SVM_IRIS_WITH_MODEL_SELECTION.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM_IRIS_WITH MODEL SELECTION",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKCUbJ7sxk6U"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uj-1MMV3x_zU",
        "outputId": "245dea62-7e55-40eb-a331-e19fdbce3040",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import seaborn as sns \n",
        "df = sns.load_dataset(\"iris\")\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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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>species</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fyit_0hAyYxr",
        "outputId": "2962100f-bc7c-4a2d-bdbc-c3b234c48943",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df[47:52 ]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length  sepal_width  petal_length  petal_width     species\n",
              "47           4.6          3.2           1.4          0.2      setosa\n",
              "48           5.3          3.7           1.5          0.2      setosa\n",
              "49           5.0          3.3           1.4          0.2      setosa\n",
              "50           7.0          3.2           4.7          1.4  versicolor\n",
              "51           6.4          3.2           4.5          1.5  versicolor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FreB-ZASyhhX"
      },
      "source": [
        "x = df.iloc[: , :-1]\n",
        "y = df.iloc[: , -1]\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZR65eaZsy8ZF"
      },
      "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 = 0.3, random_state = 101)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPYvGOk05IW_"
      },
      "source": [
        "# SVM CLASSIFICATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pO8aiGmG3zyG"
      },
      "source": [
        "from sklearn import svm"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Fq3-Cre4Esg",
        "outputId": "a3e3ccd7-f7b1-4c38-fb09-f9aef3b0bd61",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf' , random_state=1, C=30, gamma='auto')\n",
        "classifier.fit(x_train, y_train)\n",
        "classifier.score(x_test,y_test)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trnksD9z5FOL"
      },
      "source": [
        "# K CROSS VALIDATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yICQeart4lv0"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PF4ykf9j5nQd",
        "outputId": "744a03c0-cc20-4741-937b-99649c8c1c2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'),x,y,cv=5)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.        , 1.        , 0.9       , 0.96666667, 1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtXA2S36InT",
        "outputId": "339e73aa-96fe-44b2-cee9-6565f4d60c5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cross_val_score(svm.SVC(kernel='rbf', C=10, gamma='auto'),x,y,cv=5)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.96666667, 1.        , 0.96666667, 0.96666667, 1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuAc0iJi6OC0",
        "outputId": "16b2e3fa-f308-4350-bc0e-e0225f898cb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cross_val_score(svm.SVC(kernel='rbf', C=20, gamma='auto'),x,y,cv=5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.96666667, 1.        , 0.9       , 0.96666667, 1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XU1dIlTX6YDG"
      },
      "source": [
        "### same thing using a loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Nc9_Z_k6QKX",
        "outputId": "6fa201a7-0c31-4251-d655-efdb0e5a9f8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "kernels = ['rbf', 'linear']\n",
        "C = [1,10,20]\n",
        "avg_scores = {}\n",
        "for kval in kernels:\n",
        "  for cval in C:\n",
        "    cv_scores = cross_val_score(svm.SVC(kernel=kval,C=cval, gamma='auto'),x,y,cv=5)\n",
        "    avg_scores[kval+'_'+str(cval)]=np.average(cv_scores)\n",
        "avg_scores"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'linear_1': 0.9800000000000001,\n",
              " 'linear_10': 0.9733333333333334,\n",
              " 'linear_20': 0.9666666666666666,\n",
              " 'rbf_1': 0.9800000000000001,\n",
              " 'rbf_10': 0.9800000000000001,\n",
              " 'rbf_20': 0.9666666666666668}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZ39J0pF7ZV5"
      },
      "source": [
        "# GRID SEARCH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "567qHLHX7EZq"
      },
      "source": [
        "from sklearn.model_selection import  GridSearchCV\n",
        "parameters = {'kernel':('linear', 'rbf'), 'C':[1, 20]}\n",
        "svc = svm.SVC()\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZFFGA3q8noS"
      },
      "source": [
        "clf = GridSearchCV(svc, parameters)\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PZpKqBCrWUV",
        "outputId": "63b2d3e6-44f6-49d6-b2cd-5927da5d28bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "clf.fit(x_train,y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
              "                           class_weight=None, coef0=0.0,\n",
              "                           decision_function_shape='ovr', degree=3,\n",
              "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
              "                           probability=False, random_state=None, shrinking=True,\n",
              "                           tol=0.001, verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'C': [1, 20], 'kernel': ('linear', 'rbf')},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlyqvqC_sPLr",
        "outputId": "ab0edfec-2642-49e7-862a-6fdabd1695b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(clf.best_params_)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 1, 'kernel': 'linear'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAh8gUtmsfEJ"
      },
      "source": [
        "done bro\n"
      ]
    }
  ]
}