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plot_complexity.py
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plot_complexity.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy.optimize import curve_fit
import argparse
from sklearn import linear_model
import math
SMOOTHING_FACTOR = 5
COLOR_1 = 'navy'
COLOR_2 = 'firebrick'
COLOR_3 = 'tab:green'
TITLE = 'PGEL-SAT'
X_LABEL = 'number of uncertain axioms'
Y_LABEL_RIGHT = 'number of iterations'
Y_LABEL_LEFT = 'PGEL-SAT time (s)'
DF_TIME_MEAN_LABEL = 'time_mean'
DF_TIME_STD_LABEL = 'time_std'
DF_ITERS_MEAN_LABEL = 'iters_mean'
DF_ITERS_TIME_MEAN_LABEL = 'iters_time_mean'
# NEED LATEX FONT PACKAGES TO WORK PROPERLY
plt.rcParams.update({
'text.usetex': True,
'text.latex.preamble': ''' \\usepackage{libertine}
\\usepackage[libertine]{newtxmath}
\\usepackage[T1]{fontenc}
'''
})
def main():
parser = init_argparse()
args = parser.parse_args()
experiment_path = args.experiment[0]
data = get_data_from_experiment(experiment_path)
if not args.complexity_2:
complexity_1_plot(data, args, experiment_path)
else:
complexity_2_plot(data, args, experiment_path)
def complexity_1_plot(data, args, experiment_path):
for idx, values in enumerate(data):
(key, counts, times_mean, iters_mean, iters_time_mean) = values
key = [
'number of axioms',
'number of concepts',
'number of uncertain axioms'
][idx]
axes = init_subplots(args, idx, key)
plot_curve(counts, times_mean*1000, args, axes[0, 0], COLOR_1)
plot_curve(counts, iters_time_mean*1000, args, axes[0, 1], COLOR_3)
plot_curve(counts, iters_mean, args, axes[1, 0], COLOR_2)
filename = extract_filename(experiment_path)
plt.savefig(f'data/plots/complexity-1/{filename}-({idx}).png',
bbox_inches='tight', dpi=300)
if args.show:
plt.show()
plt.close()
def complexity_2_plot(data, args, experiment_path):
ax1 = init_plot(args)
(key, counts, times_mean, iters_mean, iters_time_mean) = data[2]
plot_curve(counts, times_mean, args, plt, COLOR_3)
plot_polynomial_fit(counts, times_mean, ax1, COLOR_2)
plot_exponential_fit(counts, times_mean, ax1, COLOR_1)
# -----
ax1.legend([
'PGEL-SAT time',
'polynomial approximation',
'exponential approximation'],
loc='upper left')
filename = extract_filename(experiment_path)
plt.savefig(
f'data/plots/complexity-2/{filename}.png',
bbox_inches='tight',
dpi=300)
if args.show:
plt.show()
plt.close()
def init_argparse():
parser = argparse.ArgumentParser(
usage='%(prog)s [options] experiment',
description='Plot experiment for PGEL-SAT algorithm.'
)
parser.add_argument('experiment', nargs=1, type=str,
help='path of the CSV file of the experiment')
parser.add_argument('--complexity-2', action='store_true',
help='run the complexity 2 plot')
parser.add_argument('-w', '--moving-average-size', nargs='?',
default=5, type=int, help='size of the moving average smoothing')
parser.add_argument('-p', '--prob-axioms-count', nargs='?', default=10,
type=int, help='number of probabilistic axioms tested')
parser.add_argument('-f', '--font-size', nargs='?',
default=12, type=int, help='font size for labels')
parser.add_argument('--no-title', action='store_true',
help='remove the title')
parser.add_argument('--no-objective-curves', action='store_true',
help='remove the objecive curves from the plot')
parser.add_argument('--show', action='store_true', help='show the plot')
return parser
def get_data_from_experiment(experiment_path):
df = pd.read_csv(experiment_path)
param_groups = df.groupby(['moving_param'])
param_dfs = ((gp, param_groups.get_group(gp))
for gp in param_groups.groups)
data = []
for param_key, param_df in param_dfs:
gp = param_df.groupby([param_key])
param_counts = [j for j in gp.groups.keys()]
