deltaLDA-0.1.1/ 0000755 0003103 0000163 00000000000 11200314022 013100 5 ustar andrzeje medinfo deltaLDA-0.1.1/test/ 0000755 0003103 0000163 00000000000 11200314022 014057 5 ustar andrzeje medinfo deltaLDA-0.1.1/test/testDeltaLDA.py 0000644 0003103 0000163 00000017342 11146326116 016732 0 ustar andrzeje medinfo import unittest
from numpy import *
from deltaLDA import deltaLDA
class TestDeltaLDA(unittest.TestCase):
def setUp(self):
"""
Set up base data/parameter values
"""
self.alpha = .1 * ones((1,3))
self.beta = ones((3,5))
self.docs = [[1,1,2],
[1,1,1,1,2],
[3,3,3,4],
[3,3,4,4,3,3],
[0,0,0,0,0],
[0,0,0,0]]
self.f = [0 for doc in self.docs]
self.init = [[0 for w in doc] for doc in self.docs]
self.numsamp = 50
self.randseed = 821945
# equality tolerance for testing
self.tol = 1e-6
def matProb(self,mat):
"""
Given a NumPy matrix,
check that all values >= 0, sum to 1 (valid prob dist)
"""
sumto1 = all([abs(val - float(1)) < self.tol
for val in mat.sum(axis=1)])
geq0 = all([val >= 0 for val in
mat.reshape(mat.size)])
return sumto1 and geq0
#
# These are to test *correct* operation
#
def testStandard(self):
"""
Test standard LDA with base data/params
"""
(phi,theta,sample) = deltaLDA(self.docs,self.alpha,self.beta,
self.numsamp,self.randseed)
# theta should clust docs [0,1], [2,3], [4,5]
maxtheta = argmax(theta,axis=1)
self.assert_(maxtheta[0] == maxtheta[1])
self.assert_(maxtheta[2] == maxtheta[3])
self.assert_(maxtheta[4] == maxtheta[5])
# theta valid prob matrix
self.assert_(self.matProb(theta))
# corresponding phi should emph [1,2], [3,4], [0]
maxphi = argmax(phi,axis=1)
self.assert_(maxphi[maxtheta[0]] == 1)
self.assert_(maxphi[maxtheta[2]] == 3)
self.assert_(maxphi[maxtheta[4]] == 0)
# phi valid prob matrix
self.assert_(self.matProb(phi))
def testDelta(self):
"""
Test DeltaLDA with base data/params + f-values
"""
our_f = [0, 0, 0, 0, 1, 1]
alpha = array([[.1, .1, 0],[.1, .1, .1]])
(phi,theta,sample) = deltaLDA(self.docs,alpha,self.beta,
self.numsamp,self.randseed,f=our_f)
# theta should assign special topic to docs [4,5]
maxtheta = argmax(theta,axis=1)
self.assert_(maxtheta[4] == 2)
self.assert_(maxtheta[5] == 2)
# theta valid prob matrix
self.assert_(self.matProb(theta))
# theta rows should sum to 1
self.assert_(reduce(lambda x,y: x and y,
[abs(val - float(1)) < self.tol
for val in theta.sum(axis=1)]))
# phi for special topic should emph [0]
maxphi = argmax(phi,axis=1)
self.assert_(maxphi[2] == 0)
# phi valid prob matrix
self.assert_(self.matProb(phi))
def testInit(self):
"""
Test standard LDA with init from previous sample
(this doesn't test how the init could affect behavior,
just checks that using an init doesn't fail completely...)
