AI News, Patterns for Research in Machine Learning
Patterns for Research in Machine Learning
My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute.
This is useful because: Create spatial separation between your source code and your data files: This is useful because: Even better would be to always assume that your code and your data are located independently: This is useful because: The data folder will often be too large to store on your machine, and you will have no choice but to separate the two.
For completeness you may also want to: The function get_configurations() can be written in such a way to make the above code segment train 4 different models, one for each valid combination of variables that are being swept over, and store the results in separate directories: This is useful because it makes it easier to try out different algorithm options.
Since the run_experiment() function might potentially be performing complex tasks such as: loading options from disk, sweeping parameters, communicating with a cluster of computers or managing the storing of results, you do not want to have to run the script manually for every run.
Queries and Mutations
GraphQL queries can traverse related objects and their fields, letting clients fetch lots of related data in one request, instead of making several roundtrips as one would need in a classic REST architecture.
If you have a sharp eye, you may have noticed that, since the result object fields match the name of the field in the query but don't include arguments, you can't directly query for the same field with different arguments.
The concept of fragments is frequently used to split complicated application data requirements into smaller chunks, especially when you need to combine lots of UI components with different fragments into one initial data fetch.
Up until now, we have been using a shorthand syntax where we omit both the query keyword and the query name, but in production apps it's useful to use these to make our code less ambiguous.
Here’s an example that includes the keyword query as operation type and HeroNameAndFriends as operation name : The operation type is either query, mutation, or subscription and describes what type of operation you're intending to do.
The operation type is required unless you're using the query shorthand syntax, in which case you can't supply a name or variable definitions for your operation.
In the same way, GraphQL query and mutation names, along with fragment names, can be a useful debugging tool on the server side to identify different GraphQL requests.
But in most applications, the arguments to fields will be dynamic: For example, there might be a dropdown that lets you select which Star Wars episode you are interested in, or a search field, or a set of filters.
It wouldn't be a good idea to pass these dynamic arguments directly in the query string, because then our client-side code would need to dynamically manipulate the query string at runtime, and serialize it into a GraphQL-specific format.
When we start working with variables, we need to do three things: Here's what it looks like all together: Now, in our client code, we can simply pass a different variable rather than needing to construct an entirely new query.
This is also in general a good practice for denoting which arguments in our query are expected to be dynamic - we should never be doing string interpolation to construct queries from user-supplied values.
We discussed above how variables enable us to avoid doing manual string interpolation to construct dynamic queries.
Passing variables in arguments solves a pretty big class of these problems, but we might also need a way to dynamically change the structure and shape of our queries using variables.
The core GraphQL specification includes exactly two directives, which must be supported by any spec-compliant GraphQL server implementation: Directives can be useful to get out of situations where you otherwise would need to do string manipulation to add and remove fields in your query.
This is especially useful when mutating existing data, for example, when incrementing a field, since we can mutate and query the new value of the field with one request.
There's one important distinction between queries and mutations, other than the name: While query fields are executed in parallel, mutation fields run in series, one after the other.
This means that if we send two incrementCredits mutations in one request, the first is guaranteed to finish before the second begins, ensuring that we don't end up with a race condition with ourselves.
If you are querying a field that returns an interface or a union type, you will need to use inline fragments to access data on the underlying concrete type.
R for Data Science
Normally, each knit of a document starts from a completely clean slate.
For example, here the processed_data chunk depends on the raw_data chunk: Caching the processed_data chunk means that it will get re-run if the dplyr pipeline is changed, but it won’t get rerun if the read_csv() call changes.
You can avoid that problem with the dependson chunk option: dependson should contain a character vector of every chunk that the cached chunk depends on.
Note that the chunks won’t update if a_very_large_file.csv changes, because knitr caching only tracks changes within the .Rmd file.
Then you can write: As your caching strategies get progressively more complicated, it’s a good idea to regularly clear out all your caches with knitr::clean_cache().
A Complete Tutorial on SAS Macros For Faster Data Manipulation
If you’ve been writing the same lines of code repeatedly in SAS, you can stop now.
I’ll take a simple example so that you can understand this concept better: Example: Let’s look at the following SAS program: Above SAS code is written to extract policy level details for 09-Sep-14 and let us say that the user needs to run this code on a daily basis after changing the date (at both the places) to current date.
Macro programming is typically covered as advanced topic in SAS, but the basic concepts of SAS macros are easy to understand.
Whenever we submit a program, it gets copied in memory (called input stack) followed by word scanner and there after it goes to compiler and gets executed.
macro variable is just like a standard variable, except that its value is not a data set and has only a single character value.
Scope of Macro variable can be local or global,Add Media depending on how we have defined it. If it is defined inside a macro program, then scope is local (only available for that macro code).
Macro variable name follows the SAS naming convention and if variable already exists then value is overwritten.
Value of macro variable in %Let statement can be any string and it has following characteristics:- Macro variables are referenced by using ampersand (&) followed by macro variable name.
Now, to use period as a separator between library name and dataset, we need to provide period (.) twice.
We can pass the parameters in two ways:- Positional Parameters: In this method, we supply parameter name at time of defining the macro and values are passed at time of Macro call.
Syntax:- Definition Calling At calling stage, values to parameters are passed in similar order as they are defined in the macro definition.
Keyword Parameters: In this method, we provide parameter name with equals to sign and also can assign default value to the parameters.
For example:- SAS Macros are typically considered as part of advance SAS Programming and are used widely in reporting, data manipulation and automation of SAS programs.
They do not help to reduce the time of execution, but instead, they reduce repetition of similar steps in your program and enhance the readability of Programs.
This flexibility can be exploited to reach next level of sophistication with use of conditional statements and loops using macro statements such as %IF, %DO.
As the name implies, conditional processing is used when we want to execute a piece of code based on the output of single or multiple conditions.
Loops are used to create a dynamic program, which executes for a number of iterations, based on some conditions (called conditional iteration).
Let’s say we have a series of SAS data sets YR1990 – YR2013 that contain business detail and now we want to calculate the average sales for each of these years.
These Macro functions have similar syntax, compared to their counterpart functions in data steps and they also return results in similar manner.
%SUBSTR (argument, position [, number of characters]) If number of character is not supplied, %SUBSTR function will return characters from given position till end of the string.
Example: Here we have first extracted “country” from macro variable abc and store it into def and after that used it to show title in upper case.
%SCAN (argument, n [, delimiter]) It will return NULL value, if string does not have n words separated by delimiter and if we have not given delimiter then it will use default value for it.
ABC will store string “Vidhya”, here it automatically identify the second word based on default delimiter and BCD will have value “Analyt” because we have mentioned that i is a delimiter and we required the first word.
Remember, macro variable contains only text (numerical values are also stored as text) and therefore we can’t perform any arithmetical and logical operation, if we try to use them directly.
As we know, macro variables store value as text, macro variable B will store 3+1 and with the use of %EVAL, C will store 4.
We also looked at how SAS Macros can be used in iterative and conditional circumstances, followed by several functions to perform text manipulations and to apply arithmetical and logical operations in SAS Macros.
- On Monday, September 23, 2019
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