diff --git a/CHANGELOG.md b/CHANGELOG.md index d5db7cd..e2d6f21 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Expose merge threshold in python bindings. This makes it possible to merge communities when they share many edges together after Leiden. +- Add NetworkX community detection bindings. This makes it possible to use pixelator-core + with NetworkX graphs. ### Changed diff --git a/packages/python_bindings/README.md b/packages/python_bindings/README.md index 7353029..4c40dd0 100644 --- a/packages/python_bindings/README.md +++ b/packages/python_bindings/README.md @@ -1,4 +1,4 @@ -# pixelator_core_py +# `pixelator_core` Python Bindings [![Python versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https://github.com/PixelgenTechnologies/pixelator-core/actions/workflows/wheels.yml) @@ -13,7 +13,8 @@ statistics and community detection algorithms for fast execution from Python. - Leiden - Hybrid FLP + Leiden flow (`run_hybrid_community_detection`) -> NB: for the sake of memory efficiency, all these bindings read and write the data from and to parquet files. +> NB: There are two main types of APIs provided by this package: parquet-backed APIs for workflows where +> memory-efficiency is a high priority and dedicated NetworkX APIs for in-memory graphs. ## Requirements @@ -44,13 +45,10 @@ maturin build --release --out dist/ pip install dist/*.whl ``` -## Quickstart +## Quick Start ```python from pixelator_core import ( - find_graph_statistics, - run_label_propagation, - run_leiden, run_hybrid_community_detection, ) @@ -80,9 +78,46 @@ print("Filtered edge list written to:", output_file) print("Pre recovery nodes:", pre_recovery_stats.node_count) ``` +### NetworkX usage + +NetworkX APIs are provided for ease of use, for scenarios when the lowest possible +memory usage is not a priority. + +```python +import networkx as nx +from pixelator_core import run_label_propagation_networkx, run_leiden_networkx + +G = nx.Graph() +G.add_edge("cell_a", "cell_b", weight=2) +G.add_edge("cell_b", "cell_c") +G.add_node("isolated_cell") + +flp_communities = run_label_propagation_networkx(G, epochs=2) +leiden_communities = run_leiden_networkx( + G, + resolution=1.0, + randomness=0.1, + seed=42, +) + +# Both return: +# A list of disjoint sets (partition of G). Each set represents one community. +# All communities together contain all the nodes in G. +print(flp_communities) +print(leiden_communities) +``` + +Weight constraints for NetworkX APIs: +- `run_leiden_networkx` accepts non-negative integer-like weights and rejects fractional weights (for example `0.5`). +- `run_label_propagation_networkx` uses the same non-negative integer-like requirement and also requires weights to fit in `u8` (`0..=255`). + ## Input and Output -- Input is expected to be an edge-list Parquet file compatible with `pixelator-core`. +- `find_graph_statistics`, `run_label_propagation`, `run_leiden`, and `run_hybrid_community_detection` + expect an edge-list Parquet file compatible with `pixelator-core`. +- `run_label_propagation_networkx` and `run_leiden_networkx` accept a `networkx.Graph` object + and return communities in-memory as a list of disjoint sets over original node labels. +- NetworkX edge weights for Leiden/FLP must be non-negative integer-like values; FLP additionally enforces the `u8` range (`0..