svc_example.ipynb

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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Galtan-dev/Learning/blob/master/svc_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thPS9MReWYeK"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "NOJE87C_WYDi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "import itertools\n",
        "\n",
        "# sklearn\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score as accuracy_score\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1q1WDH8WqLK"
      },
      "source": [
        "# Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NbR1I3zGSIAU",
        "outputId": "1d51ae9e-e5ad-41df-8b63-57b1be324fb2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 159 entries, 0 to 158\n",
            "Data columns (total 7 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Species  159 non-null    object \n",
            " 1   Weight   159 non-null    float64\n",
            " 2   Length1  159 non-null    float64\n",
            " 3   Length2  159 non-null    float64\n",
            " 4   Length3  159 non-null    float64\n",
            " 5   Height   159 non-null    float64\n",
            " 6   Width    159 non-null    float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 8.8+ KB\n"
          ]
        }
      ],
      "source": [
        "# Data and columns\n",
        "data = pd.read_html(\"https://web.vscht.cz/~steinbaj/python_ml/fish.htm\")[0]\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Information on the numerical data\n",
        "data.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Weight     Length1     Length2     Length3      Height       Width\n",
              "count   159.000000  159.000000  159.000000  159.000000  159.000000  159.000000\n",
              "mean    398.326415   26.247170   28.415723   31.227044    8.970994    4.417486\n",
              "std     357.978317    9.996441   10.716328   11.610246    4.286208    1.685804\n",
              "min       0.000000    7.500000    8.400000    8.800000    1.728400    1.047600\n",
              "25%     120.000000   19.050000   21.000000   23.150000    5.944800    3.385650\n",
              "50%     273.000000   25.200000   27.300000   29.400000    7.786000    4.248500\n",
              "75%     650.000000   32.700000   35.500000   39.650000   12.365900    5.584500\n",
              "max    1650.000000   59.000000   63.400000   68.000000   18.957000    8.142000"
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              "      <th>Weight</th>\n",
              "      <th>Length1</th>\n",
              "      <th>Length2</th>\n",
              "      <th>Length3</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>398.326415</td>\n",
              "      <td>26.247170</td>\n",
              "      <td>28.415723</td>\n",
              "      <td>31.227044</td>\n",
              "      <td>8.970994</td>\n",
              "      <td>4.417486</td>\n",
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              "      <th>std</th>\n",
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              "      <td>9.996441</td>\n",
              "      <td>10.716328</td>\n",
              "      <td>11.610246</td>\n",
              "      <td>4.286208</td>\n",
              "      <td>1.685804</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>8.800000</td>\n",
              "      <td>1.728400</td>\n",
              "      <td>1.047600</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>120.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>23.150000</td>\n",
              "      <td>5.944800</td>\n",
              "      <td>3.385650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>273.000000</td>\n",
              "      <td>25.200000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>29.400000</td>\n",
              "      <td>7.786000</td>\n",
              "      <td>4.248500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>650.000000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>39.650000</td>\n",
              "      <td>12.365900</td>\n",
              "      <td>5.584500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1650.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>63.400000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>18.957000</td>\n",
              "      <td>8.142000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 523.52371054653,\n        \"min\": 0.0,\n        \"max\": 1650.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          398.3264150943396,\n          273.0,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.76394993452418,\n        \"min\": 7.5,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          26.247169811320756,\n          25.2,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.4240506754142,\n        \"min\": 8.4,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.415723270440253,\n          27.3,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.09863853684949,\n        \"min\": 8.8,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.227044025157234,\n          29.4,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.44397133500888,\n        \"min\": 1.7284,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.970993710691824,\n          7.786,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.81966876572199,\n        \"min\": 1.0476,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.417485534591195,\n          4.2485,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Information on the features\n",
        "data.groupby(\"Species\")[\"Species\"].count()"
      ],
      "metadata": {
        "id": "DnepcEpxyc_d",
        "outputId": "632723eb-ad71-4f2e-e6b4-597ab0071488",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species\n",
              "Bream        35\n",
              "Parkki       11\n",
              "Perch        56\n",
              "Pike         17\n",
              "Roach        20\n",
              "Smelt        14\n",
              "Whitefish     6\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QhwPJyDrSQw7"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "8Dy5Yy_wX0_K"
      },
      "outputs": [],
      "source": [
        "# Columns for the features and output\n",
        "columns = list(data.columns)\n",
        "y_col = columns[0]\n",
        "x_cols = columns[1:]\n",
        "# Scaler\n",
        "scale = MinMaxScaler()\n",
        "# Scaling features and defining the arrays with features and output\n",
