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