442 lines
19 KiB
Plaintext
442 lines
19 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Final Group Allocation:\n",
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"Group 1: Ships_idx = [1003, 1028, 1049, 1044, 1020, 1041, 1045, 1036, 1005, 1006], PD type = 537, PD = 2006, SD = 14719\n",
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"Group 2: Ships_idx = [1025, 1035, 1021, 1026, 1002, 1030, 1024, 1037, 1038, 1029], PD type = 537, PD = 1958, SD = 8173\n",
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"Group 3: Ships_idx = [1016, 1046, 1031, 1009, 1048, 1043, 1042, 1019, 1018, 1007, 1000], PD type = 534, PD = 2079, SD = 15310\n",
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"Group 4: Ships_idx = [1004, 1032, 1039, 1014, 1040, 1017, 1022, 1051, 1008, 1050, 1013], PD type = 532, PD = 2066, SD = 12882\n",
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"Group 5: Ships_idx = [1047, 1015, 1027, 1010, 1011, 1001, 1034, 1023, 1012, 1033], PD type = 531, PD = 2064, SD = 10988\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from collections import defaultdict\n",
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"\n",
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"# Function to calculate the number of unique combinations and total count for each ship\n",
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"def calculate_ship_count(group):\n",
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" ship_count = group.groupby('ships_idx')['thing_property'].agg(['nunique', 'size']).reset_index()\n",
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" ship_count.columns = ['ships_idx', 'comb_count', 'total_count']\n",
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" return ship_count\n",
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"\n",
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"# Function to calculate the combination count and total count for a group\n",
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"def calculate_group_count(group):\n",
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" comb_count = group['thing_property'].nunique()\n",
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" total_count = group['thing_property'].size\n",
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" return comb_count, total_count\n",
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"\n",
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"# Function to calculate the increase in combination count when a ship is added to a group\n",
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"def calculate_comb_count_increase(groups, g, ship_idx, mdm):\n",
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" temp_groups = defaultdict(list, {k: v.copy() for k, v in groups.items()})\n",
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" temp_groups[g].append(ship_idx)\n",
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" \n",
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" group_ships = temp_groups[g]\n",
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" group_data = mdm[mdm['ships_idx'].isin(group_ships)]\n",
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" \n",
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" new_comb_count, _ = calculate_group_count(group_data)\n",
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" \n",
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" current_group_data = mdm[mdm['ships_idx'].isin(groups[g])]\n",
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" current_comb_count, _ = calculate_group_count(current_group_data)\n",
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" \n",
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" increase = new_comb_count - current_comb_count\n",
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" \n",
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" return increase\n",
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"\n",
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"# Function to calculate the increase in total count when a ship is added to a group\n",
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"def calculate_total_count_increase(groups, g, ship_idx, mdm):\n",
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" temp_groups = defaultdict(list, {k: v.copy() for k, v in groups.items()})\n",
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" temp_groups[g].append(ship_idx)\n",
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" \n",
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" group_ships = temp_groups[g]\n",
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" group_data = mdm[mdm['ships_idx'].isin(group_ships)]\n",
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" \n",
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" _, new_total_count = calculate_group_count(group_data)\n",
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" \n",
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" current_group_data = mdm[mdm['ships_idx'].isin(groups[g])]\n",
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" _, current_total_count = calculate_group_count(current_group_data)\n",
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" \n",
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" increase = new_total_count - current_total_count\n",
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" \n",
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" return increase\n",
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"\n",
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"# Function to find the ship that will bring the total count closest to the target\n",
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"def find_closest_total_count_ship(groups, g, remaining_ships, mdm, target_total_count):\n",
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" total_count_differences = []\n",
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"\n",
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" current_group_data = mdm[mdm['ships_idx'].isin(groups[g])]\n",
