finish all analysis and upload data and results
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import pandas as pd
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import os
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import matplotlib.pyplot as plt
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from sklearn.metrics import mean_squared_error, mean_absolute_error
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raw_path = "results/real_data/raw/dimacs_s42.csv"
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pred_path = "results/real_data/derived/distribution/dimacs_prediction_result/prediction.csv"
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save_folder = "results/real_data/derived/comparison"
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os.makedirs(save_folder, exist_ok=True)
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raw = pd.read_csv(raw_path)
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pred = pd.read_csv(pred_path)
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# real: trial 평균
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agg = (
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raw.groupby("file")
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.agg(
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nodes = ("nodes", "first"),
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density = ("density", "first"),
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real_time = ("time", "mean"),
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real_relax = ("relax_success","mean"),
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)
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.reset_index()
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)
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# prediction: file 기준으로 merge
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pred_cols = pred[["file", "predict_time", "relax_success"]].rename(
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columns={"relax_success": "pred_relax"}
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)
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merged = agg.merge(pred_cols, on="file", how="inner")
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# 오차 계산
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for real_col, pred_col, prefix in [
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("real_time", "predict_time", "time"),
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("real_relax", "pred_relax", "relax"),
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]:
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merged[f"{prefix}_abs_err"] = (merged[pred_col] - merged[real_col]).abs()
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merged[f"{prefix}_rel_err"] = merged[f"{prefix}_abs_err"] / merged[real_col]
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cols = [
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"file", "nodes", "density",
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"real_time", "predict_time", "time_abs_err", "time_rel_err",
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"real_relax", "pred_relax", "relax_abs_err", "relax_rel_err",
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]
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merged[cols].to_csv(os.path.join(save_folder, "comparison.csv"), index=False)
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print(merged[cols].to_string(index=False))
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print(f"\nSaved → {save_folder}/comparison.csv")
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for real_col, pred_col, label in [
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("real_time", "predict_time", "time"),
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("real_relax", "pred_relax", "relax_success"),
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]:
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y = merged[real_col].to_numpy()
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y_hat = merged[pred_col].to_numpy()
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ss_res = ((y - y_hat) ** 2).sum()
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ss_tot = ((y - y.mean()) ** 2).sum()
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r2 = 1 - ss_res / ss_tot
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rmse = mean_squared_error(y, y_hat) ** 0.5
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mae = mean_absolute_error(y, y_hat)
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print(f"\n[{label}] R²={r2:.4f} RMSE={rmse:.4g} MAE={mae:.4g}")
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# ── Plot ───────────────────────────────────────────────────────────────────────
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fig, axes = plt.subplots(1, 2, figsize=(12, 5))
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for ax, real_col, pred_col, label in [
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(axes[0], "real_time", "predict_time", "Runtime [s]"),
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(axes[1], "real_relax", "pred_relax", "Relax Success"),
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]:
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x = merged[real_col].to_numpy()
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y = merged[pred_col].to_numpy()
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mn, mx = min(x.min(), y.min()), max(x.max(), y.max())
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ax.scatter(x, y, s=60, zorder=3)
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for _, row in merged.iterrows():
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ax.annotate(
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row["file"].replace("USA-road-d.", "").replace(".gr", ""),
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(row[real_col], row[pred_col]),
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fontsize=6, textcoords="offset points", xytext=(4, 2)
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)
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ax.plot([mn, mx], [mn, mx], color="red", linestyle="--", linewidth=1.2, label="y = x")
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ax.set_xlabel(f"Real {label}")
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ax.set_ylabel(f"Predicted {label}")
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ax.set_title(f"Predicted vs Real — {label}")
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ax.legend(fontsize=8)
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ax.grid(True, alpha=0.4)
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fig.tight_layout()
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fig.savefig(os.path.join(save_folder, "comparison_plot.png"), dpi=300)
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plt.close(fig)
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print(f"Saved → {save_folder}/comparison_plot.png")
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@@ -0,0 +1,78 @@
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from experiments.synthetic_data.analysis.processing_time_analysis import mean_l
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from experiments.synthetic_data.analysis.processing_time_analysis import regression_k3
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from experiments.synthetic_data.analysis.processing_time_analysis import regression_m
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from experiments.synthetic_data.analysis.call_number_analysis import (
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nonlinear_regression,
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)
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import numpy as np
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import pandas as pd
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import os
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def k3(V):
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return k3_a * np.exp(-k3_b * V) + k3_c
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def m(V):
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return m_a * np.log2(V) + m_b
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def relax_success(E, S, AVG_DEG):
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relax_attempts = E
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relax_success_ratio = (rs_a * np.log(S) + rs_b) * AVG_DEG**rs_c
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return relax_attempts * relax_success_ratio
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def runtime_predict(V, D, S):
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E = V * (V - 1) * D
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AVG_DEG = (V - 1) * D
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if AVG_DEG < 1:
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return False, False
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runtime = V * l + E * k3(V) + relax_success(E, S, AVG_DEG) * m(V)
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print(relax_success(E, S, AVG_DEG))
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# 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}]"
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return runtime, relax_success(E, S, AVG_DEG)
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l = mean_l()
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k3_a, k3_b, k3_c = regression_k3()
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m_a, m_b = regression_m()
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rs_a, rs_b, rs_c = nonlinear_regression()
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csv_file = "results/real_data/derived/dimacs_graph_distribution/distribution.csv"
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save_folder = f"results/real_data/derived/{os.path.splitext(os.path.basename(csv_file))[0]}/dimacs_prediction_result"
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os.makedirs(save_folder, exist_ok=True)
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df = pd.read_csv(csv_file)
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records = []
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for row in df.itertuples():
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nodes = row.nodes
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density = row.density
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sigma = np.sqrt(np.log((row.std / row.mean) ** 2 + 1))
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pred_time, pred_relax_success = runtime_predict(nodes, density, sigma)
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if pred_time is False:
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continue
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records.append(
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{
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"file": row.file,
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"nodes": nodes,
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"density": density,
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"sigma": sigma,
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"predict_time": pred_time,
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"relax_success": pred_relax_success,
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}
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)
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print(f"finished {row.file}")
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result = pd.DataFrame(records)
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out_path = os.path.join(save_folder, "prediction.csv")
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result.to_csv(out_path, index=False)
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print(f"Saved {len(result)} rows → {out_path}")
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@@ -102,18 +102,18 @@ def visualize_weights(file):
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folder = "experiments/real_data/data/dimacs_data"
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files = [
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# "USA-road-d.BAY.gr",
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# "USA-road-d.CAL.gr",
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# "USA-road-d.COL.gr",
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# "USA-road-d.CTR.gr",
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# "USA-road-d.E.gr",
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# "USA-road-d.FLA.gr",
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# "USA-road-d.LKS.gr",
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# "USA-road-d.NE.gr",
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# "USA-road-d.NW.gr",
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# "USA-road-d.NY.gr",
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"USA-road-d.BAY.gr",
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"USA-road-d.CAL.gr",
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"USA-road-d.COL.gr",
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"USA-road-d.CTR.gr",
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"USA-road-d.E.gr",
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"USA-road-d.FLA.gr",
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"USA-road-d.LKS.gr",
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"USA-road-d.NE.gr",
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"USA-road-d.NW.gr",
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"USA-road-d.NY.gr",
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"USA-road-d.USA.gr",
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# "USA-road-d.W.gr"
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"USA-road-d.W.gr"
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]
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res_folder = "results/real_data/derived/dimacs_graph_distribution"
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