7. External-validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "479e6a58-8aaf-46ed-b03e-eafd62ecd2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "### General analysis package\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm, pearsonr, spearmanr\n",
    "from scipy.spatial import distance\n",
    "### Drawing package\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.font_manager import FontProperties\n",
    "#import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42 # Output editable PDF text\n",
    "### else\n",
    "#import cloudpickle as pickle\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # Error alert\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all' #last_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06012cef-b183-4e0b-bc57-35e3a9e2fd40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using torch 1.10.1\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.preprocessing import StandardScaler, Normalizer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, recall_score, precision_score, roc_auc_score, average_precision_score\n",
    "\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as TorchData\n",
    "from torchmetrics import Accuracy\n",
    "from pytorch_lightning import LightningModule, Trainer, seed_everything\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "SEED = 42\n",
    "seed_everything(SEED, workers=True)\n",
    "\n",
    "print(\"Using torch\", torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed43ba40-529f-4d1d-83c5-658066ea40c2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MaskedModel(LightningModule):\n",
    "    def __init__(self, mirN, geneN, gene_mask, moduleN, module_mask, pathwayN, pathway_mask, learning_rate):\n",
    "        super(MaskedModel, self).__init__()\n",
    "        self.save_hyperparameters()\n",
    "        self.learning_rate = learning_rate\n",
    "        # microRNA 2 Gene\n",
    "        self.mir2gene = nn.Linear(mirN, geneN)\n",
    "        self.mir2gene.weight.data = self.mir2gene.weight * gene_mask\n",
    "        self.gene_mask = gene_mask\n",
    "        self.gene_active = nn.ReLU() # ReLU Tanh\n",
    "        self.gene_bn = nn.BatchNorm1d(geneN, eps=1e-05, momentum=0.5, affine=True)\n",
    "        self.gene_dropout = nn.Dropout(p=0.8)\n",
    "        \n",
    "        ## Gene 2 Module\n",
    "        self.gene2module = nn.Linear(geneN, moduleN)\n",
    "        self.gene2module.weight.data = self.gene2module.weight * module_mask\n",
    "        self.module_mask = module_mask\n",
    "        self.module_active = nn.ReLU()\n",
    "        self.module_bn = nn.BatchNorm1d(moduleN, eps=1e-05, momentum=0.5, affine=True)\n",
    "        self.module_dropout = nn.Dropout(p=0.5)\n",
    "        \n",
    "        ## Module 2 Pathway\n",
    "        self.module2pathway = nn.Linear(moduleN, pathwayN)\n",
    "        self.module2pathway.weight.data = self.module2pathway.weight * pathway_mask\n",
    "        self.pathway_mask = pathway_mask\n",
    "        self.pathway_active = nn.ReLU()\n",
    "        self.pathway_bn = nn.BatchNorm1d(pathwayN, eps=1e-05, momentum=0.5, affine=True)\n",
    "        self.pathway_dropout = nn.Dropout(p=0.2)\n",
    "        \n",
    "        ## Output\n",
    "        self.pathway2out = nn.Linear(pathwayN, 2)\n",
    "        self.out_bn = nn.BatchNorm1d(2, eps=1e-05, momentum=0.5, affine=True)\n",
    "        #self.sigmoid = nn.Sigmoid()\n",
    "        \n",
    "        # Result save\n",
    "        self.gene_result = None\n",
    "        self.module_result = None\n",
