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Merge branch '0.7-prpack' into develop
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Conflicts:
	interfaces/R/types-C.def
	interfaces/R/types-R.def
	src/Makefile.am
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@gaborcsardi
gaborcsardi committed Jan 29, 2014
2 parents 873cee1 + 2589450 commit ef0659d
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Showing 4 changed files with 91 additions and 16 deletions.
26 changes: 22 additions & 4 deletions igraph/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -768,7 +768,8 @@ def path_length_hist(self, directed=True):
return hist

def pagerank(self, vertices=None, directed=True, damping=0.85,
weights=None, arpack_options=None):
weights=None, arpack_options=None, implementation="prpack",
niter=1000, eps=0.001):
"""Calculates the Google PageRank values of a graph.
@param vertices: the indices of the vertices being queried.
Expand All @@ -781,13 +782,30 @@ def pagerank(self, vertices=None, directed=True, damping=0.85,
or even an edge attribute name.
@param arpack_options: an L{ARPACKOptions} object used to fine-tune
the ARPACK eigenvector calculation. If omitted, the module-level
variable called C{arpack_options} is used.
variable called C{arpack_options} is used. This argument is
ignored if not the ARPACK implementation is used, see the
I{implementation} argument.
@param implementation: which implementation to use to solve the
PageRank eigenproblem. Possible values are:
- C{"prpack"}: use the PRPACK library. This is a new
implementation in igraph 0.7
- C{"arpack"}: use the ARPACK library. This implementation
was used from version 0.5, until version 0.7.
- C{"power"}: use a simple power method. This is the
implementation that was used before igraph version 0.5.
@param niter: The number of iterations to use in the power method
implementation. It is ignored in the other implementations
@param eps: The power method implementation will consider the
calculation as complete if the difference of PageRank values between
iterations change less than this value for every node. It is
ignored by the other implementations.
@return: a list with the Google PageRank values of the specified
vertices."""
if arpack_options is None:
arpack_options = _igraph.arpack_options
return self.personalized_pagerank(vertices, directed, damping, None, \
None, weights, arpack_options)
return self.personalized_pagerank(vertices, directed, damping, None,\
None, weights, arpack_options, \
implementation, niter, eps)

def spanning_tree(self, weights=None, return_tree=True):
"""Calculates a minimum spanning tree for a graph.
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16 changes: 16 additions & 0 deletions src/convert.c
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Original file line number Diff line number Diff line change
Expand Up @@ -2880,3 +2880,19 @@ int igraphmodule_PyObject_to_attribute_combination_t(PyObject* object,
return 0;
}

/**
* \ingroup python_interface_conversion
* \brief Converts a Python object to an igraph \c igraph_pagerank_algo_t
*/
int igraphmodule_PyObject_to_pagerank_algo_t(PyObject *o,
igraph_pagerank_algo_t *result) {
static igraphmodule_enum_translation_table_entry_t pagerank_algo_tt[] = {
{"prpack", IGRAPH_PAGERANK_ALGO_PRPACK},
{"arpack", IGRAPH_PAGERANK_ALGO_ARPACK},
{"power", IGRAPH_PAGERANK_ALGO_POWER},
{0,0}
};

return igraphmodule_PyObject_to_enum(o, pagerank_algo_tt, (int*)result);
}

1 change: 1 addition & 0 deletions src/convert.h
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,7 @@ int igraphmodule_PyObject_to_connectedness_t(PyObject *o, igraph_connectedness_t
int igraphmodule_PyObject_to_degseq_t(PyObject *o, igraph_degseq_t *result);
int igraphmodule_PyObject_to_fas_algorithm_t(PyObject *o, igraph_fas_algorithm_t *result);
int igraphmodule_PyObject_to_neimode_t(PyObject *o, igraph_neimode_t *result);
int igraphmodule_PyObject_to_pagerank_algo_t(PyObject *o, igraph_pagerank_algo_t *result);
int igraphmodule_PyObject_to_reciprocity_t(PyObject *o, igraph_reciprocity_t *result);
int igraphmodule_PyObject_to_rewiring_t(PyObject *o, igraph_rewiring_t *result);
int igraphmodule_PyObject_to_spinglass_implementation_t(PyObject *o, igraph_spinglass_implementation_t *result);
Expand Down
64 changes: 52 additions & 12 deletions src/graphobject.c
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Original file line number Diff line number Diff line change
Expand Up @@ -4784,7 +4784,7 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
{
static char *kwlist[] =
{ "vertices", "directed", "damping", "reset", "reset_vertices", "weights",
"arpack_options", NULL };
"arpack_options", "implementation", "niter", "eps", NULL };
PyObject *directed = Py_True;
PyObject *vobj = Py_None, *wobj = Py_None, *robj = Py_None, *rvsobj = Py_None;
PyObject *list;
Expand All @@ -4796,12 +4796,21 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
igraph_vector_t weights;
igraph_bool_t return_single = 0;
igraph_vs_t vs, reset_vs;
igraph_pagerank_algo_t algo=IGRAPH_PAGERANK_ALGO_PRPACK;
PyObject *algo_o = Py_None;
long niter=1000;
float eps=0.001f;
igraph_pagerank_power_options_t popts;
void *opts;
int retval;

