{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "llama-7b finished\n", "Llama-2-7b-hf finished\n", "llama-13b finished\n", "Llama-2-13b-hf finished\n", "llama-30b finished\n", "llama-65b finished\n" ] } ], "source": [ "import json\n", "from urllib.parse import urlparse\n", "# import ndjson\n", "\n", "id2model2loss = {}\n", "# model_list = [\"deepseek-llm-7b-base\", \"llama-7b\", \"Llama-2-7b-hf\", \"Mistral-7B-v0.1\", \"llama-13b\", \"Llama-2-13b-hf\", \"llama-30b\", \"llama-65b\"]\n", "model_list = [\"llama-7b\", \"Llama-2-7b-hf\", \"llama-13b\", \"Llama-2-13b-hf\", \"llama-30b\", \"llama-65b\"]\n", "\n", "\n", "for i in range(0,len(model_list)):\n", " for j in range(0,16):\n", " with open(f\"./bpc_calculation_results/{model_list[i]}/{j}.json\", \"r\") as f:\n", " for line in f:\n", " data = json.loads(line)\n", " if data[\"id\"] not in id2model2loss:\n", " id2model2loss[data[\"id\"]] = {}\n", " # raise ValueError(\"Duplicate ID\")\n", " id2model2loss[data[\"id\"]][model_list[i]] = data[\"total_loss\"]\n", " \n", "\n", " print(f\"{model_list[i]} finished\")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "id2charnum = {}\n", "id2url = {}\n", "for i in range(0,16):\n", "\n", " with open(f\"./bpc_calculation_16/{i}.json\", \"r\") as f:\n", " for line in f:\n", " data = json.loads(line)\n", " if data[\"id\"] not in id2charnum:\n", " id2charnum[data[\"id\"]] = len(data[\"text\"])\n", " else:\n", " raise ValueError(\"Duplicate ID\")\n", " if data[\"id\"] not in id2url:\n", " id2url[data[\"id\"]] = urlparse(data[\"url\"]).netloc\n", " else:\n", " raise ValueError(\"Duplicate ID\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model2benchmark = {\n", " # \"deepseek-llm-7b-base\": {\n", " # \"arc_e\": 75.42,\n", " # \"arc_e_norm\": 70.75,\n", " # \"hellaSwag\": 76.1\n", "\n", " # },\n", " \"llama-7b\": {\n", " \"arc_e\":75.38,\n", " \"arc_e_norm\":72.85,\n", " \"hellaSwag\": 76.2,\n", " \"ceval\":27.4\n", " },\n", " \"Llama-2-7b-hf\": {\n", " \"arc_e\":76.30,\n", " \"arc_e_norm\":74.49,\n", " \"hellaSwag\": 76,\n", " \"ceval\":27.4\n", "\n", " },\n", " # \"Mistral-7B-v0.1\": {\n", " # \"arc_e\":80.80,\n", " # \"arc_e_norm\":79.50,\n", " # \"hellaSwag\": 81.1\n", "\n", " # },\n", " \"llama-13b\": {\n", " \"arc_e\":77.27,\n", " \"arc_e_norm\":74.62,\n", " \"hellaSwag\": 79.1,\n", " \"ceval\":32.0\n", "\n", "\n", " },\n", " \"Llama-2-13b-hf\": {\n", " \"arc_e\":79.46,\n", " \"arc_e_norm\":77.40,\n", " \"hellaSwag\": 79.4,\n", " \"ceval\":40.2\n", " },\n", " \"llama-30b\": {\n", " \"arc_e\":80.47,\n", " \"arc_e_norm\":78.99,\n", " \"hellaSwag\": 82.6,\n", " \"ceval\":35.3\n", "\n", "\n", " },\n", " \"llama-65b\": {\n", " \"arc_e\":81.19,\n", " \"arc_e_norm\":79.88,\n", " \"hellaSwag\": 84.2,\n", " \"ceval\":40.5\n", "\n", " }, \n", "}\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000 finished\n", "200000 finished\n", "300000 finished\n", "400000 finished\n", "500000 finished\n", "600000 finished\n", "700000 finished\n", "800000 finished\n", "212952\n", "100000 finished\n", "200000 finished\n", "300000 finished\n", "400000 finished\n", "500000 finished\n", "600000 finished\n", "700000 finished\n", "800000 finished\n", "203414\n" ] } ], "source": [ "def correct_order(model2loss, model2benchmark):\n", " if len(model2loss) < 6:\n", " return False\n", " temp_model2benchmark_sort = sorted(model2benchmark.items(), key=lambda x: x[1][\"arc_e\"], reverse=True)\n", " # print(temp_model2benchmark_sort)\n", " temp_model2loss_sort = sorted(model2loss.items(), key=lambda x: x[1], reverse=False)\n", " # print(temp_model2loss_sort)\n", " count = 0\n", " for i in range(0, len(temp_model2benchmark_sort)):\n", " if