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Commit 60138094 authored by Tamino Huxohl's avatar Tamino Huxohl
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add util script of random search eval

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import json
import os
from typing import Any, Callable, Dict, List, Union, Tuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mu_map.random_search.cgan import load_params
SIZE_DEFAULT = 12
plt.rc("font", family="Roboto") # controls default font
plt.rc("font", weight="normal") # controls default font
plt.rc("font", size=SIZE_DEFAULT) # controls default text sizes
plt.rc("axes", titlesize=18) # fontsize of the axes title
class ColorList:
def __init__(self, *colors):
self.colors = colors
def __len__(self):
return len(self.colors)
def __getitem__(self, i):
return self.colors[i % len(self)]
color_lists = {
"default": ColorList(
"#1f78b4",
"#33a02c",
"#e31a1c",
"#ff7f00",
"#cab2d6",
"#a6cee3",
"#b2df8a",
"#fb9a99",
"#fdbf6f",
"#6a3d9a",
),
"printer_friendly": ColorList(
"#1b9e77",
"#d95f02",
"#7570b3",
"#e7298a",
"#66a61e",
"#e6ab02",
)
}
# https://colorbrewer2.org/#type=qualitative&scheme=Dark2&n=5
short_color_list = ColorList(
"#1b9e77",
"#d95f02",
"#7570b3",
"#e7298a",
"#66a61e",
)
# https://colorbrewer2.org/#type=qualitative&scheme=Set3&n=10
COLORS = [
"#8dd3c7",
"#fb8072",
"#80b1d3",
"#fdb462",
"#b3de69",
"#fccde5",
"#d9d9d9",
"#bc80bd",
"#ffffb3",
"#bebada",
]
def jitter(data: np.ndarray, amount: float = 0.1) -> np.ndarray:
"""
Jitter the all values in an array.
This is useful to scatter values which are all displayed for
the same x value. The amount should be chosen in relation to
the values in the data. For example, if the smallest change
in x is 1 the amount should be lower than this.
Parameters
----------
data: np.ndarray
the data which is jittered
amount: float
the maximal value added to the data for jittering
Returns
-------
np.ndarray
"""
return data + (np.random.rand(*data.shape) - 0.5) * amount
def load_data(
dir_random_search: str,
file_measures: str = "measures.csv",
file_params: str = "params.json",
) -> Dict[int, Dict[str, Any]]:
dirs_run = sorted(os.listdir(dir_random_search))
dirs_run = map(lambda f: os.path.join(dir_random_search, f), dirs_run)
dirs_run = filter(lambda f: os.path.isdir(f), dirs_run)
dirs_run = filter(lambda f: not os.path.islink(f), dirs_run)
dirs_run = map(lambda f: os.path.basename(f), dirs_run)
data = {}
for dir_run in dirs_run:
measures = pd.read_csv(os.path.join(dir_random_search, dir_run, file_measures))
params = load_params(os.path.join(dir_random_search, dir_run, file_params))
data[int(dir_run)] = {"measures": measures, "params": params, "dir": dir_run}
return data
def remove_outliers(
data: Dict[int, Dict[str, Any]], file_outliers: str = "outliers.csv"
):
outlier_runs = pd.read_csv(file_outliers)
outlier_runs = outlier_runs[outlier_runs["outlier"]]
outlier_runs = list(outlier_runs["run"])
return dict(filter(lambda i: i[0] not in outlier_runs, data.items()))
def filter_by_params(
data: Dict[int, Dict[str, Any]],
value: Union[Any, Tuple[Any]],
fields: Union[str, List[str]],
):
if type(value) is not tuple:
value = (value,)
if type(fields) is not list:
fields = [fields]
return dict(
(k, v)
for (k, v) in data.items()
if tuple(map(lambda f: v["params"][f], fields)) == value
)
class TablePrinter():
def __init__(self):
self.vert = ""
self.hori = ""
self.t_up = ""
self.t_down = ""
self.t_right = ""
self.t_left = ""
self.top_left = ""
self.top_right = ""
self.bottom_right = ""
self.bottom_left = ""
self.cross = ""
self.formatter = {
float: "{:.5f}",
np.float64: "{:.5f}",
}
self.color_formatter = {}
def print(self, table: Dict[str, List[Any]]):
headers = list(table.keys())
table = dict([(header, list(map(lambda value: self.format(value, header), column))) for header, column in table.items()])
lenghtes = dict([(header, max(len(header), *map(len, column))) for header, column in table.items()])
line_top = f"{self.vert}{self.t_down}{self.vert}".join(map(lambda header: self.vert * lenghtes[header], headers))
line_top = self.top_left + self.vert + line_top + self.vert + self.top_right
print(line_top)
line_headers = f" {self.hori} ".join(map(lambda header: f"{header:>{lenghtes[header]}}", table.keys()))
line_headers = self.hori + " " + line_headers + " " + self.hori
print(line_headers)
line_mid = f"{self.vert}{self.cross}{self.vert}".join(map(lambda header: self.vert * lenghtes[header], headers))
line_mid = self.t_left + self.vert + line_mid + self.vert + self.t_right
print(line_mid)
for i in range(len(table[headers[0]])):
values = map(lambda header: self.color(f"{table[header][i]:>{lenghtes[header]}}", header), headers)
line = f" {self.hori} ".join(values)
line = self.hori + " " + line + " " + self.hori
print(line)
line_bot = f"{self.vert}{self.t_up}{self.vert}".join(map(lambda header: self.vert * lenghtes[header], headers))
line_bot = self.bottom_left + self.vert + line_bot + self.vert + self.bottom_right
print(line_bot)
def format(self, value: Any, header: str):
if header in self.formatter:
return self.formatter[header].format(value)
if type(value) in self.formatter:
return self.formatter[type(value)].format(value)
return str(value)
def color(self, value_str: str, header: str):
if header in self.color_formatter:
return self.color_formatter[header](value_str)
return value_str
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("random_search_dir", type=str)
args = parser.parse_args()
data = load_data(args.random_search_dir)
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