"""
# Give stupid init
(phi,theta,sample) = deltaLDA(self.docs,self.alpha,self.beta,
self.numsamp,self.randseed,init=self.init)
# theta should clust docs [0,1], [2,3], [4,5]
maxtheta = argmax(theta,axis=1)
self.assert_(maxtheta[0] == maxtheta[1])
self.assert_(maxtheta[2] == maxtheta[3])
self.assert_(maxtheta[4] == maxtheta[5])
# corresponding phi should emph [1,2], [3,4], [0]
maxphi = argmax(phi,axis=1)
self.assert_(maxphi[maxtheta[0]] == 1)
self.assert_(maxphi[maxtheta[2]] == 3)
self.assert_(maxphi[maxtheta[4]] == 0)
#
# These test that the program fails correctly on *bad input*
#
"""
Test bad document data
"""
def testNonListDoc(self):
""" Non-list doc """
self.docs[0] = None
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed)
def testNegWord(self):
""" Bad word (negative) """
self.docs[0][-1] = -1
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed)
def testBigWord(self):
""" Bad word (too big) """
self.docs[0][-1] = 5
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed)
def testNonNumWord(self):
""" Bad word (non-numeric) """
self.docs[0][-1] = ''
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed)
"""
Test bad alpha/beta values
"""
def testNegAlpha(self):
""" Negative alpha """
self.alpha[0,1] = -1
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,self.beta,
self.numsamp,self.randseed)
def testNegBeta(self):
""" Negative beta """
self.beta[1,2] = -1
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,self.beta,
self.numsamp,self.randseed)
def testAlphaBetaDim(self):
""" Alpha/Beta dim mismatch """
self.alpha = .1 * ones((1,4))
self.beta = ones((3,5))
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,self.beta,
self.numsamp,self.randseed)
"""
Test bad init samples
"""
def testTooFewInit(self):
""" Too few docs """
self.init = self.init[:-1]
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
def testTooManyInit(self):
""" Too many docs """
self.init = self.init + [[]]
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
def testLenMisInit(self):
""" Doc length mismatch """
self.init[2].append(0)
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
def testNonListInit(self):
""" Non-list doc sample """
self.init[0] = None
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
def testNegInit(self):
""" Out-of-range topic value (negative) """
self.init[0][0] = -2
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
def testBigInit(self):
""" Out-of-range topic value (too big) """
self.init[0][0] = 3
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
def testNonNumInit(self):
""" Out-of-range topic value (non-numeric) """
self.init[0][0] = ''
self.assertRaises(RuntimeError,deltaLDA,self.docs,self.alpha,
self.beta,
self.numsamp,self.randseed,init=self.init)
""" Run the unit tests! """
if __name__ == '__main__':
unittest.main()
deltaLDA-0.1.1/LICENSE.txt 0000644 0003103 0000163 00000104513 11106611320 014735 0 ustar andrzeje medinfo GNU GENERAL PUBLIC LICENSE
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.
deltaLDA-0.1.1/README 0000640 0003103 0000163 00000005312 11145052265 013775 0 ustar andrzeje medinfo DELTA LATENT DIRICHLET ALLOCATION (DeltaLDA)
Version 0.1.1
David Andrzejewski (andrzeje@cs.wisc.edu)
Department of Computer Sciences
University of Wisconsin-Madison, USA
This software implements the DeltaLDA model [1] for discrete count
data. DeltaLDA is a modification of the Latent Dirichlet Allocation
(LDA) model [2] which uses two different topic mixing weight priors to
jointly model two corpora with a shared set of topics. The inference
method is Collapsed Gibbs sampling [3]. This code can also be used to
do "standard" LDA, similar to [3] (see example code).
This code implements DeltaLDA as a Python C extension module.
BUILD/INSTALL
Building this module requires installation of Python and NumPy, as
well as a C compiler. From the command-line, do:
% python setup.py install
(Note that if NumPy or Python are installed to non-standard locations,
you may need to make the appropriate changes in setup.py)
You can then test the installation with:
% python test/TestDeltaLDA.py
There is also a simple example scipt showing how to use deltaLDA:
% python example.py
LOCAL INSTALL
If you do not have write access to your Python installation directories,
you will need to tell setup.py to install this module somewhere else.
For example:
% python setup.py install --prefix=~/local
will install the module under a subdirectory of your home directory called
"local".
It may then be necessary to let Python know where that is by setting
the PYTHONPATH environment variable (e.g., in .bashrc or .cshrc). For
our example this might involve adding something like the line:
setenv PYTHONPATH ~/local/lib/python2.5/site-packages
HOW TO USE
The commenting in the example.py script explains the meanings and
types of all input and return arguments. For more detailed examples
of good/bad inputs, you can examine the tests in TestDeltaLDA.py.
LICENSE
This software is open-source, released under the terms of the GNU
General Public License version 3, or any later version of the GPL (see
LICENSE.txt).
REFERENCES
[1] Andrzejewski, D., Mulhern, A., Liblit, B., and Zhu, X. (2007).
Statistical debugging using latent topic models.