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" _, current_total_count = calculate_group_count(current_group_data)\n",
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"\n",
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" for ship_idx in remaining_ships:\n",
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" increase = calculate_total_count_increase(groups, g, ship_idx, mdm)\n",
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" new_total_count = current_total_count + increase\n",
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" difference = abs(target_total_count - new_total_count)\n",
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" total_count_differences.append((ship_idx, difference, increase))\n",
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"\n",
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" if not total_count_differences:\n",
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" return None, 0\n",
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" \n",
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" closest_ship = min(total_count_differences, key=lambda x: x[1])\n",
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" selected_ship_idx, _, selected_increase = closest_ship\n",
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"\n",
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" return selected_ship_idx, selected_increase\n",
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"\n",
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"# Function to find the ship that gives the maximum increase in combination count\n",
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"def find_max_increase_ship(groups, g, remaining_ships, mdm):\n",
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" comb_count_increase = []\n",
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"\n",
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" for ship_idx in remaining_ships:\n",
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" increase = calculate_comb_count_increase(groups, g, ship_idx, mdm)\n",
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" comb_count_increase.append((ship_idx, increase))\n",
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"\n",
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" max_increase_ship = max(comb_count_increase, key=lambda x: x[1])\n",
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" selected_ship_idx, max_increase = max_increase_ship\n",
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" \n",
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" return selected_ship_idx, max_increase\n",
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"\n",
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"# Function to find the ship that will bring the combination count closest to the target\n",
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"def find_closest_comb_count_ship(groups, g, remaining_ships, mdm, target_comb_count):\n",
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" comb_count_differences = []\n",
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"\n",
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" current_group_data = mdm[mdm['ships_idx'].isin(groups[g])]\n",
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" current_comb_count, _ = calculate_group_count(current_group_data)\n",
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"\n",
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" for ship_idx in remaining_ships:\n",
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" increase = calculate_comb_count_increase(groups, g, ship_idx, mdm)\n",
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" new_comb_count = current_comb_count + increase\n",
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" difference = abs(target_comb_count - new_comb_count)\n",
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" comb_count_differences.append((ship_idx, difference, increase))\n",
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"\n",
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" if not comb_count_differences:\n",
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" return None, 0\n",
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"\n",
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" closest_ship = min(comb_count_differences, key=lambda x: x[1])\n",
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" selected_ship_idx, _, selected_increase = closest_ship\n",
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"\n",
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" return selected_ship_idx, selected_increase\n",
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"\n",
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"# Function to find the group with the maximum combination count\n",
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"def find_group_with_max_comb_count(groups, mdm):\n",
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" max_comb_count = -1\n",
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" max_group_idx = -1\n",
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"\n",
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" for g in range(len(groups)):\n",
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" group_ships = groups[g]\n",
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" group_data = mdm[mdm['ships_idx'].isin(group_ships)]\n",
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" comb_count, _ = calculate_group_count(group_data)\n",
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" \n",
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" if comb_count > max_comb_count:\n",
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" max_comb_count = comb_count\n",
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" max_group_idx = g\n",
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"\n",
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" return max_group_idx, max_comb_count\n",
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"\n",
