KNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VAu8ecZHwzxv"
      },
      "source": [
        "Repeat KNN algorithm process with stratified sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4adJP_wRswu"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy import linalg\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing \n",
        "from sklearn.feature_selection import chi2, SelectKBest\n",
        "from sklearn.naive_bayes import GaussianNB, ComplementNB, MultinomialNB, BernoulliNB, CategoricalNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from matplotlib.pyplot import figure"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaPxSIsexERf"
      },
      "source": [
        "#data imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def data_cleaner(df):\n",
        "    data = df.copy()\n",
        "    #remove the id, partlybad columns\n",
        "    data.drop([\"id\",\"partlybad\"],1,inplace=True)\n",
        "    #set new index using the date column\n",
        "    data.set_index([\"date\"],inplace=True)\n",
        "    #add binary 'class2' col for event/non-event\n",
        "    data[\"class2\"] = np.where(data[\"class4\"] == \"nonevent\", \"nonevent\",\"event\")\n",
        "    #arrange to put class2 col at the front\n",
        "    cols = ['class2'] + [col for col in data if col != 'class2']\n",
        "    data = data[cols]\n",
        "    return data\n",
        "\n",
        "##### New way to generate the data\n",
        "\n",
        "def data_generator(df):\n",
        "    '''\n",
        "    input:\n",
        "        df: dataframe to split stratified by variable class\n",
        "        class: string with variable to use as response and as variable to stratify by\n",
        "    output:\n",
        "        X_train, y_train: variables and response 60% data \n",
        "        X_val, y_val: variables and response 20% data \n",
        "        X_trainval, y_trainval: variables and response 80% data (for cross validation)\n",
        "        X_test, y_test: variables and response 20% data \n",
        "    '''\n",
        "    \n",
        "    X = df.drop([\"class2\",\"class4\"], axis=1)\n",
        "    y = df['class4']\n",
        "    \n",
        "    # 60%, 20%, 20%\n",
        "    # Generate TrainVal 80% and test 20% \n",
        "    X_trainval, X_test, y_trainval, y_test = train_test_split( X, y, test_size=0.20, random_state=42, stratify=y)\n",
        "    \n",
        "    # Furthermore split train into Train and Val\n",
        "    X_train, X_val, y_train, y_val= train_test_split( X_trainval, y_trainval, test_size=0.25, random_state=42, stratify=y_trainval)\n",
        "    \n",
        "    return X_train, X_val, X_trainval, X_test, y_train, y_val, y_trainval, y_test\n",
        "\n",
        "\n",
        "def convert_binary(y):\n",
        "    '''\n",
        "    Parameters\n",
        "    ----------\n",
        "    y : repsonse variable with 4 classes\n",
        "    Returns\n",
        "    -------\n",
        "    y : reponse variable 2 classes\n",
        "    '''\n",
        "    y_bin = np.where(y == \"nonevent\", \"nonevent\",\"event\")\n",
        "    return y_bin\n",
        "\n",
        "url = \"https://raw.githubusercontent.com/williwilliams3/TermProjectIML/master/data/train.csv\"\n",
        "df = pd.read_csv(url)\n",
        "df = data_cleaner(df)\n",
        "\n",
        "\n",
        "X_train, X_val, X_trainval, X_test, y_train, y_val, y_trainval, y_test = data_generator(df)\n",
        "\n",
        "# If needed conversion from 4 classes to binary classes\n",
        "y_train_bin = convert_binary(y_train)\n",
        "y_val_bin = convert_binary(y_val)\n",
        "y_trainval_bin = convert_binary(y_trainval)\n",
        "y_test_bin = convert_binary(y_test)\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "id": "NTrDLpC2RvAa",
        "outputId": "f5f3f2cc-9794-4409-b89e-cdd536dc89c9"
      },
      "source": [
        "#unscaled data\n",
        "#set up for storing error rate\n",
        "pred_rate = np.zeros(40)\n",
        "\n",
        "#find error rate on the validation set \n",
        "for k in range(1,40):\n",