In Proceedings of the 18th European Conference on Machine Learning
(ECML), 6-17.
[2] Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet
allocation. Journal of Machine Learning Research (JMLR) 3
(Mar. 2003), 993-1022.
[3] Griffiths, T., and Steyvers, M. (2004). Finding Scientific
Topics. Proceedings of the National Academy of Sciences (PNAS), 101
(suppl. 1), 5228-5235.
VERSION HISTORY
0.1 Initial release
0.1.1 Fixed some npy_intp[] memory leaks
Fixed bug where betasum was taken over *wrong dimension* of
beta, causing phi to not be normalized correctly
deltaLDA-0.1.1/deltaLDA.c 0000644 0003103 0000163 00000046073 11200313733 014700 0 ustar andrzeje medinfo /**
deltaLDA - Implementation of the Delta Latent Dirichlet Allocation model
Copyright (C) 2008 David Andrzejewski (andrzeje@cs.wisc.edu)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
*/
#include
#include
#include
#include
#include
#include "deltaLDA.h"
/**
* This is the exposed method which is called from Python
*/
static PyObject * deltaLDA(PyObject *self, PyObject *args, PyObject* keywds)
{
// Null-terminated list of arg keywords
//
static char *kwlist[] = {"docs","alpha","beta","numsamp",
"randseed","f","init","verbose",NULL};
// Required args
//
PyObject* docs_arg; // List of Lists
PyArrayObject* alpha; // NumPy Array
PyArrayObject* beta; // NumPy Array
int numsamp;
int randseed;
// Optional args
//
PyObject* f_arg = NULL; // List of f-labels foreach doc
PyObject* init = NULL; // List of Lists to initialize Gibbs
int verbose = 0; // 1 = verbose output
// Parse function args
//
if(!PyArg_ParseTupleAndKeywords(args,keywds,"O!O!O!ii|O!O!i",kwlist,
&PyList_Type,&docs_arg,
&PyArray_Type,&alpha,
&PyArray_Type,&beta,
&numsamp,&randseed,
&PyList_Type,&f_arg,
&PyList_Type,&init,
&verbose))
// ERROR - bad args
return NULL;
// Use args to populate structs
// (also check for *validity*)
//
model_params* mp;
dataset* ds;
if(ARGS_BAD == convert_args(docs_arg,alpha,beta,f_arg,init,&mp,&ds))
{
// Args bad! Return to Python...error condition should be set
return NULL;
}
// Init random number generator
//
srand((unsigned int) randseed);
// Init from previous sample, or do online init?
//
int si,d,i;
counts* c;
if(init == NULL)
{
c = online_init(mp,ds);
}
else
{
c = given_init(mp,ds,init);
if(c == NULL)
{
// ERROR - something went wrong with user-supplied init
Py_DECREF(mp->alphasum);
Py_DECREF(mp->betasum);
free(mp);
for(d = 0; d < ds->D; d++)
free(ds->docs[d]);
free(ds->docs);
free(ds->doclens);
free(ds->f);
free(ds);
return NULL;
}
}
// Do the requested number of Gibbs samples
for(si=0; si < numsamp; si++)
{
// In order to make it easier to test...
srand((unsigned int) randseed + si);
if(verbose == 1)
printf("Gibbs sample %d of %d\n",si,numsamp);
gibbs_chain(mp,ds,c);
}
// Estimate phi and theta
PyArrayObject* phi = est_phi(mp, ds, c);
PyArrayObject* theta = est_theta(mp, ds, c);
// Convert final sample back to List of Lists for return
PyObject* finalsamp = PyList_New(ds->D);
for(d = 0; d < ds->D; d++)
{
PyObject* docz = PyList_New(ds->doclens[d]);
for(i = 0; i < ds->doclens[d]; i++)
PyList_SetItem(docz,i,PyInt_FromLong(ds->sample[d][i]));
PyList_SetItem(finalsamp,d,docz);
}
// Package phi, theta, and final sample in tuple for return
PyObject* retval = PyTuple_New(3);
PyTuple_SetItem(retval,0,(PyObject*) phi);
PyTuple_SetItem(retval,1,(PyObject*) theta);
PyTuple_SetItem(retval,2,finalsamp);
// Do memory cleanup...
