core file

GPy/core/parameterised.py

@@ -0,0 +1,333 @@
+import numpy as np
+import re
+import copy
+import cPickle
+import os
+from ..util.squashers import sigmoid
+
+def truncate_pad(string,width,align='m'):
+    """
+    A helper function to make aligned strings for parameterised.__str__
+    """
+    width=max(width,4)
+    if len(string)>width:
+        return string[:width-3]+'...'
+    elif len(string)==width:
+        return string
+    elif len(string)<width:
+        diff = width-len(string)
+        if align=='m':
+            return ' '*np.floor(diff/2.) + string + ' '*np.ceil(diff/2.)
+        elif align=='l':
+            return string + ' '*diff
+        elif align=='r':
+            return ' '*diff + string
+        else:
+            raise ValueError
+
+class parameterised:
+    def __init__(self):
+        """
+        This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
+        """
+        self.tied_indices = []
+        self.constrained_fixed_indices = []
+        self.constrained_fixed_values = []
+        self.constrained_positive_indices = np.empty(shape=(0,),dtype=np.int64)
+        self.constrained_negative_indices = np.empty(shape=(0,),dtype=np.int64)
+        self.constrained_bounded_indices = []
+        self.constrained_bounded_uppers = []
+        self.constrained_bounded_lowers = []
+
+    def pickle(self,filename,protocol=-1):
+        f = file(filename,'w')
+        cPickle.dump(self,f,protocol)
+        f.close()
+
+    def copy(self):
+        """
+        Returns a (deep) copy of the current model
+        """
+
+        return copy.deepcopy(self)
+
+
+    def tie_param(self, which):
+        matches = self.grep_param_names(which)
+        assert matches.size > 0, "need at least something to tie together"
+        if len(self.tied_indices):
+            assert not np.any(matches[:,None]==np.hstack(self.tied_indices)), "Some indices are already tied!"
+        self.tied_indices.append(matches)
+        #TODO only one of the priors will be evaluated. Give a warning message if the priors are not identical
+        if hasattr(self,'prior'):
+            pass
+
+        self.expand_param(self.extract_param())# sets tied parameters to single value
+
+    def untie_everything(self):
+        """Unties all parameters by setting tied_indices to an empty list."""
+        self.tied_indices = []
+
+    def all_constrained_indices(self):
+        """Return a np array of all the constrained indices"""
+        ret =  [np.hstack(i) for i in [self.constrained_bounded_indices, self.constrained_positive_indices, self.constrained_negative_indices, self.constrained_fixed_indices] if len(i)]
+        if len(ret):
+            return np.hstack(ret)
+        else:
+            return []
+    def grep_param_names(self, expr):
+        """
+        Arguments
+        ---------
+        expr -- can be a regular expression object or a string to be turned into regular expression object.
+
+        Returns
+        -------
+        the indices of self.get_param_names which match the regular expression.
+
+        Notes
+        -----
+        Other objects are passed through - i.e. integers which were'nt meant for grepping
+        """
+
+        if type(expr) is str:
+            expr = re.compile(expr)
+            return np.nonzero([expr.search(name) for name in self.get_param_names()])[0]
+        elif type(expr) is re._pattern_type:
+            return np.nonzero([expr.search(name) for name in self.get_param_names()])[0]
+        else:
+            return expr
+
+
+    def constrain_positive(self, which):
+        """
+        Set positive constraints.
