Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1470de32-5a54-4ac9-991b-914e6561d96c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 0. HG-PD：即我们的模型，$\\text{exp}_3$/HG-PD: our proposed model, $\\text{exp}_3$\n",
    "\n",
    "这个jupyter文件仅包含了我们所提出的模型HG-PD的训练过程（即 $\\text{exp}_3$）\n",
    "\n",
    "This jupyter file only includes all training processes related to our proposed model HG-PD. i.e., $\\text{exp}_3$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e267f0aa-8e56-4473-b110-6e45e2274c9e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 0.1 导入库/Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "566eea86-9d89-4734-b270-030c43d26774",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Basic'''\n",
    "import os\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import plotly.express as px\n",
    "from datetime import datetime\n",
    "import re\n",
    "import emoji\n",
    "import multiprocessing\n",
    "\n",
    "'''Sklearn'''\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "'''Cluster'''\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "'''Text processing'''\n",
    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
    "from transformers import BertTokenizer, BertModel\n",
    "import jieba\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "'''Hetero graph'''\n",
    "from torch_geometric.data import HeteroData\n",
    "\n",
    "'''Training and NN'''\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from mcr2_HGPD import MCR2_HGPD\n",
    "from mcrLoss import MaximalCodingRateReduction\n",
    "from augment import AugmentLoader\n",
    "import savePara\n",
    "\n",
    "'''Random seed'''\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24a88e1-ba32-4147-aa31-bfb40246e77b",
   "metadata": {},
   "source": [
    "确定transformers的版本号/Confirm library version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfc15cf0-91e0-46b1-a812-41f977f7ec01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.31.0\n",
      "2.0.0+cu118\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "print(transformers.__version__)\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c06a850-26c7-4156-ba95-4db32b424f02",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## 0.2 读取数据/Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdd69a8a-fff8-4da6-952e-ae71800a7921",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''csv文件/csv file'''\n",
    "user_data=pd.read_csv('Datasets/UserData.csv')\n",
    "profile_data=pd.read_csv('Datasets/UserProfile.csv')\n",
    "\n",
    "user_feature = pd.read_csv('Datasets/UserFeature_Graph.csv')\n",
    "user_inter = pd.read_csv('Datasets/UserInter_Graph.csv')\n",
    "hetero_data = pd.read_csv('Datasets/Sina_allInfo.csv')\n",
    "user_comment = pd.read_csv('Datasets/UserPost.csv')\n",
    "\n",
    "'''Tensor'''\n",
    "user_f = torch.load(\"Tensor/user_f.pt\")\n",
    "user_embed_idx = torch.load(\"Tensor/user_embed_idx.pt\")\n",
    "userInter_data = torch.load(\"Tensor/userInter_data.pt\")\n",
    "userInter_self = torch.load(\"Tensor/userInter_self.pt\")\n",
    "s_user_f = torch.load('Tensor/s_user_f.pt')\n",
    "heteroData = torch.load('Tensor/HeteroData.pt')\n",
    "\n",
    "comm_f = torch.load(\"Tensor/Comment_Vector.pt\")\n",
    "userPost_comm = torch.load(\"Tensor/userPost_comm.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5f76e1-bd04-4d90-bd91-2fb05f536a20",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. HG-PD 帮助文档/HG-PD Helper\n",
    "\n",
    "模型训练涉及的脚本将在这里被介绍\n",
    "\n",
    "The documentation of each Python script related to model training will be introduced in this section"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed60ee67-0b35-407f-827f-b2629e193ffa",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1.1 Model: mcr2_HGPD.py\n",
    "- Red: the default value in our case (based on our Genshin Dataset)\n",
    "- Yellow: hyperparameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc517271-f002-4eb0-ae85-0629906ad1ab",
   "metadata": {},
   "source": [
    "- __ init __(self, node_num, feature_dim, cate_dim, hidden_dim, num_hop)\n",
    "  - Parameter\n",
    "    - node_num: the number of user nodes with missing profiles<br>**<span style=\"color:red\">980</span>**\n",
    "    - feature_dim: the number/dimension of the initial user nodes' features<br>**<span style=\"color:red\">10</span>**\n",
    "    - cate_dim: the dimension of each binary variable transformed by a embedding layer (e.g., if $v_{n \\times 1}$ is transformed to $v'_{n \\times 10}$, then cate_dim=10)<br>**<span style=\"color:yellow\">Customizable</span>**\n",
    "    - hidden_dim: the dimension of GNNs' hidden layer<br>**<span style=\"color:yellow\">Customizable</span>**\n",
    "    - num_hop: the number of hop/range of neighbour nodes for message passing<br>**<span style=\"color:yellow\">Customizable</span>**\n",
    "  - self.embedding: generate \"pseudo profiles\" for users without profiles via embedding layer\n",
    "  - self.convs = torch.nn.ModuleList(): HeteroGNNs\n",
    "- forward\n",
    "  - Parameter\n",
    "    - no_Nidx: the embedding index for users withough profiles<br>**<span style=\"color:red\">user_embed_idx</span>**\n",
    "    - u_feature: the standardized user nodes' features for users with profiles<br>**<span style=\"color:red\">s_user_f</span>**\n",
    "    - heteData: the pre-defined HeteroG<br>**<span style=\"color:red\">heteroData</span>**\n",
    "  - Remeber: we only need the representations of user nodes for user clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a6e867-265a-4d91-8d72-34764971457a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1.2 Loss function: mcrLoss.py\n",
    "\n",
    "<font color='red'>注意以下参数名的对应</font>\n",
    "- <font color='red'>论文：$\\gamma_1$ -> 代码：gam2</font>\n",
    "- <font color='red'>论文：$\\epsilon^2$ -> 代码：eps</font>\n",
    "\n",
    "<font color='yellow'>Hint: the correspondence of the parameters names between thesis and code are:</font>\n",
    "- <font color='yellow'>Paper: $\\gamma_1$ -> Code: gam2</font>\n",
    "- <font color='yellow'>Paper: $\\epsilon^2$ -> Code: eps</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2d00dc-3045-46b0-a46c-20a42b1f5a18",
   "metadata": {},
   "source": [
    "- self.eff: whether smoothing is needed between the discriminative and compressive term<br>**<span style=\"color:yellow\">Default `False`</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c87a76f-446b-4311-986c-6e0d6dbe580f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1.3 Data augmentation and batch allocation: AugmentLoader.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbe7e60-4882-4349-8617-487d7b3c2de8",
   "metadata": {},
   "source": [
    "- Key parameters:\n",
    "  - data: user nodes representation from the model\n",
    "  - batch_size: the size of each batch\n",
    "  - aug_num: the number of the specific data point after augmentation (e.g., if 1 data point is augmented to 10, then aug_num=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d33ee8e-910a-46d8-873e-67b9b1bce2dc",
   "metadata": {},
   "source": [
    "## 1.4 Transform the pseudo label to the membership matrix $\\Pi$: Other_function.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c70d51b-58b5-46ef-9a47-d16e49beb695",
   "metadata": {},
   "source": [
    "This python scripted is called in mcrLoss.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9d3fff-be05-464e-8bda-1127cef86d21",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1.5 Save parameters and model states: savePara.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eafafac7-1569-4a7e-9acf-31bd8a6eb41a",
   "metadata": {},
   "source": [
    "This script will be used in the model training to save parameters and model states"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017228d3-3b3a-43dd-975a-747e018374db",
   "metadata": {},
   "source": [
    "# 2. 模型训练/Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93d2b64-e518-402a-9916-4404e3245c34",
   "metadata": {
    "tags": []
   },
   "source": [
    "##  2.1 Model 1 (M1: $\\gamma_1 = 1$, $\\mathrm{smooth} = \\mathrm{False}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d12be7-3385-4ac2-ad18-fdad335b94f5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) 参数/Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496c1397-30d9-4f29-888c-fbf066e94c03",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
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   "source": [
    "- MCR2_HGPD\n",
    "  - cate_dim = 8\n",
    "  - hidden_dim = 16\n",
    "  - num_hop = 2\n",
    "- mcr_loss\n",
    "  - eff = False (without smooth)\n",
    "  - eps = 0.5\n",
    "  - gam2 = 1 (without scale of Discrim in loss calculation)\n",
    "- optimizer & train\n",
    "  - torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "  - epoch = 200\n",
    "- AugmentLoader\n",
    "  - batch_size = 20\n",
    "  - aug_num = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dfb343d-4744-47c1-a878-c843e9048921",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### (2) 模型训练/Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a855d7f1-ce8d-4714-b5fb-35259f438bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "net = MCR2_HGPD(node_num=980, feature_dim=10, cate_dim=8, hidden_dim=16, num_hop=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3bf49fd-92bc-44a3-b99b-a7bf0600aba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mcr_loss = MaximalCodingRateReduction(eps = 0.5, gam2 = 1)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bbaf171-da9b-47b9-aa6a-dbb9e93a8a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_folder = 'Train_record/Model/Model1/Best'\n",
    "model_folder = 'Train_record/Model/Model1/Model state'\n",
    "params_folder = 'Train_record/Model/Model1/Parameter'\n",
    "np_path = 'Train_record/Model/Model1/All_loss.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9bdcfc4-0523-4f78-a926-fa1d43ce5886",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, -5182.87744140625\n",
      "Now the best is: 1\n",
      "2, -5230.09326171875\n",
      "Now the best is: 2\n",
      "3, -5237.19970703125\n",
      "Now the best is: 3\n",
      "4, -5252.37744140625\n",
      "Now the best is: 4\n",
      "5, -5273.7060546875\n",
      "Now the best is: 5\n",
      "6, -5281.34423828125\n",
      "Now the best is: 6\n",
      "7, -5286.69384765625\n",
      "Now the best is: 7\n",
      "8, -5302.775390625\n",
      "Now the best is: 8\n",
      "9, -5309.05615234375\n",
      "Now the best is: 9\n",
      "10, -5314.20751953125\n",
      "Now the best is: 10\n",
      "Epoch 10 finished!\n",
      "11, -5318.869140625\n",
      "Now the best is: 11\n",
      "12, -5330.78662109375\n",
      "Now the best is: 12\n",
      "13, -5335.51806640625\n",
      "Now the best is: 13\n",
      "14, -5344.2861328125\n",
      "Now the best is: 14\n",
      "15, -5352.35986328125\n",
      "Now the best is: 15\n",
      "16, -5352.349609375\n",
      "17, -5358.18994140625\n",
      "Now the best is: 17\n",
      "18, -5363.99853515625\n",
      "Now the best is: 18\n",
      "19, -5370.05224609375\n",
      "Now the best is: 19\n",
      "20, -5370.73046875\n",
      "Now the best is: 20\n",
      "Epoch 20 finished!\n",
      "21, -5376.15185546875\n",
      "Now the best is: 21\n",
      "22, -5380.06005859375\n",
      "Now the best is: 22\n",
      "23, -5378.05224609375\n",
      "24, -5387.22998046875\n",
      "Now the best is: 24\n",
      "25, -5390.79052734375\n",
      "Now the best is: 25\n",
      "26, -5393.9677734375\n",
      "Now the best is: 26\n",
      "27, -5400.5078125\n",
      "Now the best is: 27\n",
      "28, -5400.64013671875\n",
      "Now the best is: 28\n",
      "29, -5402.75390625\n",
      "Now the best is: 29\n",
      "30, -5409.1982421875\n",
      "Now the best is: 30\n",
      "Epoch 30 finished!\n",
      "31, -5408.36572265625\n",
      "32, -5412.2685546875\n",
      "Now the best is: 32\n",
      "33, -5411.076171875\n",
      "34, -5417.20751953125\n",
      "Now the best is: 34\n",
      "35, -5416.1611328125\n",
      "36, -5419.83544921875\n",
      "Now the best is: 36\n",
      "37, -5421.0673828125\n",
      "Now the best is: 37\n",
      "38, -5425.017578125\n",
      "Now the best is: 38\n",
      "39, -5428.9130859375\n",
      "Now the best is: 39\n",
      "40, -5428.62841796875\n",
      "Epoch 40 finished!\n",
      "41, -5432.19384765625\n",
      "Now the best is: 41\n",
      "42, -5432.1748046875\n",
      "43, -5437.6005859375\n",
      "Now the best is: 43\n",
      "44, -5440.8271484375\n",
      "Now the best is: 44\n",
      "45, -5441.78515625\n",
      "Now the best is: 45\n",
      "46, -5444.919921875\n",
      "Now the best is: 46\n",
      "47, -5445.11767578125\n",
      "Now the best is: 47\n",
      "48, -5447.4091796875\n",
      "Now the best is: 48\n",
      "49, -5447.64404296875\n",
      "Now the best is: 49\n",
      "50, -5450.93701171875\n",
      "Now the best is: 50\n",
      "Epoch 50 finished!\n",
      "51, -5451.58935546875\n",
      "Now the best is: 51\n",
      "52, -5455.9111328125\n",
      "Now the best is: 52\n",
      "53, -5459.47509765625\n",
      "Now the best is: 53\n",
      "54, -5459.15576171875\n",
      "55, -5461.806640625\n",
      "Now the best is: 55\n",
      "56, -5461.13525390625\n",
      "57, -5463.50927734375\n",
      "Now the best is: 57\n",
      "58, -5462.908203125\n",
      "59, -5464.9677734375\n",
      "Now the best is: 59\n",
      "60, -5468.04052734375\n",
      "Now the best is: 60\n",
      "Epoch 60 finished!\n",
      "61, -5470.3017578125\n",
      "Now the best is: 61\n",
      "62, -5471.677734375\n",
      "Now the best is: 62\n",
      "63, -5474.0244140625\n",
      "Now the best is: 63\n",
      "64, -5474.58251953125\n",
      "Now the best is: 64\n",
      "65, -5474.20361328125\n",
      "66, -5476.447265625\n",
      "Now the best is: 66\n",
      "67, -5474.81005859375\n",
      "68, -5479.1796875\n",
      "Now the best is: 68\n",
      "69, -5480.51806640625\n",
      "Now the best is: 69\n",
      "70, -5481.3603515625\n",
      "Now the best is: 70\n",
      "Epoch 70 finished!\n",
      "71, -5487.68310546875\n",
      "Now the best is: 71\n",
      "72, -5487.40234375\n",
      "73, -5485.66845703125\n",
      "74, -5489.8017578125\n",
      "Now the best is: 74\n",
      "75, -5490.21337890625\n",
      "Now the best is: 75\n",
      "76, -5494.9404296875\n",
      "Now the best is: 76\n",
      "77, -5494.7421875\n",
      "78, -5497.31689453125\n",
      "Now the best is: 78\n",
      "79, -5493.6005859375\n",
      "80, -5499.4111328125\n",
      "Now the best is: 80\n",
      "Epoch 80 finished!\n",
      "81, -5495.03759765625\n",
      "82, -5502.1416015625\n",
      "Now the best is: 82\n",
      "83, -5500.42333984375\n",
      "84, -5503.24267578125\n",
      "Now the best is: 84\n",
      "85, -5505.82568359375\n",
      "Now the best is: 85\n",
      "86, -5501.56005859375\n",
      "87, -5510.05126953125\n",
      "Now the best is: 87\n",
      "88, -5506.7197265625\n",
      "89, -5511.115234375\n",
      "Now the best is: 89\n",
      "90, -5511.90478515625\n",
      "Now the best is: 90\n",
      "Epoch 90 finished!\n",
      "91, -5512.77685546875\n",
      "Now the best is: 91\n",
      "92, -5513.5380859375\n",
      "Now the best is: 92\n",
      "93, -5517.15478515625\n",
      "Now the best is: 93\n",
      "94, -5519.1396484375\n",
      "Now the best is: 94\n",
      "95, -5517.05517578125\n",
      "96, -5518.0263671875\n",
      "97, -5522.142578125\n",
      "Now the best is: 97\n",
      "98, -5520.12255859375\n",
      "99, -5524.2216796875\n",
      "Now the best is: 99\n",
      "100, -5525.99658203125\n",
      "Now the best is: 100\n",
      "Epoch 100 finished!\n",
      "101, -5528.1689453125\n",
      "Now the best is: 101\n",
      "102, -5529.654296875\n",
      "Now the best is: 102\n",
      "103, -5528.8447265625\n",
      "104, -5531.34814453125\n",
      "Now the best is: 104\n",
      "105, -5531.759765625\n",
      "Now the best is: 105\n",
      "106, -5532.3369140625\n",
      "Now the best is: 106\n",
      "107, -5531.7646484375\n",
      "108, -5535.7861328125\n",
      "Now the best is: 108\n",
      "109, -5536.7333984375\n",
      "Now the best is: 109\n",
      "110, -5537.9423828125\n",
      "Now the best is: 110\n",
      "Epoch 110 finished!\n",
      "111, -5538.70947265625\n",
      "Now the best is: 111\n",
      "112, -5539.08935546875\n",
      "Now the best is: 112\n",
      "113, -5543.9384765625\n",
      "Now the best is: 113\n",
      "114, -5543.21923828125\n",
      "115, -5547.52783203125\n",
      "Now the best is: 115\n",
      "116, -5548.07177734375\n",
      "Now the best is: 116\n",
      "117, -5550.4326171875\n",
      "Now the best is: 117\n",
