Derived Column return WRONG datatype.

Hi, friend,
l've 2 variables A & B from source database which i used to calculate C with the following formula, i.e.

C = 100 * A/B

In case of B = 0 (DIV BY ZERO), C should be equal to -9999.

l've set the datatype for A & B are INT, C is FLOAT at targetting database.

So l used to derived column to calculate C as,
B == 0 ? -9999 : 100 * A/B

After l run the package, l realize that all my C is INTEGER rather than FLOAT.....it seems that SQL server has evaluate the wrong datatype for me....anyway to overcome this?

This should not be a problem - you can map an integer in the data flow to a float at the destination without any issues.

Donald Farmer

|||Fact is, l get a Integer at the end :(, even the data and meta data is float........
|||

Try casting A and B as floats before using them in your calculation.

-Jamie

Derived Column from a Condition statement

I've found that there is no such thing as a Case Statement in the Derived Column task in the data flow objects. I've written a ternary statement instead. I can't seem to get it to work exactly how I want it to. For example

AccountCategory == "E" ? 1 : 2

Works fine but the following doesn't

AccountCategory == "E" ? CreditAmount : DebitAmount

The CreditAmount and DebitAmount Fields are spelled correctly, the field type of AccountCategory is String and the Field type of CreditAmount and DebitAmount is numeric, but that seems to be the same situation that I have in the first example that works. Am I missing something? I'd appreciate any advice anyone has to offer. Thanks,

Bill

Only thing you did not mention is what is the data type set for the derived column itself, it should be numeric to match the Credit and Debit columns. The scale and precison must match for all three too.

That is all just guess work, and the syntax looks fine, but what would really help is the actual error message you get.

|||It looks like I stumbled on to how to fix this, I don't know if it is a bug or it is designed to work this way. If I copied the second statement and then deleted the whole row for that particular derived column then copied the statement back into the expression column before naming the column it works. Go figure.|||

Glad to hear you have it working.

Most likely, as Darren suggests, it was a datatype mismatch. However, if you could repro and report the error message, we could give you better advice.

Donald

Derived column expression question......

Greetings,

I have an existing 2000 DTS package that uses the following case statement:

Case

When TERMS_PERCENT ='0'

then 0

else cast(TERMS_PERCENT as decimal(6,2))/100

end as TermsPct

to convert a source DT_STR(4) datatype to a DT_Numeric(5,2) destination column and would like to use an equivelent derived column expression in 2005. Being a DBA by nature and experience I'm having trouble converting this statement to a valid expression without failure, any help would be greatly appreciated.

mjanzou wrote:

Greetings,

I have an existing 2000 DTS package that uses the following case statement:

Case

When TERMS_PERCENT ='0'

then 0

else cast(TERMS_PERCENT as decimal(6,2))/100

end as TermsPct

to convert a source DT_STR(4) datatype to a DT_Numeric(5,2) destination column and would like to use an equivelent derived column expression in 2005. Being a DBA by nature and experience I'm having trouble converting this statement to a valid expression without failure, any help would be greatly appreciated.

Something like this perhaps?

[TERMS_PERCENT] == "0" ? (DT_NUMERIC, 5, 2)[TERMS_PERCENT] : (DT_NUMERIC, 5, 2)0

-Jamie

|||

Jamie Thomson wrote:

[TERMS_PERCENT] == "0" ? (DT_NUMERIC, 5, 2)[TERMS_PERCENT] : (DT_NUMERIC, 5, 2)0

-Jamie

The TRUE and FALSE values are reversed . Try:

Code Snippet

[TERMS_PERCENT]== "0" ? (DT_NUMERIC,6,2)0 : ((DT_NUMERIC,6,2)[TERMS_PERCENT]) / 100

|||

Ahhh goddamnit!!!

dependency relations are different from different mining structures with same mining algorithms?

Hi, all here..

I got one problem in my data analysis case with SQL Server 2005 data mining algorithms-in the dependency network, the attributes mostly affected the predictive value are different in different mining structures tho.

For example, I got a, b,c as input attributes to predict attribute d, in mining structure 1 (with all attributes discretized), i got a as the attribute which has the strongest relation with predictive attribute d. However in mining structure 2 (with all attributes values continuous), I got attribute b as the strongest attribute with predictive attribute d?

So what is the problem tho? In this case, how can I tell which attribute actually has the strongest relation with predictive attribute?

Thanks a lot in advance for help.

This is expected. You are modeling two different problems here. In the discretized space you are asking "are ranges of data predictive of my target", in the other you are using the raw values.

For example, say you are predicting credit worthyness based on age and salary. If you discretize you are asking "which age and salary ranges indicate creditworthiness." Your ranges for age could be 0-10,11-20,21-40, etc. and salary could be 0-30k, 30-60k, etc.

In this model you may find that people who are in the 0-10 bucket have very little creditworthiness and therefore Age is the strongest predictor.

If you choose continuous, you may find that overall, a very low salary, e.g. < 13K was a stronger indicator and age overall played a minor role. This could be because the bucketing hid detail from the algorithm, or even (if predicting a continuous) because the salary played a stronger role in a regression formula.

|||Hi, Jamie,thanks a lot .