def gibbs_doc_mp(model, doc, params=None, callback=None):
"""Runs Gibbs iterations on a single document, by sampling with a prior constructed from each sample in the given Model. params applies to each sample, so should probably be much more limited than usual - the default if its undefined is to use 1 run and 1 sample and a burn in of only 500. Returns a DocModel with all the relevant samples in."""
# Initialisation stuff - handle params, create the state and the DocModel object, plus a reporter...
if params == None:
params = Params()
params.runs = 1
params.samples = 1
params.burnIn = 500
state = State(doc, params)
dm = DocModel()
# Create a pool of worker processes...
pool = mp.Pool()
# Create a value for sub-processes to report back their progress with...
manager = mp.Manager()
doneIters = manager.Value('i', 0)
totalIters = model.sampleCount() * params.runs * (
params.burnIn + params.samples + (params.samples - 1) * params.lag)
# Create a callback for when a job completes...
def onComplete(s):
dm.addFrom(s.getModel())
# Create all the jobs, wait for their completion, report progress...
try:
jobs = []
for sample in model.sampleList():
tempState = State(state)
tempState.setGlobalParams(sample)
tempState.addPrior(sample)
jobs.append(
pool.apply_async(gibbs_run_wrap, (tempState, doneIters),
callback=onComplete))
finally:
# Close the pool and wait for all the jobs to complete...
pool.close()
while len(jobs) != 0:
if jobs[0].ready():
del jobs[0]
continue
time.sleep(0.01)
if callback != None: callback(doneIters.value, totalIters)
pool.join()
# Return...
return dm
def gibbs_doc_mp(model, doc, params = None, callback = None):
"""Runs Gibbs iterations on a single document, by sampling with a prior constructed from each sample in the given Model. params applies to each sample, so should probably be much more limited than usual - the default if its undefined is to use 1 run and 1 sample and a burn in of only 500. Returns a DocModel with all the relevant samples in."""
# Initialisation stuff - handle params, create the state and the DocModel object, plus a reporter...
if params==None:
params = Params()
params.runs = 1
params.samples = 1
params.burnIn = 500
state = State(doc, params)
dm = DocModel()
# Create a pool of worker processes...
pool = mp.Pool()
# Create a value for sub-processes to report back their progress with...
manager = mp.Manager()
doneIters = manager.Value('i',0)
totalIters = model.sampleCount() * params.runs * (params.burnIn + params.samples + (params.samples-1)*params.lag)
# Create a callback for when a job completes...
def onComplete(s):
dm.addFrom(s.getModel())
# Create all the jobs, wait for their completion, report progress...
try:
jobs = []
for sample in model.sampleList():
tempState = State(state)
tempState.setGlobalParams(sample)
tempState.addPrior(sample)
jobs.append(pool.apply_async(gibbs_run_wrap,(tempState,doneIters), callback = onComplete))
finally:
# Close the pool and wait for all the jobs to complete...
pool.close()
while len(jobs)!=0:
if jobs[0].ready():
del jobs[0]
continue
time.sleep(0.01)
if callback!=None: callback(doneIters.value,totalIters)
pool.join()
# Return...
return dm
def gibbs_doc(model, doc, params = None, callback = None):
"""Runs Gibbs iterations on a single document, by sampling with a prior constructed from each sample in the given Model. params applies to each sample, so should probably be much more limited than usual - the default if its undefined is to use 1 run and 1 sample and a burn in of only 500. Returns a DocModel with all the relevant samples in."""
# Initialisation stuff - handle params, create the state and the DocModel object, plus a reporter...
if params==None:
params = Params()
params.runs = 1
params.samples = 1
params.burnIn = 500
state = State(doc, params)
dm = DocModel()
reporter = ProgReporter(params,callback,model.sampleCount())
# Iterate and run for each sample in the model...
for sample in model.sampleList():
tempState = State(state)
tempState.setGlobalParams(sample)
tempState.addPrior(sample)
gibbs_run(tempState,reporter.next)
dm.addFrom(tempState.getModel())
# Return...
return dm
def gibbs_doc(model, doc, params=None, callback=None):
"""Runs Gibbs iterations on a single document, by sampling with a prior constructed from each sample in the given Model. params applies to each sample, so should probably be much more limited than usual - the default if its undefined is to use 1 run and 1 sample and a burn in of only 500. Returns a DocModel with all the relevant samples in."""