Py_DECREF(c->nw);
Py_DECREF(c->nd);
Py_DECREF(c->nw_colsum);
free(c);
Py_DECREF(mp->alphasum);
Py_DECREF(mp->betasum);
free(mp);
for(d = 0; d < ds->D; d++)
{
free(ds->docs[d]);
free(ds->sample[d]);
}
free(ds->docs);
free(ds->sample);
free(ds->doclens);
free(ds->f);
free(ds);
return (PyObject*) retval;
}
/**
* Do an "online" init of Gibbs chain, adding one word
* position at a time and then sampling for each new position
*/
static counts* online_init(model_params* mp, dataset* ds)
{
// Do some init
//
int W = ds->W;
int D = ds->D;
int T = mp->T;
// Alloc and init count matrices and init sample
//
counts* c = (counts*) malloc(sizeof(counts));
npy_intp* nwdims = malloc(sizeof(npy_intp)*2);
nwdims[0] = W;
nwdims[1] = T;
c->nw = (PyArrayObject*) PyArray_ZEROS(2,nwdims,PyArray_INT,0);
free(nwdims);
npy_intp* nddims = malloc(sizeof(npy_intp)*2);
nddims[0] = D;
nddims[1] = T;
c->nd = (PyArrayObject*) PyArray_ZEROS(2,nddims,PyArray_INT,0);
c->nw_colsum = (PyArrayObject*) PyArray_Sum(c->nw,0,PyArray_INT,NULL);
free(nddims);
ds->sample = malloc(sizeof(int*) * D);
// Build init z sample, one word at a time
//
// Temporary array used for sampling
double* num = malloc(sizeof(double)*T);
// For each doc in corpus
int d,i,j;
for(d = 0; d < D; d++)
{
// Get this doc and f-label
int* doc = ds->docs[d];
int doclen = ds->doclens[d];
int f = ds->f[d];
// Create this sample
ds->sample[d] = malloc(sizeof(int) * doclen);
int* sample = ds->sample[d];
// For each word in doc
for(i = 0; i < doclen; i++)
{
int w_i = doc[i];
// For each topic, calculate numerators
double norm_sum = 0;
for(j = 0; j < T; j++)
{
double alpha_j = *((double*)PyArray_GETPTR2(mp->alpha,f,j));
double beta_i = *((double*)PyArray_GETPTR2(mp->beta,j,w_i));
double betasum = *((double*)PyArray_GETPTR1(mp->betasum,j));
double denom_1 = *((int*)PyArray_GETPTR1(c->nw_colsum,j)) + betasum;
// Calculate numerator for this topic
// (NOTE: alpha denom omitted, since same for all topics)
num[j] = ((*((int*)PyArray_GETPTR2(c->nw,w_i,j)))+beta_i) / denom_1;
num[j] = num[j] * (*((int*)PyArray_GETPTR2(c->nd,d,j))+alpha_j);
norm_sum += num[j];
}
// Draw a sample
//
j = mult_sample(num,norm_sum);
// Update count/cache matrices and initial sample vec
//
sample[i] = j;
(*((int*)PyArray_GETPTR2(c->nw,w_i,j)))++;
(*((int*)PyArray_GETPTR2(c->nd,d,j)))++;
(*((int*)PyArray_GETPTR1(c->nw_colsum,j)))++;
}
}
// Cleanup, put all counts in struct, and return
//
free(num);
return c;
}
/**
* Init counts and sampler from a user-supplied initial state
*/
static counts* given_init(model_params* mp, dataset* ds, PyObject* init)
{
// Do some init
//
int W = ds->W;
int D = ds->D;
int T = mp->T;
// Make sure initial sample has correct number of docs
if(D != PyList_Size(init))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Number of docs / number of init samples mismatch");
return NULL;
}
// Alloc and init count matrices and init sample
//
counts* c = (counts*) malloc(sizeof(counts));
npy_intp* nwdims = malloc(sizeof(npy_intp)*2);
nwdims[0] = W;
nwdims[1] = T;
c->nw = (PyArrayObject*) PyArray_ZEROS(2,nwdims,PyArray_INT,0);
free(nwdims);
npy_intp* nddims = malloc(sizeof(npy_intp)*2);
nddims[0] = D;
nddims[1] = T;
c->nd = (PyArrayObject*) PyArray_ZEROS(2,nddims,PyArray_INT,0);
c->nw_colsum = (PyArrayObject*) PyArray_Sum(c->nw,0,PyArray_INT,NULL);
free(nddims);
