confusion_mt.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import copy\n",
    "import random\n",
    "import time\n",
    "import math\n",
    "\n",
    "import utils\n",
    "import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "BS: 256, epoch: 200\nIteration: 641\n"
     ]
    }
   ],
   "source": [
    "def setup_seed(seed):\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "setup_seed(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "data, labels = utils.load_data('seed4')\n",
    "source_data, source_label = data[0], labels[0]\n",
    "target_data, target_label = source_data.pop(), source_label.pop()\n",
    "for i in range(len(source_data)):\n",
    "    source_data[i] = utils.norminy(source_data[i])\n",
    "target_data = utils.norminy(target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "class DANNet():\n",
    "    def __init__(self, model=models.DAN(), source_loader=0, target_loader=0, batch_size=64, iteration=10000, lr=0.001, momentum=0.9, log_interval=10):\n",
    "        self.model = model \n",
    "        self.model.to(device)\n",
    "        self.source_loader = source_loader\n",
    "        self.target_loader = target_loader \n",
    "        self.batch_size = batch_size\n",
    "        self.iteration = iteration\n",
    "        self.lr = lr\n",
    "        self.momentum = momentum\n",
    "        self.log_interval = log_interval\n",
    "\n",
    "    def __getModel__(self):\n",
    "        return self.model\n",
    "\n",
    "    def train(self):\n",
    "        best_model_wts = copy.deepcopy(self.model.state_dict())\n",
    "        source_iter = iter(self.source_loader)\n",
    "        target_iter = iter(self.target_loader)\n",
    "        correct = 0\n",
    "\n",
    "        for i in range(1, self.iteration+1):\n",
    "            self.model.train()\n",
    "            LEARNING_RATE = self.lr\n",
    "            if (i - 1) % 100 == 0:\n",
    "                print(\"Learning rate: \", LEARNING_RATE)\n",
    "            optimizer = torch.optim.Adam(self.model.parameters(), lr=LEARNING_RATE)\n",
    "            \n",
    "            try:\n",
    "                source_data, source_label = next(source_iter)\n",
    "            except Exception as err:\n",
    "                source_iter = iter(self.source_loader)\n",
    "                source_data, source_label = next(source_iter)\n",
    "            try:\n",
    "                target_data, _ = next(target_iter)\n",
    "            except Exception as err:\n",
    "                target_iter = iter(self.target_loader)\n",
    "                target_data, _ = next(target_iter)\n",
    "            source_data, source_label = source_data.to(device), source_label.to(device)\n",
    "            target_data = target_data.to(device)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            source_prediction, mmd_loss, _ = self.model(source_data, data_tgt=target_data)\n",
    "            cls_loss = F.nll_loss(F.log_softmax(source_prediction, dim=1), source_label.squeeze())\n",
    "            gamma = 2 / (1 + math.exp(-10 * (i) / (iteration))) - 1\n",
    "            loss = cls_loss + gamma * mmd_loss\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            if i % log_interval == 0:\n",
    "                print('Iter: {} [({:.0f}%)]\\tLoss: {:.6f}\\tsoft_loss: {:.6f}\\tmmd_loss {:.6f}'.format(\n",
    "                    i, 100.*i/self.iteration, loss.item(), cls_loss.item(), mmd_loss.item()\n",
    "                    )\n",
    "                )\n",
    "            if i % (log_interval * 20) == 0:\n",
    "                t_correct = self.test(i)\n",
    "                if t_correct > correct:\n",
    "                    correct = t_correct\n",
    "                best_model_wts = copy.deepcopy(self.model.state_dict())\n",
