make_discrete

getml.datasets.make_discrete(n_rows_population: int = 500, n_rows_peripheral: int = 125000, random_state: Optional[int] = None, population_name: str = '', peripheral_name: str = '', aggregation: str = 'COUNT') → Tuple[DataFrame, DataFrame][source]

Generate a random dataset with categorical variables

The dataset consists of a population table and one peripheral table.

The peripheral table has 3 columns:

  • column_01: random integer between -10 and 10

  • join_key: random integer in the range from 0 to n_rows_population

  • time_stamp: random number between 0 and 1

The population table has 4 columns:

  • column_01: random number between -1 and 1

  • join_key: unique integer in the range from 0 to n_rows_population

  • time_stamp: random number between 0 and 1

  • targets: target variable. Defined as the minimum value greater than 0 in the peripheral table for which time_stamp_peripheral < time_stamp_population and the join key matches

SELECT aggregation( column_01 )
FROM POPULATION t1
LEFT JOIN PERIPHERAL t2
ON t1.join_key = t2.join_key
WHERE (
   ( t2.column_01 > 0 )
) AND t2.time_stamp <= t1.time_stamp
GROUP BY t1.join_key,
         t1.time_stamp;
Args:
n_rows_population (int, optional):

Number of rows in the population table.

n_row_peripheral (int, optional):

Number of rows in the peripheral table.

random_state (Optional[int], optional):

Seed to initialize the random number generator used for the dataset creation. If set to None, the seed will be the ‘microsecond’ component of datetime.datetime.now().

population_name (string, optional):

Name assigned to the create DataFrame holding the population table. If set to a name already existing on the getML engine, the corresponding DataFrame will be overwritten. If set to an empty string, a unique name will be generated by concatenating discrete_population_ and the seed of the random number generator.

peripheral_name (string, optional):

Name assigned to the create DataFrame holding the peripheral table. If set to a name already existing on the getML engine, the corresponding DataFrame will be overwritten. If set to an empty string, a unique name will be generated by concatenating discrete_peripheral_ and the seed of the random number generator.

aggregation(string, optional):

aggregations used to generate the ‘target’ column.

Returns:
tuple:

tuple containing: