On the client side, the client has a stub referred to as just a client in some languages that provides the same methods as the server. The first step when working with protocol buffers is to define the structure for the data you want to serialize in a proto file : this is an ordinary text file with a.
Protocol buffer data is structured as messages , where each message is a small logical record of information containing a series of name-value pairs called fields. You can then use this class in your application to populate, serialize, and retrieve Person protocol buffer messages. You define gRPC services in ordinary proto files, with RPC method parameters and return types specified as protocol buffer messages:.
Active Oldest Votes. WOW, this is so beautiful. I experienced problems using this method on some complex dictionaries such has having nested set values e.
Therefore, I recommend blender's pickle method that works in my case. Note that python's json translates dict keys to strings when doing json.
Better to use pickle if you are not going to read the saved file using other languages than Python. Is there a way to using something like json. When I use your code the entire dict is written to one line, no matter how large it is, and it's not easily human readable. Show 2 more comments. Example Here is the simplest way: feed the string into eval. EDIT Actually, best practice in Python is to use a with statement to make sure the file gets properly closed.
Rewriting the above to use a with statement: import ast def reading self : with open 'deed. It won't evaluate a string. Blender Oh, you are right and I'm tired. I'll edit the answer. Nick: Try running it first ; — Blender. Blender, thank you for the heads-up. As for your error: self. You'd probably want to evaluate the string back into a Python object: self. Blender Blender k 49 49 gold badges silver badges bronze badges. That text isn't in your code. Before asking a question, explain what's wrong.
To store Python objects in files, use the pickle module Unless these are config files so you want them to be editable by human - see Why bother with python and config files? This is exactly what I need: to store native python structure state in a file that is not intended to be user editable.
Eric Leung 1, 11 11 silver badges 22 22 bronze badges. Manouchehr Rasouli Manouchehr Rasouli 5 5 silver badges 12 12 bronze badges. I created my own functions which work really nicely: def writeDict dict, filename, sep : with open filename, "a" as f: for i in dict.
Great code if your values are not too long. My dictionary included multiple long strings which just got cut off with ' For example, every decimal number could be mapped to a single token 0.
This keeps the vocabulary small and improves the accuracy of many language modeling tasks. Tokenization is the task of cutting a string into identifiable linguistic units that constitute a piece of language data. Although it is a fundamental task, we have been able to delay it until now because many corpora are already tokenized, and because NLTK includes some tokenizers.
Now that you are familiar with regular expressions, you can learn how to use them to tokenize text, and to have much more control over the process. The very simplest method for tokenizing text is to split on whitespace. Consider the following text from Alice's Adventures in Wonderland :. We could split this raw text on whitespace using raw. Other whitespace characters, such as carriage-return and form-feed should really be included too. The above statement can be rewritten as re. Important: Remember to prefix regular expressions with the letter r meaning "raw" , which instructs the Python interpreter to treat the string literally, rather than processing any backslashed characters it contains.
Splitting on whitespace gives us tokens like ' not' and 'herself,'. Observe that this gives us empty strings at the start and the end to understand why, try doing 'xx'. We get the same tokens, but without the empty strings, with re. Now that we're matching the words, we're in a position to extend the regular expression to cover a wider range of cases. This means that punctuation is grouped with any following letters e. We need to include? We'll also add a pattern to match quote characters so these are kept separate from the text they enclose.
The above expression also included « [-. However, nltk. For readability we break up the regular expression over several lines and add a comment about each line. The special? When set to True , the regular expression specifies the gaps between tokens, as with re. We can evaluate a tokenizer by comparing the resulting tokens with a wordlist, and reporting any tokens that don't appear in the wordlist, using set tokens.
You'll probably want to lowercase all the tokens first. Tokenization turns out to be a far more difficult task than you might have expected. No single solution works well across-the-board, and we must decide what counts as a token depending on the application domain. When developing a tokenizer it helps to have access to raw text which has been manually tokenized, in order to compare the output of your tokenizer with high-quality or "gold-standard" tokens.