+
+        Arguments
+        ---------
+        which -- np.array(dtype=int), or regular expression object or string
+        """
+        matches = self.grep_param_names(which)
+        assert not np.any(matches[:,None]==self.all_constrained_indices()), "Some indices are already constrained"
+        self.constrained_positive_indices = np.hstack((self.constrained_positive_indices, matches))
+        #check to ensure constraint is in place
+        x = self.get_param()
+        for i,xx in enumerate(x):
+            if (xx<0) & (i in matches):
+                x[i] = -xx
+        self.set_param(x)
+
+
+    def unconstrain(self,which):
+        """Unconstrain matching parameters.  does not untie parameters"""
+        matches = self.grep_param_names(which)
+        #positive/negative
+        self.constrained_positive_indices = np.delete(self.constrained_positive_indices,np.nonzero(np.sum(self.constrained_positive_indices[:,None]==matches[None,:],1))[0])
+        self.constrained_negative_indices = np.delete(self.constrained_negative_indices,np.nonzero(np.sum(self.constrained_negative_indices[:,None]==matches[None,:],1))[0])
+        #bounded
+        if len(self.constrained_bounded_indices):
+            self.constrained_bounded_indices = [np.delete(a,np.nonzero(np.sum(a[:,None]==matches[None,:],1))[0]) for a in self.constrained_bounded_indices]
+            if np.hstack(self.constrained_bounded_indices).size:
+                self.constrained_bounded_uppers, self.constrained_bounded_lowers, self.constrained_bounded_indices = zip(*[(u,l,i) for u,l,i in zip(self.constrained_bounded_uppers, self.constrained_bounded_lowers, self.constrained_bounded_indices) if i.size])
+                self.constrained_bounded_uppers, self.constrained_bounded_lowers, self.constrained_bounded_indices = list(self.constrained_bounded_uppers), list(self.constrained_bounded_lowers), list(self.constrained_bounded_indices)
+            else:
+                self.constrained_bounded_uppers, self.constrained_bounded_lowers, self.constrained_bounded_indices = [],[],[]
+        #fixed:
+        for i,indices in enumerate(self.constrained_fixed_indices):
+            self.constrained_fixed_indices[i] = np.delete(indices,np.nonzero(np.sum(indices[:,None]==matches[None,:],1))[0])
+        #remove empty elements
+        tmp = [(i,v) for i,v in zip(self.constrained_fixed_indices, self.constrained_fixed_values) if len(i)]
+        if tmp:
+            self.constrained_fixed_indices, self.constrained_fixed_values = zip(*tmp)
+            self.constrained_fixed_indices, self.constrained_fixed_values = list(self.constrained_fixed_indices), list(self.constrained_fixed_values)
+        else:
+            self.constrained_fixed_indices, self.constrained_fixed_values = [],[]
+
+
+
+
+
+    def constrain_negative(self,which):
+        """
+        Set negative constraints.
+
+        :param which: which variables to constrain
+        :type which: regular expression string
+
+        """
+        matches = self.grep_param_names(which)
+        assert not np.any(matches[:,None]==self.all_constrained_indices()), "Some indices are already constrained"
+        self.constrained_negative_indices = np.hstack((self.constrained_negative_indices, matches))
+        #check to ensure constraint is in place
+        x = self.get_param()
+        for i,xx in enumerate(x):
+            if (xx>0.) and (i in matches):
+                x[i] = -xx
+        self.set_param(x)
+
+
+
+    def constrain_bounded(self, which, lower, upper):
+        """Set bounded constraints.
+
+        Arguments
+        ---------
+        which -- np.array(dtype=int), or regular expression object or string
+        upper -- (float) the upper bound on the constraint
+        lower -- (float) the lower bound on the constraint
+        """
+        matches = self.grep_param_names(which)
+        assert not np.any(matches[:,None]==self.all_constrained_indices()), "Some indices are already constrained"
+        assert lower < upper, "lower bound must be smaller than upper bound!"
+        self.constrained_bounded_indices.append(matches)
+        self.constrained_bounded_uppers.append(upper)
+        self.constrained_bounded_lowers.append(lower)
+        #check to ensure constraint is in place
+        x = self.get_param()
+        for i,xx in enumerate(x):
+            if ((xx<=lower)|(xx>=upper)) & (i in matches):
+                x[i] = sigmoid(xx)*(upper-lower) + lower
+        self.set_param(x)
+
+
+    def constrain_fixed(self, which, value = None):
+        """
+        Arguments
+        ---------
+        :param which: np.array(dtype=int), or regular expression object or string
+        :param value: a float to fix the matched values to. If the value is not specified,
+                 the parameter is fixed to the current value
+
+        Notes
+        -----
+        Fixing a parameter which is tied to another, or constrained in some way will result in an error.
+        To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter names, or pass both of the indexes
+        """
+        matches = self.grep_param_names(which)
+        assert not np.any(matches[:,None]==self.all_constrained_indices()), "Some indices are already constrained"
+        self.constrained_fixed_indices.append(matches)
+        if value != None:
+            self.constrained_fixed_values.append(value)
+        else:
+            self.constrained_fixed_values.append(self.get_param()[self.constrained_fixed_indices[-1]])
+
+        self.constrained_fixed_values.append(value)
+        self.expand_param(self.extract_param())
+
+    def extract_param(self):
+        """use self.get_param to get the 'true' parameters of the model, which are then tied, constrained and fixed"""
+        x = self.get_param()
+        x[self.constrained_positive_indices] = np.log(x[self.constrained_positive_indices])
+        x[self.constrained_negative_indices] = np.log(-x[self.constrained_negative_indices])
+        [np.put(x,i,np.log(np.clip(x[i]-l,1e-10,np.inf)/np.clip(h-x[i],1e-10,np.inf))) for i,l,h in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers)]
+
+        to_remove = self.constrained_fixed_indices+[t[1:] for t in self.tied_indices]
+        if len(to_remove):
+            return np.delete(x,np.hstack(to_remove))
+        else:
+            return x
+
+
+    def expand_param(self,x):
+        """ takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self.set_param"""
+
+        #work out how many places are fixed, and where they are. tricky logic!
