def convertLimits(limits, form = None):
if not isinstance(limits, list):
raise ValueError("No way to convert limits because {0} is not a list.".format(limits))
if not all(limits):
return limits ## return list because there is a None in the list
if form is None:
if len(limits) < 2:
raise ValueError("List {0} does not have two or more elements".format(limits))
if isinstance(limits[0], int):
dist = rint(limits[0], limits[-1])
elif isinstance(limits[0], float):
dist = rfloat(limits[0], limits[-1])
elif isinstance(limits[0], str):
dist = limits
else:
raise ValueError("Not sure how to process list {0}.".format(limits))
else:
if form == "uniform":
if len(limits) < 2:
raise ValueError("List {0} does not have two or more elements".format(limits))
if isinstance(limits[0], int):
dist = rint(limits[0], limits[-1])
elif isinstance(limits[0], float):
dist = rfloat(limits[0], limits[-1])
else:
raise ValueError("Form {0} is not supported.".format(form))
return dist
def getDecisionTreeClassifierParams():
treeParams = {
"max_depth": [2, 4, 6, None],
"max_features": ['auto', 'sqrt', 'log2', None],
"min_impurity_decrease": rfloat(0.0, 0.25),
"min_samples_leaf": rint(1, 10)
}
params = treeParams
params["criterion"] = ["gini", "entropy"]
param_grid = {}
for param, dist in params.iteritems():
param_grid[param] = convertDistribution(dist)
retval = {"dist": params, "grid": param_grid}
return retval
def getExtraTreesClassifierParams():
treeParams = {"max_depth": [2, 4, 6, 8, None],
"max_features": ['auto', 'sqrt', 'log2', None],
"min_impurity_decrease": rfloat(0.0, 0.25),
"min_samples_leaf": rint(1, 10)}
params = treeParams
#params["bootstrap"] = [False, True]
#params["criterion"] = ["mae", "mse"]
params["n_estimators"] = [100]
param_grid = {}
for param,dist in params.iteritems():
param_grid[param] = convertDistribution(dist)
retval = {"dist": params, "grid": param_grid}
return retval