Initial commit

run_ca_detailed.py

@@ -0,0 +1,93 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import prince
+
+# Load data
+df = pd.read_excel('Data_VN_filter_v5.xlsx')
+
+# Extract variables
+ugs_cols = ['Park', 'Residential', 'Garden', 'Rooftop', 'Recreation', 'Agriculture', 'Nature']
+
+# Detailed ESS and DES variables
+ess_des_detailed = [
+    # RES (Điều hòa)
+    'Temperature', 'Noise', 'Stormwind', 'Respiratory',
+    # CES (Văn hóa)
+    'Exercises', 'Culture', 'Beauty', 'Education', 'Society', 'Spirit',
+    # DES (Bất lợi)
+    'Dirty', 'Unsafe', 'Danger'
+]
+
+# Ensure the columns exist in df
+available_detailed = [col for col in ess_des_detailed if col in df.columns]
+
+# We want to run CA on the relationships between the 7 UGS types and the 13 specific services.
+# Dữ liệu thang đo Likert thường là dữ liệu thứ bậc (Ordinal), nên dùng Spearman's rho sẽ chính xác hơn Pearson.
+data = df[ugs_cols + available_detailed].dropna()
+corr = data.corr(method='spearman').loc[ugs_cols, available_detailed]
+
+# Tương tự như trước, dịch chuyển ma trận hệ số RHO để các giá trị đều dương (dùng cho CA)
+# Alternatively, since prince CA handles frequencies, we can just pass the raw data? 
+# If we pass raw data of shape (307, 20), CA will treat rows as individuals. 
+# We want to see relationships between UGS and Detailed Services. Passing the correlation matrix (shifted) is a good proxy for similarity.
+corr_shifted = corr + 1
+
+# Initialize CA
+ca = prince.CA(n_components=2, n_iter=3, copy=True, check_input=True, engine='scipy', random_state=42)
+ca = ca.fit(corr_shifted)
+
+# Extract column and row coordinates
+row_coords = ca.row_coordinates(corr_shifted) # UGS
+col_coords = ca.column_coordinates(corr_shifted) # Detailed Services
+
+# Save to CSV
+corr.to_csv('CA_Detailed_Correlation_Matrix.csv')
+row_coords.to_csv('CA_Detailed_UGS_Coords.csv')
+col_coords.to_csv('CA_Detailed_Services_Coords.csv')
+
+# Plot Biplot
+fig, ax = plt.subplots(figsize=(20, 15))
+
+# Plot UGS points (blue dots)
+p_ugs = ax.scatter(row_coords[0], row_coords[1], c='blue', label='UGS Types', s=80, marker='o', edgecolors='black')
+
+# Plot ESS/DES points (red/green squares)
+res_cols = ['Temperature', 'Noise', 'Stormwind', 'Respiratory']
+ces_cols = ['Exercises', 'Culture', 'Beauty', 'Education', 'Society', 'Spirit']
+des_cols = ['Dirty', 'Unsafe', 'Danger']
+
+p_ess = []
+for col in available_detailed:
+    color = 'green' if col in res_cols else ('purple' if col in ces_cols else 'red')
+    p = ax.scatter(col_coords.loc[col, 0], col_coords.loc[col, 1], c=color, marker='s', s=80, edgecolors='black')
+    p_ess.append(p)
+
+# Gắn nhãn (Text) tĩnh để đảm bảo tính nhất quán (Deterministic) 100% mỗi lần chạy
+# Phân tách vị trí nhãn: UGS nằm lệch trên-trái, ESS/DES nằm lệch dưới-phải
+for i, txt in enumerate(ugs_cols):
+    ax.annotate(txt, (row_coords.iloc[i, 0], row_coords.iloc[i, 1]), 
+                xytext=(-10, 10), textcoords='offset points', 
+                color='blue', fontweight='bold', fontsize=12, ha='right', va='bottom')
+
+for col in available_detailed:
+    color = 'green' if col in res_cols else ('purple' if col in ces_cols else 'red')
+    ax.annotate(col, (col_coords.loc[col, 0], col_coords.loc[col, 1]), 
+                xytext=(10, -10), textcoords='offset points', 
+                color=color, fontsize=12, fontweight='bold', ha='left', va='top')
+
+# Add dummy plots for legend
+ax.scatter([], [], c='green', marker='s', label='RES (Điều hòa)')
+ax.scatter([], [], c='purple', marker='s', label='CES (Văn hóa)')
+ax.scatter([], [], c='red', marker='s', label='DES (Bất lợi)')
+
+ax.axhline(0, color='grey', linestyle='--', linewidth=1)
+ax.axvline(0, color='grey', linestyle='--', linewidth=1)
+ax.set_title('Detailed CA Biplot (UGS vs Specific ESS/DES)', fontsize=16)
+ax.set_xlabel('Component 0', fontsize=12)
+ax.set_ylabel('Component 1', fontsize=12)
+ax.legend(loc='center left', bbox_to_anchor=(1.02, 0.5), fontsize=12)
+plt.grid(True, linestyle=':', alpha=0.6)
+plt.savefig('CA_plot_detailed.png', dpi=300, bbox_inches='tight')
+print("\nDetailed CA Plot saved to CA_plot_detailed.png")
+print("Detailed data exported to CSV files.")