ds->sample = malloc(sizeof(int*) * D);
// Now populate count matrices from given init sample
//
int d,i;
for(d = 0; d < D; d++)
{
ds->sample[d] = malloc(sizeof(int) * ds->doclens[d]);
PyObject* docinit = PyList_GetItem(init,d);
// Verify that this is a List, and that it has the right length
if(!PyList_Check(docinit))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Non-List element in initial sample");
Py_DECREF(c->nw);
Py_DECREF(c->nd);
Py_DECREF(c->nw_colsum);
free(c);
for(i = 0; i <= d; i++)
free(ds->sample[i]);
free(ds->sample);
return NULL;
}
else if(ds->doclens[d] != PyList_Size(docinit))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Init sample/doc length mismatch");
Py_DECREF(c->nw);
Py_DECREF(c->nd);
Py_DECREF(c->nw_colsum);
free(c);
for(i = 0; i <= d; i++)
free(ds->sample[i]);
free(ds->sample);
return NULL;
}
for(i = 0; i < ds->doclens[d]; i++)
{
// Get topic from init sample
int zi = PyInt_AsLong(PyList_GetItem(docinit,i));
if(zi < 0 || zi > (T-1))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Non-numeric or out of range init sample value");
Py_DECREF(c->nw);
Py_DECREF(c->nd);
Py_DECREF(c->nw_colsum);
free(c);
for(i = 0; i <= d; i++)
free(ds->sample[i]);
free(ds->sample);
return NULL;
}
// Get word
int wi = ds->docs[d][i];
// Store topic and increment count matrices
ds->sample[d][i] = zi;
(*((int*)PyArray_GETPTR2(c->nw,wi,zi)))++;
(*((int*)PyArray_GETPTR2(c->nd,d,zi)))++;
(*((int*)PyArray_GETPTR1(c->nw_colsum,zi)))++;
}
}
return c;
}
/**
* Run Gibbs chain to get a new full sample
*/
void gibbs_chain(model_params* mp, dataset* ds, counts* c)
{
// Do some init
//
int D = ds->D;
int T = mp->T;
// Temporary array used for sampling
double* num = malloc(sizeof(double)*T);
// Use Gibbs sampling to get a new z
// sample, one position at a time
//
// foreach doc in corpus
//
int d,j,i;
for(d = 0; d < D; d++)
{
// Get this doc and f-label
int doclen = ds->doclens[d];
int* doc = ds->docs[d];
int f = ds->f[d];
// Get this sample
int* sample = ds->sample[d];
// For each word in doc
for(i = 0; i < doclen; i++)
{
// remove this w/z pair from all count/cache matrices
int z_i = sample[i];
int w_i = doc[i];
(*((int*)PyArray_GETPTR2(c->nw,w_i,z_i)))--;
(*((int*)PyArray_GETPTR2(c->nd,d,z_i)))--;
(*((int*)PyArray_GETPTR1(c->nw_colsum,z_i)))--;
// For each topic, calculate numerators
double norm_sum = 0;
for(j = 0; j < T; j++)
{
double alpha_j = *((double*)PyArray_GETPTR2(mp->alpha,f,j));
double beta_i = *((double*)PyArray_GETPTR2(mp->beta,j,w_i));
double betasum = *((double*)PyArray_GETPTR1(mp->betasum,j));
double denom_1 = *((int*)PyArray_GETPTR1(c->nw_colsum,j)) + betasum;
// Calculate numerator for each topic
// (NOTE: alpha denom omitted, since same for all topics)
num[j] = ((*((int*)PyArray_GETPTR2(c->nw,w_i,j)))+beta_i) / denom_1;
num[j] = num[j] * (*((int*)PyArray_GETPTR2(c->nd,d,j))+alpha_j);
norm_sum += num[j];
}
// Draw a sample
//
j = mult_sample(num,norm_sum);
// update count/cache matrices and sample vec
//
sample[i] = j;
(*((int*)PyArray_GETPTR2(c->nw,w_i,j)))++;
(*((int*)PyArray_GETPTR2(c->nd,d,j)))++;
(*((int*)PyArray_GETPTR1(c->nw_colsum,j)))++;
}
}
// Just cleanup and return
// (new sample will be returned in ds->sample
//
free(num);
return;
}
/**
* Use final sample to estimate theta = P(z|d)
*/
PyArrayObject* est_theta(model_params* mp, dataset* ds, counts* c)
{
int D = ds->D;