        "    #run algorithm\n",
        "    knn = KNeighborsClassifier(n_neighbors=k) \n",
        "    #fit to training data\n",
        "    knn.fit(X_train, y_train_bin) \n",
        "    #predicted values for class2 on validation set\n",
        "    y_pred_val_bin = knn.predict(X_val)\n",
        "    #compare predictions with actual results\n",
        "    pred_rate[k] = metrics.accuracy_score(y_val_bin, y_pred_val_bin)\n",
        "    print('rate of correct predictions for k = %i' %k,pred_rate[k])\n",
        "\n",
        "f, ax = plt.subplots(1)\n",
        "plt.plot(pred_rate,color=\"green\")\n",
        "ax.set_xlim(1, 40)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rate of correct predictions for k = 1 0.9038461538461539\n",
            "rate of correct predictions for k = 2 0.7884615384615384\n",
            "rate of correct predictions for k = 3 0.7692307692307693\n",
            "rate of correct predictions for k = 4 0.7692307692307693\n",
            "rate of correct predictions for k = 5 0.8461538461538461\n",
            "rate of correct predictions for k = 6 0.8076923076923077\n",
            "rate of correct predictions for k = 7 0.8461538461538461\n",
            "rate of correct predictions for k = 8 0.8269230769230769\n",
            "rate of correct predictions for k = 9 0.7884615384615384\n",
            "rate of correct predictions for k = 10 0.8269230769230769\n",
            "rate of correct predictions for k = 11 0.7884615384615384\n",
            "rate of correct predictions for k = 12 0.8076923076923077\n",
            "rate of correct predictions for k = 13 0.8076923076923077\n",
            "rate of correct predictions for k = 14 0.7884615384615384\n",
            "rate of correct predictions for k = 15 0.8076923076923077\n",
            "rate of correct predictions for k = 16 0.8076923076923077\n",
            "rate of correct predictions for k = 17 0.8461538461538461\n",
            "rate of correct predictions for k = 18 0.8269230769230769\n",
            "rate of correct predictions for k = 19 0.8269230769230769\n",
            "rate of correct predictions for k = 20 0.8076923076923077\n",
            "rate of correct predictions for k = 21 0.8269230769230769\n",
            "rate of correct predictions for k = 22 0.8269230769230769\n",
            "rate of correct predictions for k = 23 0.8076923076923077\n",
            "rate of correct predictions for k = 24 0.8076923076923077\n",
            "rate of correct predictions for k = 25 0.8076923076923077\n",
            "rate of correct predictions for k = 26 0.7692307692307693\n",
            "rate of correct predictions for k = 27 0.8076923076923077\n",
            "rate of correct predictions for k = 28 0.8076923076923077\n",
            "rate of correct predictions for k = 29 0.7884615384615384\n",
            "rate of correct predictions for k = 30 0.7884615384615384\n",
            "rate of correct predictions for k = 31 0.7884615384615384\n",
            "rate of correct predictions for k = 32 0.8076923076923077\n",
            "rate of correct predictions for k = 33 0.7884615384615384\n",
            "rate of correct predictions for k = 34 0.7884615384615384\n",
            "rate of correct predictions for k = 35 0.7884615384615384\n",
            "rate of correct predictions for k = 36 0.7884615384615384\n",
            "rate of correct predictions for k = 37 0.7884615384615384\n",
            "rate of correct predictions for k = 38 0.7884615384615384\n",
            "rate of correct predictions for k = 39 0.7884615384615384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 40.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "KiXJ1z9JSmKm",
        "outputId": "e02a2c93-faae-4dad-96a8-371ca014a56d"
      },
      "source": [
        "#k=3 \n",
        "#run on test set \n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=3) \n",
        "#fit with optimum k to training data\n",
        "knn.fit(X_train, y_train_bin) \n",
        "\n",
        "y_pred_test = knn.predict(X_test)\n",
        "\n",
        "print('multiclass accuracy for 3-NN on test set=', metrics.accuracy_score(y_test_bin, y_pred_test))\n",
