I have a field in my Kibana index whose value varies from -100 to 100.
I want to classify data as follows
If value lies between -100 to -10 it is termed as highly negative.
If value lies between -10 to -2 it is termed as negative.
If value lies between -2 to 2 it is termed as neutral.
and so on.
And I also want the count of how much data is Highly negative, negative or neutral.
Can anyone suggest how can this be done in Kibana?
In your Visualize tab, you can use the range aggregation and define all ranges of interest like this:
Related
I am trying to create a Kibana TSVB visualization that displays an “events per second (EPS)” metric for the last created elasticsearch index of a particular index pattern. Currently I’m using a Count aggregator that pipes to the Math aggregator with the formula params.Count / (params._interval / 1000).
But this calculation is only accurate if the chart’s timerange is set to exactly the first and last timestamps in the index. Otherwise the empty data sets (both before and after the index’s timeframe) is being included in calculating the EPS. Currently I have to manually query the min/max timestamps of the index and then manually set the chart’s timeframe in the upper right corner to match that, only then it calculates the EPS correctly.
So my question… is there a way to automatically do this? Such as having the chart’s Start and End timerange as variables equal to the Min and Max timestamps of the particular index I’m looking at? Or have it ignore the out of bounds time range?
Thanks
I would like to run an exact match by state but at the same time I would like a ratio of 1 to 4 case:control. Will the following code do the trick? I understand method=exact won't work since that method does not support the ratio parameter. If yes, how do I check if matching is exact? If not, how can I fix my code? I have plenty of data (255 cases & 7500 control so exact shouldn't be a problem at all)
Thank you!
exact_match <- matchit(case ~ state , ratio = 4, data = case_crude, exact="statabbr")
I'm not sure why you would want to throw out any units that are more than the 4th exact match. For example, if 5 control units resemble your treated unit, why would you only want to retain 4 of them?
Otherwise, that code should work to do what you want. It performs 4:1 nearest neighbor matching on a propensity score estimated with state as the sole predictor with exact matching on statabbr.
To check if exact matching was successful, check balance using summary(). You should see that the mean differences are 0 and the pair distance are also 0 (i.e., implying that within each pair, the values of statabbr are identical.
I'm hoping that this is a fairly simple solution, but I'm fairly new to Dynamics 365 development and the documentation and prior questions I've seen so far haven't been too helpful.
I need to create a specifically rounded calculated field within an entity. Currently the field has been set up as a decimal type and I have the precision value set to 0 to produce a whole number.
The calculation I am currently using in the field calculation editor is simply x + y / 100. However whatever result comes from that needs to be always rounded up to the next whole number rather than the nearest one. Is there a way to force the field logic to always round upwards?
If a direct answer isn't available, any resources would be appreciated.
I would say it is working as expected since less than 0.5 will round off to lower nearest number (floor) and greater than 0.5 will round off to higher nearest number (ceiling).
To handle your scenario, to always round off to higher nearest number (ceiling) - I may recommend this: Add 49 like (x + y + 49)/100, in other words 0.49 to formula. This quirk came from top of my head but this may be the best option in calculated field perspective.
I'm using Elasticsearch 5.3.1 and I'm evaluating BM25 and Classic TF/IDF.
I came across the discount_overlaps property which is optional.
Determines whether overlap tokens (Tokens with 0 position increment)
are ignored when computing norm. By default this is true, meaning
overlap tokens do not count when computing norms.
Can someone explain what the above means with an example if possible.
First off, the norm is calculated as boost / √length, and this value is stored at index time. This causes matches on shorter fields to get a higher score (because 1 in 10 is generally a better match than 1 in 1000).
For an example, let's say we have a synonym filter on our analyzer, that is going index a bunch of synonyms in the indexed form of our field. Then we index this text:
The man threw a frisbee
Once the analyzer adds all the synonyms to the field, it looks like this:
Now when we search for "The dude pitched a disc", we'll get a match.
The question is, for the purposes the norm calculation above, what is the length?
if discount_overlaps = false, then length = 12
if discount_overlaps = true, then length = 5
I want to score my documents based on on how close a number is to a query. Given I have two documents document1.field = 1 and document2.field = 10, a query field = 3 then I want document1._score > document2._score. Or in other words I want something like a fuzzy query against number. How would I achieve this? The use case is I want to support price queries (exact or range), but want to rank stuff that isn't exactly in the boundaries.
You are looking for Decay functions:
Decay functions score a document with a function that decays depending on the distance of a numeric field value of the document from a user given origin. This is similar to a range query, but with smooth edges instead of boxes.
It can be implemented using custom_score query where script will determine boost depending on absolute value of the difference between exact price and desired price. The desired price should be passed to the script as a parameter to avoid script recompilation for every request.
Alternatively, it can be implemented using custom_filters_score query. Filters here will contain different ranges around desired price. Smaller ranges will have higher boost and appear higher in the list than larger ranges.