autoprognosis.explorers.core.optimizers.hyperband module

class HyperbandOptimizer

Bases: object

Optimization helper based on HyperBand.

Parameters:

• name – str ID

• category – str classification/regression. Impacts the objective function

• classifier_seed – list List of classification methods to evaluate. Used when category = “classifier”

• regression_seed – list List of regression methods to evaluate. Used when category = “regression”

• max_iter – int maximum iterations per configuration

• eta – int configuration downsampling rate

• random_state – int random seed

evaluate() → Tuple[List[float], List[dict]]

evaluate_ensemble() → Tuple[float, dict]

class NpEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: JSONEncoder

default(obj: Any) → Any

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

encode(o)

Return a JSON string representation of a Python data structure.

>>> from json.encoder import JSONEncoder
>>> JSONEncoder().encode({"foo": ["bar", "baz"]})
'{"foo": ["bar", "baz"]}'

item_separator = ', '

iterencode(o, _one_shot=False)

Encode the given object and yield each string representation as available.

For example:

for chunk in JSONEncoder().iterencode(bigobject):
    mysocket.write(chunk)

key_separator = ': '