linting

grid_search.py

@@ -0,0 +1,111 @@
+import subprocess
+import re
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+def run_script(pweight, nstart, ncand, gamma):
+    command = [
+        './scripts/runResistnet.py',
+        '-g', '/Users/tyler/projects/resistnet_validation/simulation/output/tc1_s2_30_1.ResistanceMatrix.tsv',
+        '-n', '/Users/tyler/projects/resistnet_validation/simulation/template_networks/tc1.net',
+        '-c', '/Users/tyler/projects/resistnet_validation/simulation/output/tc1_s2_30_1.coords',
+        '-V', '/Users/tyler/projects/resistnet_validation/simulation/selected_vars.txt',
+        '-F', '50',
+        '-i', '100',
+        '-t', '1',
+        '--reps', str(10),
+        '-P', str(pweight),
+        '-G', str(gamma),
+        '-S', str(nstart),
+        '-C', str(ncand)
+    ]
+
+    result = subprocess.run(command, capture_output=True, text=True)
+    if result.returncode != 0:
+        print(f"Error running command: {command}")
+        print(result.stderr)
+        return None
+    return result.stdout
+
+def extract_losses(output):
+    pattern = r"Worker \d+: Best Loss = ([\d\.]+)"
+    matches = re.findall(pattern, output)
+    losses = [float(match) for match in matches]
+    return losses
+
+def grid_search(pweight_values, nstart_values, ncand_values, gamma_values):
+    results = []
+
+    with ThreadPoolExecutor(max_workers=6) as executor:
+        future_to_params = {
+            executor.submit(run_script, pweight, nstart, ncand, gamma): (pweight, nstart, ncand, gamma)
+            for pweight in pweight_values
+            for nstart in nstart_values
+            for ncand in ncand_values
+            for gamma in gamma_values
+        }
+
+        for future in as_completed(future_to_params):
+            params = future_to_params[future]
+            try:
+                output = future.result()
+                if output:
+                    losses = extract_losses(output)
+                    if losses:
+                        loss_spread = np.std(losses)
+                        results.append((params[0], params[1], params[2], params[3], loss_spread, np.mean(losses)))
+                    else:
+                        print(f"No losses found for parameters: {params}")
+            except Exception as exc:
+                print(f"Generated an exception: {params} - {exc}")
+
+    return results
+
+def main():
+    # Parameter grid for TPE
+    pweight_values = [0.3, 0.5, 0.7, 0.9]
+    nstart_values = [10, 30, 50]
+    ncand_values = [20, 80, 120]
+    gamma_values = [0.1, 0.2, 0.3, 0.4, 0.5]
+
+    results = grid_search(pweight_values, nstart_values, ncand_values, gamma_values)
+
+    if not results:
+        print("No valid results found.")
+        return
+
+    # Find the best parameter combination
+    results = sorted(results, key=lambda x: x[5])  # Sort by mean loss
+
+    # Prepare data for plotting
+    pw, ns, nc, g, spreads, means = zip(*results)
+
+    # Data for seaborn
+    import pandas as pd
+    df = pd.DataFrame({
+        'Prior Weight (pweight)': pw,
+        'Initial Random Evaluations (nstart)': ns,
+        'EI Candidate Points (ncand)': nc,
+        'Exploration Factor (gamma)': g,
+        'Mean Best Loss': means,
+        'Loss Spread': spreads
+    })
+
+    # Pairplot to show relationships between all parameters
+    sns.pairplot(df, hue='Mean Best Loss', palette='viridis')
+    plt.show()
+
+    # Parallel coordinates plot
+    from pandas.plotting import parallel_coordinates
+    plt.figure(figsize=(12, 6))
+    parallel_coordinates(df[['Prior Weight (pweight)', 'Initial Random Evaluations (nstart)', 'EI Candidate Points (ncand)', 'Exploration Factor (gamma)', 'Mean Best Loss']], class_column='Mean Best Loss', colormap=plt.get_cmap("viridis"))
+    plt.title('Parallel Coordinates Plot')
+    plt.show()
+
+    print("Best parameter combination (pweight, nstart, ncand, gamma):", results[0][:4])
+    print("With mean best loss:", results[0][5])
+
+if __name__ == "__main__":
+    main()

scripts/runResistnet.py

@@ -5,7 +5,7 @@
 
 from resistnet.params import parseArgs
 from resistnet.resistance_network import ResistanceNetwork
-from resistnet.model_optimisation import ModelRunnerTPE, ModelRunnerGA
+from resistnet.model_optimisation import ModelRunnerTPE
 
 
 def main():
@@ -86,7 +86,6 @@ def main():
     #     verbose=True
     # )
 
-
     # # Step 3: Run GA optimisation
     # runner.run_ga(
     #     maxgens=params.maxGens,