    "        self.pathway_result = None\n",
    "    \n",
    "    def forward(self, x):\n",
    "        ## microRNA 2 Gene\n",
    "        #print(self.mir2gene.weight.data.shape, self.mir2gene.weight.shape, self.gene_mask.shape)\n",
    "        self.mir2gene.weight.data = self.mir2gene.weight * self.gene_mask\n",
    "        #print(self.mir2gene.weight.data.shape)\n",
    "        gene_x = self.mir2gene(x)\n",
    "        gene_x = self.gene_active(gene_x)\n",
    "        gene_x = self.gene_bn(gene_x)\n",
    "        gene_x = self.gene_dropout(gene_x)\n",
    "        self.gene_result = gene_x # save result\n",
    "        \n",
    "        ## Gene 2 Module\n",
    "        self.gene2module.weight.data = self.gene2module.weight * self.module_mask\n",
    "        module_x = self.gene2module(gene_x)\n",
    "        module_x = self.module_active(module_x)\n",
    "        module_x = self.module_bn(module_x)\n",
    "        module_x = self.module_dropout(module_x)\n",
    "        self.module_result = module_x # save result\n",
    "        \n",
    "        ## Module 2 Pathway\n",
    "        self.module2pathway.weight.data = self.module2pathway.weight * self.pathway_mask\n",
    "        pathway_x = self.module2pathway(module_x)\n",
    "        pathway_x = self.pathway_active(pathway_x)\n",
    "        pathway_x = self.pathway_bn(pathway_x)\n",
    "        pathway_x = self.pathway_dropout(pathway_x)\n",
    "        self.pathway_result = pathway_x # save result\n",
    "        \n",
    "        ## Output\n",
    "        out_x = self.pathway2out(pathway_x)\n",
    "        out_x = self.out_bn(out_x)\n",
    "        #out_x = self.sigmoid(out_x)\n",
    "        return out_x\n",
    "    \n",
    "    def training_step(self, batch, batch_nb):\n",
    "        x, y = batch\n",
    "        loss = F.cross_entropy(self.forward(x), y)\n",
    "        self.log(\"train_loss\", loss, prog_bar=True)\n",
    "        return loss\n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "    \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        logits = F.softmax(self(x))\n",
    "        loss = F.cross_entropy(logits, y)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        probs = logits[:, 1]\n",
    "        auc = roc_auc_score(y, probs)\n",
    "        bac = balanced_accuracy_score(y, preds)\n",
    "        aps = average_precision_score(y, probs)\n",
    "        recall = recall_score(y, preds)\n",
    "        precision = precision_score(y, preds)\n",
    "        #self.auc(probs, y)\n",
    "\n",
    "        # Calling self.log will surface up scalars for you in TensorBoard\n",
    "        self.log(\"val_loss\", loss, prog_bar=True)\n",
    "        self.log(\"val_bac\", bac, prog_bar=True)\n",
    "        self.log(\"val_auc\", auc, prog_bar=True)\n",
    "        self.log(\"val_aps\", aps, prog_bar=True)\n",
    "        self.log(\"val_rec\", recall, prog_bar=True)\n",
    "        self.log(\"val_pre\", precision, prog_bar=True)\n",
    "        return loss\n",
    "    \n",
    "    def validation_epoch_end(self, validation_step_outputs):\n",
    "        avg_loss = torch.stack(validation_step_outputs).mean()\n",
    "        #avg_loss = torch.stack([x[\"val_loss\"] for x in outputs]).mean() # Outputs are the values of return for validation\n",
    "        self.logger.experiment.add_scalar('loss', avg_loss, self.current_epoch)\n",
    "    \n",
    "    def test_step(self, batch, batch_idx):\n",
    "        # Here we just reuse the validation_step for testing\n",
    "        return self.validation_step(batch, batch_idx)\n",
