Any ideas on how to perform addition of several columns in idiorm query
I want get the value of alias 'Due' n 'TotDue' as an addition of those specified columns,but i keep on getting a fatal Error
any assistance i shall realy appreciate;
This is my sample query ;
$d = ORM::for_table('transaction_s')
->select_many(array('ACCT_TYPE'=>'transaction_s.TRANS_TYP',
'ADate'=>'transaction_s.ACCTDATE',
'Str'=>'accounts_master.PHYS_ADDRESS',
'Zone'=>'accounts_master.ZONE_ID',
'Name'=>'customers.CUSTOMER_NAME',
'Address'=>'customers.POST_ADDRESS',
'TelNo'=>'customers.TEL_NO',
'AcctNo'=>'transaction_s.ACCT_NO',
'smscost'=>'transaction_s.INT_AMOUNT',
'Due'=>'(transaction_s.water_DUE+transaction_s.METER_RENT+transaction_s.SEWER+
transaction_s.Conserve+transaction_s.INT_AMOUNT+transaction_s.BIN_HIRE)',
'TotalDue' =>'(transaction_s.water_DUE+transaction_s.METER_RENT+transaction_s.SEWER
+transaction_s.Conserve+transaction_s.PREVBAL
+transaction_s.INT_AMOUNT+transaction_s.BIN_HIRE)',
'OutStanding'=>'transaction_s.water_OUTSTANDING'
)
->inner_join('accounts_master', 'customers.CUSTOMER_NO = accounts_master.CUSTOMER_NO')
->inner_join('customers', 'customers.CUSTOMER_NO = accounts_master.CUSTOMER_NO')
->find_many();
Related
I never use DAX and I find it very complicated!
I read this https://www.daxpatterns.com/abc-classification/ but I find it complicated.
My dataset is like this https://1drv.ms/x/s!AvMy-Bzzx3mqgwmKSZm6wr_W4VXr?e=Jwwbji&nav=MTVfezAwMDAwMDAwLTAwMDEtMDAwMC0wMDAwLT...
I need to sum column Value if the Name and date are the sums after doing the ABC analysis. The problem is that I can't aggregate before in transform because I need the column Name Fatt!
It is not necessary for a dynamic ABC analysis. The value for each Name and Date is the same...
I appreciate every help! Also video tutorial or something can help me. Thanks!
'Aggregazione fatturato = SUMX(UNION(VALUES(Name),VALUES(Date)),SUM(Value)
Fatturato Cumulato =
VAR CurrentProductSales = 'Misure'[Aggregazione fatturato]
VAR BetterProducts =
FILTER (
Name,
'Misure'[Aggregazione fatturato] >= CurrentProductSales
)
VAR Result =
SUMX (
BetterProducts,
'Misure'[Aggregazione fatturato]
)
RETURN
Result
Cumulated Pct =
DIVIDE (
'Misure'[Fatturato Cumulato],
SUM ('Misure'[Aggregazione fatturato])
ERROR 'Misure'[Aggregazione fatturato] not found
i have 2 tables and, i would like to check if table 1 (Type_Sorting) == (CCSClassCode_Type) is matched with table 2 (_Type Sorting) == (_CCS Class Type):
for example, you can see vi got the wrong value in table 1 (CCSClassCode_Type)
and, the right value is XLBas you can see in table 2 (_CCS Class Type) not ULM,
the idea of table 2 to check if people type the right values, Please not that table 2 (_CCS Class Type) have duplicate values
thank you in advance :)
You can calculate this like that:
Table 2 =
Var trt =
SELECTCOLUMNS(Table_2, "XX"
, COMBINEVALUES(",",Table_2[_CCS Class Type],Table_2[_Type Sorting]))
return
SUMMARIZECOLUMNS(Table_1[Column1]
, Table_1[CCSClassCode_Type]
, Table_1[Type_Sorting]
, FILTER(ALL(Table_1[CCSClassCode_Type],Table_1[Type_Sorting]), not( COMBINEVALUES(",",Table_1[CCSClassCode_Type],Table_1[Type_Sorting])
in trt )
))
File.txt
123,abc,4,Mony,Wa
123,abc,4, ,War
234,xyz,5, ,update
234,xyz,5,Rheka,sild
179,ijo,6,all,allSingle
179,ijo,6,ball,ballTwo
1) column1,column2,colum3 are primary Keys
2) column4,column5 are comparision Keys
I have a file with duplicate records like above In this duplicate record i need to get only one record among duplicates based on sorting order.
Expected Output:
123,abc,4, ,War
234,xyz,5, ,update
179,ijo,6,all,allSingle
Please help me. Thanks in advance.