        "x_scaled = scale.fit_transform(data[x_cols].to_numpy())\n",
        "y = data[y_col].to_numpy()\n",
        "# Split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, y, test_size=0.4,\n",
        "                                                    random_state=5, stratify=y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dox6pQUDZq-y"
      },
      "source": [
        "# Fitting an SVM classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "7sktHpByZuO6",
        "outputId": "a409d727-6053-4e61-fe81-f5c3d7cbe07d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.78125"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# SVM classifier\n",
        "clf = svm.SVC()\n",
        "# Training\n",
        "clf.fit(x_train, y_train)\n",
        "# Prediction for validation\n",
        "y_pred = clf.predict(x_test)\n",
        "accuracy_score(y_pred, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJIlepj_OB3L"
      },
      "source": [
        "# Comparison between different kernel transformations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Y8TT9cJIc_Uh",
        "outputId": "fe457b89-b618-4976-f55b-5c4dc84237d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done!\n"
          ]
        }
      ],
      "source": [
        "# Kernels and regularization params\n",
        "kernels = [\"linear\", \"poly\", \"rbf\", \"sigmoid\"]  # rbf radio based function\n",
        "# regularizační parametry\n",
        "C_params = np.array([0.1, 1, 5, 10, 50, 100, 500])\n",
        "# gama určije jak moc se má funkce kenelovská omotat kolem dat, zůžení hranice, nízké gama vede k regularizaci, nízké k přetrénování\n",
        "gammas = [0.1, 1, 10, 100, 1000, 10000]\n",
        "# Table for saving results\n",
        "df_accuracy = pd.DataFrame({\"kernel\":[], \"C\":[], \"gamma\":[], \"fit_acc\": [],\n",
        "                            \"validation_acc\":[]})\n",
        "# Checking accuracy of different combinations\n",
        "for kernel, C, gamma in itertools.product(kernels, C_params, gammas):\n",
        "  # vytvořím klasifikátor\n",
        "    clf = svm.SVC(kernel=kernel, C=C, gamma=gamma)\n",
        "    # nafituju data\n",
        "    clf.fit(x_train, y_train)\n",
        "    # uložim si perdikce\n",
        "    y_pred_train = clf.predict(x_train)\n",
        "    y_pred_test = clf.predict(x_test)\n",
        "    # do tabulky uložim výsledky pomocí lokátoru\n",
        "    df_accuracy.loc[df_accuracy.shape[0]] = \\\n",
        "     [kernel, C, gamma, accuracy_score(y_pred_train, y_train),\n",
        "      accuracy_score(y_pred_test, y_test)]\n",
        "print(\"Done!\")\n",
        "# při spištení to řve že tam některé věci nejsou ale asi v pohodě"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "DSZ3DpiROBI6",
        "outputId": "c3b89e4b-98f4-45d2-8eff-b42f6103248d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "kernel                poly\n",
              "C                      0.1\n",
              "gamma               1000.0\n",
              "fit_acc                1.0\n",
              "validation_acc    0.984375\n",
              "Name: 46, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Finding the best score based on the accuracy\n",
        "best_params = df_accuracy[df_accuracy.validation_acc ==\n",
        "                          max(df_accuracy.validation_acc)].iloc[0]\n",
        "best_params"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance of the classifier with the best parameters\n",
        "clf = svm.SVC(kernel=best_params[\"kernel\"], C=best_params[\"C\"],\n",
        "              gamma=best_params[\"gamma\"])\n",
        "# nafitujem na trenovacich datech\n",
        "clf.fit(x_train, y_train)\n",
        "# spočitame si predikci testovacích dat\n",
        "y_pred = clf.predict(x_test)\n",
        "# udělame si matici záměn\n",
        "cm = ConfusionMatrixDisplay(confusion_matrix = confusion_matrix(y_test, y_pred),\n",
        "                            display_labels = clf.classes_)\n",
        "cm.plot(colorbar=False)"
      ],
      "metadata": {
        "id": "Hd5OeI5y0YgZ",
        "outputId": "58f6bb33-0a2c-460b-821f-d8f25bece58d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x79afeafd8c40>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Overfitting a classifier"
      ],
      "metadata": {
        "id": "TYifo_s54rtz"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GU6WKbZOBV-"
      },
      "source": [
        "Vliv velikosti **gamma** parametru na přesnost fitu a přesnost validace.\n",
        "Příliš nízké hodnoty gamma způsobí ztrátu \"tvaru\" hyperplane, takže chování klasifikátoru se bude blížit lineárnímu.\n",
        "Příliš vysoké hodnoty gamma zvyšují vliv okolních bodů na tvar hyperplane, což může vést k overfittingu."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gammas = [0.1, 1, 10, 100, 1000, 10000]\n",
        "for gamma in gammas:\n",
        "  # udělame si undentifikator kam uložime gamu a nastavíme kernel\n",
        "  clf = svm.SVC(kernel=\"rbf\", gamma=gamma)\n",
        "  # nafitujem\n",
        "  clf.fit(x_train, y_train)\n",
        "  y_pred_train = clf.predict(x_train)\n",
        "  y_pred_test = clf.predict(x_test)\n",
        "  print(f\"Kernel: rbf. Gamma parameter: {gamma:.0e}.\")\n",
        "  print(f\"\\t - Training Accuracy: {accuracy_score(y_pred_train, y_train):.2%}.\"\n",
        "    f\"Validation accuracy: {accuracy_score(y_pred_test, y_test):.2%}.\")"
      ],
      "metadata": {
        "id": "FO4k7w_t5B-6",
        "outputId": "f7dcf8ff-a1b2-4636-8dce-f34f17b8508a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel: rbf. Gamma parameter: 1e-01.\n",
            "\t - Training Accuracy: 46.32%.Validation accuracy: 48.44%.\n",
            "Kernel: rbf. Gamma parameter: 1e+00.\n",
            "\t - Training Accuracy: 71.58%.Validation accuracy: 76.56%.\n",
            "Kernel: rbf. Gamma parameter: 1e+01.\n",
            "\t - Training Accuracy: 81.05%.Validation accuracy: 81.25%.\n",
            "Kernel: rbf. Gamma parameter: 1e+02.\n",
            "\t - Training Accuracy: 84.21%.Validation accuracy: 78.12%.\n",
            "Kernel: rbf. Gamma parameter: 1e+03.\n",
            "\t - Training Accuracy: 97.89%.Validation accuracy: 46.88%.\n",
            "Kernel: rbf. Gamma parameter: 1e+04.\n",
            "\t - Training Accuracy: 100.00%.Validation accuracy: 37.50%.\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}