    "    \n",
    "    def predict_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        logits = F.softmax(self(x))\n",
    "        loss = F.cross_entropy(logits, y)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        probs = logits[:, 1]\n",
    "        return preds, probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb27b137-4a5c-45e7-8a36-a07bbdb12598",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c643f6cb-f9fd-4953-84ff-418ba6919d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "microRNA = pd.read_csv('20220610_Results/1. diff_microRNA(FC2_FDR0.05).csv', index_col=0)['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07d17be5-e05a-48b8-99b1-52c5a549f793",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cloudpickle as pickle\n",
    "[mir2gene, GENE_LIST, GENE_MASK, gene2module, MODULE_LIST, MODULE_MASK,\n",
    " module2pathway, PATHWAY_LIST, PATHWAY_MASK] = pickle.load(open('20220610_Results/6. KEGG_MappingData.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8afa7e36-5426-4721-b04b-80f02d873f65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    }
   ],
   "source": [
    "model = MaskedModel.load_from_checkpoint(checkpoint_path=\"Temp/BioNet_SMOTE_val_loss.ckpt\")\n",
    "trainer = Trainer()\n",
    "#trainer.test(model, dataloaders=test_data_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d0c2146-27e7-44c8-8e1b-e86561c54d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(710, 523)"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hsa-miR-9-5p</th>\n",
       "      <th>hsa-miR-126-3p</th>\n",
       "      <th>hsa-miR-140-5p</th>\n",
       "      <th>hsa-miR-105-5p</th>\n",
       "      <th>hsa-miR-297</th>\n",
       "      <th>hsa-let-7i-5p</th>\n",
       "      <th>hsa-miR-130a-3p</th>\n",
       "      <th>hsa-miR-146b-5p</th>\n",
       "      <th>hsa_negative_control_6</th>\n",
       "      <th>hsa_negative_control_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hsa-miR-205-3p</th>\n",
       "      <th>hsa-miR-1260a</th>\n",
       "      <th>hsa-miR-525-5p</th>\n",
       "      <th>hsa-miR-187-5p</th>\n",
       "      <th>hsa-miRPlus-A1086</th>\n",
       "      <th>hsa-miR-551b-5p</th>\n",
       "      <th>hsa-miR-300</th>\n",
       "      <th>hsa-miR-193b-5p</th>\n",
       "      <th>Label</th>\n",
       "      <th>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.877643</td>\n",
       "      <td>8.080215</td>\n",
       "      <td>6.245265</td>\n",
       "      <td>6.304782</td>\n",
       "      <td>6.686064</td>\n",
       "      <td>7.805950</td>\n",
       "      <td>6.814781</td>\n",
       "      <td>7.146955</td>\n",
       "      <td>6.170680</td>\n",
       "      <td>6.129586</td>\n",
       "      <td>...</td>\n",
       "      <td>7.176684</td>\n",
       "      <td>7.724408</td>\n",
       "      <td>6.035476</td>\n",
       "      <td>6.255048</td>\n",
       "      <td>6.572019</td>\n",
       "      <td>6.251777</td>\n",
       "      <td>5.873303</td>\n",
       "      <td>6.362097</td>\n",
       "      <td>1</td>\n",
       "      <td>primary breast cancer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.544287</td>\n",
       "      <td>6.882722</td>\n",
       "      <td>5.874236</td>\n",
       "      <td>6.324987</td>\n",
       "      <td>6.638048</td>\n",
       "      <td>6.946567</td>\n",
       "      <td>6.488574</td>\n",
       "      <td>6.690773</td>\n",
       "      <td>6.160272</td>\n",
       "      <td>6.104929</td>\n",
       "      <td>...</td>\n",
       "      <td>7.249094</td>\n",
       "      <td>7.308890</td>\n",