    "                print('to target max correct: ', correct.item(), \"\\n\")\n",
    "        self.model.load_state_dict(best_model_wts)\n",
    "        return 100. * correct / len(self.target_loader.dataset), self.model\n",
    "\n",
    "    def test(self, i):\n",
    "        self.model.eval()\n",
    "        test_loss = 0\n",
    "        correct = 0\n",
    "        with torch.no_grad():\n",
    "            for data, target in self.target_loader:\n",
    "                data = data.to(device)\n",
    "                target = target.to(device)\n",
    "                preds, mmd_loss, _ = self.model(data, data)\n",
    "                test_loss += F.nll_loss(F.log_softmax(preds, dim=1), target.squeeze(), reduction='sum').item()\n",
    "                pred = preds.data.max(1)[1]\n",
    "                correct += pred.eq(target.data.squeeze()).cpu().sum()\n",
    "            test_loss /= len(self.target_loader.dataset)\n",
    "        return correct\n",
    "\n",
    "class MSMDAER():\n",
    "    def __init__(self, model=models.MSMDAERNet(), source_loaders=0, target_loader=0, batch_size=64, iteration=10000, lr=0.001, momentum=0.9, log_interval=10):\n",
    "        self.model = model\n",
    "        self.model.to(device)\n",
    "        self.source_loaders = source_loaders\n",
    "        self.target_loader = target_loader\n",
    "        self.batch_size = batch_size\n",
    "        self.iteration = iteration\n",
    "        self.lr = lr\n",
    "        self.momentum = momentum\n",
    "        self.log_interval = log_interval\n",
    "\n",
    "    def __getModel__(self):\n",
    "        return self.model\n",
    "\n",
    "    def train(self):\n",
    "        best_model_wts = copy.deepcopy(self.model.state_dict())\n",
    "        source_iters = []\n",
    "        for i in range(len(self.source_loaders)):\n",
    "            source_iters.append(iter(self.source_loaders[i]))\n",
    "        target_iter = iter(self.target_loader)\n",
    "        correct = 0\n",
    "\n",
    "        for i in range(1, self.iteration+1):\n",
    "            self.model.train()\n",
    "            LEARNING_RATE = self.lr\n",
    "            if (i - 1) % 100 == 0:\n",
    "                print(\"Learning rate: \", LEARNING_RATE)\n",
    "            optimizer = torch.optim.Adam(self.model.parameters(), lr=LEARNING_RATE)\n",
    "            \n",
    "            for j in range(len(source_iters)):\n",
    "                try:\n",
    "                    source_data, source_label = next(source_iters[j])\n",
    "                except Exception as err:\n",
    "                    source_iters[j] = iter(self.source_loaders[j])\n",
    "                    source_data, source_label = next(source_iters[j])\n",
    "                try:\n",
    "                    target_data, _ = next(target_iter)\n",
    "                except Exception as err:\n",
    "                    target_iter = iter(self.target_loader)\n",
    "                    target_data, _ = next(target_iter)\n",
    "                source_data, source_label = source_data.to(device), source_label.to(device)\n",
    "                target_data = target_data.to(device)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "                cls_loss, mmd_loss, l1_loss = self.model(source_data, number_of_source=len(source_iters), data_tgt=target_data, label_src=source_label, mark=j)\n",
    "                gamma = 2 / (1 + math.exp(-10 * (i) / (self.iteration))) - 1\n",
    "                beta = gamma/100\n",
    "                loss = cls_loss + gamma * mmd_loss + beta * l1_loss\n",
    "\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                if i % log_interval == 0: \n",