        "\n",
        "#map predictions on heat map\n",
        "confusion_matrix = pd.crosstab(y_test_bin, y_pred_test, rownames=['Actual'], colnames=['Predicted'])\n",
        "sns.heatmap(confusion_matrix, annot=True)\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "multiclass accuracy for 3-NN on test set= 0.75\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc28acb5b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "id": "C384sEBqsmB3",
        "outputId": "14b6728b-54b2-468d-f72a-f8b3e183a098"
      },
      "source": [
        "#multiclass unscaled\n",
        "#set up for storing error rate\n",
        "pred_rate = np.zeros(40)\n",
        "\n",
        "#find error rate on the validation set \n",
        "for k in range(1,40):\n",
        "    #run algorithm\n",
        "    knn = KNeighborsClassifier(n_neighbors=k) \n",
        "    #fit to training data\n",
        "    knn.fit(X_train, y_train) \n",
        "    #predicted values for class2 on validation set\n",
        "    y_pred_val = knn.predict(X_val)\n",
        "    #compare predictions with actual results\n",
        "    pred_rate[k] = metrics.accuracy_score(y_val, y_pred_val)\n",
        "    print('rate of correct predictions for k = %i' %k,pred_rate[k])\n",
        "\n",
        "f, ax = plt.subplots(1)\n",
        "plt.plot(pred_rate,color=\"fuchsia\")\n",
        "ax.set_xlim(1, 40)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rate of correct predictions for k = 1 0.6153846153846154\n",
            "rate of correct predictions for k = 2 0.5192307692307693\n",
            "rate of correct predictions for k = 3 0.5769230769230769\n",
            "rate of correct predictions for k = 4 0.6153846153846154\n",
            "rate of correct predictions for k = 5 0.5961538461538461\n",
            "rate of correct predictions for k = 6 0.6153846153846154\n",
            "rate of correct predictions for k = 7 0.5769230769230769\n",
            "rate of correct predictions for k = 8 0.6153846153846154\n",
            "rate of correct predictions for k = 9 0.6538461538461539\n",
            "rate of correct predictions for k = 10 0.6923076923076923\n",
            "rate of correct predictions for k = 11 0.6153846153846154\n",
            "rate of correct predictions for k = 12 0.5961538461538461\n",
            "rate of correct predictions for k = 13 0.5961538461538461\n",
            "rate of correct predictions for k = 14 0.5769230769230769\n",
            "rate of correct predictions for k = 15 0.5769230769230769\n",
            "rate of correct predictions for k = 16 0.5576923076923077\n",
            "rate of correct predictions for k = 17 0.5576923076923077\n",
            "rate of correct predictions for k = 18 0.5384615384615384\n",
            "rate of correct predictions for k = 19 0.5384615384615384\n",
            "rate of correct predictions for k = 20 0.5384615384615384\n",
            "rate of correct predictions for k = 21 0.5576923076923077\n",
            "rate of correct predictions for k = 22 0.5769230769230769\n",
            "rate of correct predictions for k = 23 0.5576923076923077\n",
            "rate of correct predictions for k = 24 0.5384615384615384\n",
            "rate of correct predictions for k = 25 0.5576923076923077\n",
            "rate of correct predictions for k = 26 0.5384615384615384\n",
            "rate of correct predictions for k = 27 0.5576923076923077\n",
            "rate of correct predictions for k = 28 0.5769230769230769\n",
            "rate of correct predictions for k = 29 0.5576923076923077\n",
            "rate of correct predictions for k = 30 0.5384615384615384\n",
            "rate of correct predictions for k = 31 0.5576923076923077\n",
            "rate of correct predictions for k = 32 0.5384615384615384\n",
            "rate of correct predictions for k = 33 0.5576923076923077\n",
            "rate of correct predictions for k = 34 0.5192307692307693\n",
            "rate of correct predictions for k = 35 0.5961538461538461\n",
            "rate of correct predictions for k = 36 0.5769230769230769\n",
