autora.variable: Variable and VariableCollection¶

autora.variable.Variable represents an experimental variable:

• an independent variable, or
• dependent variable.

They can be initialized as follows:

In [ ]:

from autora.variable import Variable

x1 = Variable(
    name="x1",
)
x2 = Variable(
    name="x2",
)
y = Variable(
    name="y",
)

from autora.variable import Variable x1 = Variable( name="x1", ) x2 = Variable( name="x2", ) y = Variable( name="y", )

A group of Variables representing the domain of an experiment is a autora.variable.VariableCollection.

They can be initialized as follows:

In [ ]:

from autora.variable import VariableCollection

VariableCollection(
    independent_variables=[x1, x2],
    dependent_variables=[y]
)

from autora.variable import VariableCollection VariableCollection( independent_variables=[x1, x2], dependent_variables=[y] )

Out[ ]:

VariableCollection(independent_variables=[Variable(name='x1', value_range=None, allowed_values=None, units='', type=<ValueType.REAL: 'real'>, variable_label='', rescale=1, is_covariate=False), Variable(name='x2', value_range=None, allowed_values=None, units='', type=<ValueType.REAL: 'real'>, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=<ValueType.REAL: 'real'>, variable_label='', rescale=1, is_covariate=False)], covariates=[])

For the full list of arguments, see the documentation in the autora.variable submodule.

Some functions included in AutoRA use specific values stored on the Variable objects. For instance, the autora.experimentalist.grid.pool uses the allowed_values field to create a grid of conditions:

In [ ]:

from autora.experimentalist.grid import grid_pool

grid_pool(
    VariableCollection(independent_variables=[
        Variable(name="x1", allowed_values=[-1, -2, -3]),
        Variable(name="x2", allowed_values=[11, 12, 13])
    ])
)

from autora.experimentalist.grid import grid_pool grid_pool( VariableCollection(independent_variables=[ Variable(name="x1", allowed_values=[-1, -2, -3]), Variable(name="x2", allowed_values=[11, 12, 13]) ]) )

Out[ ]:

	x1	x2
0	-1	11
1	-1	12
2	-1	13
3	-2	11
4	-2	12
5	-2	13
6	-3	11
7	-3	12
8	-3	13

The autora.experimentalist.random.pool uses the value_range field to sample conditions:

In [ ]:

from autora.experimentalist.random import random_pool

random_pool(
    VariableCollection(independent_variables=[
        Variable(name="x1", value_range=(-3, 3)),
        Variable(name="x2", value_range=(101, 102))
    ]), 
    random_state=180
)

from autora.experimentalist.random import random_pool random_pool( VariableCollection(independent_variables=[ Variable(name="x1", value_range=(-3, 3)), Variable(name="x2", value_range=(101, 102)) ]), random_state=180 )

Out[ ]:

	x1	x2
0	0.456338	101.527294
1	1.008636	101.297280
2	0.319617	101.962166
3	-1.753273	101.859696
4	-1.933420	101.201565

The autora.state.estimator_from_state function uses the names of the variables to pass the correct columns to a scikit-learn compatible estimator for curve fitting.

Check the documentation for any functions you are using to determine whether you need to include specific metadata.