finish all analysis and upload data and results

codes/experiments/real_data/analysis/dimacs_prediction.py

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+from experiments.synthetic_data.analysis.processing_time_analysis import mean_l
+from experiments.synthetic_data.analysis.processing_time_analysis import regression_k3
+from experiments.synthetic_data.analysis.processing_time_analysis import regression_m
+from experiments.synthetic_data.analysis.call_number_analysis import (
+    nonlinear_regression,
+)
+import numpy as np
+import pandas as pd
+import os
+
+
+def k3(V):
+    return k3_a * np.exp(-k3_b * V) + k3_c
+
+
+def m(V):
+    return m_a * np.log2(V) + m_b
+
+
+def relax_success(E, S, AVG_DEG):
+    relax_attempts = E
+    relax_success_ratio = (rs_a * np.log(S) + rs_b) * AVG_DEG**rs_c
+    return relax_attempts * relax_success_ratio
+
+
+def runtime_predict(V, D, S):
+    E = V * (V - 1) * D
+    AVG_DEG = (V - 1) * D
+    if AVG_DEG < 1:
+        return False, False
+
+    runtime = V * l + E * k3(V) + relax_success(E, S, AVG_DEG) * m(V)
+    print(relax_success(E, S, AVG_DEG))
+    # runtime_eq = f"[V * {l}] + [E * {k3_a} * exp(-{k3_b} * V) + {k3_c}] + [E * ({rs_a} * log(S) + {rs_b}) * AVG_DEG ** {rs_c} * {m_a} * log_2(V) + {m_b}]"
+
+    return runtime, relax_success(E, S, AVG_DEG)
+
+
+l = mean_l()
+k3_a, k3_b, k3_c = regression_k3()
+m_a, m_b = regression_m()
+rs_a, rs_b, rs_c = nonlinear_regression()
+
+csv_file = "results/real_data/derived/dimacs_graph_distribution/distribution.csv"
+save_folder = f"results/real_data/derived/{os.path.splitext(os.path.basename(csv_file))[0]}/dimacs_prediction_result"
+os.makedirs(save_folder, exist_ok=True)
+
+df = pd.read_csv(csv_file)
+
+records = []
+
+for row in df.itertuples():
+    nodes = row.nodes
+    density = row.density
+    sigma = np.sqrt(np.log((row.std / row.mean) ** 2 + 1))
+
+    pred_time, pred_relax_success = runtime_predict(nodes, density, sigma)
+
+    if pred_time is False:
+        continue
+    records.append(
+        {
+            "file": row.file,
+            "nodes": nodes,
+            "density": density,
+            "sigma": sigma,
+            "predict_time": pred_time,
+            "relax_success": pred_relax_success,
+        }
+    )
+    print(f"finished {row.file}")
+
+result = pd.DataFrame(records)
+out_path = os.path.join(save_folder, "prediction.csv")
+result.to_csv(out_path, index=False)
+print(f"Saved {len(result)} rows → {out_path}")
+
+