      "118, -5549.08984375\n",
      "119, -5552.73095703125\n",
      "Now the best is: 119\n",
      "120, -5553.1630859375\n",
      "Now the best is: 120\n",
      "Epoch 120 finished!\n",
      "121, -5555.4140625\n",
      "Now the best is: 121\n",
      "122, -5556.54541015625\n",
      "Now the best is: 122\n",
      "123, -5556.74169921875\n",
      "Now the best is: 123\n",
      "124, -5558.77978515625\n",
      "Now the best is: 124\n",
      "125, -5559.5087890625\n",
      "Now the best is: 125\n",
      "126, -5561.6484375\n",
      "Now the best is: 126\n",
      "127, -5560.65771484375\n",
      "128, -5562.9091796875\n",
      "Now the best is: 128\n",
      "129, -5567.1669921875\n",
      "Now the best is: 129\n",
      "130, -5563.88916015625\n",
      "Epoch 130 finished!\n",
      "131, -5568.978515625\n",
      "Now the best is: 131\n",
      "132, -5566.4345703125\n",
      "133, -5568.99658203125\n",
      "Now the best is: 133\n",
      "134, -5573.84912109375\n",
      "Now the best is: 134\n",
      "135, -5573.5478515625\n",
      "136, -5573.27197265625\n",
      "137, -5577.0048828125\n",
      "Now the best is: 137\n",
      "138, -5576.43701171875\n",
      "139, -5578.8798828125\n",
      "Now the best is: 139\n",
      "140, -5580.84814453125\n",
      "Now the best is: 140\n",
      "Epoch 140 finished!\n",
      "141, -5584.03662109375\n",
      "Now the best is: 141\n",
      "142, -5581.00439453125\n",
      "143, -5583.5625\n",
      "144, -5586.1171875\n",
      "Now the best is: 144\n",
      "145, -5586.97802734375\n",
      "Now the best is: 145\n",
      "146, -5587.14013671875\n",
      "Now the best is: 146\n",
      "147, -5587.759765625\n",
      "Now the best is: 147\n",
      "148, -5591.60693359375\n",
      "Now the best is: 148\n",
      "149, -5590.41455078125\n",
      "150, -5593.32763671875\n",
      "Now the best is: 150\n",
      "Epoch 150 finished!\n",
      "151, -5598.12353515625\n",
      "Now the best is: 151\n",
      "152, -5594.4990234375\n",
      "153, -5597.7470703125\n",
      "154, -5598.4482421875\n",
      "Now the best is: 154\n",
      "155, -5598.87890625\n",
      "Now the best is: 155\n",
      "156, -5603.072265625\n",
      "Now the best is: 156\n",
      "157, -5604.68359375\n",
      "Now the best is: 157\n",
      "158, -5606.2744140625\n",
      "Now the best is: 158\n",
      "159, -5604.61572265625\n",
      "160, -5605.9345703125\n",
      "Epoch 160 finished!\n",
      "161, -5607.53564453125\n",
      "Now the best is: 161\n",
      "162, -5610.96630859375\n",
      "Now the best is: 162\n",
      "163, -5609.837890625\n",
      "164, -5613.083984375\n",
      "Now the best is: 164\n",
      "165, -5610.462890625\n",
      "166, -5614.3525390625\n",
      "Now the best is: 166\n",
      "167, -5616.1083984375\n",
      "Now the best is: 167\n",
      "168, -5618.28271484375\n",
      "Now the best is: 168\n",
      "169, -5618.201171875\n",
      "170, -5618.9912109375\n",
      "Now the best is: 170\n",
      "Epoch 170 finished!\n",
      "171, -5623.7109375\n",
      "Now the best is: 171\n",
      "172, -5623.4130859375\n",
      "173, -5625.12060546875\n",
      "Now the best is: 173\n",
      "174, -5625.51318359375\n",
      "Now the best is: 174\n",
      "175, -5630.64404296875\n",
      "Now the best is: 175\n",
      "176, -5629.95458984375\n",
      "177, -5631.0966796875\n",
      "Now the best is: 177\n",
      "178, -5633.15380859375\n",
      "Now the best is: 178\n",
      "179, -5633.8271484375\n",
      "Now the best is: 179\n",
      "180, -5636.23779296875\n",
      "Now the best is: 180\n",
      "Epoch 180 finished!\n",
      "181, -5637.8662109375\n",
      "Now the best is: 181\n",
      "182, -5635.7626953125\n",
      "183, -5638.93212890625\n",
      "Now the best is: 183\n",
      "184, -5640.40869140625\n",
      "Now the best is: 184\n",
      "185, -5641.93408203125\n",
      "Now the best is: 185\n",
      "186, -5647.0224609375\n",
      "Now the best is: 186\n",
      "187, -5646.35498046875\n",
      "188, -5645.666015625\n",
      "189, -5646.25146484375\n",
      "190, -5650.17431640625\n",
      "Now the best is: 190\n",
      "Epoch 190 finished!\n",
      "191, -5651.89892578125\n",
      "Now the best is: 191\n",
      "192, -5650.5771484375\n",
      "193, -5654.92431640625\n",
      "Now the best is: 193\n",
      "194, -5653.9912109375\n",
      "195, -5655.759765625\n",
      "Now the best is: 195\n",
      "196, -5658.0341796875\n",
      "Now the best is: 196\n",
      "197, -5660.87060546875\n",
      "Now the best is: 197\n",
      "198, -5656.38525390625\n",
      "199, -5662.12939453125\n",
      "Now the best is: 199\n",
      "200, -5664.73486328125\n",
      "Now the best is: 200\n",
      "Epoch 200 finished!\n",
      "Training complete! Total training time: 1.4028265150388082 hours\n"
     ]
    }
   ],
   "source": [
    "epoch_num = 200\n",
    "loss_stand = 0\n",
    "loss_array = np.zeros(epoch_num)\n",
    "m_start_time = time.time()\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "net.train()\n",
    "for epoch in range(epoch_num):\n",
    "    node_user = net(no_Nidx = user_embed_idx,\n",
    "                    u_feature = s_user_f,\n",
    "                    heteData = heteroData)\n",
    "    data_loader = AugmentLoader(node_user,\n",
    "                                batch_size = 20,\n",
    "                                aug_num = 50,\n",
    "                                shuffle = False)\n",
    "    loss_s = torch.tensor(0.0,requires_grad=True,device='cuda')\n",
    "    '''把所有batch的loss加和；每个epoch结束后回传一次loss / Sum the loss of all batch; BP the loss at the end of each epoch'''\n",
    "    for step, (batch, pseudo_label) in enumerate(data_loader):\n",
    "        batch = batch.cuda()\n",
    "        loss, loss_empi, loss_theo = mcr_loss(batch, \n",
    "                                              pseudo_label)\n",
    "        loss_s = torch.add(loss_s, loss)\n",
    "        \n",
    "        if step == 0:\n",
    "            step_savename = 'Model1_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1]) \n",
    "        elif (step+1) % 50 == 0:\n",
    "            step_savename = 'Model1_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1])         \n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_s.backward()\n",
    "    optimizer.step()\n",
    "        \n",
    "    \n",
    "    loss_compare = loss_s.detach().item()\n",
    "    loss_array[epoch] = loss_compare\n",
    "    print(f'{epoch+1}, {loss_compare}')\n",
    "    if loss_compare < loss_stand:\n",
    "        model_savename = f'1Best_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, best_folder,\n",
    "                                  epoch, net, optimizer)\n",
    "        loss_stand = loss_compare\n",
    "        print('Now the best is: {}'.format(epoch+1))\n",
    "           \n",
    "    if epoch == 0:\n",
    "        model_savename = f'Model1_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    elif (epoch+1) % 25 == 0:\n",
    "        model_savename = f'Model1_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    \n",
    "    if epoch == 0:\n",
    "        params_savename = 'Model1_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        \n",
    "    elif (epoch+1) % 10 == 0:\n",
    "        params_savename = 'Model1_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        print('Epoch {} finished!'.format(epoch+1))\n",
    "        \n",
    "m_end_time = time.time()\n",
    "m_total_time = (m_end_time - m_start_time)/3600\n",
    "np.save(np_path, loss_array)\n",
    "print(\"Training complete! Total training time: {} hours\".format(m_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7341ecfb-c818-47d7-96a5-d2d7411f230c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Loss可视化/Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60a09912-6c14-40de-ba30-12248e08b1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_path = 'Train_record/Model/Model1/All_loss.npy'\n",
    "loss1 = np.load(np_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5f347c0-d531-4c90-87a4-3d7106dbc0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(loss1)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Model 1 - 200 Epoch Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a2aa7a-912c-4172-815d-9c77279fc353",
   "metadata": {
    "tags": []
   },
   "source": [
    "##  2.2 Model 2 (M1: $\\gamma_1 = 0.6$, $\\mathrm{smooth} = \\mathrm{False}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758eefb3-462b-435e-8e01-c7f02cabd6ea",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) 参数/Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17de2bfe-75cb-48b3-bfe5-007d1a661598",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "- MCR2_HGPD\n",
    "  - cate_dim = 8\n",
    "  - hidden_dim = 16\n",
    "  - num_hop = 2\n",
    "- mcr_loss\n",
    "  - eff = False (without smooth)\n",
    "  - eps = 0.5\n",
    "  - gam2 = 0.6 (Default scale of Discrim in loss calculation)\n",
    "- optimizer & train\n",
    "  - torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "  - epoch = 200\n",
    "- AugmentLoader\n",
    "  - batch_size = 20\n",
    "  - aug_num = 50\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c780c3-23d9-486c-a999-1e5023b89d9a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### (2) 模型训练/Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92d4fc86-c2ea-469a-b4eb-02e898a642fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "net = MCR2_HGPD(node_num=980, feature_dim=10, cate_dim=8, hidden_dim=16, num_hop=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b7eeb09-63c4-4524-ba4f-b2cc6860a4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mcr_loss = MaximalCodingRateReduction(eps = 0.5)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82871331-622a-4cd7-ae1d-e5d51d6d4371",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_folder = 'Train_record/Model/Model2/Best'\n",
    "model_folder = 'Train_record/Model/Model2/Model state'\n",
    "params_folder = 'Train_record/Model/Model2/Parameter'\n",
    "np_path = 'Train_record/Model/Model2/All_loss.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ece1c1c-ffbd-42c5-a8c3-ae942f6659ac",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, -1004.6990966796875\n",
      "Now the best is: 1\n",
      "2, -1032.1617431640625\n",
      "Now the best is: 2\n",
      "3, -1039.801025390625\n",
      "Now the best is: 3\n",
      "4, -1048.968505859375\n",
      "Now the best is: 4\n",
      "5, -1059.8511962890625\n",
      "Now the best is: 5\n",
      "6, -1066.9462890625\n",
      "Now the best is: 6\n",
      "7, -1069.4986572265625\n",
      "Now the best is: 7\n",
      "8, -1079.0728759765625\n",
      "Now the best is: 8\n",
      "9, -1082.3353271484375\n",
      "Now the best is: 9\n",
      "10, -1084.229736328125\n",
      "Now the best is: 10\n",
      "Epoch 10 finished!\n",
      "11, -1086.3067626953125\n",
      "Now the best is: 11\n",
      "12, -1093.0419921875\n",
      "Now the best is: 12\n",
      "13, -1094.7298583984375\n",
      "Now the best is: 13\n",
      "14, -1099.8399658203125\n",
      "Now the best is: 14\n",
      "15, -1104.28271484375\n",
      "Now the best is: 15\n",
      "16, -1104.924560546875\n",
      "Now the best is: 16\n",
      "17, -1108.646728515625\n",
      "Now the best is: 17\n",
      "18, -1112.2200927734375\n",
      "Now the best is: 18\n",
      "19, -1115.49755859375\n",
      "Now the best is: 19\n",
      "20, -1114.9970703125\n",
      "Epoch 20 finished!\n",
      "21, -1117.499755859375\n",
      "Now the best is: 21\n",
      "22, -1120.0594482421875\n",
      "Now the best is: 22\n",
      "23, -1116.955810546875\n",
      "24, -1123.9195556640625\n",
      "Now the best is: 24\n",
      "25, -1125.346923828125\n",
      "Now the best is: 25\n",
      "26, -1126.4471435546875\n",
      "Now the best is: 26\n",
      "27, -1131.5750732421875\n",
      "Now the best is: 27\n",
      "28, -1131.277587890625\n",
      "29, -1132.237060546875\n",
      "Now the best is: 29\n",
      "30, -1135.4190673828125\n",
      "Now the best is: 30\n",
      "Epoch 30 finished!\n",
      "31, -1134.6866455078125\n",
      "32, -1137.2060546875\n",
      "Now the best is: 32\n",
      "33, -1136.6641845703125\n",
      "34, -1140.3199462890625\n",
      "Now the best is: 34\n",
      "35, -1138.955078125\n",
      "36, -1140.1842041015625\n",
      "37, -1140.169677734375\n",
      "38, -1143.1522216796875\n",
      "Now the best is: 38\n",
      "39, -1145.42822265625\n",
      "Now the best is: 39\n",
      "40, -1145.1156005859375\n",
      "Epoch 40 finished!\n",
      "41, -1148.067138671875\n",
      "Now the best is: 41\n",
      "42, -1146.965087890625\n",
      "43, -1149.5743408203125\n",
      "Now the best is: 43\n",
      "44, -1152.3153076171875\n",
      "Now the best is: 44\n",
      "45, -1152.909912109375\n",
      "Now the best is: 45\n",
      "46, -1154.61767578125\n",
      "Now the best is: 46\n",
      "47, -1153.2236328125\n",
      "48, -1154.0150146484375\n",
      "49, -1154.7833251953125\n",
      "Now the best is: 49\n",
      "50, -1155.857177734375\n",
      "Now the best is: 50\n",
      "Epoch 50 finished!\n",
      "51, -1157.8656005859375\n",
      "Now the best is: 51\n",
      "52, -1160.20263671875\n",
      "Now the best is: 52\n",
      "53, -1162.119873046875\n",
      "Now the best is: 53\n",
      "54, -1161.0430908203125\n",
      "55, -1162.8310546875\n",
      "Now the best is: 55\n",
      "56, -1162.0565185546875\n",
      "57, -1163.85205078125\n",
      "Now the best is: 57\n",
      "58, -1163.3751220703125\n",
      "59, -1163.29638671875\n",
      "60, -1166.563232421875\n",
      "Now the best is: 60\n",
      "Epoch 60 finished!\n",
      "61, -1166.749755859375\n",
      "Now the best is: 61\n",
      "62, -1167.5614013671875\n",
      "Now the best is: 62\n",
      "63, -1170.3782958984375\n",
      "Now the best is: 63\n",
      "64, -1169.4658203125\n",
      "65, -1168.0899658203125\n",
      "66, -1170.328369140625\n",
      "67, -1169.6591796875\n",
      "68, -1171.599365234375\n",
      "Now the best is: 68\n",
      "69, -1173.0560302734375\n",
      "Now the best is: 69\n",
      "70, -1173.46630859375\n",
      "Now the best is: 70\n",
      "Epoch 70 finished!\n",
      "71, -1176.35595703125\n",
      "Now the best is: 71\n",
      "72, -1176.8199462890625\n",
      "Now the best is: 72\n",
      "73, -1176.6732177734375\n",
      "74, -1177.204345703125\n",
      "Now the best is: 74\n",
      "75, -1178.6163330078125\n",
      "Now the best is: 75\n",
      "76, -1180.3031005859375\n",
      "Now the best is: 76\n",
      "77, -1182.5059814453125\n",
      "Now the best is: 77\n",
      "78, -1181.4810791015625\n",
      "79, -1179.8804931640625\n",
      "80, -1182.8951416015625\n",
      "Now the best is: 80\n",
      "Epoch 80 finished!\n",
      "81, -1179.0357666015625\n",
      "82, -1184.81982421875\n",
      "Now the best is: 82\n",
      "83, -1184.497314453125\n",
      "84, -1185.09521484375\n",
      "Now the best is: 84\n",
      "85, -1187.806884765625\n",
      "Now the best is: 85\n",
      "86, -1183.9674072265625\n",
      "87, -1189.0443115234375\n",
      "Now the best is: 87\n",
      "88, -1186.8963623046875\n",
      "89, -1189.6427001953125\n",
      "Now the best is: 89\n",
      "90, -1189.8636474609375\n",
      "Now the best is: 90\n",
      "Epoch 90 finished!\n",
      "91, -1190.117919921875\n",
      "Now the best is: 91\n",
      "92, -1190.63330078125\n",
      "Now the best is: 92\n",
      "93, -1192.3250732421875\n",
      "Now the best is: 93\n",
      "94, -1193.681640625\n",
      "Now the best is: 94\n",
      "95, -1192.2064208984375\n",
      "96, -1192.8802490234375\n",
      "97, -1196.258544921875\n",
      "Now the best is: 97\n",
      "98, -1193.7591552734375\n",
      "99, -1197.21826171875\n",
      "Now the best is: 99\n",
      "100, -1198.339111328125\n",
      "Now the best is: 100\n",
      "Epoch 100 finished!\n",
      "101, -1198.636962890625\n",
      "Now the best is: 101\n",
      "102, -1200.2823486328125\n",
      "Now the best is: 102\n",
      "103, -1198.7581787109375\n",
      "104, -1200.6949462890625\n",
      "Now the best is: 104\n",
      "105, -1200.815185546875\n",
      "Now the best is: 105\n",
      "106, -1201.1173095703125\n",
      "Now the best is: 106\n",
      "107, -1200.2945556640625\n",
      "108, -1204.0684814453125\n",
      "Now the best is: 108\n",
      "109, -1203.140869140625\n",
      "110, -1204.7330322265625\n",
      "Now the best is: 110\n",
      "Epoch 110 finished!\n",
      "111, -1204.609619140625\n",
      "112, -1204.4388427734375\n",
      "113, -1207.0948486328125\n",
