refactor: separate statistic computation

Snakefile

@@ -2,10 +2,11 @@ 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
 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
@@ -34,7 +35,6 @@ def get_dataset(_datasets, label):
 algorithms = list(algorithm_params)
 algorithms_with_params = [f'{algorithm}-params-{params_hash}' for algorithm, param_combos in algorithm_params.items() for params_hash in param_combos.keys()]
 dataset_labels = list(_config.config.datasets.keys())
-
 dataset_gold_standard_node_pairs = [f"{dataset}-{gs['label']}" for gs in _config.config.gold_standards.values() if gs['node_files'] for dataset in gs['dataset_labels']]
 dataset_gold_standard_edge_pairs = [f"{dataset}-{gs['label']}" for gs in _config.config.gold_standards.values() if gs['edge_files'] for dataset in gs['dataset_labels']]
 
@@ -282,7 +282,7 @@ rule reconstruct:
 # Original pathway reconstruction output to universal output
 # Use PRRunner as a wrapper to call the algorithm-specific parse_output
 rule parse_output:
-    input: 
+    input:
         raw_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'raw-pathway.txt']),
         dataset_file = SEP.join([out_dir, 'dataset-{dataset}-merged.pickle'])
     output: standardized_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'pathway.txt'])
@@ -310,18 +310,33 @@ rule viz_cytoscape:
     run:
         cytoscape.run_cytoscape(input.pathways, output.session, container_settings)
 
+for keys, values in statistics_computation.items():
+    pythonic_name = 'generate_' + '_and_'.join([key.lower().replace(' ', '_') for key in keys])
+    rule:
+        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]
+        run:
+            (Path(input.pathway_file).parent / 'statistics').mkdir(exist_ok=True)
+            graph = from_output_pathway(input.pathway_file)
+            for computed, output in zip(values(graph), output):
+                Path(output).write_text(str(computed))
 
 # 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 options
+        statistics = expand(
+            '{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}statistics{sep}{statistic}.txt',
+            out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params, statistic=statistics_options)
     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, input.statistics)
         summary_df.to_csv(output.summary_table, sep='\t', index=False)
 
 # Cluster the output pathways for each dataset

spras/analysis/summary.py

@@ -1,13 +1,14 @@
+import ast
 from pathlib import Path
-from statistics import median
 from typing import Iterable
 
-import networkx as nx
 import pandas as pd
 
+from spras.statistics import from_output_pathway
+
 
 def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, algo_params: dict[str, dict],
-                       algo_with_params: list) -> pd.DataFrame:
+                       algo_with_params: list[str], statistics_files: list) -> 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
@@ -17,6 +18,7 @@ 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 list of statistic files with the computed statistics.
     @return: pandas DataFrame with summary information
     """
     # Ensure that NODEID is the first column
@@ -39,52 +41,17 @@ 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
-        ]
-
-        if len(avg_path_lengths) != 0:
-            avg_path_len = sum(avg_path_lengths) / len(avg_path_lengths)
-        else:
-            avg_path_len = 0.0
+
+        # We use literal_eval here to easily coerce to either ints or floats, depending.
+        graph_statistics = [ast.literal_eval(Path(file).read_text()) for file in statistics_files]
 
         # 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:
@@ -105,8 +72,10 @@ 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
+    statistics_options = [Path(file).stem for file in statistics_files]
     # 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', *statistics_options]
     col_names.extend(nodes_by_col_labs)
     col_names.append('Parameter combination')
 

spras/config/config.py

@@ -13,7 +13,12 @@
 """
 
 import copy as copy
+import functools
+import hashlib
+import importlib.metadata
 import itertools as it
+import subprocess
+import tomllib
 import warnings
 from pathlib import Path
 from typing import Any
@@ -27,6 +32,59 @@
 
