Wrap algorithms for Python.

local_clustering_coefficient, louvain_clustering,
subgraph_extraction

The tests are insufficient and need work.

python/katana/analytics/__init__.py

@@ -30,6 +30,12 @@
 
 .. automodule:: katana.analytics._independent_set
 
+.. automodule:: katana.analytics._louvain_clustering
+
+.. automodule:: katana.analytics._local_clustering_coefficient
+
+.. automodule:: katana.analytics._subgraph_extraction
+
 .. automodule:: katana.analytics._jaccard
 
 .. automodule:: katana.analytics._k_core
@@ -67,6 +73,14 @@
     IndependentSetStatistics,
 )
 from katana.analytics._jaccard import jaccard, jaccard_assert_valid, JaccardPlan, JaccardStatistics
+from katana.analytics._louvain_clustering import (
+    louvain_clustering,
+    louvain_clustering_assert_valid,
+    LouvainClusteringPlan,
+    LouvainClusteringStatistics,
+)
+from katana.analytics._local_clustering_coefficient import local_clustering_coefficient, LocalClusteringCoefficientPlan
+from katana.analytics._subgraph_extraction import subgraph_extraction, SubGraphExtractionPlan
 from katana.analytics._k_core import k_core, k_core_assert_valid, KCorePlan, KCoreStatistics
 from katana.analytics._k_truss import k_truss, k_truss_assert_valid, KTrussPlan, KTrussStatistics
 from katana.analytics._pagerank import pagerank, pagerank_assert_valid, PagerankPlan, PagerankStatistics

python/katana/analytics/_local_clustering_coefficient.pyx

@@ -0,0 +1,119 @@
+from katana.cpp.libstd.iostream cimport ostream, ostringstream
+from katana.cpp.libgalois.graphs.Graph cimport _PropertyGraph
+from katana.cpp.libsupport.result cimport handle_result_void, handle_result_assert, raise_error_code, Result
+from katana.analytics.plan cimport Plan, _Plan
+from katana.property_graph cimport PropertyGraph
+
+from libcpp.string cimport string
+from libcpp cimport bool
+
+from enum import Enum
+
+# TODO(amp): Module needs documenting.
+
+
+cdef extern from "katana/analytics/local_clustering_coefficient/local_clustering_coefficient.h" namespace "katana::analytics" nogil:
+    cppclass _LocalClusteringCoefficientPlan "katana::analytics::LocalClusteringCoefficientPlan" (_Plan):
+        enum Algorithm:
+            kOrderedCountAtomics "katana::analytics::LocalClusteringCoefficientPlan::kOrderedCountAtomics"
+            kOrderedCountPerThread "katana::analytics::LocalClusteringCoefficientPlan::kOrderedCountPerThread"
+
+        enum Relabeling:
+            kRelabel "katana::analytics::LocalClusteringCoefficientPlan::kRelabel"
+            kNoRelabel "katana::analytics::LocalClusteringCoefficientPlan::kNoRelabel"
+            kAutoRelabel "katana::analytics::LocalClusteringCoefficientPlan::kAutoRelabel"
+
+        _LocalClusteringCoefficientPlan.Algorithm algorithm() const
+        _LocalClusteringCoefficientPlan.Relabeling relabeling() const
+        bool edges_sorted() const
+
+        # LocalClusteringCoefficientPlan()
+
+        @staticmethod
+        _LocalClusteringCoefficientPlan OrderedCountAtomics(
+                bool edges_sorted,
+                _LocalClusteringCoefficientPlan.Relabeling relabeling
+        )
+        @staticmethod
+        _LocalClusteringCoefficientPlan OrderedCountPerThread(
+                bool edges_sorted,
+                _LocalClusteringCoefficientPlan.Relabeling relabeling
+            )
+
+    _LocalClusteringCoefficientPlan.Relabeling kDefaultRelabeling "katana::analytics::LocalClusteringCoefficientPlan::kDefaultRelabeling"
+    bool kDefaultEdgesSorted "katana::analytics::LocalClusteringCoefficientPlan::kDefaultEdgesSorted"
+
+    Result[void] LocalClusteringCoefficient(_PropertyGraph* pfg, const string& output_property_name, _LocalClusteringCoefficientPlan plan)
+
+
+class _LocalClusteringCoefficientPlanAlgorithm(Enum):
+    OrderedCountAtomics = _LocalClusteringCoefficientPlan.Algorithm.kOrderedCountAtomics
+    OrderedCountPerThread = _LocalClusteringCoefficientPlan.Algorithm.kOrderedCountPerThread
+
+
+cdef _relabeling_to_python(v):
+    if v == _LocalClusteringCoefficientPlan.Relabeling.kRelabel:
+        return True
+    elif v == _LocalClusteringCoefficientPlan.Relabeling.kNoRelabel:
+        return False
+    else:
+        return None
+
+
+cdef _relabeling_from_python(v):
+    if v is None:
+        return _LocalClusteringCoefficientPlan.Relabeling.kAutoRelabel
+    elif v:
+        return _LocalClusteringCoefficientPlan.Relabeling.kRelabel
+    else:
+        return _LocalClusteringCoefficientPlan.Relabeling.kNoRelabel
+
+
+cdef class LocalClusteringCoefficientPlan(Plan):
+    cdef:
+        _LocalClusteringCoefficientPlan underlying_
+
+    cdef _Plan* underlying(self) except NULL:
+        return &self.underlying_
+
+    Algorithm = _LocalClusteringCoefficientPlanAlgorithm
+
+    @staticmethod
+    cdef LocalClusteringCoefficientPlan make(_LocalClusteringCoefficientPlan u):
+        f = <LocalClusteringCoefficientPlan>LocalClusteringCoefficientPlan.__new__(LocalClusteringCoefficientPlan)
+        f.underlying_ = u
+        return f
+
+    @property
+    def algorithm(self) -> Algorithm:
+        return _LocalClusteringCoefficientPlanAlgorithm(self.underlying_.algorithm())
+
+    @property
+    def relabeling(self):
+        return self.underlying_.relabeling()
+
+    @property
+    def edges_sorted(self) -> bool:
+        return self.underlying_.edges_sorted()
+
+    @staticmethod
+    def ordered_count_atomics(
+                relabeling = _relabeling_to_python(kDefaultRelabeling),
+                bool edges_sorted = kDefaultEdgesSorted
+            ):
+        return LocalClusteringCoefficientPlan.make(_LocalClusteringCoefficientPlan.OrderedCountAtomics(
+             edges_sorted, _relabeling_from_python(relabeling)))
+
+    @staticmethod
+    def ordered_count_per_thread(
+                relabeling = _relabeling_to_python(kDefaultRelabeling),
+                bool edges_sorted = kDefaultEdgesSorted
+            ):
+        return LocalClusteringCoefficientPlan.make(_LocalClusteringCoefficientPlan.OrderedCountPerThread(
+             edges_sorted, _relabeling_from_python(relabeling)))
+
+
+def local_clustering_coefficient(PropertyGraph pg, str output_property_name, LocalClusteringCoefficientPlan plan = LocalClusteringCoefficientPlan()):
+    cdef string output_property_name_str = bytes(output_property_name, "utf-8")
+    with nogil:
+        handle_result_void(LocalClusteringCoefficient(pg.underlying.get(), output_property_name_str, plan.underlying_))