      "Now the best is: 113\n",
      "114, -1208.2528076171875\n",
      "Now the best is: 114\n",
      "115, -1209.9674072265625\n",
      "Now the best is: 115\n",
      "116, -1208.910888671875\n",
      "117, -1210.6998291015625\n",
      "Now the best is: 117\n",
      "118, -1209.98193359375\n",
      "119, -1212.2755126953125\n",
      "Now the best is: 119\n",
      "120, -1211.9608154296875\n",
      "Epoch 120 finished!\n",
      "121, -1213.656494140625\n",
      "Now the best is: 121\n",
      "122, -1214.754150390625\n",
      "Now the best is: 122\n",
      "123, -1214.3828125\n",
      "124, -1216.8035888671875\n",
      "Now the best is: 124\n",
      "125, -1216.92333984375\n",
      "Now the best is: 125\n",
      "126, -1216.6402587890625\n",
      "127, -1215.950439453125\n",
      "128, -1218.1727294921875\n",
      "Now the best is: 128\n",
      "129, -1221.4317626953125\n",
      "Now the best is: 129\n",
      "130, -1217.6473388671875\n",
      "Epoch 130 finished!\n",
      "131, -1221.6591796875\n",
      "Now the best is: 131\n",
      "132, -1220.3504638671875\n",
      "133, -1220.9283447265625\n",
      "134, -1224.225341796875\n",
      "Now the best is: 134\n",
      "135, -1223.0018310546875\n",
      "136, -1222.8519287109375\n",
      "137, -1226.3897705078125\n",
      "Now the best is: 137\n",
      "138, -1225.58544921875\n",
      "139, -1226.0625\n",
      "140, -1226.828857421875\n",
      "Now the best is: 140\n",
      "Epoch 140 finished!\n",
      "141, -1229.7532958984375\n",
      "Now the best is: 141\n",
      "142, -1227.4171142578125\n",
      "143, -1228.841796875\n",
      "144, -1230.1016845703125\n",
      "Now the best is: 144\n",
      "145, -1230.6494140625\n",
      "Now the best is: 145\n",
      "146, -1230.207763671875\n",
      "147, -1231.2061767578125\n",
      "Now the best is: 147\n",
      "148, -1234.1673583984375\n",
      "Now the best is: 148\n",
      "149, -1233.7489013671875\n",
      "150, -1234.882080078125\n",
      "Now the best is: 150\n",
      "Epoch 150 finished!\n",
      "151, -1237.1346435546875\n",
      "Now the best is: 151\n",
      "152, -1234.8743896484375\n",
      "153, -1237.4957275390625\n",
      "Now the best is: 153\n",
      "154, -1237.3424072265625\n",
      "155, -1238.1181640625\n",
      "Now the best is: 155\n",
      "156, -1239.44287109375\n",
      "Now the best is: 156\n",
      "157, -1240.3284912109375\n",
      "Now the best is: 157\n",
      "158, -1242.27880859375\n",
      "Now the best is: 158\n",
      "159, -1240.91845703125\n",
      "160, -1241.0311279296875\n",
      "Epoch 160 finished!\n",
      "161, -1242.4951171875\n",
      "Now the best is: 161\n",
      "162, -1244.04931640625\n",
      "Now the best is: 162\n",
      "163, -1241.557373046875\n",
      "164, -1245.90234375\n",
      "Now the best is: 164\n",
      "165, -1242.817626953125\n",
      "166, -1245.3238525390625\n",
      "167, -1247.892822265625\n",
      "Now the best is: 167\n",
      "168, -1247.669677734375\n",
      "169, -1247.2962646484375\n",
      "170, -1248.8359375\n",
      "Now the best is: 170\n",
      "Epoch 170 finished!\n",
      "171, -1250.891845703125\n",
      "Now the best is: 171\n",
      "172, -1250.4241943359375\n",
      "173, -1251.82470703125\n",
      "Now the best is: 173\n",
      "174, -1251.800048828125\n",
      "175, -1255.2418212890625\n",
      "Now the best is: 175\n",
      "176, -1254.5836181640625\n",
      "177, -1254.9921875\n",
      "178, -1255.9515380859375\n",
      "Now the best is: 178\n",
      "179, -1255.2989501953125\n",
      "180, -1258.203369140625\n",
      "Now the best is: 180\n",
      "Epoch 180 finished!\n",
      "181, -1258.6610107421875\n",
      "Now the best is: 181\n",
      "182, -1257.5301513671875\n",
      "183, -1259.3284912109375\n",
      "Now the best is: 183\n",
      "184, -1259.408203125\n",
      "Now the best is: 184\n",
      "185, -1261.009765625\n",
      "Now the best is: 185\n",
      "186, -1264.11669921875\n",
      "Now the best is: 186\n",
      "187, -1263.210205078125\n",
      "188, -1262.47998046875\n",
      "189, -1263.0172119140625\n",
      "190, -1265.53564453125\n",
      "Now the best is: 190\n",
      "Epoch 190 finished!\n",
      "191, -1267.3402099609375\n",
      "Now the best is: 191\n",
      "192, -1264.2779541015625\n",
      "193, -1267.7242431640625\n",
      "Now the best is: 193\n",
      "194, -1267.156494140625\n",
      "195, -1269.584228515625\n",
      "Now the best is: 195\n",
      "196, -1270.4346923828125\n",
      "Now the best is: 196\n",
      "197, -1271.68505859375\n",
      "Now the best is: 197\n",
      "198, -1268.9444580078125\n",
      "199, -1271.9085693359375\n",
      "Now the best is: 199\n",
      "200, -1273.9832763671875\n",
      "Now the best is: 200\n",
      "Epoch 200 finished!\n",
      "Training complete! Total training time: 1.396661428478029 hours\n"
     ]
    }
   ],
   "source": [
    "epoch_num = 200\n",
    "loss_stand = 0\n",
    "loss_array = np.zeros(epoch_num)\n",
    "m_start_time = time.time()\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "net.train()\n",
    "for epoch in range(epoch_num):\n",
    "    node_user = net(no_Nidx = user_embed_idx,\n",
    "                    u_feature = s_user_f,\n",
    "                    heteData = heteroData)\n",
    "    data_loader = AugmentLoader(node_user,\n",
    "                                batch_size = 20,\n",
    "                                aug_num = 50,\n",
    "                                shuffle = False)\n",
    "    loss_s = torch.tensor(0.0,requires_grad=True,device='cuda')\n",
    "    '''把所有batch的loss加和；每个epoch结束后回传一次loss / Sum the loss of all batch; BP the loss at the end of each epoch'''\n",
    "    for step, (batch, pseudo_label) in enumerate(data_loader):\n",
    "        batch = batch.cuda()\n",
    "        loss, loss_empi, loss_theo = mcr_loss(batch, \n",
    "                                              pseudo_label)\n",
    "        loss_s = torch.add(loss_s, loss)\n",
    "        \n",
    "        if step == 0:\n",
    "            step_savename = 'Model2_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1]) \n",
    "        elif (step+1) % 50 == 0:\n",
    "            step_savename = 'Model2_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1])         \n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_s.backward()\n",
    "    optimizer.step()\n",
    "        \n",
    "    \n",
    "    loss_compare = loss_s.detach().item()\n",
    "    loss_array[epoch] = loss_compare\n",
    "    print(f'{epoch+1}, {loss_compare}')\n",
    "    \n",
    "    if loss_compare < loss_stand:\n",
    "        model_savename = f'2Best_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, best_folder,\n",
    "                                  epoch, net, optimizer)\n",
    "        loss_stand = loss_compare\n",
    "        print('Now the best is: {}'.format(epoch+1))\n",
    "        \n",
    "        \n",
    "    if epoch == 0:\n",
    "        model_savename = f'Model2_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    elif (epoch+1) % 25 == 0:\n",
    "        model_savename = f'Model2_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    \n",
    "    if epoch == 0:\n",
    "        params_savename = 'Model2_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        \n",
    "    elif (epoch+1) % 10 == 0:\n",
    "        params_savename = 'Model2_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        print('Epoch {} finished!'.format(epoch+1))\n",
    "        \n",
    "m_end_time = time.time()\n",
    "m_total_time = (m_end_time - m_start_time)/3600\n",
    "np.save(np_path, loss_array)\n",
    "print(\"Training complete! Total training time: {} hours\".format(m_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26dc5544-8b72-4649-8455-40997600d781",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Loss可视化/Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72fd5486-650e-4198-9607-b5263cc3546e",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_path = 'Train_record/Model/Model2/All_loss.npy'\n",
    "loss2 = np.load(np_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b31e6fc-e777-4904-a1d7-6b07cac59ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(loss2)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Model 2 - 200 Epoch Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff54b69-2483-4e09-b82f-15306c166577",
   "metadata": {
    "tags": []
   },
   "source": [
    "##  2.3 Model 3 (M1: $\\gamma_1 = 0.6$, $\\mathrm{smooth} = \\mathrm{True}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2fd10d-f3fe-4411-9ce4-a1e0bab115a8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) 参数/Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b13d9d-30c8-40fb-a330-57dbce2da6b9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "- MCR2_HGPD\n",
    "  - cate_dim = 8\n",
    "  - hidden_dim = 16\n",
    "  - num_hop = 2\n",
    "- mcr_loss\n",
    "  - eff = True (with smooth)\n",
    "  - eps = 0.5\n",
    "  - gam2 = 0.6 (Default scale of Discrim in loss calculation)\n",
    "- optimizer & train\n",
    "  - torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "  - epoch = 200\n",
    "- AugmentLoader\n",
    "  - batch_size = 20\n",
    "  - aug_num = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914fdb5a-357c-49b6-ab53-48d2526ff17b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### (2) 模型训练/Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f14c3d91-e106-4756-8bf5-eede40ec0dc9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "net = MCR2_HGPD(node_num=980, feature_dim=10, cate_dim=8, hidden_dim=16, num_hop=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "331fc8e1-1657-48f0-896d-6d1afaab4186",
   "metadata": {},
   "outputs": [],
   "source": [
    "mcr_loss = MaximalCodingRateReduction(eff = True, eps = 0.5)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94f195c3-4bac-4b30-8583-729a056a2e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_folder = 'Train_record/Model/Model3/Best'\n",
    "model_folder = 'Train_record/Model/Model3/Model state'\n",
    "params_folder = 'Train_record/Model/Model3/Parameter'\n",
    "np_path = 'Train_record/Model/Model3/All_loss.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a99b716-4b29-46c6-95e1-bff346fecec5",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, -4162.240234375\n",
      "Now the best is: 1\n",
      "2, -4191.11865234375\n",
      "Now the best is: 2\n",
      "3, -4193.9736328125\n",
      "Now the best is: 3\n",
      "4, -4203.603515625\n",
      "Now the best is: 4\n",
      "5, -4217.1640625\n",
      "Now the best is: 5\n",
      "6, -4220.89892578125\n",
      "Now the best is: 6\n",
      "7, -4224.4384765625\n",
      "Now the best is: 7\n",
      "8, -4233.52490234375\n",
      "Now the best is: 8\n",
      "9, -4237.3623046875\n",
      "Now the best is: 9\n",
      "10, -4240.83056640625\n",
      "Now the best is: 10\n",
      "Epoch 10 finished!\n",
      "11, -4244.4462890625\n",
      "Now the best is: 11\n",
      "12, -4251.4755859375\n",
      "Now the best is: 12\n",
      "13, -4254.673828125\n",
      "Now the best is: 13\n",
      "14, -4259.6396484375\n",
      "Now the best is: 14\n",
      "15, -4263.919921875\n",
      "Now the best is: 15\n",
      "16, -4264.29443359375\n",
      "Now the best is: 16\n",
      "17, -4268.11767578125\n",
      "Now the best is: 17\n",
      "18, -4271.7392578125\n",
      "Now the best is: 18\n",
      "19, -4275.23974609375\n",
      "Now the best is: 19\n",
      "20, -4276.228515625\n",
      "Now the best is: 20\n",
      "Epoch 20 finished!\n",
      "21, -4280.064453125\n",
      "Now the best is: 21\n",
      "22, -4282.236328125\n",
      "Now the best is: 22\n",
      "23, -4281.7900390625\n",
      "24, -4286.787109375\n",
      "Now the best is: 24\n",
      "25, -4288.87451171875\n",
      "Now the best is: 25\n",
      "26, -4290.92724609375\n",
      "Now the best is: 26\n",
      "27, -4293.97802734375\n",
      "Now the best is: 27\n",
      "28, -4294.919921875\n",
      "Now the best is: 28\n",
      "29, -4296.224609375\n",
      "Now the best is: 29\n",
      "30, -4300.43994140625\n",
      "Now the best is: 30\n",
      "Epoch 30 finished!\n",
      "31, -4300.23388671875\n",
      "32, -4302.30224609375\n",
      "Now the best is: 32\n",
      "33, -4301.78125\n",
      "34, -4305.4296875\n",
      "Now the best is: 34\n",
      "35, -4305.109375\n",
      "36, -4307.76611328125\n",
      "Now the best is: 36\n",
      "37, -4308.83935546875\n",
      "Now the best is: 37\n",
      "38, -4311.14697265625\n",
      "Now the best is: 38\n",
      "39, -4313.24853515625\n",
      "Now the best is: 39\n",
      "40, -4313.013671875\n",
      "Epoch 40 finished!\n",
      "41, -4314.92333984375\n",
      "Now the best is: 41\n",
      "42, -4315.5712890625\n",
      "Now the best is: 42\n",
      "43, -4318.70751953125\n",
      "Now the best is: 43\n",
      "44, -4319.958984375\n",
      "Now the best is: 44\n",
      "45, -4320.68359375\n",
      "Now the best is: 45\n",
      "46, -4322.84130859375\n",
      "Now the best is: 46\n",
      "47, -4323.73828125\n",
      "Now the best is: 47\n",
      "48, -4325.00341796875\n",
      "Now the best is: 48\n",
      "49, -4325.13525390625\n",
      "Now the best is: 49\n",
      "50, -4327.56640625\n",
      "Now the best is: 50\n",
      "Epoch 50 finished!\n",
      "51, -4327.197265625\n",
      "52, -4329.677734375\n",
      "Now the best is: 52\n",
      "53, -4332.060546875\n",
      "Now the best is: 53\n",
      "54, -4332.15966796875\n",
      "Now the best is: 54\n",
      "55, -4333.552734375\n",
      "Now the best is: 55\n",
      "56, -4333.322265625\n",
      "57, -4334.802734375\n",
      "Now the best is: 57\n",
      "58, -4334.6923828125\n",
      "59, -4336.154296875\n",
      "Now the best is: 59\n",
      "60, -4337.3251953125\n",
      "Now the best is: 60\n",
      "Epoch 60 finished!\n",
      "61, -4339.109375\n",
      "Now the best is: 61\n",
      "62, -4340.22119140625\n",
      "Now the best is: 62\n",
      "63, -4341.23779296875\n",
      "Now the best is: 63\n",
      "64, -4341.77294921875\n",
      "Now the best is: 64\n",
      "65, -4342.17578125\n",
      "Now the best is: 65\n",
      "66, -4343.267578125\n",
      "Now the best is: 66\n",
      "67, -4342.02685546875\n",
      "68, -4345.05078125\n",
      "Now the best is: 68\n",
      "69, -4345.52880859375\n",
      "Now the best is: 69\n",
      "70, -4346.009765625\n",
      "Now the best is: 70\n",
      "Epoch 70 finished!\n",
      "71, -4350.13232421875\n",
      "Now the best is: 71\n",
      "72, -4349.85693359375\n",
      "73, -4348.54638671875\n",
      "74, -4351.65966796875\n",
      "Now the best is: 74\n",
      "75, -4351.62451171875\n",
      "76, -4354.88818359375\n",
      "Now the best is: 76\n",
      "77, -4353.5986328125\n",
      "78, -4356.18115234375\n",
      "Now the best is: 78\n",
      "79, -4354.10791015625\n",
      "80, -4357.51171875\n",
      "Now the best is: 80\n",
      "Epoch 80 finished!\n",
      "81, -4355.71484375\n",
      "82, -4359.5712890625\n",
      "Now the best is: 82\n",
      "83, -4357.94287109375\n",
      "84, -4360.1181640625\n",
      "Now the best is: 84\n",
      "85, -4361.00927734375\n",
      "Now the best is: 85\n",
      "86, -4359.44482421875\n",
      "87, -4364.1806640625\n",
      "Now the best is: 87\n",
      "88, -4362.36328125\n",
      "89, -4365.10498046875\n",
      "Now the best is: 89\n",
      "90, -4365.2705078125\n",
      "Now the best is: 90\n",
      "Epoch 90 finished!\n",
      "91, -4366.2998046875\n",
      "Now the best is: 91\n",
      "92, -4366.8203125\n",
      "Now the best is: 92\n",
      "93, -4368.890625\n",
      "Now the best is: 93\n",
      "94, -4369.99609375\n",
      "Now the best is: 94\n",
      "95, -4369.03369140625\n",
      "96, -4369.8193359375\n",
      "97, -4371.564453125\n",
      "Now the best is: 97\n",
      "98, -4370.90673828125\n",
      "99, -4373.18359375\n",
      "Now the best is: 99\n",
      "100, -4374.19873046875\n",
      "Now the best is: 100\n",
      "Epoch 100 finished!\n",
      "101, -4375.76220703125\n",
      "Now the best is: 101\n",
      "102, -4376.29296875\n",
      "Now the best is: 102\n",
      "103, -4376.28857421875\n",
      "104, -4377.4365234375\n",
      "Now the best is: 104\n",
      "105, -4377.9521484375\n",
      "Now the best is: 105\n",
      "106, -4378.44482421875\n",
      "Now the best is: 106\n",
      "107, -4377.9833984375\n",
      "108, -4380.27734375\n",
      "Now the best is: 108\n",
      "109, -4381.08251953125\n",
      "Now the best is: 109\n",
      "110, -4381.541015625\n",