int T = mp->T;
npy_intp* tdims = malloc(sizeof(npy_intp)*2);
tdims[0] = D;
tdims[1] = T;
PyArrayObject* theta = (PyArrayObject*)
PyArray_ZEROS(2,tdims,PyArray_DOUBLE,0);
free(tdims);
PyArrayObject* rowsums = (PyArrayObject*) PyArray_Sum(c->nd,1,PyArray_DOUBLE,NULL);
int d,t;
for(d = 0; d < D; d++)
{
double rowsum = *((double*)PyArray_GETPTR1(rowsums,d));
int f = ds->f[d];
double alphasum = *((double*)PyArray_GETPTR1(mp->alphasum,f));
for(t = 0; t < T; t++)
{
double alpha_t = *((double*)PyArray_GETPTR2(mp->alpha,f,t));
int ndct = *((int*)PyArray_GETPTR2(c->nd,d,t));
// Calc and assign theta entry
double newval = (ndct + alpha_t) / (rowsum+alphasum);
*((double*)PyArray_GETPTR2(theta,d,t)) = newval;
}
}
return theta;
}
/**
* Use final sample to estimate phi = P(w|z)
*/
PyArrayObject* est_phi(model_params* mp, dataset* ds, counts* c)
{
int W = ds->W;
int T = mp->T;
npy_intp* pdims = malloc(sizeof(npy_intp)*2);
pdims[0] = T;
pdims[1] = W;
PyArrayObject* phi = (PyArrayObject*)
PyArray_ZEROS(2,pdims,PyArray_DOUBLE,0);
free(pdims);
int t,w;
for(t = 0; t < T; t++)
{
int colsum = (*((int*)PyArray_GETPTR1(c->nw_colsum,t)));
double betasum = *((double*)PyArray_GETPTR1(mp->betasum,t));
for(w = 0; w < W; w++)
{
double beta_w = *((double*)PyArray_GETPTR2(mp->beta,t,w));
int nwct = *((int*)PyArray_GETPTR2(c->nw,w,t));
double newval = (beta_w + nwct) / (betasum + colsum);
*((double*)PyArray_GETPTR2(phi,t,w)) = newval;
}
}
return phi;
}
/**
* Simultaneously check args and populate structs
*/
static int convert_args(PyObject* docs_arg, PyArrayObject* alpha,
PyArrayObject* beta, PyObject* f_arg, PyObject* init,
model_params** p_mp, dataset** p_ds)
{
int i;
int fmax = 0; // Will need to ensure alpha dim is this big
int D = PyList_Size(docs_arg);
int* f = malloc(sizeof(int)*D);
// If f-labels were not specified, just set all to zero
if(f_arg == NULL)
{
for(i = 0; i < D; i++)
f[i] = 0;
}
// Otherwise convert and check
else
{
if(D != PyList_Size(f_arg))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"f and docs have different lengths");
free(f);
return ARGS_BAD;
}
for(i = 0; i < D; i++)
{
f[i] = PyInt_AsLong(PyList_GetItem(f_arg,i));
// If PyInt_AsLong fails, it returns -1
// (also should not be getting neg f-values anyway)
if(f[i] < 0)
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Non-numeric or negative f-value");
free(f);
return ARGS_BAD;
}
else if(f[i] > fmax)
{
fmax = f[i];
}
}
}
// Get some basic information from parameters
// (and check dimensionality agreement)
int T = PyArray_DIM(beta,0);
int W = PyArray_DIM(beta,1);
int F = PyArray_DIM(alpha,0);
if(fmax != (F - 1))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Alpha/f dimensionality mismatch");
free(f);
return ARGS_BAD;
}
if(T != PyArray_DIM(alpha,1))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Alpha/Beta dimensionality mismatch");
free(f);
return ARGS_BAD;
}
// Check that all alpha/beta values are non-negative
//
double betamin = PyFloat_AsDouble(PyArray_Min(beta,NPY_MAXDIMS,NULL));
double alphamin = PyFloat_AsDouble(PyArray_Min(alpha,NPY_MAXDIMS,NULL));
if(betamin <= 0 || alphamin < 0)
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Negative Alpha or negative/zero Beta value");
free(f);
return ARGS_BAD;
}
// Convert documents from PyObject* to int[]
//
int d;
int* doclens = malloc(sizeof(int) * D);
int** docs = malloc(sizeof(int*) * D);
