{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1c5e25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import jovian\n",
    "import torchvision\n",
    "import matplotlib\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch.nn.functional as F\n",
    "from torchvision.datasets.utils import download_url\n",
    "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c77ff6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_raw = pd.read_csv(\"winequality-red.csv\")\n",
    "dataframe_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99f42861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['fixed acidity',\n",
       "  'volatile acidity',\n",
       "  'citric acid',\n",
       "  'residual sugar',\n",
       "  'chlorides',\n",
       "  'free sulfur dioxide',\n",
       "  'total sulfur dioxide',\n",
       "  'density',\n",
       "  'pH',\n",
       "  'sulphates',\n",
       "  'alcohol'],\n",
       " ['quality'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_cols=list(dataframe_raw.columns)[:-1]\n",
    "output_cols = ['quality']\n",
    "input_cols,output_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87011c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 7.4  ,  0.7  ,  0.   , ...,  3.51 ,  0.56 ,  9.4  ],\n",
       "        [ 7.8  ,  0.88 ,  0.   , ...,  3.2  ,  0.68 ,  9.8  ],\n",
       "        [ 7.8  ,  0.76 ,  0.04 , ...,  3.26 ,  0.65 ,  9.8  ],\n",
       "        ...,\n",
       "        [ 6.3  ,  0.51 ,  0.13 , ...,  3.42 ,  0.75 , 11.   ],\n",
       "        [ 5.9  ,  0.645,  0.12 , ...,  3.57 ,  0.71 , 10.2  ],\n",
       "        [ 6.   ,  0.31 ,  0.47 , ...,  3.39 ,  0.66 , 11.   ]]),\n",
       " array([[5],\n",
       "        [5],\n",
       "        [5],\n",
       "        ...,\n",
       "        [6],\n",
       "        [5],\n",
       "        [6]], dtype=int64))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dataframe_to_arrays(dataframe):\n",
    "    dataframe1 = dataframe_raw.copy(deep=True)\n",
    "    inputs_array = dataframe1[input_cols].to_numpy()\n",
    "    targets_array = dataframe1[output_cols].to_numpy()\n",
    "    return inputs_array, targets_array\n",
    "\n",
    "inputs_array, targets_array = dataframe_to_arrays(dataframe_raw)\n",
    "inputs_array, targets_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "705fb5b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 7.4000,  0.7000,  0.0000,  ...,  3.5100,  0.5600,  9.4000],\n",
       "         [ 7.8000,  0.8800,  0.0000,  ...,  3.2000,  0.6800,  9.8000],\n",
       "         [ 7.8000,  0.7600,  0.0400,  ...,  3.2600,  0.6500,  9.8000],\n",
       "         ...,\n",
       "         [ 6.3000,  0.5100,  0.1300,  ...,  3.4200,  0.7500, 11.0000],\n",
       "         [ 5.9000,  0.6450,  0.1200,  ...,  3.5700,  0.7100, 10.2000],\n",
       "         [ 6.0000,  0.3100,  0.4700,  ...,  3.3900,  0.6600, 11.0000]]),\n",
       " tensor([[5.],\n",
       "         [5.],\n",
       "         [5.],\n",
       "         ...,\n",
       "         [6.],\n",
       "         [5.],\n",
       "         [6.]]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = torch.from_numpy(inputs_array).type(torch.float)\n",
    "targets = torch.from_numpy(targets_array).type(torch.float)\n",
    "inputs,targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71f14b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.utils.data.dataset.TensorDataset at 0x16db5c32af0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = TensorDataset(inputs, targets)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4f8cd40",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds, val_ds = random_split(dataset, [1300, 299])\n",
    "batch_size=50\n",
    "train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_ds, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56f75067",
   "metadata": {},
   "outputs": [],
   "source": [
    "class WineQuality(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(input_size,output_size) \n",
    "        \n",
    "    def forward(self, xb): \n",
    "        out = self.linear(xb)\n",
    "        return out\n",
    "    \n",
    "    def training_step(self, batch):\n",
    "        inputs, targets = batch \n",
    "        # Generate predictions\n",
    "        out = self(inputs)          \n",
    "        # Calcuate loss\n",
    "        loss = F.l1_loss(out,targets) \n",