=255`). - `run_label_propagation` and `run_leiden` produce node-partition Parquet outputs. - `run_hybrid_community_detection` produces a filtered edge-list Parquet output. - Output paths are optional; defaults are used when omitted. diff --git a/packages/python_bindings/pyproject.toml b/packages/python_bindings/pyproject.toml index 61ffdba..b6da9df 100644 --- a/packages/python_bindings/pyproject.toml +++ b/packages/python_bindings/pyproject.toml @@ -7,7 +7,7 @@ readme = "README.md" dependencies = [] [project.optional-dependencies] -dev = ["pytest>=8,<9", "pyarrow>=14", "maturin>=1,<2"] +dev = ["pytest>=8,<9", "pyarrow>=14", "maturin>=1,<2", "networkx>=3,<4"] [tool.maturin] module-name = "pixelator_core" @@ -15,3 +15,8 @@ module-name = "pixelator_core" [build-system] requires = ["maturin>=1.0,<2.0"] build-backend = "maturin" + +[dependency-groups] +dev = [ + "ipykernel>=7.2.0", +] diff --git a/packages/python_bindings/src/lib.rs b/packages/python_bindings/src/lib.rs index 09298fd..7b9476f 100644 --- a/packages/python_bindings/src/lib.rs +++ b/packages/python_bindings/src/lib.rs @@ -3,11 +3,13 @@ use pixelator_core::common::io::{ write_node_partitions_to_parquet, }; use pyo3::prelude::*; +use pyo3::types::{PyDict, PySet, PyTuple}; -use pixelator_core::common::graph::GraphProperties; +use pixelator_core::common::graph::{Graph, GraphProperties}; use pixelator_core::common::node_partitioning::{ FastNodePartitioning, LeidenNodePartitioning, NodePartitioning, }; +use pixelator_core::common::types::Edge; use pixelator_core::fast_label_propagation::algorithm::fast_label_propagation; use pixelator_core::fast_label_propagation::strategies::{ AssignmentStrategy, DefaultAssignmentStrategy, @@ -29,30 +31,17 @@ use mimalloc::MiMalloc; static GLOBAL: MiMalloc = MiMalloc; #[pymodule(name = "pixelator_core")] -mod pixelator_core_py { - use pyo3::prelude::*; - - #[pymodule_export] - use super::find_graph_statistics; - - #[pymodule_export] - use super::run_label_propagation; - - #[pymodule_export] - use super::run_leiden; - - #[pymodule_export] - use super::run_hybrid_community_detection; - - #[pymodule_export] - use super::PyGraphProperties; - - #[pymodule_init] - fn init(m: &Bound<'_, PyModule>) -> PyResult<()> { - pyo3_log::init(); - m.add("__version__", env!("CARGO_PKG_VERSION"))?; - Ok(()) - } +fn pixelator_core_py(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { + pyo3_log::init(); + m.add_function(wrap_pyfunction!(find_graph_statistics, m)?)?; + m.add_function(wrap_pyfunction!(run_label_propagation, m)?)?; + m.add_function(wrap_pyfunction!(run_label_propagation_networkx, m)?)?; + m.add_function(wrap_pyfunction!(run_leiden, m)?)?; + m.add_function(wrap_pyfunction!(run_leiden_networkx, m)?)?; + m.add_function(wrap_pyfunction!(run_hybrid_community_detection, m)?)?; + m.add_class::()?; + m.add("__version__", env!("CARGO_PKG_VERSION"))?; + Ok(()) } /// @@ -224,17 +213,279 @@ pub fn run_hybrid_community_detection( pub fn run_label_propagation(parquet_file: &str, epochs: u64, output: &str) -> PyResult { let (umi_mapping, graph) = create_graph_and_umi_mapping_from_parquet_file::(parquet_file); + let node_partition = run_flp_core(&graph, epochs); + + write_node_partitions_to_parquet(output, &node_partition, &umi_mapping, None) + .map_err(|e| PyErr::new::(format!("{:?}", e)))?; + + Ok(node_partition.num_partitions()) +} + +fn validate_networkx_graph(graph: &Bound<'_, PyAny>) -> PyResult<()> { + if !graph.hasattr("nodes")? || !graph.hasattr("edges")? { + return Err(PyErr::new::( + "Expected a NetworkX graph object with `nodes` and `edges` methods", + )); + } + Ok(()) +} + +/// Resolve a Python node label to its internal contiguous node index. +/// +/// # Errors +/// Returns `TypeError` if the node label is missing from the precomputed node-index map. +fn extract_node_index( + node_to_index: &Bound<'_, PyDict>, + node: &Bound<'_, PyAny>, +) -> PyResult { + node_to_index + .get_item(node)? + .ok_or_else(|| { + PyErr::new::( + "Encountered