      "Now the best is: 110\n",
      "Epoch 110 finished!\n",
      "111, -4382.5322265625\n",
      "Now the best is: 111\n",
      "112, -4383.005859375\n",
      "Now the best is: 112\n",
      "113, -4385.56494140625\n",
      "Now the best is: 113\n",
      "114, -4384.86669921875\n",
      "115, -4387.59130859375\n",
      "Now the best is: 115\n",
      "116, -4388.30615234375\n",
      "Now the best is: 116\n",
      "117, -4389.69677734375\n",
      "Now the best is: 117\n",
      "118, -4389.34521484375\n",
      "119, -4390.94140625\n",
      "Now the best is: 119\n",
      "120, -4391.43408203125\n",
      "Now the best is: 120\n",
      "Epoch 120 finished!\n",
      "121, -4392.80712890625\n",
      "Now the best is: 121\n",
      "122, -4393.22998046875\n",
      "Now the best is: 122\n",
      "123, -4393.494140625\n",
      "Now the best is: 123\n",
      "124, -4394.28076171875\n",
      "Now the best is: 124\n",
      "125, -4395.0625\n",
      "Now the best is: 125\n",
      "126, -4396.5517578125\n",
      "Now the best is: 126\n",
      "127, -4396.5771484375\n",
      "Now the best is: 127\n",
      "128, -4397.53173828125\n",
      "Now the best is: 128\n",
      "129, -4399.3291015625\n",
      "Now the best is: 129\n",
      "130, -4398.2880859375\n",
      "Epoch 130 finished!\n",
      "131, -4400.9306640625\n",
      "Now the best is: 131\n",
      "132, -4399.32568359375\n",
      "133, -4401.1484375\n",
      "Now the best is: 133\n",
      "134, -4404.24072265625\n",
      "Now the best is: 134\n",
      "135, -4404.0283203125\n",
      "136, -4404.23828125\n",
      "137, -4406.0703125\n",
      "Now the best is: 137\n",
      "138, -4405.8359375\n",
      "139, -4407.52490234375\n",
      "Now the best is: 139\n",
      "140, -4408.99560546875\n",
      "Now the best is: 140\n",
      "Epoch 140 finished!\n",
      "141, -4410.52001953125\n",
      "Now the best is: 141\n",
      "142, -4408.884765625\n",
      "143, -4410.4765625\n",
      "144, -4412.0322265625\n",
      "Now the best is: 144\n",
      "145, -4412.9287109375\n",
      "Now the best is: 145\n",
      "146, -4412.71533203125\n",
      "147, -4413.1640625\n",
      "Now the best is: 147\n",
      "148, -4415.25634765625\n",
      "Now the best is: 148\n",
      "149, -4414.44580078125\n",
      "150, -4416.41064453125\n",
      "Now the best is: 150\n",
      "Epoch 150 finished!\n",
      "151, -4419.4609375\n",
      "Now the best is: 151\n",
      "152, -4417.43017578125\n",
      "153, -4419.08740234375\n",
      "154, -4419.78955078125\n",
      "Now the best is: 154\n",
      "155, -4419.66015625\n",
      "156, -4422.85009765625\n",
      "Now the best is: 156\n",
      "157, -4423.728515625\n",
      "Now the best is: 157\n",
      "158, -4424.33837890625\n",
      "Now the best is: 158\n",
      "159, -4423.07177734375\n",
      "160, -4424.97216796875\n",
      "Now the best is: 160\n",
      "Epoch 160 finished!\n",
      "161, -4425.48046875\n",
      "Now the best is: 161\n",
      "162, -4427.53564453125\n",
      "Now the best is: 162\n",
      "163, -4427.60400390625\n",
      "Now the best is: 163\n",
      "164, -4428.9404296875\n",
      "Now the best is: 164\n",
      "165, -4427.81689453125\n",
      "166, -4430.197265625\n",
      "Now the best is: 166\n",
      "167, -4430.7060546875\n",
      "Now the best is: 167\n",
      "168, -4432.44873046875\n",
      "Now the best is: 168\n",
      "169, -4432.79296875\n",
      "Now the best is: 169\n",
      "170, -4432.400390625\n",
      "Epoch 170 finished!\n",
      "171, -4435.82568359375\n",
      "Now the best is: 171\n",
      "172, -4435.54931640625\n",
      "173, -4436.25830078125\n",
      "Now the best is: 173\n",
      "174, -4436.869140625\n",
      "Now the best is: 174\n",
      "175, -4439.57177734375\n",
      "Now the best is: 175\n",
      "176, -4439.17529296875\n",
      "177, -4439.9326171875\n",
      "Now the best is: 177\n",
      "178, -4441.3916015625\n",
      "Now the best is: 178\n",
      "179, -4442.21044921875\n",
      "Now the best is: 179\n",
      "180, -4443.3896484375\n",
      "Now the best is: 180\n",
      "Epoch 180 finished!\n",
      "181, -4444.466796875\n",
      "Now the best is: 181\n",
      "182, -4443.00390625\n",
      "183, -4444.80029296875\n",
      "Now the best is: 183\n",
      "184, -4446.02490234375\n",
      "Now the best is: 184\n",
      "185, -4447.638671875\n",
      "Now the best is: 185\n",
      "186, -4450.20947265625\n",
      "Now the best is: 186\n",
      "187, -4450.22998046875\n",
      "Now the best is: 187\n",
      "188, -4449.4765625\n",
      "189, -4450.1396484375\n",
      "190, -4452.51318359375\n",
      "Now the best is: 190\n",
      "Epoch 190 finished!\n",
      "191, -4453.146484375\n",
      "Now the best is: 191\n",
      "192, -4453.3125\n",
      "Now the best is: 192\n",
      "193, -4455.06884765625\n",
      "Now the best is: 193\n",
      "194, -4454.99560546875\n",
      "195, -4455.42626953125\n",
      "Now the best is: 195\n",
      "196, -4457.14404296875\n",
      "Now the best is: 196\n",
      "197, -4458.6396484375\n",
      "Now the best is: 197\n",
      "198, -4456.03271484375\n",
      "199, -4459.3583984375\n",
      "Now the best is: 199\n",
      "200, -4460.927734375\n",
      "Now the best is: 200\n",
      "Epoch 200 finished!\n",
      "Training complete! Total training time: 1.4011720891793569 hours\n"
     ]
    }
   ],
   "source": [
    "epoch_num = 200\n",
    "loss_stand = 0\n",
    "loss_array = np.zeros(epoch_num)\n",
    "m_start_time = time.time()\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "net.train()\n",
    "for epoch in range(epoch_num):\n",
    "    node_user = net(no_Nidx = user_embed_idx,\n",
    "                    u_feature = s_user_f,\n",
    "                    heteData = heteroData)\n",
    "    data_loader = AugmentLoader(node_user,\n",
    "                                batch_size = 20,\n",
    "                                aug_num = 50,\n",
    "                                shuffle = False)\n",
    "    loss_s = torch.tensor(0.0,requires_grad=True,device='cuda')\n",
    "    '''把所有batch的loss加和；每个epoch结束后回传一次loss / Sum the loss of all batch; BP the loss at the end of each epoch'''\n",
    "    for step, (batch, pseudo_label) in enumerate(data_loader):\n",
    "        batch = batch.cuda()\n",
    "        loss, loss_empi, loss_theo = mcr_loss(batch, \n",
    "                                              pseudo_label)\n",
    "        loss_s = torch.add(loss_s, loss)\n",
    "        \n",
    "        if step == 0:\n",
    "            step_savename = 'Model3_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1]) \n",
    "        elif (step+1) % 50 == 0:\n",
    "            step_savename = 'Model3_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1])         \n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_s.backward()\n",
    "    optimizer.step()\n",
    "       \n",
    "    \n",
    "    loss_compare = loss_s.detach().item()\n",
    "    loss_array[epoch] = loss_compare\n",
    "    print(f'{epoch+1}, {loss_compare}')\n",
    "    if loss_compare < loss_stand:\n",
    "        model_savename = f'3Best_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, best_folder,\n",
    "                                  epoch, net, optimizer)\n",
    "        loss_stand = loss_compare\n",
    "        print('Now the best is: {}'.format(epoch+1))\n",
    "        \n",
    "        \n",
    "    if epoch == 0:\n",
    "        model_savename = f'Model3_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    elif (epoch+1) % 25 == 0:\n",
    "        model_savename = f'Model3_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    \n",
    "    if epoch == 0:\n",
    "        params_savename = 'Model3_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        \n",
    "    elif (epoch+1) % 10 == 0:\n",
    "        params_savename = 'Model3_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        print('Epoch {} finished!'.format(epoch+1))\n",
    "        \n",
    "m_end_time = time.time()\n",
    "m_total_time = (m_end_time - m_start_time)/3600\n",
    "np.save(np_path, loss_array)\n",
    "print(\"Training complete! Total training time: {} hours\".format(m_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9e55ef-d582-4b3e-9c80-0db1d1364a52",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Loss可视化/Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f374e63c-56da-4fe9-887a-49da271cdb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_path = 'Train_record/Model/Model3/All_loss.npy'\n",
    "loss3 = np.load(np_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ffa5d842-eb7e-47e3-968b-b4a7ff0af003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(loss3)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Model 3 - 200 Epoch Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b087f27-eb7d-4f39-bc7a-3e883a16e7e1",
   "metadata": {},
   "source": [
    "##  2.4 Model 4 (M2: $ \\epsilon^2 = 0.5$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e88f342-366c-4435-aa6f-1fbc14a9945d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) 参数/Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f4675b-fef1-448d-a5ee-0b7511f6a397",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "- MCR2_HGPD\n",
    "  - cate_dim = 16\n",
    "  - hidden_dim = 16\n",
    "  - num_hop = 2\n",
    "- mcr_loss\n",
    "  - eff = False (without smooth)\n",
    "  - eps = 0.5\n",
    "  - gam2 = 0.6 (Default scale of Discrim in loss calculation)\n",
    "- optimizer & train\n",
    "  - torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "  - epoch = 200\n",
    "- AugmentLoader\n",
    "  - batch_size = 20\n",
    "  - aug_num = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e751de4-c75d-43bf-9567-52ed442e845b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### (2) 模型训练/Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8be1890c-fcb1-4e16-b311-4c368e5e6996",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "net = MCR2_HGPD(node_num=980, feature_dim=10, cate_dim=16, hidden_dim=16, num_hop=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ced7345c-4885-4fbd-90af-195ec11a54fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "mcr_loss = MaximalCodingRateReduction(eps = 0.5)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d881e16-1846-4195-a720-9dfe3ced4247",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_folder = 'Train_record/Model/Model4/Best'\n",
    "model_folder = 'Train_record/Model/Model4/Model state'\n",
    "params_folder = 'Train_record/Model/Model4/Parameter'\n",
    "np_path = 'Train_record/Model/Model4/All_loss.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57786e35-3cdb-45a0-8f0c-909c10763250",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, -1009.0474243164062\n",
      "Now the best is: 1\n",
      "2, -1043.831298828125\n",
      "Now the best is: 2\n",
      "3, -1057.0701904296875\n",
      "Now the best is: 3\n",
      "4, -1072.0528564453125\n",
      "Now the best is: 4\n",
      "5, -1076.84521484375\n",
      "Now the best is: 5\n",
      "6, -1085.2042236328125\n",
      "Now the best is: 6\n",
      "7, -1090.0391845703125\n",
      "Now the best is: 7\n",
      "8, -1096.0755615234375\n",
      "Now the best is: 8\n",
      "9, -1097.932861328125\n",
      "Now the best is: 9\n",
      "10, -1102.8939208984375\n",
      "Now the best is: 10\n",
      "Epoch 10 finished!\n",
      "11, -1106.0386962890625\n",
      "Now the best is: 11\n",
      "12, -1109.5587158203125\n",
      "Now the best is: 12\n",
      "13, -1109.57080078125\n",
      "Now the best is: 13\n",
      "14, -1113.2015380859375\n",
      "Now the best is: 14\n",
      "15, -1116.61181640625\n",
      "Now the best is: 15\n",
      "16, -1117.74853515625\n",
      "Now the best is: 16\n",
      "17, -1121.521484375\n",
      "Now the best is: 17\n",
      "18, -1121.5538330078125\n",
      "Now the best is: 18\n",
      "19, -1125.905517578125\n",
      "Now the best is: 19\n",
      "20, -1126.3001708984375\n",
      "Now the best is: 20\n",
      "Epoch 20 finished!\n",
      "21, -1128.678466796875\n",
      "Now the best is: 21\n",
      "22, -1130.8143310546875\n",
      "Now the best is: 22\n",
      "23, -1129.7945556640625\n",
      "24, -1131.96923828125\n",
      "Now the best is: 24\n",
      "25, -1131.7186279296875\n",
      "26, -1134.02783203125\n",
      "Now the best is: 26\n",
      "27, -1138.5511474609375\n",
      "Now the best is: 27\n",
      "28, -1138.865234375\n",
      "Now the best is: 28\n",
      "29, -1138.7476806640625\n",
      "30, -1141.7568359375\n",
      "Now the best is: 30\n",
      "Epoch 30 finished!\n",
      "31, -1142.619140625\n",
      "Now the best is: 31\n",
      "32, -1143.8616943359375\n",
      "Now the best is: 32\n",
      "33, -1144.604248046875\n",
      "Now the best is: 33\n",
      "34, -1145.3330078125\n",
      "Now the best is: 34\n",
      "35, -1146.56591796875\n",
      "Now the best is: 35\n",
      "36, -1144.7264404296875\n",
      "37, -1147.0380859375\n",
      "Now the best is: 37\n",
      "38, -1149.6500244140625\n",
      "Now the best is: 38\n",
      "39, -1149.9427490234375\n",
      "Now the best is: 39\n",
      "40, -1152.953857421875\n",
      "Now the best is: 40\n",
      "Epoch 40 finished!\n",
      "41, -1151.7308349609375\n",
      "42, -1152.4024658203125\n",
      "43, -1153.9449462890625\n",
      "Now the best is: 43\n",
      "44, -1155.69921875\n",
      "Now the best is: 44\n",
      "45, -1155.928466796875\n",
      "Now the best is: 45\n",
      "46, -1156.7576904296875\n",
      "Now the best is: 46\n",
      "47, -1157.1451416015625\n",
      "Now the best is: 47\n",
      "48, -1156.77734375\n",
      "49, -1159.710205078125\n",
      "Now the best is: 49\n",
      "50, -1160.1239013671875\n",
      "Now the best is: 50\n",
      "Epoch 50 finished!\n",
      "51, -1161.755859375\n",
      "Now the best is: 51\n",
      "52, -1162.874267578125\n",
      "Now the best is: 52\n",
      "53, -1163.58349609375\n",
      "Now the best is: 53\n",
      "54, -1162.359619140625\n",
      "55, -1164.501708984375\n",
      "Now the best is: 55\n",
      "56, -1166.0208740234375\n",
      "Now the best is: 56\n",
      "57, -1166.2337646484375\n",
      "Now the best is: 57\n",
      "58, -1165.594970703125\n",
      "59, -1166.8994140625\n",
      "Now the best is: 59\n",
      "60, -1167.849609375\n",
      "Now the best is: 60\n",
      "Epoch 60 finished!\n",
      "61, -1169.67919921875\n",
      "Now the best is: 61\n",
      "62, -1170.9754638671875\n",
      "Now the best is: 62\n",
      "63, -1171.4566650390625\n",
      "Now the best is: 63\n",
      "64, -1170.583984375\n",
      "65, -1169.1380615234375\n",
      "66, -1173.80322265625\n",
      "Now the best is: 66\n",
      "67, -1173.0576171875\n",
      "68, -1173.534423828125\n",
      "69, -1174.4932861328125\n",
      "Now the best is: 69\n",
      "70, -1176.371826171875\n",
      "Now the best is: 70\n",
      "Epoch 70 finished!\n",
      "71, -1173.8170166015625\n",
      "72, -1178.92529296875\n",
      "Now the best is: 72\n",
      "73, -1179.22509765625\n",
      "Now the best is: 73\n",
      "74, -1177.963134765625\n",
      "75, -1181.011962890625\n",
      "Now the best is: 75\n",
      "76, -1179.6505126953125\n",
      "77, -1182.5186767578125\n",
      "Now the best is: 77\n",
      "78, -1182.6993408203125\n",
      "Now the best is: 78\n",
      "79, -1180.172119140625\n",
      "80, -1183.12109375\n",
      "Now the best is: 80\n",
      "Epoch 80 finished!\n",
      "81, -1181.5469970703125\n",
      "82, -1182.204345703125\n",
      "83, -1185.40087890625\n",
      "Now the best is: 83\n",
      "84, -1186.49609375\n",
      "Now the best is: 84\n",
      "85, -1188.8819580078125\n",
      "Now the best is: 85\n",
      "86, -1186.3140869140625\n",
      "87, -1188.2236328125\n",
      "88, -1185.929931640625\n",
      "89, -1189.1429443359375\n",
      "Now the best is: 89\n",
      "90, -1191.0069580078125\n",
      "Now the best is: 90\n",
      "Epoch 90 finished!\n",
      "91, -1190.3955078125\n",
      "92, -1190.542236328125\n",
      "93, -1193.4156494140625\n",
      "Now the best is: 93\n",
      "94, -1192.137939453125\n",
      "95, -1189.9859619140625\n",
      "96, -1193.352783203125\n",
      "97, -1194.3575439453125\n",
      "Now the best is: 97\n",
      "98, -1196.9586181640625\n",
      "Now the best is: 98\n",
      "99, -1197.48974609375\n",
      "Now the best is: 99\n",
      "100, -1196.5147705078125\n",
      "Epoch 100 finished!\n",
      "101, -1196.076904296875\n",
      "102, -1198.8739013671875\n",
      "Now the best is: 102\n",
      "103, -1196.826416015625\n",
      "104, -1198.1234130859375\n",
