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Fixed clustering maths: Previously was comparing models with unequal numbers of points and different parameters
This commit is contained in:
Michael T Smith
parent
532d7188e7
commit
84d2ffcedb
2 changed files with 47 additions and 53 deletions
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@ -86,7 +86,7 @@ class GPOffsetRegression(GP):
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self.X = self.X_fixed - offsets[self.selected]
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super(GPOffsetRegression, self).parameters_changed()
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dL_dr = self.kern.dK_dr_via_X(self.X, self.X) * self.grad_dict['dL_dK']
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dL_dr = self.kern.dK_dr_via_X(self.X, self.X) * self.grad_dict['dL_dK'] #TODO could use gradients_X, instead of r.
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dr_doff = self.dr_doffset(self.X,self.selected,self.offset.values)
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for i in range(len(dr_doff)):
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@ -5,12 +5,10 @@ import GPy
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import numpy as np
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import sys #so I can print dots
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def get_log_likelihood(inputs,data,clust):
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"""Get the LL of a combined set of clusters, ignoring time series offsets.
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def get_individual_log_likelihood_offset(inputs,data,clust,common_kern):
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"""Get the LL of a pair of clusters, but having them independent
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Get the log likelihood of a cluster without worrying about the fact
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different time series are offset. We're using it here really for those
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cases in which we only have one cluster to get the loglikelihood of.
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Get the log likelihood of a cluster pair.
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arguments:
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inputs -- the 'X's in a list, one item per cluster
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@ -18,7 +16,7 @@ def get_log_likelihood(inputs,data,clust):
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clust -- list of clusters to use
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returns a tuple:
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log likelihood and the offset (which is always zero for this model)
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log likelihood and the offset
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"""
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S = data[0].shape[0] #number of time series
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@ -29,23 +27,22 @@ def get_log_likelihood(inputs,data,clust):
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#for each person in the cluster,
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#add their inputs and data to the new dataset
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for p in clust:
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idx = np.zeros([0,1])
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for it,p in enumerate(clust):
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X = np.vstack([X,inputs[p]])
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Y = np.vstack([Y,data[p].T])
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#find the loglikelihood. We just add together the LL for each time series.
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#ll=0
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#for s in range(S):
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# m = GPy.models.GPRegression(X,Y[:,s][:,None])
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# m.optimize()
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# ll+=m.log_likelihood()
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idx = np.r_[idx,np.ones([data[p].shape[1],1])*it]
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m = GPy.models.GPRegression(X,Y)
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X = np.c_[X,idx]
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#print(X)
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k_independent = GPy.kern.IndependentOutputs(common_kern.copy(),index_dim=1)
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m = GPy.models.GPRegression(X,Y,k_independent)
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m.optimize()
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ll=m.log_likelihood()
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return ll,0
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def get_log_likelihood_offset(inputs,data,clust):
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def get_shared_log_likelihood_offset(inputs,data,clust,common_kern):
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"""Get the log likelihood of a combined set of clusters, fitting the offsets
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arguments:
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@ -56,18 +53,13 @@ def get_log_likelihood_offset(inputs,data,clust):
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returns a tuple:
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log likelihood and the offset
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"""
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#if we've only got one cluster, the model has an error, so we want to just
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#use normal GPRegression.
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if len(clust)==1:
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return get_log_likelihood(inputs,data,clust)
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S = data[0].shape[0] #number of time series
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X = np.zeros([0,2]) #notice the extra column, this is for the cluster index
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Y = np.zeros([0,S])
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#for each person in the cluster, add their inputs and data to the new
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#for each cluster, add their inputs and data to the new
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#dataset. Note we add an index identifying which person is which data point.
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#This is for the offset model to use, to allow it to know which data points
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#to shift.
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@ -75,10 +67,11 @@ def get_log_likelihood_offset(inputs,data,clust):
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idx = i*np.ones([inputs[p].shape[0],1])
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X = np.vstack([X,np.hstack([inputs[p],idx])])
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Y = np.vstack([Y,data[p].T])
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m = GPy.models.GPOffsetRegression(X,Y)
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m = GPy.models.GPOffsetRegression(X,Y,common_kern.copy())
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#TODO: How to select a sensible prior?
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m.offset.set_prior(GPy.priors.Gaussian(0,20))
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## m.offset.set_prior(GPy.priors.Gaussian(0,20))
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#TODO: Set a sensible start value for the length scale,
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#make it long to help the offset fit.