       "      <td>6.021796</td>\n",
       "      <td>6.902473</td>\n",
       "      <td>6.347183</td>\n",
       "      <td>6.625569</td>\n",
       "      <td>6.004899</td>\n",
       "      <td>6.560329</td>\n",
       "      <td>1</td>\n",
       "      <td>primary breast cancer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 1928 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hsa-miR-9-5p  hsa-miR-126-3p  hsa-miR-140-5p  hsa-miR-105-5p  hsa-miR-297  \\\n",
       "0      6.877643        8.080215        6.245265        6.304782     6.686064   \n",
       "1      6.544287        6.882722        5.874236        6.324987     6.638048   \n",
       "\n",
       "   hsa-let-7i-5p  hsa-miR-130a-3p  hsa-miR-146b-5p  hsa_negative_control_6  \\\n",
       "0       7.805950         6.814781         7.146955                6.170680   \n",
       "1       6.946567         6.488574         6.690773                6.160272   \n",
       "\n",
       "   hsa_negative_control_7  ...  hsa-miR-205-3p  hsa-miR-1260a  hsa-miR-525-5p  \\\n",
       "0                6.129586  ...        7.176684       7.724408        6.035476   \n",
       "1                6.104929  ...        7.249094       7.308890        6.021796   \n",
       "\n",
       "   hsa-miR-187-5p  hsa-miRPlus-A1086  hsa-miR-551b-5p  hsa-miR-300  \\\n",
       "0        6.255048           6.572019         6.251777     5.873303   \n",
       "1        6.902473           6.347183         6.625569     6.004899   \n",
       "\n",
       "   hsa-miR-193b-5p  Label                Disease  \n",
       "0         6.362097      1  primary breast cancer  \n",
       "1         6.560329      1  primary breast cancer  \n",
       "\n",
       "[2 rows x 1928 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hsa-miR-3926</th>\n",
       "      <th>hsa-miR-4267</th>\n",
       "      <th>hsa-miR-548ak</th>\n",
       "      <th>hsa-miR-5701</th>\n",
       "      <th>hsa-miR-4304</th>\n",
       "      <th>hsa-miR-143-3p</th>\n",
       "      <th>hsa-miR-4781-3p</th>\n",
       "      <th>hsa-miR-483-5p</th>\n",
       "      <th>hsa-miR-3622b-5p</th>\n",
       "      <th>hsa-miR-4803</th>\n",
       "      <th>...</th>\n",
       "      <th>hsa-miR-6720-3p</th>\n",
       "      <th>hsa-miR-7641</th>\n",
       "      <th>hsa-miR-1252-3p</th>\n",
       "      <th>hsa-miR-6129</th>\n",
       "      <th>hsa-miR-520g-5p</th>\n",
       "      <th>hsa-miR-670-5p</th>\n",
       "      <th>hsa-miR-6841-3p</th>\n",
       "      <th>hsa-miR-6734-5p</th>\n",
       "      <th>hsa-miR-6514-5p</th>\n",
       "      <th>hsa-miR-7974</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.379868</td>\n",
       "      <td>6.353834</td>\n",
       "      <td>5.868474</td>\n",
       "      <td>8.628912</td>\n",
       "      <td>6.183578</td>\n",
       "      <td>8.401114</td>\n",
       "      <td>5.895089</td>\n",
       "      <td>7.873301</td>\n",
       "      <td>6.428665</td>\n",
       "      <td>6.128846</td>\n",
       "      <td>...</td>\n",
       "      <td>2.613294</td>\n",
       "      <td>6.056884</td>\n",
       "      <td>2.626683</td>\n",
       "      <td>2.796097</td>\n",
       "      <td>2.627649</td>\n",
       "      <td>2.815612</td>\n",
       "      <td>3.03934</td>\n",
       "      <td>3.849228</td>\n",
       "      <td>2.654754</td>\n",
       "      <td>2.949888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.547309</td>\n",
       "      <td>6.338348</td>\n",
       "      <td>5.856222</td>\n",
       "      <td>7.647449</td>\n",
