import itertools
import copy
import pandas as pd
[docs]class DependentParameter:
def __init__(self, format_string, parameter_names=None):
self.format_string = format_string
self.parameter_names = parameter_names
[docs] def eval(self, parameters):
return self.format_string % tuple([parameters[n] for n in self.parameter_names])
[docs]class ParameterSearch:
"""Utility for evaluating online experiments with different hyperparameters. For a
brief tutorial on using this class, see :doc:`/getting_started/3-five_minute_tutorial`.
"""
def __init__(self, model, Score):
self.model = model
self.Score = Score
self.parameter_values = []
self.parameter_names = []
def _get_non_dependant_parameter_names(self):
return [k for (k, v) in zip(self.parameter_names, self.parameter_values) if not (len(v) == 1 and isinstance(v[0], DependentParameter))]
[docs] def set_parameter_values(self, parameter_name, parameter_values):
if(isinstance(parameter_values, DependentParameter)):
if(parameter_values.parameter_names is None):
parameter_values.parameter_names = self._get_non_dependant_parameter_names()
parameter_values = [parameter_values]
self.parameter_values.append(parameter_values)
self.parameter_names.append(parameter_name)
def _get_configurations(self):
names = self.parameter_names
values = self.parameter_values
configurations = [dict(zip(names, v)) for v in itertools.product(*values)]
return configurations
def _run_single(self, model, Score, parameters, run_paremeters):
model = copy.copy(model)
for k in parameters:
model.set_parameter(k, parameters[k])
scores = model.run(*run_paremeters[0], **run_paremeters[1])
score = Score(scores)
result = score.mean()
return result
def _mapper(self, parameter_tuple):
(parameters, run_paremeters) = parameter_tuple
current_parameter_values = [parameters[k] for k in self._get_non_dependant_parameter_names()]
result = self._run_single(self.model, self.Score, parameters, run_paremeters)
current_parameter_values.append(result)
return current_parameter_values
def _to_dataframe(self, results):
columns = self._get_non_dependant_parameter_names()
columns.append(self.Score.__name__)
return pd.DataFrame.from_records(results, columns=columns)
[docs] def run(self, *run_paremeters, **run_kw_parameters):
self.results = []
configurations = self._get_configurations()
parameter_tuples = [(parameters, (run_paremeters, run_kw_parameters)) for parameters in configurations]
results = list(map(self._mapper, parameter_tuples))
return self._to_dataframe(results)