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"# Function to find the group with the maximum total count\n",
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"def find_group_with_max_total_count(groups, mdm):\n",
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" max_total_count = -1\n",
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" max_group_idx = -1\n",
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"\n",
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" for g in range(len(groups)):\n",
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" group_ships = groups[g]\n",
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" group_data = mdm[mdm['ships_idx'].isin(group_ships)]\n",
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" _, total_count = calculate_group_count(group_data)\n",
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" \n",
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" if total_count > max_total_count:\n",
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" max_total_count = total_count\n",
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" max_group_idx = g\n",
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"\n",
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" return max_group_idx, max_total_count\n",
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"\n",
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"import pandas as pd\n",
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"from collections import defaultdict\n",
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"\n",
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"# Load the CSV file\n",
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"data_file_path = 'preprocessed_data.csv'\n",
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"data = pd.read_csv(data_file_path)\n",
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"\n",
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"# Filter the data where MDM is True\n",
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"mdm_true = data[data['MDM'] == True].copy() # .copy()를 사용하여 명시적으로 복사본 생성\n",
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"mdm_all = data.copy()\n",
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"\n",
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"# Create a new column combining 'thing' and 'property'\n",
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"mdm_true.loc[:, 'thing_property'] = mdm_true['thing'] + '_' + mdm_true['property']\n",
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"mdm_all.loc[:, 'thing_property'] = mdm_all['thing'] + '_' + mdm_all['property']\n",
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"\n",
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"# Initial setup for groups\n",
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"ship_count = calculate_ship_count(mdm_true)\n",
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"num_groups = 5\n",
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"groups = defaultdict(list)\n",
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"\n",
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"# Sort ships by combination count in descending order\n",
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"sorted_ships = ship_count.sort_values(by='comb_count', ascending=False)\n",
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"\n",
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"# Assign the first 5 ships to the groups\n",
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"for i in range(num_groups):\n",
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" groups[i].append(sorted_ships.iloc[i]['ships_idx'])\n",
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"\n",
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"remaining_ships = sorted_ships.iloc[num_groups:]['ships_idx'].values\n",
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"\n",
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"# Allocate remaining ships to the groups\n",
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"while len(remaining_ships) > 0:\n",
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" group_comb_counts = []\n",
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" for g in range(num_groups):\n",
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" group_ships = groups[g]\n",
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" group_data = mdm_true[mdm_true['ships_idx'].isin(group_ships)]\n",
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" comb_count, _ = calculate_group_count(group_data)\n",
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" group_comb_counts.append((g, comb_count))\n",
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"\n",
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" group_comb_counts.sort(key=lambda x: x[1])\n",
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" \n",
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" remaining_group = []\n",
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" for g, _ in group_comb_counts:\n",
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" if len(remaining_ships) == 0:\n",
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" break\n",
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" \n",
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" if group_comb_counts.index((g, _)) == 0:\n",
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" selected_ship_idx, comb_increase = find_max_increase_ship(groups, g, remaining_ships, mdm_true)\n",
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" \n",
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" else:\n",
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" max_group_idx, max_comb_count = find_group_with_max_comb_count(groups, mdm_true)\n",
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" selected_ship_idx, comb_increase = find_closest_comb_count_ship(groups, g, remaining_ships, mdm_true, max_comb_count)\n",
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"\n",
