diff --git a/Snakefile b/Snakefile index cf075b0f..9673b80b 100644 --- a/Snakefile +++ b/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 @@ -310,18 +311,33 @@ 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 diff --git a/spras/analysis/summary.py b/spras/analysis/summary.py index 2092200f..e5c0b1f7 100644 --- a/spras/analysis/summary.py +++ b/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, 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 -params- 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') diff --git a/spras/statistics.py b/spras/statistics.py new file mode 100644 index 00000000..342f1a5e --- /dev/null +++ b/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))) diff --git a/test/analysis/test_summary.py b/test/analysis/test_summary.py index 57f1f601..8618f0a2 100644 --- a/test/analysis/test_summary.py +++ b/test/analysis/test_summary.py @@ -12,9 +12,9 @@ # - 'NODEID' is required as the first column label in the node table # - file_paths must be an iterable, even if a single file path is passed -INPUT_DIR = 'test/analysis/input/' -OUT_DIR = 'test/analysis/output/' -EXPECT_DIR = 'test/analysis/expected_output/' +INPUT_DIR = Path('test', 'analysis', 'input') +OUT_DIR = Path('test', 'analysis', 'output') +EXPECT_DIR = Path('test', 'analysis', 'expected_output') class TestSummary: @@ -35,14 +35,14 @@ def test_example_networks(self): } example_dataset = Dataset(example_dict) example_node_table = example_dataset.node_table - config.init_from_file(INPUT_DIR + "config.yaml") + config.init_from_file(INPUT_DIR / "config.yaml") algorithm_params = config.config.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()] - example_network_files = Path(INPUT_DIR + "example").glob("*.txt") # must be path to use .glob() + example_network_files = Path(INPUT_DIR, "example").glob("*.txt") - out_path = Path(OUT_DIR + "test_example_summary.txt") + out_path = Path(OUT_DIR, "test_example_summary.txt") out_path.unlink(missing_ok=True) summarize_example = summarize_networks(example_network_files, example_node_table, algorithm_params, algorithms_with_params) @@ -51,7 +51,7 @@ def test_example_networks(self): # Comparing the dataframes directly with equals does not match because of how the parameter # combinations column is loaded from disk. Therefore, write both to disk and compare the files. - assert filecmp.cmp(out_path, EXPECT_DIR + "expected_example_summary.txt", shallow=False) + assert filecmp.cmp(out_path, EXPECT_DIR / "expected_example_summary.txt", shallow=False) def test_egfr_networks(self): """Test data from EGFR workflow""" @@ -64,14 +64,14 @@ def test_egfr_networks(self): egfr_dataset = Dataset(egfr_dict) egfr_node_table = egfr_dataset.node_table - config.init_from_file(INPUT_DIR + "egfr.yaml") + config.init_from_file(INPUT_DIR / "egfr.yaml") algorithm_params = config.config.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()] - egfr_network_files = Path(INPUT_DIR + "egfr").glob("*.txt") # must be path to use .glob() + egfr_network_files = Path(INPUT_DIR, "egfr").glob("*.txt") # must be path to use .glob() - out_path = Path(OUT_DIR + "test_egfr_summary.txt") + out_path = Path(OUT_DIR, "test_egfr_summary.txt") out_path.unlink(missing_ok=True) summarize_egfr = summarize_networks(egfr_network_files, egfr_node_table, algorithm_params, algorithms_with_params) @@ -80,7 +80,7 @@ def test_egfr_networks(self): # Comparing the dataframes directly with equals does not match because of how the parameter # combinations column is loaded from disk. Therefore, write both to disk and compare the files. - assert filecmp.cmp(out_path, EXPECT_DIR + "expected_egfr_summary.txt", shallow=False) + assert filecmp.cmp(out_path, EXPECT_DIR / "expected_egfr_summary.txt", shallow=False) def test_load_dataset_dict(self): """Test loading files from dataset_dict""" @@ -95,7 +95,7 @@ def test_load_dataset_dict(self): # node_table contents are not generated consistently in the same order, # so we will check that the contents are the same, but row order doesn't matter - expected_node_table = pd.read_csv((EXPECT_DIR + "expected_node_table.txt"), sep="\t") + expected_node_table = pd.read_csv((EXPECT_DIR / "expected_node_table.txt"), sep="\t") # ignore 'NODEID' column because this changes each time upon new generation cols_to_compare = [col for col in example_node_table.columns if col != "NODEID"]