How many ways are there to pick a sequence of two different letters of the alphabet from the word MATHEMATICS?
Please forward this error screen to 64. 4 Writing Structured Programs By now you will have a sense of the capabilities of the How many ways are there to pick a sequence of two different letters of the alphabet from the word MATHEMATICS? programming language for processing natural language.
However, if you’re new to Python or to programming, you may still be wrestling with Python and not feel like you are in full control yet. How can you write well-structured, readable programs that you and others will be able to re-use easily? How do the fundamental building blocks work, such as loops, functions and assignment? What are some of the pitfalls with Python programming and how can you avoid them?
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Along the way, you will consolidate your knowledge of fundamental programming constructs, learn more about using features of the Python language in a natural and concise way, and learn some useful techniques in visualizing natural language data. In the other chapters of this book, we have organized the programming concepts as dictated by the needs of NLP. Here we revert to a more conventional approach where the material is more closely tied to the structure of the programming language. There’s not room for a complete presentation of the language, so we’ll just focus on the language constructs and idioms that are most important for NLP. However, there are some surprising subtleties here. However, assignment statements do not always involve making copies in this way.
Assignment always copies the value of an expression, but a value is not always what you might expect it to be. To understand what is going on here, we need to know how lists are stored in the computer’s memory. Observe that changing one of the items inside our nested list of lists changed them all. This is because each of the three elements is actually just a reference to one and the same list in memory. Now modify one of the elements of the list, and observe that all the elements are changed.
We began with a list containing three references to a single empty list object. This last step modified one of the three object references inside the nested list. It is crucial to appreciate this difference between modifying an object via an object reference, and overwriting an object reference. This copies the object references inside the list. Equality Python provides two ways to check that a pair of items are the same.
We can use it to verify our earlier observations about objects. Now let’s put a new python in this nest. This reveals that the second item of the list has a distinct identifier. If you try running this code snippet yourself, expect to see different numbers in the resulting list, and also the interloper may be in a different position. Having two kinds of equality might seem strange. However, it’s really just the type-token distinction, familiar from natural language, here showing up in a programming language. 2 Sequences So far, we have seen two kinds of sequence object: strings and lists.
We’ve actually seen them in the previous chapters, and sometimes referred to them as “pairs”, since there were always two members. However, tuples can have any number of members. Tuples are constructed using the comma operator. Parentheses are a more general feature of Python syntax, designed for grouping. Notice in this code sample that we computed multiple values on a single line, separated by commas. These comma-separated expressions are actually just tuples — Python allows us to omit the parentheses around tuples if there is no ambiguity. When we print a tuple, the parentheses are always displayed.
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By using tuples in this way, we are implicitly aggregating items together. The sequence functions illustrated in 4. We can convert between these sequence types. Red lorry, yellow lorry, red lorry, yellow lorry. In the next example, we use tuples to re-arrange the contents of our list. We can omit the parentheses because the comma has higher precedence than assignment.
This is an idiomatic and readable way to move items inside a list. For some NLP tasks it is necessary to cut up a sequence into two or more parts. Combining Different Sequence Types Let’s combine our knowledge of these three sequence types, together with list comprehensions, to perform the task of sorting the words in a string by their length. Each of the above lines of code contains a significant feature. We began by talking about the commonalities in these sequence types, but the above code illustrates important differences in their roles.
First, strings appear at the beginning and the end: this is typical in the context where our program is reading in some text and producing output for us to read. Lists and tuples are used in the middle, but for different purposes. We often use lists to hold sequences of words. Here, a lexicon is represented as a list because it is a collection of objects of a single type — lexical entries — of no predetermined length. Note that these pronunciations are stored using a list. A good way to decide when to use tuples vs lists is to ask whether the interpretation of an item depends on its position. For example, a tagged token combines two strings having different interpretation, and we choose to interpret the first item as the token and the second item as the tag.
In contrast, the elements of a text are all tokens, and position is not significant. The linguistic meaning of the words might be different, but the interpretation of list items as tokens is unchanged. The distinction between lists and tuples has been described in terms of usage. In other words, lists can be modified, while tuples cannot. Here are some of the operations on lists that do in-place modification of the list. Generator Expressions We’ve been making heavy use of list comprehensions, for compact and readable processing of texts.
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I choose it to mean – neither more nor less. Suppose we now want to process these words further. This is more than a notational convenience: in many language processing situations, generator expressions will be more efficient. If the text is very large, this could be slow. 3 Questions of Style Programming is as much an art as a science.