temp_model2loss_sort[i][0] != temp_model2benchmark_sort[i][0] :\n", " count += 1\n", " \n", " if count <= 0:\n", " return True\n", " return False\n", " # return True\n", " # for model in model2loss.keys():\n", " \n", "def wrong_order(model2loss, model2brnchmark, key):\n", " # if len(model2loss) < 6:\n", " # return False\n", " temp_model2benchmark_sort = sorted(model2brnchmark.items(), key=lambda x: x[1][key], reverse=True)\n", " # print(temp_model2benchmark_sort)\n", " temp_model2loss_sort = sorted(model2loss.items(), key=lambda x: x[1], reverse=False)\n", " # print(temp_model2loss_sort)\n", "\n", " count = 0\n", " for i in range(0, len(temp_model2benchmark_sort)):\n", " if temp_model2loss_sort[i][0] != temp_model2benchmark_sort[i][0] :\n", " count += 1\n", " if count > 4 :\n", " return True \n", " else:\n", " return False\n", "\n", "\n", "# correct_order_id = []\n", "# count = 0\n", "# for id in id2model2loss.keys():\n", "# if correct_order(id2model2loss[id], model2benchmark):\n", "# correct_order_id.append(id)\n", "# count += 1\n", "# if (count % 100000 == 0):\n", "# print(f\"{count} finished\") \n", "# print(len(correct_order_id))\n", "\n", "# wrong_order_id = []\n", "# count = 0\n", "# for id in id2model2loss.keys():\n", "# if wrong_order(id2model2loss[id], model2benchmark, \"arc_e\"):\n", "# wrong_order_id.append(id)\n", "# count += 1\n", "# if (count % 100000 == 0):\n", "# print(f\"{count} finished\")\n", "# print(len(wrong_order_id))\n", "\n", "\n", "id2score = {}\n", "for id in id2model2loss.keys():\n", " score = 0\n", " for i in range(0, len(model_list)):\n", " for j in range(i+1, len(model_list)):\n", " if model2benchmark[model_list[i]][\"arc_e\"] > model2benchmark[model_list[j]][\"arc_e\"]:\n", " if id2model2loss[id][model_list[i]] < id2model2loss[id][model_list[j]]:\n", " score += 1\n", " elif model2benchmark[model_list[i]][\"arc_e\"] < model2benchmark[model_list[j]][\"arc_e\"]:\n", " if id2model2loss[id][model_list[i]] > id2model2loss[id][model_list[j]]:\n", " score += 1\n", " id2score[id] = score\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "sorted_id2score = sorted(id2score.items(), key=lambda x: x[1], reverse=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150231\n" ] } ], "source": [ "temp_count = 0\n", "for i in range(0,len(sorted_id2score)):\n", " if sorted_id2score[i][1] < 11:\n", " temp_count += 1\n", "print(temp_count)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "correct_order_id = []\n", "wrong_order_id = []\n", "\n", "for i in range(0, len(sorted_id2score)):\n", " if sorted_id2score[i][1] == 15:\n", " correct_order_id.append(sorted_id2score[i][0])\n", " elif sorted_id2score[i][1] <= 11:\n", " wrong_order_id.append(sorted_id2score[i][0])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "domain_count = {}\n", "for i in range(0,len(correct_order_id)):\n", " if id2url[correct_order_id[i]] not in domain_count:\n", " domain_count[id2url[correct_order_id[i]]] = 1\n", " else:\n", " domain_count[id2url[correct_order_id[i]]] += 1\n", "# domain_count = sorted(domain_count.items(), key=lambda x: x[1], reverse=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "domain_count = {}\n", "for i in range(0,len(wrong_order_id)):\n", " if id2url[wrong_order_id[i]] not in domain_count:\n", " domain_count[id2url[wrong_order_id[i]]] = 1\n", " else:\n", " domain_count[id2url[wrong_order_id[i]]] += 1\n", "# domain_count = sorted(domain_count.items(), key=lambda x: x[1], reverse=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Filter by number (domain)\n", "correct_order_id_new = []\n", "for i in range(0,len(correct_order_id)):\n", " if