Initial commit

2026-07-08 14:17:57 +07:00
commit b041ccc11a
72 changed files with 2009 additions and 0 deletions
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+import pandas as pd
+import numpy as np
+import statsmodels.api as sm
+
+df = pd.read_excel('Data_VN_filter_v5.xlsx')
+
+# Chuyển các biến Ordinal thành Numeric thay vì Dummies để giảm số chiều, tránh Phân tách hoàn hảo
+numeric_vars = ['Income', 'MEAN RES', 'MEAN CES', 'MEAN DES'] + \
+               ['Park', 'Residential', 'Garden', 'Rooftop', 'Recreation', 'Agriculture', 'Nature'] + \
+               ['Literacy', 'Frequency', 'Distance', 'Time']
+
+# Các biến Nominal (Phân loại danh nghĩa) thực sự
+categorical_vars = ['Gender', 'Career', 'Transportation']
+
+for col in numeric_vars:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+for col in categorical_vars:
+    df[col] = df[col].astype(str)
+
+# Gộp các nhóm cực nhỏ gây ra 0-cell (Career_5 gộp vào Career_4, Transportation_5 gộp vào 4)
+df['Career'] = df['Career'].replace({'5.0': '4.0', '5': '4'})
+df['Transportation'] = df['Transportation'].replace({'5.0': '4.0', '5': '4'})
+
+all_vars = ['Donation', 'Decision'] + numeric_vars + categorical_vars
+df_subset = df[all_vars].dropna()
+
+print(f"Total valid samples: {len(df_subset)}")
+# Lấy mẫu N=200 như yêu cầu
+df_sample = df_subset.sample(n=200, random_state=42)
+
+# Xử lý Dummy
+X = pd.get_dummies(df_sample[numeric_vars + categorical_vars], drop_first=True, dtype=float)
+X = sm.add_constant(X)
+y = df_sample['Donation'].astype(float)
+
+# Run model for Donation
+model = sm.Logit(y, X)
+try:
+    result = model.fit(method='newton', maxiter=1000, disp=False)
+    summary_df = pd.DataFrame({
+        'Beta (B)': result.params,
+        'P-value': result.pvalues,
+        'Odds Ratio EXP(B)': np.exp(result.params)
+    }).round(4)
+    summary_df['Significance'] = summary_df['P-value'].apply(lambda p: '***' if p < 0.001 else ('**' if p < 0.01 else ('*' if p < 0.05 else '')))
+    summary_df = summary_df.sort_values('P-value')
+    summary_df.to_csv('Logistic_Results_Donation_Final.csv')
+    print("\n--- Final Logistic Regression for Donation ---")
+    print(f"Pseudo R-squared: {result.prsquared:.4f}")
+    print(summary_df.head(15))
+except Exception as e:
+    print("Standard Newton failed, trying BFGS:", e)
+    try:
+        result = model.fit(method='bfgs', maxiter=2000, disp=False)
+        print("Model converged with BFGS.")
+        summary_df = pd.DataFrame({
+            'Beta (B)': result.params,
+            'P-value': result.pvalues,
+            'Odds Ratio EXP(B)': np.exp(result.params)
+        }).round(4)
+        print(summary_df.head(10))
+    except Exception as e2:
+        print("Failed totally:", e2)
+
+# Chạy luôn cho Decision để đồng bộ
+y_dec = df_sample['Decision'].astype(float)
+model_dec = sm.Logit(y_dec, X)
+res_dec = model_dec.fit(method='newton', maxiter=1000, disp=False)
+sum_dec = pd.DataFrame({
+    'Beta (B)': res_dec.params,
+    'P-value': res_dec.pvalues,
+    'Odds Ratio EXP(B)': np.exp(res_dec.params)
+}).round(4)
+sum_dec['Significance'] = sum_dec['P-value'].apply(lambda p: '***' if p < 0.001 else ('**' if p < 0.01 else ('*' if p < 0.05 else '')))
+sum_dec = sum_dec.sort_values('P-value')
+sum_dec.to_csv('Logistic_Results_Decision_Final.csv')
+print("\n--- Final Logistic Regression for Decision ---")
+print(sum_dec.head(10))