edge with node not present in graph.nodes()", + ) + })? + .extract::() +} + +/// Parse and validate a NetworkX edge weight as a non-negative integer-like value. +/// +/// Accepts: +/// - Python integers `>= 0` +/// - Finite floats with zero fractional part (for example `2.0`) +/// +/// # Errors +/// Returns `TypeError` for negative, fractional, non-finite, or non-numeric values. +fn extract_non_negative_integer_like_weight(weight: &Bound<'_, PyAny>) -> PyResult { + if let Ok(value) = weight.extract::() { + return Ok(value); + } + + if let Ok(value) = weight.extract::() { + if value.is_finite() && value >= 0.0 && value.fract() == 0.0 { + return Ok(value as usize); + } + } + + Err(PyErr::new::( + "Expected NetworkX edge weights to be non-negative integer-like values", + )) +} + +type NetworkxNodeLabels = Vec>; +type NetworkxWeightedEdge = (usize, usize, usize); +type NetworkxWeightedEdges = Vec; +type NetworkxWeightedGraph = (NetworkxNodeLabels, NetworkxWeightedEdges); + +/// Convert a NetworkX graph-like Python object to indexed node labels and weighted edges. +/// +/// The returned tuple contains: +/// - `node_labels`: `index -> original Python node label` +/// - `weighted_edges`: `(src_index, dst_index, weight)` using validated integer-like weights +/// +/// Edge weights are read via `edges(data="weight", default=1)`. +/// +/// # Errors +/// Returns `TypeError` when the graph shape is invalid, edges are malformed, node labels are +/// inconsistent, or weights fail validation. +fn networkx_to_weighted_edges(graph: &Bound<'_, PyAny>) -> PyResult { + validate_networkx_graph(graph)?; + let py = graph.py(); + + let node_to_index = PyDict::new(py); + let mut node_labels: Vec> = Vec::new(); + for (idx, node_item) in graph.call_method0("nodes")?.try_iter()?.enumerate() { + let node = node_item?; + node_to_index.set_item(&node, idx)?; + node_labels.push(node.unbind()); + } + + let edge_kwargs = PyDict::new(py); + edge_kwargs.set_item("data", "weight")?; + edge_kwargs.set_item("default", 1)?; + + let mut weighted_edges: Vec<(usize, usize, usize)> = Vec::new(); + for edge_item in graph + .call_method("edges", (), Some(&edge_kwargs))? + .try_iter()? + { + let edge_any = edge_item?; + let edge_tuple = edge_any.downcast::().map_err(|_| { + PyErr::new::( + "Expected NetworkX edges(data='weight') to yield (u, v, weight) tuples", + ) + })?; + if edge_tuple.len() != 3 { + return Err(PyErr::new::( + "Expected NetworkX edges(data='weight') to yield 3-tuples", + )); + } + + let source = edge_tuple.get_item(0)?; + let destination = edge_tuple.get_item(1)?; + let weight = edge_tuple.get_item(2)?; + let source_idx = extract_node_index(&node_to_index, &source)?; + let destination_idx = extract_node_index(&node_to_index, &destination)?; + let edge_weight = extract_non_negative_integer_like_weight(&weight)?; + weighted_edges.push((source_idx, destination_idx, edge_weight)); + } + + Ok((node_labels, weighted_edges)) +} + +/// Convert an internal node partition into Python communities preserving original labels. +/// +/// Communities are returned as a list of Python sets. The list order is stabilized by the +/// minimum node index in each community to improve deterministic behavior. +fn partition_to_python_communities( + py: Python<'_>, + node_partition: &P, + node_labels: &[Py], +) -> PyResult>> { + let mut communities: Vec<(usize, Py)> = Vec::new(); + for nodes in node_partition.get_partition_to_node_map().into_values() { + let mut members: Vec> = Vec::with_capacity(nodes.len()); + let mut min_index = usize::MAX; + for node_index in nodes { + min_index = min_index.min(node_index); + members.push(node_labels[node_index].clone_ref(py)); + } + let