domain_count[id2url[correct_order_id[i]]] > 20:\n", " correct_order_id_new.append(correct_order_id[i])\n", "len(correct_order_id_new)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wrong_order_id_new = []\n", "for i in range(0,len(wrong_order_id)):\n", " if domain_count[id2url[wrong_order_id[i]]] > 40:\n", " wrong_order_id_new.append(wrong_order_id[i])\n", "len(wrong_order_id_new)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "correct_order_id = correct_order_id_new\n", "wrong_order_id = wrong_order_id_new" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "text_len_count = []\n", "for i in range(0,len(correct_order_id)):\n", " text_len_count.append(id2charnum[correct_order_id[i]])\n", "print(np.mean(text_len_count), np.percentile(text_len_count, 10), np.percentile(text_len_count, 90), min(text_len_count), max(text_len_count))\n", "\n", "text_len_count = []\n", "for i in range(0,len(wrong_order_id)):\n", " text_len_count.append(id2charnum[wrong_order_id[i]])\n", "print(np.mean(text_len_count), np.percentile(text_len_count, 10), np.percentile(text_len_count, 90), min(text_len_count), max(text_len_count))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "correct_id_new = []\n", "for i in range(0,len(correct_order_id)):\n", " if id2charnum[correct_order_id[i]] > 500 and id2charnum[correct_order_id[i]] < 11500:\n", " correct_id_new.append(correct_order_id[i])\n", "\n", "correct_order_id = correct_id_new\n", "wrong_order_id_new = []\n", "for i in range(0,len(wrong_order_id)):\n", " if id2charnum[wrong_order_id[i]] > 500 and id2charnum[wrong_order_id[i]] < 11500:\n", " wrong_order_id_new.append(wrong_order_id[i])\n", "\n", "wrong_order_id = wrong_order_id_new\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "notebookRunGroups": { "groupValue": "2" } }, "outputs": [], "source": [ "# import json\n", "# from urllib.parse import urlparse\n", "# # import ndjson\n", "\n", "# model_list = [\"deepseek-llm-7b-base\", \"llama-7b\", \"Llama-2-7b-hf\", \"Mistral-7B-v0.1\", \"llama-13b\", \"Llama-2-13b-hf\", \"llama-30b\", \"llama-65b\"]\n", "\n", "# for k in range(0,8):\n", "# id2model2loss = {}\n", "\n", "# model_list = [\"deepseek-llm-7b-base\", \"llama-7b\", \"Llama-2-7b-hf\", \"Mistral-7B-v0.1\", \"llama-13b\", \"Llama-2-13b-hf\", \"llama-30b\", \"llama-65b\"]\n", "# model2benchmark = {\n", "# \"deepseek-llm-7b-base\": {\n", "# \"arc_e\": 75.42,\n", "# \"arc_e_norm\": 70.75\n", "\n", "# },\n", "# \"llama-7b\": {\n", "# \"arc_e\":75.38,\n", "# \"arc_e_norm\":72.85\n", "\n", "# },\n", "# \"Llama-2-7b-hf\": {\n", "# \"arc_e\":76.30,\n", "# \"arc_e_norm\":74.49\n", "\n", "# },\n", "# \"Mistral-7B-v0.1\": {\n", "# \"arc_e\":80.80,\n", "# \"arc_e_norm\":79.50\n", "\n", "# },\n", "# \"llama-13b\": {\n", "# \"arc_e\":77.27,\n", "# \"arc_e_norm\":74.62\n", "\n", "# },\n", "# \"Llama-2-13b-hf\": {\n", "# \"arc_e\":79.46,\n", "# \"arc_e_norm\":77.40\n", "\n", "# },\n", "# \"llama-30b\": {\n", "# \"arc_e\":80.47,\n", "# \"arc_e_norm\":78.99 \n", "\n", "# },\n", "# \"llama-65b\": {\n", "# \"arc_e\":81.19,\n", "# \"arc_e_norm\":79.88\n", "\n", "# }, \n", "# }\n", "\n", "\n", "\n", "# print(f\"except model {model_list[k]}\")\n", "\n", "# model_list.remove(model_list[k])\n", "# del model2benchmark[model_list[k]]\n", "# print(model_list)\n", "# print(model2benchmark)\n", "# for i in range(0,len(model_list)):\n", "# for j in range(0,16):\n", "# with open(f\"./bpc_calculation_results/{model_list[i]}/{j}.json\", \"r\") as f:\n", "# for line in f:\n", "# data = json.loads(line)\n", "# if data[\"id\"] not in id2model2loss:\n", "# id2model2loss[data[\"id\"]] = {}\n", "\n", "# # raise ValueError(\"Duplicate