strata | Evented I/O B-tree for Node.js | Runtime Evironment library
kandi X-RAY | strata Summary
kandi X-RAY | strata Summary
The b-tree package exports an object I like to name Strata. TODO Force the naming. In order to create a Strata b-tree you need to choose a storage strategy, you can store to either a write-ahead log or into a directory tree on the file system. Let's start with the file system. We'll be introducing different modules as needed in the final draft, but let's dump them all into the README.md for now. For our README.md examples we'll need to create some file paths. When you create a Strata b-tree you need to provide functions that will convert the objects you want to store to and from Buffers. Strata will writes those buffers to the file system or write-ahead log. You will need one serializer and deserializer pair for keys and one serializer and deserialiser pair for records. Records are inserted into Strata as an array of objects. This allows you to store an array that contains both JSON serializable (or otherwise serializalbe) objects and Buffers. We call this a parts array. WHen you serialize a record you will be given an array of parts and you must return an array of buffers. The length of the array of Buffers does not need to match the length of the array of parts. Similarly, to deserialize a record you provide a function that receives an array of buffers and returns an array of parts, that is an array of JavaScript objects that are maaningful to your application. Key serializers... write about this, please. When you create a Strata b-tree you need to provide two functions that will define how the tree indexed. The first function is an extractor. This function extracts a key from the stored record. Strata is not a key/value store, it is a record store. The key for a record in the store is extracted from the stored record. To extract the record you provide an extractor function. Strata works with compound keys. These compound keys are represented as arrays. All Strata keys are arrays. Your extractor must return an array that contains the values of the compound key. The array returned must always be the same length. WHen you insert data into a Strata b-tree you insert an array of JavaScript objects. The extractor function takes this array of objects and extracts the key values into an array that creates a compound key. Our initial example extractor merely returns a single element array, a compound key with one component. To complete the index we need to provide a comparator. A comparator should compare two arrays and return less than zero if the first array is less than the second, greater than zero if the first array is greater than the second and zero if they are equal. The arrays are equal if they are of equal length and the array element for each index in the array are equal. The first element in the first array that is less than or greater than the correspondding element in the second array makes the first array less than the second array. If one array is shorter than the other array and elements of the shorter array are equal to the correspondding elements in the longer array, then the shorter array is less than the longer array. With this sort function we can perform forward and reverse inclusive and exclusive searches against the compound keys our b-tree using whole or partial keys. To create a comparator I use Addendum which prvoides a comparator builder function that builds a comparator according to the aforementioned algorithm. In the above we have created a comparator that compares ... Strata stores data as an array of buffers. Deserialization converts that array of buffers into an array of object. Serialization converts that array into an array of objects. The extractor function accepts an array of parts. The parts are also user defined. To both insert into and retrieve objects from the tree, we must first search the tree to arrive at the appropriate page. To do this we use a Trampoline so that we do not have to surrender the process to an async call if all the pages are cached in memory. When call search with a Trampoline instance, a key and a callback function. The function is called with a Cursor object only. (This is not an error-first callback function from the good old days of Node.js.) The function is synchronous and all operations on the page must complete before the function returns. The synchronous callback function is a window in which you have sole control of the in-memory b-tree page. You should not hold onto the cursor and use it outside of the synchronous callback function. These operations are are verbose, but as noted, they are usually encapsulated in a module that provides the user with an abstraction layer. Retrieving from the Strata b-tree is similar. You invoke search with a trampoline, a key to search for, and callback function that accepts a cursor object. The synchronous function is the window in which you have sole control over the in-memory b-tree page. You should copy the values out of the in-memory page for use when the function returns.
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QUESTION
I am trying to follow this tutorial here - https://juliasilge.com/blog/xgboost-tune-volleyball/
I am using it on the most recent Tidy Tuesday dataset about great lakes fishing - trying to predict agency based on many other values.
ALL of the code below works except the final row where I get the following error:
...ANSWER
Answered 2021-Jun-15 at 04:08If we look at the documentation of last_fit() We see that split
must be
An rsplit object created from `rsample::initial_split().
You accidentally passed the cross-validation folds object stock_folds
into split
but you should have passed rsplit
object stock_split
instead
QUESTION
I have three large dataframes and I want to append some of the elements from one onto another based on several criteria. I looked up similar questions in Stack Overflow but they don't seem to work for my dataframe format (or I'm not skilled enough to adapt it properly).
