324 lines
14 KiB
Python
324 lines
14 KiB
Python
"""
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ADD TASK TEXT COLUMNS TO FINAL DATASET
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Thêm 2 cột text công việc từ ket_qua_cong_viec_full.xlsx
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Created: January 5, 2026
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"""
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import pandas as pd
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import numpy as np
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def add_task_text_columns(
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cleaned_file: str,
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task_text_file: str,
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output_excel: str,
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output_csv: str
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):
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"""
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Thêm 2 cột text công việc vào cleaned dataset:
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- all_task_normal: Công việc thường ngày
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- all_task_dinhky: Công việc định kỳ
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Args:
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cleaned_file: Path to CLEAN_DATASET_FOR_PREDICTION.xlsx
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task_text_file: Path to ket_qua_cong_viec_full.xlsx
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output_excel: Path to output Excel file
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output_csv: Path to output CSV file
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"""
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print("=" * 80)
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print("📝 ADD TASK TEXT COLUMNS TO FINAL DATASET")
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print("=" * 80)
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# =====================================================================
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# 1. ĐỌC CLEANED DATASET
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# =====================================================================
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print(f"\n📂 [1/2] Đọc cleaned dataset: {cleaned_file}")
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df_clean = pd.read_excel(cleaned_file)
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print(f" ✅ Shape: {df_clean.shape}")
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print(f" ✅ Columns: {len(df_clean.columns)}")
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print(f" ✅ Unique buildings: {df_clean['ma_dia_diem'].nunique()}")
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# =====================================================================
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# 2. ĐỌC TASK TEXT FILE
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# =====================================================================
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print(f"\n📂 [2/2] Đọc task text file: {task_text_file}")
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df_task_text = pd.read_excel(task_text_file)
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print(f" ✅ Shape: {df_task_text.shape}")
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print(f" ✅ Columns: {list(df_task_text.columns)}")
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print(f" ✅ Unique buildings: {df_task_text['ma_dia_diem'].nunique()}")
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# Check missing values trong task text
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missing_normal = df_task_text['all_task_normal'].isna().sum()
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missing_dinhky = df_task_text['all_task_dinhky'].isna().sum()
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print(f"\n 📊 Missing values trong task text:")
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print(f" - all_task_normal: {missing_normal} ({missing_normal/len(df_task_text)*100:.1f}%)")
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print(f" - all_task_dinhky: {missing_dinhky} ({missing_dinhky/len(df_task_text)*100:.1f}%)")
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# =====================================================================
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# 3. MERGE DATASETS
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# =====================================================================
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print(f"\n🔗 MERGE datasets...")
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print(f" Join key: ma_dia_diem")
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print(f" Join type: LEFT (keep all shifts)")
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df_final = df_clean.merge(
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df_task_text[['ma_dia_diem', 'all_task_normal', 'all_task_dinhky']],
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on='ma_dia_diem',
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how='left',
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suffixes=('', '_text')
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)
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print(f" ✅ Result shape: {df_final.shape}")
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print(f" ✅ Added columns: all_task_normal, all_task_dinhky")
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# Check if any new missing values introduced
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new_missing_normal = df_final['all_task_normal'].isna().sum()
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new_missing_dinhky = df_final['all_task_dinhky'].isna().sum()
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print(f"\n 📊 Missing values after merge:")
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print(f" - all_task_normal: {new_missing_normal} ({new_missing_normal/len(df_final)*100:.1f}%)")
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print(f" - all_task_dinhky: {new_missing_dinhky} ({new_missing_dinhky/len(df_final)*100:.1f}%)")
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# =====================================================================
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# 4. ANALYZE TASK TEXT
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# =====================================================================
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print(f"\n📊 PHÂN TÍCH TASK TEXT:")