community = PySet::new(py, members)?; + communities.push((min_index, community.into_any().unbind())); + } + communities.sort_by_key(|(min_index, _)| *min_index); + Ok(communities + .into_iter() + .map(|(_, community)| community) + .collect()) +} + +/// Build merge-threshold options from mutually exclusive absolute/relative parameters. +/// +/// # Errors +/// Returns `ValueError` when both threshold parameters are provided simultaneously. +fn get_merge_threshold( + merge_edge_threshold: Option, + merge_edge_threshold_relative: Option, +) -> PyResult> { + match (merge_edge_threshold, merge_edge_threshold_relative) { + (Some(_), Some(_)) => Err(PyErr::new::( + "`merge_edge_threshold` and `merge_edge_threshold_relative` cannot both be used at the same time", + )), + (Some(value), None) => Ok(Some(ThresholdOptions::Absolute(value))), + (None, Some(value)) => Ok(Some(ThresholdOptions::Relative(value))), + (None, None) => Ok(None), + } +} + +/// Run Fast Label Propagation on an already constructed unweighted/`u8` graph. +fn run_flp_core(graph: &Graph, epochs: u64) -> FastNodePartitioning { let node_partition = FastNodePartitioning::initialize_with_singlet_partitions(graph.get_num_nodes()); let assignment_strategy: &dyn AssignmentStrategy<_> = &DefaultAssignmentStrategy; + fast_label_propagation(graph, epochs, assignment_strategy, node_partition) +} - let node_partition = - fast_label_propagation(&graph, epochs, assignment_strategy, node_partition); +/// Build the initial Leiden partition from optional labels or singlet defaults. +/// +/// When `mismatch_err` is set, validates the provided partition length against `node_count`. +/// +/// # Errors +/// Returns `ValueError` if partition length validation fails. +fn build_leiden_partition( + partition: Option>, + node_count: usize, + mismatch_err: Option<&'static str>, +) -> PyResult { + if let Some(node_partition_map) = partition { + if let Some(mismatch_err) = mismatch_err { + if node_partition_map.len() != node_count { + return Err(PyErr::new::( + mismatch_err, + )); + } + } + Ok(LeidenNodePartitioning::initialize_from_partitions( + node_partition_map, + )) + } else { + Ok(LeidenNodePartitioning::initialize_with_singlet_partitions( + node_count, + )) + } +} - write_node_partitions_to_parquet(output, &node_partition, &umi_mapping, None) - .map_err(|e| PyErr::new::(format!("{:?}", e)))?; +/// Execute the shared Leiden core pipeline on an indexed graph. +/// +/// This helper is reused by both parquet-backed and NetworkX-backed public Leiden APIs. +/// It initializes the partition, runs Leiden, and returns `(partition, quality)`. +/// +/// # Errors +/// Propagates partition validation errors from `build_leiden_partition`. +#[allow(clippy::too_many_arguments)] +fn run_leiden_core( + graph: Graph, + resolution: f64, + max_iteration: Option, + partition: Option>, + randomness: f64, + seed: Option, + merge_threshold: Option, + partition_mismatch_err: Option<&'static str>, +) -> PyResult<(FastNodePartitioning, f64)> { + let partition = + build_leiden_partition(partition, graph.get_num_nodes(), partition_mismatch_err)?; + let quality_function = Modularity::new(resolution, graph.get_total_edge_weight()); + let mut wp_graph = + WeightedPartitionedGraph::new(graph, partition, quality_function, None, seed); + leiden(&mut wp_graph, randomness, max_iteration, merge_threshold); - Ok(node_partition.num_partitions()) + let node_partition = + FastNodePartitioning::initialize_from_partitions(wp_graph.get_ancestor_to_partition_map()); + Ok((node_partition, wp_graph.quality())) +} + +/// Finds community partitioning using Fast Label Propagation (FLP) on a NetworkX graph. +/// +/// # Arguments +/// * `graph` - A NetworkX graph object. +/// * `epochs` - Number of times FLP is run. Default is once. +/// +/// NetworkX edge weights are read from the `weight` attribute (default `1` when absent). +/// Weights must be non-negative integer-like values and must fit in `u8` (`0..