Here we pick up on some issues of programming style that have important ramifications for the readability of your code, including code layout, procedural vs declarative style, and the use of loop variables. Python Coding Style When writing programs you make many subtle choices about names, spacing, comments, and so on. When you look at code written by other people, needless differences in style make it harder to interpret the code. We briefly review some of its key recommendations here, and refer readers to the full guide for detailed discussion with examples. Code layout should use four spaces per indentation level. You should make sure that when you write Python code in a file, you avoid tabs for indentation, since these can be misinterpreted by different text editors and the indentation can be messed up. Python is able to detect that the line continues over to the next line.
Typing spaces instead of tabs soon becomes a chore. Procedural vs Declarative Style We have just seen how the same task can be performed in different ways, with implications for efficiency. Another factor influencing program development is programming style. This is a low-level style, not far removed from machine code, the primitive operations performed by the computer’s CPU.
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The two variables are just like a CPU’s registers, accumulating values at many intermediate stages, values that are meaningless until the end. We say that this program is written in a procedural style, dictating the machine operations step by step. The first line uses a generator expression to sum the token lengths, while the second line computes the average as before. Implementation details are left to the Python interpreter. Another case where a loop variable seems to be necessary is for printing a counter with each line of output. It’s sometimes tempting to use loop variables to store a maximum or minimum value seen so far.
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Let’s use this method to find the longest word in a text. We also need to balance our concerns about program efficiency with programmer efficiency. Some Legitimate Uses for Counters There are cases where we still want to use loop variables in a list comprehension. It is quite tricky to get the range of the loop variable right.
Here’s an example of how we can use loop variables in building multidimensional structures. Note that it would be incorrect to do this work using multiplication, for reasons concerning object copying that were discussed earlier in this section. Iteration is an important programming device. It is tempting to adopt idioms from other languages. However, Python offers some elegant and highly readable alternatives, as we have seen. 4 Functions: The Foundation of Structured Programming Functions provide an effective way to package and re-use program code, as already explained in 3.
For example, suppose we find that we often want to read text from an HTML file. This involves several steps: opening the file, reading it in, normalizing whitespace, and stripping HTML markup. Read text from a file, normalizing whitespace and stripping HTML markup. It will return a string, and we can assign this to a variable, e. Each time we want to use this series of steps we only have to call the function.
Using functions has the benefit of saving space in our program. More importantly, our choice of name for the function helps make the program readable. This naming helps to provide some “semantic interpretation” — it helps a reader of our program to see what the program “means”. Notice that the above function definition contains a string. We have seen that functions help to make our work reusable and readable. They also help make it reliable.
When we re-use code that has already been developed and tested, we can be more confident that it handles a variety of cases correctly. We also remove the risk that we forget some important step, or introduce a bug. The program that calls our function also has increased reliability. To summarize, as its name suggests, a function captures functionality. It is a segment of code that can be given a meaningful name and which performs a well-defined task.
Functions allow us to abstract away from the details, to see a bigger picture, and to program more effectively. The rest of this section takes a closer look at functions, exploring the mechanics and discussing ways to make your programs easier to read. Function Inputs and Outputs We pass information to functions using a function’s parameters, the parenthesized list of variables and constants following the function’s name in the function definition. To the calling program, it looks as if the function call had been replaced with the function’s result, e.
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A Python function is not required to have a return statement. Consider the following three sort functions. The third one is dangerous because a programmer could use it without realizing that it had modified its input. The same is true for functions.
Thus, to understand Python’s call-by-value parameter passing, it is enough to understand how assignment works. When you assign to a new variable inside the body of a function, the name is only defined within that function. The name is not visible outside the function, or in other functions. This behavior means you can choose variable names without being concerned about collisions with names used in your other function definitions.
When you refer to an existing name from within the body of a function, the Python interpreter first tries to resolve the name with respect to the names that are local to the function. If nothing is found, the interpreter checks if it is a global name within the module. Finally, if that does not succeed, the interpreter checks if the name is a Python built-in. However, this practice should be avoided as much as possible.
In general you should use parameters for function inputs and return values for function outputs. Checking Parameter Types Python does not allow us to declare the type of a variable when we write a program, and this permits us to define functions that are flexible about the type of their arguments. However, often we want to write programs for later use by others, and want to program in a defensive style, providing useful warnings when functions have not been invoked correctly. This is a slight improvement, because the function is checking the type of the argument, and trying to return a “special”, diagnostic value for the wrong input. Additionally, the error message is easy to interpret. A more fundamental approach is to document the parameters to each function using docstrings as described later in this section. Functional Decomposition Well-structured programs usually make extensive use of functions.
