deephyper.skopt.utils.use_named_args#
- deephyper.skopt.utils.use_named_args(dimensions)[source]#
Wrapper / decorator for an objective function that uses named arguments to make it compatible with optimizers that use a single list of parameters.
Your objective function can be defined as being callable using named arguments: func(foo=123, bar=3.0, baz=’hello’) for a search-space with dimensions named [‘foo’, ‘bar’, ‘baz’]. But the optimizer will only pass a single list x of unnamed arguments when calling the objective function: func(x=[123, 3.0, ‘hello’]). This wrapper converts your objective function with named arguments into one that accepts a list as argument, while doing the conversion automatically.
The advantage of this is that you don’t have to unpack the list of arguments x yourself, which makes the code easier to read and also reduces the risk of bugs if you change the number of dimensions or their order in the search-space.
Examples
>>> # Define the search-space dimensions. They must all have names! >>> from deephyper.skopt.space import Real >>> from deephyper.skopt import forest_minimize >>> from deephyper.skopt.utils import use_named_args >>> dim1 = Real(name='foo', low=0.0, high=1.0) >>> dim2 = Real(name='bar', low=0.0, high=1.0) >>> dim3 = Real(name='baz', low=0.0, high=1.0) >>> >>> # Gather the search-space dimensions in a list. >>> dimensions = [dim1, dim2, dim3] >>> >>> # Define the objective function with named arguments >>> # and use this function-decorator to specify the >>> # search-space dimensions. >>> @use_named_args(dimensions=dimensions) ... def my_objective_function(foo, bar, baz): ... return foo ** 2 + bar ** 4 + baz ** 8 >>> >>> # Not the function is callable from the outside as >>> # `my_objective_function(x)` where `x` is a list of unnamed arguments, >>> # which then wraps your objective function that is callable as >>> # `my_objective_function(foo, bar, baz)`. >>> # The conversion from a list `x` to named parameters `foo`, >>> # `bar`, `baz` >>> # is done automatically. >>> >>> # Run the optimizer on the wrapped objective function which is called >>> # as `my_objective_function(x)` as expected by `forest_minimize()`. >>> result = forest_minimize(func=my_objective_function, ... dimensions=dimensions, ... n_calls=20, base_estimator="ET", ... random_state=4) >>> >>> # Print the best-found results in same format as the expected result. >>> print("Best fitness: " + str(result.fun)) Best fitness: 0.1948080835239698 >>> print("Best parameters: {}".format(result.x)) Best parameters: [0.44134853091052617, 0.06570954323368307, 0.17586123323419825]