      "105, -1199.6011962890625\n",
      "Now the best is: 105\n",
      "106, -1204.1318359375\n",
      "Now the best is: 106\n",
      "107, -1200.9862060546875\n",
      "108, -1202.468994140625\n",
      "109, -1202.521484375\n",
      "110, -1202.2154541015625\n",
      "Epoch 110 finished!\n",
      "111, -1204.5020751953125\n",
      "Now the best is: 111\n",
      "112, -1204.02734375\n",
      "113, -1205.3515625\n",
      "Now the best is: 113\n",
      "114, -1207.614013671875\n",
      "Now the best is: 114\n",
      "115, -1209.45458984375\n",
      "Now the best is: 115\n",
      "116, -1206.5565185546875\n",
      "117, -1209.625\n",
      "Now the best is: 117\n",
      "118, -1209.2550048828125\n",
      "119, -1211.3096923828125\n",
      "Now the best is: 119\n",
      "120, -1210.8988037109375\n",
      "Epoch 120 finished!\n",
      "121, -1212.8583984375\n",
      "Now the best is: 121\n",
      "122, -1211.2174072265625\n",
      "123, -1211.537841796875\n",
      "124, -1216.0794677734375\n",
      "Now the best is: 124\n",
      "125, -1214.990478515625\n",
      "126, -1217.05859375\n",
      "Now the best is: 126\n",
      "127, -1214.489501953125\n",
      "128, -1219.45947265625\n",
      "Now the best is: 128\n",
      "129, -1219.122802734375\n",
      "130, -1216.759521484375\n",
      "Epoch 130 finished!\n",
      "131, -1220.072998046875\n",
      "Now the best is: 131\n",
      "132, -1223.7225341796875\n",
      "Now the best is: 132\n",
      "133, -1221.5091552734375\n",
      "134, -1220.546875\n",
      "135, -1221.6181640625\n",
      "136, -1220.7222900390625\n",
      "137, -1222.3006591796875\n",
      "138, -1224.046875\n",
      "Now the best is: 138\n",
      "139, -1223.8543701171875\n",
      "140, -1226.1556396484375\n",
      "Now the best is: 140\n",
      "Epoch 140 finished!\n",
      "141, -1225.956787109375\n",
      "142, -1227.9603271484375\n",
      "Now the best is: 142\n",
      "143, -1227.764892578125\n",
      "144, -1229.515869140625\n",
      "Now the best is: 144\n",
      "145, -1227.966064453125\n",
      "146, -1230.343505859375\n",
      "Now the best is: 146\n",
      "147, -1230.2904052734375\n",
      "148, -1231.4703369140625\n",
      "Now the best is: 148\n",
      "149, -1233.5240478515625\n",
      "Now the best is: 149\n",
      "150, -1234.6514892578125\n",
      "Now the best is: 150\n",
      "Epoch 150 finished!\n",
      "151, -1234.2410888671875\n",
      "152, -1234.36376953125\n",
      "153, -1236.9097900390625\n",
      "Now the best is: 153\n",
      "154, -1236.8812255859375\n",
      "155, -1240.023681640625\n",
      "Now the best is: 155\n",
      "156, -1237.58447265625\n",
      "157, -1239.800537109375\n",
      "158, -1241.2110595703125\n",
      "Now the best is: 158\n",
      "159, -1241.204345703125\n",
      "160, -1238.2208251953125\n",
      "Epoch 160 finished!\n",
      "161, -1242.9703369140625\n",
      "Now the best is: 161\n",
      "162, -1242.6837158203125\n",
      "163, -1242.6585693359375\n",
      "164, -1243.4964599609375\n",
      "Now the best is: 164\n",
      "165, -1241.2103271484375\n",
      "166, -1244.498779296875\n",
      "Now the best is: 166\n",
      "167, -1245.916748046875\n",
      "Now the best is: 167\n",
      "168, -1246.2301025390625\n",
      "Now the best is: 168\n",
      "169, -1246.5941162109375\n",
      "Now the best is: 169\n",
      "170, -1249.2032470703125\n",
      "Now the best is: 170\n",
      "Epoch 170 finished!\n",
      "171, -1249.0814208984375\n",
      "172, -1248.26611328125\n",
      "173, -1251.8153076171875\n",
      "Now the best is: 173\n",
      "174, -1250.7548828125\n",
      "175, -1251.9345703125\n",
      "Now the best is: 175\n",
      "176, -1253.5574951171875\n",
      "Now the best is: 176\n",
      "177, -1253.1866455078125\n",
      "178, -1255.7069091796875\n",
      "Now the best is: 178\n",
      "179, -1253.2764892578125\n",
      "180, -1256.7744140625\n",
      "Now the best is: 180\n",
      "Epoch 180 finished!\n",
      "181, -1255.790283203125\n",
      "182, -1259.6087646484375\n",
      "Now the best is: 182\n",
      "183, -1259.959228515625\n",
      "Now the best is: 183\n",
      "184, -1258.4818115234375\n",
      "185, -1260.1788330078125\n",
      "Now the best is: 185\n",
      "186, -1260.951416015625\n",
      "Now the best is: 186\n",
      "187, -1260.6826171875\n",
      "188, -1260.338623046875\n",
      "189, -1260.1871337890625\n",
      "190, -1263.4539794921875\n",
      "Now the best is: 190\n",
      "Epoch 190 finished!\n",
      "191, -1265.98193359375\n",
      "Now the best is: 191\n",
      "192, -1262.982421875\n",
      "193, -1265.011474609375\n",
      "194, -1267.545654296875\n",
      "Now the best is: 194\n",
      "195, -1266.434814453125\n",
      "196, -1270.0753173828125\n",
      "Now the best is: 196\n",
      "197, -1270.003662109375\n",
      "198, -1267.511962890625\n",
      "199, -1271.942138671875\n",
      "Now the best is: 199\n",
      "200, -1272.01416015625\n",
      "Now the best is: 200\n",
      "Epoch 200 finished!\n",
      "Training complete! Total training time: 1.4155543849865595 hours\n"
     ]
    }
   ],
   "source": [
    "epoch_num = 200\n",
    "loss_stand = 0\n",
    "loss_array = np.zeros(epoch_num)\n",
    "m_start_time = time.time()\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "net.train()\n",
    "for epoch in range(epoch_num):\n",
    "    node_user = net(no_Nidx = user_embed_idx,\n",
    "                    u_feature = s_user_f,\n",
    "                    heteData = heteroData)\n",
    "    data_loader = AugmentLoader(node_user,\n",
    "                                batch_size = 20,\n",
    "                                aug_num = 50,\n",
    "                                shuffle = False)\n",
    "    loss_s = torch.tensor(0.0,requires_grad=True,device='cuda')\n",
    "    '''把所有batch的loss加和；每个epoch结束后回传一次loss / Sum the loss of all batch; BP the loss at the end of each epoch'''\n",
    "    for step, (batch, pseudo_label) in enumerate(data_loader):\n",
    "        batch = batch.cuda()\n",
    "        loss, loss_empi, loss_theo = mcr_loss(batch, \n",
    "                                              pseudo_label)\n",
    "        loss_s = torch.add(loss_s, loss)\n",
    "        \n",
    "        if step == 0:\n",
    "            step_savename = 'Model4_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1]) \n",
    "        elif (step+1) % 50 == 0:\n",
    "            step_savename = 'Model4_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1])         \n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_s.backward()\n",
    "    optimizer.step()\n",
    "        \n",
    "\n",
    "    loss_compare = loss_s.detach().item()\n",
    "    loss_array[epoch] = loss_compare\n",
    "    print(f'{epoch+1}, {loss_compare}')\n",
    "    if loss_compare < loss_stand:\n",
    "        model_savename = f'4Best_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, best_folder,\n",
    "                                  epoch, net, optimizer)\n",
    "        loss_stand = loss_compare\n",
    "        print('Now the best is: {}'.format(epoch+1))\n",
    "        \n",
    "  \n",
    "    if epoch == 0:\n",
    "        model_savename = f'Model4_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    elif (epoch+1) % 25 == 0:\n",
    "        model_savename = f'Model4_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    \n",
    "    if epoch == 0:\n",
    "        params_savename = 'Model4_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        \n",
    "    elif (epoch+1) % 10 == 0:\n",
    "        params_savename = 'Model4_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        print('Epoch {} finished!'.format(epoch+1))\n",
    "        \n",
    "m_end_time = time.time()\n",
    "m_total_time = (m_end_time - m_start_time)/3600\n",
    "np.save(np_path, loss_array)\n",
    "print(\"Training complete! Total training time: {} hours\".format(m_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224180a0-2bef-4623-8da5-52b374c87748",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Loss可视化/Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a031746-7811-4fae-b3e2-4d4fc8c40bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_path = 'Train_record/Model/Model4/All_loss.npy'\n",
    "loss4 = np.load(np_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "488b7855-7e8c-4ead-8c16-a5943049b719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(loss4)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Model 4 - 200 Epoch Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e034143-931f-4109-89ee-b7d221cadb09",
   "metadata": {},
   "source": [
    "##  2.5 Model 5 (M2: $\\epsilon^2 = 0.3$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49431ca-731e-4493-b791-488a7d955533",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) 参数/Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150a0985-6507-48fb-ab41-40af074a8f93",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "- MCR2_HGPD\n",
    "  - cate_dim = 16\n",
    "  - hidden_dim = 16\n",
    "  - num_hop = 2\n",
    "- mcr_loss\n",
    "  - eff = False (without smooth)\n",
    "  - eps = 0.3\n",
    "  - gam2 = 0.6 (Default scale of Discrim in loss calculation)\n",
    "- optimizer & train\n",
    "  - torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "  - epoch = 200\n",
    "- AugmentLoader\n",
    "  - batch_size = 20\n",
    "  - aug_num = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1963cbe3-2cda-482f-9c0f-3c0f013cf8af",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### (2) 模型训练/Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b238548d-f986-4e51-b455-ff6779109ac7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "net = MCR2_HGPD(node_num=980, feature_dim=10, cate_dim=16, hidden_dim=16, num_hop=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5bd8d558-05b3-4352-89e8-823df591af57",
   "metadata": {},
   "outputs": [],
   "source": [
    "mcr_loss = MaximalCodingRateReduction(eps = 0.3)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3ca969e-1b62-4841-a414-4ea72406d3b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_folder = 'Train_record/Model/Model5/Best'\n",
    "model_folder = 'Train_record/Model/Model5/Model state'\n",
    "params_folder = 'Train_record/Model/Model5/Parameter'\n",
    "np_path = 'Train_record/Model/Model5/All_loss.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "673bbc9b-00c0-445a-88f7-d576012788d5",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, -724.4489135742188\n",
      "Now the best is: 1\n",
      "2, -759.24169921875\n",
      "Now the best is: 2\n",
      "3, -772.546630859375\n",
      "Now the best is: 3\n",
      "4, -787.4896240234375\n",
      "Now the best is: 4\n",
      "5, -792.3648071289062\n",
      "Now the best is: 5\n",
      "6, -800.7647094726562\n",
      "Now the best is: 6\n",
      "7, -805.5044555664062\n",
      "Now the best is: 7\n",
      "8, -811.6428833007812\n",
      "Now the best is: 8\n",
      "9, -813.3541870117188\n",
      "Now the best is: 9\n",
      "10, -818.3885498046875\n",
      "Now the best is: 10\n",
      "Epoch 10 finished!\n",
      "11, -821.588623046875\n",
      "Now the best is: 11\n",
      "12, -825.0465087890625\n",
      "Now the best is: 12\n",
      "13, -825.0471801757812\n",
      "Now the best is: 13\n",
      "14, -828.6268310546875\n",
      "Now the best is: 14\n",
      "15, -832.0648193359375\n",
      "Now the best is: 15\n",
      "16, -833.1910400390625\n",
      "Now the best is: 16\n",
      "17, -836.9822387695312\n",
      "Now the best is: 17\n",
      "18, -837.0381469726562\n",
      "Now the best is: 18\n",
      "19, -841.4117431640625\n",
      "Now the best is: 19\n",
      "20, -841.7573852539062\n",
      "Now the best is: 20\n",
      "Epoch 20 finished!\n",
      "21, -844.2037963867188\n",
      "Now the best is: 21\n",
      "22, -846.3472290039062\n",
      "Now the best is: 22\n",
      "23, -845.2318115234375\n",
      "24, -847.4855346679688\n",
      "Now the best is: 24\n",
      "25, -847.2131958007812\n",
      "26, -849.504150390625\n",
      "Now the best is: 26\n",
      "27, -854.1124877929688\n",
      "Now the best is: 27\n",
      "28, -854.3513793945312\n",
      "Now the best is: 28\n",
      "29, -854.19580078125\n",
      "30, -857.2599487304688\n",
      "Now the best is: 30\n",
      "Epoch 30 finished!\n",
      "31, -858.11474609375\n",
      "Now the best is: 31\n",
      "32, -859.33251953125\n",
      "Now the best is: 32\n",
      "33, -860.1093139648438\n",
      "Now the best is: 33\n",
      "34, -860.8095703125\n",
      "Now the best is: 34\n",
      "35, -862.02197265625\n",
      "Now the best is: 35\n",
      "36, -860.1807861328125\n",
      "37, -862.467041015625\n",
      "Now the best is: 37\n",
      "38, -865.0890502929688\n",
      "Now the best is: 38\n",
      "39, -865.4016723632812\n",
      "Now the best is: 39\n",
      "40, -868.4494018554688\n",
      "Now the best is: 40\n",
      "Epoch 40 finished!\n",
      "41, -867.1800537109375\n",
      "42, -867.8714599609375\n",
      "43, -869.4127807617188\n",
      "Now the best is: 43\n",
      "44, -871.1864013671875\n",
      "Now the best is: 44\n",
      "45, -871.38525390625\n",
      "Now the best is: 45\n",
      "46, -872.223388671875\n",
      "Now the best is: 46\n",
      "47, -872.53857421875\n",
      "Now the best is: 47\n",
      "48, -872.18505859375\n",
      "49, -875.1618041992188\n",
      "Now the best is: 49\n",
      "50, -875.5636596679688\n",
      "Now the best is: 50\n",
      "Epoch 50 finished!\n",
      "51, -877.1952514648438\n",
      "Now the best is: 51\n",
      "52, -878.3861083984375\n",
      "Now the best is: 52\n",
      "53, -879.021484375\n",
      "Now the best is: 53\n",
      "54, -877.8006591796875\n",
      "55, -879.935302734375\n",
      "Now the best is: 55\n",
      "56, -881.4943237304688\n",
      "Now the best is: 56\n",
      "57, -881.69677734375\n",
      "Now the best is: 57\n",
      "58, -881.0079345703125\n",
      "59, -882.273193359375\n",
      "Now the best is: 59\n",
      "60, -883.27978515625\n",
      "Now the best is: 60\n",
      "Epoch 60 finished!\n",
      "61, -885.1656494140625\n",
      "Now the best is: 61\n",
      "62, -886.418701171875\n",
      "Now the best is: 62\n",
      "63, -886.9334106445312\n",
      "Now the best is: 63\n",
      "64, -885.9996337890625\n",
      "65, -884.556640625\n",
      "66, -889.2479248046875\n",
      "Now the best is: 66\n",
      "67, -888.4814453125\n",
      "68, -888.9483032226562\n",
      "69, -889.9349365234375\n",
      "Now the best is: 69\n",
      "70, -891.783447265625\n",
      "Now the best is: 70\n",
      "Epoch 70 finished!\n",
      "71, -889.1842651367188\n",
      "72, -894.38232421875\n",
      "Now the best is: 72\n",
      "73, -894.6588745117188\n",
      "Now the best is: 73\n",
      "74, -893.365966796875\n",
      "75, -896.4595336914062\n",
      "Now the best is: 75\n",
      "76, -895.073486328125\n",
      "77, -897.9976196289062\n",
      "Now the best is: 77\n",
      "78, -898.1660766601562\n",
      "Now the best is: 78\n",
      "79, -895.564208984375\n",
      "80, -898.5535888671875\n",
      "Now the best is: 80\n",
      "Epoch 80 finished!\n",
      "81, -896.9511108398438\n",
      "82, -897.59912109375\n",
      "83, -900.9159545898438\n",
      "Now the best is: 83\n",
      "84, -901.9281005859375\n",
      "Now the best is: 84\n",
      "85, -904.349609375\n",
      "Now the best is: 85\n",
      "86, -901.7256469726562\n",
      "87, -903.63623046875\n",
      "88, -901.3226318359375\n",
      "89, -904.5404052734375\n",
      "Now the best is: 89\n",
      "90, -906.4555053710938\n",
      "Now the best is: 90\n",
      "Epoch 90 finished!\n",
      "91, -905.8019409179688\n",
      "92, -905.9817504882812\n",
      "93, -908.8570556640625\n",
      "Now the best is: 93\n",
      "94, -907.54443359375\n",
      "95, -905.3363647460938\n",
      "96, -908.74951171875\n",
      "97, -909.787841796875\n",
      "Now the best is: 97\n",
      "98, -912.389892578125\n",
      "Now the best is: 98\n",
      "99, -912.9238891601562\n",
      "Now the best is: 99\n",
      "100, -911.90478515625\n",
      "Epoch 100 finished!\n",
      "101, -911.487060546875\n",
      "102, -914.3331909179688\n",
      "Now the best is: 102\n",
      "103, -912.2091064453125\n",
      "104, -913.5068969726562\n",
      "105, -915.0599365234375\n",
      "Now the best is: 105\n",
      "106, -919.564697265625\n",
      "Now the best is: 106\n",
      "107, -916.357666015625\n",
      "108, -917.89892578125\n",
      "109, -917.8923950195312\n",
      "110, -917.6476440429688\n",
