def gridsearch(self, param_grid, **params):
""" DEPRECATED. USE OPTIMISE.
TO BE TESTED AFTER LATEST CHANGES
sklearn gridsearch and report
"""
log.warning("this needs to be tested")
sys.exit()
defaults = dict(scoring=self.scorer, cv=None)
for k, v in defaults.items():
params.setdefault(k, v)
gs = GridSearchCV(self.clf, param_grid=param_grid, **params)
gs.fit(self.xtrain, self.ytrain)
runsdf = pd.DataFrame([score.parameters for score in gs.grid_scores_])
try:
# strip pipeline step name
runsdf.columns = [col.split("__")[-1] for col in runsdf.columns]
except:
pass
runsdf["score"] = [score.mean_validation_score for score in gs.grid_scores_]
# create runs object so we can report it
runs = Runs("runs_gridsearch")
for i in range(len(runsdf)):
runs.append(runsdf.ix[i].to_dict())
runs.plot()
log.info("best model: %s"%gs.best_params_)
log.info(gs.best_score_)
def __init__(self, folder="optimise"):
self.intparams = []
self.logparams = []
self.fixedparams = dict()
self.count = 1
self.runs = Runs(folder, "w")
class Optuner():
""" wrapper for optunity """
def __init__(self, folder="optimise"):
self.intparams = []
self.logparams = []
self.fixedparams = dict()
self.count = 1
self.runs = Runs(folder, "w")
def cleanparams(self, params):
""" recursively convert search_space to optunity format
convert
bools to int
list of strings to sub-keys
single values to fixed parameters
single keys to fixed parameters
create for later processing
self.fixedparams=list of fixed parameters
self.logparams=list of logspace params (10**value)
self.intparams=list of keys to be constrained to int
remove logparams (special key listing logspace params)
"""
# logspace params e.g. -3, 3 => 10**-3, 10**3
if not self.logparams:
self.logparams = params.pop("logparams", [])
for k in list(params.keys()):
v = params[k]
if isinstance(v, dict):
if len(v.keys()) == 1:
k2, v2 = list(v.items())[0]
if not isinstance(v2, list):
self.fixedparams[k] = k2
self.cleanparams(params[k])
continue
try:
if v and not isinstance(v, list):
v = [v]
# fixed param
if len(set(v)) == 1:
v = v[0]
if v in self.logparams:
self.fixedparams[k] = 10**v
else:
self.fixedparams[k] = v
del params[k]
# convert list of strings to sub-entries
elif isinstance(v[0], str):
params[k] = {v: None for v in v}
# convert bool to int
elif all([isinstance(v, bool) for v in v]):
params[k] = sorted([x*1 for x in v])
self.intparams.append(k)
# compile list of integer parameters
elif all([isinstance(v, int) for v in v]):
self.intparams.append(k)
except (TypeError, KeyError):
pass
def make_target(self, func, **constants):
""" make target
func has signature score=func(**params)
constants fixed for all iterations e.g. x and y
"""
def target(**params):
""" target function for each optunity iteration
enables fixed params in search space
removes nulls
converts intparams to integer
converts logparams to logspace
updates params with constants
logs params, scores
params provided by optimiser for each iteration
"""
starttime = time()
# enable fixed parameters that are always the same value
params.update(self.fixedparams)
# remove k=None (bug in optunity) and v=None (na for this run)
params = {k:v for k,v in params.items() if k and v}
# convert intparams to integer
for k in set(params) & set(self.intparams):
params[k] = int(round(params[k]))
# convert logparams to logspace i.e. -3, 3 searches .001 to 1000
for k in set(params) & set(self.logparams):
params[k] = 10**params[k]
# add in constants such as x, y
params.update(constants)
# report params
if self.verbose >= 20:
paramsout = ', '.join("{!s}={!r}".format(k,v)
for (k,v) in params.items())
log.info("[%s] %s"%(self.count, paramsout))
#########################################
score = func(**params)
#########################################
# log results
params.update(score=score,
elapsed=time()-starttime)
for fixed in self.fixedparams:
del params[fixed]
self.runs.append(params)
# report score
if self.verbose >= 20:
log.info("****** %s"%score)
self.count += 1
return score
return target
def maximise(self, target, num_evals, search=None, verbose=20,
**constants):
"""
maximise target within defined search space
target has signature score=target(**params)
constants are fixed for every iteration e.g. x and y
verbose=20 report every iteration
"""
self.verbose = verbose
if search is None:
with open("search.yaml") as f:
search = yaml.load(f)
#pprint(search_space)
self.cleanparams(search)
#pprint(search_space)
# note ignore return values as using runs instead
# runs has functions such as plot, report and correlations
optunity.maximize_structured(self.make_target(target, **constants),
search_space=search,
num_evals=num_evals)
self.runs.report()