+        Nfix_places = 0.
+        if len(self.tied_indices):
+            Nfix_places += np.hstack(self.tied_indices).size-len(self.tied_indices)
+        if len(self.constrained_fixed_indices):
+            Nfix_places += np.hstack(self.constrained_fixed_indices).size
+        if Nfix_places:
+            fix_places = np.hstack(self.constrained_fixed_indices+[t[1:] for t in self.tied_indices])
+        else:
+            fix_places = []
+
+        free_places = np.setdiff1d(np.arange(Nfix_places+x.size,dtype=np.int),fix_places)
+
+        #put the models values in the vector xx
+        xx = np.zeros(Nfix_places+free_places.size,dtype=np.float64)
+
+        xx[free_places] = x
+        [np.put(xx,i,v) for i,v in zip(self.constrained_fixed_indices, self.constrained_fixed_values)]
+        [np.put(xx,i,v) for i,v in [(t[1:],xx[t[0]]) for t in self.tied_indices] ]
+        xx[self.constrained_positive_indices] = np.exp(xx[self.constrained_positive_indices])
+        xx[self.constrained_negative_indices] = -np.exp(xx[self.constrained_negative_indices])
+        [np.put(xx,i,low+sigmoid(xx[i])*(high-low)) for i,low,high in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers)]
+        self.set_param(xx)
+
+    def extract_param_names(self):
+        """
+        Returns the parameter names as propagated after constraining,
+        tying or fixing, i.e. a list of the same length as extract_param()
+        """
+        n =  self.get_param_names()
+
+        #remove/concatenate the tied parameter names
+        if len(self.tied_indices):
+            for t in self.tied_indices:
+                n[t[0]] = "<tie>".join([n[tt] for tt in t])
+            remove = np.hstack([t[1:] for t in self.tied_indices])
+        else:
+            remove=np.empty(shape=(0,),dtype=np.int)
+
+        #also remove the fixed params
+        if len(self.constrained_fixed_indices):
+            remove = np.hstack((remove, np.hstack(self.constrained_fixed_indices)))
+
+        #add markers to show that some variables are constrained
+        for i in self.constrained_positive_indices:
+            n[i] = n[i]+'(+ve)'
+        for i in self.constrained_negative_indices:
+            n[i] = n[i]+'(-ve)'
+        for i,l,h in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers):
+            for ii in i:
+                n[ii] = n[ii]+'(bounded)'
+
+        n = [nn for i,nn in enumerate(n) if not i in remove]
+        return n
+
+    def __str__(self,nw=30):
+        """
+        Return a string describing the parameter names and their ties and constraints
+        """
+        names = self.get_param_names()
+        N = len(names)
+
+        if not N:
+            return "This object has no free parameters."
+        header = ['Name','Value','Constraints','Ties']
+        values = self.get_param() #map(str,self.get_param())
+        #sort out the constraints
+        constraints = ['']*len(names)
+        for i in self.constrained_positive_indices:
+            constraints[i] = '(+ve)'
+        for i in self.constrained_negative_indices:
+            constraints[i] = '(-ve)'
+        for i in self.constrained_fixed_indices:
+            for ii in i:
+                constraints[ii] = 'Fixed'
+        for i,u,l in zip(self.constrained_bounded_indices, self.constrained_bounded_uppers, self.constrained_bounded_lowers):
+            for ii in i:
+                constraints[ii] = '('+str(l)+', '+str(u)+')'
+        #sort out the ties
+        ties = ['']*len(names)
+        for i,tie in enumerate(self.tied_indices):
+            for j in tie:
+                ties[j] = '('+str(i)+')'
+
+        values = ['%.4f' % float(v) for v in values]
+        max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
+        max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
+        max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
+        max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
+        cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
+        columns = cols.sum()
+
+        header_string = ["{h:^{col}}".format(h = header[i], col = cols[i]) for i in range(len(cols))]
+        header_string = map(lambda x: '|'.join(x), [header_string])
+        separator = '-'*len(header_string[0])
+        param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n = names[i], v = values[i], c = constraints[i], t = ties[i],
+                                                                        c0 = cols[0], c1 = cols[1], c2 = cols[2], c3 = cols[3]) for i in range(len(values))]
+
+
+        return ('\n'.join([header_string[0], separator]+param_string)) + '\n'