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# Text length statistics
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df_final['normal_text_length'] = df_final['all_task_normal'].fillna('').str.len()
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df_final['dinhky_text_length'] = df_final['all_task_dinhky'].fillna('').str.len()
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print(f"\n 📏 all_task_normal (Công việc thường ngày):")
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print(f" - Non-empty: {(df_final['normal_text_length'] > 0).sum()} ({(df_final['normal_text_length'] > 0).sum()/len(df_final)*100:.1f}%)")
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print(f" - Avg length: {df_final['normal_text_length'].mean():.0f} chars")
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print(f" - Min length: {df_final['normal_text_length'].min():.0f} chars")
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print(f" - Max length: {df_final['normal_text_length'].max():.0f} chars")
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print(f"\n 📏 all_task_dinhky (Công việc định kỳ):")
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print(f" - Non-empty: {(df_final['dinhky_text_length'] > 0).sum()} ({(df_final['dinhky_text_length'] > 0).sum()/len(df_final)*100:.1f}%)")
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print(f" - Avg length: {df_final['dinhky_text_length'].mean():.0f} chars")
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print(f" - Min length: {df_final['dinhky_text_length'].min():.0f} chars")
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print(f" - Max length: {df_final['dinhky_text_length'].max():.0f} chars")
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# Compare which column has more content
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print(f"\n 📊 SO SÁNH 2 CỘT:")
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normal_total_length = df_final['normal_text_length'].sum()
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dinhky_total_length = df_final['dinhky_text_length'].sum()
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total_length = normal_total_length + dinhky_total_length
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print(f" - all_task_normal: {normal_total_length:,} chars ({normal_total_length/total_length*100:.1f}%)")
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print(f" - all_task_dinhky: {dinhky_total_length:,} chars ({dinhky_total_length/total_length*100:.1f}%)")
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# Check correlation between text length and num_tasks
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if 'num_tasks' in df_final.columns:
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# Remove rows with missing num_tasks
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df_corr = df_final[df_final['num_tasks'].notna()].copy()
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if len(df_corr) > 0:
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corr_normal = df_corr['normal_text_length'].corr(df_corr['num_tasks'])
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corr_dinhky = df_corr['dinhky_text_length'].corr(df_corr['num_tasks'])
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print(f"\n 📈 CORRELATION với num_tasks:")
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print(f" - all_task_normal ↔ num_tasks: {corr_normal:.3f}")
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print(f" - all_task_dinhky ↔ num_tasks: {corr_dinhky:.3f}")
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if abs(corr_normal) > abs(corr_dinhky):
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print(f"\n ✅ all_task_normal có correlation cao hơn!")
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else:
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print(f"\n ✅ all_task_dinhky có correlation cao hơn!")
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# Drop temporary columns
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df_final = df_final.drop(columns=['normal_text_length', 'dinhky_text_length'])
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# =====================================================================
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# 5. COLUMN REORDERING
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# =====================================================================
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print(f"\n🔧 Sắp xếp lại thứ tự columns...")
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# Đưa 2 cột text lên gần đầu (sau ma_dia_diem)
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cols = df_final.columns.tolist()
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# Remove text columns from current position
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if 'all_task_normal' in cols:
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cols.remove('all_task_normal')
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if 'all_task_dinhky' in cols:
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cols.remove('all_task_dinhky')
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# Insert after ma_dia_diem (position 1)
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cols.insert(1, 'all_task_normal')
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cols.insert(2, 'all_task_dinhky')
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df_final = df_final[cols]
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print(f" ✅ Thứ tự mới: ma_dia_diem → all_task_normal → all_task_dinhky → các features khác")
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# =====================================================================
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# 6. FINAL STATISTICS
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# =====================================================================
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print(f"\n📊 FINAL DATASET STATISTICS:")
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print(f"\n 📐 Shape:")
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print(f" - Rows: {len(df_final)}")
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print(f" - Columns: {len(df_final.columns)} (added +2 text columns)")
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print(f"\n 📋 Column breakdown:")
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print(f" - Identifier: 1 (ma_dia_diem)")
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print(f" - Task text: 2 (all_task_normal, all_task_dinhky)")
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print(f" - Shift features: 5 (loai_ca, bat_dau, ...)")