python/katana/analytics/_louvain_clustering.pyx

@@ -0,0 +1,188 @@
+from katana.cpp.libstd.iostream cimport ostream, ostringstream
+from katana.cpp.libgalois.graphs.Graph cimport _PropertyGraph
+from katana.cpp.libsupport.result cimport handle_result_void, handle_result_assert, raise_error_code, Result
+from katana.analytics.plan cimport Plan, _Plan
+from katana.property_graph cimport PropertyGraph
+
+from libc.stdint cimport uint32_t, uint64_t
+from libcpp.string cimport string
+from libcpp cimport bool
+
+from enum import Enum
+
+# TODO(amp): Module needs documenting.
+
+
+cdef extern from "katana/analytics/louvain_clustering/louvain_clustering.h" namespace "katana::analytics" nogil:
+    cppclass _LouvainClusteringPlan "katana::analytics::LouvainClusteringPlan" (_Plan):
+        enum Algorithm:
+            kDoAll "katana::analytics::LouvainClusteringPlan::kDoAll"
+
+        _LouvainClusteringPlan.Algorithm algorithm() const
+        bool enable_vf() const
+        double modularity_threshold_per_round() const
+        double modularity_threshold_total() const
+        uint32_t max_iterations() const
+        uint32_t min_graph_size() const
+
+        # LouvainClusteringPlan()
+
+        @staticmethod
+        _LouvainClusteringPlan DoAll(
+                bool enable_vf,
+                double modularity_threshold_per_round,
+                double modularity_threshold_total,
+                uint32_t max_iterations,
+                uint32_t min_graph_size
+            )
+
+    bool kDefaultEnableVF "katana::analytics::LouvainClusteringPlan::kDefaultEnableVF"
+    double kDefaultModularityThresholdPerRound "katana::analytics::LouvainClusteringPlan::kDefaultModularityThresholdPerRound"
+    double kDefaultModularityThresholdTotal "katana::analytics::LouvainClusteringPlan::kDefaultModularityThresholdTotal"
+    uint32_t kDefaultMaxIterations "katana::analytics::LouvainClusteringPlan::kDefaultMaxIterations"
+    uint32_t kDefaultMinGraphSize "katana::analytics::LouvainClusteringPlan::kDefaultMinGraphSize"
+
+    Result[void] LouvainClustering(_PropertyGraph* pfg, const string& edge_weight_property_name,const string& output_property_name, _LouvainClusteringPlan plan)
+
+    Result[void] LouvainClusteringAssertValid(_PropertyGraph* pfg,
+            const string& edge_weight_property_name,
+            const string& output_property_name
+            )
+
+    cppclass _LouvainClusteringStatistics "katana::analytics::LouvainClusteringStatistics":
+        uint64_t n_clusters
+        uint64_t n_non_trivial_clusters
+        uint64_t largest_cluster_size
+        double largest_cluster_proportion
+        double modularity
+
+        void Print(ostream os)
+
+        @staticmethod
+        Result[_LouvainClusteringStatistics] Compute(_PropertyGraph* pfg,
+            const string& edge_weight_property_name,
+            const string& output_property_name
+            )
+
+
+class _LouvainClusteringPlanAlgorithm(Enum):
+    DoAll = _LouvainClusteringPlan.Algorithm.kDoAll
+
+
+cdef class LouvainClusteringPlan(Plan):
+    cdef:
+        _LouvainClusteringPlan underlying_
+
+    cdef _Plan* underlying(self) except NULL:
+        return &self.underlying_
+
+    Algorithm = _LouvainClusteringPlanAlgorithm
+
+    @staticmethod
+    cdef LouvainClusteringPlan make(_LouvainClusteringPlan u):
+        f = <LouvainClusteringPlan>LouvainClusteringPlan.__new__(LouvainClusteringPlan)
+        f.underlying_ = u
+        return f
+
+    @property
+    def algorithm(self) -> Algorithm:
+        return _LouvainClusteringPlanAlgorithm(self.underlying_.algorithm())
+
+    @property
+    def enable_vf(self) -> bool:
+        return self.underlying_.enable_vf()
+
+    @property
