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create_website.py
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create_website.py
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import matplotlib as mpl
mpl.use('Agg')
import argparse
import os, json, pickle, yaml
import numpy
import hashlib
from jinja2 import Environment, FileSystemLoader
from ann_benchmarks import results
from ann_benchmarks.algorithms.definitions import get_algorithm_name
from ann_benchmarks.datasets import get_dataset
from ann_benchmarks.plotting.plot_variants import all_plot_variants as plot_variants
from ann_benchmarks.plotting.metrics import all_metrics as metrics
from ann_benchmarks.plotting.utils import get_plot_label, compute_metrics, compute_all_metrics, create_pointset, create_linestyles
import plot
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 get_dataset_from_desc(desc) + " (k = " + 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)
all_runs = 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,
False, True, 'k-nn', 'qps', args.outputdir + get_algorithm_name(name, batch) + ".png",
linestyles, batch)
with open(args.outputdir + get_algorithm_name(name, batch) + ".html", "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]
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, label_func=get_algorithm_name))
def load_all_results():
import copy
"""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 properties, f in results.load_all_results():
sdn = get_run_desc(properties)
if sdn != old_sdn:
dataset = get_dataset(properties["dataset"])
cached_true_dist = numpy.array(dataset["distances"])
old_sdn = sdn
algo = properties["algo"]
ms = compute_all_metrics(cached_true_dist, f, properties, args.recompute)
algo_ds = get_dataset_label(sdn)
idx = "non-batch"
if properties["batch_mode"]:
idx = "batch"
all_runs_by_algorithm[idx].setdefault(algo, {}).setdefault(algo_ds, []).append(ms)
all_runs_by_dataset[idx].setdefault(sdn, {}).setdefault(algo, []).append(ms)
if algo == 'bruteforce-blas':
ms1 = copy.deepcopy(ms)
ms1[2]['k-nn'] = 0.000001
ms1[2]['qps'] = ms[2]['qps'] + 0.000001
all_runs_by_dataset[idx].setdefault(sdn, {}).setdefault(algo, []).append(ms1)
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")