{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "63528a79-842d-44ec-9109-29df1621cef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the basic imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "# but also reporting on the model\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "# and a couple of utilities\n",
    "from sklearn.utils import resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d106a41b-e75c-4ca0-96e1-0e5b117992bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data, extract just the features, and just the labels\n",
    "wine_data = pd.read_csv(\"./WineQT.csv\", delimiter=\",\")\n",
    "wine_features = wine_data.drop(\"quality\", axis=1).drop(\"Id\", axis=1)\n",
    "wine_labels = np.ravel(wine_data['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c446ebbc-e0a9-4322-a713-8c839c244c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into train and test subsets\n",
    "# note, while it may be tempting to get creative with variable names, such as\n",
    "# features_train, features_test, labels_train, labels_test...\n",
    "# it's WAY TOO MUCH typing, and most examples use x for features (as in, input\n",
    "# data) and y for labels (as in, result)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(wine_features, wine_labels, test_size=0.2, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "65029b18-57b9-4829-b2fa-a51447b2188d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 914 test: 229\n"
     ]
    }
   ],
   "source": [
    "print(\"train:\", len(x_train), \"test:\", len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cbb0be13-83c5-4751-9061-14ed9632a36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample:       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
      "309             7.0              0.62         0.18             1.5      0.062   \n",
      "258            12.5              0.46         0.63             2.0      0.071   \n",
      "1122            6.4              0.36         0.53             2.2      0.230   \n",
      "378            10.0              0.48         0.24             2.7      0.102   \n",
      "712             8.0              0.58         0.16             2.0      0.120   \n",
      "\n",
      "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
      "309                   7.0                  50.0  0.99510  3.08       0.60   \n",
      "258                   6.0                  15.0  0.99880  2.99       0.87   \n",
      "1122                 19.0                  35.0  0.99340  3.37       0.93   \n",
      "378                  13.0                  32.0  1.00000  3.28       0.56   \n",
      "712                   3.0                   7.0  0.99454  3.22       0.58   \n",
      "\n",
      "      alcohol  \n",
      "309       9.3  \n",
      "258      10.2  \n",
      "1122     12.4  \n",
      "378      10.0  \n",
      "712      11.2  \n"
     ]
    }
   ],
   "source": [
    "print(\"sample:\", resample(x_train, n_samples=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e3aaa169-e086-4a19-865d-557c0c2aa210",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise the data (meaning spread it ALL out on a scale between a..b)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler().fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1626bb97-75b9-4685-801e-733d624c701b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalised sample: [[ 0.03113943  1.24584416 -0.83529885 -0.48591162  0.09791733  0.05205639\n",
      "   0.53090333 -0.12485948 -0.76774246  1.02627829 -0.80008014]\n",
      " [ 0.31293584 -1.00062031  0.65403462  0.86207888  0.55581673 -0.34777171\n",
      "  -0.684197   -0.64140864 -0.5136124   0.27501217  1.41492055]\n",
      " [-0.53245339  0.42119264 -0.06495395 -0.3273245  -0.06234746  0.15201342\n",
      "   1.40328305 -0.29874731 -0.13241733 -0.60146497 -0.61549675]\n",
      " [-0.41973482 -0.14753254  0.08911503 -0.16873739  0.00633745 -1.0474709\n",
      "  -0.99576119 -0.881783    0.05818021 -0.22583191  0.95346207]\n",
      " [-0.30701626  0.67711897 -1.40021845  2.36865649  0.00633745 -1.0474709\n",
      "  -0.87113551  0.94915362  0.56644031 -0.66407048 -0.80008014]]\n"
     ]
    }
   ],
   "source": [
    "print(\"normalised sample:\", resample(x_train, n_samples=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a1ee2d09-caed-4d39-ab51-cc27504753ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** TESTING C-Support Vector Classification ****\n",
      "x: predictions, y: labels\n",
      "    0   1   2   3   4   5   6   7   8   9   10\n",
      "0    0   0   0   0   0   0   0   0   0   0   0\n",
      "1    0   0   0   0   0   0   0   0   0   0   0\n",
      "2    0   0   0   0   0   0   0   0   0   0   0\n",
      "3    0   0   0   0   0   2   0   0   0   0   0\n",
      "4    0   0   0   0   0   6   4   1   0   0   0\n",
      "5    0   0   0   0   0  78  21   0   0   0   0\n",
      "6    0   0   0   0   0  29  50   0   0   0   0\n",
      "7    0   0   0   0   0   1  27   9   0   0   0\n",
      "8    0   0   0   0   0   0   1   0   0   0   0\n",
      "9    0   0   0   0   0   0   0   0   0   0   0\n",
      "10   0   0   0   0   0   0   0   0   0   0   0\n"
     ]
    }
   ],
   "source": [
    "# train the SVC model\n",
    "print(\"**** TESTING C-Support Vector Classification ****\")\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_model = SVC()\n",
    "svc_model.fit(x_train, y_train)\n",
    "\n",
    "# now test the fitness with the test subset\n",
    "svc_y_predict = svc_model.predict(x_test)\n",
    "\n",
    "# visualise it\n",
    "print(\"x: predictions, y: labels\")\n",
    "svc_cm = np.array(confusion_matrix(y_test, svc_y_predict, labels=[0,1,2,3,4,5,6,7,8,9,10]))\n",
    "svc_conf_matrix = pd.DataFrame(svc_cm)\n",
    "print(svc_conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "82e77e4a-c700-426c-98a7-8f338bf5f393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise the SVC model in a nice picture\n",
    "sns.heatmap(svc_conf_matrix, annot=True, fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cdf2c8ae-d024-4687-b1df-23804f3d9816",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** TESTING Nu-Support Vector Classification ****\n",
      "x: predictions, y: labels\n",
      "    0   1   2   3   4   5   6   7   8   9   10\n",
      "0    0   0   0   0   0   0   0   0   0   0   0\n",
      "1    0   0   0   0   0   0   0   0   0   0   0\n",
      "2    0   0   0   0   0   0   0   0   0   0   0\n",
      "3    0   0   0   0   0   2   0   0   0   0   0\n",
      "4    0   0   0   0   0   6   4   1   0   0   0\n",
      "5    0   0   0   0   0  78  21   0   0   0   0\n",
      "6    0   0   0   0   0  29  50   0   0   0   0\n",
      "7    0   0   0   0   0   1  27   9   0   0   0\n",
      "8    0   0   0   0   0   0   1   0   0   0   0\n",
      "9    0   0   0   0   0   0   0   0   0   0   0\n",
      "10   0   0   0   0   0   0   0   0   0   0   0\n"
     ]
    }
   ],
   "source": [
    "# train the NuSVC model\n",
    "print(\"**** TESTING Nu-Support Vector Classification ****\")\n",
    "\n",
    "from sklearn.svm import NuSVC\n",
    "\n",
    "nusvc_model = NuSVC(nu=0.015)\n",
    "nusvc_model.fit(x_train, y_train)\n",
    "\n",
    "# now test the fitness with the test subset\n",
    "nusvc_y_predict = svc_model.predict(x_test)\n",
    "\n",
    "# visualise it\n",
    "print(\"x: predictions, y: labels\")\n",
    "nu_cm = np.array(confusion_matrix(y_test, nusvc_y_predict, labels=[0,1,2,3,4,5,6,7,8,9,10]))\n",
    "nu_conf_matrix = pd.DataFrame(nu_cm)\n",
    "print(nu_conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4ef4f484-5664-45dd-a5aa-2909276cedc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise the NuSVC model in a nice picture\n",
    "sns.heatmap(nu_conf_matrix, annot=True, fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b38cbca4-7cf7-421b-889d-5814b81e6f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1ff5d2c3-e979-434f-a6ac-355c68832952",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** TESTING C-Support Vector Regression ****\n"
     ]
    }
   ],
   "source": [
    "# train the SVR model\n",
    "print(\"**** TESTING C-Support Vector Regression ****\")\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "svr_model = SVR(kernel=\"rbf\")\n",
    "svr_model.fit(x_train, y_train)\n",
    "\n",
    "# now test the fitness with the test subset\n",
    "svr_y_predict = svr_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4a339cfd-fcbb-4c94-9827-b77b2d470ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x30c74aad0>"
      ]
     },
     "execution_count": 74,
     "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": [
    "# visualise it\n",
    "plt.scatter(range(0, len(y_test)), y_test, color = 'blue')\n",
    "plt.scatter(range(0, len(y_test)), svr_y_predict, color = 'green')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d490a2da-0ee2-4385-b094-94e8f7fc2f2f",
   "metadata": {},
   "source": [
    "What else is there to do?\n",
    "\n",
    "* improve visualisations\n",
    "* experiment with model settings\n",
    "* try different train/test sizes"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sklearn-16",
   "language": "python",
   "name": "sklearn-16"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}