       "      <td>6.477872</td>\n",
       "      <td>6.916796</td>\n",
       "      <td>5.913144</td>\n",
       "      <td>7.513173</td>\n",
       "      <td>6.366013</td>\n",
       "      <td>6.146907</td>\n",
       "      <td>...</td>\n",
       "      <td>2.613294</td>\n",
       "      <td>6.056884</td>\n",
       "      <td>2.626683</td>\n",
       "      <td>2.796097</td>\n",
       "      <td>2.627649</td>\n",
       "      <td>2.815612</td>\n",
       "      <td>3.03934</td>\n",
       "      <td>3.849228</td>\n",
       "      <td>2.654754</td>\n",
       "      <td>2.949888</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   hsa-miR-3926  hsa-miR-4267  hsa-miR-548ak  hsa-miR-5701  hsa-miR-4304  \\\n",
       "0      6.379868      6.353834       5.868474      8.628912      6.183578   \n",
       "1      6.547309      6.338348       5.856222      7.647449      6.477872   \n",
       "\n",
       "   hsa-miR-143-3p  hsa-miR-4781-3p  hsa-miR-483-5p  hsa-miR-3622b-5p  \\\n",
       "0        8.401114         5.895089        7.873301          6.428665   \n",
       "1        6.916796         5.913144        7.513173          6.366013   \n",
       "\n",
       "   hsa-miR-4803  ...  hsa-miR-6720-3p  hsa-miR-7641  hsa-miR-1252-3p  \\\n",
       "0      6.128846  ...         2.613294      6.056884         2.626683   \n",
       "1      6.146907  ...         2.613294      6.056884         2.626683   \n",
       "\n",
       "   hsa-miR-6129  hsa-miR-520g-5p  hsa-miR-670-5p  hsa-miR-6841-3p  \\\n",
       "0      2.796097         2.627649        2.815612          3.03934   \n",
       "1      2.796097         2.627649        2.815612          3.03934   \n",
       "\n",
       "   hsa-miR-6734-5p  hsa-miR-6514-5p  hsa-miR-7974  \n",
       "0         3.849228         2.654754      2.949888  \n",
       "1         3.849228         2.654754      2.949888  \n",
       "\n",
       "[2 rows x 711 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extenal = pd.read_csv('20220610_Results/1. GSE58606.csv', index_col=0)\n",
    "X_train_ = pd.read_csv('20220610_Results/1. X_train.csv', index_col=0)\n",
    "len(microRNA), len(set(microRNA)&set(extenal.columns))\n",
    "extenal.head(2)\n",
    "temp = extenal[list(set(microRNA)&set(extenal.columns))+['Label']]\n",
    "for i in set(microRNA)-set(extenal.columns):\n",
    "    temp[i] = X_train_[i].mean()\n",
    "extenal = temp\n",
    "extenal.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9814b19-5fa5-4fe7-bade-9d6410959b77",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_extenal = torch.from_numpy(extenal[microRNA].values).type(torch.FloatTensor)\n",
    "y_extenal = torch.from_numpy(extenal['Label'].values).type(torch.LongTensor)\n",
    "extenal_data_loader = TorchData.DataLoader(dataset=TorchData.TensorDataset(X_extenal, y_extenal), \n",
    "                                           batch_size=X_extenal.shape[0], shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a57e565-0b25-4d45-a841-07d4a3b72fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#trainer.test(model, extenal_data_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b9d5be-4a38-4926-b560-576fa3dce161",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b2d74666-ba6b-4113-9273-ae58e542ab31",
   "metadata": {},
   "source": [
    "# Migration Learning (3 layers before freezing: 3 * 4 = 12, updating only the last layer) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "192a7a72-7c93-4950-8a1e-c093252fedd2",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mir2gene.weight torch.Size([11418, 710])\n",
