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create_seed_plots.py
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import argparse
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
pgf_with_custom_preamble = {
"font.family": "serif", # use serif/main font for text elements
"text.usetex": True, # use inline math for ticks
# "pgf.rcfonts": False, # don't setup fonts from rc parameters
"pgf.preamble": [
# unicode math setup
"\\usepackage{unicode-math,amsmath,amssymb,amsthm}",
]
}
mpl.rcParams.update(pgf_with_custom_preamble)
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
def makeup_for_plot(fig1):
fig1.spines["top"].set_visible(False)
fig1.spines["bottom"].set_visible(True)
fig1.spines["right"].set_visible(False)
fig1.spines["left"].set_visible(True)
fig1.get_xaxis().tick_bottom()
fig1.get_yaxis().tick_left()
fig1.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on", labelsize=12)
grid_color = '#e3e3e3'
grid_line_style = '--'
fig1.grid(linestyle=grid_line_style, color=grid_color)
return fig1
def do_tight_layout_for_fig(fig):
fig.tight_layout()
return fig
lr_vals = [0.1]
colors = ['m', 'y', 'orange', 'red', 'green', 'c', ]
# 1 = red
# 6 = green
# 2 = c
# 3 = m
# 4 = y
# 5 = o
parser = argparse.ArgumentParser(description='Plot Experiments')
parser.add_argument('--fun_num', '--fun_num', default=0,
type=int, dest='fun_num')
args = parser.parse_args()
fun_num = args.fun_num
my_markers = ['', '', '', '', '', '', '']
if fun_num == 0:
# for L2 Regularization for U,Z and lam = 0
files = {
1: 'results/cocain_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_2_lam_val_0.0_rank_val_5_uL_est_0.01_lL_est_0.01',
2: 'results/bpg_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_2_lam_val_0.0_rank_val_5',
3: 'results/palm_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_1_beta_0.0_lam_val_0.0_rank_val_5',
4: 'results/palm_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_1_beta_0.2_lam_val_0.0_rank_val_5',
5: 'results/palm_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_1_beta_0.4_lam_val_0.0_rank_val_5',
6: 'results/bpg_mf_wb_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_2_lam_val_0.0_rank_val_5_uL_est_0.01_lL_est_0.01',
}
if fun_num == 1:
# for L2 Regularization for U,Z and lam = 1e-1
files = {
1: 'results/cocain_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_2_lam_val_0.1_rank_val_5_uL_est_0.01_lL_est_0.01',
2: 'results/bpg_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_2_lam_val_0.1_rank_val_5',
3: 'results/palm_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_1_beta_0.0_lam_val_0.1_rank_val_5',
4: 'results/palm_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_1_beta_0.2_lam_val_0.1_rank_val_5',
5: 'results/palm_mf_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_1_beta_0.4_lam_val_0.1_rank_val_5',
6: 'results/bpg_mf_wb_fun_name_1_dataset_option_3_abs_fun_num_3_breg_num_2_lam_val_0.1_rank_val_5_uL_est_0.01_lL_est_0.01',
}
if fun_num == 2:
# for L1 Regularization for U,Z and
files = {
1: 'results/cocain_mf_fun_name_2_dataset_option_3_abs_fun_num_2_breg_num_2_lam_val_0.1_rank_val_5_uL_est_0.01_lL_est_0.01',
2: 'results/bpg_mf_fun_name_2_dataset_option_3_abs_fun_num_2_breg_num_2_lam_val_0.1_rank_val_5',
3: 'results/palm_mf_fun_name_2_dataset_option_3_abs_fun_num_2_breg_num_1_beta_0.0_lam_val_0.1_rank_val_5',
4: 'results/palm_mf_fun_name_2_dataset_option_3_abs_fun_num_2_breg_num_1_beta_0.2_lam_val_0.1_rank_val_5',
5: 'results/palm_mf_fun_name_2_dataset_option_3_abs_fun_num_2_breg_num_1_beta_0.4_lam_val_0.1_rank_val_5',
6: 'results/bpg_mf_wb_fun_name_2_dataset_option_3_abs_fun_num_2_breg_num_2_lam_val_0.1_rank_val_5_uL_est_0.01_lL_est_0.01',
}
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1 = makeup_for_plot(ax1)
label_font_size = 13
legend_font_size = 17
my_line_width = 2
num_seed_exps = 50
labels_dict = {
1: r"CoCaIn BPG-MF",
6: r"BPG-MF-WB",
2: r"BPG-MF",
3: r"PALM",
4: r"iPALM ($\beta = 0.2$)",
5: r"iPALM ($\beta = 0.4$)",
}
opt_vals = np.array([3, 4, 5, 1, 6, 2])
# ignoring 2 because BPG-MF always has bad performance as
color_count = 0
f_opt = 0
for i in opt_vals:
file_name_temp = files[i]
best_train_objective_vals = []
for j in range(num_seed_exps):
file_name = file_name_temp+'_seed_exp_num_'+str(j)+'.txt'
best_train_objective_vals = best_train_objective_vals + \
[np.loadtxt(file_name)[:, 0][-1]]
ax1.hist((best_train_objective_vals[:num_seed_exps]), num_seed_exps,
label=labels_dict[i], color=colors[color_count], width=0.5)
color_count += 1
figure_name1 = 'seed_figures/'+'func_vals_fun_num_'+str(fun_num)
# legends
ax1.legend(loc='upper right', fontsize=label_font_size)
ax1.set_ylabel('Number of seeds', fontsize=legend_font_size)
ax1.set_xlabel('Function value', fontsize=legend_font_size)
do_tight_layout_for_fig(fig1)
fig1.savefig(figure_name1+'.png', dpi=fig1.dpi)
fig1.savefig(figure_name1+'.pdf', dpi=fig1.dpi)