forest | 分布式任务调度平台 , 分布式 , 任务调度 , schedule , scheduler | Job Scheduling library

 by   busgo Go Version: v0.1.6 License: Apache-2.0

kandi X-RAY | forest Summary

kandi X-RAY | forest Summary

forest is a Go library typically used in Data Processing, Job Scheduling applications. forest has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

分布式任务调度平台,分布式,任务调度,schedule,scheduler
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            kandi-support Support

              forest has a low active ecosystem.
              It has 249 star(s) with 50 fork(s). There are 12 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 6 open issues and 2 have been closed. On average issues are closed in 2 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of forest is v0.1.6

            kandi-Quality Quality

              forest has no bugs reported.

            kandi-Security Security

              forest has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              forest is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              forest releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

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            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of forest
            Get all kandi verified functions for this library.

            forest Key Features

            No Key Features are available at this moment for forest.

            forest Examples and Code Snippets

            copy iconCopy
            def includes_any(lst, values):
              for v in values:
                if v in lst:
                  return True
              return False
            
            
            includes_any([1, 2, 3, 4], [2, 9]) # True
            includes_any([1, 2, 3, 4], [8, 9]) # False
            
              
            copy iconCopy
            const lowercaseKeys = obj =>
              Object.keys(obj).reduce((acc, key) => {
                acc[key.toLowerCase()] = obj[key];
                return acc;
              }, {});
            
            
            const myObj = { Name: 'Adam', sUrnAME: 'Smith' };
            const myObjLower = lowercaseKeys(myObj); // {name: 'Adam  
            copy iconCopy
            const decapitalize = ([first, ...rest], upperRest = false) =>
              first.toLowerCase() +
              (upperRest ? rest.join('').toUpperCase() : rest.join(''));
            
            
            decapitalize('FooBar'); // 'fooBar'
            decapitalize('FooBar', true); // 'fOOBAR'
            
              
            Deletes an item from the forest .
            javadot img4Lines of Code : 30dot img4License : Permissive (MIT License)
            copy iconCopy
            private Node delete(Node node, T data) {
                    if (node == null) {
                        System.out.println("No such data present in BST.");
                    } else if (node.data.compareTo(data) > 0) {
                        node.left = delete(node.left, data);
                    } els  
            Initialize a forest saver .
            pythondot img5Lines of Code : 23dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def __init__(self, resource_handle, create_op, name):
                """Creates a _TreeEnsembleSavable object.
            
                Args:
                  resource_handle: handle to the decision tree ensemble variable.
                  create_op: the op to initialize the variable.
                  name: the n  
            Print the forest .
            javadot img6Lines of Code : 21dot img6License : Permissive (MIT License)
            copy iconCopy
            public void preorder() {
                    if (this.root == null) {
                        System.out.println("This BST is empty.");
                        return;
                    }
                    System.out.println("Preorder traversal of this tree is:");
                    Stack st = new Stack();
                    s  

            Community Discussions

            QUESTION

            Git rebase commit replays vs merge commits: a concrete example
            Asked 2021-Jun-15 at 13:22

            I have a question about how rebasing works in git, in part because whenever I ask other devs questions about it I get vague, abstract, high level "architect-y speak" that doesn't make a whole lot of sense to me.

            It sounds as if rebasing "replays" commits, one after another (so sequentially) from the source branch over the changes in my working branch, is this the case? So if I have a feature branch, say, feature/xyz-123 that was cut from develop originally, and then I rebase from origin/develop, then it replays all the commits made to develop since I branched off of it. Furthermore, it does so, one develop commit at a time, until all the changes have been "replayed" into my feature branch, yes?

            If anything I have said above is incorrect or misled, please begin by correcting me! But assuming I'm more or less correct, I'm not seeing how this is any different than merging in changes from develop by doing a git merge develop. Don't both methods result with all the latest changes from develop making their way into feature/xyz-123?

            I'm sure this is not the case but I'm just not seeing the forest through the trees here. If someone could give a concrete example (with perhaps some mock commits and git command line invocations) I might be able to understand the difference in how rebase works versus a merge. Thanks in advance!

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:22

            " It sounds as if rebasing "replays" commits, one after another (so sequentially) from the source branch over the changes in my working branch, is this the case? "

            Yes.

