PEP 570, and it’s worth talking a look at the doc to understand the feature in more detail.

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Python Tutorial - Learn Python for Machine Learning and Web Development

Python Tutorial - Learn Python for Machine Learning and Web Development

Learn Python for Machine Learning and Web Development. Can Python be used for machine learning? Python is widely considered as the preferred language for teaching and learning ML (Machine Learning). Can I use Python for web development? Python can be used to build server-side web applications. Why Python is suitable for machine learning? How Python is used in AI? What language is best for machine learning?

Python tutorial for beginners - Learn Python for Machine Learning and Web Development

TABLE OF CONTENT

  • 00:00:00 Introduction
  • 00:01:49 Installing Python 3
  • 00:06:10 Your First Python Program
  • 00:08:11 How Python Code Gets Executed
  • 00:11:24 How Long It Takes To Learn Python
  • 00:13:03 Variables
  • 00:18:21 Receiving Input
  • 00:22:16 Python Cheat Sheet
  • 00:22:46 Type Conversion
  • 00:29:31 Strings
  • 00:37:36 Formatted Strings
  • 00:40:50 String Methods
  • 00:48:33 Arithmetic Operations
  • 00:51:33 Operator Precedence
  • 00:55:04 Math Functions
  • 00:58:17 If Statements
  • 01:06:32 Logical Operators
  • 01:11:25 Comparison Operators
  • 01:16:17 Weight Converter Program
  • 01:20:43 While Loops
  • 01:24:07 Building a Guessing Game
  • 01:30:51 Building the Car Game
  • 01:41:48 For Loops
  • 01:47:46 Nested Loops
  • 01:55:50 Lists
  • 02:01:45 2D Lists
  • 02:05:11 My Complete Python Course
  • 02:06:00 List Methods
  • 02:13:25 Tuples
  • 02:15:34 Unpacking
  • 02:18:21 Dictionaries
  • 02:26:21 Emoji Converter
  • 02:30:31 Functions
  • 02:35:21 Parameters
  • 02:39:24 Keyword Arguments
  • 02:44:45 Return Statement
  • 02:48:55 Creating a Reusable Function
  • 02:53:42 Exceptions
  • 02:59:14 Comments
  • 03:01:46 Classes
  • 03:07:46 Constructors
  • 03:14:41 Inheritance
  • 03:19:33 Modules
  • 03:30:12 Packages
  • 03:36:22 Generating Random Values
  • 03:44:37 Working with Directories
  • 03:50:47 Pypi and Pip
  • 03:55:34 Project 1: Automation with Python
  • 04:10:22 Project 2: Machine Learning with Python
  • 04:58:37 Project 3: Building a Website with Django

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Using Scikit-Learn for Machine Learning Application Development in Python

Using Scikit-Learn for Machine Learning Application Development in Python

Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers don’t know where to start...

Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers don’t know where to start...

They should look into the scikit-learn library, which is one of the best for developing machine learning applications. It is free and relatively easy to install and learn.

Why Machine Learning Programmers Should Be Familiar With Scikit-Learn

If you are trying to develop machine learning applications, then you were going to need a robust toolkit. Scikit-learn is just the solution that you need. This library was developed in 2007 as part of a Google project. Three years later, the code was released as hey solution for machine learning algorithms in conjunction with Google and several other major companies.

Scikit-learn is a library that contains several implementations of machine learning algorithms. There are two essential classifiers for developing machine learning applications with this library: a supervised learning model known as an SVM and a Random Forest (RF).

There are numerous reasons that scikit-learn is one of the preferred libraries for developing machine learning solutions. Some of the Premier benefits include:

  • Regression modeling
  • Unsupervised classification and clustering
  • Decision tree pruning and induction
  • Comprehensive and neural network training with regression and classification algorithms
  • Decision boundary learning with SVMs
  • Advanced probability modeling
  • Feature analysis and selection
  • Reduction of dimensionality
  • Outlier detection and rejection

Scikit-learn has been used in a number of applications by J.P. Morgan, Spotify, Inria, and other major companies. Machine learning applications built with scikit-learn include financial cybersecurity analytics, product development, neuroimaging, barcode scanner development, medical modeling and help with handling Shopify inventory issues.

The wide range of decision modeling features makes scikit-learn. One of the most versatile machine learning environments available in any programming language. Intermediate and advanced Python programmers should be able to master the nuances of this sophisticated library in a matter of hours.

The scikit-learn library is not installed by default. Fortunately, you should be able to set it up quickly. Here are some guidelines for installation and creating the foundation for your first machine learning project.

Installation of Scikit-Learn

If you already have pip installed, it's very easy to install the scikit-learn library. The instructions are available on this page.

Data for Audio

The purpose of using classification is to create a model based on the representation of a phenomenon in vector form (i.e. as a vector) and its corresponding class. This model will then be used to assign a class to an unknown vector. MFCCs can be used for approximations of sound vectors. MFCC provides 13 values per window. One option is to try classifying the class of a sound using those values. However, the sequences of the sound are very important.

This approach resolves some vector problems. The first approach we can follow is to take a segment of MFCCs and average them. Rather than having 13 values for the size of the segment, we end up with thirteen values. Averaging them is very simple, but we can get other statistics, such as: standard deviations and quartiles. This strategy provides statistical representations of all variables.

Loading Data From a CSV File

You will save your scikit-learn data in CSV files. Each line represents a line and each regular column represents a dimension of the vector. In general, the latter represents the class. Rows are separated by a line break and columns by a column. An illustrative example would be as follows:

#!events
event_1,event_2, event_label
1,2,3
11.1,1221,11341
1322,1422,320
330,222,121

To upload a file you can execute the following code:

import numpy as np
.loadtxt('scikit_1.csv',)
data.shape

At the end of this code, the variable data contains our data. The file scikit_1.csv contains segment data..

Separating Different Data Types

In order to learn a model, we need to follow the methodology presented at the beginning. We are not going to be able to follow it to the letter, but we are going to do our best to make our model the best. The first step is to hide some examples to consider them as evidence.

scikit learn prefers separate data between dimensions and classes.

Here is the code that accomplishes this step:

[:,:2233]
[:,-3]

The first line brings $2233$ dimensions of our vectors (in this case we are ignoring those derived from these data). The data will be stored in the variable $First_variable$. The variable $Second_variable$ stores the classes (all lines, last column).

Scikit learn contains a function that allows separating the training data from the test data, and this is done automatically and shuffles the data randomly that supports our methodology.

We have four sets; two versions of the dimension data we generally call features and two versions of the classes. One version is for training (train), and another for testing (test). The train versions have half of the original data, while testing the other half.