You can try the below code:
data = LOAD 'path/to/file' using PigStorage(',') AS (col1:chararray,col2:chararray,col3:chararray,col4:chararray,col5:chararray);
B = group data by (col1,col2,col3);
C = foreach B {
sorted = order data by col4 desc;
first = limit sorted 1;
generate group, flatten(first);
};
In the above code, you can change the sorted variable to choose the column you would like to consider for sorting and the type of sorting. Also, in case you require more than one record, you can change the limit to greater than 1.
Hope this helps.
Questions isn't soo clear , but I understand this is what you need :
A = LOAD 'file.txt' using PigStorage(',') as (column1,column2,colum3,column4,column5);
B = GROUP A BY (column1,column2,colum3);
C = FOREACH B GENERATE FLATTERN(group) as (column1,column2,colum3);
DUMP C;
Or
A = LOAD 'file.txt' using PigStorage(',') as (column1,column2,colum3,column4,column5);
B = DISTINCT(FOREACH A GENERATE column1,column2,colum3);
DUMP B;
I can run a query in two different ways to return a Relation.
When I interrogate the size of the Relation one query gives a Fixnum as expected the other gives a Hash which is a hash of each value in the Relations Group By statement with the number of occurrences of each.
In Rails 3 I assume it always returned a Fixnum as I never had a problem whereeas with Rails 4 it sometimes returns a Hash and a statement like Rel.size.zero? gives the error:
undefined method `zero?' for {}:Hash
Am I best just using the .blank? method to check for zero records to be sure of avoiding unexpected errors?
Here is a snippet of code with looging statements for the two queries and the resulting log
CODE:
assessment_responses1=AssessmentResponse.select("process").where("client_id=? and final = ?",self.id,false).group("process")
logger.info("-----------------------------------------------------------")
logger.info("assessment_responses1.class = #{assessment_responses1.class}")
logger.info("assessment_responses1.size.class = #{assessment_responses1.size.class}")
logger.info("assessment_responses1.size value = #{assessment_responses1.size}")
logger.info("............................................................")
assessment_responses2=AssessmentResponse.select("distinct process").where("client_id=? and final = ?",self.id,false)
logger.info("assessment_responses2.class = #{assessment_responses2.class}")
logger.info("assessment_responses2.size.class = #{assessment_responses2.size.class}")
logger.info("assessment_responses2.size values = #{assessment_responses2.size}")
logger.info("-----------------------------------------------------------")
LOG
-----------------------------------------------------------
assessment_responses1.class = ActiveRecord::Relation::ActiveRecord_Relation_AssessmentResponse
(0.5ms) SELECT COUNT(`assessment_responses`.`process`) AS count_process, process AS process FROM `assessment_responses` WHERE `assessment_responses`.`organisation_id` = 17 AND (client_id=43932 and final = 0) GROUP BY process
assessment_responses1.size.class = Hash
CACHE (0.0ms) SELECT COUNT(`assessment_responses`.`process`) AS count_process, process AS process FROM `assessment_responses` WHERE `assessment_responses`.`organisation_id` = 17 AND (client_id=43932 and final = 0) GROUP BY process
assessment_responses1.size value = {"6 Month Review(1)"=>3, "Assessment(1)"=>28, "Assessment(2)"=>28}
............................................................
assessment_responses2.class = ActiveRecord::Relation::ActiveRecord_Relation_AssessmentResponse
(0.5ms) SELECT COUNT(distinct process) FROM `assessment_responses` WHERE `assessment_responses`.`organisation_id` = 17 AND (client_id=43932 and final = 0)
assessment_responses2.size.class = Fixnum
CACHE (0.0ms) SELECT COUNT(distinct process) FROM `assessment_responses` WHERE `assessment_responses`.`organisation_id` = 17 AND (client_id=43932 and final = 0)
assessment_responses2.size values = 3
-----------------------------------------------------------
size on an ActiveRecord::Relation object translates to count, because the former tries to get the count of the Relation. But when you call count on a grouped Relation object, you receive a hash.
The keys of this hash are the grouped column's values; the values of this hash are the respective counts.
AssessmentResponse.group(:client_id).count # this will return a Hash
AssessmentResponse.group(:client_id).size # this will also return a Hash
This is true for the following methods: count, sum, average, maximum, and minimum.
If you want to check for rows being present or not, simply use exists? i.e. do the following:
AssessmentResponse.group(:client_id).exists?
Instead of this:
AssessmentResponse.group(:client_id).count.zero?
