binary_operation

Performs a binary arithmetic operation.

Usage

This calculator allows the user to do arithmetical operation between 2 columns like an addition, multiplication, max or other. Constant value can be used instead of column name in input_columns.

This calculator can be used with the following method:

binary_operation

Examples:

• Apply a discount rate to a product price

• Round quantity to be above 0

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Main Parameters

The bold options represent the default values when the parameters are optional.

• input_columns list of columns used as input of the calculators: The list of columns that will be used to fill the output column.

• output_columns list of columns added by the calculators : Name of the filled column added to the dataset.

• global (true, false) Should this calculator be performed before data splitting during training for cross-validation

• steps [optionnal] (training, prediction, postprocessing) List of steps in a pipeline where columns from this calculator are added to the data. Note that when the training option is listed, the calculator is actually added during preprocessing.

• store_in_model [optionnal] (true, false) Please indicate whether the "calculated" columns by the calculator should be stored in the model or not to avoid recalculating them during prediction. This is only relevant if the calculated columns are added to both training and prediction. Without this parameter, the values will not be stored in the model. The following parameters only make sense if this parameter is set to true.

• stored_columns [required if store_in_model is true] List indicating the columns to be stored among the output_columns.

• stored_keys [required if store_in_model is true] List indicating the columns to use for identifying the correct values to join on the data for prediction among the stored values (logically, they are to be chosen from the input_columns).

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Specific Parameters

• operation (add, eq, ge, gt, le, lt, max, min, mod, mul, ne, sub, truediv) operation you can choose from.

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Examples

1. After doing the prediction some of the quantity predicted (qty_pred) are negative which is not logic. To solve this issue the user wants to round the quantity predicted below 0 to 0 in post processing.

   calculated_cols:
     round_negative_quantity:
         method: binary_operation
         input_columns:
         - qty_pred
         - 0
         output_columns:
         - qty_pred_wo_neg
         params:
           operation: max
         steps:
         - postprocessing

   Result :

   qty_pred	qty_pred_wo_neg
   10	10
   15	15
   -7	0
   0	0
   2	2
   9	9

2. The user wants to apply a discount rate (discount_rate) to a product price (item_price).

   calculated_cols:
     apply_discount:
         method: binary_operation
         input_columns:
         - discount_rate
         - item_price
         output_columns:
         - discounted_item_price
         params:
           operation: mul

   Result:

   discount_rate	item_price	discounted_item_price
   0.9	5	4.5
   0.9	7	6.3
   0.9	9	8.1
   0.5	5	2.5
   0.5	7	3.5
   0.5	9	4.5