What needs to happen is:
- Filter by sex in maindf1
- Search for the same ZCTA value in maindf1 in a rowname (first column) in maledflookup
- Also search for the right age strata from a row in maindf1 in the column name of maledflookup
- Add a new column of data to maindf1 row with matching ZCTA that has the census population value for that sex and age strata taken from maledflookup
- Repeat with femaledflookup
- End result is maindf1 having a censuspop value for every row that was matched by sex, ZCTA, and age strata
maindf1 is raw data where each row is an individual and columns are survey responses or collected data on individuals
The lookup table from the census website I had to use is in weird formatting so the easiest solution for me to fix one of the issues with it was to separate the lookup tables by sex first.
I had no luck in writing successful code as I'm not very experienced with coding in R yet. I tried some for & if loops and failed at adapting fuzzyjoin code for this task. I appreciate your help!
Example data:
...ANSWER
Answered 2021-Jun-12 at 17:56Use left_join
from tidyverse and a properly formatted lookup table:
QUESTION
I have the following dataset:
...ANSWER
Answered 2021-Jun-07 at 16:31We can split
the data by 'strata' into a list
and create the model by looping over the list
with lapply
QUESTION
I was trying to obtain the expected utility for each individual using R's survival
package (clogit
function) and I was not able to find a simple solution such as mlogit's logsum
.
Below I set an example of how one would do it using the mlogit
package. It is pretty straight forward: it just requires regressing the variables with the mlogit
function, save the output and use it as an argument in the logsum
function -- if needed, there is a short explanation in this vignette. And what I want is to know the similar method for clogit
. I've read the package's manual but I have failed to grasp what would be the most adequate function to perform the analsysis.
Note1: My preference for a function like mlogit's
is related to the fact that I might need to perform tons of regressions later on and being able to perform the correct estimation in different scenarios would be helpful.
Note2: I do not intend that the dataset created below be representative of how data should behave. I've set the example solely for the purpose of perfoming the function after the logit regressions.
**
...ANSWER
Answered 2021-Jun-07 at 00:20The vignette you offer says the logsum is calculated as:
To my reading that is similar to the calculation used to construct the "linear predictor". the lp is t(coef(clog)) %*% Xhat
. If I'm correct on that interpretation, then that is stored in the clog
-object:
QUESTION
I'm trying to convert the tick value of Y-Axis Scale from (0 .2 .4 .6 .8 1.0) to (0 .01 .02 .03 .04 .05), but failed. However, no such problem when converting viewmax
...ANSWER
Answered 2021-Jun-01 at 15:27It works fine for me.
QUESTION
I created the following recipe to predict my random forest in R:
...ANSWER
Answered 2021-May-27 at 16:48We recommend using DALEX for these kinds of model explainability tasks, because there is great support for tidymodels.
After you have a final fitted model (such as your random forest), you need to:
- create a DALEX explainer
- compute the PDP
QUESTION
I'm studying about bootstrap two sample t test with boot package. In gene expression matrix, I want to compare genes between conditions and my aim is to find expressed genes. I have a matrix 5*12(5 control, 7 treatment and 5 genes) and firstly I converted this data matrix to tibble format as two long vector in order to understand the tibble structure and make it easier for me.:
...ANSWER
Answered 2021-Apr-06 at 09:07I'm not sure why you want to bootstrap t-tests. It seems easier to just run the t.test
function. Here is my code for doing that:
Load packages
QUESTION
Is it possible to plot trees in random forest model ? The following is the sample dataset which can be used for explaining. Im sorry, i didnt find any such example online and hence didnt try anything by my own.The following is just a sample workaround.
...ANSWER
Answered 2021-May-23 at 13:14As far as I know, there is no built-in function to plot a ranger
tree or a randomForest
tree (see here and here). However, the forest of decision trees is made up of 500 trees by default, it seems exaggerated to have a plot for each of them. There are some methods to plot decision trees from other algorithm such as rpart
, party
or tree
. Have a look here for a brief tour of these methods for plotting trees and forests .
QUESTION
I am trying to create a function that will produce an individual plot comparing two linear regressions for type = "Plot" to type = "Strata". This comparison of linear models must be made for each unique combination of BCR # and LC type. For example (LC = UC and BCR = 30,LC = UC and BCR = 29,LC = UC and BCR = 28...once the LC "UC" has been compared for each unique BCR then the loop should move on to the next LC type and compare it against all BCR #s). Below is my data frame:
...ANSWER
Answered 2021-May-18 at 01:36You can get generate a list of plots using split
+ lapply
approach.
QUESTION
I have this pandas data frame, where I want to make a line plot, per each year strata:
...ANSWER
Answered 2021-May-12 at 13:12Why: Because after you do reset_index
, year
and month
become normal columns. And some_df.plot()
simply plots all the columns of the dataframe into one plot, resulting what you posted.
Fix: Try unstack
instead of reset_index
:
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