    "                    print('Train source' + str(j) + ', iter: {} [({:.0f}%)]\\tLoss: {:.6f}\\tsoft_loss: {:.6f}\\tmmd_loss {:.6f}\\tl1_loss: {:.6f}'.format(i, 100.*i/self.iteration, loss.item(), cls_loss.item(), mmd_loss.item(), l1_loss.item()))\n",
    "\n",
    "            if i % (log_interval * 20) == 0:\n",
    "                t_correct = self.test(i)\n",
    "                if t_correct > correct:\n",
    "                    correct = t_correct\n",
    "                best_model_wts = copy.deepcopy(self.model.state_dict())\n",
    "                print('to target max correct: ', correct.item(), \"\\n\")\n",
    "        self.model.load_state_dict(best_model_wts)\n",
    "        return 100. * correct / len(self.target_loader.dataset), self.model\n",
    "\n",
    "    def test(self, i):\n",
    "        self.model.eval()\n",
    "        test_loss = 0\n",
    "        correct = 0\n",
    "        corrects = []\n",
    "        for i in range(len(self.source_loaders)):\n",
    "            corrects.append(0)\n",
    "        with torch.no_grad():\n",
    "            for data, target in self.target_loader:\n",
    "                data = data.to(device)\n",
    "                target = target.to(device)\n",
    "                preds, _ = self.model(data, len(self.source_loaders))\n",
    "                for i in range(len(preds)):\n",
    "                    preds[i] = F.softmax(preds[i], dim=1)\n",
    "                pred = sum(preds)/len(preds)\n",
    "                test_loss += F.nll_loss(F.log_softmax(pred, dim=1), target.squeeze()).item()\n",
    "                pred = pred.data.max(1)[1]\n",
    "                correct += pred.eq(target.data.squeeze()).cpu().sum()\n",
    "                for j in range(len(self.source_loaders)):\n",
    "                    pred = preds[j].data.max(1)[1]\n",
    "                    corrects[j] += pred.eq(target.data.squeeze()).cpu().sum()\n",
    "            \n",
    "            test_loss /= len(self.target_loader.dataset)\n",
    "        return correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = 'seed4'\n",
    "batch_size = 256\n",
    "epoch = 200\n",
    "lr = 0.01\n",
    "\n",
    "trial_total, category_number, _ = utils.get_number_of_label_n_trial(dataset_name=dataset_name)\n",
    "\n",
    "# training settings\n",
    "print('BS: {}, epoch: {}'.format(batch_size, epoch))\n",
    "momentum = 0.9\n",
    "log_interval = 10\n",
    "iteration = 0\n",
    "if dataset_name == 'seed3':\n",
    "    iteration = math.ceil(epoch*3394/batch_size)\n",
    "elif dataset_name == 'seed4':\n",
    "    iteration = math.ceil(epoch*820/batch_size)\n",
    "else:\n",
    "    iteration = 5000\n",
    "print('Iteration: {}'.format(iteration))\n",
    "\n",
    "source_train_data, source_train_label = copy.deepcopy(source_data), copy.deepcopy(source_label)\n",
    "target_train_data, target_train_label = copy.deepcopy(target_data), copy.deepcopy(target_label)\n",
    "\n",
    "# ms-mdaer\n",
    "source_train_loaders = []\n",
    "for j in range(len(source_train_data)):\n",
    "    source_train_loaders.append(torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_train_data[j], source_train_label[j]), batch_size=batch_size, shuffle=True, drop_last=True))\n",
    "target_train_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_train_data, target_train_label), batch_size=batch_size, shuffle=True, drop_last=True) \n",
    "\n",
    "# dan\n",
    "# source_train_data_comb = source_train_data[0]\n",
    "# source_train_label_comb = source_train_label[0]\n",
    "# for j in range(1, len(source_train_data)):\n",
    "#     source_train_data_comb = np.vstack((source_train_data_comb, source_train_data[j]))\n",
    "#     source_train_label_comb = np.vstack((source_train_label_comb, source_train_label[j]))\n",
