> For the complete documentation index, see [llms.txt](https://doc.verteego.com/verteego-doc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.verteego.com/verteego-doc/pipelines/forecasting-pipelines/calculators/transformation/cyclic.md).

# cyclic

Extracts cyclic features as SIN and COS.

## Usage

{% hint style="info" %}
This calculator allows you to do cyclic encoding, i.e. to introduce the periodicity components of a feature (such as month, day, week, etc.).
{% endhint %}

This calculator can be used with the following method:

<mark style="color:red;">**`cyclic`**</mark>

Examples:

* Extract the cos and sin components of the month of the year. This will enable the model to understand that the month feature takes values that repeat cyclically over time (month 1 comes after month 12).
* Same for weekday or week.

***

## Main Parameters

{% hint style="success" %}
**The bold options** represent the default values when the parameters are optional.
{% endhint %}

* *<mark style="color:blue;">input\_columns</mark>* \
  list of columns used as input of the calculators: The list of columns that will be used to fill the output column.
* *<mark style="color:blue;">output\_columns</mark>* \
  list of columns added by the calculators : Name of the filled column added to the dataset.
* *<mark style="color:blue;">global</mark>* *(true, **false)*** \
  Should this calculator be performed before data splitting during training for cross-validation
* *<mark style="color:blue;">steps</mark>* \[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.
* *<mark style="color:blue;">store\_in\_model</mark>* \[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*.
* *<mark style="color:blue;">stored\_columns</mark>* \[required if *<mark style="color:blue;">store\_in\_model</mark> is true*] \
  List indicating the columns to be stored among the *<mark style="color:blue;">output\_columns</mark>*.
* *<mark style="color:blue;">stored\_keys</mark>* \[required if *<mark style="color:blue;">store\_in\_model</mark> 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 *<mark style="color:blue;">input\_columns</mark>*).

***

## Specific Parameters

* <mark style="color:blue;">uniques\_values</mark>\
  Number of unique values in the serie (eg. 7 for weekdays, 12 for months etc…).

***

## Examples

1. The user wants to extract the cyclic features (sin and cos) of the column weekday.

   ```yaml
   calculated_cols:
     cyclic_days:
       method: cyclic
       params:
           unique_values: 7
       input_columns:
       - weekday
       output_columns:
       - weekday_sin
       - weekday_cos
   ```
2. The user wants to extract the cyclic features (sin and cos) of the column month.

   ```yaml
   calculated_cols:
     cyclic_months:
       method: cyclic
       params:
           unique_values: 12
       input_columns:
       - month
       output_columns:
       - month_sin
       - month_cos
   ```