=255`). +/// +/// # Returns +/// A list of disjoint sets representing communities of the graph. +/// +/// # Errors +/// Returns: +/// * `TypeError` when the input is not NetworkX-like or when an edge weight is not +/// non-negative integer-like. +/// * `ValueError` when an edge weight exceeds the `u8` range. +#[pyfunction(signature = (graph, epochs=1))] +pub fn run_label_propagation_networkx( + py: Python<'_>, + graph: Py, + epochs: u64, +) -> PyResult>> { + let graph = graph.bind(py); + let (node_labels, weighted_edges) = networkx_to_weighted_edges(graph)?; + let edges = weighted_edges + .into_iter() + .map(|(src, dest, weight)| { + let converted_weight = u8::try_from(weight).map_err(|_| { + PyErr::new::( + "NetworkX edge weight exceeds FLP u8 weight range", + ) + })?; + Ok(Edge::new(src, dest, Some(converted_weight))) + }) + .collect::>>>()?; + let rust_graph = Graph::::from_edges(edges.into_iter(), node_labels.len()); + + let node_partition = run_flp_core(&rust_graph, epochs); + + partition_to_python_communities(py, &node_partition, &node_labels) } /// Finds community partitioning using the Leiden algorithm @@ -287,36 +538,90 @@ pub fn run_leiden( merge_edge_threshold: Option, merge_edge_threshold_relative: Option, ) -> PyResult<(usize, f64)> { - let merge_threshold = match (merge_edge_threshold, merge_edge_threshold_relative) { - (Some(_), Some(_)) => { - return Err(PyErr::new::( - "`merge_edge_threshold` and `merge_edge_threshold_relative` cannot both be used at the same time", - )); - } - (Some(v), None) => Some(ThresholdOptions::Absolute(v)), - (None, Some(v)) => Some(ThresholdOptions::Relative(v)), - (None, None) => None, - }; + let merge_threshold = get_merge_threshold(merge_edge_threshold, merge_edge_threshold_relative)?; let (umi_mapping, graph) = create_graph_and_umi_mapping_from_parquet_file::(parquet_file); - let partition = if let Some(node_partition_map) = partition { - LeidenNodePartitioning::initialize_from_partitions(node_partition_map) - } else { - LeidenNodePartitioning::initialize_with_singlet_partitions(graph.get_num_nodes()) - }; - let quality_function = Modularity::new(resolution, graph.get_total_edge_weight()); - - let mut wp_graph = - WeightedPartitionedGraph::new(graph, partition, quality_function, None, seed); - - leiden(&mut wp_graph, randomness, max_iteration, merge_threshold); - - let node_partition = - FastNodePartitioning::initialize_from_partitions(wp_graph.get_ancestor_to_partition_map()); + let (node_partition, quality) = run_leiden_core( + graph, + resolution, + max_iteration, + partition, + randomness, + seed, + merge_threshold, + None, + )?; write_node_partitions_to_parquet(output, &node_partition, &umi_mapping, None) .map_err(|e| PyErr::new::(format!("{:?}", e)))?; - Ok((node_partition.num_partitions(), wp_graph.quality())) + Ok((node_partition.num_partitions(), quality)) +} + +/// Finds community partitioning using the Leiden algorithm on a NetworkX graph. +/// +/// # Arguments +/// * `graph` - A NetworkX graph object. +/// * `max_iteration` - Maximum number of Leiden iterations to perform. +/// * `partition` - Optional initial node partition labels aligned with graph node iteration order. +/// * `resolution` - Resolution parameter used in the modularity quality function. +/// * `randomness` - Randomness of node transitions