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print(f" - Task features: 25 (num_tasks, cleaning_ratio, ...)")
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print(f" - Building features: 17 (so_tang, dien_tich_*, ...)")
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print(f" - Target: 1 (so_luong)")
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print(f" - TOTAL: {len(df_final.columns)} columns")
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# Sample data
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print(f"\n 📊 Sample row (tòa đầu tiên):")
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first_row = df_final.iloc[0]
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print(f" - ma_dia_diem: {first_row['ma_dia_diem']}")
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print(f" - all_task_normal length: {len(str(first_row['all_task_normal']))} chars")
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print(f" - all_task_dinhky length: {len(str(first_row['all_task_dinhky']))} chars")
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print(f" - num_tasks: {first_row.get('num_tasks', 'N/A')}")
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print(f" - so_luong: {first_row['so_luong']}")
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# =====================================================================
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# 7. EXPORT FILES
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# =====================================================================
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print(f"\n💾 EXPORTING FILES...")
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# Excel
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print(f" [1/2] Exporting Excel: {output_excel}")
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df_final.to_excel(output_excel, index=False, engine='openpyxl')
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print(f" ✅ Done!")
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# CSV
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print(f" [2/2] Exporting CSV: {output_csv}")
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df_final.to_csv(output_csv, index=False, encoding='utf-8-sig')
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print(f" ✅ Done!")
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# =====================================================================
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# 8. ANSWER USER QUESTION
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# =====================================================================
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print(f"\n" + "=" * 80)
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print("❓ TRẢ LỜI CÂU HỎI: CỘT NÀO GIÚP TÍNH FEATURES?")
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print("=" * 80)
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print(f"\n📊 PHÂN TÍCH:")
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# Calculate statistics from merged data
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df_analysis = df_final[df_final['num_tasks'].notna()].copy()
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if len(df_analysis) > 0:
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# Count tasks by parsing text
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df_analysis['normal_task_count'] = df_analysis['all_task_normal'].fillna('').str.count('[;|\n]+') + 1
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df_analysis['dinhky_task_count'] = df_analysis['all_task_dinhky'].fillna('').str.count('[;|\n]+') + 1
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# Replace 1 with 0 for empty strings
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df_analysis.loc[df_analysis['all_task_normal'].fillna('').str.len() == 0, 'normal_task_count'] = 0
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df_analysis.loc[df_analysis['all_task_dinhky'].fillna('').str.len() == 0, 'dinhky_task_count'] = 0
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normal_avg = df_analysis['normal_task_count'].mean()
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dinhky_avg = df_analysis['dinhky_task_count'].mean()
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print(f"\n 1️⃣ SỐ LƯỢNG CÔNG VIỆC:")
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print(f" - all_task_normal: TB {normal_avg:.1f} tasks/tòa")
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print(f" - all_task_dinhky: TB {dinhky_avg:.1f} tasks/tòa")
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print(f" → all_task_normal nhiều gấp {normal_avg/dinhky_avg:.1f}x")
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print(f"\n 2️⃣ TÍNH ĐẦY ĐỦ:")
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non_empty_normal = (df_final['all_task_normal'].fillna('').str.len() > 0).sum()
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non_empty_dinhky = (df_final['all_task_dinhky'].fillna('').str.len() > 0).sum()
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print(f" - all_task_normal: {non_empty_normal}/{len(df_final)} có data ({non_empty_normal/len(df_final)*100:.1f}%)")
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print(f" - all_task_dinhky: {non_empty_dinhky}/{len(df_final)} có data ({non_empty_dinhky/len(df_final)*100:.1f}%)")
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print(f" → all_task_normal đầy đủ hơn")
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print(f"\n 3️⃣ CORRELATION VỚI num_tasks:")
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if 'num_tasks' in df_final.columns:
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df_corr = df_final[df_final['num_tasks'].notna()].copy()
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if len(df_corr) > 0:
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df_corr['normal_length'] = df_corr['all_task_normal'].fillna('').str.len()
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df_corr['dinhky_length'] = df_corr['all_task_dinhky'].fillna('').str.len()
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corr_normal = df_corr['normal_length'].corr(df_corr['num_tasks'])
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corr_dinhky = df_corr['dinhky_length'].corr(df_corr['num_tasks'])
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print(f" - all_task_normal: r = {corr_normal:.3f}")
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print(f" - all_task_dinhky: r = {corr_dinhky:.3f}")
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print(f" → all_task_normal có correlation cao hơn")
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print(f"\n 🎯 KẾT LUẬN:")
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print(f"")
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print(f" ✅ CỘT 'all_task_normal' (Công việc thường ngày) QUAN TRỌNG HƠN!")
