metrics | This is not the Java library.

 by   codahale Go Version: Current License: MIT

kandi X-RAY | metrics Summary

kandi X-RAY | metrics Summary

metrics is a Go library. metrics has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This is not the Java library.

            kandi-support Support

              metrics has a low active ecosystem.
              It has 447 star(s) with 48 fork(s). There are 29 watchers for this library.
              It had no major release in the last 6 months.
              There are 0 open issues and 6 have been closed. On average issues are closed in 28 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of metrics is current.

            kandi-Quality Quality

              metrics has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              metrics is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              metrics releases are not available. You will need to build from source code and install.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of metrics
            Get all kandi verified functions for this library.

            metrics Key Features

            No Key Features are available at this moment for metrics.

            metrics Examples and Code Snippets

            Collect metrics from a list of outputs .
            pythondot img1Lines of Code : 85dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def collect_per_output_metric_info(metrics,
            Wrap metrics into a dict .
            pythondot img2Lines of Code : 57dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _wrap_and_check_metrics(self, metrics):
                """Handle the saving of metrics.
                Metrics is either a tuple of (value, update_op), or a dict of such tuples.
                Here, we separate out the tuples and create a dict with names to tensors.
            Wrap metrics into a dict .
            pythondot img3Lines of Code : 57dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _wrap_and_check_metrics(self, metrics):
                """Handle the saving of metrics.
                Metrics is either a tuple of (value, update_op), or a dict of such tuples.
                Here, we separate out the tuples and create a dict with names to tensors.

            Community Discussions


            how to calculate model accuracy in rstudio for logistic regression
            Asked 2021-Jun-15 at 22:26

            How do you calculate the model accuracy in RStudio for logistic regression. The dataset is from Kaggle.



            Answered 2021-Jun-15 at 21:39

            use the package ML metrics



            How to reply_count & quote_count using tweepy 3.10.0?
            Asked 2021-Jun-15 at 22:22

            I am trying to execute quote_count & reply_count using the Twitter Tweepy API, but I can't find proper updated documentation on how to do it.


            I have some working code from Tweepy for Twitter API version 1 to get some data I use, but I cant find good info about how to extract reply_count & quote_count using Twitter API version 2 via Tweepy.



            Answered 2021-Jun-15 at 22:22

            Tweepy v3.10.0 does not support Twitter API v2. You'll have to use the latest development version of Tweepy on the master branch or wait for Tweepy v4.0 to be released.

            As that documentation says, you need to pass the specific fields and expansions you want when making the API request. For example, for the version currently on the master branch, the equivalent of the public metrics example request in that documentation would be:



            Apereo CAS HTML template does not seem to load
            Asked 2021-Jun-15 at 18:37

            So I initialized CAS using cas-initializr with the following command inside the cas folder:



            Answered 2021-Jun-15 at 18:37

            Starting with 6.4 RC5 (which is the version you run as of this writing and should provide this in your original post):

            The collection of thymeleaf user interface template pages are no longer found in the context root of the web application resources. Instead, they are organized and grouped into logical folders for each feature category. For example, the pages that deal with login or logout functionality can now be found inside login or logout directories. The page names themselves remain unchecked. You should always cross-check the template locations with the CAS WAR Overlay and use the tooling provided by the build to locate or fetch the templates from the CAS web application context.


            Please read the release notes and adjust your setup.

            All templates are listed here:



            Pandas RMSE Groupby Multiple Conditions
            Asked 2021-Jun-15 at 17:13

            I am trying to compute the RMSE of a panda dataframe based on multiple conditions: (plant_name, year, month). My datafram (df3m) looks like this:



            Answered 2021-Jun-15 at 17:13

            You can use .GroupBy.apply() and put the call to mean_squared_error inside it, as follows:



            How does Prometheus labeling syntax works?
            Asked 2021-Jun-15 at 17:00

            I'm new to Prometheus and I have a very basic question.

            What is the syntax to add a label to my Metrics? I tried the following:



            Answered 2021-Jun-15 at 16:18

            Your question lacks helpful detail to aid answering.

            I assume you're using the Java SDK.

            Here's the link to the documentation:


            It appears you should use:



            Need to Calculate few metrics from dataset using SQL - separate queries
            Asked 2021-Jun-15 at 16:59

            Dataset looks like this : This is a sample dataset for number of employee login activity named - activity

            I need to calculate few metrics, was able to do in python data frames, but new in mySQL.

            1. what is the average number of employee active per day for month of jan 2018 by dept ( was able to do somewhat half of it, but results coming are not correct.

            2. number of unique active employee (login >0) per month for jan 2018 for each dept_id (was able to do it)

            3. month over month growth for all dept_id from dec-2017 to jan 2018 where at least one employee was active (login >0) - no idea how to do this in sql

            4. fraction of users who were active in each dept_id for dec 2017 and were also active in the same dept_id for jan 2018

            5. how many employee login in on 3 or more consecutive days in jan 2018

            Any help would be appreciated.

            Query written for case 1:



            Answered 2021-Jun-15 at 16:59

            Let me know if this works otherwise I will update the answer, I don't have MYSQL installed so wasn't able to check.

            And the date is a keyword in oracle but not sure in MYSQL so use it in quotes like "date".

