I my BIRT Report i am using a data cube to show some data with the help of cross tab.In the cross tab i added a derived measure with the help of other measures.
Question:
How to calculate the average of that derived measure in the cross tab?
Please give the answer of my question. Thanks
In my case i made a derived measures depending on the other measures.
Suppose i have 2 measures names are monthlyRent and totalRent. With the help of these two measure i made a derived measure like this:
derived measure = (monthlyRent/totalRent)*100
Now i want to calculate the grand total for this derived measure.
Steps:
Right click on cell.
then insert.
Dynamic Text.
Available Column Bindings.
cross tab.
then selected the both measures monthlyRent and totalRent.
It will be like this: (data["monthlyRent"]/data["totalRent"])*100
Regarding any help you can contact me i will provide the practical example for this.
Thanks
Related
To speed up processing time in tableau: Is it best to combine multiple calculated fields into one calculated field or best to have the equation broken out into pieces?
Thanks!
Both options are not related to optimization. However, it is a good practice to hide unused fields from the "tables" sidebar
I was wondering if it is possible to have more than one time series identifier column in the model? Let's assume I'd like to create a forecast at a product and store level (which the documentation suggests should be possible).
If I select product as the series identifier, the only options I have left for store is either a covariate or an attribute and neither is applicable in this scenario.
Would concatenating product and store and using the individual product and store code values for that concatenated ID as attributes be a solution? It doesn't feel right, but I can't see any other option - am I missing something?
Note: I understand that this feature of Vertex AI is currently in preview and that because of that the options may be limited.
There isn't an alternate way to assign 2 or more Time Series Identifiers in the Forecasting Model on Vertex AI. The "Forecasting model" is in the "Preview" Product launch stage, as you are aware, with all consequences of that fact the options are limited. Please refer to this doc for more information about the best practices for data preparation to train the forecasting model.
As a workaround, the two columns can be concatenated and assigned a Time Series Identifier on that concatenated column, as you have mentioned in the question. This way, the concatenated column carries more contextual information into the training of the model.
Just to follow up on Vishal's (correct) answer in case someone is looking this up in the future.
Yes, concatenating is the only option for now as there can only be one time series identifier (I would hope this changes in the future). Having said that, I've experimented with adding the individual identifiers in the data as categorical attributes and it works actually pretty well. This way I have forecast generated at a product/store level, but I can aggregate all forecasts for individual products and the results are not much off from the models trained on aggregated data (obviously that would depend on the demand classification and selected optimisation method amongst other factors).
Also, an interesting observation. When you include things like product descriptions, you can classify them either as categorical or text. I wasn't able to find in the documentation if the model would only use unigrams (which is what the column statistics in the console would suggest) or a number of n-grams but it is definitely something you would want to experiment with with your data. My dataset was actually showing a better accuracy when the categorical classification was used, which is a bit counter-intuitive as it feels like redundant information, although it's hard to tell as the documentation isn't very detailed. It is likely to be specific to my data set, so as I said make sure you experiment with yours.
i have a table i need to design where rows are the actual values as below
i tried to use table, the results were as below, which is not what i want:
i tried to use a matrix , i couldnt reach the desired design as well:
can any one have any idea how to do such thing?
i solved it using same concept adopted in the below solution
Custom aggregate column in power bi matrix
best regards,
Georges
Firstly, I will illustrate the scenario that I have a dataset like;
ProductID, ProductType, MachineID, MachineModel, MachineSpeed, RejectDate, RejectVolume etc.
And I want to find which field(s) is the reason for the increase in my RejectVolume? Also, in the scenario, all products have a RejectVolume. I mean RejectVolume is nonzero and there are continuous but different values. Thanks to this, I can recognize the reason(s) and find the solution for reducing the value of RejectVolume.
Can you give me any ideas for creating the model?
Thank you.
You want to look at Feature Selection methods.
In this scenario you could start with Linear Regression using Lasso for feature selection. This is done by successively increasing the lasso regularization term, which will decrease the weight of unimportant features, leaving you with the features with the most impact.
As far as I can tell it doesn't do anything, and the best thing to do is to add all measures as a list in one column, but then there would be a "Measures List" instead of grid.... so what's the "grid" part do?
The main reason I can imagine is that you can put a measure below a column to which it refers somehow. This is obviously the case for the standard case of a measure being the sum of a specific column. But other than that, I agree there is no real use.