def get_parameter_distribution(distribution, **kwargs):
if "choice" in distribution.lower():
parameter_distr = choice(
kwargs.get("options", [])
)
elif "randint" in distribution.lower():
parameter_distr = randint(
upper=kwargs.get("upper", None)
)
elif "uniform" in distribution.lower():
parameter_distr = uniform(
min_value=kwargs.get("min_value", None),
max_value=kwargs.get("max_value", None)
)
elif "quniform" in distribution.lower():
parameter_distr = quniform(
min_value=kwargs.get("min_value", None),
max_value=kwargs.get("max_value", None),
q=kwargs.get("q", None)
)
elif "loguniform" in distribution.lower():
parameter_distr = loguniform(
min_value=kwargs.get("min_value", None),
max_value=kwargs.get("max_value", None),
)
elif "qloguniform" in distribution.lower():
parameter_distr = qloguniform(
min_value=kwargs.get("min_value", None),
max_value=kwargs.get("max_value", None),
q=kwargs.get("q", None)
)
elif "normal" in distribution.lower():
parameter_distr = normal(
mu=kwargs.get("mu", None),
sigma=kwargs.get("sigma", None)
)
elif "qnormal" in distribution.lower():
parameter_distr = qnormal(
mu=kwargs.get("mu", None),
sigma=kwargs.get("sigma", None),
q=kwargs.get("q", None)
)
elif "lognormal" in distribution.lower():
parameter_distr = lognormal(
mu=kwargs.get("mu", None),
sigma=kwargs.get("sigma", None)
)
elif "qlognormal" in distribution.lower():
parameter_distr = qlognormal(
mu=kwargs.get("mu", None),
sigma=kwargs.get("sigma", None),
q=kwargs.get("q", None)
)
else:
parameter_distr = None
raise RunConfigurationException(f"Parameter distribution for parameter not defined in settings. Please choose between \'choice\', \'randint\', \'uniform\', \'quniform\', \'loguniform\', \'qloguniform\', \'normal\', \'qnormal\', \'lognormal\' and \'qlognormal\'")
return parameter_distr
def get_parameter_distribution(parameter_name, parameter_setting):
if "choice" in parameter_setting["distribution"]:
parameter_distr = choice(parameter_setting["parameters"]["options"])
elif "randint" in parameter_setting["distribution"]:
parameter_distr = randint(
upper=parameter_setting["parameters"]["upper"])
elif "uniform" in parameter_setting["distribution"]:
parameter_distr = uniform(
min_value=parameter_setting["parameters"]["min_value"],
max_value=parameter_setting["parameters"]["max_value"])
elif "quniform" in parameter_setting["distribution"]:
parameter_distr = quniform(
min_value=parameter_setting["parameters"]["min_value"],
max_value=parameter_setting["parameters"]["max_value"],
q=parameter_setting["parameters"]["q"])
elif "loguniform" in parameter_setting["distribution"]:
parameter_distr = loguniform(
min_value=parameter_setting["parameters"]["min_value"],
max_value=parameter_setting["parameters"]["max_value"])
elif "qloguniform" in parameter_setting["distribution"]:
parameter_distr = qloguniform(
min_value=parameter_setting["parameters"]["min_value"],
max_value=parameter_setting["parameters"]["max_value"],
q=parameter_setting["parameters"]["q"])
elif "normal" in parameter_setting["distribution"]:
parameter_distr = normal(
mu=parameter_setting["parameters"]["mu"],
sigma=parameter_setting["parameters"]["sigma"])
elif "qnormal" in parameter_setting["distribution"]:
parameter_distr = qnormal(
mu=parameter_setting["parameters"]["mu"],
sigma=parameter_setting["parameters"]["sigma"],
q=parameter_setting["parameters"]["q"])
elif "lognormal" in parameter_setting["distribution"]:
parameter_distr = lognormal(
mu=parameter_setting["parameters"]["mu"],
sigma=parameter_setting["parameters"]["sigma"])
elif "qlognormal" in parameter_setting["distribution"]:
parameter_distr = qlognormal(
mu=parameter_setting["parameters"]["mu"],
sigma=parameter_setting["parameters"]["sigma"],
q=parameter_setting["parameters"]["q"])
else:
parameter_distr = None
raise RunConfigurationException(
"Parameter distribution for parameter {} not defined in settings. Please choose between \'choice\', \'randint\', \'uniform\', \'quniform\', \'loguniform\', \'qloguniform\', \'normal\', \'qnormal\', \'lognormal\' and \'qlognormal\'"
.format(parameter_name))
return parameter_distr
# -loguniform
#continuous hyperparams
#normal
#uniform
#lognormal
#loguniform
from azureml.train.hyperdrive import choice, normal
param_sapce = {
'--batch_size': choice(10,12,15),
'--learning_rate': normal(10,3)
}
#configuring sample - grid,random, baysian
from azureml.train.hyperdrive import GridParameterSampling, RandomParameterSampling, BayesianParameterSampling
param_sampling = GridParameterSampling(param_sapce)
#configuring the early stopping
- bandit policy -> BanditPolicy(slack_amount = 0.2, evaluation_interval = 1, delay_evaluation = 5)
- median stopping policy -> MedianStoppingPolicy(evaluation_interval = 1, delay_evaluation = 5)