{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8159f2eb-88ce-4c45-b1ae-584ce3a1976f", "metadata": {}, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 2, "id": "179a6741-6649-4bea-be83-7fc9fd6c13c6", "metadata": {}, "outputs": [], "source": [ "filename = \"gpt2_gene_multiv1_ft_en.jsonl\"\n", "data_list = []\n", "for line in open(filename):\n", " data = json.loads(line)\n", " data_list.append(data)\n", " " ] }, { "cell_type": "code", "execution_count": 10, "id": "c8cc78e9-fbdf-4c95-847f-44ea953a38ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "en: 0.899\n", "fr: 0.8245\n", "de: 0.79\n", "zh: 0.7395\n", "dna_sim_pair_simple_150bp: 0.955\n", "dna_sim_pair_150bp: 0.86975\n", "dna_sim_pair_50bp: 0.8185\n", "protein_sim_pair_150bp: 0.9738888888888889\n", "protein_sim_pair_450bp: 0.975\n", "dna_protein_pair: 0.5575\n", "dna_protein_pair_100: 0.5725\n", "dna_protein_pair_full: 0.7324999999999999\n", "dna_protein_pair_rand: 0.6\n", "dna_protein_pair_rand_100: 0.60375\n", "dna_protein_pair_rand_full: 0.61125\n" ] } ], "source": [ "# 假设您的数据存储在一个名为data_list的列表中\n", "# 初始化一个字典来保存每个键的最大accuracy值\n", "max_accuracies = {}\n", "\n", "dna_protein_pair_full_list = []\n", "\n", "\n", "# 遍历列表中的每个字典\n", "for data in data_list:\n", " for key, metrics in data.items():\n", " if key not in ['seed']: # 忽略非目标键,例如'seed'\n", " if isinstance(metrics, dict) and 'accuracy' in metrics:\n", " accuracy = metrics['accuracy']\n", " if accuracy<0.5:\n", " accuracy = 1-accuracy\n", "\n", " if key==\"dna_protein_pair_full\":\n", " dna_protein_pair_full_list.append(accuracy)\n", " \n", " if key not in max_accuracies or accuracy > max_accuracies[key]:\n", " max_accuracies[key] = accuracy\n", "\n", "# 打印每个键的最大accuracy值\n", "for key, max_accuracy in max_accuracies.items():\n", " print(f\"{key}: {max_accuracy}\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "0d2f40f8-a817-4b6b-ae17-310478f6f8d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.505,\n", " 0.4975,\n", " 0.575,\n", " 0.5025,\n", " 0.45,\n", " 0.4775,\n", " 0.515,\n", " 0.505,\n", " 0.475,\n", " 0.515,\n", " 0.5675,\n", " 0.4275,\n", " 0.4875,\n", " 0.5125,\n", " 0.505,\n", " 0.5025,\n", " 0.53,\n", " 0.5425,\n", " 0.51,\n", " 0.49,\n", " 0.3925,\n", " 0.4825,\n", " 0.5425,\n", " 0.385,\n", " 0.36,\n", " 0.5125,\n", " 0.535,\n", " 0.4825,\n", " 0.5025,\n", " 0.485,\n", " 0.5125,\n", " 0.3325,\n", " 0.6225,\n", " 0.4975,\n", " 0.5375,\n", " 0.4975,\n", " 0.5325,\n", " 0.2925,\n", " 0.4825,\n", " 0.4875,\n", " 0.4975,\n", " 0.53,\n", " 0.285,\n", " 0.4625,\n", " 0.4275,\n", " 0.48,\n", " 0.4225,\n", " 0.55,\n", " 0.385,\n", " 0.5175,\n", " 0.53,\n", " 0.4375,\n", " 0.495,\n", " 0.485,\n", " 0.3425,\n", " 0.4875,\n", " 0.5575,\n", " 0.4825,\n", " 0.2675,\n", " 0.4975,\n", " 0.5375,\n", " 0.5375,\n", " 0.475,\n", " 0.3525,\n", " 0.485,\n", " 0.34,\n", " 0.4625,\n", " 0.5,\n", " 0.505,\n", " 0.5075,\n", " 0.515,\n", " 0.4925,\n", " 0.445,\n", " 0.3675,\n", " 0.5125,\n", " 0.495,\n", " 0.4175,\n", " 0.4725,\n", " 0.5025,\n", " 0.4875,\n", " 0.53,\n", " 0.5425,\n", " 0.4175,\n", " 0.34,\n", " 0.5225,\n", " 0.49,\n", " 0.4125,\n", " 0.3575,\n", " 0.4925,\n", " 0.535,\n", " 0.51,\n", " 0.49,\n", " 0.535,\n", " 0.4975,\n", " 0.3825,\n", " 0.48,\n", " 0.485,\n", " 0.5]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dna_protein_pair_full_list" ] }, { "cell_type": "code", "execution_count": null, "id": "110b1efd-1ccb-43d1-9033-53cbf92146e2", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }