def __init__(self,
states_df,
score_type,
max_num_mtries,
alpha=0,
num_starts=10,
ess=1.0,
verbose=False,
vtx_to_states=None):
"""
Constructor
Parameters
----------
alpha : float
states_df : pandas.DataFrame
score_type : str
max_num_mtries : int
num_starts : int
ess : float
Equivalent Sample Size, a parameter in BDEU scorer. Fudge factor
that is supposed to grow as the amount of prior knowledge grows.
verbose : bool
vtx_to_states : dict[str, list[str]]
A dictionary mapping each node name to a list of its state names.
This information will be stored in self.bnet. If
vtx_to_states=None, constructor will learn vtx_to_states
from states_df
Returns
-------
None
"""
# this is a good default value
self.alpha = 5 / len(states_df.index)
lner = MB_MMPC_Lner(states_df,
alpha,
verbose,
vtx_to_states,
learn_later=True)
lner.find_PC()
self.vtx_to_nbors = lner.vtx_to_nbors
HC_RandRestartLner.__init__(self, states_df, score_type,
max_num_mtries, num_starts, ess, verbose,
vtx_to_states)
def __init__(self, states_df, score_type, max_num_mtries,
alpha=0, tabu_len=10,
ess=1.0, verbose=False, vtx_to_states=None):
"""
Constructor
Parameters
----------
tabu_len : int
alpha : float
states_df : pandas.DataFrame
score_type : str
max_num_mtries : int
ess : float
Equivalent Sample Size, a parameter in BDEU scorer. Fudge factor
that is supposed to grow as the amount of prior knowledge grows.
verbose : bool
vtx_to_states : dict[str, list[str]]
A dictionary mapping each node name to a list of its state names.
This information will be stored in self.dag. If
vtx_to_states=None, constructor will learn vtx_to_states
from states_df
Returns
-------
None
"""
# this is a good default value
self.alpha = 5/len(states_df.index)
lner = MB_MMPC_Lner(states_df, alpha, verbose,
vtx_to_states, learn_later=True)
lner.find_PC()
self.vtx_to_nbors = lner.vtx_to_nbors
HC_TabuLner.__init__(
self, states_df, score_type, max_num_mtries,
tabu_len,
ess, verbose, vtx_to_states)