 config = None
 
+@functools.cache
+def spras_revision() -> str:
+    """
+    Gets the revision of the current SPRAS repository. This function is meant to be user-friendly to warn for bad SPRAS installs.
+    1. If this file is inside the correct `.git` repository, we use the revision hash. This is for development in SPRAS as well as SPRAS installs via a cloned git repository.
+    2. If SPRAS was installed via a PyPA-compliant package manager, we use the hash of the RECORD file (https://packaging.python.org/en/latest/specifications/recording-installed-packages/#the-record-file).
+        which contains the hashes of all installed files to the package.
+    """
+    clone_tip = "Make sure SPRAS is installed through the installation instructions: https://spras.readthedocs.io/en/latest/install.html."
+
+    # Check if we're inside the right git repository
+    try:
+        project_directory = subprocess.check_output(
+            ["git", "rev-parse", "--show-toplevel"],
+            encoding='utf-8',
+            # In case the CWD is not inside the actual SPRAS directory
+            cwd=Path(__file__).parent.resolve()
+        ).strip()
+
+        # We check the pyproject.toml name attribute to confirm that this is the SPRAS project. This is susceptible
+        # to false negatives, but we use this as a preliminary check against bad SPRAS installs.
+        pyproject_path = Path(project_directory, 'pyproject.toml')
+        try:
+            pyproject_toml = tomllib.loads(pyproject_path.read_text())
+            if "project" not in pyproject_toml or "name" not in pyproject_toml["project"]:
+                raise RuntimeError(f"The git top-level `{pyproject_path}` does not have the expected attributes. {clone_tip}")
+            if pyproject_toml["project"]["name"] != "spras":
+                raise RuntimeError(f"The git top-level `{pyproject_path}` is not the SPRAS pyproject.toml. {clone_tip}")
+
+            return subprocess.check_output(
+                ["git", "rev-parse", "--short", "HEAD"],
+                encoding='utf-8',
+                cwd=project_directory
+            ).strip()
+        except FileNotFoundError as err:
+            # pyproject.toml wasn't found during the `read_text` call
+            raise RuntimeError(f"The git top-level {pyproject_path} wasn't found. {clone_tip}") from err
+        except tomllib.TOMLDecodeError as err:
+            raise RuntimeError(f"The git top-level {pyproject_path} is malformed. {clone_tip}") from err
+    except subprocess.CalledProcessError:
+        try:
+            # `git` failed: use the truncated hash of the RECORD file in .dist-info instead.
+            record_path = str(importlib.metadata.distribution('spras').locate_file(f"spras-{importlib.metadata.version('spras')}.dist-info/RECORD"))
+            with open(record_path, 'rb', buffering=0) as f:
+                # Truncated to the magic value 8, the length of the short git revision.
+                return hashlib.file_digest(f, 'sha256').hexdigest()[:8]
+        except importlib.metadata.PackageNotFoundError as err:
+            # The metadata.distribution call failed.
+            raise RuntimeError(f"The spras package wasn't found: {clone_tip}") from err
+
+def attach_spras_revision(label: str) -> str:
+    return f"{label}_{spras_revision()}"
+
 # This will get called in the Snakefile, instantiating the singleton with the raw config
 def init_global(config_dict):
     global config
@@ -117,6 +175,12 @@ def process_datasets(self, raw_config: RawConfig):
         # Currently assumes all datasets have a label and the labels are unique
         # When Snakemake parses the config file it loads the datasets as OrderedDicts not dicts
         # Convert to dicts to simplify the yaml logging
+
+        for dataset in raw_config.datasets:
+            dataset.label = attach_spras_revision(dataset.label)
+        for gold_standard in raw_config.gold_standards:
+            gold_standard.label = attach_spras_revision(gold_standard.label)
+
         for dataset in raw_config.datasets:
             label = dataset.label
             if label.lower() in [key.lower() for key in self.datasets.keys()]:
@@ -130,8 +194,11 @@ def process_datasets(self, raw_config: RawConfig):
         dataset_labels = set(self.datasets.keys())
         gold_standard_dataset_labels = {dataset_label for value in self.gold_standards.values() for dataset_label in value['dataset_labels']}
         for label in gold_standard_dataset_labels:
-            if label not in dataset_labels:
+            if attach_spras_revision(label) not in dataset_labels:
                 raise ValueError(f"Dataset label '{label}' provided in gold standards does not exist in the existing dataset labels.")
+        # We attach the SPRAS revision to the individual dataset labels afterwards for a cleaner error message above.
+        for key, gold_standard in self.gold_standards.items():
+            self.gold_standards[key]["dataset_labels"] = map(attach_spras_revision, gold_standard["dataset_labels"])
 
