/
create_website.py
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/
create_website.py
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import matplotlib as mpl
mpl.use("Agg") # noqa
import argparse
import hashlib
import os
from jinja2 import Environment, FileSystemLoader
import plot
from ann_benchmarks import results
from ann_benchmarks.datasets import get_dataset
from ann_benchmarks.plotting.metrics import all_metrics as metrics
from ann_benchmarks.plotting.plot_variants import \
all_plot_variants as plot_variants
from ann_benchmarks.plotting.utils import (compute_all_metrics,
create_linestyles, create_pointset,
get_plot_label)
colors = [
"rgba(166,206,227,1)",
"rgba(31,120,180,1)",
"rgba(178,223,138,1)",
"rgba(51,160,44,1)",
"rgba(251,154,153,1)",
"rgba(227,26,28,1)",
"rgba(253,191,111,1)",
"rgba(255,127,0,1)",
"rgba(202,178,214,1)",
]
point_styles = {
"o": "circle",
"<": "triangle",
"*": "star",
"x": "cross",
"+": "rect",
}
def convert_color(color):
r, g, b, a = color
return "rgba(%(r)d, %(g)d, %(b)d, %(a)d)" % {"r": r * 255, "g": g * 255, "b": b * 255, "a": a}
def convert_linestyle(ls):
new_ls = {}
for algo in ls.keys():
algostyle = ls[algo]
new_ls[algo] = (
convert_color(algostyle[0]),
convert_color(algostyle[1]),
algostyle[2],
point_styles[algostyle[3]],
)
return new_ls
def get_run_desc(properties):
return "%(dataset)s_%(count)d_%(distance)s" % properties
def get_dataset_from_desc(desc):
return desc.split("_")[0]
def get_count_from_desc(desc):
return desc.split("_")[1]
def get_distance_from_desc(desc):
return desc.split("_")[2]
def get_dataset_label(desc):
return "{} (k = {})".format(get_dataset_from_desc(desc), get_count_from_desc(desc))
def directory_path(s):
if not os.path.isdir(s):
raise argparse.ArgumentTypeError("'%s' is not a directory" % s)
return s + "/"
def prepare_data(data, xn, yn):
"""Change format from (algo, instance, dict) to (algo, instance, x, y)."""
res = []
for algo, algo_name, result in data:
res.append((algo, algo_name, result[xn], result[yn]))
return res
parser = argparse.ArgumentParser()
parser.add_argument(
"--plottype",
help="Generate only the plots specified",
nargs="*",
choices=plot_variants.keys(),
default=plot_variants.keys(),
)
parser.add_argument("--outputdir", help="Select output directory", default=".", type=directory_path, action="store")
parser.add_argument("--latex", help="generates latex code for each plot", action="store_true")
parser.add_argument("--scatter", help="create scatterplot for data", action="store_true")
parser.add_argument("--recompute", help="Clears the cache and recomputes the metrics", action="store_true")
args = parser.parse_args()
def get_lines(all_data, xn, yn, render_all_points):
"""For each algorithm run on a dataset, obtain its performance
curve coords."""
plot_data = []
for algo in sorted(all_data.keys(), key=lambda x: x.lower()):
xs, ys, ls, axs, ays, als = create_pointset(prepare_data(all_data[algo], xn, yn), xn, yn)
if render_all_points:
xs, ys, ls = axs, ays, als
plot_data.append({"name": algo, "coords": zip(xs, ys), "labels": ls, "scatter": render_all_points})
return plot_data
def create_plot(all_data, xn, yn, linestyle, j2_env, additional_label="", plottype="line"):
xm, ym = (metrics[xn], metrics[yn])
render_all_points = plottype == "bubble"
plot_data = get_lines(all_data, xn, yn, render_all_points)
latex_code = j2_env.get_template("latex.template").render(
plot_data=plot_data, caption=get_plot_label(xm, ym), xlabel=xm["description"], ylabel=ym["description"]
)
plot_data = get_lines(all_data, xn, yn, render_all_points)
button_label = hashlib.sha224((get_plot_label(xm, ym) + additional_label).encode("utf-8")).hexdigest()
return j2_env.get_template("chartjs.template").render(
args=args,
latex_code=latex_code,
button_label=button_label,
data_points=plot_data,
xlabel=xm["description"],
ylabel=ym["description"],
plottype=plottype,
