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