When a block of program code grows longer than 10-20 lines, it is a great help to readability if the code is broken up into one or more functions, each one having a clear purpose. This is analogous to the way a good essay is divided into paragraphs, each expressing one main idea. Functions provide an important kind of abstraction. They allow us to group multiple actions into a single, complex action, and associate a name with it.
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When we use functions, the main program can be written at a higher level of abstraction, making its structure transparent, e. Appropriate use of functions makes programs more readable and maintainable. Additionally, it becomes possible to reimplement a function — replacing the function’s body with more efficient code — without having to be concerned with the rest of the program. This function has a number of problems. The function has two side-effects: it modifies the contents of its second parameter, and it prints a selection of the results it has computed.
Given that its task is to identify frequent words, it should probably just return a list, not the whole frequency distribution. This is no different to any other variable except it signals to the reader that we don’t have a use for the information it holds. Documenting Functions If we have done a good job at decomposing our program into functions, then it should be easy to describe the purpose of each function in plain language, and provide this in the docstring at the top of the function definition. You should provide a triple-quoted string containing a complete sentence on a single line. For non-trivial functions, you should still provide a one sentence summary on the first line, since many docstring processing tools index this string.
Docstrings should document the type of each parameter to the function, and the return type. At a minimum, that can be done in plain text. However, note that NLTK uses the Sphinx markup language to document parameters. Calculate the fraction of test items that equal the corresponding reference items.
Lists must have the same length. Illustration of a complete docstring, consisting of a one-line summary, a more detailed explanation, a doctest example, and Sphinx markup specifying the parameters, types, return type, and exceptions. 5 Doing More with Functions This section discusses more advanced features, which you may prefer to skip on the first time through this chapter. Functions as Arguments So far the arguments we have passed into functions have been simple objects like strings, or structured objects like lists. Python also lets us pass a function as an argument to another function. However, we can supply our own sort function, e. The calling program gets the first word and does any necessary processing.
Here’s a more sophisticated example of a generator which produces all permutations of a list of words. Higher-Order Functions Python provides some higher-order functions that are standard features of functional programming languages such as Haskell. We illustrate them here, alongside the equivalent expression using list comprehensions. We can also provide a lambda expression.
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Here’s a pair of equivalent examples which count the number of vowels in each word. The solutions based on list comprehensions are usually more readable than the solutions based on higher-order functions, and we have favored the former approach throughout this book. Named Arguments When there are a lot of parameters it is easy to get confused about the correct order. Instead we can refer to parameters by name, and even assign them a default value just in case one was not provided by the calling program. Now the parameters can be specified in any order, and can be omitted.
If we mix these two kinds of parameters, then we must ensure that the unnamed parameters precede the named ones. It has to be this way, since unnamed parameters are defined by position. Dictionaries will be presented in 3. A side-effect of having named arguments is that they permit optionality.
Another common use of optional arguments is to permit a flag. Take care not to use a mutable object as the default value of a parameter. A series of calls to the function will use the same object, sometimes with bizarre results as we will see in the discussion of debugging below. If your program will work with a lot of files, it is a good idea to close any open files once they are no longer required. 6 Program Development Programming is a skill that is acquired over several years of experience with a variety of programming languages and tasks.
Key high-level abilities are algorithm design and its manifestation in structured programming. Key low-level abilities include familiarity with the syntactic constructs of the language, and knowledge of a variety of diagnostic methods for trouble-shooting a program which does not exhibit the expected behavior. This section describes the internal structure of a program module and how to organize a multi-module program. Then it describes various kinds of error that arise during program development, what you can do to fix them and, better still, to avoid them in the first place.
Structure of a Python Module The purpose of a program module is to bring logically-related definitions and functions together in order to facilitate re-use and abstraction. Python modules are nothing more than individual . For example, if you were working with a particular corpus format, the functions to read and write the format could be kept together. If the format was updated, you would know that only one file needed to be changed. Similarly, a module could contain code for creating and manipulating a particular data structure such as syntax trees, or code for performing a particular processing task such as plotting corpus statistics. When you start writing Python modules, it helps to have some examples to emulate.
This returns the location of the compiled . The file that you will need to open is the corresponding . Like every other NLTK module, distance. Since the code is distributed, it also includes the URL where the code is available, a copyright statement, and license information. As metrics, they must satisfy the following three requirements: 1.
Some module variables and functions are only used within the module. These should have names beginning with an underscore, e. Multi-Module Programs Some programs bring together a diverse range of tasks, such as loading data from a corpus, performing some analysis tasks on the data, then visualizing it. We may already have stable modules that take care of loading data and producing visualizations. Our work might involve coding up the analysis task, and just invoking functions from the existing modules.