            "rate of correct predictions for k = 37 0.5769230769230769\n",
            "rate of correct predictions for k = 38 0.5769230769230769\n",
            "rate of correct predictions for k = 39 0.5576923076923077\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 40.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "xmwJiG5Xs72v",
        "outputId": "9dd90365-14e9-476f-ceb3-12b7f8f390af"
      },
      "source": [
        "#k=10 gives lowest error\n",
        "#run on test set\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=10) \n",
        "#fit with optimum k to training data\n",
        "knn.fit(X_train, y_train) \n",
        "\n",
        "y_pred_test = knn.predict(X_test)\n",
        "\n",
        "print('multiclass accuracy for 10-NN on test set=', metrics.accuracy_score(y_test, y_pred_test))\n",
        "\n",
        "#map predictions on heat map\n",
        "confusion_matrix = pd.crosstab(y_test, y_pred_test, rownames=['Actual'], colnames=['Predicted'])\n",
        "sns.heatmap(confusion_matrix, annot=True)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "multiclass accuracy for 10-NN on test set= 0.5769230769230769\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc28ac547b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "id": "pPGTJ8K_T5Us",
        "outputId": "bf950788-f684-4eaf-bde6-5edd7d2fec23"
      },
      "source": [
        "#normalised x data\n",
        "#normalise 0to1\n",
        "def normalize_0to1(X):\n",
        "    # Normalize X values to be between 0 and 1\n",
        "    x = X.values\n",
        "    min_max_scaler = preprocessing.MinMaxScaler()\n",
        "    x_scaled = min_max_scaler.fit_transform(x)\n",
        "    return pd.DataFrame(x_scaled)\n",
        "\n",
        "X_train_norm = normalize_0to1(X_train)\n",
        "X_val_norm = normalize_0to1(X_val)\n",
        "X_test_norm = normalize_0to1(X_test)\n",
        "\n",
        "#repeat \n",
        "#set up for storing error rate\n",
        "pred_rate = np.zeros(40)\n",
        "\n",
        "#find error rate on the validation set \n",
        "for k in range(1,40):\n",
        "    #run algorithm\n",
        "    knn = KNeighborsClassifier(n_neighbors=k) \n",
        "    #fit to training data\n",
        "    knn.fit(X_train_norm, y_train) \n",
        "    #predicted values for class2 on validation set\n",
        "    y_pred_val = knn.predict(X_val_norm)\n",
        "    #compare predictions with actual results\n",
        "    pred_rate[k] = metrics.accuracy_score(y_val, y_pred_val)\n",
        "    print('rate of correct predictions for k = %i' %k,pred_rate[k])\n",
        "\n",
        "f, ax = plt.subplots(1)\n",
        "plt.plot(pred_rate,color=\"blue\")\n",
        "ax.set_xlim(1, 40)\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rate of correct predictions for k = 1 0.5576923076923077\n",
            "rate of correct predictions for k = 2 0.4807692307692308\n",
            "rate of correct predictions for k = 3 0.5961538461538461\n",
            "rate of correct predictions for k = 4 0.6153846153846154\n",
            "rate of correct predictions for k = 5 0.6153846153846154\n",
            "rate of correct predictions for k = 6 0.5961538461538461\n",
            "rate of correct predictions for k = 7 0.6346153846153846\n",
            "rate of correct predictions for k = 8 0.5961538461538461\n",
            "rate of correct predictions for k = 9 0.6730769230769231\n",
            "rate of correct predictions for k = 10 0.5769230769230769\n",
            "rate of correct predictions for k = 11 0.5961538461538461\n",
            "rate of correct predictions for k = 12 0.5961538461538461\n",
            "rate of correct predictions for k = 13 0.5961538461538461\n",
            "rate of correct predictions for k = 14 0.6153846153846154\n",
            "rate of correct predictions for k = 15 0.5384615384615384\n",
            "rate of correct predictions for k = 16 0.5576923076923077\n",
            "rate of correct predictions for k = 17 0.5384615384615384\n",
            "rate of correct predictions for k = 18 0.5769230769230769\n",
            "rate of correct predictions for k = 19 0.5576923076923077\n",
