Graph-Feature-Fusion/plot_mp.py at main · ParisaArbab/Graph-Feature-Fusion · GitHub

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import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import math

# Sample data: Replace with your 10 lists of training losses
np.random.seed(0)
epochs = list(range(1, 1001))

num_mp = [1, 5, 9, 12]

# for i in range(35, 81, 5):
#     num_dim.append(i)


training_loss = []
val_loss = []

for num in num_mp:
    path = "plots/MNISTSuperpixels/4000_no_lr_decay/dln_mp_dim_100/mp_"+str(num)+"_dln_fl_dim_300/fl_1"
    loss_list = [0] * 1000
    val_loss_list = [0] * 1000
    for i in range(10):
        with open(path + "/train_loss_fold_" + str(i+1) + ".txt", "r") as f:
            lines = f.readlines()
            for j in range(1000):
                loss_list[j] += math.log10(float(lines[j].strip()))
        with open(path + "/test_loss_fold_" + str(i+1) + ".txt", "r") as f:
            lines = f.readlines()
            for j in range(1000):
                val_loss_list[j] += math.log10(float(lines[j].strip()))

    loss_list = [x / 10 for x in loss_list]
    val_loss_list = [x / 10 for x in val_loss_list]
    training_loss.append(loss_list)
    val_loss.append(val_loss_list)

# Set the seaborn style to "whitegrid"
sns.set(style="whitegrid", font_scale=1.2, rc={"lines.linewidth": 1.5})

# Plot the figure
plt.figure(figsize=(10, 6))
# palette = sns.color_palette("coolwarm", 40)  # High-contrast palette
# palette = sns.color_palette("Set1", 10)  # Colorblind-friendly palette
# palette = sns.color_palette("Blues", 10)
# palette = sns.color_palette("Dark2", 10)
palette = [
    '#FF00FF', '#00FFFF',
    '#FFFF00',
    '#0000FF',
    '#00FF00',
    # '#FF0000',
    # "#FFA500",
    # "#FF4500",
    "#ADFF2F",
    "#7FFF00",
    "#00BFFF",
    "#1E90FF",
    "#FF69B4",
    "#FF1493",
    "#DA70D6",
    "#BA55D3",
    "#32CD32",
    "#FFD700"
]

for idx, loss in enumerate(training_loss):
    sns.lineplot(x=epochs, y=loss, label=f'train_mp={num_mp[idx]}', color=palette[idx])
    v_loss = val_loss[idx]
    sns.lineplot(x=epochs, y=v_loss, label=f'test_mp={num_mp[idx]}', color=palette[idx], linestyle='--')

# Labeling and aesthetics
plt.title('Loss Across Epochs for Our Model with Untrained MP', fontsize=16, weight='bold')
plt.xlabel('Epoch', fontsize=14)
plt.ylabel('Loss (log scale)', fontsize=14)
plt.ylim(-5, 3)
plt.legend(title='Models', fontsize=10, title_fontsize=12)
# plt.grid(True, linestyle='--', alpha=0.7)

plt.tight_layout()
plt.savefig('loss_non_linear_untrained_n_mp_mnist.pdf', format='pdf', bbox_inches='tight')
plt.show()

# sns.set(style="whitegrid", font_scale=1.2, rc={"lines.linewidth": 1.5})

# # Plot the figure
# plt.figure(figsize=(10, 6))
# palette = [
#     '#FF00FF', '#00FFFF', '#FFFF00', '#0000FF', '#00FF00', '#FF0000',
#     "#FFA500",
#     "#FF4500",
#     "#ADFF2F",
#     "#7FFF00",
#     "#00BFFF",
#     "#1E90FF",
#     "#FF69B4",
#     "#FF1493",
#     "#DA70D6",
#     "#BA55D3",
#     "#32CD32",
#     "#FFD700"
# ]

# for idx, loss in enumerate(val_loss):
#     sns.lineplot(x=epochs, y=loss, label=f'n_mp={num_mp[idx]}', color=palette[idx])

# # Labeling and aesthetics
# plt.title('Testing Loss Across Epochs for ours', fontsize=16, weight='bold')
# plt.xlabel('Epoch', fontsize=14)
# plt.ylabel('Testing Loss', fontsize=14)
# plt.ylim(-1, 1.5)
# plt.legend(title='Models', fontsize=10, title_fontsize=12)
# plt.grid(True, linestyle='--', alpha=0.7)

# plt.tight_layout()
# plt.savefig('ours_val_loss_non_linear_n_mp.png', dpi=300, bbox_inches='tight')
# plt.show()