            " Furthermore, it does so, one develop commit at a time, until all the changes have been "replayed" into my feature branch, yes? "

            No, it's the contrary. If you rebase your branch on origin/develop, all your branch's commits are to be replayed on top of origin/develop, not the other way around.

            Finally, the difference between merge and rebase scenarios has been described in details everywhere, including on this site, but very broadly the merge workflow will add a merge commit to history. For that last part, take a look here for a start.

            Source https://stackoverflow.com/questions/67986445

            QUESTION

            compare array of object of array with an another array in javascript
            Asked 2021-Jun-15 at 11:51

            have two arrays one with a simple array with all the elements have integer value and another one with array of objects with an array (nested object).

            need to compare both the array and remove the value which is not equilant.

            ...

            ANSWER

            Answered 2021-Jun-15 at 11:29

            You can easily achieve this result using map and filter.

            Source https://stackoverflow.com/questions/67985171

            QUESTION

            How to sort lines of text alphabetically based on a part of each line?
            Asked 2021-Jun-12 at 08:18

            I have a text file that contains abbreviations like so (simplified example):

            ...

            ANSWER

            Answered 2021-Jun-11 at 10:22

            Here’s a ‘tidyverse’ solution:

            Source https://stackoverflow.com/questions/67934669

            QUESTION

            How to create a list with the y-axis labels of a TreeExplainer shap chart?
            Asked 2021-Jun-10 at 17:29

            How to create a list with the y-axis labels of a TreeExplainer shap chart?

            Hello,

            I was able to generate a chart that sorts my variables by order of importance on the y-axis. It is an impotant solution to visualize in graph form, but now I need to extract the list of ordered variables as they are on the y-axis of the graph. Does anyone know how to do this? I put here an example picture.

            Obs.: Sorry, I was not able to add a minimal reproducible example. I don't know how to paste the Jupyter Notebook cells here, so I've pasted below the link to the code shared via Github.

            In this example, the list would be "vB0 , mB1 , vB1, mB2, mB0, vB2".

            minimal reproducible example

            ...

            ANSWER

            Answered 2021-Jun-09 at 16:36

            QUESTION

            Implementing Longitudinal Random Forest with LongituRF package in R
            Asked 2021-Jun-09 at 21:44

            I have some high dimensional repeated measures data, and i am interested in fitting random forest model to investigate the suitability and predictive utility of such models. Specifically i am trying to implement the methods in the LongituRF package. The methods behind this package are detailed here :

            Capitaine, L., et al. Random forests for high-dimensional longitudinal data. Stat Methods Med Res (2020) doi:10.1177/0962280220946080.

            Conveniently the authors provide some useful data generating functions for testing. So we have

            ...

            ANSWER

            Answered 2021-Apr-09 at 14:46

            When the function DataLongGenerator() creates Z, it's a random uniform data in a matrix. The actual coding is

            Source https://stackoverflow.com/questions/66188123

            QUESTION

            Newbie : How evaluate model to increase accuracy model in classification
            Asked 2021-Jun-09 at 08:41

            my data

            how do I increase the accuracy of the model, if some of my models when run produce results like the one below `

            ...

            ANSWER

            Answered 2021-Jun-09 at 05:44

            There are several ways to achieve this:

            1. Look at the data. Are they in the best shape for the algorithm? Regarding NaN, Covariance and so on? Are they normalized, are the categorical ones translated well? This is a question too far-reaching for a forum.

            2. Look at the problem and the different algorithm suitable for this problem. Maybe

            • Logistic Regression
            • SVN
            • XGBoost
            • ....
            1. Try hyper parameter tuning with RandomisedsearvCV or GridSearchCV

            This is quite high-level.

            Source https://stackoverflow.com/questions/67897317

            QUESTION

            Create new variables based on specific factor levels in time series data with dplyr
            Asked 2021-Jun-08 at 21:34

            I've got some time series data where both the steps of the sequence (ranging from 1 to 8) as well as its topic (>100) are encoded as character factor levels within a single variable. Here is a minimal example (I omitted timestamps which would be increasing within each id):

            ...

