5.7.10. Vectorized Calculator (High-Speed clip0090 action)

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5.7.10. Vectorized Calculator (High-Speed clip0090 action)

 

Icon: ANATEL~3_img658

 
Function: CalculatorVectorized
 

Property window:
 

ANATEL~3_img657

 

Short description:

Compute many new

columns easily.

 

Long Description:

 
The first step before creating a predictive model is to build a learning dataset that contains as many “good” features as possible (this is named “feature engineering”). For example, in telecom, if you want to predict if a subscriber will renew its subscription the next month (i.e. if you want to do a “churn” model), it’s interesting to look at the consumptions patterns of the previous months (i.e. to look at the Month “M-1”, “M-2” and also maybe “M-3”). This means that you’ll have a learning dataset that will include features such as:
 

Month_M-1_Number_of_Voice_Calls

Month_M-2_Number_of_Voice_Calls

Month_M-1_Number_of_SMS

Month_M-2_Number_of_SMS

Month_M-1_Total_Duration_of_Voice_Calls

Month_M-2_Total_Duration_of_Voice_Calls

Month_M-1_Numbers_of_International_Calls

Month_M-2_Numbers_of_International_Calls

 
The above 8 features are interesting in themselves but there exist some even better, more meaningful features: These features are named the “Evolution” features and represents the evolution over time of the “original, raw” features. Here are 4 “Evolution” features that are based on the list of 8 features shown here above:

 

clip0224

One of the (many) objective of the ANATEL~3_img658 Vectorized Calculator Action is to allow you to quickly create thousands of “Evolution” features. For example, to create the 4 “EVO_*” features shown here above, we’ll have:

clip0225

About the above example:
 

The equation that is used to compute the “Evolution” variables is “a/(1+b)” but many other variations exist: For example: “a-b”, “b?a/b:0”, “2*a/(b+c)”, etc.

We created 4 new “Evolution” variables/columns. In most “real-life” examples, the ANATEL~3_img658 Vectorized Calculator Action is rather used to create between 100 and 1000 new variables/columns.

 

The different variables declared in the left panel of the ANATEL~3_img658 Vectorized Calculator Action (in the above example: the variables “a” and “b”) can either be:
 

A list of several columns. All the list that are declared must have the same size. In the example above, the list size is “4”, and there are two lists (“a” and “b”):

clip0226

The procedure to create each of these 2 list is quite simple too: For example, to create the “a” list:
 

oOpen the “column chooser” Window

 

oEnter “m-1” inside the “filter” box: number1

 

ANATEL~3_img672

 

oClick the “Add to selection” button number2
 

oSort the selected columns by clicking on the header of the list, here: number3
 

This last “sort” step is required to ensure the consistency between all the different lists (between the “a” and “b” list), to be sure that the variables/columns are all in the same order in all the lists.

 

One unique column.

 

Here is second example (demonstrating the “one unique column” option):

 

We start with a table that contains the sales of the 12 months of the year (in absolute value):
 

Sales_January

Sales_February

Sales_March

Sales_April

Sales_May

Sales_June

Sales_July

Sales_August

Sales_September

Sales_October

Sales_November

Sales_December

20

40

80

30

70

90

80

70

20

30

20

10

 

…and we want to convert all these “absolute values” to some percentage.

 

 

To arrive to the solution to this small problem, we need two steps:

Compute the sum of all the sales for the whole year (using the ANATEL~3_img673 AggregateOnColumns Action)

Compute the actual percentages (using the ANATEL~3_img658 Vectorized Calculator Action)

 

More precisely, we have:

clip0227

 

In the above example, the ANATEL~3_img658 Vectorized Calculator Action adds these columns to the final, result table:

 

Percent_January

Percent_February

Percent_March

Percent_April

Percent_May

Percent_June

Percent_July

Percent_August

Percent_September

Percent_October

Percent_November

Percent_December

3.57143

7.14286

14.2857

5.35714

12.5

16.0714

14.2857

12.5

3.57143

5.35714

3.57143

1.78571