Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Algorithm Comparisons #14

Merged
merged 3 commits into from
Aug 14, 2024
Merged

Algorithm Comparisons #14

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0968dac
fixrd path to comparisons
Almog-David Aug 4, 2024
630b57f
Fixed issue with plotting for minbudget multiple nodes
20shaked20 Aug 4, 2024
bd81fbe
graphs for erel - spreading minbudget
Almog-David Aug 4, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
45 changes: 25 additions & 20 deletions experiments/firefighter_problem/comparisons.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ def Compare_NonSpread():
This function runs multiple experiments on randomly generated layered networks
and plots the results comparing the budget used by different algorithms.
"""
ex1 = experiments_csv.Experiment("./experiments/", "non_spreading.csv", backup_folder=None)
ex1 = experiments_csv.Experiment("./experiments/firefighter_problem/", "non_spreading.csv", backup_folder=None)
ex1.clear_previous_results() # to clear previous experiments

input_ranges = {
Expand All @@ -144,7 +144,7 @@ def multiple_runs(runs=30):
ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9)

# Preprocess the DataFrame to extract numeric budget values
results_csv_file = "./experiments/non_spreading_minbudget.csv"
results_csv_file = "./experiments/firefighter_problem/non_spreading_minbudget.csv"
results = pd.read_csv(results_csv_file)

# Extract the numeric budget from the 'Budget' column
Expand All @@ -164,7 +164,7 @@ def extract_budget_numeric(budget):
results = results.dropna(subset=['Budget_numeric'])

# Save the preprocessed DataFrame to a temporary CSV file
preprocessed_csv_file = "./experiments/non_spreading_minbudget_preprocessed.csv"
preprocessed_csv_file = "./experiments/firefighter_problem/non_spreading_minbudget_preprocessed.csv"
results.to_csv(preprocessed_csv_file, index=False)

print("\n DataFrame-NonSpread: \n", results)
Expand All @@ -177,7 +177,7 @@ def extract_budget_numeric(budget):
y_field="Budget_numeric",
z_field="algorithm",
mean=True,
save_to_file="./experiments/non_spreading.png"
save_to_file="./experiments/firefighter_problem/non_spreading.png"
)

print("\n DataFrame-NonSpread: \n", ex1.dataFrame)
Expand All @@ -189,7 +189,7 @@ def Compare_SpreadingMaxSave():
This function runs multiple experiments on randomly generated directed graphs
and plots the results comparing the number of nodes saved by different algorithms.
"""
ex2 = experiments_csv.Experiment("./experiments/", "spreading_maxsave.csv", backup_folder=None)
ex2 = experiments_csv.Experiment("./experiments/firefighter_problem/", "spreading_maxsave.csv", backup_folder=None)
ex2.clear_previous_results() # to clear previous experiments

input_ranges = {
Expand Down Expand Up @@ -224,7 +224,7 @@ def multiple_runs(runs=10):

## DATA ISSUE WE HAD SO THIS IS A FIX ##
# Load the results
results_csv_file = "./experiments/spreading_maxsave.csv"
results_csv_file = "./experiments/firefighter_problem/spreading_maxsave.csv"
results = pd.read_csv(results_csv_file)

# Ensure 'algorithm' column is of type string
Expand All @@ -245,7 +245,7 @@ def multiple_runs(runs=10):
results['Nodes_Saved'] = results['Nodes_Saved'].astype(int)

# Save the cleaned DataFrame to a new CSV file (optional, for debugging)
cleaned_csv_file = "./experiments/spreading_maxsave_preprocessed.csv"
cleaned_csv_file = "./experiments/firefighter_problem/spreading_maxsave_preprocessed.csv"
results.to_csv(cleaned_csv_file, index=False)