ID\")\n", "# id2model2loss[data[\"id\"]][model_list[i]] = data[\"total_loss\"]\n", " \n", "\n", "# # print(f\"{model_list[i]} finished\")\n", "\n", "\n", "# def correct_order(model2loss, model2brnchmark, key):\n", "# temp_model2benchmark_sort = sorted(model2brnchmark.items(), key=lambda x: x[1][key], reverse=True)\n", "# # print(temp_model2benchmark_sort)\n", "# temp_model2loss_sort = sorted(model2loss.items(), key=lambda x: x[1], reverse=False)\n", "# # print(temp_model2loss_sort)\n", "# for i in range(0, len(temp_model2benchmark_sort)):\n", "# if temp_model2loss_sort[i][0] != temp_model2benchmark_sort[i][0] :\n", "# return False\n", "# return True\n", "# # for model in model2loss.keys():\n", " \n", "\n", "\n", "# correct_order_id = []\n", "\n", "# for id in id2model2loss.keys():\n", "# if correct_order(id2model2loss[id], model2benchmark, key=\"arc_e\"):\n", "# correct_order_id.append(id)\n", " \n", "# print(f\"key: arc_e, correct order: {len(correct_order_id)}\")\n", "# correct_order_id = []\n", "\n", "# for id in id2model2loss.keys():\n", "# if correct_order(id2model2loss[id], model2benchmark,key=\"arc_e_norm\"):\n", "# correct_order_id.append(id)\n", " \n", "# print(f\"key: arc_e_norm, correct order: {len(correct_order_id)}\")\n", "# print(\"------------\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "all_data = {}\n", "for i in range(0,16):\n", "\n", " with open(f\"./bpc_calculation_16/{i}.json\", \"r\") as f:\n", " for line in f:\n", " data = json.loads(line)\n", " if data[\"id\"] not in all_data:\n", " all_data[data[\"id\"]] = data[\"text\"]\n", " else:\n", " raise ValueError(\"Duplicate ID\")\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "positive: 73293, negative: 242668\n" ] } ], "source": [ "data_positive = []\n", "data_negative = []\n", "for i in range(0,len(correct_order_id)):\n", " data_positive.append(all_data[correct_order_id[i]])\n", "for i in range(0,len(wrong_order_id)):\n", " data_negative.append(all_data[wrong_order_id[i]])\n", "\n", "print(f\"positive: {len(data_positive)}, negative: {len(data_negative)}\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "with open(\"../Clustering/fasttext/data/rank/6_model_pos_2_neg_mismatch_5_ceval.txt\",\"w\") as f:\n", "\n", " \n", " for i in range(0,len(data_positive)):\n", " f.write(\"__label__1 \" + data_positive[i].replace(\"\\n\", \" \") + \"\\n\")\n", " for i in range(0,len(data_negative)):\n", " f.write(\"__label__0 \" + data_negative[i].replace(\"\\n\", \" \") + \"\\n\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Read 93M words\n", "Number of words: 3154145\n", "Number of labels: 2\n", "Progress: 100.0% words/sec/thread: 83988 lr: 0.000000 avg.loss: 0.969708 ETA: 0h 0m 0s\n" ] } ], "source": [ "import fasttext\n", "model = fasttext.train_supervised(\n", " input=\"../Clustering/fasttext/data/rank/6_model_pos_2_neg_mismatch_5_ceval.txt\",\n", " epoch=3,\n", " lr=0.1,\n", " wordNgrams=2,\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "/ssddata/ksshumab/Pretrain/Clustering/fasttext/model/6_model_pos_2_neg_mismatch_5_ceval.bin cannot be opened for loading!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfasttext\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m fasttext\u001b[38;5;241m.\u001b[39mload_model(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/ssddata/ksshumab/Pretrain/Clustering/fasttext/model/6_model_pos_2_neg_mismatch_5_ceval.bin\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m/home/data/ksshumab/anaconda3/lib/python3.11/site-packages/fasttext/FastText.py:445\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model\u001b[39m(path):\n\u001b[1;32m 444\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Load a model given a filepath and return a model object.