    "        return loss\n",
    "    \n",
    "    def validation_step(self, batch):\n",
    "        inputs, targets = batch\n",
    "        # Generate predictions\n",
    "        out = self(inputs)\n",
    "        # Calculate loss\n",
    "        loss = F.l1_loss(out,targets)   \n",
    "        return {'val_loss': loss.detach()}\n",
    "        \n",
    "    def validation_epoch_end(self, outputs):\n",
    "        batch_losses = [x['val_loss'] for x in outputs]\n",
    "        epoch_loss = torch.stack(batch_losses).mean() \n",
    "        return {'val_loss': epoch_loss.item()}\n",
    "    \n",
    "    def epoch_end(self, epoch, result, num_epochs):\n",
    "        # Print result every 100th epoch\n",
    "        if (epoch+1) % 100 == 0 or epoch == num_epochs-1:\n",
    "            print(\"Epoch [{}], val_loss: {:.4f}\".format(epoch+1, result['val_loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57f354ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = len(input_cols)\n",
    "output_size = len(output_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a926cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=WineQuality()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3df1733d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, val_loader):\n",
    "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
    "    return model.validation_epoch_end(outputs)\n",
    "\n",
    "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
    "    history = []\n",
    "    optimizer = opt_func(model.parameters(), lr)\n",
    "    for epoch in range(epochs):\n",
    "        for batch in train_loader:\n",
    "            loss = model.training_step(batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "        result = evaluate(model, val_loader)\n",
    "        model.epoch_end(epoch, result, epochs)\n",
    "        history.append(result)\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ed5f872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [100], val_loss: 5.1117\n",
      "Epoch [200], val_loss: 1.7651\n",
      "Epoch [300], val_loss: 1.4800\n",
      "Epoch [400], val_loss: 1.3942\n",
      "Epoch [500], val_loss: 1.3119\n",
      "Epoch [600], val_loss: 1.2326\n",
      "Epoch [700], val_loss: 1.1571\n",
      "Epoch [800], val_loss: 1.0863\n",
      "Epoch [900], val_loss: 1.0224\n",
      "Epoch [1000], val_loss: 0.9642\n",
      "Epoch [1100], val_loss: 0.9100\n",
      "Epoch [1200], val_loss: 0.8617\n",
      "Epoch [1300], val_loss: 0.8200\n",
      "Epoch [1400], val_loss: 0.7816\n",
      "Epoch [1500], val_loss: 0.7484\n"
     ]
    }
   ],
   "source": [
    "epochs = 1500\n",
    "lr = 1e-6\n",
    "history5 = fit(epochs, lr, model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "413ab394",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_single(input, target, model):\n",
    "    inputs = input.unsqueeze(0)\n",
    "    predictions = model(inputs)\n",
    "    prediction = predictions[0].detach()\n",
    "    #print(\"Input:\", input)\n",
    "    #print(\"Target:\", target)\n",
    "    #print(\"Prediction:\", prediction)\n",
    "    print(\"Target: \", target, \"-----   Prediction: \", prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1ab4522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target:  tensor([6.]) -----   Prediction:  tensor([5.1011])\n",
      "Target:  tensor([6.]) -----   Prediction:  tensor([7.1398])\n",
      "Target:  tensor([5.]) -----   Prediction:  tensor([5.1009])\n",
      "Target:  tensor([6.]) -----   Prediction:  tensor([5.2282])\n",
      "Target:  tensor([5.]) -----   Prediction:  tensor([4.8219])\n",
      "Target:  tensor([6.]) -----   Prediction:  tensor([4.8082])\n",
      "Target:  tensor([7.]) -----   Prediction:  tensor([5.0764])\n",
      "Target:  tensor([5.]) -----   Prediction:  tensor([6.3668])\n",
      "Target:  tensor([6.]) -----   Prediction:  tensor([5.0642])\n",
      "Target:  tensor([5.]) -----   Prediction:  tensor([5.4656])\n"
     ]
    }
   ],
   "source": [
    "#wylosuj 10 próbek predykcji\n",
    "for i in random.sample(range(0, len(val_ds)), 10):\n",
    "    input_, target = val_ds[i]\n",
    "    predict_single(input_, target, model)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0237aad2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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