feat: heuristics

Snakefile

@@ -2,11 +2,12 @@ import os
 from spras import runner
 import shutil
 import yaml
-from spras.dataset import Dataset
-from spras.evaluation import Evaluation
 from spras.analysis import ml, summary, cytoscape
 from spras.config.revision import detach_spras_revision
 import spras.config.config as _config
+from spras.dataset import Dataset
+from spras.evaluation import Evaluation
+from spras.statistics import from_output_pathway, statistics_computation, statistics_options
 
 # Snakemake updated the behavior in the 6.5.0 release https://github.com/snakemake/snakemake/pull/1037
 # and using the wrong separator prevents Snakemake from matching filenames to the rules that can produce them
@@ -292,6 +293,9 @@ rule parse_output:
         params = reconstruction_params(wildcards.algorithm, wildcards.params).copy()
         params['dataset'] = input.dataset_file
         runner.parse_output(detach_spras_revision(_config.config.immutable_files, wildcards.algorithm), input.raw_file, output.standardized_file, params)
+        # TODO: cache heuristics result, store partial heuristics configuration file
+        # to allow this rule to update when heuristics change
+        _config.config.heuristics.validate_graph_from_file(output.standardized_file)
 
 # TODO: reuse in the future once we make summary work for mixed graphs. See https://github.com/Reed-CompBio/spras/issues/128
 # Collect summary statistics for a single pathway
@@ -312,18 +316,48 @@ rule viz_cytoscape:
     run:
         cytoscape.run_cytoscape(input.pathways, output.session, container_settings)
 
+# We generate new Snakemake rules for every statistic
+# to allow parallel and lazy computation of individual statistics
+for keys in statistics_computation.keys():
+    pythonic_name = 'generate_' + '_and_'.join([key.lower().replace(' ', '_') for key in keys])
+    rule:
+        # (See https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#procedural-rule-definition)
+        name: pythonic_name
+        input: pathway_file = rules.parse_output.output.standardized_file
+        output: [SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'statistics', f'{key}.txt']) for key in keys]
+        # It is very tempting to use `.items()` instead of `.keys()` above, but
+        # We instead need to pass keys in via parameters, else the job would use the latest values in the statistics_computation.
+        # More info is in the procedural rule link ab
+        params: statistics_names=keys
+        run:
+            (Path(input.pathway_file).parent / 'statistics').mkdir(exist_ok=True)
+            graph = from_output_pathway(input.pathway_file)
+            for computed, output in zip(statistics_computation[params.statistics_names](graph), output):
+                Path(output).write_text(str(computed))
+
+# We isolate this to a separate input function, as we want to preserve the dictionary structure
+def summary_files(wildcards):
+    return {
+        algorithm_param: expand(
+            '{out_dir}{sep}{dataset}-{algorithm_param}{sep}statistics{sep}{statistic}.txt',
+            out_dir=out_dir, sep=SEP, algorithm_param=algorithm_param, statistic=statistics_options,
+            dataset=wildcards.dataset
+        ) for algorithm_param in algorithms_with_params
+    }
 
 # Write a single summary table for all pathways for each dataset
 rule summary_table:
     input:
         # Collect all pathways generated for the dataset
         pathways = expand('{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}pathway.txt', out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params),
-        dataset_file = SEP.join([out_dir, 'dataset-{dataset}-merged.pickle'])
+        dataset_file = SEP.join([out_dir, 'dataset-{dataset}-merged.pickle']),
+        # Collect all possible statistics from the `summary_files` dictionary-based input function
+        statistics = lambda wildcards: flatten(list(summary_files(wildcards).values()))
     output: summary_table = SEP.join([out_dir, '{dataset}-pathway-summary.txt'])
     run:
         # Load the node table from the pickled dataset file
         node_table = Dataset.from_file(input.dataset_file).node_table
-        summary_df = summary.summarize_networks(input.pathways, node_table, algorithm_params, algorithms_with_params)
+        summary_df = summary.summarize_networks(input.pathways, node_table, algorithm_params, algorithms_with_params, summary_files(wildcards))
         summary_df.to_csv(output.summary_table, sep='\t', index=False)
 