            ANSWER

            Answered 2021-Jun-08 at 20:52

            This isn't particularly elegant, but it works:

            Source https://stackoverflow.com/questions/67894160

            QUESTION

            Kotlin by lazy throws NullPointerException
            Asked 2021-Jun-08 at 16:39

            I am currently trying to learn Kotlin with the help of the book "Kotlin Programming The Big Nerd Ranch Guide" and so far everything worked. But now I am struggling with the "lazy" initialization which throws a NullPointerException which says

            Cannot invoke "kotlin.Lazy.getValue()" because "< local1>" is null

            The corresponding lines are:

            ...

            ANSWER

            Answered 2021-Jun-08 at 16:39

            When something like this happens, it's usually due to bad ordering of initialization.

            The initialization of the Player class goes this way:

            1. the name property has its backing field initialized with the _name value
            2. the init block is run, and tries to access name
            3. the getter of name tries to read the hometown property, but fails because hometown is still not initialized
            4. ...if things had gone right, the hometown property would be initialized now with the lazy delegate

            So basically you're trying to access hometown before the lazy delegate is configured. If you move hometown's declaration above the init block, you should be fine.

            You can see the fix in action on the playground

            Source https://stackoverflow.com/questions/67890451

            QUESTION

            Why are feature selection results different for Random forest classifier when applied in two different ways
            Asked 2021-Jun-06 at 17:10

            I want to do feature selection and I used Random forest classifier but did differently.

            I used sklearn.feature_selection.SelectfromModel(estimator=randomforestclassifer...) and used random forest classifier standalone. It was surprising to find that although I used the same classifier, the results were different. Except for some two features, all others were different. Can someone explain why is it so? Maybe is it because the parameters change in these two cases?

            ...

            ANSWER

            Answered 2021-Jun-06 at 17:10

            This could be because select_from_model refits the estimator by default and sklearn.ensembe.RandomForestClassifier has two pseudo random parameters: bootsrap, which is set to True by default, and max_features, which is set to 'auto' by default.

            If you did not set a random_state in your randomforestclassifier estimator, then it will most likely yield different results every time you fit the model because of the randomness introduced by the bootstrap and max_features parameters, even on the same training data.

            • bootstrap=True means that each tree will be trained on a random sample (with replacement) of a certain percentage of the observations from the training dataset.

            • max_features='auto' means that when building each node, only the square root of the number of features in your training data will be considered to pick the cutoff point that reduces the gini impurity most.

            You can do two things to ensure you get the same results:

            1. Train your estimator first and then use select_from_model(randomforestclassifier, refit=False).
            2. Declare randomforestclassifier with a random seed and then use select_from_model.

            Needless to say, both options require you to pass the same X and y data.

            Source https://stackoverflow.com/questions/67860624

            QUESTION

            using random forest as base classifier with adaboost
            Asked 2021-Jun-06 at 12:54

            Can I use AdaBoost with random forest as a base classifier? I searched on the internet and I didn't find anyone who does it.

            Like in the following code; I try to run it but it takes a lot of time:

            ...

            ANSWER

            Answered 2021-Apr-07 at 11:30

            No wonder you have not actually seen anyone doing it - it is an absurd and bad idea.

            You are trying to build an ensemble (Adaboost) which in itself consists of ensemble base classifiers (RFs) - essentially an "ensemble-squared"; so, no wonder about the high computation time.

            But even if it was practical, there are good theoretical reasons not to do it; quoting from my own answer in Execution time of AdaBoost with SVM base classifier:

            Adaboost (and similar ensemble methods) were conceived using decision trees as base classifiers (more specifically, decision stumps, i.e. DTs with a depth of only 1); there is good reason why still today, if you don't specify explicitly the base_classifier argument, it assumes a value of DecisionTreeClassifier(max_depth=1). DTs are suitable for such ensembling because they are essentially unstable classifiers, which is not the case with SVMs, hence the latter are not expected to offer much when used as base classifiers.

            On top of this, SVMs are computationally much more expensive than decision trees (let alone decision stumps), which is the reason for the long processing times you have observed.

            The argument holds for RFs, too - they are not unstable classifiers, hence there is not any reason to actually expect performance improvements when using them as base classifiers for boosting algorithms, like Adaboost.

            Source https://stackoverflow.com/questions/66977025

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install forest

            You can download it from GitHub.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            gh repo clone busgo/forest

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            git@github.com:busgo/forest.git

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