create configuration from HPOlib pkl

I want to create configuration from a HPOlib-pkl-file :

hpolib_pkl = cPickle.load(open(path_to_pkl,"r"))
config = configuration_space.Configuration(config_space, hpolib_pkl["trials"][0]["params"])

But it crashes with the error message:

Value 1.0, for instantiation of hyperparameter 'random_forest:max_features, Type: UniformFloat, Range: [0.5, 5.0], Default: 1' is not a legal value

Did I miss something? Are the dictionaries for a parameter instantiation created by HPOlib in a different format than HPOlibConfigSpace expects?

The HPOlib dictionary looks as follows:

In [350]: hpolib_pkl["trials"][0]["params"]
Out[350]: OrderedDict([('classifier', 'random_forest'), ('imputation:strategy', 'mean'), ('preprocessor', 'None'), ('random_forest:bootstrap', 'True'), ('random_forest:criterion', 'gini'), ('random_forest:max_depth', 'None'), ('random_forest:max_features', '1.0'), ('random_forest:max_leaf_nodes', 'None'), ('random_forest:min_samples_leaf', '1'), ('random_forest:min_samples_split', '2'), ('random_forest:n_estimators', '100'), ('rescaling:strategy', 'min/max')])

Start at the configuration_space.Configuration constructor and the validation of hyperparameter values, using the HPOlib pickle example in the issue as the reproduction case. Compare the parameter dictionary format with the format accepted by Configuration, then verify that the reported random_forest values are handled consistently and add or run a focused regression test if the project provides one.

Written by the indexing model from the issue text.