A final issue for tokenization is the presence of contractions, such as didn't. If we are analyzing the meaning of a sentence, it would probably be more useful to normalize this form to two separate forms: did and n't or not. We can do this work with the help of a lookup table. This section discusses more advanced concepts, which you may prefer to skip on the first time through this chapter.
Tokenization is an instance of a more general problem of segmentation. In this section we will look at two other instances of this problem, which use radically different techniques to the ones we have seen so far in this chapter. Manipulating texts at the level of individual words often presupposes the ability to divide a text into individual sentences. As we have seen, some corpora already provide access at the sentence level. In the following example, we compute the average number of words per sentence in the Brown Corpus:.
In other cases, the text is only available as a stream of characters. Before tokenizing the text into words, we need to segment it into sentences. Here is an example of its use in segmenting the text of a novel. Note that if the segmenter's internal data has been updated by the time you read this, you will see different output :.
Notice that this example is really a single sentence, reporting the speech of Mr Lucian Gregory. However, the quoted speech contains several sentences, and these have been split into individual strings.
This is reasonable behavior for most applications. Sentence segmentation is difficult because period is used to mark abbreviations, and some periods simultaneously mark an abbreviation and terminate a sentence, as often happens with acronyms like U. For another approach to sentence segmentation, see 6. For some writing systems, tokenizing text is made more difficult by the fact that there is no visual representation of word boundaries.
A similar problem arises in the processing of spoken language, where the hearer must segment a continuous speech stream into individual words.
A particularly challenging version of this problem arises when we don't know the words in advance. This is the problem faced by a language learner, such as a child hearing utterances from a parent. Consider the following artificial example, where word boundaries have been removed:.
Our first challenge is simply to represent the problem: we need to find a way to separate text content from the segmentation. We can do this by annotating each character with a boolean value to indicate whether or not a word-break appears after the character an idea that will be used heavily for "chunking" in 7. Let's assume that the learner is given the utterance breaks, since these often correspond to extended pauses.
Here is a possible representation, including the initial and target segmentations:. Observe that the segmentation strings consist of zeros and ones. They are one character shorter than the source text, since a text of length n can only be broken up in n-1 places.
The segment function in 3. Now the segmentation task becomes a search problem: find the bit string that causes the text string to be correctly segmented into words. We assume the learner is acquiring words and storing them in an internal lexicon. Given a suitable lexicon, it is possible to reconstruct the source text as a sequence of lexical items. Following Brent, , we can define an objective function , a scoring function whose value we will try to optimize, based on the size of the lexicon and the amount of information needed to reconstruct the source text from the lexicon.
We illustrate this in 3. It is a simple matter to implement this objective function, as shown in 3. The final step is to search for the pattern of zeros and ones that minimizes this objective function, shown in 3. Notice that the best segmentation includes "words" like thekitty , since there's not enough evidence in the data to split this any further.
With enough data, it is possible to automatically segment text into words with a reasonable degree of accuracy. Such methods can be applied to tokenization for writing systems that don't have any visual representation of word boundaries. Often we write a program to report a single data item, such as a particular element in a corpus that meets some complicated criterion, or a single summary statistic such as a word-count or the performance of a tagger.
More often, we write a program to produce a structured result; for example, a tabulation of numbers or linguistic forms, or a reformatting of the original data. When the results to be presented are linguistic, textual output is usually the most natural choice.
However, when the results are numerical, it may be preferable to produce graphical output. In this section you will learn about a variety of ways to present program output.
The simplest kind of structured object we use for text processing is lists of words. When we want to output these to a display or a file, we must convert these lists into strings.
To do this in Python we use the join method, and specify the string to be used as the "glue". Many people find this notation for join counter-intuitive. The join method only works on a list of strings — what we have been calling a text — a complex type that enjoys some privileges in Python. The print command yields Python's attempt to produce the most human-readable form of an object. The second method — naming the variable at a prompt — shows us a string that can be used to recreate this object.
