import pathlib

import time

import pandas as pd

import matplotlib.pyplot as plt

import matplotlib.ticker as ticker

from deephyper.hpo import CBO

from ackley import hp_problem

NUM_WORKERS = 5

SEARCH_TIMEOUT = 20

RUN_SLEEP = 1

def execute_search(evaluator):

t = time.time()

init_duration = t - evaluator.timestamp

evaluator.timestamp = t

search = CBO(

hp_problem, evaluator, surrogate_model="DUMMY", filter_duplicated=False

)

results = search.search(timeout=SEARCH_TIMEOUT)

results.to_csv("results.csv")

return init_duration

def get_profile_from_hist(hist):

n_processes = 0

profile_dict = dict(t=[0], n_processes=[0])

for e in sorted(hist):

t, incr = e

n_processes += incr

profile_dict["t"].append(t)

profile_dict["n_processes"].append(n_processes)

profile = pd.DataFrame(profile_dict)

return profile

def get_perc_util(profile):

csum = 0

for i in range(len(profile) - 1):

csum += (profile.loc[i + 1, "t"] - profile.loc[i, "t"]) * profile.loc[

i, "n_processes"

]

perc_util = csum / (profile["t"].iloc[-1] * 6)

return perc_util

def plot_profile(ax, profile, ylabel="None", color="blue"):

ax.step(profile["t"], profile["n_processes"], where="post", color=color)

ax.set_ylabel(ylabel)

ax.set_ylim(0, NUM_WORKERS + 1)

ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

ax.grid()

def plot_sum_up(name, init_duration):

pathlib.Path("plots").mkdir(parents=False, exist_ok=True)

results = pd.read_csv("results.csv")

# compute profiles from results.csv

jobs_hist = []

runs_hist = []

for _, row in results.iterrows():

jobs_hist.append((row["timestamp_submit"], 1))

jobs_hist.append((row["timestamp_gather"], -1))

runs_hist.append((row["timestamp_start"], 1))

runs_hist.append((row["timestamp_end"], -1))

jobs_profile = get_profile_from_hist(jobs_hist)

runs_profile = get_profile_from_hist(runs_hist)

# compute average job and run durations

job_avrg_duration = (

results["timestamp_gather"] - results["timestamp_submit"]

).mean()

# compute perc_util

jobs_perc_util = get_perc_util(jobs_profile)

runs_perc_util = get_perc_util(runs_profile)

# compute total number of evaluations

total_num_eval = len(results)

# plot

fig, axs = plt.subplots(2, sharex=True)

fig.suptitle(name, fontsize=17)

plot_profile(axs[0], jobs_profile, ylabel="# jobs submitted", color="blue")

plot_profile(axs[1], runs_profile, ylabel="# jobs running", color="crimson")

fig.text(0.1, -0.1, f"init_duration: {init_duration:.2f}s.", fontsize=12)

fig.text(0.1, -0.2, f"job_avrg_duration: {job_avrg_duration:.2f}s.", fontsize=12)

fig.text(0.1, -0.3, f"total_num_eval: {total_num_eval}", fontsize=12)

fig.text(

0.6,

-0.1,

f"jobs_perc_util: {100*jobs_perc_util:.1f}%",

fontsize=12,

color="blue",

)

fig.text(

0.6,

-0.2,

f"runs_perc_util: {100*runs_perc_util:.1f}%",

fontsize=12,

color="crimson",

)

fig.tight_layout()

plt.savefig(f"plots/{name}.jpg", bbox_inches="tight")

def evaluate_and_plot(evaluator, name):

init_duration = execute_search(evaluator)

plot_sum_up(name, init_duration)