From b1be3a8b6fb54c17f3ff948ce053361f3e9fee70 Mon Sep 17 00:00:00 2001
From: nuluh <dam.ar@outlook.com>
Date: Thu, 16 Oct 2025 12:12:34 +0700
Subject: [PATCH] feat(src): add grid result summary function to export
 DataFrame to LaTeX

---
 code/src/representation.py | 68 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 code/src/representation.py

diff --git a/code/src/representation.py b/code/src/representation.py
new file mode 100644
index 0000000..b39c2f6
--- /dev/null
+++ b/code/src/representation.py
@@ -0,0 +1,68 @@
+import pandas as pd
+import numpy as np
+
+def grid_result_summary(df: pd.DataFrame, output_path: str, include_efficiency: bool = True, verbose=False) -> None:
+    """
+    Export a DataFrame to a LaTeX file with optional inclusion of the efficiency column.
+
+    Parameters:
+    -----------
+    df : pd.DataFrame
+        The DataFrame to export.
+    output_path : str
+        The file path where the LaTeX file will be saved.
+    include_efficiency : bool, optional
+        Whether to include the efficiency column (E_i) in the output. Default is True.
+
+    Returns:
+    --------
+    None
+    """
+    # Define the columns to include
+    columns: list[str] = [
+        "param_pca__n_components", 
+        "param_svc__C", 
+        "param_svc__gamma", 
+        "mean_test_score", 
+        "mean_fit_time"
+    ]
+    # Filter DataFrame to include only the best mean_test_score for each param_pca__n_components
+    df = df.loc[df.groupby('param_pca__n_components')['mean_test_score'].idxmax()][columns]
+
+    # Convert C and gamma to log2 scale
+    df['param_svc__C'] = np.log2(df['param_svc__C']).astype(int)
+    df['param_svc__gamma'] = np.log2(df['param_svc__gamma']).astype(int)
+
+    if include_efficiency:
+        columns.append("time_score_ratio")
+        df['time_score_ratio'] = df['mean_test_score'] / df['mean_fit_time'] * 1e3
+    
+    # Rename columns for LaTeX formatting
+    column_names = {
+        "param_pca__n_components": r"$n_{\text{components}}$",
+        "param_svc__C": r"$C (\log_2)$",
+        "param_svc__gamma": r"$\gamma (\log_2)$",
+        "mean_test_score": r"$S_i$",
+        "mean_fit_time": r"$T_i$",
+        "time_score_ratio": r"$E_i (\times 10^{-3})$"
+    }
+    
+    # Filter and rename columns
+    df_filtered = df[columns].rename(columns=column_names)
+    
+    # Export to LaTeX
+    latex_table = df_filtered.to_latex(
+        index=False,
+        float_format="%.5f",
+        column_format="cccccc" if include_efficiency else "ccccc",
+        # caption="Best hyperparameters for each PCA component size.",
+        # label="tab:best_hyperparams"
+    )
+    
+    # Save to file
+    with open(output_path, "w") as f:
+        f.write(latex_table)
+    
+    if verbose:
+        print(latex_table)
+    print(f"LaTeX table exported to '{output_path}'")
\ No newline at end of file