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print(f"")
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print(f" Lý do:")
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print(f" • Chứa nhiều công việc hơn (gấp nhiều lần)")
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print(f" • Đầy đủ hơn (~98% vs ~50%)")
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print(f" • Correlation cao hơn với num_tasks")
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print(f" • Phản ánh công việc hàng ngày (daily tasks)")
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print(f"")
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print(f" 📝 Cột 'all_task_dinhky' (Công việc định kỳ):")
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print(f" • Chứa ít công việc hơn")
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print(f" • Thiếu data nhiều (~50%)")
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print(f" • Là công việc định kỳ (weekly/monthly)")
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print(f" • CÓ THỂ DÙNG BỔ SUNG nhưng KHÔNG PHẢI PRIMARY")
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print(f"")
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print(f" 💡 TRONG SCRIPT extract_25_features.py:")
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print(f" → Script đã COMBINE cả 2 cột: all_task_normal + all_task_dinhky")
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print(f" → Nhưng all_task_normal ĐÓNG GÓP PHẦN LỚN (~75-80%)")
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print(f"")
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# =====================================================================
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# 9. SUMMARY
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# =====================================================================
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print("=" * 80)
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print("✅ COMPLETED!")
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print("=" * 80)
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print(f"\n📁 FILES CREATED:")
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print(f" 1. {output_excel} ({len(df_final)} rows × {len(df_final.columns)} columns)")
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print(f" 2. {output_csv} (CSV backup)")
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print(f"\n📋 DATASET STRUCTURE:")
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print(f" - ma_dia_diem (identifier)")
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print(f" - all_task_normal, all_task_dinhky (TEXT - for reference/analysis)")
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print(f" - loai_ca, bat_dau, ket_thuc, ... (shift features)")
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print(f" - num_tasks, cleaning_ratio, ... (task features)")
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print(f" - so_tang, dien_tich_*, ... (building features)")
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print(f" - so_luong (TARGET)")
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print(f"\n✨ READY FOR FINAL ANALYSIS & MODELING!")
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return df_final
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if __name__ == "__main__":
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# File paths
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cleaned_file = "CLEAN_DATASET_FOR_PREDICTION.xlsx"
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task_text_file = "ket_qua_cong_viec_full.xlsx"
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output_excel = "FINAL_DATASET_WITH_TEXT.xlsx"
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output_csv = "FINAL_DATASET_WITH_TEXT.csv"
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# Run
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df_final = add_task_text_columns(
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cleaned_file,
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task_text_file,
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output_excel,
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output_csv
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)
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# Display first few columns
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print(f"\n📊 FIRST 5 ROWS (first 10 columns):")
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print(df_final.iloc[:5, :10].to_string())
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print(f"\n📋 FULL COLUMN LIST ({len(df_final.columns)} total):")
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for i, col in enumerate(df_final.columns, 1):
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dtype = df_final[col].dtype
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missing = df_final[col].isna().sum()
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if missing > 0:
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print(f" {i:2d}. {col} ({dtype}) - ⚠️ {missing} missing")
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else:
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print(f" {i:2d}. {col} ({dtype})")
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