Author: bugman
Date: Fri Jul 18 14:43:07 2014
New Revision: 24568
URL: http://svn.gna.org/viewcvs/relax?rev=24568&view=rev
Log:
More modifications of the minimise.grid_search user function backend.
The grid_bounds() function has been renamed to grid_setup(), and it now
accepts and processes the
inc user function argument. The error checking code of the relaxation
curve-fitting
grid_search_setup() optimisation function has been shifted into this analysis
independent
grid_setup() function to shift the minimise.grid_search user function error
checking out of the
specific analyses. The function now scales the parameter bounds, using the
yet-to-be implemented
scaling() method of the parameter object. And the grid search increments are
converted into a
per-model list of lists.
Modified:
branches/zooming_grid_search/pipe_control/minimise.py
Modified: branches/zooming_grid_search/pipe_control/minimise.py
URL:
http://svn.gna.org/viewcvs/relax/branches/zooming_grid_search/pipe_control/minimise.py?rev=24568&r1=24567&r2=24568&view=diff
==============================================================================
--- branches/zooming_grid_search/pipe_control/minimise.py (original)
+++ branches/zooming_grid_search/pipe_control/minimise.py Fri Jul 18
14:43:07 2014
@@ -23,6 +23,7 @@
"""Module for model minimisation/optimisation."""
# relax module imports.
+from lib.errors import RelaxError, RelaxIntListIntError, RelaxLenError
from multi import Processor_box
from pipe_control.mol_res_spin import return_spin, spin_loop
from pipe_control import pipes
@@ -82,51 +83,6 @@
processor.run_queue()
-def grid_bounds(lower=None, upper=None):
- """Determine the per-model grid bounds, allowing for the zooming grid
search.
-
- @keyword lower: The lower bounds of the grid search which must be
equal to the number of parameters in the model.
- @type lower: array of numbers
- @keyword upper: The upper bounds of the grid search which must be
equal to the number of parameters in the model.
- @type upper: array of numbers
- @return: The per-model grid upper and lower bounds. The
first dimension of each structure corresponds to the model, the second the
model parameters.
- @rtype: tuple of 2 lists of lists of float
- """
-
- # The specific analysis API object and parameter object.
- api = return_api()
- param_object = return_parameter_object()
-
- # Initialise.
- model_lower = []
- model_upper = []
-
- # Loop over the models.
- for model_info in api.model_loop():
- # The lower and upper bounds have been supplied by the user, so use
those unmodified instead.
- if lower != None or upper != None:
- model_lower.append(lower)
- model_upper.append(upper)
- continue
-
- # Print out the model title.
- api.print_model_title(model_info)
-
- # Get the parameter names and current values.
- names = api.get_param_names(model_info)
- values = api.get_param_values(model_info)
-
- # Build the bounds.
- model_lower.append([])
- model_upper.append([])
- for i in range(len(names)):
- model_lower[-1].append(param_object.grid_lower(names[i],
model_info=model_info))
- model_upper[-1].append(param_object.grid_upper(names[i],
model_info=model_info))
-
- # Return the bounds.
- return model_lower, model_upper
-
-
def grid_search(lower=None, upper=None, inc=None, constraints=True,
verbosity=1):
"""The grid search function.
@@ -135,7 +91,7 @@
@param upper: The upper bounds of the grid search which must be
equal to the number of parameters in the model.
@type upper: array of numbers
@param inc: The increments for each dimension of the space for
the grid search. The number of elements in the array must equal to the
number of parameters in the model.
- @type inc: array of int
+ @type inc: int or list of int
@param constraints: If True, constraints are applied during the grid
search (elinating parts of the grid). If False, no constraints are used.
@type constraints: bool
@param verbosity: The amount of information to print. The higher the
value, the greater the verbosity.
@@ -148,8 +104,8 @@
# The specific analysis API object.
api = return_api()
- # Determine the model specific grid bounds, and allow for the zooming
grid search.
- model_lower, model_upper = grid_bounds(lower, upper)
+ # Determine the model specific grid bounds, and allow for the zooming
grid search, and check the inc argument.
