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fix indexing
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Aspen Cherie Smith committed Dec 15, 2024
1 parent 4662e10 commit 405e6d8
Showing 1 changed file with 29 additions and 26 deletions.
55 changes: 29 additions & 26 deletions utils/comparisons.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@ def run_implementation(exec_path: str, dataset: str) -> float:

def plot_table(results, datasets, implementations):
"""Generate a table of execution times with proper axis labels and save as an image"""
dataset_sizes = [10, 15, 20, 25, 100, 1000, "10k", "100k"]
data = []
for dataset in datasets:
row = []
Expand All @@ -30,7 +31,7 @@ def plot_table(results, datasets, implementations):
row.append("") # Leave blank if no data
data.append(row)

df = pd.DataFrame(data, columns=implementations, index=datasets)
df = pd.DataFrame(data, columns=implementations, index=dataset_sizes)
fig, ax = plt.subplots(figsize=(8, 6))
ax.axis('tight')
ax.axis('off')
Expand Down Expand Up @@ -66,7 +67,7 @@ def plot_table(results, datasets, implementations):
table.set_fontsize(10)
ax.add_table(table)
ax.text(0.5, 1.05, 'Algorithm', fontsize=12, ha='center', transform=ax.transAxes)
ax.text(-0.05, 0.5, 'Datasets', fontsize=12, va='center', rotation='vertical', transform=ax.transAxes)
ax.text(-0.05, 0.5, 'Number of Cities', fontsize=12, va='center', rotation='vertical', transform=ax.transAxes)

plt.savefig("execution_times_table.png", dpi=300, bbox_inches="tight")
print("Execution times table saved as execution_times_table.png")
Expand All @@ -75,19 +76,21 @@ def plot_results(results):
"""Plot timing results with multiple views"""
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 6))

# Define line styles and valid single-character color codes or explicit color definitions
colors = {
'brute': 'b', # Blue
'greedy': 'g', # Green
'genetic': 'r', # Red
'dp': 'c', # Cyan
'greedy_cuda': 'y', # Yellow
'genetic_cuda': 'm', # Magenta
'dp_omp': 'orange', # Orange
'dp_cuda': 'purple' # Purple
'brute': 'b', # Blue
'greedy': 'g', # Green
'genetic': 'm', # Red
'dp': 'c', # Cyan
'greedy_cuda': 'y', # Yellow
'genetic_cuda': 'r', # Magenta
'dp_omp': '#FFA500', # Orange (RGB Hex Code)
'dp_cuda': '#800080', # Purple (RGB Hex Code)
'dp_omp_numa': '#008080'
}
datasets = ['tiny', 'small', 'medium', 'large','huge','gigantic']
dataset_sizes = [10, 27, 100, 1000, '10k', '100k'] # New tick labels

datasets = ['tiny', 'small_15', 'small_20', 'small_25', 'medium', 'large', 'huge', 'gigantic']
dataset_sizes = [10, 15, 20, 25, 100, 1000, "10k", "100k"] # New tick labels
x_pos = range(len(datasets))

# Plot 1: Log scale (good for small differences)
Expand All @@ -96,13 +99,12 @@ def plot_results(results):
filtered_datasets = [d for d in datasets if d in timings] # Filter valid datasets for this implementation
x_pos_filtered = [datasets.index(d) for d in filtered_datasets] # Map filtered datasets to x_pos indices
ax1.plot(x_pos_filtered, [timings[d] for d in filtered_datasets],
f'{colors[impl]}-o',
label=f'{impl.capitalize()}')
'-o', color=colors[impl], label=f'{impl.capitalize()}') # Use explicit color
ax1.set_yscale('log')
ax1.set_title('Log Scale Comparison')
ax1.set_xticks(x_pos)
ax1.set_xticklabels(dataset_sizes)
ax1.set_xlabel('Number of Cities')
ax1.set_xticklabels(dataset_sizes)
ax1.grid(True)
ax1.legend()

Expand All @@ -112,11 +114,10 @@ def plot_results(results):
filtered_datasets = [d for d in datasets if d in timings] # Filter valid datasets for this implementation
x_pos_filtered = [datasets.index(d) for d in filtered_datasets] # Map filtered datasets to x_pos indices
ax2.plot(x_pos_filtered, [timings[d] for d in filtered_datasets],
f'{colors[impl]}-o',
label=f'{impl.capitalize()}')
'-o', color=colors[impl], label=f'{impl.capitalize()}') # Use explicit color
ax2.set_title('Linear Scale Comparison (Seconds)')
ax2.set_xticks(x_pos)
ax2.set_xticklabels(dataset_sizes)
ax2.set_xticklabels(dataset_sizes)
ax2.set_xlabel('Number of Cities')
ax2.grid(True)

Expand All @@ -129,12 +130,11 @@ def plot_results(results):
x_pos_filtered = [datasets.index(d) for d in filtered_datasets] # Map filtered datasets to x_pos indices
speedups = [baseline[d] / timings[d] for d in filtered_datasets]
ax3.plot(x_pos_filtered, speedups,
f'{colors[impl]}-o',
label=f'{impl.capitalize()}')
'-o', color=colors[impl], label=f'{impl.capitalize()}') # Use explicit color
ax3.axhline(y=1.0, color='k', linestyle='--')
ax3.set_title('Speedup vs. Greedy')
ax3.set_xticks(x_pos)
ax3.set_xticklabels(dataset_sizes)
ax3.set_xticklabels(dataset_sizes)
ax3.set_xlabel('Number of Cities')
ax3.grid(True)
ax3.legend()
Expand All @@ -149,7 +149,7 @@ def plot_results(results):
print(f"Could not display plot: {e}")

if __name__ == "__main__":
all_implementations = ['brute', 'greedy', 'genetic', 'dp', 'greedy_cuda']
all_implementations = ['brute', 'greedy', 'genetic', 'dp', 'greedy_cuda', 'genetic_cuda', 'dp_omp', 'dp_cuda', 'dp_omp_numa']

parser = argparse.ArgumentParser(description='Compare TSP implementations')
parser.add_argument('implementations',
Expand All @@ -159,10 +159,13 @@ def plot_results(results):

args = parser.parse_args()
implementations = args.implementations if args.implementations else all_implementations
datasets = ['tiny', 'small', 'medium', 'large','huge','gigantic']
datasets = ['tiny', 'small_15', 'small_20', 'small_25', 'medium', 'large', 'huge', 'gigantic']
cutoff = {
'brute': 'small',
'brute': 'small_15',
'dp': 'medium',
'dp_omp': 'medium',
'dp_cuda': 'medium',
'dp_omp_numa': 'medium'
}

results = {}
Expand All @@ -181,4 +184,4 @@ def plot_results(results):
print(f"{impl.capitalize()}, {dataset}: {execution_time:.6f} seconds")

plot_results(results)
plot_table(results, datasets, implementations)
plot_table(results, datasets, implementations)

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