 # Cluster the output pathways for each dataset

spras/analysis/summary.py

@@ -1,14 +1,17 @@
+import ast
+import itertools
 import json
+import os
 from pathlib import Path
-from statistics import median
-from typing import Iterable
+from typing import Iterable, Mapping
 
-import networkx as nx
 import pandas as pd
 
+from spras.statistics import from_output_pathway, statistics_options
+
 
 def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, algo_params: dict[str, dict],
-                       algo_with_params: list[str]) -> pd.DataFrame:
+                       algo_with_params: list[str], statistics_files: Mapping[str, Iterable[str | os.PathLike]]) -> pd.DataFrame:
     """
     Generate a table that aggregates summary information about networks in file_paths, including which nodes are present
     in node_table columns. Network directionality is ignored and all edges are treated as undirected. The order of the
@@ -18,6 +21,7 @@ def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, alg
     @param algo_params: a nested dict mapping algorithm names to dicts that map parameter hashes to parameter
     combinations.
     @param algo_with_params: a list of <algorithm>-params-<params_hash> combinations
+    @param statistics_files: a dictionary from algo_with_params to lists of statistic files with the computed statistics.
     @return: pandas DataFrame with summary information
     """
     # Ensure that NODEID is the first column
@@ -40,52 +44,22 @@ def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, alg
 
     # Iterate through each network file path
     for index, file_path in enumerate(sorted(file_paths)):
-        with open(file_path, 'r') as f:
-            lines = f.readlines()[1:]  # skip the header line
-
         # directed or mixed graphs are parsed and summarized as an undirected graph
-        nw = nx.read_edgelist(lines, data=(('weight', float), ('Direction', str)))
+        nw = from_output_pathway(file_path)
 
         # Save the network name, number of nodes, number edges, and number of connected components
         nw_name = str(file_path)
-        number_nodes = nw.number_of_nodes()
-        number_edges = nw.number_of_edges()
-        ncc = nx.number_connected_components(nw)
-
-        # Save the max/median degree, average clustering coefficient, and density
-        if number_nodes == 0:
-            max_degree = 0
-            median_degree = 0.0
-            density = 0.0
-        else:
-            degrees = [deg for _, deg in nw.degree()]
-            max_degree = max(degrees)
-            median_degree = median(degrees)
-            density = nx.density(nw)
-
-        cc = list(nx.connected_components(nw))
-        # Save the max diameter
-        # Use diameter only for components with ≥2 nodes (singleton components have diameter 0)
-        diameters = [
-            nx.diameter(nw.subgraph(c).copy()) if len(c) > 1 else 0
-            for c in cc
-        ]
-        max_diameter = max(diameters, default=0)
 
-        # Save the average path lengths
-        # Compute average shortest path length only for components with ≥2 nodes (undefined for singletons, set to 0.0)
-        avg_path_lengths = [
-            nx.average_shortest_path_length(nw.subgraph(c).copy()) if len(c) > 1 else 0.0
-            for c in cc
+        # We use ast.literal_eval here to convert statistic file outputs to ints or floats depending on their string representation.
+        # (e.g. "5.0" -> float(5.0), while "5" -> int(5).)
+        graph_statistics = [
+            ast.literal_eval(Path(file).read_text()) for file in
+            # along with sorting to keep the output stable (this happens again)
+            sorted(statistics_files[algo_with_params[index]], key=lambda x: statistics_options.index(Path(x).stem))
         ]
 
-        if len(avg_path_lengths) != 0:
-            avg_path_len = sum(avg_path_lengths) / len(avg_path_lengths)
-        else:
-            avg_path_len = 0.0
-
         # Initialize list to store current network information
-        cur_nw_info = [nw_name, number_nodes, number_edges, ncc, density, max_degree, median_degree, max_diameter, avg_path_len]
+        cur_nw_info = [nw_name, *graph_statistics]
 
         # Iterate through each node property and save the intersection with the current network
         for node_list in nodes_by_col:
@@ -107,8 +81,13 @@ def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, alg
         # Save the current network information to the network summary list
         nw_info.append(cur_nw_info)
 