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 445\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _FastText(model_path\u001b[38;5;241m=\u001b[39mpath)\n", "File \u001b[0;32m/home/data/ksshumab/anaconda3/lib/python3.11/site-packages/fasttext/FastText.py:93\u001b[0m, in \u001b[0;36m_FastText.__init__\u001b[0;34m(self, model_path, args)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf \u001b[38;5;241m=\u001b[39m fasttext\u001b[38;5;241m.\u001b[39mfasttext()\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf\u001b[38;5;241m.\u001b[39mloadModel(model_path)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "\u001b[0;31mValueError\u001b[0m: /ssddata/ksshumab/Pretrain/Clustering/fasttext/model/6_model_pos_2_neg_mismatch_5_ceval.bin cannot be opened for loading!" ] } ], "source": [ "import fasttext\n", "model = fasttext.load_model(\"/ssddata/ksshumab/Pretrain/Clustering/fasttext/model/6_model_pos_2_neg_mismatch_5_ceval.bin\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3154145" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(model.get_words())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "finish 0\n", "finish 1000\n", "finish 2000\n", "finish 3000\n", "finish 4000\n", "finish 5000\n", "finish 6000\n", "finish 7000\n", "finish 8000\n", "finish 9000\n", "finish 10000\n", "finish 11000\n", "finish 12000\n", "finish 13000\n", "finish 14000\n", "finish 15000\n", "finish 16000\n", "finish 17000\n", "finish 18000\n", "finish 19000\n", "finish 20000\n", "finish 21000\n", "finish 22000\n", "finish 23000\n", "finish 24000\n", "finish 25000\n", "finish 26000\n", "finish 27000\n", "finish 28000\n", 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= np.matmul(np.array(vector_input).transpose(), vector_output.transpose())\n", "\n", " contribution = result[1] - result[0]\n", "\n", " score_list[word] = contribution\n", " # temp = {word: contribution}\n", " # score_list.append(temp)\n", " if i % 1000 == 0:\n", " print(f\"finish {i}\")\n", "else:\n", " for i in range(0,len(model.get_words())):\n", " word = word_list[i]\n", " vector_input = input_matrix[i]\n", " result = np.matmul(np.array(vector_input).transpose(), vector_output.transpose())\n", "\n", " contribution = result[0] - result[1]\n", "\n", " score_list[word] = contribution\n", " # temp = {word: contribution}\n", " # score_list.append(temp)\n", " if i % 1000 == 0:\n", " print(f\"finish {i}\")\n", " # sorted(score_list, key=lambda x: x.values())\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_negative = data_negative[0:len(data_positive)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "word = \"+0000\"\n", "import re\n", "list_occur = []\n", "count = 0\n", "for i in range(0,len(data_negative)):\n", " if word in data_negative[i]:\n", " tokens = re.split(\"\\n|\\t| |\\r|\\v|\\f|\\0\", data_negative[i])\n", " count += tokens.count(word)\n", "print(count)\n", "\n", "count = 0\n", "for i in range(0,len(data_positive)):\n", " if word in data_positive[i]:\n", " tokens = re.split(\"\\n|\\t| |\\r|\\v|\\f|\\0\", data_positive[i])\n", "\n", " count += tokens.count(word)\n", "print(count)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(data_positive)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('says.', 122.0203), ('2017', 95.16321), ('2018', 90.108505), ('@', 75.229095), ('Title:', 71.8569), ('“I', 65.72561), ('+0000', 65.46805), ('2019', 64.15744), ('“We', 61.93983), ('(UTC)', 61.671017), ('said.', 59.503487), ('Tue,', 58.890873), ('chapter', 58.600258), ('evidence', 57.67717), ('whose', 57.24536), ('Main', 56.244358), ('From:', 