iris_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%matplotlib.inline` not found.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd    \n",
    "\n",
    "import torch\n",
    "import torch.nn as nn \n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib.inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeurlNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.lay1 = nn.Linear(4,8)\n",
    "        self.out = nn.Linear(8,3)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.lay1(x))\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(32)\n",
    "model = NeurlNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
       "0             5.1          3.5           1.4          0.2     Setosa\n",
       "1             4.9          3.0           1.4          0.2     Setosa\n",
       "2             4.7          3.2           1.3          0.2     Setosa\n",
       "3             4.6          3.1           1.5          0.2     Setosa\n",
       "4             5.0          3.6           1.4          0.2     Setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  Virginica\n",
       "146           6.3          2.5           5.0          1.9  Virginica\n",
       "147           6.5          3.0           5.2          2.0  Virginica\n",
       "148           6.2          3.4           5.4          2.3  Virginica\n",
       "149           5.9          3.0           5.1          1.8  Virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data = pd.read_csv('datasets/iris.csv')\n",
    "iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Setosa        50\n",
       "Versicolor    50\n",
       "Virginica     50\n",
       "Name: variety, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data['variety'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data['variety'] = iris_data['variety'].replace(\"Setosa\",0.0)\n",
    "iris_data['variety'] = iris_data['variety'].replace(\"Versicolor\",1.0)\n",
    "iris_data['variety'] = iris_data['variety'].replace(\"Virginica\",2.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width  variety\n",
       "0           5.1          3.5           1.4          0.2      0.0\n",
       "1           4.9          3.0           1.4          0.2      0.0\n",
       "2           4.7          3.2           1.3          0.2      0.0\n",
       "3           4.6          3.1           1.5          0.2      0.0\n",
       "4           5.0          3.6           1.4          0.2      0.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurs = iris_data.iloc[:,:-1].values\n",
    "labels = iris_data.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data\n",
    "x_train, x_test, y_train,y_test = train_test_split(featurs, labels, test_size=0.2, random_state=32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = torch.FloatTensor(x_train)\n",
    "x_test = torch.FloatTensor(x_test)\n",
    "y_train = torch.FloatTensor(y_train).view(-1)\n",
    "y_test = torch.FloatTensor(y_test).view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([120])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100], Loss: 1.1633\n",
      "Epoch [11/100], Loss: 0.8488\n",
      "Epoch [21/100], Loss: 0.6763\n",
      "Epoch [31/100], Loss: 0.5421\n",
      "Epoch [41/100], Loss: 0.4411\n",
      "Epoch [51/100], Loss: 0.3626\n",
      "Epoch [61/100], Loss: 0.2969\n",
      "Epoch [71/100], Loss: 0.2416\n",
      "Epoch [81/100], Loss: 0.1977\n",
      "Epoch [91/100], Loss: 0.1648\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "num_epochs = 100\n",
    "losses = []\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    output_batch = model.forward(x_train)\n",
    "    loss = criterion(output_batch, y_train.type(torch.LongTensor))\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    losses.append(loss.detach().numpy())\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epochs')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.plot(range(num_epochs), losses)\n",
    "plt.ylabel(\"loss/error\")\n",
    "plt.xlabel(\"epochs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.1365)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with torch.no_grad(): # turn off back propagation\n",
    "    model.eval()\n",
    "    y_ped_eval = model(x_test)\n",
    "    loss = criterion(y_ped_eval, y_test.type(torch.LongTensor))\n",
    "\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 0., 1., 2., 2., 0., 0., 1., 0., 1., 2., 1., 1., 2., 2., 1., 2.,\n",
       "        1., 0., 0., 2., 2., 0., 0., 1., 0., 2., 0., 0.])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " y_test.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "Accuracy on test set: 1.0000\n"
     ]
    }
   ],
   "source": [
    "model.eval()  # Set model to evaluation mode\n",
    "\n",
    "with torch.no_grad():\n",
    "    output_test = model(x_test)\n",
    "    predicted_labels = output_test.argmax(dim=1)\n",
    "\n",
    "correct_predictions = (predicted_labels == y_test.squeeze()).sum().item()\n",
    "print(correct_predictions)\n",
    "total_predictions = len(y_test)\n",
    "\n",
    "accuracy = correct_predictions / total_predictions\n",
    "print(f\"Accuracy on test set: {accuracy:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.7754,  2.2511,  1.0855])  1  1\n",
      "tensor([ 5.0900,  0.3369, -9.3457])  0  0\n",
      "tensor([ 4.2231,  0.8891, -8.1976])  0  0\n",
      "tensor([-1.8695,  2.4113,  1.0194])  1  1\n",
      "tensor([-2.8221,  1.8163,  3.3331])  2  2\n",
      "tensor([-3.1500,  2.3788,  3.1325])  2  2\n",
      "tensor([ 3.7972,  1.0610, -7.6338])  0  0\n",
      "tensor([ 4.2668,  1.0327, -8.4191])  0  0\n",
      "tensor([-1.6253,  2.2586,  0.7910])  1  1\n",
      "tensor([ 4.7767,  0.6649, -8.9761])  0  0\n",
      "tensor([-2.0099,  2.7778,  0.7827])  1  1\n",
      "tensor([-3.0750,  1.5617,  3.9739])  2  2\n",
      "tensor([-1.2427,  2.3969,  0.0233])  1  1\n",
      "tensor([-1.0526,  2.5489, -0.5310])  1  1\n",
      "tensor([-2.8156,  1.8950,  3.1553])  2  2\n",
      "tensor([-3.0320,  1.3521,  4.1565])  2  2\n",
      "tensor([-1.8587,  2.0543,  1.4556])  1  1\n",
      "tensor([-2.5838,  2.0249,  2.6214])  2  2\n",
      "tensor([-1.2777,  2.5859, -0.1581])  1  1\n",
      "tensor([ 5.1120,  0.7765, -9.7729])  0  0\n",
      "tensor([ 4.3887,  0.6771, -8.2552])  0  0\n",
      "tensor([-3.4735,  2.0065,  4.1171])  2  2\n",
      "tensor([-2.3511,  1.9254,  2.3837])  2  2\n",
      "tensor([ 4.4147,  1.0267, -8.6669])  0  0\n",
      "tensor([ 4.4755,  0.7033, -8.4770])  0  0\n",
      "tensor([-1.1468,  2.3073, -0.0842])  1  1\n",
      "tensor([ 5.2372,  0.5934, -9.8547])  0  0\n",
      "tensor([-3.4417,  1.5105,  4.6119])  2  2\n",
      "tensor([ 4.8569,  0.8467, -9.3321])  0  0\n",
      "tensor([ 4.7294,  0.8818, -9.1118])  0  0\n",
      "Accuracy of the model is: 100%\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "correct = 0\n",
    "all_ = len(y_test)\n",
    "\n",
    "with torch.no_grad():\n",
    "    for i, data in enumerate(x_test):\n",
    "        pred = model(data)\n",
    "        prediction_best = pred.argmax(dim=-1)\n",
    "        print(f'{pred}  {prediction_best }  {y_test[i].type(torch.int)}')\n",
    "        if prediction_best == y_test[i]:\n",
    "            correct += 1\n",
    "    \n",
    "    print(f'Accuracy of the model is: {round((correct / all_)* 100)}%')\n"
   ]
  }
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