BDA_project/plot.py at master · ksukrit/BDA_project · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Read the CSV file
df = pd.read_csv('results.csv')

# Create a color mapping for each unique model base name (without bit specification)
model_bases = df['model_name'].apply(lambda x: x.split('_')[0] + '_' + x.split('_')[1] if 'video' in x else x.split('_')[0])
unique_models = model_bases.unique()
colors = sns.color_palette("husl", len(unique_models))
color_map = dict(zip(unique_models, colors))

# Function to get color based on model name
def get_color(model_name):
    base = model_name.split('_')[0] + '_' + model_name.split('_')[1] if 'video' in model_name else model_name.split('_')[0]
    return color_map[base]

# Function to convert memory to GB
def bytes_to_gb(bytes_val):
    return bytes_val / (1024 ** 3)

# Get unique benchmarks
benchmarks = df['benchmark'].unique()

# Create a figure for each benchmark
plt.style.use('seaborn')
for i, benchmark in enumerate(benchmarks):
    benchmark_data = df[df['benchmark'] == benchmark]

    # Create figure and primary axis
    fig, ax1 = plt.subplots(figsize=(14, 7))

    # Plot bars on primary axis (accuracy)
    bars = ax1.bar(range(len(benchmark_data)), benchmark_data['accuracy'])

    # Set colors for each bar based on model name
    for bar, model_name in zip(bars, benchmark_data['model_name']):
        bar.set_color(get_color(model_name))

    # Customize primary axis (accuracy)
    ax1.set_xlabel('Model Configuration')
    ax1.set_ylabel('Accuracy', color='darkblue')
    ax1.tick_params(axis='y', labelcolor='darkblue')

    # Create secondary axis (memory utilization)
    ax2 = ax1.twinx()

    # Plot line on secondary axis (memory)
    memory_gb = benchmark_data['memory_utilization'].apply(bytes_to_gb)
    line = ax2.plot(range(len(benchmark_data)), memory_gb,
                    color='darkred', marker='o', linewidth=2,
                    linestyle='--', label='Memory Usage (GB)')

    # Customize secondary axis (memory)
    ax2.set_ylabel('Memory Usage (GB)', color='darkred')
    ax2.tick_params(axis='y', labelcolor='darkred')

    # Set title and x-axis labels
    plt.title(f'{benchmark.upper()} Benchmark Results: Accuracy vs Memory Usage', pad=20)
    ax1.set_xticks(range(len(benchmark_data)))
    ax1.set_xticklabels(benchmark_data['model_name'], rotation=45, ha='right')

    # Add grid for better readability (only for primary axis)
    ax1.grid(True, axis='y', linestyle='--', alpha=0.3)

    # Add legend
    lines = ax2.get_lines()
    labels = [line.get_label() for line in lines]
    ax2.legend(lines, labels, loc='upper right')

    plt.tight_layout()

    plt.savefig(f'benchmark_results/{benchmark}_results_with_memory.png', dpi=300, bbox_inches='tight')
    plt.close()


for i, benchmark in enumerate(benchmarks):
    benchmark_data = df[df['benchmark'] == benchmark]

    fig, ax1 = plt.subplots(figsize=(14, 7))

    bars = ax1.bar(range(len(benchmark_data)), benchmark_data['additional_results'])

    for bar, model_name in zip(bars, benchmark_data['model_name']):
        bar.set_color(get_color(model_name))

    ax1.set_xlabel('Model Configuration')
    ax1.set_ylabel('Accuracy', color='darkblue')
    ax1.tick_params(axis='y', labelcolor='darkblue')

    ax2 = ax1.twinx()

    memory_gb = benchmark_data['memory_utilization'].apply(bytes_to_gb)
    line = ax2.plot(range(len(benchmark_data)), memory_gb,
                    color='darkred', marker='o', linewidth=2,
                    linestyle='--', label='Memory Usage (GB)')

    ax2.set_ylabel('Memory Usage (GB)', color='darkred')
    ax2.tick_params(axis='y', labelcolor='darkred')

    plt.title(f'{benchmark.upper()} Benchmark Results: Accuracy vs Memory Usage', pad=20)
    ax1.set_xticks(range(len(benchmark_data)))
    ax1.set_xticklabels(benchmark_data['model_name'], rotation=45, ha='right')

    ax1.grid(True, axis='y', linestyle='--', alpha=0.3)

    lines = ax2.get_lines()
    labels = [line.get_label() for line in lines]
    ax2.legend(lines, labels, loc='upper right')

    plt.tight_layout()

    plt.savefig(f'benchmark_results/{benchmark}_results_with_bertF1.png', dpi=300, bbox_inches='tight')
    plt.close()


print("Plots have been generated and saved as PNG files.")