56.16172), ('AM]', 55.269646), ('Basic', 54.257042), ('bass', 53.282196), ('shall', 52.8376), ('UNICEF', 51.577404), ('URL', 51.395737), ('Sort', 50.58952), ('Their', 50.583023), ('example,', 50.300304), ('“It', 49.74859), ('Meeting', 49.176216), ('Wed,', 48.969433), ('▶', 48.581314), ('“It’s', 48.055084), ('Archives', 47.855087), ('Section', 47.796097), ('mailing', 46.939297), ('Date', 46.565964), ('Description:', 46.508663), ('…', 46.245163), ('Members', 46.178635), ('useful', 46.14344), ('Tractor', 46.036285), ('writes:', 45.996693), ('questions', 45.92608), ('posts', 45.395187), ('Zoom', 45.28817), ('Skater', 45.24904), ('eyes', 45.23216), ('?', 45.200817), ('[Skip', 45.07399), ('General', 44.666023), ('pts', 44.259605)]\n", "\n", "\n", "[('', -350.10492), ('–\\xa0', -129.51299), ('share|improve', -117.07978), ('in:', -111.954544), ('Defines', -111.211586), ('Disqus', -107.212616), ('(AP)', -98.500435), ('\\xa0\\xa0\\xa0', -86.929214), ('Weather', -86.80957), ('Comments', -85.3295), ('Print', -83.82944), ('powered', -83.40584), ('Posters', -80.42235), ('shoes', -80.08198), ('Leave', -77.75506), ('COVID-19', -77.0975), ('Published:', -75.73395), ('Recommended', -73.279205), ('Answer', -72.77182), ('Posts', -72.03338), ('Sponsored', -69.2242), ('Sports', -68.86692), ('Popular', -68.68242), ('Answers', -68.484604), ('Recent', -67.89334), ('Images)', -67.45068), ('Gray', -66.20628), ('Stories', -64.50835), ('PHOTOS:', -64.49107), ('Tags:', -64.21125), ('Privacy', -61.684895), ('▲', -61.31809), ('Trending', -60.62216), ('Filed', -59.713905), ('Living', -59.11519), ('Answered:', -58.992577), ('Also', -58.649796), ('macrumors', -58.478264), ('Articles', -57.99871), ('Horse', -57.493103), ('Subscribe', -57.03083), ('Tools', -56.834404), ('Company', -56.50789), ('Follow', -56.369225), ('Images', -56.124928), ('louboutin', -55.7736), ('Posted:', -55.64402), ('Bookmark', -55.18771), ('PM,', -54.186577), ('Current', -54.101524)]\n" ] } ], "source": [ "sorted_list = sorted(score_list.items(), key=lambda item: item[1],reverse=True)\n", "reverse_sorted_list = sorted(score_list.items(), key=lambda item: item[1],reverse=False)\n", "print(sorted_list[:50])\n", "print(\"\\n\")\n", "print(reverse_sorted_list[:50])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reverse_sorted_list[:50]\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b99350ed167a42eea3d0ba09d0a4c904", "version_major": 2, "version_minor": 0 }, "text/plain": [ "6_model_pos_0_neg_mismatch_4.bin: 0%| | 0.00/3.84G [00:00 correlation\n", "## 每一个domain 要有model -> bpc\n", "import numpy as np\n", "domain2model2totalloss = {}\n", "domain2model2total_char = {}\n", "domain2model2bpc = {}\n", "basic_dict = {}\n", "for i in range(0,len(model_list)):\n", " basic_dict[model_list[i]] = 0\n", "print(basic_dict)\n", "for key in id2url.keys():\n", " if id2url[key] not in domain2model2bpc:\n", " domain2model2bpc[id2url[key]] = basic_dict.copy()\n", " if id2url[key] not in domain2model2totalloss:\n", " domain2model2totalloss[id2url[key]] = basic_dict.copy()\n", " if id2url[key] not in domain2model2total_char:\n", " domain2model2total_char[id2url[key]] = basic_dict.copy()\n", "\n", "\n", "\n", "for key in id2model2loss.keys():\n", " for model in id2model2loss[key].keys():\n", " domain2model2totalloss[id2url[key]][model] += id2model2loss[key][model]\n", " domain2model2total_char[id2url[key]][model] += id2charnum[key]\n", "\n", "for domain in domain2model2totalloss.keys():\n", " for model in domain2model2totalloss[domain].keys():\n", " domain2model2bpc[domain][model] = domain2model2totalloss[domain][model] / (domain2model2total_char[domain][model] * np.log(2))\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "domain2correlation = {}\n", "\n", "for domain in domain2model2bpc.keys():\n", " x = []\n", " y = []\n", " for model in domain2model2bpc[domain].keys():\n", " x.append(domain2model2bpc[domain][model])\n", " y.append(model2benchmark[model][\"arc_e\"]) \n", "\n", " if len(x) == 7:\n", " correlation = np.corrcoef(x,y)[0,1]\n", " domain2correlation[domain] = correlation\n", " else:\n", " domain2correlation[key] = 0\n", "domain2correlation_sorted = sorted(domain2correlation.items(), key=lambda x: x[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "domain2correlation_sorted[2000:3000]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key in domain2correlation:\n", " if \"sex\" in key:\n", " print(f\"{key} : {domain2correlation[key]}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "positive_domain = []\n", "negative_domain = []\n", "for i in range(0,100):\n", " positive_domain.append(domain2correlation_sorted[i][0])\n", "for i in range(0,100):\n", " negative_domain.append(domain2correlation_sorted[-i-1][0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "positive_data = []\n", "negative_data = []\n", "for key in all_data.keys():\n", " if id2url[key] in positive_domain:\n", " positive_data.append(all_data[key])\n", " if id2url[key] in negative_domain:\n", " negative_data.append(all_data[key])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open(\"../Clustering/fasttext/data/correlation/domain_pos_100_neg_100_3w_arce.txt\",\"w\") as f:\n", "\n", " \n", " for i in range(0,len(positive_data)):\n", " f.write(\"__label__1 \" + data_positive[i].replace(\"\\n\", \" \") + \"\\n\")\n", " for i in range(0,len(negative_data)):\n", " f.write(\"__label__0 \" + data_negative[i].replace(\"\\n\", \" \") + \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import fasttext\n", "model = fasttext.train_supervised(\n", " input=\"../Clustering/fasttext/data/correlation/domain_pos_100_neg_100_3w_arce.txt\",\n", " epoch=3,\n", " lr=0.1,\n", " wordNgrams=2,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import fasttext\n", "# model = fasttext.load_model(\"./fasttext/model/cluster_top30.bin\")\n", "import numpy as np\n", "score_list = {}\n", "vector_output = np.array(model.get_output_matrix())\n", "word_list = model.get_words()\n", "input_matrix = model.get_input_matrix()\n", "if(model.get_labels()[1] == \"__label__1\"):\n", " for i in range(0,len(model.get_words())):\n", " word = word_list[i]\n", " vector_input = input_matrix[i]\n", " result = np.matmul(np.array(vector_input).transpose(), vector_output.transpose())\n", "\n", " contribution = result[1] - result[0]\n", "\n", " score_list[word] = contribution\n", " # temp = {word: contribution}\n", " # score_list.append(temp)\n", " if i % 1000 == 0:\n", " print(f\"finish {i}\")\n", "else:\n", " for i in range(0,len(model.get_words())):\n", " word = word_list[i]\n", " vector_input = input_matrix[i]\n", " result = np.matmul(np.array(vector_input).transpose(), vector_output.transpose())\n", "\n", " contribution = result[0] - result[1]\n", "\n", " score_list[word] = contribution\n", " # temp = {word: contribution}\n", " # score_list.append(temp)\n", " if i % 1000 == 0:\n", " print(f\"finish {i}\")\n", " # sorted(score_list, key=lambda x: x.values())\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted_list = sorted(score_list.items(), key=lambda item: item[1],reverse=True)\n", "reverse_sorted_list = sorted(score_list.items(), key=lambda item: item[1],reverse=False)\n", "print(sorted_list[:50])\n", "print(\"\\n\")\n", "print(reverse_sorted_list[:50])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }