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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"!pip intall numpy pandas FlagEmbedding scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.metrics import precision_score, recall_score, f1_score\n",
"from FlagEmbedding import FlagReranker\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_path = '...'\n",
"qd_df = pd.read_parquet('AutoRAG-example-korean-embedding-benchmark/data/qa_v4.parquet')\n",
"qd_df['retrieval_gt'] = qd_df['retrieval_gt'].apply(lambda x : x[0][0])\n",
"\n",
"corpus_df = pd.read_parquet('AutoRAG-example-korean-embedding-benchmark/data/ocr_corpus_v3.parquet')\n",
"corpus_id = {}\n",
"for idx, row in corpus_df.iterrows():\n",
" corpus_id[row[0]] = row[1]\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_3861538/48936308.py:10: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
" corpus_id[row[0]] = row[1]\n",
"/tmp/ipykernel_3861538/48936308.py:18: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
" query_id[row[0]] = row[1]\n"
]
}
],
"source": [
"qd_df = qd_df[['qid','query','generation_gt','retrieval_gt']]\n",
"\n",
"query_id = {}\n",
"for idx, row in qd_df.iterrows():\n",
" query_id[row[0]] = row[1]\n",
"\n",
"qrel = qd_df[['qid','retrieval_gt']]\n",
"qrel_id = {}\n",
"for idx, row in qrel.iterrows():\n",
" q_id = row.iloc[0]\n",
" relevant_copus_id = row.iloc[1]\n",
" if q_id not in qrel_id:\n",
" qrel_id[q_id] = set()\n",
" qrel_id[q_id].add(relevant_copus_id)\n",
"\n",
"corpus_df = corpus_df[['doc_id','contents']]\n",
"\n",
"valid_dict = {}\n",
"valid_dict['qrel'] =qrel_id"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"doc_id commerce - B2BDigComm.pdf - 1\n",
"contents Adobe\\n디지털 커머스 시대,\\nB2B 비즈니스 생존 전략\\nB2B 비즈니스를 ...\n",
"Name: 0, dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corpus_df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['qid', 'query', 'generation_gt', 'retrieval_gt'], dtype='object')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qd_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"corpus_df = corpus_df.reset_index(drop=True)\n",
"qd_df = qd_df.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def calculate_accuracy(ranks_list, valid_dict, qd_df, k_values=[1, 3, 5]):\n",
" accuracies = {k: 0 for k in k_values}\n",
" total_queries = len(qd_df)\n",
" \n",
" for i in range(total_queries):\n",
" search_idx = ranks_list[i]\n",
" true_doc_idx = corpus_df[corpus_df['doc_id'] == list(valid_dict['qrel'][qd_df.loc[i, 'qid']])[0]].index[0]\n",
" \n",
" for k in k_values:\n",
" top_k_preds = search_idx[:k]\n",
" if true_doc_idx in top_k_preds:\n",
" accuracies[k] += 1\n",
" \n",
" return {k: accuracies[k] / total_queries for k in k_values}\n",
"\n",
"def calculate_f1_recall_precision(ranks_list, valid_dict, qd_df, k_values=[1, 3, 5]):\n",
" f1_scores = {k: 0 for k in k_values}\n",
" recall_scores = {k: 0 for k in k_values}\n",
" precision_scores = {k: 0 for k in k_values}\n",
" \n",
" total_queries = len(qd_df)\n",
" \n",
" for i in range(total_queries):\n",
" search_idx = ranks_list[i]\n",
" true_doc_idx = corpus_df[corpus_df['doc_id'] == list(valid_dict['qrel'][qd_df.loc[i, 'qid']])[0]].index[0]\n",
" \n",
" for k in k_values:\n",
" top_k_preds = search_idx[:k]\n",
" y_true = [1 if idx == true_doc_idx else 0 for idx in top_k_preds]\n",
" y_pred = [1] * len(top_k_preds)\n",
" \n",
" # Precision, Recall, F1\n",
" precision_scores[k] += precision_score(y_true, y_pred)\n",
" recall_scores[k] += recall_score(y_true, y_pred)\n",
" f1_scores[k] += f1_score(y_true, y_pred)\n",
" \n",
" return {k: f1_scores[k] / total_queries for k in k_values}, \\\n",
" {k: recall_scores[k] / total_queries for k in k_values}, \\\n",
" {k: precision_scores[k] / total_queries for k in k_values}\n",
"\n",
"\n",
"def evaluate_model(corpus_df, qd_df, valid_dict, reranker):\n",
" scores_list = []\n",
" ranks_list = []\n",
" \n",
" for c, query in enumerate(qd_df['query'], start=1):\n",
" corpus_df['query'] = query\n",
" pair_df = corpus_df[['query', 'contents']]\n",
" scores = reranker.compute_score(pair_df.values.tolist(), normalize=True)\n",
" scores = np.array(scores)\n",
" \n",
" sorted_idxs = np.argsort(-scores)\n",
" scores_list.append(scores[sorted_idxs])\n",
" ranks_list.append(sorted_idxs)\n",
" print(f'{c}/{len(qd_df)}')\n",
"\n",
" k_values = [1, 3, 5, 10]\n",
" accuracies = calculate_accuracy(ranks_list, valid_dict, qd_df, k_values=k_values)\n",
" f1_scores, recalls, precisions = calculate_f1_recall_precision(ranks_list, valid_dict, qd_df, k_values=k_values)\n",
" \n",
" return accuracies, f1_scores, recalls, precisions\n",
"\n",
"\n",
"# 모델 평가 \n",
"reranker = FlagReranker(model_path, use_fp16=True)\n",
"\n",
"accuracies, f1_scores, recalls, precisions = evaluate_model(\n",
" corpus_df.copy(), qd_df, valid_dict, reranker)\n",
"\n",
"print(f'Model: {model_path}')\n",
"for k in [1, 3, 5, 10]:\n",
" print(f'Accuracy@{k}: {accuracies[k]:.4f}')\n",
" print(f'F1@{k}: {f1_scores[k]:.4f}')\n",
" print(f'Recall@{k}: {recalls[k]:.4f}')\n",
" print(f'Precision@{k}: {precisions[k]:.4f}')\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sbert3",
"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": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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