if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOdOOOO!", kwlist, &vobj,
&directed, &damping, &robj, &rvsobj, &wobj,
if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOdOOOO!Oid", kwlist, &vobj,
&directed, &damping, &robj,
&rvsobj, &wobj,
&igraphmodule_ARPACKOptionsType,
&arpack_options_o))
&arpack_options_o, &algo_o, &niter, &eps))


return NULL;

if (robj != Py_None && rvsobj != Py_None) {
Expand All @@ -4814,6 +4823,7 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
return NULL;
}

arpack_options = (igraphmodule_ARPACKOptionsObject*)arpack_options_o;
if (robj != Py_None) {
if (igraphmodule_attrib_to_vector_t(robj, self, &reset, ATTRIBUTE_TYPE_VERTEX)) {
igraph_vs_destroy(&vs);
Expand Down Expand Up @@ -4845,13 +4855,25 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
return igraphmodule_handle_igraph_error();
}

arpack_options = (igraphmodule_ARPACKOptionsObject*)arpack_options_o;
if (igraphmodule_PyObject_to_pagerank_algo_t(algo_o, &algo))
return NULL;

popts.niter = (igraph_integer_t) niter; popts.eps = eps;

if (algo == IGRAPH_PAGERANK_ALGO_POWER) {
opts = &popts;
} else if (algo == IGRAPH_PAGERANK_ALGO_ARPACK) {
opts = igraphmodule_ARPACKOptions_get(arpack_options);
} else {
opts = 0;
}

if (rvsobj != Py_None)
retval = igraph_personalized_pagerank_vs(&self->g, &res, 0, vs, PyObject_IsTrue(directed),
damping, reset_vs, &weights, igraphmodule_ARPACKOptions_get(arpack_options));
retval = igraph_personalized_pagerank_vs(&self->g, algo, &res, 0, vs,
PyObject_IsTrue(directed), damping, reset_vs, &weights, opts);
else
retval = igraph_personalized_pagerank(&self->g, &res, 0, vs, PyObject_IsTrue(directed),
damping, reset, &weights, igraphmodule_ARPACKOptions_get(arpack_options));
retval = igraph_personalized_pagerank(&self->g, algo, &res, 0, vs,
PyObject_IsTrue(directed), damping, reset, &weights, opts);

if (retval) {
igraphmodule_handle_igraph_error();
Expand Down Expand Up @@ -12892,8 +12914,10 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
/* interface to igraph_personalized_pagerank */
{"personalized_pagerank", (PyCFunction) igraphmodule_Graph_personalized_pagerank,
METH_VARARGS | METH_KEYWORDS,
"personalized_pagerank(vertices=None, directed=True, damping=0.85, "
"reset=None, reset_vertices=None, weights=None, arpack_options=None)\n\n"
"personalized_pagerank(vertices=None, directed=True, damping=0.85,\n"
" reset=None, reset_vertices=None, weights=None, \n"
" arpack_options=None, implementation=\"prpack\", niter=1000,\n"
" eps=0.001)\n\n"
"Calculates the personalized PageRank values of a graph.\n\n"
"The personalized PageRank calculation is similar to the PageRank\n"
"calculation, but the random walk is reset to a non-uniform distribution\n"
Expand All @@ -12919,7 +12943,23 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
" even an edge attribute name.\n"
"@param arpack_options: an L{ARPACKOptions} object used to fine-tune\n"
" the ARPACK eigenvector calculation. If omitted, the module-level\n"
" variable called C{arpack_options} is used.\n"
" variable called C{arpack_options} is used. This argument is\n"
" ignored if not the ARPACK implementation is used, see the \n"
" I{implementation} argument.\n"
"@param implementation: which implementation to use to solve the \n"
" PageRank eigenproblem. Possible values are:\n\n"
" - C{\"prpack\"}: use the PRPACK library. This is a new \n"
" implementation in igraph 0.7\n\n"
" - C{\"arpack\"}: use the ARPACK library. This implementation\n"
" was used from version 0.5, until version 0.7.\n\n"
" - C{\"power\"}: use a simple power method. This is the\n"
" implementation that was used before igraph version 0.5.\n\n"
"@param niter: The number of iterations to use in the power method\n"
" implementation. It is ignored in the other implementations.\n"
"@param eps: The power method implementation will consider the\n"
" calculation as complete if the difference of PageRank values between\n"
" iterations change less than this value for every node. It is \n"
" ignored by the other implementations.\n"
"@return: a list with the personalized PageRank values of the specified\n"
" vertices.\n"},

Expand Down

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