+    def modularity_threshold_per_round(self) -> double:
+        return self.underlying_.modularity_threshold_per_round()
+
+    @property
+    def modularity_threshold_total(self) -> double:
+        return self.underlying_.modularity_threshold_total()
+
+    @property
+    def max_iterations(self) -> uint32_t:
+        return self.underlying_.max_iterations()
+
+    @property
+    def min_graph_size(self) -> uint32_t:
+        return self.underlying_.min_graph_size()
+
+
+    @staticmethod
+    def do_all(
+                bool enable_vf = kDefaultEnableVF,
+                double modularity_threshold_per_round = kDefaultModularityThresholdPerRound,
+                double modularity_threshold_total = kDefaultModularityThresholdTotal,
+                uint32_t max_iterations = kDefaultMaxIterations,
+                uint32_t min_graph_size = kDefaultMinGraphSize
+            ) -> LouvainClusteringPlan:
+        return LouvainClusteringPlan.make(_LouvainClusteringPlan.DoAll(
+             enable_vf, modularity_threshold_per_round, modularity_threshold_total, max_iterations, min_graph_size))
+
+
+def louvain_clustering(PropertyGraph pg, str edge_weight_property_name, str output_property_name, LouvainClusteringPlan plan = LouvainClusteringPlan()):
+    cdef string edge_weight_property_name_str = bytes(edge_weight_property_name, "utf-8")
+    cdef string output_property_name_str = bytes(output_property_name, "utf-8")
+    with nogil:
+        handle_result_void(LouvainClustering(pg.underlying.get(), edge_weight_property_name_str, output_property_name_str, plan.underlying_))
+
+
+def louvain_clustering_assert_valid(PropertyGraph pg, str edge_weight_property_name, str output_property_name ):
+    cdef string edge_weight_property_name_str = bytes(edge_weight_property_name, "utf-8")
+    cdef string output_property_name_str = bytes(output_property_name, "utf-8")
+    with nogil:
+        handle_result_assert(LouvainClusteringAssertValid(pg.underlying.get(),
+                edge_weight_property_name_str,
+                output_property_name_str
+                ))
+
+
+cdef _LouvainClusteringStatistics handle_result_LouvainClusteringStatistics(Result[_LouvainClusteringStatistics] res) nogil except *:
+    if not res.has_value():
+        with gil:
+            raise_error_code(res.error())
+    return res.value()
+
+
+cdef class LouvainClusteringStatistics:
+    cdef _LouvainClusteringStatistics underlying
+
+    def __init__(self, PropertyGraph pg,
+            str edge_weight_property_name,
+            str output_property_name
+            ):
+        cdef string edge_weight_property_name_str = bytes(edge_weight_property_name, "utf-8")
+        cdef string output_property_name_str = bytes(output_property_name, "utf-8")
+        with nogil:
+            self.underlying = handle_result_LouvainClusteringStatistics(_LouvainClusteringStatistics.Compute(
+                pg.underlying.get(),
+                edge_weight_property_name_str,
+                output_property_name_str
+                ))
+
+    @property
+    def n_clusters(self) -> uint64_t:
+        return self.underlying.n_clusters
+
+    @property
+    def n_non_trivial_clusters(self) -> uint64_t:
+        return self.underlying.n_non_trivial_clusters
+
+    @property
+    def largest_cluster_size(self) -> uint64_t:
+        return self.underlying.largest_cluster_size
+
+    @property
+    def largest_cluster_proportion(self) -> double:
+        return self.underlying.largest_cluster_proportion
+
+    @property
+    def modularity(self) -> double:
+        return self.underlying.modularity
+
+
+    def __str__(self) -> str:
+        cdef ostringstream ss
+        self.underlying.Print(ss)
+        return str(ss.str(), "ascii")