# Initialisation stuff - handle params, create the state and the DocModel object, plus a reporter...
if params == None:
params = Params()
params.runs = 1
params.samples = 1
params.burnIn = 500
state = State(doc, params)
dm = DocModel()
reporter = ProgReporter(params, callback, model.sampleCount())
# Iterate and run for each sample in the model...
for sample in model.sampleList():
tempState = State(state)
tempState.setGlobalParams(sample)
tempState.addPrior(sample)
gibbs_run(tempState, reporter.next)
dm.addFrom(tempState.getModel())
# Return...
return dm
def leftRightNegLogProbWord(sample, doc, cluster, particles, cap):
"""Does a left to right estimate of the negative log probability of the words in the given document, given a sample, the documents abnormalities and a cluster assignment. cap defines a cap on the number of documents resampled before each word is sampled for inclusion - set to a negative number for no cap, but be warned that the algorithm is then O(n^2) with regard to the number of words in the document. Should be set quite high in practise for a reasonable trade off between quality and run-time."""
code = start_cpp(shared_code) + """
// Setup - create the state, extract the document, set its cluster...
State state;
StatePyToCpp(stateIn, &state);
Document & doc = state.doc[0];
if (cluster>=0)
{
// Existing cluster...
doc.SetCluster(state.clusters.Index(cluster));
}
else
{
// New cluster...
ItemRef<Cluster,Conc> * newC = state.clusters.Append();
newC->Body().alpha = state.rho.alpha;
newC->Body().beta = state.rho.beta;
newC->Body().conc = state.rho.conc;
float * bmn = new float[state.behCount];
float bmnDiv = 0.0;
for (int b=0;b<state.behCount;b++)
{
bmn[b] = state.phi[b];
bmnDiv += state.phi[b];
}
for (int b=0;b<state.behCount;b++) bmn[b] /= bmnDiv;
newC->SetBMN(bmn);
doc.SetCluster(newC);
}
// If the cap is negative set it to include all words, otherwise we need some storage...
int * samIndex = 0;
if (cap<0) cap = doc.SampleCount();
else
{
samIndex = new int[cap];
}
// Create some memory for storing the results into, zeroed out...
float * samProb = new float[doc.SampleCount()];
for (int s=0;s<doc.SampleCount();s++) samProb[s] = 0.0;
// Do all the particles, summing the results into the samProb array...
for (int p=0;p<particles;p++)
{
// Reset the document to have no assignments to words...
for (int s=0;s<doc.SampleCount();s++)
{
doc.GetSample(s).SetDocInst(0);
}
// Iterate and factor in the result from each sample...
for (int s=0;s<doc.SampleCount();s++)
{
// Resample preceding samples - 3 scenarios with regards to the cap...
// (Note that duplication is allowed in the random sample selection - whilst strictly forbidden the situation is such that it can not cause any issues.)
if (s<=cap)
{
// Less or equal number of samples than the cap - do them all...
for (int s2=0;s2<s;s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
}
else
{
if (s<=cap*2)
{
// Need to miss some samples out, but due to numbers its best to randomly select the ones to miss rather than the ones to do...
int missCount = s-cap;
for (int m=0;m<missCount;m++) samIndex[m] = sample_nat(s);
qsort(samIndex, missCount, sizeof(int), compareInt);
for (int s2=0;s2<samIndex[0];s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
for (int m=0;m<missCount-1;m++)
{
for (int s2=samIndex[m]+1;s2<samIndex[m+1];s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
}
for (int s2=samIndex[missCount-1]+1;s2<s;s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
}
else
{
// Need to select a subset of samples to do...
for (int m=0;m<cap;m++) samIndex[m] = sample_nat(s);
qsort(samIndex, cap, sizeof(int), compareInt);
for (int m=0;m<cap;m++)
{
ResampleSample(state, doc, doc.GetSample(samIndex[m]));
}
}
}
// Calculate the contribution of this sample, whilst simultaneously filling out so we can make a draw from them...
float pSum = CalcSampleProb(state, doc, doc.GetSample(s));
samProb[s] += (pSum - samProb[s]) / (p+1);
// Draw an assignment for the current sample, ready for the next iteration...