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" if comb_increase == 0:\n",
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" remaining_group.append(g)\n",
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" else:\n",
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" groups[g].append(selected_ship_idx)\n",
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" remaining_ships = remaining_ships[remaining_ships != selected_ship_idx]\n",
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"\n",
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" for g in remaining_group:\n",
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" if len(remaining_ships) == 0:\n",
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" break\n",
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" max_group_idx, max_total_count = find_group_with_max_total_count(groups, mdm_true)\n",
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" selected_ship_idx, count_increase = find_closest_total_count_ship(groups, g, remaining_ships, mdm_true, max_total_count)\n",
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" if selected_ship_idx is not None:\n",
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" groups[g].append(selected_ship_idx)\n",
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" remaining_ships = remaining_ships[remaining_ships != selected_ship_idx]\n",
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"\n",
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"# Calculate comb_count for each group and store it in a list\n",
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"group_comb_counts = []\n",
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"for g in range(num_groups):\n",
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" group_ships = groups[g]\n",
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" group_data_true = mdm_true[mdm_true['ships_idx'].isin(group_ships)]\n",
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" comb_count, total_count = calculate_group_count(group_data_true)\n",
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"\n",
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" # Calculate total count including MDM=False\n",
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" group_data_all = mdm_all[mdm_all['ships_idx'].isin(group_ships)]\n",
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" _, total_count_all = calculate_group_count(group_data_all)\n",
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" \n",
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" group_comb_counts.append((g, comb_count, total_count_all))\n",
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"\n",
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"# Sort the groups by comb_count in descending order\n",
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"group_comb_counts.sort(key=lambda x: x[1], reverse=True)\n",
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"\n",
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"# Reorder the groups dictionary based on the sorted order\n",
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"sorted_groups = defaultdict(list)\n",
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"for i, (g, _, _) in enumerate(group_comb_counts):\n",
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" sorted_groups[i] = groups[g]\n",
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"\n",
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"# Final output of group allocation\n",
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"print(\"Final Group Allocation:\")\n",
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"for g in range(num_groups):\n",
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" group_ships = sorted_groups[g]\n",
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" group_data_true = mdm_true[mdm_true['ships_idx'].isin(group_ships)]\n",
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" comb_count, total_count = calculate_group_count(group_data_true)\n",
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"\n",
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" # Calculate total count including MDM=False\n",
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" group_data_all = mdm_all[mdm_all['ships_idx'].isin(group_ships)]\n",
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" _, total_count_all = calculate_group_count(group_data_all)\n",
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"\n",
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" print(f\"Group {g + 1}: Ships_idx = {group_ships}, PD type = {comb_count}, PD = {total_count}, SD = {total_count_all}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CSV file has been generated: 'combined_group_allocation.csv'\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.model_selection import GroupKFold\n",
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"\n",
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"# Prepare data for custom group allocation (BGKF)\n",
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"comb_counts = []\n",
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"total_counts = []\n",
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"ship_counts = []\n",
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"custom_results = []\n",
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"\n",
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"for g in range(num_groups):\n",
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" group_ships = groups[g]\n",
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" group_data_true = mdm_true[mdm_true['ships_idx'].isin(group_ships)]\n",
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" comb_count, total_count = calculate_group_count(group_data_true)\n",
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" \n",
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" # Calculate total count including MDM=False\n",
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" group_data_all = mdm_all[mdm_all['ships_idx'].isin(group_ships)]\n",