I'm trying to classify an unlabelled string using Weka, I'm not an expert in data mining so i have been struggling with the different terms. What I'm doing is I am providing the training data and setting the unlabeled string after running the M5Rules classifier, I'm actually getting an output but i have no idea what it mean:
run:
{17 1,35 1,64 1,135 1,205 1,214 1,215 1,284 1,288 1,309 1,343 1,461 1,493 1,500 1,552 1,806 -0.038168} | -0.03816793850062397
-0.03816793850062397 ->
Results
======
Correlation coefficient 0
Mean absolute error 0
Root mean squared error 0
Relative absolute error 0 %
Root relative squared error 0 %
Total Number of Instances 1
BUILD SUCCESSFUL (total time: 1 second)
The source code is as follows:
public Categorizer(){
try{
//*** READ ARRF FILES *///////////////////////////////////////////////////////
//BufferedReader trainReader = new BufferedReader(new FileReader("c:/Users/Yehia A.Salam/Desktop/dd/training-data.arff"));//File with text examples
//BufferedReader classifyReader = new BufferedReader(new FileReader("c:/Users/Yehia A.Salam/Desktop/dd/test-data.arff"));//File with text to classify
// Create trainning data instance
TextDirectoryLoader loader = new TextDirectoryLoader();
loader.setDirectory(new File("c:/Users/Yehia A.Salam/Desktop/dd/training-data"));
Instances dataRaw = loader.getDataSet();
StringToWordVector filter = new StringToWordVector();
filter.setInputFormat(dataRaw);
Instances dataTraining = Filter.useFilter(dataRaw, filter);
dataTraining.setClassIndex(dataRaw.numAttributes() - 1);
// Create test data instances
loader.setDirectory(new File("c:/Users/Yehia A.Salam/Desktop/dd/test-data"));
dataRaw = loader.getDataSet();
Instances dataTest = Filter.useFilter(dataRaw, filter);
dataTest.setClassIndex(dataTest.numAttributes() - 1);
// Classify
FilteredClassifier model = new FilteredClassifier();
model.setFilter(new StringToWordVector());
model.setClassifier(new M5Rules());
model.buildClassifier(dataTraining);
for (int i = 0; i < dataTest.numInstances(); i++) {
dataTest.instance(i).setClassMissing();
double cls = model.classifyInstance(dataTest.instance(i));
dataTest.instance(i).setClassValue(cls);
System.out.println(dataTest.instance(i).toString() + " | " + cls);
System.out.println(cls + " -> " + dataTest.instance(i).classAttribute().value((int) cls));
// evaluate classifier and print some statistics
Evaluation eval = new Evaluation(dataTraining);
eval.evaluateModelOnce(cls, dataTest.instance(i));
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}
}
catch(FileNotFoundException e){
System.err.println(e.getMessage());
}
catch(IOException i){
System.err.println(i.getMessage());
}
catch(Exception o){
System.err.println(o.getMessage());
}
}
And finally a couple of screenshots in case i made anything wrong in the folder hierarchy:
tl;dr:
You set the class index to a random feature
You have to use a classifier, not a regression algorithm
The problem is how you initialize your data sets. Although weka usually puts the class in the last column, the TextDirectoryLoader doesn't. In fact, you don't need to set the class index manually, it is already set, so remove the lines
dataTraining.setClassIndex(dataRaw.numAttributes() - 1);
dataTest.setClassIndex(dataTest.numAttributes() - 1);
(The first line is wrong anyway, because you use the number of attributes from the raw data set, but choose the column of the already filtered data set.)
If you then run your code, you will get this:
weka.classifiers.functions.LinearRegression: Cannot handle binary class!
As I already guessed, M5Rules is not a classifier, but for regression. If you use a classifier like J48 or RandomForest, you will get a more sensible output. Just change the line
model.setClassifier(new M5Rules());
to
model.setClassifier(new RandomForest());
As for your output, here is what I make of it:
{17 1,35 1,64 1,135 1,205 1,214 1,215 1,284 1,288 1,309 1,343 1,461 1,493 1,500 1,552 1,806 -0.038168} | -0.03816793850062397
-0.03816793850062397 ->
is the result of the lines
System.out.println(dataTest.instance(i).toString() + " | " + cls);
System.out.println(cls + " -> " + dataTest.instance(i).classAttribute().value((int) cls));
So you see the features of your instance serialized as sparse ARFF followed by | and the class.
Usually, the class should be an integer, but from the documentation of M5Rules I get that it is a classifier for regression problems, so you won't get discrete classes, but continuous values, in your case -0.03816793850062397
Since you (incorrectly) set a numerical feature as class label, M5Rules didn't complain and gave you an output. If you use an actual classifier, you will get your labels "health" or "travel".
The rest are standard statistics about the classifiers performance, but they are pretty useless for only one classifier instance. It looks like the one sample was classified correctly, so all errors are zero.
Correlation coefficient 0
Mean absolute error 0
Root mean squared error 0
Relative absolute error 0 %
Root relative squared error 0 %
Total Number of Instances 1
Just in case someone else got the same error with M5P, try to see if the Arff is just a header or empty.
Otherwise try
model.buildClassifier(....)
instead of
model.setClassifier(....);
That solved it for me.