    "# source_train_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_train_data_comb, source_train_label_comb), batch_size=batch_size, shuffle=True, drop_last=True)\n",
    "# target_train_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_train_data, target_train_label), batch_size=batch_size, shuffle=True, drop_last=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MSMDAER(model=models.MSMDAERNet_tsne(pretrained=False, number_of_source=len(source_train_data), number_of_category=category_number), source_loaders=source_train_loaders, target_loader=target_train_loader, batch_size=batch_size, iteration=iteration, lr=lr, momentum=momentum, log_interval=log_interval) \n",
    "\n",
    "# model = DANNet(model=models.DAN_tsne(pretrained=False, number_of_category=category_number), source_loader=source_train_loader, target_loader=target_train_loader, batch_size=batch_size, iteration=iteration, lr=lr, momentum=momentum, log_interval=log_interval)\n",
    "\n",
    "\n",
    "acc, model = model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "model.to(device)\n",
    "# target_data.to(device)\n",
    "# source_data.to(device)\n",
    "\n",
    "# ms-mdaer\n",
    "tsne_source_loaders = []\n",
    "for i in range(len(source_data)):\n",
    "    tsne_source_loaders.append(torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data[i], source_label[i]), batch_size=len(source_data[i]), shuffle=True, drop_last=True))\n",
    "tsne_target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label), batch_size=len(target_data), shuffle=True, drop_last=True)\n",
    "\n",
    "# dan\n",
    "# source_data_comb = np.vstack((source_data[0], source_data[1]))\n",
    "# source_label_comb = np.vstack((source_label[0], source_label[1]))\n",
    "# for j in range(2, len(source_data)):\n",
    "#     source_data_comb = np.vstack((source_data_comb, source_data[j]))\n",
    "#     source_label_comb = np.vstack((source_label_comb, source_label[j]))\n",
    "# tsne_source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_data_comb), batch_size=len(source_data_comb), shuffle=True, drop_last=True)\n",
    "# tsne_target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label), batch_size=len(target_data), shuffle=True, drop_last=True)    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([3, 1, 3, 0, 0, 3, 2, 1, 0, 1, 3, 2, 3, 2, 1, 0, 2, 1, 3, 1, 2, 1, 0, 3,\n        3, 1, 2, 2, 0, 1, 2, 0, 3, 2, 0, 3, 1, 3, 1, 2, 0, 2, 1, 1, 1, 3, 0, 2,\n        3, 3, 2, 1, 1, 3, 2, 3, 2, 1, 0, 0, 1, 1, 3, 0, 3, 3, 2, 3, 0, 0, 0, 3,\n        0, 0, 1, 0, 3, 3, 3, 3, 3, 1, 1, 2, 0, 0, 0, 2, 0, 1, 0, 0, 3, 0, 3, 2,\n        0, 1, 2, 2, 3, 1, 2, 1, 0, 1, 0, 1, 2, 0, 3, 3, 3, 1, 0, 1, 1, 1, 0, 1,\n        0, 0, 2, 1, 3, 0, 3, 1, 2, 1, 2, 1, 0, 2, 1, 1, 1, 3, 1, 1, 1, 1, 0, 0,\n        1, 1, 2, 2, 3, 0, 1, 3, 3, 1, 3, 1, 3, 2, 3, 0, 3, 1, 3, 1, 3, 1, 1, 3,\n        0, 2, 2, 3, 3, 2, 2, 1, 1, 0, 1, 3, 2, 0, 1, 1, 2, 0, 1, 1, 1, 2, 1, 1,\n        1, 3, 2, 0, 3, 3, 2, 2, 0, 3, 0, 1, 0, 2, 0, 1, 2, 2, 3, 2, 0, 0, 1, 1,\n        0, 0, 0, 1, 2, 2, 1, 2, 3, 3, 3, 2, 1, 2, 1, 0, 0, 1, 2, 0, 3, 0, 1, 1,\n        2, 3, 0, 0, 2, 2, 3, 2, 0, 0, 1, 0, 3, 3, 2, 2, 3, 1, 0, 2, 3, 0, 1, 3,\n        0, 1, 3, 1, 1, 1, 2, 1, 3, 0, 0, 2, 1, 0, 2, 2, 1, 1, 1, 1, 1, 0, 2, 3,\n        0, 1, 0, 2, 3, 3, 0, 0, 1, 2, 3, 0, 3, 2, 1, 1, 3, 0, 2, 1, 1, 3, 2, 2,\n        1, 3, 1, 3, 3, 2, 3, 3, 1, 2, 0, 3, 1, 0, 0, 2, 2, 3, 0, 0, 1, 2, 0, 1,\n        0, 1, 1, 3, 0, 1, 1, 2, 1, 2, 2, 2, 3, 3, 0, 1, 1, 1, 0, 0, 3, 1, 3, 0,\n        3, 0, 0, 0, 3, 3, 1, 1, 3, 0, 1, 0, 2, 3, 1, 1, 2, 1, 0, 1, 0, 1, 2, 0,\n        0, 2, 0, 1, 1, 3, 1, 3, 2, 0, 1, 1, 2, 0, 3, 1, 2, 3, 3, 3, 2, 1, 1, 0,\n        2, 0, 3, 2, 2, 0, 3, 1, 0, 2, 3, 2, 1, 1, 2, 0, 0, 2, 0, 3, 1, 0, 0, 2,\n        1, 0, 3, 1, 3, 1, 1, 2, 1, 3, 3, 0, 0, 