         # Code snipped from Snakefile that may be useful for assigning default labels
         # dataset_labels = [dataset.get('label', f'dataset{index}') for index, dataset in enumerate(datasets)]
@@ -187,7 +254,10 @@ def process_algorithms(self, raw_config: RawConfig):
                             run_dict[param] = float(value)
                         if isinstance(value, np.ndarray):
                             run_dict[param] = value.tolist()
-                    params_hash = hash_params_sha1_base32(run_dict, self.hash_length, cls=NpHashEncoder)
+                    # Incorporates the `spras_revision` into the hash
+                    hash_run_dict = copy.deepcopy(run_dict)
+                    hash_run_dict["_spras_rev"] = spras_revision()
+                    params_hash = hash_params_sha1_base32(hash_run_dict, self.hash_length, cls=NpHashEncoder)
                     if params_hash in prior_params_hashes:
                         raise ValueError(f'Parameter hash collision detected. Increase the hash_length in the config file '
                                         f'(current length {self.hash_length}).')

spras/statistics.py

@@ -0,0 +1,70 @@
+"""
+Graph statistics, used to power summary.py.
+
+We allow for arbitrary computation of any specific statistic on some graph,
+computing more than necessary if we have dependencies. See the top level
+`statistics_computation` dictionary for usage.
+"""
+
+import itertools
+from statistics import median
+from typing import Callable
+
+import networkx as nx
+
+
+def compute_degree(graph: nx.DiGraph) -> tuple[int, float]:
+    """
+    Computes the (max, median) degree of a `graph`.
+    """
+    # number_of_nodes is a cheap call
+    if graph.number_of_nodes() == 0:
+        return (0, 0.0)
+    else:
+        degrees = [deg for _, deg in graph.degree()]
+        return max(degrees), median(degrees)
+
+def compute_on_cc(directed_graph: nx.DiGraph) -> tuple[int, float]:
+    graph: nx.Graph = directed_graph.to_undirected()
+    cc = list(nx.connected_components(graph))
+    # Save the max diameter
+    # Use diameter only for components with ≥2 nodes (singleton components have diameter 0)
+    diameters = [
+        nx.diameter(graph.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(graph.subgraph(c).copy()) if len(c) > 1 else 0.0
+        for c in cc
+    ]
+
+    if len(avg_path_lengths) != 0:
+        avg_path_len = sum(avg_path_lengths) / len(avg_path_lengths)
+    else:
+        avg_path_len = 0.0
+
+    return max_diameter, avg_path_len
+
+# The type signature on here is quite bad. I would like to say that an n-tuple has n-outputs.
+statistics_computation: dict[tuple[str, ...], Callable[[nx.DiGraph], tuple[float | int, ...]]] = {
+    ('Number of nodes',): lambda graph : (graph.number_of_nodes(),),
+    ('Number of edges',): lambda graph : (graph.number_of_edges(),),
+    ('Number of connected components',): lambda graph : (nx.number_connected_components(graph.to_undirected()),),
+    ('Density',): lambda graph : (nx.density(graph),),
+
+    ('Max degree', 'Median degree'): compute_degree,
+    ('Max diameter', 'Average path length'): compute_on_cc,
+}
+
+# All of the keys inside statistics_computation, flattened.
+statistics_options: list[str] = list(itertools.chain(*(list(key) for key in statistics_computation.keys())))
+
+def from_output_pathway(lines) -> nx.Graph:
+    with open(lines, 'r') as f:
+        lines = f.readlines()[1:]
+
+    return nx.read_edgelist(lines, data=(('Rank', int), ('Direction', str)))