for(d = 0; d < D; d++)
{
PyObject* doc = PyList_GetItem(docs_arg,d);
if(!PyList_Check(doc))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Non-List element in docs List");
free(f);
for(i = 0; i < d; i++)
free(docs[i]);
free(docs);
free(doclens);
return ARGS_BAD;
}
doclens[d] = PyList_Size(doc);
docs[d] = malloc(sizeof(int) * doclens[d]);
for(i = 0; i < doclens[d]; i++)
{
docs[d][i] = PyInt_AsLong(PyList_GetItem(doc,i));
if(docs[d][i] < 0 || docs[d][i] > (W - 1))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Non-numeric or out of range word");
free(f);
for(i = 0; i <= d; i++)
free(docs[i]);
free(docs);
free(doclens);
return ARGS_BAD;
}
}
}
// Populate dataset struct
//
dataset* ds = (dataset*) malloc(sizeof(dataset));
ds->D = D;
ds->W = W;
ds->doclens = doclens;
ds->docs = docs;
ds->sample = NULL;
ds->f = f;
// Populate model params struct
//
model_params* mp = (model_params*) malloc(sizeof(model_params));
mp->alpha = alpha;
mp->beta = beta;
mp->T = T;
mp->alphasum = (PyArrayObject*) PyArray_Sum(alpha,1,PyArray_DOUBLE,NULL);
mp->betasum = (PyArrayObject*) PyArray_Sum(beta,1,PyArray_DOUBLE,NULL);
*(p_ds) = ds;
*(p_mp) = mp;
return ARGS_OK;
}
/**
* Draw a multinomial sample propto vals
*
* (!!! we're assuming sum is the correct sum for vals !!!)
*
*/
static int mult_sample(double* vals, double norm_sum)
{
double rand_sample = unif() * norm_sum;
double tmp_sum = 0;
int j = 0;
while(tmp_sum < rand_sample || j == 0) {
tmp_sum += vals[j];
j++;
}
return j - 1;
}
//
// PYTHON EXTENSION BOILERPLATE BELOW
//
// Defines the module method table
PyMethodDef methods[] =
{
{"deltaLDA", (PyCFunction) deltaLDA,
METH_VARARGS | METH_KEYWORDS, "Run DeltaLDA"},
{NULL, NULL, 0, NULL} // Is a 'sentinel' (?)
};
// This is a macro that does stuff for us (linkage, declaration, etc)
PyMODINIT_FUNC
initdeltaLDA() // Passes method table to init our module
{
(void) Py_InitModule("deltaLDA", methods);
import_array(); // Must do this to satisfy NumPy (!)
}
deltaLDA-0.1.1/deltaLDA.h 0000644 0003103 0000163 00000004023 11147053650 014703 0 ustar andrzeje medinfo /**
deltaLDA - Implementation of the Delta Latent Dirichlet Allocation model
Copyright (C) 2008 David Andrzejewski (andrzeje@cs.wisc.edu)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
*/
#include
#include
#include
#include
#include
// Uniform rand between [0,1] (inclusive)
#define unif() ((double) rand()) / ((double) RAND_MAX)
#define ARGS_OK 0
#define ARGS_BAD 1
typedef struct {
PyArrayObject* nw;
PyArrayObject* nd;
PyArrayObject* nw_colsum;
} counts;
typedef struct {
PyArrayObject* alpha;
PyArrayObject* alphasum;
PyArrayObject* beta;
PyArrayObject* betasum;
int T;
} model_params;
typedef struct {
int D;
int W;
int* doclens;
int** docs;
int** sample;
int* f;
} dataset;
static PyObject* deltaLDA(PyObject *self, PyObject *args, PyObject* keywds);
static int convert_args(PyObject* docs_arg, PyArrayObject* alpha,
PyArrayObject* beta, PyObject* f_arg, PyObject* init,
model_params** mp, dataset** ds);
static counts* given_init(model_params* mp, dataset* ds, PyObject* init);
static counts* online_init(model_params* mp, dataset* ds);
static void gibbs_chain(model_params* mp, dataset* ds, counts* c);
static int mult_sample(double* vals, double sum);
static PyArrayObject* est_phi(model_params* mp, dataset* ds, counts* c);
