"""

Quick Plot

----------

A tool to have quick and simple visualization from your data.

It can be use such as:

.. code-block:: console

$ deephyper-analytics quickplot nas_big_data/combo/exp_sc21/combo_1gpu_8_age/infos/results.csv

$ deephyper-analytics quickplot save/history/*.json --xy time val_r2

$ deephyper-analytics quickplot save/history/*.json --xy epochs val_r2

"""

import json

from datetime import datetime

import matplotlib

import matplotlib.pyplot as plt

import pandas as pd

from deephyper.core.exceptions import DeephyperRuntimeError

width = 8

height = width / 1.618

fontsize = 18

matplotlib.rcParams.update(

{

"font.size": fontsize,

"figure.figsize": (width, height),

"figure.facecolor": "white",

"savefig.dpi": 72,

"figure.subplot.bottom": 0.125,

"figure.edgecolor": "white",

"xtick.labelsize": fontsize,

"ytick.labelsize": fontsize,

}

)

def add_subparser(subparsers):

subparser_name = "quickplot"

function_to_call = main

parser = subparsers.add_parser(

subparser_name, help="Tool to generate a quick 2D plot from file."

)

# best search_spaces

parser.add_argument("path", nargs="+", type=str)

parser.add_argument(

"--xy",

metavar="xy",

type=str,

nargs=2,

default=[],

help="name of x y variables in the CSV file.",

)

return subparser_name, function_to_call

def plot_for_single_csv(path: str, xy: list):

"""Generate a plot from a single CSV file.

:meta private:

Args:

path (str): Path to the CSV file.

xy (list): If empty ``list`` then it will use ``"elapsed_sec"`` for x-axis and ``"objective"`` for the y-axis.

Raises:

DeephyperRuntimeError: if only 1 or more than 2 arguments are provided.

"""

if len(xy) == 0:

xy = ["elapsed_sec", "objective"]

elif len(xy) != 2:

raise DeephyperRuntimeError(

"--xy must take two arguments such as '--xy elapsed_sec objective'"

)

df = pd.read_csv(path)

plt.figure()

plt.scatter(df[xy[0]], df[xy[1]], s=5, alpha=1.0)

plt.xlabel(xy[0])

plt.ylabel(xy[1])

plt.grid()

plt.tight_layout()

plt.show()

def plot_for_single_json(path: str, xy: list):

"""[summary]

:meta private:

Args:

path (str): [description]

xy (list): [description]

Raises:

DeephyperRuntimeError: [description]

"""

if len(xy) == 0:

xy = ["epochs", "val_loss"]

elif len(xy) != 2:

raise DeephyperRuntimeError(

"--xy must take two arguments such as '--xy epochs val_loss'"

)

xlabel, ylabel = xy

with open(path, "r") as f:

history = json.load(f)

x = list(range(len(history[ylabel]))) if xlabel == "epochs" else history[xlabel]

y = history[ylabel]

plt.figure()

plt.plot(x, y)

plt.xlabel(xlabel)

plt.ylabel(ylabel)

plt.grid()

plt.tight_layout()

plt.show()

def plot_multiple_training(path: list, ylabel: str):

"""[summary]

:meta private:

Args:

path (list): [description]

ylabel (str): [description]

"""

for p in path:

with open(p, "r") as f:

history = json.load(f)

x = list(range(len(history[ylabel])))

y = history[ylabel]

plt.plot(x, y)

plt.xlabel("Epochs")

def plot_multiple_objective_wrp_time(path: list, ylabel: str):

"""[summary]

:meta private:

Args:

path (list): [description]

ylabel (str): [description]

"""

times = []

objectives = []

for p in path:

with open(p, "r") as f:

history = json.load(f)

time = "_".join(p[:-5].split("_")[-2:])

time = datetime.strptime(time, "%d-%b-%Y_%H-%M-%S").timestamp()

times.append(time)

objective = max(history[ylabel])

objectives.append(objective)

plt.scatter(times, objectives)

plt.xlabel("Time")

def plot_for_multiple_json(path: list, xy: list):

"""

:meta private:

"""

if len(xy) == 0:

xy = ["epochs", "val_loss"]

elif len(xy) != 2:

raise DeephyperRuntimeError(

"--xy must take two arguments such as '--xy epochs val_loss'"

)

xlabel, ylabel = xy

plt.figure()

if xlabel == "epochs":

plot_multiple_training(path, ylabel)

elif xlabel == "time":

plot_multiple_objective_wrp_time(path, ylabel)

plt.ylabel(ylabel)

plt.grid()

plt.tight_layout()

plt.show()

def main(path: list, xy: list, *args, **kwargs):

"""

:meta private:

"""

def extension(path):

return path.split(".")[-1]

if len(path) == 1:

if extension(path[0]) == "csv":

plot_for_single_csv(path[0], xy)

elif extension(path[0]) == "json":

plot_for_single_json(path[0], xy)

else:

raise DeephyperRuntimeError(

f"Extension of input file '{extension(path[0])}' is not yet supported."

)

else:

# Comparing multiple results.csv files (different search experiments)

if all([extension(p) == "csv" for p in path]):

raise DeephyperRuntimeError(

"Comparison of multiple experiments is not yet supported."

)

# Comparing multiple history.json files (different neural networks)

elif all([extension(p) == "json" for p in path]):

plot_for_multiple_json(path, xy)

else:

raise DeephyperRuntimeError(

"Multiple input files should all have the same extension '.csv' or '.json'"

)