4. Multi Class Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bd38cb7b",
   "metadata": {},
   "source": [
    "# Notebook 4:  Multi Class Classification\n",
    "__Author: Bibek Poudel__\n",
    "\n",
    "Using the results from Cross Validation (Notebook 3), I train the neural network on the entire training dataset and see its test performance here "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "353d5699",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import glob\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from scipy.io import loadmat\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a1a4652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f4d397c7a30>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set seeds for reproducibility of results\n",
    "SEED = 22\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "860cd3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a dataset class (Required by pytorch)\n",
    "class MultiDataset(Dataset):\n",
    "    def __init__(self, inputs, targets):\n",
    "        self.inputs_list = inputs\n",
    "        self.target_list = targets\n",
    "        assert (len(self.inputs_list) == len(self.target_list))\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.inputs_list)\n",
    "    \n",
    "    def __getitem__(self, key):\n",
    "        input_idx = self.inputs_list[key]\n",
    "        target_idx = self.target_list[key]\n",
    "        return [input_idx.astype(np.float32), target_idx.astype(np.float32) ]#.astype(np.long)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfeab750",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a neural network with a single hidden layer\n",
    "class model(nn.Module):\n",
    "    def __init__(self, input_units, hidden_units_l1, hidden_units_l2, output_units):\n",
    "        super().__init__()\n",
    "        self.activation = nn.ReLU() # ReLU activation function\n",
    "        self.fc1 = nn.Linear(input_units, hidden_units_l1) # Hidden units can be specified arbitrarily\n",
    "        self.fc2 = nn.Linear(hidden_units_l1, hidden_units_l2) # Hidden units can be specified arbitrarily\n",
    "        self.fc3 = nn.Linear(hidden_units_l2, output_units) # Output units can be specified arbitrarily\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.activation(self.fc1(x))\n",
    "        x = self.activation(self.fc2(x)) \n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4498ef90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performance metric\n",
    "def akkuracy(ground_truths, predictions):\n",
    "    num_correct = np.sum(ground_truths==predictions)\n",
    "    total = ground_truths.shape[0]\n",
    "    return num_correct*100.0 / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cef3e10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a method to train neural network model\n",
    "def train(model, dataloader, optimizer, learning_rate, loss_function, epochs, train_size):\n",
    "    \n",
    "    epoch_losses = np.zeros(epochs)\n",
    "    for epoch in range(epochs):\n",
    "        predictions =  np.zeros(train_size)\n",
    "        ground_truths_total= np.zeros(train_size)\n",
    "        count =0\n",
    "        \n",
    "        #print(\"Epoch:{} \".format(epoch), end = \"\\t\")\n",
    "        total_loss = 0.0\n",
    "\n",
    "        for bi, (inputs, targets) in enumerate(dataloader):\n",
    "            current_size=targets.shape[0]\n",
    "            ground_truths_total[count: count+current_size] = targets\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            outputs = model(inputs)\n",
    "\n",
    "            #print(outputs)\n",
    "            #print(targets)\n",
    "            \n",
    "            loss = loss_function(outputs.float(), targets.long()) # targets should be long [batch size], outputs are distributed across the classes?\n",
    "            \n",
    "            loss.backward()\n",
    "\n",
    "            optimizer.step()\n",
    "            \n",
    "            total_loss+=loss.item()\n",
    "            \n",
    "            _, predicted = torch.max(outputs.data, 1) \n",
    "            predicted_np = np.squeeze(predicted.cpu().detach().numpy())\n",
    "            predictions[count: count+current_size] = predicted_np\n",
    "            \n",
    "            count+=current_size\n",
    "        \n",
    "        avg_batch_loss = round(total_loss/len(dataloader),3)\n",
    "        #print(\"Average batch loss:{}\".format(avg_batch_loss))\n",
    "        epoch_losses[epoch] = avg_batch_loss\n",
    "        torch.save(model, \"./saved_models/training_model_multi.pt\")\n",
    "        train_accuracy = akkuracy(ground_truths_total, predictions)\n",
    "    \n",
    "    return train_accuracy, epoch_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc25cb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a method to perform model test\n",
    "def test(trained_model, dataloader, test_size):\n",
    "    predictions =  np.zeros(test_size)\n",