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@ -88,7 +81,7 @@ def get_log_likelihood_offset(inputs,data,clust):
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offset = m.offset.values[0]
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return ll,offset
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def cluster(data,inputs,verbose=False):
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def cluster(data,inputs,common_kern,verbose=False):
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"""Clusters data
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Using the new offset model, this method uses a greedy algorithm to cluster
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@ -111,12 +104,11 @@ def cluster(data,inputs,verbose=False):
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for p in range(0,N):
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active.append([p])
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loglikes = np.zeros(len(active))
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loglikes[:] = None
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pairloglikes = np.zeros([len(active),len(active)])
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pairloglikes[:] = None
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pairoffset = np.zeros([len(active),len(active)])
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individualloglikes = np.zeros([len(active),len(active)])
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individualloglikes[:] = None
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sharedloglikes = np.zeros([len(active),len(active)])
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sharedloglikes[:] = None
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sharedoffset = np.zeros([len(active),len(active)])
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it = 0
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while True:
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@ -125,23 +117,17 @@ def cluster(data,inputs,verbose=False):
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it +=1
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print("Iteration %d" % it)
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#Compute the log-likelihood of each cluster (add them together)
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#Compute the log-likelihood of each cluster
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for clusti in range(len(active)):
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if verbose:
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sys.stdout.write('.')
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sys.stdout.flush()
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if np.isnan(loglikes[clusti]):
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loglikes[clusti], unused_offset = get_log_likelihood_offset(inputs,data,[clusti])
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#try combining with each other cluster...
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for clustj in range(clusti): #count from 0 to clustj-1
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temp = [clusti,clustj]
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if np.isnan(pairloglikes[clusti,clustj]):
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pairloglikes[clusti,clustj],pairoffset[clusti,clustj] = get_log_likelihood_offset(inputs,data,temp)
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if np.isnan(sharedloglikes[clusti,clustj]):
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sharedloglikes[clusti,clustj],sharedoffset[clusti,clustj] = get_shared_log_likelihood_offset(inputs,data,temp,common_kern)
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individualloglikes[clusti,clustj], unused_offset = get_individual_log_likelihood_offset(inputs,data,temp,common_kern)
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seploglikes = np.repeat(loglikes[:,None].T,len(loglikes),0)+np.repeat(loglikes[:,None],len(loglikes),1)
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loglikeimprovement = pairloglikes - seploglikes #how much likelihood improves with clustering
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top = np.unravel_index(np.nanargmax(pairloglikes-seploglikes), pairloglikes.shape)
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loglikeimprovement = sharedloglikes - individualloglikes #how much likelihood improves with clustering
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top = np.unravel_index(np.nanargmax(sharedloglikes-individualloglikes), sharedloglikes.shape)
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#if loglikeimprovement.shape[0]<3:
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# #no more clustering to do - this shouldn't happen really unless
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@ -149,9 +135,15 @@ def cluster(data,inputs,verbose=False):
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# break
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#if theres further clustering to be done...
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# print("SHARED")
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# print(sharedloglikes)
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# print("IND")
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# print(individualloglikes)
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# print("----")
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if loglikeimprovement[top[0],top[1]]>0:
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#print(loglikeimprovement[top[0],top[1]])
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active[top[0]].extend(active[top[1]])
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offset=pairoffset[top[0],top[1]]
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offset=sharedoffset[top[0],top[1]]
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inputs[top[0]] = np.vstack([inputs[top[0]],inputs[top[1]]-offset])
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data[top[0]] = np.hstack([data[top[0]],data[top[1]]])
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del inputs[top[1]]
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@ -159,12 +151,14 @@ def cluster(data,inputs,verbose=False):
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del active[top[1]]
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#None = message to say we need to recalculate
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pairloglikes[:,top[0]] = None
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pairloglikes[top[0],:] = None
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pairloglikes = np.delete(pairloglikes,top[1],0)
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pairloglikes = np.delete(pairloglikes,top[1],1)
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loglikes[top[0]] = None
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loglikes = np.delete(loglikes,top[1])
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sharedloglikes[:,top[0]] = None
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sharedloglikes[top[0],:] = None
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sharedloglikes = np.delete(sharedloglikes,top[1],0)
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sharedloglikes = np.delete(sharedloglikes,top[1],1)
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individualloglikes[:,top[0]] = None
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individualloglikes[top[0],:] = None
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individualloglikes = np.delete(individualloglikes,top[1],0)
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individualloglikes = np.delete(individualloglikes,top[1],1)
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else:
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break
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