It is important to keep in mind that both of these are just strings, displayed for the benefit of you, the user. They do not give us any clue as to the actual internal representation of the object. There are many other useful ways to display an object as a string of characters.
This may be for the benefit of a human reader, or because we want to export our data to a particular file format for use in an external program. Formatted output typically contains a combination of variables and pre-specified strings, e. Apart from the problem of unwanted whitespace, print statements that contain alternating variables and constants can be difficult to read and maintain.
A better solution is to use string formatting expressions. To understand what is going on here, let's test out the string formatting expression on its own. By now this will be your usual method of exploring new syntax.
Let's unpack the above code further, in order to see this behavior up close:. We can also provide the values for the placeholders indirectly. Here's an example using a for loop:. It is right-justified by default , but we can include a minus sign to make it left-justified. In case we don't know in advance how wide a displayed value should be, the width value can be replaced with a star in the formatting string, then specified using a variable.
Other control characters are used for decimal integers and floating point numbers. An important use of formatting strings is for tabulating data. Recall that in 2. Let's perform the tabulation ourselves, exercising full control of headings and column widths, as shown in 3. Note the clear separation between the language processing work, and the tabulation of results.
Recall from the listing in 3. This allows us to specify the width of a field using a variable. Remember that the comma at the end of print statements adds an extra space, and this is sufficient to prevent the column headings from running into each other. We have seen how to read text from files 3. It is often useful to write output to files as well. The following code opens a file output. When we write non-text data to a file we must convert it to a string first.
We can do this conversion using formatting strings, as we saw above. Let's write the total number of words to our file, before closing it. You should avoid filenames that contain space characters like output file.
When the output of our program is text-like, instead of tabular, it will usually be necessary to wrap it so that it can be displayed conveniently. Consider the following output, which overflows its line, and which uses a complicated print statement:. We can take care of line wrapping with the help of Python's textwrap module.
For maximum clarity we will separate each step onto its own line:. Notice that there is a linebreak between more and its following number. If we wanted to avoid this, we could redefine the formatting string so that it contained no spaces, e.
For example, this documentation covers "universal newline support," explaining how to work with the different newline conventions used by various operating systems.
For more extensive discussion of text processing with Python see Mertz, For information about normalizing non-standard words see Sproat et al, There are many references for regular expressions, both practical and theoretical.
For a comprehensive and detailed manual in using regular expressions, covering their syntax in most major programming languages, including Python, see Friedl, Other presentations include Section 2.
There are many online resources for Unicode. Useful discussions of Python's facilities for handling Unicode are:. Our method for segmenting English text follows Brent, ; this work falls in the area of language acquisition Niyogi, Collocations are a special case of multiword expressions.
A multiword expression is a small phrase whose meaning and other properties cannot be predicted from its words alone, e. Simulated annealing is a heuristic for finding a good approximation to the optimum value of a function in a large, discrete search space, based on an analogy with annealing in metallurgy.
The technique is described in many Artificial Intelligence texts. The approach to discovering hyponyms in text using search patterns like x and other ys is described by Hearst, Write a Python statement that changes this to "colourless" using only the slice and concatenation operations. For example, 'dogs' [] removes the last character of dogs , leaving dog. Use slice notation to remove the affixes from these words we've inserted a hyphen to indicate the affix boundary, but omit this from your strings : dish-es , run-ning , nation-ality , un-do , pre-heat.
Is it possible to construct an index that goes too far to the left, before the start of the string? The following returns every second character within the slice: monty[]. It also works in the reverse direction: monty[] Try these for yourself, then experiment with different step values.
