From 37031d744ac5c5e7591649baa7ca72ceb3bab36f Mon Sep 17 00:00:00 2001
From: Martin Larralde <martin.larralde@embl.de>
Date: Thu, 4 Aug 2022 00:17:06 +0200
Subject: [PATCH] Add benchmarks for next `pyfastani` release
---
benches/mapping/plot.py | 57 +-
benches/mapping/v0.4.0.json | 6004 +++++++++++++++++++++++++++++++++++
benches/mapping/v0.4.0.svg | 2851 +++++++++++++++++
3 files changed, 8885 insertions(+), 27 deletions(-)
create mode 100644 benches/mapping/v0.4.0.json
create mode 100644 benches/mapping/v0.4.0.svg
diff --git a/benches/mapping/plot.py b/benches/mapping/plot.py
index c8da6f4..dffff89 100644
--- a/benches/mapping/plot.py
+++ b/benches/mapping/plot.py
@@ -2,6 +2,7 @@ import argparse
import itertools
import json
import os
+import random
import re
import math
@@ -10,8 +11,10 @@ import matplotlib.pyplot as plt
import scipy.stats
from scipy.optimize import curve_fit
from numpy.polynomial import Polynomial
-from palettable.cartocolors.qualitative import Bold_9
+from palettable.cartocolors.qualitative import Prism_10
+from mpl_toolkits.mplot3d import Axes3D
+random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", required=True)
@@ -25,12 +28,25 @@ def exp_decay(x, a, b, c):
with open(args.input) as f:
data = json.load(f)
+# fig = plt.figure(1)
+# ax = fig.add_subplot(111, projection='3d')
+#
+# X = [ bench["nucleotides"] / 1e6 for bench in data["results"] ]
+# Y = [ bench["threads"] for bench in data["results"] ]
+# Z = [ bench["mean"] for bench in data["results"] ]
+# c = [ Bold_9.hex_colors[ X.index(bench["nucleotides"] / 1e6) % 9 ] for i, bench in enumerate(data["results"]) ]
+#
+# ax.scatter(X, Y, Z, c=c)
+# ax.set_xlabel("Genome size (Mbp)")
+# ax.set_ylabel("Number of Threads")
+# ax.set_zlabel("Time (s)")
+
plt.figure(1, figsize=(12, 6))
plt.subplot(1, 2, 1)
data["results"].sort(key=lambda r: (r["threads"], r["nucleotides"]))
for color, (threads, group) in zip(
- itertools.cycle(Bold_9.hex_colors),
+ itertools.cycle(Prism_10.hex_colors),
itertools.groupby(data["results"], key=lambda r: r["threads"])
):
group = list(group)
@@ -42,7 +58,7 @@ for color, (threads, group) in zip(
# reg = scipy.stats.linregress(X, Y)
# plt.plot([ 0, max(X) ], [ reg.intercept, reg.slope*max(X) + reg.intercept ], color=color, linestyle="--", marker="")
# ci = [1.96 * r["stddev"] / math.sqrt(len(r["times"])) for r in group]
- plt.scatter(X, Y, marker="+", color=color, label=f"threads={threads}")
+ plt.scatter(X, Y, marker="+", color=color, label=f"Threads={threads}")
# plt.plot(pX, p(pX), color=color, linestyle="--")
plt.legend()
@@ -50,44 +66,31 @@ plt.xlabel("Genome size (Mbp)")
plt.ylabel("Query Time (s)")
plt.subplot(1, 2, 2)
+
+values = list({ r["nucleotides"] for r in data["results"] })
+choices = { random.choice(values) for _ in range(5) }
+data["results"] = [ r for r in data["results"] if r["nucleotides"] in choices ]
+
data["results"].sort(key=lambda r: (r["nucleotides"], r["threads"]))
for color, (nucleotides, group) in zip(
- itertools.cycle(Bold_9.hex_colors),
+ itertools.cycle(Prism_10.hex_colors),
itertools.groupby(data["results"], key=lambda r: r["nucleotides"])
):
group = list(group)
- X = numpy.array([r["threads"] for r in group])
+ X = numpy.array([r["threads"] for r in group if r["mean"]])
Y = numpy.array([r["mean"] for r in group])
popt, pcov = curve_fit(exp_decay, X, Y)
pX = numpy.linspace(1, max(r["threads"] for r in group), 100)
- plt.scatter(X, Y, marker="+", color=color, label=f"{group[0]['genome']} ({nucleotides/1e6:.1f} Mbp)")
- plt.plot(pX, exp_decay(pX, *popt), color=color, linestyle="--")
+ name = ".".join(group[0]['genome'].split(".")[:2])
+ plt.scatter(X, Y, marker="+", color=color, label=f"{name} ({nucleotides/1e6:.1f} Mbp)")
+ plt.plot(pX, exp_decay(pX, *popt), color=color, linestyle="--", linewidth=0.3)
plt.legend()
-plt.xlabel("Threads")
+plt.xlabel("Number of Threads")
plt.ylabel("Query Time (s)")
-# plt.subplot(1, 2, 2)
-# data["results"].sort(key=lambda r: (r["backend"], r["residues"]))
-# for color, (backend, group) in zip(
-# Bold_4.hex_colors, itertools.groupby(data["results"], key=lambda r: r["backend"])
-# ):
-# group = list(group)
-# X = numpy.array([r["residues"] for r in group])
-# Y = numpy.array([r["mean"] for r in group])
-# p = Polynomial.fit(X, Y, 2)
-# # reg = scipy.stats.linregress(X, Y)
-# # plt.plot([ 0, max(X) ], [ reg.intercept, reg.slope*max(X) + reg.intercept ], color=color, linestyle="--", marker="")
-# # ci = [1.96 * r["stddev"] / math.sqrt(len(r["times"])) for r in group]
-# plt.scatter(X, Y, marker="+", color=color, label=f"{backend}")
-# plt.plot(X, p(X), color=color, linestyle="--")
-#
-# plt.legend()
-# plt.xlabel("Number of residues")
-# plt.ylabel("Time (s)")
-
plt.tight_layout()
output = args.output or args.input.replace(".json", ".svg")
plt.savefig(output, transparent=True)
diff --git a/benches/mapping/v0.4.0.json b/benches/mapping/v0.4.0.json
new file mode 100644
index 0000000..83aa674
--- /dev/null
+++ b/benches/mapping/v0.4.0.json
@@ -0,0 +1,6004 @@
+{
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