Add script to run benchmarks and script to download data (#39)

Example usage:
```
$ python scripts/bench.py -b graphblas -f pagerank -d amazon0302
```

environment.yml

@@ -46,6 +46,10 @@ dependencies:
     - pre-commit
     # For testing
     - pytest-cov
+    # For benchmarking
+    - requests
     # For debugging
     - icecream
     - ipython
+    # For type annotations
+    - mypy

scripts/bench.py

@@ -0,0 +1,246 @@
+#!/usr/bin/env python
+import argparse
+import json
+import os
+import statistics
+import sys
+import timeit
+
+import download_data
+import graphblas as gb
+import networkx as nx
+import numpy as np
+import scipy.sparse
+
+import graphblas_algorithms as ga
+import scipy_impl
+from graphblas_algorithms.interface import Dispatcher as ga_dispatcher
+
+thisdir = os.path.dirname(__file__)
+datapaths = [
+    os.path.join(thisdir, "..", "data"),
+    os.path.curdir,
+]
+
+
+def find_data(dataname):
+    if os.path.exists(dataname):
+        return os.path.relpath(dataname)
+    for path in datapaths:
+        path = os.path.join(path, dataname) + ".mtx"
+        if os.path.exists(path):
+            return os.path.relpath(path)
+        path = path.removesuffix(".mtx")
+        if os.path.exists(path):
+            return os.path.relpath(path)
+    if dataname in download_data.data_urls:
+        return os.path.relpath(download_data.main([dataname])[0])
+    raise FileNotFoundError(f"Unable to find data file for {dataname}")
+
+
+def get_symmetry(file_or_mminfo):
+    if not isinstance(file_or_mminfo, tuple):
+        mminfo = scipy.io.mminfo(file_or_mminfo)
+    else:
+        mminfo = file_or_mminfo
+    return mminfo[5]
+
+
+def readfile(filename, is_symmetric, backend):
+    name = filename.split(".", 1)[0].rsplit("/", 1)[0]
+    if backend == "graphblas":
+        A = gb.io.mmread(filename, name=name)
+        A.wait()
+        if is_symmetric:
+            return ga.Graph(A)
+        return ga.DiGraph(A)
+    a = scipy.io.mmread(filename)
+    if backend == "networkx":
+        create_using = nx.Graph if is_symmetric else nx.DiGraph
+        return nx.from_scipy_sparse_array(a, create_using=create_using)
+    if backend == "scipy":
+        return scipy.sparse.csr_array(a)
+    raise ValueError(
+        f"Backend {backend!r} not understood; must be 'graphblas', 'networkx', or 'scipy'"
+    )
+
+
+def best_units(num):
+    """Returns scale factor and prefix such that 1 <= num*scale < 1000"""
+    if num < 1e-12:
+        return 1e15, "f"
+    if num < 1e-9:
+        return 1e12, "p"
+    if num < 1e-6:
+        return 1e9, "n"
+    if num < 1e-3:
+        return 1e6, "\N{MICRO SIGN}"
+    if num < 1:
+        return 1e3, "m"
+    if num < 1e3:
+        return 1.0, ""
+    if num < 1e6:
+        return 1e-3, "k"
+    if num < 1e9:
+        return 1e-6, "M"
+    if num < 1e12:
+        return 1e-9, "G"
+    return 1e-12, "T"
+
+
+def stime(time):
+    scale, units = best_units(time)
+    return f"{time * scale:4.3g} {units}s"
+
+
+# Functions that aren't available in the main networkx namespace
+functionpaths = {
+    "inter_community_edges": "community.quality.inter_community_edges",
+    "intra_community_edges": "community.quality.intra_community_edges",
+    "is_tournament": "tournament.is_tournament",
+    "mutual_weight": "structuralholes.mutual_weight",
+    "score_sequence": "tournament.score_sequence",
+    "tournament_matrix": "tournament.tournament_matrix",
+}
+functioncall = {
+    "s_metric": "func(G, normalized=False)",
+}
+poweriteration = {"eigenvector_centrality", "katz_centrality", "pagerank"}
+directed_only = {
+    "in_degree_centrality",
+    "is_tournament",
+    "out_degree_centrality",
+    "score_sequence",
+    "tournament_matrix",
+    "reciprocity",
+    "overall_reciprocity",
+}
+# Is square_clustering undirected only? graphblas-algorthms doesn't implement it for directed
+undirected_only = {"generalized_degree", "k_truss", "triangles", "square_clustering"}
+
+
+def getfunction(functionname, backend):
+    if backend == "graphblas":
+        return getattr(ga_dispatcher, functionname)
+    if backend == "scipy":
+        return getattr(scipy_impl, functionname)
+    if functionname in functionpaths:
+        func = nx
+        for attr in functionpaths[functionname].split("."):