# Plot the results using the cleaned DataFrame
Expand All @@ -261,7 +261,7 @@ def multiple_runs(runs=10):
sharex=True,
sharey=True,
mean=True,
save_to_file="./experiments/spreading_maxsave_budget.png"
save_to_file="./experiments/firefighter_problem/spreading_maxsave_budget.png"
)

multi_plot_results(
Expand All @@ -276,7 +276,7 @@ def multiple_runs(runs=10):
sharex=True,
sharey=True,
mean=True,
save_to_file="./experiments/spreading_maxsave_100_edge_prob.png"
save_to_file="./experiments/firefighter_problem/spreading_maxsave_100_edge_prob.png"
)

multi_plot_results(
Expand All @@ -291,7 +291,7 @@ def multiple_runs(runs=10):
sharex=True,
sharey=True,
mean=True,
save_to_file="./experiments/spreading_maxsave_200_edge_prob.png"
save_to_file="./experiments/firefighter_problem/spreading_maxsave_200_edge_prob.png"
)

multi_plot_results(
Expand All @@ -306,7 +306,7 @@ def multiple_runs(runs=10):
sharex=True,
sharey=True,
mean=True,
save_to_file="./experiments/spreading_maxsave_400_edge_prob.png"
save_to_file="./experiments/firefighter_problem/spreading_maxsave_400_edge_prob.png"
)

print("\n DataFrame-NonSpread: \n", ex2.dataFrame)
Expand All @@ -318,7 +318,7 @@ def Compare_SpreadingMinBudget():
This function runs multiple experiments on randomly generated directed graphs
and plots the results comparing the budget used by different algorithms.
"""
ex3 = experiments_csv.Experiment("./experiments/", "spreading_minbudget.csv", backup_folder=None)
ex3 = experiments_csv.Experiment("./experiments/firefighter_problem/", "spreading_minbudget.csv", backup_folder=None)
ex3.clear_previous_results() # to clear previous experiments

input_ranges = {
Expand All @@ -328,7 +328,7 @@ def Compare_SpreadingMinBudget():
node_counts = [100, 200, 400]
edge_probabilities = [0.1, 0.5, 0.8]

def multiple_runs(runs=10):
def multiple_runs(runs=15):
for num_nodes in node_counts:
for edge_prob in edge_probabilities:
graph = generate_random_DiGraph(num_nodes=num_nodes, edge_probability=edge_prob, seed=None)
Expand All @@ -349,7 +349,7 @@ def multiple_runs(runs=10):
ex3.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9)

# Preprocess the DataFrame to extract numeric budget values
results_csv_file = "./experiments/spreading_minbudget.csv"
results_csv_file = "./experiments/firefighter_problem/spreading_minbudget.csv"
results = pd.read_csv(results_csv_file)

# Extract the numeric budget from the 'Budget' column
Expand All @@ -369,7 +369,7 @@ def extract_budget_numeric(budget):
results = results.dropna(subset=['Budget_numeric'])

# Save the preprocessed DataFrame to a temporary CSV file
preprocessed_csv_file = "./experiments/spreading_minbudget_preprocessed.csv"
preprocessed_csv_file = "./experiments/firefighter_problem/spreading_minbudget_preprocessed.csv"
results.to_csv(preprocessed_csv_file, index=False)

print("\n DataFrame-NonSpread: \n", results)
Expand All @@ -382,17 +382,22 @@ def extract_budget_numeric(budget):
y_field="Budget_numeric",
z_field="algorithm",
mean=True,
save_to_file="./experiments/spreading_minbudget.png"
save_to_file="./experiments/firefighter_problem/spreading_minbudget.png"
)

single_plot_results(
multi_plot_results(
results_csv_file=preprocessed_csv_file,
filter={"edge_probability":0.1},
x_field="graph_nodes",
filter={},
subplot_rows=3,
subplot_cols=1,
x_field="edge_probability",
y_field="Budget_numeric",
z_field="algorithm",
subplot_field="graph_nodes",
sharex=True,
sharey=True,
mean=True,
save_to_file="./experiments/spreading_minbudget_edge.png"
save_to_file="./experiments/firefighter_problem/spreading_minbudget_edge_prob.png"
)

print("\n DataFrame-NonSpread: \n", ex3.dataFrame)
Expand Down
Loading
Loading