    "    ground_truths_total= np.zeros(test_size)\n",
    "    count =0\n",
    "    with torch.no_grad():\n",
    "        for bi, (inputs, ground_truths) in enumerate(dataloader):\n",
    "            \n",
    "            outputs = trained_model(inputs)\n",
    "            _, predicted = torch.max(outputs.data, 1) \n",
    "            predicted_np = np.squeeze(predicted.cpu().detach().numpy())\n",
    "            current_size=ground_truths.shape[0]\n",
    "            predictions[count: count+current_size] = predicted_np\n",
    "            ground_truths_total[count: count+current_size] = ground_truths\n",
    "            count+=current_size\n",
    "\n",
    "    test_accuracy = akkuracy(ground_truths_total, predictions)\n",
    "    return test_accuracy, predictions, ground_truths_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ec6a22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "__________________________________________________________\n",
      "Started Training .... it may take a while...\n",
      "Training time = 37 seconds\n",
      "Finished Training, training accuracy at last epoch = 98.17%:\n",
      "\n",
      "__________________________________________________________\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3200x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "__________________________________________________________\n",
      "Test accuracy = 85.3 %\n",
      "Confusion Matrix:\n",
      " Total Predicted = 1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "__________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def return_data(dataset_path):\n",
    "    path_name_end = dataset_path.split('/')[-1]\n",
    "    d_set = loadmat(dataset_path)\n",
    "    d_name = path_name_end.split('.')[0]\n",
    "    data = d_set[d_name]\n",
    "    return data\n",
    "\n",
    "datasets = glob.glob('./datasets/multi-class/*.mat')\n",
    "\n",
    "train_X = return_data(datasets[0])\n",
    "test_Y = np.squeeze(return_data(datasets[1]))\n",
    "train_Y = np.squeeze(return_data(datasets[2]))\n",
    "test_X = return_data(datasets[3])\n",
    "\n",
    "# Using the results from cross validation\n",
    "hidden_units_l1 = 100\n",
    "hidden_units_l2 = 20\n",
    "output_units = 10\n",
    "\n",
    "# Training\n",
    "net = model(train_X.shape[1], hidden_units_l1, hidden_units_l2, output_units).train()\n",
    "\n",
    "# Using some standard hyper-parameter values for cross validation\n",
    "epochs =100\n",
    "loss = nn.CrossEntropyLoss() # Loss function as Cross Entropy\n",
    "lr = 0.0001 # Learning rate \n",
    "optimizer = torch.optim.Adam(net.parameters(), lr = lr) # Optimizer as Adam\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "\n",
    "train_dataset = MultiDataset(train_X, train_Y)\n",
    "train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=None,\n",
    "                                           shuffle=False)\n",
    "\n",
    "print(\"\\n__________________________________________________________\")\n",
    "print(\"Started Training .... it may take a while...\")\n",
    "start = time.time()\n",
    "train_acc,epoch_losses = train(net, train_dataloader, optimizer, lr, loss, epochs,train_Y.shape[0])\n",
    "print(\"Training time = {} seconds\".format(round(time.time() - start),3))\n",
    "print(\"Finished Training, training accuracy at last epoch = {}%:\".format(round(train_acc,3)))\n",
    "print(\"\\n__________________________________________________________\")\n",
    "# Plot the training profile\n",
    "fig, ax = plt.subplots(figsize=(16,5), dpi =200)\n",
    "ax.plot(epoch_losses)\n",
    "ax.set_title(\"Training Loss vs. Epoch \", fontsize=14)\n",
    "ax.set_ylabel(\"Cross Entropy Loss (Average Batch Loss Per Epoch)\", fontsize=12)\n",
    "ax.set_xlabel(\"Epoch Number\", fontsize=12)\n",
    "plt.show()\n",
    "print(\"\\n__________________________________________________________\")\n",
    "# Test\n",
    "trained_model = torch.load(\"./saved_models/training_model_multi.pt\")\n",
    "\n",
    "test_dataset = MultiDataset(test_X, test_Y)\n",
    "test_dataloader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=None,\n",
    "                                           shuffle=False)\n",
    "\n",
    "test_acc, preds, gts = test(trained_model, test_dataloader, test_Y.shape[0])\n",
    "\n",
    "# Plot confusion matrix\n",
    "print(\"Test accuracy = {} %\".format(round(test_acc,3)))\n",
    "print(\"Confusion Matrix:\\n Total Predicted = {}\".format(test_Y.shape[0]))\n",
    "fig_2,ax_2 = plt.subplots(figsize=(5,5), dpi=200)\n",
    "ConfusionMatrixDisplay.from_predictions(preds,gts, ax=ax_2) #gts, preds\n",
    "ax_2.set_title(\"Multi class dataset\", fontsize=14)\n",
    "plt.show()\n",
    "print(\"\\n__________________________________________________________\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2403964c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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