      "Epoch 110 finished!\n",
      "111, -919.8859252929688\n",
      "Now the best is: 111\n",
      "112, -919.3472290039062\n",
      "113, -920.7598876953125\n",
      "Now the best is: 113\n",
      "114, -923.0262451171875\n",
      "Now the best is: 114\n",
      "115, -924.8836669921875\n",
      "Now the best is: 115\n",
      "116, -921.94921875\n",
      "117, -924.9796752929688\n",
      "Now the best is: 117\n",
      "118, -924.640380859375\n",
      "119, -926.7565307617188\n",
      "Now the best is: 119\n",
      "120, -926.2628173828125\n",
      "Epoch 120 finished!\n",
      "121, -928.2412719726562\n",
      "Now the best is: 121\n",
      "122, -926.6002807617188\n",
      "123, -926.9539794921875\n",
      "124, -931.4644165039062\n",
      "Now the best is: 124\n",
      "125, -930.4262084960938\n",
      "126, -932.490478515625\n",
      "Now the best is: 126\n",
      "127, -929.8274536132812\n",
      "128, -934.9173583984375\n",
      "Now the best is: 128\n",
      "129, -934.5866088867188\n",
      "130, -932.1524047851562\n",
      "Epoch 130 finished!\n",
      "131, -935.490966796875\n",
      "Now the best is: 131\n",
      "132, -939.1808471679688\n",
      "Now the best is: 132\n",
      "133, -936.9234008789062\n",
      "134, -935.9196166992188\n",
      "135, -937.0367431640625\n",
      "136, -936.100341796875\n",
      "137, -937.7022705078125\n",
      "138, -939.48876953125\n",
      "Now the best is: 138\n",
      "139, -939.2572021484375\n",
      "140, -941.578369140625\n",
      "Now the best is: 140\n",
      "Epoch 140 finished!\n",
      "141, -941.420654296875\n",
      "142, -943.357666015625\n",
      "Now the best is: 142\n",
      "143, -943.1182861328125\n",
      "144, -944.8722534179688\n",
      "Now the best is: 144\n",
      "145, -943.3614501953125\n",
      "146, -945.7619018554688\n",
      "Now the best is: 146\n",
      "147, -945.7359008789062\n",
      "148, -946.9232177734375\n",
      "Now the best is: 148\n",
      "149, -948.918212890625\n",
      "Now the best is: 149\n",
      "150, -950.1213989257812\n",
      "Now the best is: 150\n",
      "Epoch 150 finished!\n",
      "151, -949.6365966796875\n",
      "152, -949.78271484375\n",
      "153, -952.3359375\n",
      "Now the best is: 153\n",
      "154, -952.3387451171875\n",
      "Now the best is: 154\n",
      "155, -955.4402465820312\n",
      "Now the best is: 155\n",
      "156, -952.9229125976562\n",
      "157, -955.1990356445312\n",
      "158, -956.6532592773438\n",
      "Now the best is: 158\n",
      "159, -956.6099243164062\n",
      "160, -953.5633544921875\n",
      "Epoch 160 finished!\n",
      "161, -958.414794921875\n",
      "Now the best is: 161\n",
      "162, -958.1199340820312\n",
      "163, -958.0065307617188\n",
      "164, -958.9193725585938\n",
      "Now the best is: 164\n",
      "165, -956.531982421875\n",
      "166, -959.912353515625\n",
      "Now the best is: 166\n",
      "167, -961.3515014648438\n",
      "Now the best is: 167\n",
      "168, -961.6336669921875\n",
      "Now the best is: 168\n",
      "169, -961.9512329101562\n",
      "Now the best is: 169\n",
      "170, -964.6126708984375\n",
      "Now the best is: 170\n",
      "Epoch 170 finished!\n",
      "171, -964.4633178710938\n",
      "172, -963.7150268554688\n",
      "173, -967.2796630859375\n",
      "Now the best is: 173\n",
      "174, -966.1430053710938\n",
      "175, -967.3213500976562\n",
      "Now the best is: 175\n",
      "176, -969.0369873046875\n",
      "Now the best is: 176\n",
      "177, -968.617919921875\n",
      "178, -971.1151733398438\n",
      "Now the best is: 178\n",
      "179, -968.6178588867188\n",
      "180, -972.2294311523438\n",
      "Now the best is: 180\n",
      "Epoch 180 finished!\n",
      "181, -971.1561889648438\n",
      "182, -975.051025390625\n",
      "Now the best is: 182\n",
      "183, -975.4254760742188\n",
      "Now the best is: 183\n",
      "184, -973.8848266601562\n",
      "185, -975.6295776367188\n",
      "Now the best is: 185\n",
      "186, -976.38525390625\n",
      "Now the best is: 186\n",
      "187, -976.0648803710938\n",
      "188, -975.725830078125\n",
      "189, -975.6033935546875\n",
      "190, -978.845703125\n",
      "Now the best is: 190\n",
      "Epoch 190 finished!\n",
      "191, -981.3875122070312\n",
      "Now the best is: 191\n",
      "192, -978.363525390625\n",
      "193, -980.3861694335938\n",
      "194, -982.856201171875\n",
      "Now the best is: 194\n",
      "195, -981.8168334960938\n",
      "196, -985.5156860351562\n",
      "Now the best is: 196\n",
      "197, -985.372802734375\n",
      "198, -982.9948120117188\n",
      "199, -987.40869140625\n",
      "Now the best is: 199\n",
      "200, -987.47802734375\n",
      "Now the best is: 200\n",
      "Epoch 200 finished!\n",
      "Training complete! Total training time: 1.4074804492791493 hours\n"
     ]
    }
   ],
   "source": [
    "epoch_num = 200\n",
    "loss_stand = 0\n",
    "loss_array = np.zeros(epoch_num)\n",
    "m_start_time = time.time()\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "net.train()\n",
    "for epoch in range(epoch_num):\n",
    "    node_user = net(no_Nidx = user_embed_idx,\n",
    "                    u_feature = s_user_f,\n",
    "                    heteData = heteroData)\n",
    "    data_loader = AugmentLoader(node_user,\n",
    "                                batch_size = 20,\n",
    "                                aug_num = 50,\n",
    "                                shuffle = False)\n",
    "    loss_s = torch.tensor(0.0,requires_grad=True,device='cuda')\n",
    "    '''把所有batch的loss加和；每个epoch结束后回传一次loss / Sum the loss of all batch; BP the loss at the end of each epoch'''\n",
    "    for step, (batch, pseudo_label) in enumerate(data_loader):\n",
    "        batch = batch.cuda()\n",
    "        loss, loss_empi, loss_theo = mcr_loss(batch, \n",
    "                                              pseudo_label)\n",
    "        loss_s = torch.add(loss_s, loss)\n",
    "        \n",
    "        if step == 0:\n",
    "            step_savename = 'Model5_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1]) \n",
    "        elif (step+1) % 50 == 0:\n",
    "            step_savename = 'Model5_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1])         \n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_s.backward()\n",
    "    optimizer.step()\n",
    "        \n",
    "\n",
    "    loss_compare = loss_s.detach().item()\n",
    "    loss_array[epoch] = loss_compare\n",
    "    print(f'{epoch+1}, {loss_compare}')\n",
    "    if loss_compare < loss_stand:\n",
    "        model_savename = f'5Best_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, best_folder,\n",
    "                                  epoch, net, optimizer)\n",
    "        loss_stand = loss_compare\n",
    "        print('Now the best is: {}'.format(epoch+1))\n",
    "        \n",
    "    \n",
    "    if epoch == 0:\n",
    "        model_savename = f'Model5_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    elif (epoch+1) % 25 == 0:\n",
    "        model_savename = f'Model5_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    \n",
    "    if epoch == 0:\n",
    "        params_savename = 'Model5_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        \n",
    "    elif (epoch+1) % 10 == 0:\n",
    "        params_savename = 'Model5_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        print('Epoch {} finished!'.format(epoch+1))\n",
    "        \n",
    "m_end_time = time.time()\n",
    "m_total_time = (m_end_time - m_start_time)/3600\n",
    "np.save(np_path, loss_array)\n",
    "print(\"Training complete! Total training time: {} hours\".format(m_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32323966-ff85-47fe-9d3a-4fe0c74df3a9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Loss可视化/Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f1fe7c8-fc82-4a47-9453-387406c104ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_path = 'Train_record/Model/Model5/All_loss.npy'\n",
    "loss5 = np.load(np_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d02fcb9-6d7c-40de-8bb0-4a56d73d8e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(loss5)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Model 5 - 200 Epoch Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46b22f7-2f26-4874-8a7b-14add213a97c",
   "metadata": {},
   "source": [
    "##  2.6 Model 6 (M2: $\\epsilon^2 = 0.7$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ad9192-c297-4c65-aef7-bf7587d47383",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) 参数/Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85e2321-baac-41eb-9dfd-8a019afd4af6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "- MCR2_HGPD\n",
    "  - cate_dim = 16\n",
    "  - hidden_dim = 16\n",
    "  - num_hop = 2\n",
    "- mcr_loss\n",
    "  - eff = False (without smooth)\n",
    "  - eps = 0.7\n",
    "  - gam2 = 0.6 (Default scale of Discrim in loss calculation)\n",
    "- optimizer & train\n",
    "  - torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "  - epoch = 200\n",
    "- AugmentLoader\n",
    "  - batch_size = 20\n",
    "  - aug_num = 50\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d222e0-58a2-476b-8f7d-91bc543e2057",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### (2) 模型训练/Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4121e36-52aa-4f9d-9dcc-c85d0d87ee99",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "net = MCR2_HGPD(node_num=980, feature_dim=10, cate_dim=16, hidden_dim=16, num_hop=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cee4a6b5-69ba-4e47-b574-33871908ceda",
   "metadata": {},
   "outputs": [],
   "source": [
    "mcr_loss = MaximalCodingRateReduction(eps = 0.7)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46f4dd00-726c-4781-bae4-b9345affd2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_folder = 'Train_record/Model/Model6/Best'\n",
    "model_folder = 'Train_record/Model/Model6/Model state'\n",
    "params_folder = 'Train_record/Model/Model6/Parameter'\n",
    "np_path = 'Train_record/Model/Model6/All_loss.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c7bdc0a-d886-4a3f-a102-48f4be1e8937",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, -1188.32666015625\n",
      "Now the best is: 1\n",
      "2, -1223.1009521484375\n",
      "Now the best is: 2\n",
      "3, -1236.1861572265625\n",
      "Now the best is: 3\n",
      "4, -1251.339599609375\n",
      "Now the best is: 4\n",
      "5, -1256.0450439453125\n",
      "Now the best is: 5\n",
      "6, -1264.2244873046875\n",
      "Now the best is: 6\n",
      "7, -1269.3565673828125\n",
      "Now the best is: 7\n",
      "8, -1275.2513427734375\n",
      "Now the best is: 8\n",
      "9, -1277.095458984375\n",
      "Now the best is: 9\n",
      "10, -1282.16845703125\n",
      "Now the best is: 10\n",
      "Epoch 10 finished!\n",
      "11, -1285.104736328125\n",
      "Now the best is: 11\n",
      "12, -1288.6844482421875\n",
      "Now the best is: 12\n",
      "13, -1288.8682861328125\n",
      "Now the best is: 13\n",
      "14, -1292.4661865234375\n",
      "Now the best is: 14\n",
      "15, -1295.783935546875\n",
      "Now the best is: 15\n",
      "16, -1296.93359375\n",
      "Now the best is: 16\n",
      "17, -1300.6844482421875\n",
      "Now the best is: 17\n",
      "18, -1300.7593994140625\n",
      "Now the best is: 18\n",
      "19, -1305.1175537109375\n",
      "Now the best is: 19\n",
      "20, -1305.545166015625\n",
      "Now the best is: 20\n",
      "Epoch 20 finished!\n",
      "21, -1307.842529296875\n",
      "Now the best is: 21\n",
      "22, -1309.96923828125\n",
      "Now the best is: 22\n",
      "23, -1309.0576171875\n",
      "24, -1311.16162109375\n",
      "Now the best is: 24\n",
      "25, -1310.909423828125\n",
      "26, -1313.2564697265625\n",
      "Now the best is: 26\n",
      "27, -1317.6971435546875\n",
      "Now the best is: 27\n",
      "28, -1318.0701904296875\n",
      "Now the best is: 28\n",
      "29, -1317.99609375\n",
      "30, -1320.9488525390625\n",
      "Now the best is: 30\n",
      "Epoch 30 finished!\n",
      "31, -1321.8106689453125\n",
      "Now the best is: 31\n",
      "32, -1323.063720703125\n",
      "Now the best is: 32\n",
      "33, -1323.79345703125\n",
      "Now the best is: 33\n",
      "34, -1324.549072265625\n",
      "Now the best is: 34\n",
      "35, -1325.7806396484375\n",
      "Now the best is: 35\n",
      "36, -1323.94677734375\n",
      "37, -1326.2696533203125\n",
      "Now the best is: 37\n",
      "38, -1328.8817138671875\n",
      "Now the best is: 38\n",
      "39, -1329.18115234375\n",
      "Now the best is: 39\n",
      "40, -1332.13037109375\n",
      "Now the best is: 40\n",
      "Epoch 40 finished!\n",
      "41, -1330.9437255859375\n",
      "42, -1331.6224365234375\n",
      "43, -1333.1685791015625\n",
      "Now the best is: 43\n",
      "44, -1334.9144287109375\n",
      "Now the best is: 44\n",
      "45, -1335.15234375\n",
      "Now the best is: 45\n",
      "46, -1335.9786376953125\n",
      "Now the best is: 46\n",
      "47, -1336.4278564453125\n",
      "Now the best is: 47\n",
      "48, -1336.055419921875\n",
      "49, -1338.938720703125\n",
      "Now the best is: 49\n",
      "50, -1339.3594970703125\n",
      "Now the best is: 50\n",
      "Epoch 50 finished!\n",
      "51, -1341.0054931640625\n",
      "Now the best is: 51\n",
      "52, -1342.05078125\n",
      "Now the best is: 52\n",
      "53, -1342.8209228515625\n",
      "Now the best is: 53\n",
      "54, -1341.59375\n",
      "55, -1343.739990234375\n",
      "Now the best is: 55\n",
      "56, -1345.24169921875\n",
      "Now the best is: 56\n",
      "57, -1345.443359375\n",
      "Now the best is: 57\n",
      "58, -1344.8375244140625\n",
      "59, -1346.182861328125\n",
      "Now the best is: 59\n",
      "60, -1347.052001953125\n",
      "Now the best is: 60\n",
      "Epoch 60 finished!\n",
      "61, -1348.84912109375\n",
      "Now the best is: 61\n",
      "62, -1350.1640625\n",
      "Now the best is: 62\n",
      "63, -1350.655029296875\n",
      "Now the best is: 63\n",
      "64, -1349.837646484375\n",
      "65, -1348.411376953125\n",
      "66, -1353.0264892578125\n",
      "Now the best is: 66\n",
      "67, -1352.2911376953125\n",
      "68, -1352.7738037109375\n",
      "69, -1353.717529296875\n",
      "Now the best is: 69\n",
      "70, -1355.6087646484375\n",
      "Now the best is: 70\n",
      "Epoch 70 finished!\n",
      "71, -1353.0911865234375\n",
      "72, -1358.1378173828125\n",
      "Now the best is: 72\n",
      "73, -1358.4158935546875\n",
      "Now the best is: 73\n",
      "74, -1357.2005615234375\n",
      "75, -1360.2099609375\n",
      "Now the best is: 75\n",
      "76, -1358.8648681640625\n",
      "77, -1361.6866455078125\n",
      "Now the best is: 77\n",
      "78, -1361.8770751953125\n",
      "Now the best is: 78\n",
      "79, -1359.4337158203125\n",
      "80, -1362.3721923828125\n",
      "Now the best is: 80\n",
      "Epoch 80 finished!\n",
      "81, -1360.8255615234375\n",
      "82, -1361.4639892578125\n",
      "83, -1364.598388671875\n",
      "Now the best is: 83\n",
      "84, -1365.7308349609375\n",
      "Now the best is: 84\n",
      "85, -1368.09326171875\n",
      "Now the best is: 85\n",
      "86, -1365.5794677734375\n",
      "87, -1367.463623046875\n",
      "88, -1365.22802734375\n",
      "89, -1368.434814453125\n",
      "Now the best is: 89\n",
      "90, -1370.2064208984375\n",
      "Now the best is: 90\n",
      "Epoch 90 finished!\n",
      "91, -1369.6617431640625\n",
      "92, -1369.7786865234375\n",
      "93, -1372.6812744140625\n",
      "Now the best is: 93\n",
      "94, -1371.39208984375\n",
      "95, -1369.2979736328125\n",
      "96, -1372.652099609375\n",
      "97, -1373.5706787109375\n",
      "Now the best is: 97\n",
      "98, -1376.18115234375\n",
      "Now the best is: 98\n",
      "99, -1376.7332763671875\n",
      "Now the best is: 99\n",
      "100, -1375.791015625\n",
      "Epoch 100 finished!\n",
      "101, -1375.3448486328125\n",
      "102, -1378.1187744140625\n",
      "Now the best is: 102\n",
      "103, -1376.115234375\n",
      "104, -1377.38720703125\n",
      "105, -1378.8153076171875\n",
      "Now the best is: 105\n",
      "106, -1383.3922119140625\n",
      "Now the best is: 106\n",
      "107, -1380.3240966796875\n",
      "108, -1381.6981201171875\n",
      "109, -1381.79541015625\n",
      "110, -1381.4747314453125\n",
      "Epoch 110 finished!\n",
      "111, -1383.7843017578125\n",
      "Now the best is: 111\n",
      "112, -1383.35498046875\n",
      "113, -1384.611328125\n",
      "Now the best is: 113\n",
      "114, -1386.8782958984375\n",
