Author: bugman
Date: Wed Apr 5 11:34:20 2006
New Revision: 2449
URL: http://svn.gna.org/viewcvs/relax?rev=2449&view=rev
Log:
Model selection is now enabled for hybrid models. This completes task #3122.
The location of the task is
https://gna.org/task/?func=detailitem&item_id=3122.
Modified:
1.2/generic_fns/model_selection.py
1.2/specific_fns/hybrid.py
1.2/specific_fns/model_free.py
1.2/specific_fns/specific_setup.py
Modified: 1.2/generic_fns/model_selection.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/generic_fns/model_selection.py?rev=2449&r1=2448&r2=2449&view=diff
==============================================================================
--- 1.2/generic_fns/model_selection.py (original)
+++ 1.2/generic_fns/model_selection.py Wed Apr 5 11:34:20 2006
@@ -69,20 +69,65 @@
if len(self.runs) == 0:
raise RelaxError, "No runs are availible for use in model
selection."
- # Store the first run.
+ # Initialise.
+ self.first_run = None
+ self.function_type = {}
+ self.duplicate_data = {}
+ self.count_num_instances = {}
+ self.model_statistics = {}
+ self.skip_function = {}
+
+ # Cross validation setup.
if type(self.runs[0]) == list:
- self.first_run = self.runs[0][0]
+ # No runs.
+ if len(run) == 0:
+ raise RelaxError, "No runs are availible for use in model
selection in the array " + `run` + "."
+
+ # Loop over the runs.
+ for i in xrange(len(self.runs)):
+ for j in xrange(len(self.runs[i])):
+ # The run name.
+ run = self.runs[i][j]
+
+ # Function type.
+ self.function_type[run] =
self.relax.data.run_types[self.relax.data.run_names.index(run)]
+
+ # Store the first non-hybrid run.
+ if not self.first_run and self.function_type[run] !=
'hybrid':
+ self.first_run = run
+
+ # Specific duplicate data, number of instances, and
model statistics functions.
+ self.duplicate_data[run] =
self.relax.specific_setup.setup('duplicate_data', self.function_type[run])
+ self.count_num_instances[run] =
self.relax.specific_setup.setup('num_instances', self.function_type[run])
+ self.model_statistics[run] =
self.relax.specific_setup.setup('model_stats', self.function_type[run])
+ self.skip_function[run] =
self.relax.specific_setup.setup('skip_function', self.function_type[run])
+
+ # Run various tests.
+ self.tests(run)
+
+ # All other model selection setup.
else:
- self.first_run = self.runs[0]
-
- # Function type.
- self.function_type =
self.relax.data.run_types[self.relax.data.run_names.index(self.first_run)]
-
- # Specific duplicate data, number of instances, and model statistics
functions.
- self.duplicate_data =
self.relax.specific_setup.setup('duplicate_data', self.function_type)
- self.count_num_instances =
self.relax.specific_setup.setup('num_instances', self.function_type)
- self.model_statistics =
self.relax.specific_setup.setup('model_stats', self.function_type)
- self.skip_function =
self.relax.specific_setup.setup('skip_function', self.function_type)
+ # Loop over the runs.
+ for i in xrange(len(self.runs)):
+ # The run name.
+ run = self.runs[i]
+
+ # Function type.
+ self.function_type[run] =
self.relax.data.run_types[self.relax.data.run_names.index(run)]
+
+ # Store the first non-hybrid run.
+ if not self.first_run and self.function_type[run] !=
'hybrid':
+ self.first_run = run
+
+ # Specific duplicate data, number of instances, and model
statistics functions.
+ self.duplicate_data[run] =
self.relax.specific_setup.setup('duplicate_data', self.function_type[run])
+ self.count_num_instances[run] =
self.relax.specific_setup.setup('num_instances', self.function_type[run])
+ self.model_statistics[run] =
self.relax.specific_setup.setup('model_stats', self.function_type[run])
+ self.skip_function[run] =
self.relax.specific_setup.setup('skip_function', self.function_type[run])
+
+ # Run various tests.
+ self.tests(run)
+
# Number of instances. If the number is not the same for each run,
then the minimum number will give the specific function self.model_statistics
the
# opportunity to consolidate the instances to the minimum number if
possible.
@@ -96,7 +141,7 @@
# Loop over the nested array.
for j in xrange(len(self.runs[i])):
# Number of instances.
- num = self.count_num_instances(self.runs[i][j])
+ num =
self.count_num_instances[self.runs[i][j]](self.runs[i][j])
self.num_instances[i].append(num)
# Minimum.
@@ -105,30 +150,12 @@
else:
# Number of instances.
- num = self.count_num_instances(self.runs[i])
+ num = self.count_num_instances[self.runs[i]](self.runs[i])
self.num_instances.append(num)
# Minimum.
if num < self.min_instances:
self.min_instances = num
-
- # Tests for all runs.
- for run in self.runs:
- # An array of arrays - for cross validation model selection.
- if type(run) == list:
- # No runs.
- if len(run) == 0:
- raise RelaxError, "No runs are availible for use in
model selection in the array " + `run` + "."
-
- # Loop over the nested array.
- for run2 in run:
- # Run various tests.
- self.tests(run2)
-
- # runs is a normal array.
- else:
- # Run various tests.
- self.tests(run)
# Loop over the number of instances.
@@ -145,20 +172,20 @@
for j in xrange(len(self.runs)):
# Single-item-out cross validation.
if method == 'CV':
- # Reassign the run.
- run = self.runs[j][0]
-
# Sum of chi-squared values.
sum_crit = 0.0
# Loop over the validation samples and sum the
chi-squared values.
for k in xrange(len(self.runs[j])):
+ # Reassign the run.
+ run = self.runs[j][k]
+
# Skip function.
- if self.skip_function(run=self.runs[j][k],
instance=i):
+ if self.skip_function[run](run=run, instance=i):
continue
# Get the model statistics.
- k, n, chi2 =
self.model_statistics(run=self.runs[j][k], instance=i,
min_instances=self.min_instances, num_instances=self.num_instances[j][k])
+ k, n, chi2 = self.model_statistics[run](run=run,
instance=i, min_instances=self.min_instances,
num_instances=self.num_instances[j][k])
# Missing data sets.
if k == None or n == None or chi2 == None:
@@ -176,11 +203,11 @@
run = self.runs[j]
# Skip function.
- if self.skip_function(run=run, instance=i,
min_instances=self.min_instances, num_instances=self.num_instances[j]):
+ if self.skip_function[run](run=run, instance=i,
min_instances=self.min_instances, num_instances=self.num_instances[j]):
continue
# Get the model statistics.
- k, n, chi2 = self.model_statistics(run=run, instance=i,
min_instances=self.min_instances, num_instances=self.num_instances[j])
+ k, n, chi2 = self.model_statistics[run](run=run,
instance=i, min_instances=self.min_instances,
num_instances=self.num_instances[j])
# Missing data sets.
if k == None or n == None or chi2 == None:
@@ -202,7 +229,7 @@
# Duplicate the data from the 'best_model' to the model
selection run 'modsel_run'.
if best_model != None:
- self.duplicate_data(new_run=modsel_run, old_run=best_model,
instance=i)
+ self.duplicate_data[best_model](new_run=modsel_run,
old_run=best_model, instance=i)
def aic(self, chi2, k, n):
@@ -264,9 +291,14 @@
# Find the index of the run.
index = self.relax.data.run_names.index(run)
- # Test if the function type is the same as 'self.function_type'.
- if self.relax.data.run_types[index] != self.function_type:
+ # Test if the function type is the same as 'self.function_type'
(skip the test if self.function_type is a hybrid).
+ #if self.function_type != 'hybrid' and
self.relax.data.run_types[index] != self.function_type:
+ if self.relax.data.run_types[index] != self.function_type[run]:
raise RelaxError, "The runs supplied are not all of the same
function type."
+
+ # Skip all tests if the model is a hybrid.
+ if self.relax.data.run_types[index] == 'hybrid':
+ return
# Test if sequence data is loaded.
if not self.relax.data.res.has_key(run):
Modified: 1.2/specific_fns/hybrid.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/specific_fns/hybrid.py?rev=2449&r1=2448&r2=2449&view=diff
==============================================================================
--- 1.2/specific_fns/hybrid.py (original)
+++ 1.2/specific_fns/hybrid.py Wed Apr 5 11:34:20 2006
@@ -26,6 +26,28 @@
"""Class containing function specific to hybrid models."""
self.relax = relax
+
+
+ def duplicate_data(self, new_run=None, old_run=None, instance=None):
+ """Function for duplicating data."""
+
+ # Test that the new run exists.
+ if not new_run in self.relax.data.run_names:
+ raise RelaxNoRunError, new_run
+
+ # Test that the old run exists.
+ if not old_run in self.relax.data.run_names:
+ raise RelaxNoRunError, old_run
+
+ # Test that the new run has no sequence loaded.
+ if self.relax.data.res.has_key(new_run):
+ raise RelaxSequenceError, new_run
+
+ # Reset the new run type to hybrid!
+ self.relax.data.run_types[self.relax.data.run_names.index(new_run)]
= 'hybrid'
+
+ # Duplicate the hybrid run data structure.
+ self.relax.data.hybrid_runs[new_run] =
self.relax.data.hybrid_runs[old_run]
def hybridise(self, hybrid=None, runs=None):
@@ -65,3 +87,52 @@
# Create the data structure of the runs which form the hybrid.
self.relax.data.hybrid_runs[hybrid] = runs
+
+
+ def model_statistics(self, run=None, instance=None, min_instances=None,
num_instances=None):
+ """Function for returning the values k, n, and chi2 of the hybrid.
+
+ k - number of parameters.
+ n - number of data points.
+ chi2 - the chi-squared value.
+ """
+
+ # Arguments.
+ self.run = run
+
+ # Initialise.
+ k_total = 0
+ n_total = 0
+ chi2_total = 0.0
+
+ # Specific setup.
+ for run in self.relax.data.hybrid_runs[self.run]:
+ # Function type.
+ function_type =
self.relax.data.run_types[self.relax.data.run_names.index(run)]
+
+ # Specific model statistics functions.
+ model_statistics =
self.relax.specific_setup.setup('model_stats', function_type)
+
+ # Get the statistics.
+ k, n, chi2 = model_statistics(run, instance=0, min_instances=1)
+
+ # Sum the stats.
+ k_total = k_total + k
+ n_total = n_total + n
+ chi2_total = chi2_total + chi2
+
+ # Return the totals.
+ return k_total, n_total, chi2_total
+
+
+ def num_instances(self, run=None):
+ """Function for returning the number of instances, which for hybrids
is always 1."""
+
+ return 1
+
+
+ def skip_function(self, run=None, instance=None, min_instances=None,
num_instances=None):
+ """Dummy function."""
+
+ return
+
Modified: 1.2/specific_fns/model_free.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/specific_fns/model_free.py?rev=2449&r1=2448&r2=2449&view=diff
==============================================================================
--- 1.2/specific_fns/model_free.py (original)
+++ 1.2/specific_fns/model_free.py Wed Apr 5 11:34:20 2006
@@ -2507,6 +2507,10 @@
global_stats = 0
break
+ # Determine the parameter set type.
+ self.param_set = self.determine_param_set_type()
+
+ # Sequence specific data.
# Statistics for a single residue.
if not global_stats and not combine:
# Missing data sets.
Modified: 1.2/specific_fns/specific_setup.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/specific_fns/specific_setup.py?rev=2449&r1=2448&r2=2449&view=diff
==============================================================================
--- 1.2/specific_fns/specific_setup.py (original)
+++ 1.2/specific_fns/specific_setup.py Wed Apr 5 11:34:20 2006
@@ -84,6 +84,10 @@
# Model-free analysis.
if function_type == 'mf':
function = self.mf_funcs()
+
+ # Hybrid models.
+ if function_type == 'hybrid':
+ function = self.hybrid_funcs()
# Catch all errors.
except:
@@ -103,8 +107,28 @@
return function
+ def hybrid_funcs(self):
+ """Hybrid model specific functions."""
+
+ # Duplicate data function.
+ if self.eqi == 'duplicate_data':
+ return self.relax.specific.hybrid.duplicate_data
+
+ # Model statistics.
+ if self.eqi == 'model_stats':
+ return self.relax.specific.hybrid.model_statistics
+
+ # Number of instances.
+ if self.eqi == 'num_instances':
+ return self.relax.specific.hybrid.num_instances
+
+ # Skip function.
+ if self.eqi == 'skip_function':
+ return self.relax.specific.hybrid.skip_function
+
+
def jw_funcs(self):
- """Model-free analysis specific functions."""
+ """Reduced spectral density mapping specific functions."""
# Calculate function.
if self.eqi == 'calculate':
r2449 - in /1.2: generic_fns/ specific_fns/