      "mir2gene.bias torch.Size([11418])\n",
      "gene_bn.weight torch.Size([11418])\n",
      "gene_bn.bias torch.Size([11418])\n",
      "gene2module.weight torch.Size([116, 11418])\n",
      "gene2module.bias torch.Size([116])\n",
      "module_bn.weight torch.Size([116])\n",
      "module_bn.bias torch.Size([116])\n",
      "module2pathway.weight torch.Size([70, 116])\n",
      "module2pathway.bias torch.Size([70])\n",
      "pathway_bn.weight torch.Size([70])\n",
      "pathway_bn.bias torch.Size([70])\n",
      "pathway2out.weight torch.Size([2, 70])\n",
      "pathway2out.bias torch.Size([2])\n",
      "out_bn.weight torch.Size([2])\n",
      "out_bn.bias torch.Size([2])\n"
     ]
    }
   ],
   "source": [
    "model = MaskedModel.load_from_checkpoint(checkpoint_path=\"Temp/BioNet_SMOTE_val_loss.ckpt\")\n",
    "for name, param in model.named_parameters():\n",
    "    print(name, param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "454b719e-3dc3-4750-92a1-89689f56cb28",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 0\n",
    "for param in model.parameters():\n",
    "    if k<12:\n",
    "        param.requires_grad = False\n",
    "    k += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5560a84-865f-446e-872b-986c084733a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    }
   ],
   "source": [
    "logger = TensorBoardLogger(\"tb_logs\", name=\"BioNet_extend\")\n",
    "# Initialize a trainer\n",
    "trainer = Trainer(\n",
    "    accelerator=\"cpu\",\n",
    "    #gpus=AVAIL_GPUS,\n",
    "    num_processes=1,\n",
    "    min_epochs=100,\n",
    "    max_epochs=500,\n",
    "    #progress_bar_refresh_rate=20,\n",
    "    callbacks=[EarlyStopping(monitor=\"val_loss\", mode='min')], # validate Stops when the AUC in the data is highest\n",
    "    auto_lr_find=True,\n",
    "    stochastic_weight_avg=False,\n",
    "    #deterministic=False, # GPU repeatable\n",
    "    #val_check_interval=100,\n",
    "    logger=logger,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ecba9c4-7e34-4caa-b1c7-54c3e5d2b542",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "   | Name            | Type        | Params\n",
      "-------------------------------------------------\n",
      "0  | mir2gene        | Linear      | 8.1 M \n",
      "1  | gene_active     | ReLU        | 0     \n",
      "2  | gene_bn         | BatchNorm1d | 22.8 K\n",
      "3  | gene_dropout    | Dropout     | 0     \n",
      "4  | gene2module     | Linear      | 1.3 M \n",
      "5  | module_active   | ReLU        | 0     \n",
      "6  | module_bn       | BatchNorm1d | 232   \n",
      "7  | module_dropout  | Dropout     | 0     \n",
      "8  | module2pathway  | Linear      | 8.2 K \n",
      "9  | pathway_active  | ReLU        | 0     \n",
      "10 | pathway_bn      | BatchNorm1d | 140   \n",
      "11 | pathway_dropout | Dropout     | 0     \n",
      "12 | pathway2out     | Linear      | 142   \n",
      "13 | out_bn          | BatchNorm1d | 4     \n",
      "-------------------------------------------------\n",
      "146       Trainable params\n",
      "9.5 M     Non-trainable params\n",
      "9.5 M     Total params\n",
      "37.897    Total estimated model params size (MB)\n"
     ]
    },
    {
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    },
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    {
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     "output_type": "stream",
     "text": [
      "Trainer was signaled to stop but required minimum epochs (100) or minimum steps (None) has not been met. Training will continue...\n"
