from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from lagom.utils import pickle_load
from lagom.utils import yaml_load
from lagom.transform import interp_curves
from lagom.transform import smooth_filter
[docs]def set_ticker(ax, axis='x', num=None, KM_format=False, integer=False):
if axis == 'x':
axis = ax.xaxis
elif axis == 'y':
axis = ax.yaxis
if num is not None:
axis.set_major_locator(plt.MaxNLocator(num))
if KM_format:
def tick_formatter(x, pos):
if abs(x) >= 0 and abs(x) < 1000:
return int(x) if integer else x
elif abs(x) >= 1000 and abs(x) < 1000000:
return f'{int(x/1000)}K' if integer else f'{x/1000}K'
elif abs(x) >= 1000000:
return f'{int(x/1000000)}M' if integer else f'{x/1000000}M'
axis.set_major_formatter(plt.FuncFormatter(tick_formatter))
return ax
[docs]def read_xy(log_folder, file_name, get_x, get_y, smooth_out=False, smooth_kws=None, point_step=1):
glob_dir = lambda x: [p for p in x.glob('*/') if p.is_dir() and str(p.name).isdigit()]
dfs = []
for id_folder in glob_dir(Path(log_folder)):
x = []
y = []
for seed_folder in glob_dir(id_folder):
logs = pickle_load(seed_folder / file_name)
x.append([get_x(log) for log in logs])
y.append([get_y(log) for log in logs])
new_x, ys = interp_curves(x, y) # all seeds share same x values
if smooth_out:
if smooth_kws is None:
smooth_kws = {'window_length': 51, 'polyorder': 3}
ys = [smooth_filter(y, **smooth_kws) for y in ys]
if point_step > 1:
idx = np.arange(0, new_x.size, step=point_step)
new_x = new_x[idx, ...]
ys = [y[idx, ...] for y in ys]
df = pd.DataFrame({'x': np.tile(new_x, len(ys)), 'y': np.hstack(ys)})
config = yaml_load(id_folder / 'config.yml')
config = pd.DataFrame([config.values()], columns=config.keys())
config = config.applymap(lambda x: tuple(x) if isinstance(x, list) else x)
df = pd.concat([df, config], axis=1, ignore_index=False)
df = df.fillna(method='pad') # padding all NaN configs
dfs.append(df)
dfs = pd.concat(dfs, axis=0, ignore_index=True)
return dfs