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" _, total_count_all = calculate_group_count(group_data_all)\n",
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" \n",
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" custom_results.append({\n",
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" 'Group': g + 1,\n",
|
||
|
|
" 'Allocation': 'BGKF',\n",
|
||
|
|
" 'Comb_count': comb_count,\n",
|
||
|
|
" 'Total_count': total_count,\n",
|
||
|
|
" 'Total_count_all': total_count_all,\n",
|
||
|
|
" 'Ship_count': len(group_ships),\n",
|
||
|
|
" 'Ships_idx': list(group_ships)\n",
|
||
|
|
" })\n",
|
||
|
|
"\n",
|
||
|
|
"# Sort the custom group allocation by comb_count in descending order\n",
|
||
|
|
"custom_results.sort(key=lambda x: x['Comb_count'], reverse=True)\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust group numbers after sorting\n",
|
||
|
|
"for i, result in enumerate(custom_results):\n",
|
||
|
|
" result['Group'] = i + 1\n",
|
||
|
|
"\n",
|
||
|
|
"# Prepare data for GroupKFold allocation (GKF)\n",
|
||
|
|
"gkf = GroupKFold(n_splits=5)\n",
|
||
|
|
"gkf_results = []\n",
|
||
|
|
"\n",
|
||
|
|
"for i, (train_idx, test_idx) in enumerate(gkf.split(mdm_true, groups=mdm_true['ships_idx'])):\n",
|
||
|
|
" test_group = mdm_true.iloc[test_idx]\n",
|
||
|
|
" comb_count, total_count = calculate_group_count(test_group)\n",
|
||
|
|
" \n",
|
||
|
|
" # Calculate total count including MDM=False\n",
|
||
|
|
" test_group_ships = test_group['ships_idx'].unique()\n",
|
||
|
|
" test_group_all = mdm_all[mdm_all['ships_idx'].isin(test_group_ships)]\n",
|
||
|
|
" _, total_count_all = calculate_group_count(test_group_all)\n",
|
||
|
|
" \n",
|
||
|
|
" gkf_results.append({\n",
|
||
|
|
" 'Group': i + 1,\n",
|
||
|
|
" 'Allocation': 'GKF',\n",
|
||
|
|
" 'Comb_count': comb_count,\n",
|
||
|
|
" 'Total_count': total_count,\n",
|
||
|
|
" 'Total_count_all': total_count_all,\n",
|
||
|
|
" 'Ship_count': test_group['ships_idx'].nunique(),\n",
|
||
|
|
" 'Ships_idx': list(test_group['ships_idx'].unique())\n",
|
||
|
|
" })\n",
|
||
|
|
"\n",
|
||
|
|
"# Sort the GKF allocation by comb_count in descending order\n",
|
||
|
|
"gkf_results.sort(key=lambda x: x['Comb_count'], reverse=True)\n",
|
||
|
|
"\n",
|
||
|
|
"# Adjust group numbers after sorting\n",
|
||
|
|
"for i, result in enumerate(gkf_results):\n",
|
||
|
|
" result['Group'] = i + 1\n",
|
||
|
|
"\n",
|
||
|
|
"# Combine BGKF and GKF results into one DataFrame\n",
|
||
|
|
"combined_results = custom_results + gkf_results\n",
|
||
|
|
"combined_df = pd.DataFrame(combined_results)\n",
|
||
|
|
"\n",
|
||
|
|
"# Output the combined results to a single CSV file\n",
|
||
|
|
"combined_df.to_csv('combined_group_allocation.csv', index=False)\n",
|
||
|
|
"\n",
|
||
|
|
"print(\"CSV file has been generated: 'combined_group_allocation.csv'\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Group 1 datasets saved in dataset/1\n",
|
||
|
|
"Group 2 datasets saved in dataset/2\n",
|
||
|
|
"Group 3 datasets saved in dataset/3\n",
|
||
|
|
"Group 4 datasets saved in dataset/4\n",
|
||
|
|
"Group 5 datasets saved in dataset/5\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import os\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"from sklearn.model_selection import KFold\n",
|
||
|
|
"\n",
|
||
|
|
"def save_datasets_for_group(groups, mdm, data, output_dir='dataset', n_splits=4):\n",
|
||
|
|
" for i in range(len(groups)):\n",
|
||
|
|
" group_folder = os.path.join(output_dir, str(i + 1))\n",
|
||
|
|
" os.makedirs(group_folder, exist_ok=True)\n",
|
||
|
|
" \n",
|
||
|
|
" # Create the test dataset by including only group i\n",
|
||
|
|
" test_group_ships = groups[i]\n",
|
||
|
|
" test_data = mdm[mdm['ships_idx'].isin(test_group_ships)]\n",
|
||
|
|
" \n",
|
||
|
|
" # Extract corresponding entries from the external test dataset\n",
|
||
|
|
" test_all_data = data[data['ships_idx'].isin(test_group_ships)]\n",
|
||
|
|
" \n",
|
||
|
|
" # Create the train dataset by excluding group i\n",
|
||
|
|
" train_group_ships = []\n",
|
||
|
|
" for g in range(len(groups)):\n",
|
||
|
|
" if g != i:\n",
|
||
|
|
" train_group_ships.extend(groups[g])\n",
|
||
|
|
" train_data = mdm[mdm['ships_idx'].isin(train_group_ships)]\n",
|
||
|
|
" \n",
|
||
|
|
" # Use KFold to split train_data into train and valid datasets\n",
|
||
|
|
" kf_inner = KFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
|
||
|
|
" train_idx_inner, valid_idx_inner = next(kf_inner.split(train_data))\n",
|
||
|
|
" \n",
|
||
|
|
" final_train_data = train_data.iloc[train_idx_inner]\n",
|
||
|
|
" valid_data = train_data.iloc[valid_idx_inner]\n",
|
||
|
|
" \n",
|
||
|
|
" # Combine train and valid data to create train_all\n",
|
||
|
|
" train_all_data = pd.concat([final_train_data, valid_data])\n",
|
||
|
|
" \n",
|
||
|
|
" # Save datasets to CSV files\n",
|
||
|
|
" train_file_path = os.path.join(group_folder, 'train.csv')\n",
|
||
|
|
" valid_file_path = os.path.join(group_folder, 'valid.csv')\n",
|
||
|
|
" test_file_path = os.path.join(group_folder, 'test.csv')\n",
|
||
|
|
" test_all_file_path = os.path.join(group_folder, 'test_all.csv')\n",
|
||
|
|
" train_all_file_path = os.path.join(group_folder, 'train_all.csv')\n",
|
||
|
|
" \n",
|
||
|
|
" final_train_data.to_csv(train_file_path, index=False, encoding='utf-8-sig')\n",
|
||
|
|
" valid_data.to_csv(valid_file_path, index=False, encoding='utf-8-sig')\n",
|
||
|
|
" # test_data.to_csv(test_file_path, index=False, encoding='utf-8-sig')\n",
|
||
|
|
" test_all_data.to_csv(test_file_path, index=False, encoding='utf-8-sig')\n",
|
||
|
|
" train_all_data.to_csv(train_all_file_path, index=False, encoding='utf-8-sig')\n",
|
||
|
|
" \n",
|
||
|
|
" print(f\"Group {i + 1} datasets saved in {group_folder}\")\n",
|
||
|
|
"\n",
|
||
|
|
"# Example usage:\n",
|
||
|
|
"save_datasets_for_group(groups, mdm_true, data, n_splits=4)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "torch",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.14"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|