If you like what we're working on, please  star us on GitHub. This enables us to continue to give back to the community.
DEEPCHECKS GLOSSARY

Pattern Matching

What is Pattern Matching?

The process of algorithmically searching for patterns in sequences of unprocessed data or tokens is called pattern matching. This job is limited to finding precise matches within a pre existing database and cannot create new patterns. Contrary to popular belief, pattern matching is not a deep learning method but rather a fundamental approach for testing and validating code and data.

How does a match pattern work?

Any string, not only discrete variables, may be used as a pattern in pattern matching since it is simply filtering and/or replacing data. How exactly patterns are discovered changes based on the data being examined. Instead of doing a full, “brute force” search of all the data, most instances verify and match regular expressions or tree patterns (strings) via a process-of-elimination strategy, like backtracking.

Regular expressions are used in pattern matching algorithms (or regex). You might think of a regular expression as a language that allows you to establish a pattern and communicate it to another person (or in this example, a computer program).

With the use of regular expressions, testing data may be analyzed for specific patterns. Regular expressions (regexps) may be automatically generated by certain programs if they are smart enough to recognize patterns in a given collection of data values. Common regular expressions such as those for credit card numbers, phone numbers in the United States, date/time formats, and email addresses may already be included in certain applications and tools.

Match patterns

There are many ways to match patterns in different programming languages. Here are a few examples:

  • Regular expressions. Many programming languages support the use of regular expressions, a sequence of characters that define a search pattern. Regular expressions can be used to search for patterns in strings, and to perform operations such as search and replace based on those patterns.
  • String methods. Many programming languages have built-in string methods that can be used to search for patterns within strings. For example, the find() method in Python can be used to search for a substring within a larger string, and the index() method can be used to find the index of a substring within a string.
  • Conditional statements. In some cases, you may be able to use conditional statements (such as “if…” and “…else”) to check for patterns within a string or other data structure. For example, you might use an if statement to check if a string starts with a certain letter or if it contains a certain sequence of characters.
  • Loop constructs such as for and while loops can be used to iterate over the elements of a string or other data structure, and to perform operations based on the values of those elements.
  • Custom functions. You can also define custom functions to perform pattern matching in your code. These functions can use any of the techniques described above, or other approaches, to search for and identify patterns within data.

It’s worth noting that the approach you should use to match patterns will depend on the nature of the data you are working with and the specific requirements of your task.

Open source package for ml validation

Build Test Suites for ML Models & Data with Deepchecks

Get StartedOur GithubOur Github

Regex pattern matches

Regular expression (regex) pattern matching is a technique for identifying and extracting patterns in strings. A regular expression is a sequence of characters that defines a search pattern and can be used to match, search, and manipulate strings.

To use a regex pattern to match against a string, you can use a regex library or built-in function in your programming language of choice. For example, in Python, you can use the re-module to work with regular expressions.

There are many different regex patterns that you can use to match different kinds of patterns in strings. Some common regex patterns include:

  • \d: matches any digit (0-9)
  • \w: matches any word character (a-z, A-Z, 0-9, and _)
  • \s: matches any whitespace character (space, tab, newline, etc.)
  • ^: matches the start of a string
  • $: matches the end of a string
  • *: matches zero or more repetitions of the preceding character or group
  • +: matches one or more repetitions
  • ?: matches zero or one repetition

There are many other regex patterns that you can use, and you can also combine multiple patterns to create more complex search criteria.

To perform a regex pattern match, you can use a function like re.search() or re.match() in Python that returns a Match object if the pattern is found in the string. You can then use the methods of the Match object to extract information about the pattern match such as the start and end indices of the match, or the specific characters that were matched.

There are also many algorithms and approaches that can be used for pattern matching, depending on the specific requirements of the task. Some common approaches include brute force search, Boyer-Moore string matching, and the Knuth-Morris-Pratt algorithm.