+    # Get the list of statistic names by their file names (via finding all requested statistics in the provided files)
+    current_statistics_options = sorted(
+        set(Path(file).stem for file in itertools.chain(*statistics_files.values())),
+        key=lambda x: statistics_options.index(x)
+    )
     # Prepare column names
-    col_names = ['Name', 'Number of nodes', 'Number of edges', 'Number of connected components', 'Density', 'Max degree', 'Median degree', 'Max diameter', 'Average path length']
+    col_names = ['Name', *current_statistics_options]
     col_names.extend(nodes_by_col_labs)
     col_names.append('Parameter combination')
 
@@ -120,67 +99,3 @@ def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, alg
     )
 
     return nw_info
-
-
-def degree(g):
-    return dict(g.degree)
-
-# TODO: redo .run code to work on mixed graphs
-# stats is just a list of functions to apply to the graph.
-# They should take as input a networkx graph or digraph but may have any output.
-# stats = [degree, nx.clustering, nx.betweenness_centrality]
-
-
-# def produce_statistics(g: nx.Graph, s=None) -> dict:
-#     global stats
-#     if s is not None:
-#         stats = s
-#     d = dict()
-#     for s in stats:
-#         sname = s.__name__
-#         d[sname] = s(g)
-#     return d
-
-
-# def load_graph(path: str) -> nx.Graph:
-#     g = nx.read_edgelist(path, data=(('weight', float), ('Direction',str)))
-#     return g
-
-
-# def save(data, pth):
-#     fout = open(pth, 'w')
-#     fout.write('#node\t%s\n' % '\t'.join([s.__name__ for s in stats]))
-#     for node in data[stats[0].__name__]:
-#         row = [data[s.__name__][node] for s in stats]
-#         fout.write('%s\t%s\n' % (node, '\t'.join([str(d) for d in row])))
-#     fout.close()
-
-
-# def run(infile: str, outfile: str) -> None:
-#     """
-#     run function that wraps above functions.
-#     """
-#     # if output directory doesn't exist, make it.
-#     outdir = os.path.dirname(outfile)
-#     if not os.path.exists(outdir):
-#         os.makedirs(outdir)
-
-#     # load graph, produce stats, and write to human-readable file.
-#     g = load_graph(infile)
-#     dat = produce_statistics(g)
-#     save(dat, outfile)
-
-
-# def main(argv):
-#     """
-#     for testing
-#     """
-#     g = load_graph(argv[1])
-#     print(g.nodes)
-#     dat = produce_statistics(g)
-#     print(dat)
-#     save(dat, argv[2])
-
-
-# if __name__ == '__main__':
-#     main(sys.argv)

spras/config/config.py

@@ -61,6 +61,8 @@ def __init__(self, raw_config: dict[str, Any]):
         self.hash_length = parsed_raw_config.hash_length
         # Container settings used by PRMs.
         self.container_settings = ProcessedContainerSettings.from_container_settings(parsed_raw_config.containers, self.hash_length)
+        # The heuristic handler
+        self.heuristics = parsed_raw_config.heuristics
         # A nested dict mapping algorithm names to dicts that map parameter hashes to parameter combinations.
         # Only includes algorithms that are set to be run with 'include: true'.
         self.algorithm_params: dict[str, dict[str, Any]] = dict()