in Leiden. +/// * `seed` - Seed for the random generator. +/// * `merge_edge_threshold` - Absolute merge threshold. +/// * `merge_edge_threshold_relative` - Relative merge threshold. +/// +/// NetworkX edge weights are read from the `weight` attribute (default `1` when absent). +/// Weights must be non-negative integer-like values. Fractional values are rejected. +/// +/// # Returns +/// A list of disjoint sets representing communities of the graph. +/// +/// # Errors +/// Returns: +/// * `TypeError` when the input is not NetworkX-like or when an edge weight is not +/// non-negative integer-like. +/// * `ValueError` when both merge thresholds are set or when the provided partition length +/// does not match the number of graph nodes. +#[pyfunction(signature = ( + graph, + resolution, + max_iteration=None, + partition=None, + randomness=0.1, + seed=None, + merge_edge_threshold=None, + merge_edge_threshold_relative=None +))] +#[allow(clippy::too_many_arguments)] +pub fn run_leiden_networkx( + py: Python<'_>, + graph: Py, + resolution: f64, + max_iteration: Option, + partition: Option>, + randomness: f64, + seed: Option, + merge_edge_threshold: Option, + merge_edge_threshold_relative: Option, +) -> PyResult>> { + let graph = graph.bind(py); + let merge_threshold = get_merge_threshold(merge_edge_threshold, merge_edge_threshold_relative)?; + let (node_labels, weighted_edges) = networkx_to_weighted_edges(graph)?; + let edges = weighted_edges + .into_iter() + .map(|(src, dest, weight)| Edge::new(src, dest, Some(weight))); + let rust_graph = Graph::::from_edges(edges, node_labels.len()); + + let (node_partition, _) = run_leiden_core( + rust_graph, + resolution, + max_iteration, + partition, + randomness, + seed, + merge_threshold, + Some("Length of `partition` must match the number of nodes in the NetworkX graph"), + )?; + partition_to_python_communities(py, &node_partition, &node_labels) } diff --git a/packages/python_bindings/tests/test_pixelator_core.py b/packages/python_bindings/tests/test_pixelator_core.py index 212374a..346ef54 100644 --- a/packages/python_bindings/tests/test_pixelator_core.py +++ b/packages/python_bindings/tests/test_pixelator_core.py @@ -4,6 +4,7 @@ from pathlib import Path +import networkx as nx import pyarrow.parquet as pq import pytest @@ -14,6 +15,20 @@ SMALL_PARQUET = _TEST_DATA / "mix_40cells_1pc_1000rows.parquet" +def _assert_partition_of_nodes(communities: object, nodes: set[object]) -> None: + assert isinstance(communities, list) + assert all(isinstance(community, set) for community in communities) + assert all(community for community in communities) + + flattened: set[object] = set().union(*communities) if communities else set() + assert flattened == nodes + + visited: set[object] = set() + for community in communities: + assert visited.isdisjoint(community) + visited.update(community) + + @pytest.fixture(scope="module") def small_parquet_path() -> str: assert SMALL_PARQUET.is_file(), f"missing test fixture: {SMALL_PARQUET}" @@ -24,7 +39,9 @@ def test_public_api() -> None: for name in ( "find_graph_statistics", "run_label_propagation", + "run_label_propagation_networkx", "run_leiden", + "run_leiden_networkx", "run_hybrid_community_detection", "PyGraphProperties", "__version__", @@ -156,3 +173,153 @@ def test_run_hybrid_community_detection(small_parquet_path: str, tmp_path: Path) assert stats.edge_weight_sum > 0 assert stats.n_connected_components >= 1 assert 0.0 < stats.fraction_in_largest_component <= 1.0 + + +def test_run_leiden_networkx_partition_shape_and_labels() -> None: + graph = nx.Graph() + graph.add_edge("A", "B", weight=2) + graph.add_edge("B", "C") + graph.add_edge(("tuple", 