means = gp.mean()
def get_vals(label): return means.get(label).values
vals = map(get_vals, [DF_TIME_MEAN_LABEL,
DF_ITERS_MEAN_LABEL, DF_ITERS_TIME_MEAN_LABEL])
data += [(param_key, np.array(param_counts), *vals)]
return data
def init_subplots(args, idx, key):
plt.rcParams.update({'font.size': args.font_size})
fig, axes = plt.subplots(2, 2)
axes[0, 0].set_xlabel(key)
axes[0, 0].set_ylabel('PGEL-SAT time (ms)')
axes[0, 1].set_xlabel(key)
axes[0, 1].set_ylabel('avg. iteration time (ms)')
axes[1, 0].set_xlabel(key)
axes[1, 0].set_ylabel('avg. number of iterations')
axes[-1, -1].axis('off')
fig.tight_layout()
return axes
def init_plot(args):
plt.rcParams.update({'font.size': args.font_size})
fig, ax1 = plt.subplots()
ax1.set_xlabel(X_LABEL)
ax1.set_ylabel(Y_LABEL_LEFT)
fig.tight_layout()
return ax1
def running_average(data_list, size):
window = np.ones(size)/size
def smooth_data(data):
return np.convolve(data, window, mode='valid')
return list(map(smooth_data, data_list))
def plot_curve(axioms_counts, values_mean, args, ax, color):
axioms_counts, values_mean = running_average(
[axioms_counts, values_mean], args.moving_average_size)
ax.plot(axioms_counts, values_mean, color=color)
def plot_logit_fit(axioms_counts, sats_mean, ax1):
def logit_fn(x, k, x0, A, off):
return A / (1 + np.exp(k * (x - x0))) + off
popt, _ = curve_fit(logit_fn, axioms_counts, sats_mean)
logit_vals = logit_fn(axioms_counts, *popt)
ax1.plot(axioms_counts, logit_vals, color=COLOR_1, ls='--')
def plot_linear_fit(axioms_counts, times_mean, ax2):
coef = np.polyfit(axioms_counts, times_mean, 1)
poly1d_fn = np.poly1d(coef)
ax2.plot(axioms_counts, poly1d_fn(axioms_counts), color=COLOR_2, ls='--')
def plot_polynomial_fit(axioms_counts, times_mean, ax1, color):
def polynomial_fn(x, a, b, c, d, e, f, g, h):
return np.polyval([a, b, c, d, e, f, g, h], x)
popt, _ = curve_fit(polynomial_fn, axioms_counts, times_mean)
poly_vals = polynomial_fn(axioms_counts, *popt)
print('POLYNOMIAL FIT')
print(' + '.join((f'{a}*x^{len(popt) - i - 1}'
for i, a in enumerate(popt))))
print()
print(' + '.join((f'{a:.10f}*x^{len(popt) - i - 1}'
for i, a in enumerate(popt))))
print()
ax1.plot(axioms_counts, poly_vals, color=color, ls='--')
def plot_exponential_fit(axioms_counts, times_mean, ax1, color):
def exponential_fn(x, a, b, d):
return a*2**(b*x) + d
popt, _ = curve_fit(exponential_fn, axioms_counts,
times_mean, [1, 1/100, -1])
print('EXPONENTIAL FIT')
print('{}*2**({}*x) + {}'.format(*popt))
exp_vals = exponential_fn(axioms_counts, *popt)
ax1.plot(axioms_counts, exp_vals, color=color, ls=':')
def plot_fit_linear_regression(counts, times_mean, plt):
logx = np.log(counts)
logy = np.log(times_mean)
# log-log fit
loglogmod = linear_model.LinearRegression()
x = np.reshape(logx, (len(counts), 1))
y = logy
loglogmod.fit(x, y)
loglogmod_r2 = loglogmod.score(x, y)
# log fit
logmod = linear_model.LinearRegression()
x = np.reshape(counts, (len(counts), 1))
logmod.fit(x, y)
logmod_r2 = logmod.score(x, y)
# polynomial plot
m = loglogmod.coef_[0]
c = loglogmod.intercept_
polynomial = math.exp(c)*np.power(counts, m)
plt.plot(counts, polynomial,
label='$y = {:.3f} \\cdot x^{{{:.3f}}}$ ($r^2={:.2f}$)'.format(
math.exp(c), m, loglogmod_r2),
ls='--', color=COLOR_2)
print(m)
# exponential plot
m = logmod.coef_[0]
c = logmod.intercept_
print(m)
exponential = np.exp(counts * m) * math.exp(c)
plt.plot(counts, exponential,
label='$y = {:.3f} \\cdot e^{{{:.3f}x}}$ ($r^2={:.2f}$)'.format(
math.exp(c), m, logmod_r2),
ls=':', color=COLOR_3)
plt.legend(loc='upper left')
def extract_filename(path):
return path.split('/')[-1].split('.')[0]
if __name__ == '__main__':
main()