            Case 1:



            Model.evaluate returns 0 loss when using custom model
            Asked 2021-Jun-15 at 15:52

            I am trying to use my own train step in with Keras by creating a class that inherits from Model. It seems that the training works correctly but the evaluate function always returns 0 on the loss even if I send to it the train data, which have a big loss value during the training. I can't share my code but was able to reproduce using the example form the Keras api in I changed the Dense layer to have 2 units instead of one, and made its activation to sigmoid.

            The code:



            Answered 2021-Jun-12 at 17:27

            As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.



            Differnces between __execute-count value and values gathered by the Metrics Reporting API v2
            Asked 2021-Jun-15 at 15:18

            I have run a topology, and I used the Meter type in metric Reporting API v2. In the execute method I mark this metric. So it will mark an event whenever the execute method is called. But when I compare this value with the __execute-count, I see huge differences. Does anyone know why this happens?

            These are the values from my log which are gathered at the same time:

            9:v7 __execute-count {v0:v7=44500}
            9:v7 tuple_inRate.count 664129

            Update: When I use the mark method on the Meter metric, I will get different results in comparison with the Counter metric. But still, I do not understand why the values from the counter metric (tuple counter) are not the same as the __execute-count.



            Answered 2021-Jun-11 at 06:51

            As given in this answer, Storms Internal Metrics are just estimated by a percentage of the real data flow. Initially, it uses 5% of incoming tuples to make those estimations. This may lead to inaccuracies for extreme high or low throughputs.

            EDIT: The documentation describes the following:

            In general all of these tuple count metrics are randomly sub-sampled unless otherwise stated. This means that the counts you see both on the UI and from the built in metrics are not necessarily exact. In fact by default we sample only 5% of the events and estimate the total number of events from that. The sampling percentage is configurable per topology through the topology.stats.sample.rate config. Setting it to 1.0 will make the counts exact, but be aware that the more events we sample the slower your topology will run (as the metrics are counted in the same code path as tuples are processed). This is why we have a 5% sample rate as the default.

            EDIT 2 In this post, there is more information about the estimation:

            The way it works is that if you choose a sampling rate of 0.05, it will pick a random element of the next 20 events in which to increase the count by 20. So if you have 20 tasks for that bolt, your stats could be off by +-380.

            By the way, execute_count is just an increasing number, while your tuple_inRate.count is a rate, isn`t it?



            Azure Data Explorer High Ingestion Latency with Streaming
            Asked 2021-Jun-15 at 08:34

            We are using stream ingestion from Event Hubs to Azure Data Explorer. The Documentation states the following:

            The streaming ingestion operation completes in under 10 seconds, and your data is immediately available for query after completion.

            I am also aware of the limitations such as

            Streaming ingestion performance and capacity scales with increased VM and cluster sizes. The number of concurrent ingestion requests is limited to six per core. For example, for 16 core SKUs, such as D14 and L16, the maximal supported load is 96 concurrent ingestion requests. For two core SKUs, such as D11, the maximal supported load is 12 concurrent ingestion requests.

            But we are currently experiencing ingestion latency of 5 minutes (as shown on the Azure Metrics) and see that data is actually available for quering 10 minutes after ingestion.

            Our Dev Environment is the cheapest SKU Dev(No SLA)_Standard_D11_v2 but given that we only ingest ~5000 Events per day (per metric "Events Received") in this environment this latency is very high and not usable in the streaming scenario where we need to have the data available < 1 minute for queries.

            Is this the latency we have to expect from the Dev Environment or are the any tweaks we can apply in order to achieve lower latency also in those environments? How will latency behave with a production environment loke Standard_D12_v2? Do we have to expect those high numbers there as well or is there a fundamental difference in behavior between Dev/test and Production Environments in this concern?



            Answered 2021-Jun-15 at 08:34

            Did you follow the two steps needed to enable the streaming ingestion for the specific table, i.e. enabling streaming ingestion on the cluster and on the table?

            In general, this is not expected, the Dev/Test cluster should exhibit the same behavior as the production cluster with the expected limitations around the size and scale of the operations, if you test it with a few events and see the same latency it means that something is wrong.

            If you did follow these steps, and it still does not work please open a support ticket.



            What are the different use cases for AWS VPC in the area of Data Analytics?
            Asked 2021-Jun-15 at 07:40

            I am new to AWS VPC and exploring everything about it. I understood that VPC is majorly used to have a secure and isolated environment. What are the different use cases for AWS VPC in the area of Data Analytics? I have a data lake pipeline currently which is as follows:

            1. Extract data using APIs
            2. Store raw data in S3
            3. Create Lambda functions or Glue Jobs to perform business metrics
            4. Store metric outputs in S3
            5. Create tables in Athena for all the data stored in S3
            6. Import tables in Quicksight to produce business insights from visuals

            In this process how can VPC be used or make this process efficient/better?



            Answered 2021-Jun-15 at 07:40

            The services you mention (mostly) live outside of VPCs.

            VPCs are used for services that use virtual computers, such as Amazon EC2 computers and Amazon RDS databases.

            By using services that don't involve specific 'computers' (such as Amazon S3, Athena, QuickSight) you can take advantage of much lower costs, paying only what you use. These services do not mimic traditional servers and therefore don't need VPCs. All the networking complexity is hidden and you can concentrate on using the service instead of running a network.

            Yes, VPCs add extra security, but that's only because resources on a VPC need securing due to potential security holes. The services you mention are all secured via IAM and do not expose themselves outside the published APIs.


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


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            Install metrics

            You can download it from GitHub.


            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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