1), "isolated-link") + graph.add_node("isolated") + + communities = pixelator_core.run_leiden_networkx( + graph, + resolution=1.0, + randomness=0.1, + seed=42, + max_iteration=None, + partition=None, + merge_edge_threshold=None, + merge_edge_threshold_relative=None, + ) + + _assert_partition_of_nodes(communities, set(graph.nodes())) + + +def test_run_leiden_networkx_deterministic_with_seed() -> None: + graph = nx.Graph() + graph.add_edges_from([(0, 1), (1, 2), (2, 0), (3, 4)]) + + first = pixelator_core.run_leiden_networkx( + graph, + resolution=1.0, + randomness=0.1, + seed=123, + max_iteration=None, + partition=None, + merge_edge_threshold=None, + merge_edge_threshold_relative=None, + ) + second = pixelator_core.run_leiden_networkx( + graph, + resolution=1.0, + randomness=0.1, + seed=123, + max_iteration=None, + partition=None, + merge_edge_threshold=None, + merge_edge_threshold_relative=None, + ) + assert {frozenset(s) for s in first} == {frozenset(s) for s in second} + + +def test_run_label_propagation_networkx_partition_shape() -> None: + graph = nx.Graph() + graph.add_edge("x", "y") + graph.add_edge("y", "z", weight=3) + graph.add_node("w") + + communities = pixelator_core.run_label_propagation_networkx(graph, epochs=2) + + _assert_partition_of_nodes(communities, set(graph.nodes())) + + +def test_networkx_api_rejects_non_graph_objects() -> None: + with pytest.raises(TypeError, match="NetworkX"): + pixelator_core.run_leiden_networkx( + object(), + resolution=1.0, + randomness=0.1, + seed=1, + max_iteration=None, + partition=None, + merge_edge_threshold=None, + merge_edge_threshold_relative=None, + ) + + with pytest.raises(TypeError, match="NetworkX"): + pixelator_core.run_label_propagation_networkx(object(), epochs=1) + + +def test_run_label_propagation_networkx_empty_graph() -> None: + graph = nx.Graph() + communities = pixelator_core.run_label_propagation_networkx(graph, epochs=1) + assert communities == [] + + +def test_run_leiden_networkx_with_both_merge_thresholds_raises() -> None: + graph = nx.Graph() + graph.add_edge(0, 1) + with pytest.raises( + ValueError, + match="`merge_edge_threshold` and `merge_edge_threshold_relative` cannot both be used at the same time", + ): + pixelator_core.run_leiden_networkx( + graph, + resolution=1.0, + randomness=0.1, + seed=1, + max_iteration=None, + partition=None, + merge_edge_threshold=2, + merge_edge_threshold_relative=0.2, + ) + + +def test_run_leiden_networkx_partition_length_mismatch_raises() -> None: + graph = nx.Graph() + graph.add_edges_from([(0, 1), (1, 2)]) + with pytest.raises( + ValueError, + match="Length of `partition` must match the number of nodes in the NetworkX graph", + ): + pixelator_core.run_leiden_networkx( + graph, + resolution=1.0, + randomness=0.1, + seed=1, + max_iteration=None, + partition=[0, 1], + merge_edge_threshold=None, + merge_edge_threshold_relative=None, + ) + + +def test_run_leiden_networkx_rejects_fractional_weights() -> None: + graph = nx.Graph() + graph.add_edge("a", "b", weight=0.5) + with pytest.raises(TypeError, match="non-negative integer-like"): + pixelator_core.run_leiden_networkx( + graph, + resolution=1.0, + randomness=0.1, + seed=1, + max_iteration=None, + partition=None, + merge_edge_threshold=None, + merge_edge_threshold_relative=None, + ) + + +def test_run_label_propagation_networkx_rejects_fractional_weights() -> None: + graph = nx.Graph() + graph.add_edge("a", "b", weight=0.5) + with pytest.raises(TypeError, match="non-negative integer-like"): + pixelator_core.run_label_propagation_networkx(graph, epochs=1) + + +def 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