static PyArrayObject* est_theta(model_params* mp, dataset* ds, counts* c);
deltaLDA-0.1.1/example.py 0000644 0003103 0000163 00000004051 11106613634 015125 0 ustar andrzeje medinfo from numpy import *
from deltaLDA import deltaLDA
# alpha and beta *must* be NumPy Array objects
#
# Their dimensionalities implicitly specify:
# -the number of topics T
# -the vocabulary size W
# -number of f-label values F (just 1 for 'standard' LDA)
#
# alpha = F x T
# beta = T x W
#
alpha = .1 * ones((1,3))
beta = ones((3,5))
# docs must be a Python List of Python lists
# Each individual List contains the indices of word tokens
# (That is, docs[0] = "w1 w1 w2", docs[1] = "w1 w1 w1 w1 w2", etc)
#
docs = [[1,1,2],
[1,1,1,1,2],
[3,3,3,4],
[3,3,4,4,3,3],
[0,0,0,0,0],
[0,0,0,0]]
# numsamp specifies how many samples to take from the Gibbs sampler
numsamp = 50
# randseed is used to initialize the Gibbs sampler random number generator
randseed = 194582
# This command will run the standard LDA model
#
(phi,theta,sample) = deltaLDA(docs,alpha,beta,numsamp,randseed)
# This command will initialize the Gibbs sampler from a user-supplied sample
#
#(phi,theta,sample) = deltaLDA(docs,alpha,beta,numsamp,randseed,init=sample)
# This command will run standard LDA, but show Gibbs sampler output
# ("Gibbs sample X of Y")
#
#(phi,theta,sample) = deltaLDA(docs,alpha,beta,numsamp,randseed,verbose=1)
# These commands will run deltaLDA
# (use different alpha vectors for different docs, depending on value of f)
#
#delta_f = [0, 0, 0, 0, 1, 1]
#delta_alpha = array([[.1, .1, 0],[.1, .1, .1]])
#(phi,theta,sample) = deltaLDA(docs,delta_alpha,beta,numsamp,randseed,f=delta_f)
# theta is the matrix of document-topic probabilities
# (estimated from final sample)
#
# theta = D x T
# theta[di,zj] = P(z=zj | d=di)
#
print ''
print 'Theta - P(z|d)'
print str(theta)
print ''
# phi is the matrix of topic-word probabilities
# (estimated from final sample)
#
# phi = T x W
# phi[zj,wi] = P(w=wi | z=zj)
#
print ''
print 'Phi - P(w|z)'
print str(phi)
print ''
# Since the simple documents we created and fed into deltaLDA exhibit such
# clearly divided word usage patterns, the resulting phi and theta
# should reflect these patterns nicely
deltaLDA-0.1.1/setup.py 0000644 0003103 0000163 00000001463 11145052100 014622 0 ustar andrzeje medinfo from distutils.core import setup, Extension
import os
from numpy.distutils.misc_util import *
numpyincl = get_numpy_include_dirs()
dldamodule = Extension("deltaLDA",
sources = ["deltaLDA.c"],
include_dirs = [os.getcwd()] + numpyincl,
library_dirs = [],
libraries = [],
extra_compile_args = ['-O3','-Wall'],
extra_link_args = [])
setup(name = 'deltaLDA',
description = 'Delta LDA model',
version = '0.1.1',
author = 'David Andrzejewski',
author_email = 'andrzeje@cs.wisc.edu',
license = 'GNU General Public License (Version 3 or later)',
url = 'http://pages.cs.wisc.edu/~andrzeje/research/df_lda.html',
ext_modules = [dldamodule],
py_modules = [])
deltaLDA-0.1.1/PKG-INFO 0000644 0003103 0000163 00000000456 11200314022 014202 0 ustar andrzeje medinfo Metadata-Version: 1.0
Name: deltaLDA
Version: 0.1.1
Summary: Delta LDA model
Home-page: http://pages.cs.wisc.edu/~andrzeje/research/df_lda.html
Author: David Andrzejewski
Author-email: andrzeje@cs.wisc.edu
License: GNU General Public License (Version 3 or later)
Description: UNKNOWN
Platform: UNKNOWN