+ model_lower, model_upper, model_inc = grid_setup(lower, upper, inc)
# Deselect spins lacking data:
api.overfit_deselect()
@@ -169,7 +125,7 @@
status.mc_number = i
# Optimisation.
- api.grid_search(lower=model_lower, upper=model_upper, inc=inc,
constraints=constraints, verbosity=verbosity-1, sim_index=i)
+ api.grid_search(lower=model_lower, upper=model_upper,
inc=model_inc, constraints=constraints, verbosity=verbosity-1, sim_index=i)
# Print out.
if verbosity and not processor.is_queued():
@@ -183,10 +139,99 @@
# Grid search.
else:
- api.grid_search(lower=model_lower, upper=model_upper, inc=inc,
constraints=constraints, verbosity=verbosity)
+ api.grid_search(lower=model_lower, upper=model_upper, inc=model_inc,
constraints=constraints, verbosity=verbosity)
# Execute any queued commands.
processor.run_queue()
+
+
+def grid_setup(lower=None, upper=None, inc=None):
+ """Determine the per-model grid bounds, allowing for the zooming grid
search.
+
+ @keyword lower: The user supplied lower bounds of the grid search
which must be equal to the number of parameters in the model.
+ @type lower: list of numbers
+ @keyword upper: The user supplied upper bounds of the grid search
which must be equal to the number of parameters in the model.
+ @type upper: list of numbers
+ @keyword inc: The user supplied grid search increments.
+ @type inc: int or list of int
+ @return: The per-model grid upper and lower bounds. The
first dimension of each structure corresponds to the model, the second the
model parameters.
+ @rtype: tuple of lists of lists of float, lists of lists of
float, list of lists of int
+ """
+
+ # The specific analysis API object and parameter object.
+ api = return_api()
+ param_object = return_parameter_object()
+
+ # Initialise.
+ model_lower = []
+ model_upper = []
+ model_inc = []
+
+ # Loop over the models.
+ for model_info in api.model_loop():
+ # Get the parameter names and current values.
+ names = api.get_param_names(model_info)
+ values = api.get_param_values(model_info)
+
+ # The parameter number.
+ n = len(names)
+
+ # Make sure that the length of the parameter array is > 0.
+ if n == 0:
+ raise RelaxError("Cannot run a grid search on a model with zero
parameters.")
+
+ # Check the user supplied increments.
+ if isinstance(inc, list) and len(inc) != n:
+ raise RelaxLenError('increment', n)
+ if isinstance(inc, list):
+ for i in range(n):
+ if not (isinstance(inc[i], int) or inc[i] == None):
+ raise RelaxIntListIntError('increment', inc)
+ elif not isinstance(inc, int):
+ raise RelaxIntListIntError('increment', inc)
+
+ # Convert to the model increment list.
+ if isinstance(inc, int):
+ model_inc.append([inc]*n)
+ else:
+ model_inc.append(inc)
+
+ # The lower and upper bounds have been supplied by the user, so use
those unmodified instead.
+ if lower != None or upper != None:
+ # Check that the user supplied bound lengths are ok.
+ if len(lower) != n:
+ raise RelaxLenError('lower bounds', n)
+ if len(upper) != n:
+ raise RelaxLenError('upper bounds', n)
+
+ # Append the values.
+ model_lower.append(lower)
+ model_upper.append(upper)
+
+ # Skip the rest of the loop.
+ continue
+
+ # Print out the model title.
+ api.print_model_title(model_info)
+
+ # Build the bounds.
+ model_lower.append([])
+ model_upper.append([])
+ for i in range(n):
+ # Obtain the bounds.
+ lower_i = param_object.grid_lower(names[i],
model_info=model_info)
+ upper_i = param_object.grid_upper(names[i],
model_info=model_info)
+
+ # Scale the bounds.
+ lower_i /= param_object.scaling(names[i], model_info=model_info)
+ upper_i /= param_object.scaling(names[i], model_info=model_info)
+
+ # Append.
+ model_lower[-1].append(lower_i)
+ model_upper[-1].append(upper_i)
+
+ # Return the bounds.
+ return model_lower, model_upper, model_inc
def grid_zoom(level=0):
r24568 - /branches/zooming_grid_search/pipe_control/minimise.py