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    },
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    {
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     "text": [
      "Trainer was signaled to stop but required minimum epochs (100) or minimum steps (None) has not been met. Training will continue...\n"
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   ],
   "source": [
    "# Tune the model\n",
    "trainer.fit(model, extenal_data_loader, extenal_data_loader)\n",
    "trainer.save_checkpoint(\"Temp/BioNet_extend_mean.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e0815ee-3a99-42ac-9615-ffe24a850770",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "Predicting: 1it [00:00, ?it/s]"
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    {
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n6C9atKhq1qyp/PnzKzIyUgEBAYYF/qNHj6pevXrau3evzaXh7WGdaNO4cWO75j+8BYT0b9JHlSpVVKJECXl5eSk+Pl7BwcE6duyYYVH14sWLat68uQ4dOmT39g6hoaF64YUXdOHCBcs5/fz8VLx4ccXExOjChQu6dOlSknPHjBmjiRMnGvpMJpMqV66sihUrytPTU7dv39bBgwcVHBxsGbNo0SJdu3ZNGzduTLGChfX1MJlMKlu2rCpWrKg8efLIyclJt2/f1qlTpwwx3r9/X71795arq6u6detm1/V4EgQEBBjaFSpUsGu+9WvM+ngZYciQIdq9e7el/dVXX6lZs2apzpsxY4ah3bt372S397BOtksu+So51uPPnDmjhIQEOTgkvnc+O695kyZNDO2NGzcmShJE+pGYACBNJk58Vjt2XNS+fcbszv79a2vGjLbKnTt9meEAAAAAAADp8vffkp3lhpEOJ09K1apldxRptnbt2kR3QKe2uPPnn4lv2qlbt26GxFOnTh1D+/79+zp69Kj8/PxSjCFv3rx2L+RkpX379iVKSihVqpTGjx+v7t27y83NLdGcK1euaMWKFfr++++zMlSb9enTR2azWT4+Ppo+fbpatWpluPs/Pj7ecod89erVDfvOHzhwQCdOnFCNGjVsOteWLVsUGBhoab/wwgsqXLhwkmMDAgLUo0cPQ1JC27ZtNX78+CS3TThy5IiGDh1qKbkeFBSknj17aseOHckuWKbV3r17De1atWrZfYwiRYqoT58+euGFF1S/fv0kF+4TEhK0ZcsW/fe//9WRI0ckSWFhYfL3908UQ2rGjRun8PBwubq6asyYMXrrrbfk4eFhGPPPP/8kmjd//nxDUoKDg4OGDBmi4cOHq1SpUoaxZrNZa9as0dChQy0JBFu3btWHH36oyZMnpxifm5ubevTooY4dO6pFixaJYnvg5MmTmjBhgpYtW2bp69evn5o0aSJvb++UL0IaREVF6fr16xl+XGuOjo4ZnkRz/vx5Q9v6+UpN6dKlDe2QkBDdvn070bZB6dGrVy/99NNPWrt2rSRpxYoVmjRpkj744INk5/zvf//Tl19+aWl7e3tr7NixyY4vUKCAoX3t2jW7YrQeHxUVpUuXLqlMmTKJxqb3mluPt6WiygM1a9aUyWSyJJnt2bPHrnPDNiQmAEgTJ6dcWrKkq2rVmqW7d6OVJ4+Lvv/+BXXvzn/4AQAAAAAA8Pg4ffp0ou0PSpUqpXbt2qU4z3rf7cKFCydawEmrpPbwvnLlSqLEBOuS8FWqVLGrJH5WiomJUffu3Q1JCc8884zWrl2b7N7mklSiRAkNHTpUb7/9tm7evJkVodolPDxcfn5+2rx5s7y8vBI9nitXLsNiWf/+/Q37zs+ZM8dQRSElc+bMMbST28YhISFB3bt3V2RkpKVv3LhxKS4+1q5dW9u2bVP37t0t1Tp27dqlxYsX65VXXrEpPlsdPHjQ0LY1MeOBXr16aejQoYkqiFhzcHBQ69at1bRpU3Xs2FGbNm2S9G+CzI4dO2y6s/yB8PBwOTs7a8OGDcnOK1u2rKEdGBioN954w9J2cXHR6tWr1bZt2yTnm0wmderUSQ0bNtQzzzxjWaT95JNPNHDgwETHf6BSpUoKCgqyacG7evXq+vnnnzVu3DiNHz/e8r198803GjduXKrz7bV3715DifzMUrp06UQVbNLr4a0OJNmduOHh4SFXV1dFRUVZ+sLCwjI0MUGSfvrpJ7366qtaunSpJGnUqFFat26d+vXrJ19fX3l5eSkiIkLHjh3TwoULDVsUeHt7a+PGjSpUqFCyx69ataqhbW9ST1Ljw8LCkhyb3mtuPT658yTFw8NDZcuWtVRFOXHihKKiomza1gm2S1wnAwBsVLZsPs2e3V716hXXkSODSEoAAAAAAADAIy8hIUGhoaHatWuX3n33XdWtW1c3btywPO7g4KBvv/02Uflqa9b7bmfkYpOrq6tcXFxSPF9Sffbu/Z2V5s+fb7jbv3jx4lqzZk2KSQkPM5lMyVYHyE4uLi768ccfk0xKSEqPHj0M+9QvWrRIMTExqc4LDQ3VmjVrLO1ixYrpueeeS3LsypUrDXu1v/TSSykmJTzg6Oio+fPnGxb3Pv3001Tn2evhu5i9vLxsvnYPFCtWLNWkhIe5uLho7ty5hjmLFy+265ySNHr0aLuSGT755BPDVivTp09PNinhYd7e3vrxxx8t7fj4eE2fPj3Z8Xny5LH7/WfMmDF66qmnLO20XI+cLiIiwtBOqqJLaqznhIeHpyumpLi6umrJkiXatGmT2rVrJ2dnZ+3du1cDBw5U3bp1VbFiRdWuXVt9+/a1JCU4Ozurf//+On78uGrXrp3i8Zs2bWpo//333zZXE9i1a5dlof9hyV2H9F7z9F7vh6tcxMXFJRk70ofEBADp8tJL1fTnn/1UrlzGZvkBAAAAAAAA6fXss8/KZDIZvnLlyqUCBQqocePGmj59uqHUvZubmxYuXJhqtQQp85MCrI8XEhKSaIx136OcmPDtt98a2h999FGGVZjITt26dbNr+wxPT0+99NJLlnZISIgh4SA5ixYtMlSb6Nu3b7JbLMycOdPyb5PJpClTptgcn4eHhwYNGmRpnzhxIkPvRL9+/bphsT6jy+8np2jRomrYsKGlvXv3brvmu7u76+2337Z5fGRkpH744QdLu1y5cobrmho/Pz81btzY0n5Qqj+jODg4qHPnzpb2+fPnFRwcnKHneNxZL5Kn5c5564