+            func = getattr(func, attr)
+        return func
+    return getattr(nx, functionname)
+
+
+def main(dataname, backend, functionname, time=3.0, n=None, extra=None, display=True):
+    filename = find_data(dataname)
+    is_symmetric = get_symmetry(filename) == "symmetric"
+    if not is_symmetric and functionname in undirected_only:
+        # Should we automatically symmetrize?
+        raise ValueError(
+            f"Data {dataname!r} is not symmetric, but {functionname} only works on undirected"
+        )
+    if is_symmetric and functionname in directed_only:
+        is_symmetric = False  # Make into directed graph
+    G = readfile(filename, is_symmetric, backend)
+    func = getfunction(functionname, backend)
+    benchstring = functioncall.get(functionname, "func(G)")
+    if extra is not None:
+        benchstring = f"{benchstring[:-1]}, {extra})"
+    globals = {"func": func, "G": G}
+    if functionname in poweriteration:
+        benchstring = f"try:\n    {benchstring}\nexcept exc:\n    pass"
+        globals["exc"] = nx.PowerIterationFailedConvergence
+    if backend == "graphblas":
+        benchstring = f"G._cache.clear()\n{benchstring}"
+    timer = timeit.Timer(benchstring, globals=globals)
+    if display:
+        line = f"Backend = {backend}, function = {functionname}, data = {dataname}"
+        if extra is not None:
+            line += f", extra = {extra}"
+        print("=" * len(line))
+        print(line)
+        print("-" * len(line))
+    info = {"backend": backend, "function": functionname, "data": dataname}
+    if extra is not None:
+        info["extra"] = extra
+    try:
+        first_time = timer.timeit(1)
+    except Exception as exc:
+        if display:
+            print(f"EXCEPTION: {exc}")
+            print("=" * len(line))
+            raise
+        info["exception"] = str(exc)
+        return info
+    if time == 0:
+        n = 1
+    elif n is None:
+        n = 2 ** max(0, int(np.ceil(np.log2(time / first_time))))
+    if display:
+        print("Number of runs:", n)
+        print("first: ", stime(first_time))
+    info["n"] = n
+    info["first"] = first_time
+    if n > 1:
+        results = timer.repeat(n - 1, 1)
+        results.append(first_time)
+        if display:
+            print("median:", stime(statistics.median(results)))
+            print("mean:  ", stime(statistics.mean(results)))
+            print("stdev: ", stime(statistics.stdev(results)))
+            print("min:   ", stime(min(results)))
+            print("max:   ", stime(max(results)))
+        info["median"] = statistics.median(results)
+        info["mean"] = statistics.mean(results)
+        info["stdev"] = statistics.stdev(results)
+        info["min"] = min(results)
+        info["max"] = max(results)
+    if display:
+        print("=" * len(line))
+    return info
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=f"Example usage: python {sys.argv[0]} -b graphblas -f pagerank -d amazon0302"
+    )
+    parser.add_argument(
+        "-b", "--backend", choices=["graphblas", "networkx", "scipy"], default="graphblas"
+    )
+    parser.add_argument(
+        "-t", "--time", type=float, default=3.0, help="Target minimum time to run benchmarks"
+    )
+    parser.add_argument(
+        "-n",
+        type=int,
+        help="The number of times to run the benchmark (the default is to run according to time)",
+    )
+    parser.add_argument(
+        "-d",
+        "--data",
+        required=True,
+        help="The path to a mtx file or one of the following data names: {"
+        + ", ".join(sorted(download_data.data_urls))
+        + "}; data will be downloaded if necessary",
+    )
+    parser.add_argument(
+        "-j",
+        "--json",
+        action="store_true",
+        help="Print results as json instead of human-readable text",
+    )
+    parser.add_argument("-f", "--func", required=True, help="Which function to benchmark")
+    parser.add_argument("--extra", help="Extra string to add to the function call")
+    args = parser.parse_args()
+    info = main(
+        args.data,
+        args.backend,
+        args.func,
+        time=args.time,
+        n=args.n,
+        extra=args.extra,
+        display=not args.json,
+    )
+    if args.json:
+        print(json.dumps(info))