2, 2, 1, 0, 1, 1, 0, 0, 0, 3, 3,\n        2, 0, 1, 1, 3, 0, 1, 3, 1, 1, 0, 0, 0, 1, 0, 2, 0, 1, 1, 1, 0, 1, 2, 3,\n        2, 1, 2, 3, 0, 1, 2, 2, 2, 1, 2, 1, 1, 3, 2, 3, 1, 3, 0, 2, 0, 2, 3, 2,\n        2, 2, 1, 2, 0, 1, 0, 0, 2, 3, 2, 1, 2, 1, 3, 2, 1, 3, 3, 1, 1, 0, 3, 1,\n        0, 2, 0, 1, 2, 1, 2, 1, 0, 0, 0, 2, 3, 2, 3, 1, 0, 0, 2, 3, 0, 2, 3, 1,\n        2, 2, 3, 3, 0, 3, 0, 2, 1, 3, 3, 0, 2, 3, 3, 0, 2, 1, 2, 2, 0, 3, 1, 3,\n        3, 1, 1, 3, 0, 2, 0, 0, 1, 0, 3, 3, 1, 0, 0, 0, 0, 3, 2, 2, 3, 0, 1, 0,\n        1, 0, 2, 3, 1, 1, 3, 3, 3, 1, 0, 3, 0, 1, 1, 0, 3, 3, 1, 2, 2, 3, 0, 2,\n        3, 3, 1, 1, 1, 3, 3, 2, 3, 1, 0, 2, 3, 1, 3, 1, 1, 2, 1, 0, 0, 3, 0, 1,\n        1, 2, 1, 3, 3, 2, 0, 2, 1, 2, 1, 0, 3, 1, 2, 1, 0, 2, 1, 0, 0, 3, 3, 1,\n        1, 1, 1, 0, 0, 1, 0, 2, 2, 1, 0, 3, 0, 1, 1, 1, 3, 2, 0, 0, 2, 2, 1, 2,\n        1, 3, 1, 3, 1, 0, 2, 0, 1, 3, 1, 2, 3, 1, 2, 2, 1, 0, 0, 2, 1, 2, 2, 0,\n        1, 1, 1, 0, 0, 2, 2, 1, 0, 3, 0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 1, 1,\n        3, 3, 3, 3, 2, 2, 0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 1, 3, 0, 2, 3, 3, 2, 1,\n        1, 0, 3, 0, 0, 1, 1, 1, 3, 2, 3, 0, 0, 1, 0, 1, 1, 1, 1, 2, 1, 2, 0, 0,\n        0, 1, 2, 1, 1, 0, 2, 1, 0, 3, 2, 1, 0, 0, 2, 3, 2, 2, 3, 3, 1, 0, 3, 0,\n        2, 0, 1, 1, 3, 0, 0, 0, 2, 0, 1, 3, 0, 1, 1, 1, 1, 3, 0, 2, 1, 1, 2, 3,\n        1, 1, 3, 0, 2, 0, 0, 1, 1, 2, 3])\n[[217   0   0   0]\n [  0  96 136  27]\n [  0  96  22  64]\n [  0   0  24 169]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "correct = 0\n",
    "\n",
    "for data, target in tsne_target_loader:\n",
    "    data = data.to(device)\n",
    "    target = target.to(device)\n",
    "    preds, _ = model(data, 14)\n",
    "    for i in range(len(preds)):\n",
    "        preds[i] = F.softmax(preds[i], dim=1)\n",
    "    pred = sum(preds)/len(preds)\n",
    "    pred = pred.data.max(1)[1]\n",
    "    print(target.data.squeeze().cpu())\n",
    "    print(confusion_matrix(target.data.squeeze().cpu(), pred.cpu()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ary = target.data.squeeze().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "217\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(ary==0)) # seed: 0 negative, 1 neutral 2 positive\n",
    "# seed4: 0 neutral, 1 sad, 2 fear, 3 happy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "259\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(ary==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "182\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(ary==2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x504 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# array = [[882, 104, 134],\n",
    "#          [176, 785, 113],\n",
    "#          [42, 56, 1102]]\n",
    "# array = [[926, 81, 113],\n",
    "#          [116, 905, 53],\n",
    "#          [30, 36, 1134]]\n",
    "array = [[791, 176, 153],\n",
    "         [179, 798, 97],\n",
    "         [83, 98, 1019]]\n",
    "# array = [[209,   0,   0,   8],\n",
    "#          [  0,  157, 76,  26],\n",
    "#          [  0,  44,  109,  29],\n",
    "#          [  11,   0,  24, 158]]\n",
    "\n",
    "# (\"negative\", \"neutral\", \"positive\")(\"neutral\", \"sad\", \"fear\", \"happy\")\n",
    "# cmap = sns.color_palette(\"rocket_r\")\n",
    "cmap = sns.color_palette(\"light:b\")\n",
    "\n",
    "df_cm = pd.DataFrame(array, index = [i for i in (\"negative\", \"neutral\", \"positive\")],\n",
    "                  columns = [i for i in (\"negative\", \"neutral\", \"positive\")])\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.set(font_scale=1.4) # for label size\n",
    "sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}, cmap=cmap, fmt='g') # font size\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}