continue_cascade¶

Continue Cascade From a Fit Node¶

Prototype¶

# at_cascade.continue_cascade
def continue_cascade(
   all_node_database = None,
   fit_database      = None,
   fit_goal_set      = None,
   fit_type_list     = [ 'both', 'fixed' ],
   shared_unique     = '',
) :
   assert type(all_node_database) == str
   assert type(fit_database) == str
   assert type(fit_goal_set)      == set
   assert type(fit_type_list)     == list
   assert type(shared_unique)     == str

Purpose¶

Sometimes when running the cascade, the fit or statistics for a node fails. This may be because of something that happened on the system, or because of some of the settings in the root_database. This routine enables you to continue the cascade from such a node.

all_node_database¶

is a python string specifying the location of the all_node_db relative to the current working directory. This argument can’t be None.

fit_database¶

is a python string specifying the location of a dismod_at database relative to the current working directory. This is a fit_database with the final state after a run of cascade_root_node or continue_cascade that includes fitting this database. The fit_database is not changed, it is only used to identify which child jobs to fit.

fit_goal_set¶

This is a set with elements of type int (str) specifying the node_id (node_name) for each element of the fit_goal_set . Each such node must be the root node, or a descendant of the root node. In addition, it must be in the fit_goal_table , or an ancestor of a node in the fit goal table.

fit_type_list¶

This is a list with one or two elements and its possible elements are both and fixed. For each job, the first type of fit is attempted. If it fails, and there is a second type of fit, it is attempted. If it also fails, the corresponding job fails.

shared_unique¶

Under normal circumstances, you should use the empty string (default value) for this parameter.

Parallel runs of continue_cascade must use different values of shared_unique so the corresponding shared memory names are different; see shared_unique .
The shared_unique special case is where you are running (in parallel) continue_cascade with the same fit_database, and disjoint fit_goal_set . (The intersection of disjoint sets is empty.)
In the special case above, the suggested value for shared_unique is _ node_name , where node_name is the name of one of the nodes in the fit_goal_set. It may be necessary to include the value of the splitting covariate in shared_unique . (The splitting covariate is sex in the csv.fit case.)