ResampleSample(state, doc, doc.GetSample(s), pSum);
}
}
// Sumarise the results buffer into a single log probability and return it...
float ret = 0.0;
for (int s=0;s<doc.SampleCount();s++) ret += log(samProb[s]);
return_val = ret;
// Clean up...
delete[] samIndex;
delete[] samProb;
"""
stateIn = State(doc, Params())
stateIn.setGlobalParams(sample)
stateIn.addPrior(sample)
ret = weave.inline(code,['stateIn','cluster','particles','cap'] , support_code=shared_code)
return -ret # Convert to negative log on the return - before then stick to positive.
def leftRightNegLogProbWord(sample, doc, cluster, particles, cap):
"""Does a left to right estimate of the negative log probability of the words in the given document, given a sample, the documents abnormalities and a cluster assignment. cap defines a cap on the number of documents resampled before each word is sampled for inclusion - set to a negative number for no cap, but be warned that the algorithm is then O(n^2) with regard to the number of words in the document. Should be set quite high in practise for a reasonable trade off between quality and run-time."""
code = start_cpp(shared_code) + """
// Setup - create the state, extract the document, set its cluster...
State state;
StatePyToCpp(stateIn, &state);
Document & doc = state.doc[0];
if (cluster>=0)
{
// Existing cluster...
doc.SetCluster(state.clusters.Index(cluster));
}
else
{
// New cluster...
ItemRef<Cluster,Conc> * newC = state.clusters.Append();
newC->Body().alpha = state.rho.alpha;
newC->Body().beta = state.rho.beta;
newC->Body().conc = state.rho.conc;
float * bmn = new float[state.behCount];
float bmnDiv = 0.0;
for (int b=0;b<state.behCount;b++)
{
bmn[b] = state.phi[b];
bmnDiv += state.phi[b];
}
for (int b=0;b<state.behCount;b++) bmn[b] /= bmnDiv;
newC->SetBMN(bmn);
doc.SetCluster(newC);
}
// If the cap is negative set it to include all words, otherwise we need some storage...
int * samIndex = 0;
if (cap<0) cap = doc.SampleCount();
else
{
samIndex = new int[cap];
}
// Create some memory for storing the results into, zeroed out...
float * samProb = new float[doc.SampleCount()];
for (int s=0;s<doc.SampleCount();s++) samProb[s] = 0.0;
// Do all the particles, summing the results into the samProb array...
for (int p=0;p<particles;p++)
{
// Reset the document to have no assignments to words...
for (int s=0;s<doc.SampleCount();s++)
{
doc.GetSample(s).SetDocInst(0);
}
// Iterate and factor in the result from each sample...
for (int s=0;s<doc.SampleCount();s++)
{
// Resample preceding samples - 3 scenarios with regards to the cap...
// (Note that duplication is allowed in the random sample selection - whilst strictly forbidden the situation is such that it can not cause any issues.)
if (s<=cap)
{
// Less or equal number of samples than the cap - do them all...
for (int s2=0;s2<s;s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
}
else
{
if (s<=cap*2)
{
// Need to miss some samples out, but due to numbers its best to randomly select the ones to miss rather than the ones to do...
int missCount = s-cap;
for (int m=0;m<missCount;m++) samIndex[m] = sample_nat(s);
qsort(samIndex, missCount, sizeof(int), compareInt);
for (int s2=0;s2<samIndex[0];s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
for (int m=0;m<missCount-1;m++)
{
for (int s2=samIndex[m]+1;s2<samIndex[m+1];s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
}
for (int s2=samIndex[missCount-1]+1;s2<s;s2++)
{
ResampleSample(state, doc, doc.GetSample(s2));
}
}
else
{
// Need to select a subset of samples to do...
for (int m=0;m<cap;m++) samIndex[m] = sample_nat(s);
qsort(samIndex, cap, sizeof(int), compareInt);
for (int m=0;m<cap;m++)
{
ResampleSample(state, doc, doc.GetSample(samIndex[m]));
}
}
}
// Calculate the contribution of this sample, whilst simultaneously filling out so we can make a draw from them...
float pSum = CalcSampleProb(state, doc, doc.GetSample(s));
samProb[s] += (pSum - samProb[s]) / (p+1);
// Draw an assignment for the current sample, ready for the next iteration...
ResampleSample(state, doc, doc.GetSample(s), pSum);
}
}
// Sumarise the results buffer into a single log probability and return it...
float ret = 0.0;
for (int s=0;s<doc.SampleCount();s++) ret += log(samProb[s]);
return_val = ret;
// Clean up...
delete[] samIndex;
delete[] samProb;
"""
stateIn = State(doc, Params())
stateIn.setGlobalParams(sample)
stateIn.addPrior(sample)
ret = weave.inline(code, ['stateIn', 'cluster', 'particles', 'cap'],
support_code=shared_code)
return -ret # Convert to negative log on the return - before then stick to positive.