      "Now the best is: 114\n",
      "115, -1388.688232421875\n",
      "Now the best is: 115\n",
      "116, -1385.789794921875\n",
      "117, -1388.8580322265625\n",
      "Now the best is: 117\n",
      "118, -1388.5067138671875\n",
      "119, -1390.5465087890625\n",
      "Now the best is: 119\n",
      "120, -1390.11279296875\n",
      "Epoch 120 finished!\n",
      "121, -1392.0806884765625\n",
      "Now the best is: 121\n",
      "122, -1390.4876708984375\n",
      "123, -1390.793701171875\n",
      "124, -1395.2562255859375\n",
      "Now the best is: 124\n",
      "125, -1394.2120361328125\n",
      "126, -1396.274658203125\n",
      "Now the best is: 126\n",
      "127, -1393.7335205078125\n",
      "128, -1398.6602783203125\n",
      "Now the best is: 128\n",
      "129, -1398.307861328125\n",
      "130, -1395.9581298828125\n",
      "Epoch 130 finished!\n",
      "131, -1399.28076171875\n",
      "Now the best is: 131\n",
      "132, -1402.939208984375\n",
      "Now the best is: 132\n",
      "133, -1400.725830078125\n",
      "134, -1399.78857421875\n",
      "135, -1400.869140625\n",
      "136, -1399.9169921875\n",
      "137, -1401.56640625\n",
      "138, -1403.3282470703125\n",
      "Now the best is: 138\n",
      "139, -1403.1209716796875\n",
      "140, -1405.3619384765625\n",
      "Now the best is: 140\n",
      "Epoch 140 finished!\n",
      "141, -1405.193603515625\n",
      "142, -1407.2537841796875\n",
      "Now the best is: 142\n",
      "143, -1407.0120849609375\n",
      "144, -1408.8004150390625\n",
      "Now the best is: 144\n",
      "145, -1407.2689208984375\n",
      "146, -1409.5498046875\n",
      "Now the best is: 146\n",
      "147, -1409.53564453125\n",
      "148, -1410.71142578125\n",
      "Now the best is: 148\n",
      "149, -1412.7476806640625\n",
      "Now the best is: 149\n",
      "150, -1413.83935546875\n",
      "Now the best is: 150\n",
      "Epoch 150 finished!\n",
      "151, -1413.4908447265625\n",
      "152, -1413.5517578125\n",
      "153, -1416.147216796875\n",
      "Now the best is: 153\n",
      "154, -1416.132080078125\n",
      "155, -1419.189697265625\n",
      "Now the best is: 155\n",
      "156, -1416.8509521484375\n",
      "157, -1419.0704345703125\n",
      "158, -1420.4119873046875\n",
      "Now the best is: 158\n",
      "159, -1420.4686279296875\n",
      "Now the best is: 159\n",
      "160, -1417.5419921875\n",
      "Epoch 160 finished!\n",
      "161, -1422.1585693359375\n",
      "Now the best is: 161\n",
      "162, -1421.892333984375\n",
      "163, -1421.9454345703125\n",
      "164, -1422.688232421875\n",
      "Now the best is: 164\n",
      "165, -1420.4847412109375\n",
      "166, -1423.7542724609375\n",
      "Now the best is: 166\n",
      "167, -1425.1622314453125\n",
      "Now the best is: 167\n",
      "168, -1425.52001953125\n",
      "Now the best is: 168\n",
      "169, -1425.9193115234375\n",
      "Now the best is: 169\n",
      "170, -1428.380126953125\n",
      "Now the best is: 170\n",
      "Epoch 170 finished!\n",
      "171, -1428.33447265625\n",
      "172, -1427.5177001953125\n",
      "173, -1431.0595703125\n",
      "Now the best is: 173\n",
      "174, -1430.034912109375\n",
      "175, -1431.1944580078125\n",
      "Now the best is: 175\n",
      "176, -1432.814453125\n",
      "Now the best is: 176\n",
      "177, -1432.4364013671875\n",
      "178, -1434.94287109375\n",
      "Now the best is: 178\n",
      "179, -1432.6033935546875\n",
      "180, -1436.02001953125\n",
      "Now the best is: 180\n",
      "Epoch 180 finished!\n",
      "181, -1435.0230712890625\n",
      "182, -1438.849853515625\n",
      "Now the best is: 182\n",
      "183, -1439.16162109375\n",
      "Now the best is: 183\n",
      "184, -1437.7412109375\n",
      "185, -1439.48779296875\n",
      "Now the best is: 185\n",
      "186, -1440.1278076171875\n",
      "Now the best is: 186\n",
      "187, -1440.0018310546875\n",
      "188, -1439.605224609375\n",
      "189, -1439.4185791015625\n",
      "190, -1442.6827392578125\n",
      "Now the best is: 190\n",
      "Epoch 190 finished!\n",
      "191, -1445.11328125\n",
      "Now the best is: 191\n",
      "192, -1442.2437744140625\n",
      "193, -1444.284423828125\n",
      "194, -1446.83984375\n",
      "Now the best is: 194\n",
      "195, -1445.6475830078125\n",
      "196, -1449.2860107421875\n",
      "Now the best is: 196\n",
      "197, -1449.127685546875\n",
      "198, -1446.7559814453125\n",
      "199, -1451.1256103515625\n",
      "Now the best is: 199\n",
      "200, -1451.20947265625\n",
      "Now the best is: 200\n",
      "Epoch 200 finished!\n",
      "Training complete! Total training time: 1.4400329813692305 hours\n"
     ]
    }
   ],
   "source": [
    "epoch_num = 200\n",
    "loss_stand = 0\n",
    "loss_array = np.zeros(epoch_num)\n",
    "m_start_time = time.time()\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "net.train()\n",
    "for epoch in range(epoch_num):\n",
    "    node_user = net(no_Nidx = user_embed_idx,\n",
    "                    u_feature = s_user_f,\n",
    "                    heteData = heteroData)\n",
    "    data_loader = AugmentLoader(node_user,\n",
    "                                batch_size = 20,\n",
    "                                aug_num = 50,\n",
    "                                shuffle = False)\n",
    "    loss_s = torch.tensor(0.0,requires_grad=True,device='cuda')\n",
    "    '''把所有batch的loss加和；每个epoch结束后回传一次loss / Sum the loss of all batch; BP the loss at the end of each epoch'''\n",
    "    for step, (batch, pseudo_label) in enumerate(data_loader):\n",
    "        batch = batch.cuda()\n",
    "        loss, loss_empi, loss_theo = mcr_loss(batch, \n",
    "                                              pseudo_label)\n",
    "        loss_s = torch.add(loss_s, loss)\n",
    "        \n",
    "        if step == 0:\n",
    "            step_savename = 'Model6_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1]) \n",
    "        elif (step+1) % 50 == 0:\n",
    "            step_savename = 'Model6_step.csv'\n",
    "            savePara.save_step_DC(step_savename, params_folder,\n",
    "                                  epoch, step,\n",
    "                                  loss.item(), loss_empi[0], loss_empi[1], \n",
    "                                  loss_theo[0], loss_theo[1])         \n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_s.backward()\n",
    "    optimizer.step()\n",
    "        \n",
    "    \n",
    "    loss_compare = loss_s.detach().item()\n",
    "    loss_array[epoch] = loss_compare\n",
    "    print(f'{epoch+1}, {loss_compare}')\n",
    "    if loss_compare < loss_stand:\n",
    "        model_savename = f'6Best_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, best_folder,\n",
    "                                  epoch, net, optimizer)\n",
    "        loss_stand = loss_compare\n",
    "        print('Now the best is: {}'.format(epoch+1))\n",
    "        \n",
    "    \n",
    "    if epoch == 0:\n",
    "        model_savename = f'Model6_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    elif (epoch+1) % 25 == 0:\n",
    "        model_savename = f'Model6_epoch{epoch + 1}.pt'\n",
    "        savePara.save_model_state(model_savename, model_folder, \n",
    "                                  epoch, net, optimizer)\n",
    "    \n",
    "    if epoch == 0:\n",
    "        params_savename = 'Model6_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        \n",
    "    elif (epoch+1) % 10 == 0:\n",
    "        params_savename = 'Model6_params.csv'\n",
    "        savePara.save_parameters_to_csv(params_savename, params_folder, \n",
    "                                        epoch, loss_s.item(), 0.001)\n",
    "        print('Epoch {} finished!'.format(epoch+1))\n",
    "        \n",
    "m_end_time = time.time()\n",
    "m_total_time = (m_end_time - m_start_time)/3600\n",
    "np.save(np_path, loss_array)\n",
    "print(\"Training complete! Total training time: {} hours\".format(m_total_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebe55e8-d6fb-4028-9364-dfd5394e7154",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Loss可视化/Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cb0e945-4ca8-47be-8d1c-4716f190a6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_path = 'Train_record/Model/Model6/All_loss.npy'\n",
    "loss6 = np.load(np_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77d666bb-594c-4075-ab59-e12c89d1b39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vSNVNM1Yrt7jc7FIBAAAAAMAZZDfzwydOnKhrrrnmuG2aN29+Un3GxMQoJiZG7du3V0REhAYNGqQnn3xSjRs3VkxMjHfm3BHp6emSfptZd6w2drtdERERR/1Mh8NRaTktcCKRgQ79977ztDoxW+UujyrcHpW7PapwG/q/hbuVmFWsSZ9u1Hs39pbVevSAGAAAAAAANCymBnWRkZGKjIystf6PzIQrKyuTJA0YMECPPfaYysvL5evrK0lasGCBYmNjvYHggAEDNG/evEr9LFiwQL1795aPj0+t1Yqzj5+vTee1japyvUPjIF3xj2VauCNd037Zq7uGtDahOgAAAAAAcKbVmz3qkpOTtWHDBiUnJ8vtdmvDhg3asGGDCgsPb8Y/f/58vf/++9qyZYsSExM1f/583XnnnTrnnHO8Idy1114rh8Oh8ePHa8uWLfrqq6/03HPPadKkSd5lrRMmTFBSUpImTZqk7du3a8aMGZo+fboeeughs4aOs0yn2BD9bXQnSdIr3+/U8r1ZJlcEAAAAAADOBItxZNpZHTd+/HjNmjWryvVFixZpyJAhWrRokR5//HFt27ZNZWVliouL05gxY/TII48oNDTU237z5s26++67tWrVKoWFhWnChAl66qmnKu0/t3jxYj3wwAPaunWrYmNj9fDDD2vChAnVrrW6R+4Cx2IYhh76bJO+WLdfkYEOzb/vXDUKcppdFgAAAAAAOAXVzYrqTVBXnxDUoSaUlLt1+dtLtfNQgfq3DNfsW/vJbqs3k2ABAAAAAMCvCOpMRFCHmrI3o1CXvfU/FZW7dW7rSHVtGqK4cH/FhfkrLtxPsaF+8iG8AwAAAACgTiOoMxFBHWrSfzalauJH6496LyLAV1NGd9IlXWPPcFUAAAAAAKC6COpMRFCHmrY+OUdrEnOUklOslOxipeSUKCW7WGUujyRpdPdYPXNZZ4X4czIxAAAAAAB1DUGdiQjqcCaUuzx6a+Fuvb1ojzyGFBPs1Ctju+ncNpFmlwYAAAAAAH6nulkRm1sB9ZSv3aoHL2ynz+8cqBaRAUrLL9X101dq8tytKil3m10eAAAAAAA4SQR1QD3XMz5M3957rm7o30ySNHNZoka8/ov+tzvT5MoAAAAAAMDJIKgDGgB/X7v+dnlnzbqlr2JDnErOLtb101fqoc82Kre4vEr7/NIK/bDtkJbuyRSr3wEAAAAAqBvYo64WsEcdzFRY5tLL3+3QByuSZBhSZKCvnrq0k+LC/PTLrkwt2Z2h9Sm5cnsO/19/UJtITb28i+Ij/E2uHAAAAACAhonDJExEUIe6YG1Sjh75YpN2pxce9X7zCH+l5pWq3OWR08eqBy5oq1vPbSG7jYm2AAAAAADUJII6ExHUoa4oc7k17ed9evvnPXLarTq3TaQGtYnSua0jFRfur30ZhXr8qy1avi9LktSxcbCmjO6kMH8fZRdVKKe4XLnF5Sosc2tw2yi1bhRo8ogAAAAAAKh/COpMRFCHusbl9kjSUWfLGYahz9fu19T525VbXHHMPnztVj0ysr3GD2wuq9VSa7UCAAAAANDQENSZiKAO9VFmYZmmfrtd3246KKePVeEBvgr191V4gK/ySyq0JilH0uE97V4Z203RwU6TKwYAAAAAoH4gqDMRQR3qM8MwZLFYqlybvTJZU7/dptIKj8L8ffT8mK4a2TnGpCoBAAAAAKg/qpsVndKu8SkpKdq/f7/3/apVq3T//ffrX//616l0B6AO+WNId+TaDf2b6T/3DFLnJsHKKa7QhNlr9cTXm0XWDwAAAABAzTiloO7aa6/VokWLJElpaWkaPny4Vq1apccee0zPPPNMjRYIoO5o3ShQX955ju4c0koWizR7RbKm/y/B7LIAAAAAAGgQTimo27Jli/r27StJ+vTTT9W5c2ctW7ZMH330kWbOnFmT9QGoY3ztVj08sr2euayTJOmF/+7Q+uQck6sCAAAAAKD+O6WgrqKiQg6HQ5L0448/6rLLLpMktW/fXgcPHqy56gDUWdf3b6aLuzaWy2No4kfrlVtcbnZJAAAAAADUa6cU1HXq1EnTpk3TkiVL9MMPP2jkyJGSpNTUVEVERNRogQDqJovFohfGdFGzCH8dyC3RQ59tYr86AAAAAABOwykFdS+++KL++c9/asiQIRo3bpy6desmSZo7d653SSyAhi/I6aO3r+0pX5tVP24/pBlLE80uCQAAAACAestinOIUGLfbrfz8fIWFhXmvJSYmyt/fX40aNaqxAuuj6h65CzQUHy5P1JPfbJWPzaLPJgxU97hQs0sCAAAAAKDOqG5WZD+VzktKSmQYhjekS0pK0ldffaUOHTpoxIgRp1YxgHrr+v7NtHxfluZvTtNds9fqqt5xahziVOMQp2JD/RQd5JTVKlW4DbncHpW7PXK5DTUJ85OP7ZQm9gIAAAAA0OCcUlA3evRojRkzRhMmTFBubq769esnHx8fZWZm6rXXXtOdd95Z03UCqMMsFoteuLKrthzIV3J2sd78aXe1nosNcWr6+D7q0JiZpwAAAAAAnNJUlnXr1mnQoEGSpM8//1zR0dFKSkrSBx98oDfffLNGCwRQPwQ7ffT5hAH6y4h2urZfvIa2i1L7mCCF+PlUamezWuSwW+Vrsyo1r1R/mrZcy/ZmmlQ1AAAAAAB1xynNqCsuLlZQUJAkacGCBRozZoysVqv69++vpKSkGi0QQP3RKNipu4e2rnK9tMItSfKxWWWzWiRJecUVuu3DNVqVkK2bZqzSK2O7aXT3Jme0XgAAAAAA6pJTmlHXunVrff3110pJSdH333+vCy+8UJKUnp7O4QkAqnD62OT0sXlDOkkK8ffRB7f01cVdGqvCbei+TzboX7/s1SmebwMAAAAAQL13SkHdU089pYceekjNmzdX3759NWDAAEmHZ9f16NGjRgsE0HA5fWx6a1wP3XJOC0nSc/N36Mlvtighs4jADgAAAABw1rEYp/jdcFpamg4ePKhu3brJaj2c961atUrBwcFq3759jRZZ31T3yF0Av3lvyT49++127/vGIU4NaBWhc1pFamDrCDUO8TOxOgAAAAAATl11s6JTDuqO2L9/vywWi5o0YW+pIwjqgFOzYGuapv8vQeuTc1Xu9lS61yM+VGN7xemSbo0V7PQ5Rg8AAAAAANQ9tRrUeTwePfvss3r11VdVWFgoSQoKCtKDDz6oxx9/3DvD7mxFUAecnpJyt9Ym5Wjp3kwt25ulzftz5fn1v1QOu1UjO8foql5NNbBVZKV97wAAAAAAqIuqmxWd0qmvjz/+uKZPn64XXnhB55xzjgzD0NKlSzV58mSVlpZq6tSpp1w4APj52nRum0id2yZSkpReUKqv1x/QZ2v2a3d6ob7ZkKpvNqQq2GlX9/gw9YwPVc/4MHWPD2W2HQAAAACg3jqlGXWxsbGaNm2aLrvsskrXv/nmG9111106cOBAjRVYHzGjDqgdhmFo84E8fbZmv+ZuTFVeSUWl+xaL1LZRkHo2C1WP+DD1jA9Tq6gAWSwnnnVXWuHWx6uSlVNUrtsHt1Kg45R+jgEAAAAAQBW1uvTV6XRq06ZNatu2baXrO3fuVPfu3VVSUnLyFTcgBHVA7XO5PdqRVqB1yTlal5Sjdcm5Ss4urtIuxM9HPeNDdVHnxhrVtXGVAM7jMTRvU6pe+m6nDuQe/m9XfLi//n51d/VqFnZGxgIAAAAAaNhqNajr16+f+vXrpzfffLPS9XvuuUerVq3SypUrT77iBoSgDjBHRkHZ4eAuOUfrk3K1cX+uyly/HUrh52PTyM4xGtOziQa2itTqxGw9N3+7Nu3PkyRFBztks1iUmlcqq0W6e2hr3Xt+G/nYzu59NwEAAAAAp6dWg7rFixfr4osvVnx8vAYMGCCLxaJly5YpJSVF8+fP16BBg06r+PqOoA6oG8pdHm0/mK//7cnUF+v2a19GkfdemL+PcooPL50N8LXpziGtdOu5LVXu9ujpb7bo6w2pkqSuTUP096u7q1VUoCljAAAAAADUf7Ua1ElSamqq3n77be3YsUOGYahjx466/fbbNXnyZM2YMeOUC28ICOqAuscwDG1IydUX6/Zr3saDyiupkM1q0bV943XfBW0UGeio1H7exlQ98fUW5ZVUyOlj1bOXd9FVvZqaVD0AAAAAoD6r9aDuaDZu3KiePXvK7XbXVJf1EkEdULeVudxam5ijpmH+io/wP2a7tLxSPfTZRv1vT6Yk6fr+8Xrqkk7ytbMUFgAAAABQfdXNivhuE8BZx2G3aWDryOOGdJIUE+LUB7f01QMXtJXFIs1ekayr/7VcaXmlZ6hSAAAAAMDZhKAOAI7DarXovgvaaMZNfRTstGt9cq4ueWuJVuzLMrs0AAAAAEADYze7AACoD4a2b6R595yrCbPXafvBfF333koNbhulqECHIoN8FRnoUGSgQ92ahp5wph4AAAAAAEdzUkHdmDFjjns/Nzf3dGoBgDqtWUSAvrxzoB77arO+Wn9AC3ekV2njY7PolbHdNLp7ExMqBAAAAADUZycV1IWEhJzw/o033nhaBQFAXebna9Nrf+qma/rEaW9GkTILy7yvhMxibT+Yr/s+2aC0vFLdfl5LWSwWs0sGAAAAANQTNXrqKw7j1Ffg7OTxGHr22+2asTRBkjR+YHM9eUlH2ayVw7oDuSX6eWe6IgJ81aVpqGJDnAR6AAAAANCAVTcrYo86AKghVqtFT13aUbGhTj377XbNXJaotLxSvX5Nd5VWuDV/c5q+Xn9AqxKzKz0XGeirLk1C1KVpqM5rE6lezcII7gAAAADgLMSMulrAjDoA8zam6sFPN6rc7VHTMD8dyi9Vhfvwf24tFqlXfJiKy93aeahAbk/l/wx3jwvVhMGtdGHHaFmtBHYAAAAAUN9VNysiqKsFBHUAJGn53izd/uEaFZS6JEkdGgfr8u6xuqx7rBqH+EmSSivc2n4wX5sP5GldUo7+uyVNZS6PJKllVIDuOK+lLu/RRA67zbRxAAAAAABOD0GdiQjqAByxL6NQP24/pMFtG6ldTNAJ22cUlGnmsgR9uDxJ+b8GfNHBDk0c1kZX946Tr91a2yUDAAAAAGoYQZ2JCOoAnK7CMpc+Xpms6f9LUFp+qSQpPtxfDwxvo8u6NalyQAUAAAAAoO4iqDMRQR2AmlLmcuvfq1P05k97lFlYJklqFx2kSRe2Vf+WEQp22jl4AgAAAADqOII6ExHUAahpxeUuzVyWqGk/7/UuiZUkm9WiED8fhfr5KMTfRy0iA9S/ZYQGtIxQ0zA/QjwAAAAAqAMI6kxEUAegtuQVV+hfS/Zq9opk5ZVUHLdtbIhT/VtGqFfzMLWICFBcuL9iQ/2qLJstKnPpYF6J0gvK1LFxsEL9fWtzCAAAAABw1iGoMxFBHYAzobTCrbySCuUWVyi3uFw5xeXafCBPK/dla+P+XFW4q/7n3W61qGmYn2JCnMotrlBqbkmlGXph/j5698be6t08/EwOBQAAAAAaNII6ExHUATBbcblL65JytWJflrak5ik5u1j7s0tU7vYctX2Qwy6Hj1WZheXytVv16thuurRb7BmuGgAAAAAapupmRfYzWBMA4Azx97Xr3DaROrdNpPea22PoUH6pkrKKdSi/VGEBvmoc4lTjEKeCnD4qKXfrvk/Wa8G2Q7rn4/Xan1OiCYNbss8dAAAAAJwhzKirBcyoA1BfuT2Gpn67XTOWJkiSxvWN199Gd5LdZjW5MgAAAACov5hRBwA4aTarRU9d2lFx4X565j/b9PGqZO1Iy1e/FodPkW0a5qe4cH81CfWT08dmdrkAAAAA0KAQ1AEAqrj5nBZqEuqnez9Zr/XJuVqfnFvpvt1q0WXdY3XPsDZqERlgTpEAAAAA0MCw9LUWsPQVQEOxL6NQC3eka39OifbnFCslu0QpOcUqLndLkqwWaXT3Jrp7aGu1bhRocrUAAAAAUDdx6quJCOoANGSGYWjj/jy99dNu/bQjXZJksUiXdo3Vzec0V7emobJaOYACAAAAAI4gqDMRQR2As8Xm/Xl6c+Fu/bDtkPdadLBDwztGa0SnGPVrESFf+4kPosgoKNOKfVnq3CSEpbQAAAAAGhyCOhMR1AE422xNzdO/ftmnn7anq7DM5b0e5LTrvLZR6ts8XL2ahalD42DZfp1tV1rh1o/bD+nLdQe0eFeG3B5Dvjar7h7aWncOaVWtgA8AAAAA6gOCOhMR1AE4W5W53Fq2N0sLth7SD9sOKbOwrNL9QIddPeJDFRXk0A/bDqmg9LdQr2mYn/bnlEiS2kUH6cWruqp7XOiZLB8AAAAAagVBnYkI6gBAcnsMrU/O0bK9WVqTlKP1STkq+N1sO0mKDXFqTM+muqJnE7WMDNC8TQc1Ze5WZRWVy2o5fPrsgxe2lb8vh5QDAAAAqL8I6kxEUAcAVbk9hnamFWhtUrb255ZocJso9W8ZUeXgieyicv3tP9v01foDkqSYYKduObe5rukbr2CnjxmlAwAAAMBpIagzEUEdAJy+RTvT9cRXW3Qg9/By2ECHXdf0idPN57ZQk1C/Ez5fWuHWnvRChQX4Vqs9AAAAANQWgjoTEdQBQM0orXBr7oZUvbtkn3anF0qSbFaLzm/fSHHh/gpy2hXs9FGQ0y4/X5uSsoq17WC+dhzMV0JmkTy//g03qE2krusXr/M7RMvHxiEVAAAAAM4sgjoTEdQBQM3yeAwt3p2hd3/Zp2V7s6r9XKi/j/JKKnTkb7pGQQ5d3SdOV/eJU9Mw/1qqFgAAAAAqI6gzEUEdANSeLQfy9MvuDOWVVKig1KX8X38tKnOpSZif2scEq0PjIHVsHKyoIIdSskv08epkfbYmRZmF5d5+2jQK1DmtIzWwVYT6tYxQiB/73wEAAACoHQR1JiKoA4C6p9zl0YJtaZqzIlkrErL0+7/9rBapS9NQ3dC/ma7o0US2PxxwAQAAAACng6DORAR1AFC35RSVa/m+LC3bm6lle7K0L7PIe699TJAeHtleQ9pFyWIhsAMAAABw+qqbFdWbHbWnTp2qgQMHyt/fX6GhoVXuZ2VlaeTIkYqNjZXD4VBcXJwmTpyo/Px8b5vExERZLJYqr++++65SX4sXL1avXr3kdDrVsmVLTZs2rbaHBwA4g8ICfDWqS2M9e3kXLXxoiJY9MkwPj2yvYKddO9IKdPPM1brmXyu0PjmnRj+3tMKtL9bu1yerksXPyQAAAAD8kd3sAqqrvLxcY8eO1YABAzR9+vQq961Wq0aPHq1nn31WUVFR2rNnj+6++25lZ2fro48+qtT2xx9/VKdOnbzvw8PDvf87ISFBo0aN0m233abZs2dr6dKluuuuuxQVFaUrr7yy9gYIADBNbKif7hzSSuP6xumdn/fq/WWJWpmQrSv+sUzD2jfSdf3iNbhtlOyneGJsam6JZq9I0serkpVTXCFJKnd7dOOA5jU4CgAAAAD1Xb1b+jpz5kzdf//9ys3NPWHbN998Uy+//LJSUlIkHZ5R16JFC61fv17du3c/6jMPP/yw5s6dq+3bt3uvTZgwQRs3btTy5curVSNLXwGgfjuQW6K//7BLX6zb793LLibYqT/1bqo/VfPE2DKXW2sTczR7ZZK+33pIbs/hjsL8fZRTXCFfu1XzJp6rdjFBtTkUAAAAAHVAdbOiejOj7mSlpqbqyy+/1ODBg6vcu+yyy1RaWqo2bdrogQce0FVXXeW9t3z5cl144YWV2o8YMULTp09XRUWFfHyqngpYVlamsrIy7/vfL7cFANQ/TUL99MrYbrpzSCt9sipZn6/dr7T8Ur25cI/eWrRHfZuHq11MkFpEBnhfkYEObdyfq5X7srUyIUvrk3NV5vJ4+xzQMkLjz2mu89s30m0frNGinRm65+N1mjvxXDl9bCaOFgAAAEBd0eCCunHjxumbb75RSUmJLr30Ur333nvee4GBgXrttdd0zjnnyGq1au7cubr66qs1a9YsXX/99ZKktLQ0RUdHV+ozOjpaLpdLmZmZaty4cZXPfP755zVlypTaHRgA4IxrFRWoxy/uqIdGtNOCrYf0yepkLd2TpZUJ2VqZkH3C5yMDfTW8Y7RuGthc7WN++6nZy2O7aeTrS7TrUKGem79dz4zuXJvDAAAAAFBPmLr0dfLkyScMuFavXq3evXt7359o6WtaWppyc3O1c+dOPfbYYxo8eLD+8Y9/HLP/e+65R4sXL9amTZskSW3bttXNN9+sRx991Ntm6dKlOvfcc3Xw4EHFxMRU6eNoM+ri4uJY+goADVBSVpFW7MtSQmaxEjILlZBZpMSsYpW7PGoU5FC/lhHq1yJc/VtGqFVUwDFPjv1lV4ZunLFKkvTujb01vGP0UdsBAAAAqP/qxdLXiRMn6pprrjlum+bNm59UnzExMYqJiVH79u0VERGhQYMG6cknnzzqTDhJ6t+/f6VZdzExMUpLS6vUJj09XXa7XREREUftw+FwyOFwnFSdAID6qVlEgJpFBFS65vYYyiupUJi/zzGDuT86r22UbhvUQu8uSdBfP9+o7+4/T9HBTu/9g3kl2rw/TwEOu5pHBqhxsFNWa/X6BgAAAFA/mRrURUZGKjIystb6PzJZ8Pez3f5o/fr1lUK8AQMGaN68eZXaLFiwQL179z7q/nQAANisFoUH+J70cw+NaKdle7O0NTVf932yXue3j9b6lBytS8pVWn5ppbYOu1XNIvzVPCJAHRoH67y2UeoeFyrbSYZ3hmHIY+iknwMAAABQ++rNHnXJycnKzs5WcnKy3G63NmzYIElq3bq1AgMDNX/+fB06dEh9+vRRYGCgtm3bpr/+9a8655xzvLPyZs2aJR8fH/Xo0UNWq1Xz5s3Tm2++qRdffNH7ORMmTND//d//adKkSbrtttu0fPlyTZ8+XR9//LEJowYANGQOu01vjuuhS978n1bsy9aKfb/te2ezWtSmUaDK3R4lZxWrzOXRrkOF2nWoUAu2HdIbP+1WqL+PBrWJ0uC2h19RQcef3b0qIVuTPt2gQIddn00YoCAnP4ACAAAA6hJT96g7GePHj9esWbOqXF+0aJGGDBmiRYsW6fHHH9e2bdtUVlamuLg4jRkzRo888ohCQ0MlHQ7qXnzxRSUlJclms6lt27a6//77vQdJHLF48WI98MAD2rp1q2JjY/Xwww9rwoQJ1a61uuuOAQCQpHkbU/X3H3apVaNA9YgPVc/4MHVtGiJ/38M/T3O5PTqQW6KEzCIlZBZpTWKOluzOUH6py9uH1SJd0zdek4a3VWRg5cDO4zH0z1/26ZUFO+X2HP5r/7p+8Zp6RZczN0gAAADgLFbdrKjeBHX1CUEdAKC2udwebUjJ1c87M/TzrnRtOZAvSQpy2DVxWGuNP6e5HHabcorK9eBnG7VwR7okaVCbSC3ZnSlJ+ujP/TSwde1tQQEAAADgMII6ExHUAQDOtJX7svS3b7d5A7v4cH/dck5zvbskQQdyS+Rrt2rKZZ10TZ84PfH1Fs1Zmay4cD99d995CnDUm50wAAAAgHqJoM5EBHUAADN4PIa+WLdfL3+/U+kFvx2k1DzCX29f11OdYkMkSQWlFRrx91+Umleq8QOba/JlncwqGQAAADgrVDcrsp7BmgAAQC2yWi0a2ztOix4aonuGtVaQw67LusVq3j3nekM6SQpy+uj5K7tKkmYtT9TqxOxjdQkAAADgDGJGXS1gRh0AoC4wDEMWi+WY9//6+UZ9uma/WkQG6L/3DZLTx3bc/jILy/TdljQ1DfPTkHaNarpcAAAAoMGqblbEpjQAADRQxwvpJOnxiztq8a4MJWQW6bUfdumxUR2qtCmtcOvH7Yf05boDWrwrw3tq7GOj2uv281rVSt0AAADA2YoZdbWAGXUAgPrip+2HdOusNZKkUH8fRQT4KiLAofAAX/nYrfp5Z7oKSl3e9s0j/JWYVSxJum1QCz16UQdZrccPBAEAAICzHTPqAADACZ3fIVq3nttC0/+XoNziCuUWV2hvRlGlNrEhTl3Rs4mu6NFUraIC9K9f9un5/+7Qu0sSlFlYrpeu6iof22/b3qZkF+uT1cn6ZVembj6nucb0bHqmhwUAAADUS8yoqwXMqAMA1DfZReXKLCxTZmGZsovKlVVYroLSCvVqFq5+LcKrzJr7Yu1+/fWLTXJ7DA1uG6W3ru2hlfuyNWdlkhbvytDv/3Vx99BWenB4u2rPvHN7DP1j0R59tna/7h7aSlf3ia/JoQIAAABnXHWzIoK6WkBQBwA4Gyzaka675qxTSYVbvjaryt0e771zW0cqPsJfH61MliSN6hKj1/7U/YQHVhzKL9X9n2zQ8n1Z3mv3nt9GD1zQ5oR77gEAAAB1FUGdiQjqAABni3XJObpl5mrlFlcoPMBXY3s11bi+8WoeGSBJ+nztfj365SZVuA11iwvVuzf2UqMg51H7WrQzXQ9+ulHZReXy97Xpgg7RmrsxVZJ0Va+men5Ml0pLbCUpNbdEs5YnKjW3VO1jgtQpNlidYkMUFeSo3YEDAAAAJ4GgzkQEdQCAs8nBvBJtP5ivc1pHymGvOmNu5b4s3TF7rXKLK9Qk1E+PXNReEQG+CnL6KMhpl7/Dpnd/2ad3lyRIkjo2DtZb1/ZQq6hAfbQyWU98vVkeQzqvbZT+cV1PBTrs2n2oQNMW79M3Gw7I5an6T5lGQQ71iA/VwyPbq2VUYK3/HgAAAADHQ1BnIoI6AAAqS8ws0i0zV2tfZtFx240f2FyPXNS+0hLZn7Yf0sSP1qukwq1OscFqHOLUj9vTvff7twzXoDZR2plWoC2peUrILPLukRcR4KsPb+2njrH8fQwAAADzENSZiKAOAICqcovL9cJ/d2jXoQIVlLpUUOpSYdnhV1SQQ1Mv76wLO8Uc9dmNKbm6ZeZqZRWVS5IsFmlExxhNGNJK3eNCK7UtKnNp+8F8PT13q7am5ivYadf7N/dVr2ZhtT1EAAAA4KgI6kxEUAcAQPW5PYYs0glPhU3KKtKT32xVk1A//XlQC7U6wZLWvJIK3TpztdYk5cjf16Z3b+ytc1pH1mDlAAAAQPUQ1JmIoA4AgLqhuNylOz5cqyW7M+Vrs+r/ru1xzFl7AAAAQG2pblZkPeYdAACAes7f1673buqtEZ2iVe726M456/Tqgp3am1FodmkAAABAFcyoqwXMqAMAoG5xuT366+eb9OX6A95rHRsH65JujXVp11jFhftXq5+C0goFOuyyWI69TNcwDH257oA27s/Vfee3UUSg47TrBwAAQP3G0lcTEdQBAFD3eDyG5m5M1VfrD2jpnky5PL/9E2hgqwi9MKar4iOOHti53B69+sMuTVu8V51jQ/TkJR3Vt0V4lXYH80r0yBebtXhXhiSpTaNAzflzPzUKdtbOoAAAAFAvENSZiKAOAIC6LaeoXN9tTdO8jalasS9LHkMKdNg15bJOGtOzSaUZc2l5pbr34/ValZhdqY9RXWL0yMgOio/wl2EY+mLdAU2Zt1UFpS752q0KdtqVWViuFpEBmvPnfooN9TvTwwQAAEAdQVBnIoI6AADqj5TsYk36dINWJ+ZIki7p2lhTL++iEH8fLdmdofs/2aCsonIFOux66tKO2pCSq09WJctjSL42q8af01z7Mgr14/Z0SVK3uFC9OrarfGxWXfvuSh3ILVHTMD99fFv/ai+xBQAAQMNCUGcigjoAAOoXt8fQOz/v0d9/3C23x1BsiFMXdIzWhyuSZBhSh8bB+sd1PdUiMkCStCMtX8/+Z7v+tyfT24evzar7h7fR7YNaym47fF7XgdwSXffuCiVmFatxiFNz/txPLaMCTRkjAAAAzENQZyKCOgAA6qcNKbm675P1Ssoq9l4b1zdeT1/aUU4fW6W2hmFo4Y50vbJglwJ8bZp6RRe1iwmq0ueh/FJd++4K7c0oUmSgQ/+8oad6Nau6vx0AAAAaLoI6ExHUAQBQfxWVufTst9u1eGe6/jKyna7o0fS0+8wsLNP1763UjrQCSdKFHaP1lxHt1Ca6arBXWzweQxmFZYrmYAsAAIAzjqDORAR1AADgj3KLy/Xc/O36fO1+eQzJapGu7NlU9w9vqyahfiqtcCshs0i70wu151CBMgrL5bBb5fSxyeljlZ+PTaH+PhrVpbGCnD4n9dk70vL16JebtT45V38+t4Uev7hDpQMzAAAAULsI6kxEUAcAAI5l96ECvbJgp77fekiS5Gu3qkmon5KyiuSpxr/KWjcK1Pvj+1TrYIrSCrfe/Gm3/vXLPrl+1zlhHQAAwJlFUGcigjoAAHAi65Jz9NJ3O7RiX7b3WrDTrjbRQWrTKFAxIU6VuzwqrfCo1OVWaYVb/9udqfSCMkUGOvT++D7q0jTkmP3/b3emHv96s3e/vRGdotUjPkwv/HeHJOn281rq0YvaE9YBAACcAQR1JiKoAwAA1WEYhjak5KqozK220YGKCnIcNzg7mFeim99frR1pBfLzsent63poWPto7323x9AvuzP00cpk/bDt8Iy9mGCnnhndSRd2ipEkzV6RpCe+3iJJmjC4lR4e2Y6wDgAAoJYR1JmIoA4AANSWgtIK3TVnnZbszpTVIj0zurMGt43Sp2tS9Pna/TqYVypJslikmwY010Mj2inQYa/Ux4fLE/XkN1slSXcNaaW/jCCsAwAAqE0EdSYiqAMAALWpwu3RY19u1mdr91e5F+rvoyt6NNG4vvFqe5xTZWctS9TTcw+HdRd0aKThHaM1qE2UYkP9aq1uAACAsxVBnYkI6gAAQG0zDENvLdyj137YJYtFOrd1pK7uE6fhHaPlsNuq1cf7SxM0Zd62StdaNwrUeW2idHHXGPVqFl4bpQMAAJx1COpMRFAHAADOlO0H8xXs56MmpzgTbsuBPP2w7ZB+2Z2hjSm5lU6enTi0tR4Y3lY2K8tiAQAATgdBnYkI6gAAQH2UW1yuZXuz9N2WNM3dmCpJOq9tlN64urvCAnxNrg4AAKD+IqgzEUEdAACo777ZcEAPf7FJpRUeNQ3z07Tre6lzk5CT6qO0wq39OcVKzi5WUlaxbFaLrurVVP6+9hM/DAAA0IAQ1JmIoA4AADQE2w/ma8LstUrKKpav3apnLuuky3s0kdOn6h54hmEoIbNIP+/M0C+7M7T9YL4O5ZdVadexcbDeu6n3cQ+tSMkulmFI8RH+NToeAAAAsxDUmYigDgAANBR5JRWa9O8N+mlHuiTJapHiw/3VJjpIbRoFqlmEv7YcyNfPu9KVkl1S5flAh13x4f6KD/fX6sRsZRWVKyrIoX/d0Es94sMqtc0qLNOL3+3Qp2sOn2bbq1mY/tS7qS7uGqtAB7PwAABA/UVQZyKCOgAA0JB4PIbeWbxX7y3Zp5ziimO287VZ1adFmIa0baTezcPUPCJAof4+slgOH0aRkl2s2z5Yox1pBfK1W/XyVV01unsTuT2GPl6VrJe/36m8ksP9Wy3yHmzh52PTxV0b60+949SneZi3PwAAgPqCoM5EBHUAAKAhMgxDGYVl2nOoULsOFWhXeqGSsorUIjJAQ9o20oBWEQo4wcy3wjKX7v9kvX7cfniG3viBzbU2KUebD+RJkjo0Dtazl3dSXJi/vlx/QJ+uSdG+jCLv8+1jgnTzOc01uvvRl+ACAADURQR1JiKoAwAAODa3x9BL3+/QPxfv814Lctj14IVtdX3/ZrLbrN7rhmFoXXKO/r06RfM2HlRJhVuSFOrvo3F943VD/2bH3e8OAACgLiCoMxFBHQAAwIl9vna/Xvxuhwa1idSjF3VQVJDjuO3ziiv06ZoUzVqeqP05h/fDs1ktmnp5Z13TN/5MlAwAAHBKCOpMRFAHAABQe9weQz9tP6QZSxO0Yl+2fG1WfXX3QHWKDTG7NAAAgKOqblZkPeYdAAAAoA6yWS26sFOMPr6tvy7oEK1yt0f3fLxexeWuYz6zeFeG3l60R/tzis9gpQAAACeHoA4AAAD1ksVi0ctXdVVMsFP7Moo0Ze62o7b7cEWSxr+/Si9/v1PnvbRIf561Wj/vTJfHw8ISAABQt7D0tRaw9BUAAODMWb43S9e+t0KGIb01rocu7RYr6fBBFP+3cI9e/WGXJKl1o0DtSS/0Phcf7q+r+8SpaZifHHarfO1WOew2+dqtyi+pUHpBmTIKypReUKqMgjI1CnLqvgvaKDLw+HvpAQAA/BF71JmIoA4AAODMenXBTr21cI+CHHbNv2+QmoT66W/fbtP7SxMlSfee30YPXNBG+zKLNGdFsj5bm6KC0mMvlT2WyEBfvTCmqy7oGF3DIwAAAA0ZQZ2JCOoAAADOLJfboz/9c7nWJeeqZ3yomkcE6Mv1ByRJT1/aUTef06JS++Jyl+ZtTNUP29JVXO5SucujMpfn11/dCnTa1SjIqUZBDkUFORQR4KuPV6Vo56ECSdK4vnF64uKOCnDYz/hYAQBA/UNQZyKCOgAAgDMvJbtYo95YooKywzPlbFaLXhnbVVf0aFoj/ZdWuPXqgp16738JMozDS2dfuLKLgp0+SskuVkpOsVKyS5SWX6oOMUG6sldTNYsIqJHPBgAA9RtBnYkI6gAAAMzxn02pmvjRejnsVr1zfU8Na1/zS1SX7c3UQ59uVGpe6Qnb9m0RrrG9mmpUl8bMvgMA4CxGUGcigjoAAADzrErIVlSQQy0ia282W15JhabM26p5G1MV4ueruHA/xYX5Ky7cTxEBDv28K0NLdmfoyL+0/X1turJnU/1lZDsFO31qrS4AAFA3EdSZiKAOAADg7ODxGLJaLUe9dzCvRF+uO6DP1+5XQmaRJCk2xKmXruqmc9tEnskyAQCAyQjqTERQBwAAgCMMw9CyvVl67KvNSsoqliTdOKCZHrmovfx9T7wcNq+4Qk/N3SKn3abJl3WSn6+ttksGAAA1jKDORAR1AAAA+KPicpeen79DH65IkiQ1j/DXq3/qpl7Nwo/5TEp2sW6euVp70gslSb2bhWn6+D4K8WP5LAAA9Ul1syLrGawJAAAAOGv5+9r1t8s768Nb+6pxiFOJWcW6atpy/eWzjTqYV1Kl/ab9ubriH8u0J71QMcFOBTvtWpOUo6v/uVzp+Sc+yAIAANQ/BHUAAADAGTSoTZS+u/88XdmzqQxD+mztfg15+We9+N0O5ZVUSJJ+3HZIV/9zhTILy9ShcbC+vvsc/fuOAYoKcmhHWoGumrZcyb8uowUAAA0HS19rAUtfAQAAUB3rknP0wvwdWpWYLUkK9ffRRZ1j9O/VKfIY0nlto/T2tT0U9OtJsUlZRbph+iolZxcrKsihD27pq5hgp9LyS5WWX6pDeaXKL63Q8I4xtXrqLQAAODnsUWcigjoAAABUl2EY+ml7ul74bod3LzpJuqZPnP52eWf52CovgknPL9WNM1ZpR1rBMfv0tVk1YXBL3TW0tZw+HD4BAIDZCOpMRFAHAACAk+Vye/T52v2aszJZl3ZrrNsGtZTFYjlq27ziCt324RqtSjg8Ey88wFfRwU7FBDtUVOb2ztBrFuGvKZd10pB2jc7YOAAAQFUEdSYiqAMAAEBtMwxDGQVlCvH3kcNuq3T9uy1pmjJvm9J+PXRiVJcYjerSWPtzSpSSXayUX3/1tVk1cVhrXdK18TFDQcMwtDYpR0XlbkUG+ioqyKGIAIds1qO3BwAAVRHUmYigDgAAAGYrLHPp7z/s0sxliXJ7jv9P/v4twzXlss5qFxPkveb2HA783l60R9sO5ldqb7FIEQG+6hkfplf+1E3Bv+6hBwAAjo6gzkQEdQAAAKgrtqXm69UFO5VdXK74cH/Fh/srLsxfTcP9tCYxR28v2qMyl0c2q0U3Dmime4a10U/bD+mdxXu1L6NIkuTva1N8uL8yC8uVVVSm338HMapLjN6+tucxZ+QBAACCOlMR1AEAAKC+2J9TrKnfbtd/t6RJOjxb7sh3CCF+Pho/sLnGD2yusABfSYdn2mUXlWvzgVzd8eFaVbgN/e3yzrqhfzOzhgAAQJ1HUGcigjoAAADUN0t2Z2jy3K3am1GkyECHbhvUQtf1b6ZAh/2Yz7y3ZJ+e/Xa7fO1WfXXXQHWKDTmDFQMAUH8Q1JmIoA4AAAD1UbnLo037c9W5SYicPrYTtjcMQ7d9sEY/bk9Xi8gAzbvn3OMGe9VRWOY67T4AAKhrqpsVWc9gTQAAAADqMF+7Vb2bh1crpJMki8WiV8Z2U2yIUwmZRXr8q8364zyAvJIKLdqRrv05xcftK7OwTDdMX6luUxbomw0HTnkMAADUZ/yoCgAAAMApC/X31VvX9tCf/rlC32xI1YCWERravpEWbDukBVvTtHxvllweQ752q+4e0loThrSUw145CFydmK2JH63TofwySdITX21Rn+bhig31M2NIAACYhqWvtYClrwAAADjbvPPzXr343Q7ZrBa5PZW/xYgKciij4HAI1zIqQFMv76IBrSJkGIbeW5KgF77bIbfHUKuoAPn52rTlQL4GtYnUB7f05TRZAECDwB51JiKoAwAAwNnG4zF0y6zV+nlnhiSpZ3yoLuwUows7Rh/ev27TQT0zb5syCw8HdmN6NlFhqUsLth2SJI3uHqvnruiitPxSjXpjicpcHj13RRdd2y++2jWs3JellQnZGn9OcwU7fWp+kAAAnCKCOhMR1AEAAOBsVFrh1i+7MtQ9LlSNgp1V7ueVVOjl73dozspkHfkuxNdm1VOXdtR1/eK9s+eOnCYb4GvTd/efp7hw/+N+bpnLrVe+36l3lyRIkrrFheqDW/oqxI+wDgBQNxDUmYigDgAAADi29ck5mjxvm4rKXHrtT93UtWlopfsej6Fr/rVCqxKzNaBlhOb8uZ+s1qMvgd11qED3frxeO9IKJEl+PjaVVLgJ6wAAdQpBnYkI6gAAAIDTk5RVpJGvL1FJhVtTLuukmwY2r3TfMAzNXJao5/+7Q+Uuj8IDfPXilV3VJNRP1723QjnFFYR1AIA6o7pZkfUM1nRapk6dqoEDB8rf31+hoaHHbZuVlaWmTZvKYrEoNze30r3Nmzdr8ODB8vPzU5MmTfTMM89UOUJ+8eLF6tWrl5xOp1q2bKlp06bV8GgAAAAAHE+ziAA9Oqq9JOmF/+7QnJVJenvRHj359Rbd9sEaXfj3XzRl3jaVuzwa3DZK390/SMM7RqtjbLDm/Lm/wvx9tDElVzfOWKW8kgqTRwMAQPXUm6CuvLxcY8eO1Z133nnCtrfeequ6du1a5Xp+fr6GDx+u2NhYrV69Wm+99ZZeeeUVvfbaa942CQkJGjVqlAYNGqT169frscce07333qsvvviiRscDAAAA4Piu79dMA1tFqKTCrce/2qKXv9+pD1ck6Ydth7Q7vVAOu1VTLuukmTf3UaOg3/bEI6wDANRX9W7p68yZM3X//fdXmSl3xDvvvKN///vfeuqpp3T++ecrJyfHOwPvnXfe0aOPPqpDhw7J4XBIkl544QW99dZb2r9/vywWix5++GHNnTtX27dv9/Y5YcIEbdy4UcuXL69WjSx9BQAAAGpGam6JHv5ikyQpOtipmGCnokMO/9qlSYhiQqoeWnHEttR87zLY3s3C9OGt/eTna6v2Z7s9hjbuz9WWA3nqFBuiHnGhx9wrT5IO5JaorMKtllGB1R8gAOCsUN2syH4Ga6p127Zt0zPPPKOVK1dq3759Ve4vX75cgwcP9oZ0kjRixAg9+uijSkxMVIsWLbR8+XJdeOGFlZ4bMWKEpk+froqKCvn4VN3foqysTGVlZd73+fn5NTgqAAAA4OwVG+qnD2/td0rPHplZd82/lmtNUo7u+Xidpl3fS3bbsRcWpeeX6pfdmfp5Z7r+tydTucW/zcSLDnZoZKcYjezcWH1bhMvtMbQmMVs/78rQoh3p2p1eKEka2CpC953fRv1aRpxS3QCAs1eDCerKyso0btw4vfzyy4qPjz9qUJeWlqbmzZtXuhYdHe2916JFC6WlpXmv/b6Ny+VSZmamGjduXKXf559/XlOmTKm5wQAAAACoER1jgzV9fB9d/95K/bg9XY99tVkvXtlVFkvlmXHp+aV68pst+n7roUrXg512dW0aqg0puTqUX6ZZy5M0a3mSIgJ8VVrhVlG529vWapGsFouW7c3Ssr1Z6t8yXPed31YDWhHYAQCqx9SgbvLkyScMuFavXq3evXufsK9HH31UHTp00PXXX3/cdn/8C/nIyt/fX69Omz9+9qRJk7zv8/PzFRcXd8KaAQAAANS+Ps3D9X/X9tQdH67Rp2v2KyLQoYdHHj6owjAMfb3hgCbP3ebdx65r0xANbhulwW2j1D0uVHabVWUut5buydT8zWn6YdshZRWVS5IiAx0a3DZKQ9tHaVDrKBWUVegfP+/VZ2tStGJftlbsW6G+LcL10IXt1LdFuGm/BwCA+sHUoG7ixIm65pprjtvmjzPgjmXhwoXavHmzPv/8c0m/hWuRkZF6/PHHNWXKFMXExCgtLa3Sc+np6ZJ+m1l3rDZ2u10REUf/SZjD4ai0nBYAAABA3TK8Y7SeH9NFD3+xWe/8vFeRgQ5d2rWxHvtqi37cfngWXecmwXplbDe1j6m6d5DDbtOw9tEa1j5aFW6P1ifnyt/Xpo6NgyvtWxfi76Pnruiiu4e21js/79Gnq/drVUK2/vTP5RraLkoPjWinTrEhZ2zcAID6xdSgLjIyUpGRkTXS1xdffKGSkhLv+9WrV+uWW27RkiVL1KpVK0nSgAED9Nhjj6m8vFy+vr6SpAULFig2NtYbCA4YMEDz5s2r1PeCBQvUu3fvo+5PBwAAAKB+uLpPvDILy/Xy9zv1t/9s0+s/7FJBmUs+NovuO7+N7hjcSj7H2b/uCB+b9YSz45qE+unZyw8Hdm8t3KN/r07Rop0ZWrQzQ5d1i9Wk4W3VPDJAkuTxGCoqdym/1KUQPx8FOhrMDkUAgJNUb059TU5OVnZ2tubOnauXX35ZS5YskSS1bt1agYFVT1X6+eefNXTo0Eqnvubl5aldu3YaNmyYHnvsMe3evVvjx4/XU089pQcffFCSlJCQoM6dO+uOO+7QbbfdpuXLl2vChAn6+OOPdeWVV1arVk59BQAAAOomwzA0Zd42zVyWKEnq0iREr4ztpnYxQbX6uQmZRXrth12atzFVkmS3WtQ41Kn8EpcKSivk+fW7MqePVX8d0V7jBzY/7gmzAID6pbpZUb0J6saPH69Zs2ZVub5o0SINGTKkyvWjBXWStHnzZt19991atWqVwsLCNGHCBD311FOV9p9bvHixHnjgAW3dulWxsbF6+OGHNWHChGrXSlAHAAAA1F0ej6F3l+yT08ema/vFV2sWXU3ZciBPL3+/U4t3ZVS5Z7Na5P41sevbIlyvXNVN8RH+Z6w2AEDtaXBBXX1CUAcAAADgeHYdKlBhmUvBTruCnT4K9vORr82qOauS9fz87Soud8vf16ZHR3XQ9f3ij3mwHQCgfiCoMxFBHQAAAIBTlZxVrIc+36hVCdmSpD7NwxQX7q+iMpeKytwqLHOpzOXR4LZRumdYawWwpx0A1HkEdSYiqAMAAABwOjweQzOXJeql73eotMJzzHaxIU5NGd1ZwztGn8HqAAAni6DORAR1AAAAAGpCQmaR5m8+KJvVogCHXYEOmwJ87Soqd+nVBbu0P6dEkjS8Y7SmXNZJsaF+JlcMADgagjoTEdQBAAAAqG0l5W69uXC33v1ln1weQ/6+Nl3StbGKy93KLa5QTnG5cosrZLFIN/RvppsGNpfTx3bUvkor3Jq7MVUBvnaN6hLDnngAUMMI6kxEUAcAAADgTNmZVqDHv9qsNUk5x23XOMSpBy5oqzE9m8j+60m3OUXl+nBFkmYtS1RWUbkk6fz2jfT8lV3UKMhZ67UDwNmCoM5EBHUAAAAAziSPx9C3mw9q96EChfr7KtTfR2G//ronvVCv/7hbB3IPL5Nt0yhQE4e11vrkXP17dYpKKtySDu93l1lYrnK3R2H+Ppp6RReN6tLYzGEBQINBUGcigjoAAAAAdUlphVuzVyTp/xbtUW5xRaV7nWKDdcfgVhrVOUZ7Mgo16d8bte1gviTp8u6xmnJZZ4X4+5hRNgA0GAR1JiKoAwAAAFAX5ZVU6F+/7NWX6w6odaNA3XFeK53TOqLSnnTlLo/e/Gm3/vHzHnkMKdBhV5DTLsOQDBnyGJLNYlHfFuG6vEesBrWJks+vS2kBAEdHUGcigjoAAAAA9d265Bw9+OlGJWQWHbddmL+PLu7aWJd3b6JezcI4iAIAjoKgzkQEdQAAAAAagnKXR7sOFchjGLL+GsBZLFJBqUvfbUnTfzalKrOw3Nu+fUyQ/nZ5Z/VpHm5WyQBQJxHUmYigDgAAAMDZwOX2aNneLH2zIVXfbTmoovLDB1OM7dVUj47qoPAAX5MrBIC6gaDORAR1AAAAAM42OUXlevG7HfpkdYokKdTfR4+MbK8/9Y6T1cpyWABnN4I6ExHUAQAAADhbrU3K1uNfbdGOtAJJUremIRrbO04jOsUoKshhcnUAYA6COhMR1AEAAAA4m7ncHs1clqi//7DLuxzWapH6NA/XqC6NdX6HRnJ7DB3KL1NafqkO5ZXqUH6pKtwe2W1W2W0W2a0W2a1WtYgM0GXdYpmVB6BeI6gzEUEdAAAAAEjp+aX6cv0B/XfzQW3cn3fK/Qxr30h//1N3hfj71GB1AHDmENSZiKAOAAAAACpLyS7W91vT9O3mg1qfnCuH3aqYEKeig52KCXYqOtghh90ml8eQy+2Ry2OopNytrzccUJnLo/hwf71zfU91ig0xeygAcNII6kxEUAcAAAAAx1bmcsvXZpXFcuLlrFtT8zRh9lqlZJfIYbfquSu66MpeTc9AlQBQcwjqTERQBwAAAAA1J6+4Qvf/e70W7cyQJI3rG6f+LSMkSYYhGTJkkUXntI7kwAoAdRJBnYkI6gAAAACgZnk8ht5auEev/7RLx/ouNjzAV7Nu7qsuTY+9PNbl9uhAboniw/2rNaMPAGoCQZ2JCOoAAAAAoHYs3pWhmUsTVFrhkdUqWWSRxSIlZxcrKatYAb42vXtTbw1sFVnl2T3phbr/3+u15UC+hneM1tQrOqtRkLNG6ipzueWw22qkLwAND0GdiQjqAAAAAODMKixz6fYP1mjZ3iz52qx6c1x3jezcWJJkGIZmr0jS1PnbVVrh8T4T4uejKZd10ujusac8u84wDP3rl316dcEuXd+/mZ66tGONjAdAw1LdrMh6BmsCAAAAAKBWBDrsmjG+j0Z2ilG526O75qzTJ6uSlVFQpltmrtaT32xVaYVHg9pEavat/dQpNlh5JRW6/98bdMeHa5VeUHrSn+n2GHp67lY9/98dKnd7NGNpguZvPlgLowNwtmBGXS1gRh0AAAAAmMPtMfT4V5v1yeoUSVKQ066CUpd87VY9MrK9xg9sLqvVogq3R+/8vFdvLdytCrehUH8f3Tusja7s2VQh/j4n/JyScrfu/WS9fth2SBaL1Ld5uFYmZCvYadd/7z9PTUL9anuoAOoRlr6aiKAOAAAAAMxjGIZe/n6n/vHzXklS+5ggvXFND7WLCarSdvvBfD346UZtO5gvSXLYrbqka6yu6x+vHnGhR10Sm1VYpltnrdGGlFz52q16/eruGt4xWldNW66NKbnq2zxcH9/eXzYrh1UAOIygzkQEdQAAAABgvm82HFB6fpluHNjsuAc9VLg9+mR1iuasSNKOtALv9fYxQRravpF8bVbZrRbZbBbZrRZ9tDJZiVnFCvX30bs39laf5uGSpKSsIo16Y4mKyt2aNLyt7j2/TY2PaW9GoQ7klGhQm0hOrQXqEYI6ExHUAQAAAED9YxiG1iXnas7KJH276aDKXJ5jtm0a5qeZN/dV60aBla5/uW6/Jn26UTarRZ/e0V+9moXXSG3lLo/eXrRHby/aI5fH0B2DW+qRke0J64B6gqDORAR1AAAAAFC/5RaX65sNqUrILJLbY8jl8cjlNuT2GArx99GdQ1qpUZDzqM/e/8l6fb0hVU3D/DT/vkEKdh7e867M5VZOUYUKy1yHZ+hZLbLbDv/qsNkU7Gc/avC25UCeHvpsY6XZfpJ067kt9MTFHQjrgHqAoM5EBHUAAAAAcPbKL63QxW8uUUp2iZqE+slmtSi7qFyFZa7jPhcT7FTPZqHqGR+mHvFhahcTpH8u3qt//LxXbo+h8ABfPTO6k3KLK/TE11skSeMHNtfTl3YkrAPqOII6ExHUAQAAAMDZbV1yjsZOWy63p/K33DarRQG+NhmGVOHxyO0xVOE+8bflF3dprCmjOyky0CFJ+mRVsh79arMMQ7q+f7yeuayzrBxeAdRZ1c2K7GewJgAAAAAAzgo948M0d+I52p9ToogAX4X/+gp2+hw1UCsud2nz/jytS87V2qQcrU/OUVZRuSICfPXM6M66uGvjSu2v6Rsvq9Wih7/YpNkrkuX2GJp6eRfCOqCeY0ZdLWBGHQAAAADgdBiGoYN5pQoP8JXT59gn1n65br8e+myjPIbUMjJANwxopqt6NVXQr/viAagbWPpqIoI6AAAAAMCZMndjqh7/crMKft0DL8DXpjE9m+qmgc3UulGQydUBkAjqTEVQBwAAAAA4kwrLXPpq/QF9sCxRu9MLvdfjwv3UOMRPTUL91DjEqcahfooNcapxiJ9iQ50K8fPhIArgDCCoMxFBHQAAAADADIZhaPneLM1clqgftx+S5wTf8fv52NQ41KkWEQG65/w26h4XekbqBM42BHUmIqgDAAAAAJgts7BMCZlFSs0t0cG8Uh3MLVFqXqkO5pUoNbdU2UXlldrbrRY9PLK9bj23BYdSADWMU18BAAAAADiLRQY6FBnoOOb90gq3N8CbszJZ324+qKnzt2v5viy9MrabwgN8q/U5ZS63cosrFB3srKnSgbMWM+pqATPqAAAAAAD1iWEY+mhVsqbM26Zyl0cxwU69cU139WsZUaVtaYVb65JztHJftlYmZGl9cq7KXB49cXEH/XlQSxOqB+o+lr6aiKAOAAAAAFAfbUvN18SP12lfRpGsFql9TLA8hiG359eXYehgbqnK3Z6jPv/CmC66pm/8Ga4aqPsI6kxEUAcAAAAAqK+Kylx66put+mLd/mO2iQ52qF+LCPVrGa5+LSL02ZoU/fOXfbJYpLfG9dAlXWPPYMVA3UdQZyKCOgAAAABAfbflQJ6yispls1hktUp2q1U2qxQV6FRcuJ8slt8OnDAMQ499tVkfr0qRj82if93YW0PbNarSZ2GZS1aL5O/Llvk4u3CYBAAAAAAAOGWdm4RUu63FYtGzl3dRQalL/9l0UHfOXqsPbumnvi3ClZJdrB+2HdIP2w5pVWK2bBaLhrSL0mXdY3V++2j5+dpqcRRA/cKMulrAjDoAAAAAwNmowu3RHR+u1cId6Qpy2NUkzE870gqO2T7A16bhHaM1ukcTDW4TJavVcsy2QH3G0lcTEdQBAAAAAM5WpRVu3ThjlVYlZEuSbFaL+jQP0/COMRreIVrFFS7N3ZCquRtTtT+nxPtcu+gg3T2stS7u0lg2Ajs0MAR1JiKoAwAAAACczQpKKzRzaaKahPlpaLtGCgvwrdLGMAytS87VvI2p+mLtfhWUuSRJLaMCdPeQ1hrdPVZ2m1XS4QMuDuaVKC2vTK0bBSomxHnCGjwegxl6qDMI6kxEUAcAAAAAQPXllRwO9mYsTVBeSYUkqUmon4KcdqXmlii/1OVt6/Sx6pnLOmts76aVDrQ4orTCrVe+36mPViVrwuBWuvf8NmdsHMCxENSZiKAOAAAAAICTV1BaodkrkvXekn3KKiqvdC/IaVeQw67UvFJJ0pieTfTs5Z0rnSC7Iy1f93+yodK+eA8Ob6t7COtgMoI6ExHUAQAAAABw6orLXfplV6b8fG2KDXEqJsSpIKePPB5D7yzeq1cX7JTHkNo0CtQ/ruupVlGBen9Zol78bofKXR5FBPjqwk4x+nhVsiTp0Yva647BrUweFc5mBHUmIqgDAAAAAKD2rNiXpXs/Xq/0gjL5+djUoXGQ1iXnSpKGtW+kF6/sqqggh976abde/WGXJOmpSzrqlnNbVOmrsMylsgq3IgIdZ3IIOMsQ1JmIoA4AAAAAgNqVUVCmB/69Qf/bkynp8N51T1zcUdf1i6+0d92rC3bqrYV7JEnPXt5Z1/dvpuSsYv24/ZB+2nFIqxKyZbVYNPPmvhrQKsKUsaDhI6gzEUEdAAAAAAC1z+0x9N6Sfdq4P1eThrdT60aBVdoYhqEXvtuhfy7eJ0lqGRmgfZlFVdoFO+368q6Bat0oqNbrxtmHoM5EBHUAAAAAANQdhmHomf9s0/tLEyVJNqtFfZqH6YIO0Tq3TaQe/2qL1iblqEmon766e6AaBTnNLRgNDkGdiQjqAAAAAACoWwzD0NcbDshmtWpwmyiF+Pt472UXlWvMP5YqMatYXZuG6JPb+1c6TRY4XQR1JiKoAwAAAACgfknILNKYfyxVTnGFLugQrX/e0Es2q+WY7Q3D0PJ9WfpoZbIq3B79eVBL9WkefgYrRn1CUGcigjoAAAAAAOqfNYnZuva9lSp3eTR+YHM9fWnHSgdTSFJxuUtfr0/VrGWJ2nmooNK9Ie2i9NCF7dS5SciZLBv1AEGdiQjqAAAAAACon77ddFB3f7ROkuRjsygq0KGooMOvQIddC3ekK7/UJUny87FpTM8m8hjSp2tS5PYcjlgu7tpYDw5vq5ZRVQ+3OFmGYaikwq3CUpcqPIaahPqddp848wjqTERQBwAAAABA/TVzaYKem79D5W7PUe/Hh/vrxgHNNLZ3nEL8Du91l5hZpL//uEtzN6bqSNLSLMJfnWND1DE2WJ1ig9UxNlhOH5tKK9wqLfeopMKt0gq3sorKdCC3VAdySnQgt0SpuSU6lF+qglKXCstc3gBQki7o0Ej/d21POX1stf77gJpDUGcigjoAAAAAAOq3MpdbmYXlyigo876yi8rUMTZYg9s2Oub+ddsP5uuV73fqpx3pNVrPkY/zGNKgNpF698behHX1CEGdiQjqAAAAAAA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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(loss6)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Model 6 - 200 Epoch Loss')\n",
    "plt.savefig('W:/0_Levia/Code/7002/0_Final/Model20/M20_loss.eps')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}