spras/config/heuristics.py

@@ -0,0 +1,105 @@
+import os
+
+import networkx as nx
+from pydantic import BaseModel, ConfigDict
+
+from spras.interval import Interval
+from spras.statistics import compute_statistics, statistics_options
+
+all = ['GraphHeuristicsError', 'GraphHeuristic']
+
+class GraphHeuristicsError(RuntimeError):
+    """
+    Represents an error arising from a graph algorithm output
+    not meeting the necessary graph heuristisc.
+    """
+    failed_heuristics: list[tuple[str, float | int, list[Interval]]]
+
+    @staticmethod
+    def format_failed_heuristic(heuristic: tuple[str, float | int, list[Interval]]) -> str:
+        name, desired, intervals = heuristic
+        if len(intervals) == 1:
+            interval_string = str(intervals[0])
+        else:
+            formatted_intervals = ", ".join([str(interval) for interval in intervals])
+            interval_string = f"one of the intervals ({formatted_intervals})"
+        return f"{name} expected {desired} in interval {interval_string}"
+    @staticmethod
+    def to_string(failed_heuristics: list[tuple[str, float | int, list[Interval]]]):
+        formatted_heuristics = [
+            GraphHeuristicsError.format_failed_heuristic(heuristic) for heuristic in failed_heuristics
+        ]
+
+        formatted_heuristics = "\n".join([f"- {formatted_heuristics}" for heuristic in formatted_heuristics])
+        return f"The following heuristics failed:\n{formatted_heuristics}"
+
+    def __init__(self, failed_heuristics: list[tuple[str, float | int, list[Interval]]]):
+        super().__init__(GraphHeuristicsError.to_string(failed_heuristics))
+
+        self.failed_heuristics = failed_heuristics
+
+    def __str__(self) -> str:
+        return GraphHeuristicsError.to_string(self.failed_heuristics)
+
+class GraphHeuristics(BaseModel):
+    number_of_nodes: Interval | list[Interval] = []
+    number_of_edges: Interval | list[Interval] = []
+    number_of_connected_components: Interval | list[Interval] = []
+    density: Interval | list[Interval] = []
+
+    max_degree: Interval | list[Interval] = []
+    median_degree: Interval | list[Interval] = []
+    max_diameter: Interval | list[Interval] = []
+    average_path_length: Interval | list[Interval] = []
+
+    def validate_graph(self, graph: nx.DiGraph):
+        statistics_dictionary = {
+            'Number of nodes': self.number_of_nodes,
+            'Number of edges': self.number_of_edges,
+            'Number of connected components': self.number_of_connected_components,
+            'Density': self.density,
+            'Max degree': self.max_degree,
+            'Median degree': self.median_degree,
+            'Max diameter': self.max_diameter,
+            'Average path length': self.average_path_length
+        }
+
+        # quick assert: is statistics_dictionary exhaustive?
+        assert set(statistics_dictionary.keys()) == set(statistics_options)
+
+        stats = compute_statistics(
+            graph,
+            list(k for k, v in statistics_dictionary.items() if not isinstance(v, list) or len(v) != 0)
+        )
+
+        failed_heuristics: list[tuple[str, float | int, list[Interval]]] = []
+        for key, value in stats.items():
+            intervals = statistics_dictionary[key]
+            if not isinstance(intervals, list): intervals = [intervals]
+
+            for interval in intervals:
+                if not interval.mem(value):
+                    failed_heuristics.append((key, value, intervals))
+                    break
+
+        if len(failed_heuristics) != 0:
+            raise GraphHeuristicsError(failed_heuristics)
+
+    model_config = ConfigDict(extra='forbid')
+
+    def validate_graph_from_file(self, path: str | os.PathLike):
+        """
+        Takes in a graph produced by PRM#parse_output,
+        and throws a GraphHeuristicsError if it fails the heuristics in `self`.
+        """
+        # TODO: re-use from summary.py once we have a mixed/hypergraph library
+        G: nx.DiGraph = nx.read_edgelist(path, data=(('Rank', str), ('Direction', str)), create_using=nx.DiGraph)
+
+        # We explicitly use `list` here to stop add_edge
+        # from expanding our iterator infinitely.
+        for source, target, data in list(G.edges(data=True)):
+            if data["Direction"] == 'U':
+                G.add_edge(target, source, data=data)
+            pass
+
+        return self.validate_graph(G)

spras/config/schema.py

@@ -17,6 +17,7 @@
 from spras.config.algorithms import AlgorithmUnion
 from spras.config.container_schema import ContainerSettings
 from spras.config.dataset import DatasetSchema
+from spras.config.heuristics import GraphHeuristics
 from spras.config.util import CaseInsensitiveEnum, label_validator
 
 # Most options here have an `include` property,
@@ -122,6 +123,8 @@ class RawConfig(BaseModel):
 
     reconstruction_settings: ReconstructionSettings
 
+    heuristics: GraphHeuristics = GraphHeuristics()
+
     # We include use_attribute_docstrings here to preserve the docstrings
     # after attributes at runtime (for future JSON schema generation)
     model_config = ConfigDict(extra='forbid', use_attribute_docstrings=True)