Vy62NmpLi4ODk5OSW5rcnD3N3dNWrUKH300Uc2JXsVKVJELVq0MPS99957io+PTzWe9957L8nHbE1MsPeap/d6W78fZXQVDpCYADzxzp4N0eTJO9N1jFy5eCsBAAAAAADA48vLy0tvvPGGTp8+rV69emV3OJL0yG7JkBahoaE6evSope3l5aWePXtmY0QZp1OnTnbPGTBggKFtvUVDUh5e5DaZTOrXr1+S4yIjIw2l0/38/JLdAiA51qX3d+5M39+PH2a9HUdGl7W/d++ebty4ocDAQF28eNHw9fC5zpw5Y9lL3hbPPvusXZUddu3aZUjA6Natmxwc7Ps7+sPPQ2BgoC5dumTXfEmKjo7WrVu3krwe1ou4p0+ftvv4qWnWrJnMZnOmf2XFAnJa3pOz4n08KChIzZs31/PPP681a9YYku2Scu/ePY0dO1ZlypTRe++9Z3idJmfUqFGG9p49e9S7d29DstTDoqKi5O/vr/379yf5uK0/C/Zev/Reb+v3o0dx+6DHXcppMwBytAULjmnw4PWKjIxV2bL51KMHWzEAAAAAAADgyXPv3j05ODjYtVWA9RYE9uxlbQvrvbaT2vIgs2PIKHv27DEsAtevXz/RVhWPq9TKoCelYcOGqlq1qmUhePPmzbp8+bJKliyZ5PiDBw/q2LFjlnazZs1Uvnz5JMfu3btXsbGxlna5cuXsXrRNSEgwtP/++2+75qfEetE0LeXxH0hISNCOHTu0fPlyHThwQAEBAakuyj489+7duzYnG9j7PO/atcvQLlasmN3Pg/V2MhcuXFCpUqVSnHPy5En99NNP2rlzp06dOqVbt27ZfL7bt2/bFV9O5+HhYWjbsoB9tI68AAAgAElEQVRvzXqO9THT68qVK2rcuLHhteXu7q4BAwaoc+fOqlGjhry8vBQeHq4zZ87ol19+0bfffqvbt28rOjpan3/+uXbu3KlNmzalmCT07LPP6u233zZUY1myZIn27t2rIUOGqFGjRsqfP79CQ0O1c+dOff3115aYXF1dlTt3bkOFn+Sq+3h4eBheh/Ze8/Reb3d3d0M7MjLSrvlIHYkJwBMoPDxagwf/qkWLjlv6Bg5cp6efLs6WDAAAAAAAAMgxlixZovr16xv6IiMjdeHCBW3cuFFz587V/fv3FRsbq6+//lonTpzQxo0bbVostU4KyMhFvaioKEVFRRn6ktr2wLrPOpnhUXHt2jVDu1q1atkUScbz9vZO07z+/ftr+PDhkv5dJJ83b54+/PDDJMc+XC1BSlxx4WGXL182tJcuXaqlS5emKcYHrLctSY+4uDhDO7Wy88nZuXOn3nzzTR0/fjz1wckICwuzOTHB3ufZ+nkYNmyYhg0bZtcxrKX0PAQGBmrYsGFavXp1mo//qCY2ZZfHITHh5ZdfNiQlVKhQQb/++qsqVqxoGJcvXz41aNBADRo00JAhQ9SpUycdOHBAknTgwAH5+/tr/fr1KVYc+PTTTxUcHKwlS5ZY+v755x/L+1hSHBwctHDhQg0fPvyxSEywfj96OMkLGYP668AT5uDBq6pde5YhKUGSwsNj1LPnCsXGprwvEAAAAAAAAPC4KFKkiMqUKWP4qlatmtq3b6+vv/5ax44dM5S5/+OPP/T666/bdOzixYsb2tevXzcsvKTHqVOnUj2f9O9d2A87c+ZMhpw/o1lfl4wu35+dPD090zTvlVdekZOTk6U9d+7cJLcWuH//vmEhMF++fOrSpUuyx82o1+DDktsPPi2sk37Ssti7fPlytWjRIl1JCVLiyhApsfd5zsrn4fTp02rQoEG6khIk+67Hk8A6aSU4ONiu+REREYle38ktyKfFpk2b9Pvvv1vazs7OWr9+faKkBGvFihXT+vXrDcl1GzZs0C+//JLiPCcnJ/3444+aPn16khV8rJUtW1Zbt25V165dEyWnJVedKL3X3HrrBXuvt/XzlTt3brvmI3VUTACeEAkJZn3xxV79979bFBub9AeM/fuDNHr0Nk2d2iqLowMAAAAAAMhg5ctLJ09mdxRPjmTKyj/qKlasqF9++UVPP/20pWTzggUL1KFDB3Xt2jXFuQ0bNkzUd/DgQbVp0ybdcR08eNDQdnNzU61atRKNe+aZZwzt27dv69y5c6kuTGW3rNh3/VFXqFAhtW/fXitXrpT0753H27ZtU4sWLQzjVqxYYaiE4e/vL1dX12SPGxMTk+GxJpUwkVZ58uQxtO0tlR4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\n",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "pred, probs = trainer.predict(model, extenal_data_loader)[0]\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_extenal, probs.numpy(), pos_label=1)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "fig = plt.figure(figsize=(8, 6), dpi=300)\n",
    "_ = plt.plot(fpr, tpr, color=\"red\", lw=2, label=\"ROC curve (area = %0.3f)\" % auc)\n",
    "_ = plt.plot([0, 1], [0, 1], color=\"navy\", lw=2, linestyle=\"--\")\n",
    "_ = plt.xlim([-0.01, 1.01])\n",
    "_ = plt.ylim([-0.01, 1.01])\n",
    "_ = plt.xlabel(\"False Positive Rate\")\n",
    "_ = plt.ylabel(\"True Positive Rate\")\n",
    "_ = plt.legend(loc=\"lower right\")\n",
    "_ = plt.show()\n",
    "fig.savefig('20220610_Results/7. ROC_extend_mean.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3398149f-7c44-4702-950f-df415ca6931c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x1800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, _ = metrics.precision_recall_curve(y_extenal, pred.numpy())\n",
    "disp = metrics.PrecisionRecallDisplay(precision=precision, recall=recall)\n",
    "fig = plt.figure(figsize=(8, 6), dpi=300)\n",
    "_ = disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08ed415-791b-4f13-9f9a-4e4ad4048b2c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}