Hyperparameters

Here you’ll find the explanation of how to specify random hyper-parameters. Those hyper-parameters are used to generate random values of the parameters of a given model.

Example, to generate a random integer between 1 and 10 (included) you can use HyperRangeInt:

hyper = HyperRangeInt(start = 1, end = 10)
[hyper.get_rand() for _ in range(5)]

>> [4,1,10,3,3]

The complete list of HyperParameters are available here :module:`aikit.ml_machine.hyper_parameters` Each class implements a ‘get_rand’ method.

HyperCrossProduct

This class is used to combine hyper-parameters together which is needed to generate complexe dictionnary-like hyper-parameters

class aikit.ml_machine.hyper_parameters.HyperCrossProduct(list_of_hyperparameters, random_state=None)

Cartesian Product of Distribution

The list of hyperameters can be :

• dict or OrderedDict : in that case the object will draw dictionnary with them key and value from the hyper-parameters
• list or tuple : in that case the object will draw list (resp. tuple) from each element within the list (resp. tuple)

Parameters:list_of_hyperparameters (list of hyperameters like object) –

Examples

>>> hp = HyperCrossProduct([HyperRangeInt(0,10), HyperRangeFloat(0,1)])
>>> hp.get_rand()

>>> hp = HyperCrossProduct({"int_value":HyperRangeInt(0,10), "float_value":HyperRangeFloat(0,1)})
>>> hp.get_rand()

>>> hp = HyperCrossProduct({"int_value":scipy.stats.randint(0,10), "float_value":HyperRangeFloat(0,1) , "choice":("a","b","c"),"constant":10})
>>> hp.get_rand()

Example:

hp = HyperCrossProduct({"int_value":HyperRangeInt(0,10), "float_value":HyperRangeFloat(0,1)})
hp.get_rand()

This will generate random dictionnary with keys ‘int_value’ and ‘float_value’

HyperComposition

This class is used to include dependency between parameters or to create an hyper parameters from two different distributions

class aikit.ml_machine.hyper_parameters.HyperComposition(dict_vals, random_state=None)

Composition of Distributions : randomly choice among several distributions

the size of the values can be : * if size 1 : list of HyperParameters * if size 2 : list of weight * HyperParameters

Parameters:dict_vals (list or tuple of size 2 or 1) –

Examples

>>> hp = HyperComposition([ HyperRangeInt(0,100) , HyperRangeInt(100,1000)])
>>> hp.get_rand()

>>> hp = HyperComposition([ (0.9,HyperRangeInt(0,100)) ,(0.1,HyperRangeInt(100,1000)) ])
>>> hp.get_rand()

>>> hp = HyperComposition([ (0.9,"choice_a")  ,(0.1, "choice_b") ])
>>> hp.get_rand()

Example:

hp = HyperComposition([ (0.9,HyperRangeInt(0,100)) ,(0.1,HyperRangeInt(100,1000)) ])
hp.get_rand()

This will generate a random number between 0 and 100 with probability 0.9 and one between 100 and 1000 with probability 0.1

hp = HyperComposition([
    (0.5 , HyperComposition({"analyzer":"char" , "n_gram_range":[1,4]})),
    (0.5 , HyperComposition({"analyzer":"word" , "n_gram_range":1}) )

])
hp.get_rand()

This will generate with probability:

• 1/2 a dictionnary with “analyzer”:”char” and “n_gram_range”: random between 1 and 4
• 1/2 a dictionnary with “analyzer”:”word” and “n_gram_range”: 1