Explain why this is a reasonable result. Use urllib. Define a function load f that reads from the file named in its sole argument, and returns a string containing the text of the file. Now, split raw on some character other than space, such as 's'. What happens when the string being split contains tab characters, consecutive space characters, or a sequence of tabs and spaces? Experiment with words. What is the difference? Try converting between strings and integers using int "3" and str 3.
If you haven't already done this or can't find the file , go ahead and do it now. Next, start up a new session with the Python interpreter, and enter the expression monty at the prompt. You will get an error from the interpreter. Now, try the following note that you have to leave off the. This time, Python should return with a value. You can also try import test , in which case Python should be able to evaluate the expression test. Print them in order. Are any words duplicated in this list, because of the presence of case distinctions or punctuation?
Read the file into a Python list using open filename. Next, break each line into its two fields using split , and convert the number into an integer using int. The result should be a list of the form: [[ 'fuzzy' , 53], For example, access a weather site and extract the forecast top temperature for your town or city today. In order to do this, extract all substrings consisting of lowercase letters using re. Try to categorize these words manually and discuss your findings.
You will see that there is still a fair amount of non-textual data there, particularly Javascript commands. You may also find that sentence breaks have not been properly preserved. Define further regular expressions that improve the extraction of text from this web page. Normalize the text to lowercase before converting it. Add more substitutions of your own. Each word of the text is converted as follows: move any consonant or consonant cluster that appears at the start of the word to the end, then append ay , e.
Hungarian , extract the vowel sequences of words, and create a vowel bigram table. Write a generator expression that produces a sequence of randomly chosen letters drawn from the string "aehh " , and put this expression inside a call to the ''. You should get a result that looks like uncontrolled sneezing or maniacal laughter: he haha ee heheeh eha.
Use split and join again to normalize the whitespace in this string. Should we say that the numeric expression 4. Or should we say that it's a single compound word? Or should we say that it is actually nine words, since it's read "four point five three, plus or minus fifteen percent"?
Or should we say that it's not a "real" word at all, since it wouldn't appear in any dictionary? Discuss these different possibilities. Can you think of application domains that motivate at least two of these answers? Compute the ARI score for various sections of the Brown Corpus, including section f popular lore and j learned. Make use of the fact that nltk. Do the same thing with the Lancaster Stemmer and see if you observe any differences. Process this list using a for loop, and store the result in a new list lengths.
Then each time through the loop, use append to add another length value to the list. This happens to be the legitimate interpretation that bilingual English-Spanish speakers can assign to Chomsky's famous nonsense phrase, colorless green ideas sleep furiously according to Wikipedia. Now write code to perform the following tasks:.
For example, 'inexpressible'. Investigate this phenomenon with the help of a corpus and the findall method for searching tokenized text described in 3. Use re. Implement this algorithm in Python.
Use Punkt to perform sentence segmentation. Extend the concordance search program in 3. FreqDist , nltk. For simplicity, work with a single character encoding and just a few languages. For each word, compute the WordNet similarity between all synsets of the word and all synsets of the words in its context. Note that this is a crude approach; doing it well is a difficult, open research problem.
The goal of this chapter is to answer the following questions: How can we write programs to access text from local files and from the web, in order to get hold of an unlimited range of language material? How can we split documents up into individual words and punctuation symbols, so we can carry out the same kinds of analysis we did with text corpora in earlier chapters?
How can we write programs to produce formatted output and save it in a file? Note The read process will take a few seconds as it downloads this large book. In order for a child to have blonde to have blonde hair , it must have the gene on both sides of the family in the gra there is a disadvantage of having that gene or by chance.
They don ' t disappear ondes would disappear is if having the gene was a disadvantage and I do not think. Processing Search Engine Results The web can be thought of as a huge corpus of unannotated text. Google hits adore love like prefer absolutely , , 16, definitely 1, 51, , 62, ratio Table 3.
Note Your Turn: Search the web for "the of" inside quotes. Processing RSS Feeds The blogosphere is an important source of text, in both formal and informal registers.
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