85 lines
2.8 KiB
Plaintext
85 lines
2.8 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": 2,
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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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"The file with the updated p_dup and p_map columns has been saved: 0.class_document/knn_tfidf/1/test_p_c_r.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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"\n",
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"group_number = 1\n",
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"method_name='knn_tfidf'\n",
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"# Read the test file\n",
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"test_path = f'0.class_document/{method_name}/{group_number}/test_p_c_r.csv'\n",
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"df = pd.read_csv(test_path)\n",
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"\n",
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"# Concatenate p_thing and p_property into p_tp in the test data\n",
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"df['p_tp'] = df['p_thing'] + \" \" + df['p_property']\n",
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"\n",
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"# Read the train_all file\n",
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"train_all_path = f'../../data_preprocess/dataset/{group_number}/train_all.csv'\n",
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"train_all_df = pd.read_csv(train_all_path)\n",
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"\n",
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"# Concatenate thing and property into tp in the train_all data\n",
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"train_all_df['tp'] = train_all_df['thing'] + \" \" + train_all_df['property']\n",
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"\n",
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"# Initialize the p_map column in the test data\n",
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"df['p_map'] = 0\n",
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"\n",
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"# Group by ships_idx and then group p_tp within each ships_idx group\n",
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"grouped = df.groupby('ships_idx')['p_tp']\n",
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"\n",
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"# Iterate through each ships_idx group\n",
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"for ships_idx, group in grouped:\n",
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" # Count the occurrences of each p_tp within the test group\n",
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" p_tp_counts = group.value_counts()\n",
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" \n",
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" # Assign the count as an integer to p_dup for rows with the corresponding p_tp within the group\n",
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" for p_tp, count in p_tp_counts.items():\n",
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" # Update p_dup\n",
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" df.loc[(df['ships_idx'] == ships_idx) & (df['p_tp'] == p_tp), 'p_dup'] = int(count)\n",
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" \n",
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" # Calculate p_map by counting matching tp in train_all_df\n",
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" p_map_count = train_all_df['tp'].eq(p_tp).sum()\n",
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" df.loc[(df['ships_idx'] == ships_idx) & (df['p_tp'] == p_tp), 'p_map'] = int(p_map_count)\n",
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"\n",
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"# Save the modified DataFrame\n",
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"output_path = f'0.class_document/{method_name}/{group_number}/test_p_c_r.csv'\n",
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"df.to_csv(output_path, index=False, encoding='utf-8-sig')\n",
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"\n",
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"print(\"The file with the updated p_dup and p_map columns has been saved:\", output_path)\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "torch",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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