metrics | FID score Pytorch and TF implementation | Machine Learning library

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.

https://developer.twitter.com/en/docs/twitter-api/metrics

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.

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

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:

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:

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:

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 https://keras.io/guides/customizing_what_happens_in_fit/ I changed the Dense layer to have 2 units instead of one, and made its activation to sigmoid.

The code:

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.

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?

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?