plot_label=get_plot_label(xm, ym),
label=additional_label,
linestyle=linestyle,
render_all_points=render_all_points,
)
def build_detail_site(data, label_func, j2_env, linestyles, batch=False):
for (name, runs) in data.items():
print("Building '%s'" % name)
runs.keys()
label = label_func(name)
data = {"normal": [], "scatter": []}
for plottype in args.plottype:
xn, yn = plot_variants[plottype]
data["normal"].append(create_plot(runs, xn, yn, convert_linestyle(linestyles), j2_env))
if args.scatter:
data["scatter"].append(
create_plot(runs, xn, yn, convert_linestyle(linestyles), j2_env, "Scatterplot ", "bubble")
)
# create png plot for summary page
data_for_plot = {}
for k in runs.keys():
data_for_plot[k] = prepare_data(runs[k], "k-nn", "qps")
plot.create_plot(
data_for_plot, False, "linear", "log", "k-nn", "qps", args.outputdir + name + ".png", linestyles, batch
)
output_path = args.outputdir + name + ".html"
with open(output_path, "w") as text_file:
text_file.write(
j2_env.get_template("detail_page.html").render(title=label, plot_data=data, args=args, batch=batch)
)
def build_index_site(datasets, algorithms, j2_env, file_name):
dataset_data = {"batch": [], "non-batch": []}
for mode in ["batch", "non-batch"]:
distance_measures = sorted(set([get_distance_from_desc(e) for e in datasets[mode].keys()]))
sorted_datasets = sorted(set([get_dataset_from_desc(e) for e in datasets[mode].keys()]))
for dm in distance_measures:
d = {"name": dm.capitalize(), "entries": []}
for ds in sorted_datasets:
matching_datasets = [
e
for e in datasets[mode].keys()
if get_dataset_from_desc(e) == ds and get_distance_from_desc(e) == dm # noqa
]
sorted_matches = sorted(matching_datasets, key=lambda e: int(get_count_from_desc(e)))
for idd in sorted_matches:
d["entries"].append({"name": idd, "desc": get_dataset_label(idd)})
dataset_data[mode].append(d)
with open(args.outputdir + "index.html", "w") as text_file:
text_file.write(
j2_env.get_template("summary.html").render(
title="ANN-Benchmarks", dataset_with_distances=dataset_data, algorithms=algorithms
)
)
def load_all_results():
"""Read all result files and compute all metrics"""
all_runs_by_dataset = {"batch": {}, "non-batch": {}}
all_runs_by_algorithm = {"batch": {}, "non-batch": {}}
cached_true_dist = []
old_sdn = None
for mode in ["non-batch", "batch"]:
for properties, f in results.load_all_results(batch_mode=(mode == "batch")):
sdn = get_run_desc(properties)
if sdn != old_sdn:
dataset, _ = get_dataset(properties["dataset"])
cached_true_dist = list(dataset["distances"])
old_sdn = sdn
algo_ds = get_dataset_label(sdn)
desc_suffix = "-batch" if mode == "batch" else ""
algo = properties["algo"] + desc_suffix
sdn += desc_suffix
ms = compute_all_metrics(cached_true_dist, f, properties, args.recompute)
all_runs_by_algorithm[mode].setdefault(algo, {}).setdefault(algo_ds, []).append(ms)
all_runs_by_dataset[mode].setdefault(sdn, {}).setdefault(algo, []).append(ms)
return (all_runs_by_dataset, all_runs_by_algorithm)
j2_env = Environment(loader=FileSystemLoader("./templates/"), trim_blocks=True)
j2_env.globals.update(zip=zip, len=len)
runs_by_ds, runs_by_algo = load_all_results()
dataset_names = [get_dataset_label(x) for x in list(runs_by_ds["batch"].keys()) + list(runs_by_ds["non-batch"].keys())]
algorithm_names = list(runs_by_algo["batch"].keys()) + list(runs_by_algo["non-batch"].keys())
linestyles = {**create_linestyles(dataset_names), **create_linestyles(algorithm_names)}
build_detail_site(runs_by_ds["non-batch"], lambda label: get_dataset_label(label), j2_env, linestyles, False)
build_detail_site(runs_by_ds["batch"], lambda label: get_dataset_label(label), j2_env, linestyles, True)
build_detail_site(runs_by_algo["non-batch"], lambda x: x, j2_env, linestyles, False)
build_detail_site(runs_by_algo["batch"], lambda x: x, j2_env, linestyles, True)
build_index_site(runs_by_ds, runs_by_algo, j2_env, "index.html")