kernel_pca.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunnyy6/first-repo/blob/main/kernel_pca.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQ3syspj_rKn"
      },
      "source": [
        "# Kernel PCA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJGl9TcT_skx"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNEgrGwd_29D"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hyp1gza1_6qX"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPLTDBVI__ZQ"
      },
      "source": [
        "dataset = pd.read_csv('Wine.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bUhSHktAcOe"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7hGLt1qAced"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wrHODfJAEiI"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-UCD7ezAJG2"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3i3lRiwASAX"
      },
      "source": [
        "## Applying Kernel PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAi_sSw9AVzI"
      },
      "source": [
        "from sklearn.decomposition import KernelPCA\n",
        "kpca = KernelPCA(n_components = 2, kernel = 'rbf')\n",
        "X_train = kpca.fit_transform(X_train)\n",
        "X_test = kpca.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBx16JVLAuel"
      },
      "source": [
        "## Training the Logistic Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDQahsqTAy44",
        "outputId": "f4652d65-17b2-4471-e60c-12de403a4967",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTck416XBPnD"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LO7H5LsBS1b",
        "outputId": "16cee330-be69-4838-b340-1f64a7f1e46b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[14  0  0]\n",
            " [ 0 16  0]\n",
            " [ 0  0  6]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6pZMBrUBXwb"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FK_LpLOeBdQ4",
        "outputId": "37225feb-9c48-437f-adcf-51e118f5ed4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
        "plt.title('Logistic Regression (Training set)')\n",
        "plt.xlabel('PC1')\n",
        "plt.ylabel('PC2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Dbzx_KqBguX"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk07XbUHBl0W",
        "outputId": "4c02d4a8-ed35-4ecb-b6b8-e18879457d38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
        "plt.title('Logistic Regression (Test set)')\n",
        "plt.xlabel('PC1')\n",
        "plt.ylabel('PC2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}