            "rate of correct predictions for k = 20 0.5576923076923077\n",
            "rate of correct predictions for k = 21 0.5576923076923077\n",
            "rate of correct predictions for k = 22 0.5769230769230769\n",
            "rate of correct predictions for k = 23 0.5576923076923077\n",
            "rate of correct predictions for k = 24 0.5576923076923077\n",
            "rate of correct predictions for k = 25 0.5769230769230769\n",
            "rate of correct predictions for k = 26 0.5769230769230769\n",
            "rate of correct predictions for k = 27 0.5576923076923077\n",
            "rate of correct predictions for k = 28 0.5576923076923077\n",
            "rate of correct predictions for k = 29 0.5384615384615384\n",
            "rate of correct predictions for k = 30 0.5961538461538461\n",
            "rate of correct predictions for k = 31 0.5576923076923077\n",
            "rate of correct predictions for k = 32 0.5576923076923077\n",
            "rate of correct predictions for k = 33 0.5384615384615384\n",
            "rate of correct predictions for k = 34 0.5\n",
            "rate of correct predictions for k = 35 0.5\n",
            "rate of correct predictions for k = 36 0.5192307692307693\n",
            "rate of correct predictions for k = 37 0.5769230769230769\n",
            "rate of correct predictions for k = 38 0.5961538461538461\n",
            "rate of correct predictions for k = 39 0.5769230769230769\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 40.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "OMeuLx4PVQdw",
        "outputId": "330eb103-2cbf-48a9-e684-31a10c9c41f7"
      },
      "source": [
        "#k=9\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=9) \n",
        "#fit with optimum k to training data\n",
        "knn.fit(X_train_norm, y_train) \n",
        "\n",
        "y_pred_test = knn.predict(X_test_norm)\n",
        "\n",
        "print('multiclass accuracy for 9-NN on test set=', metrics.accuracy_score(y_test, y_pred_test))\n",
        "\n",
        "#map predictions on heat map\n",
        "confusion_matrix = pd.crosstab(y_test, y_pred_test, rownames=['Actual'], colnames=['Predicted'])\n",
        "sns.heatmap(confusion_matrix, annot=True)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "multiclass accuracy for 9-NN on test set= 0.6153846153846154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc28aea33c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zx8cLqCbGXJk"
      },
      "source": [
        "def select_n_best_feature(X, y, n):\n",
        "    # Normalize X values to be between 0 and 1\n",
        "    x = X.values\n",
        "    min_max_scaler = preprocessing.MinMaxScaler()\n",
        "    x_scaled = min_max_scaler.fit_transform(x)\n",
        "    X = pd.DataFrame(x_scaled)\n",
        "\n",
        "    # Use SelectKBest class to extract top n best features\n",
        "    bestfeatures = SelectKBest(score_func=chi2, k=n)\n",
        "    fit = bestfeatures.fit(X,y)\n",
        "    dfscores = pd.DataFrame(fit.scores_)\n",
        "    dfcolumns = pd.DataFrame(X.columns)\n",
        "\n",
        "    # Concat two dataframes for better visualization and print best features\n",
        "    featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
        "    featureScores.columns = ['Feature_index','Score']\n",
        "    #print(featureScores.nlargest(n,'Score'))\n",
        "\n",
        "    # Filter X. Leave only top n features\n",
        "    #X = bestfeatures.fit_transform(X, y)\n",
        "    \n",
        "    # Return X\n",
        "    #return X\n",
        "    return bestfeatures"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDYWOa27ZZwl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3b853763-74ba-4b72-f1d5-c0dcea6a13b3"
      },
      "source": [
        "#feature selection \n",
        "\n",
        "#select optimum number of features on validation set\n",
        "acc_rate = np.zeros(100)\n",
        "\n",
        "for i in range(1,100): \n",
        "    bestFeaturesFilter = select_n_best_feature(X_train, y_train, i)\n",
        "    X_train_filter = bestFeaturesFilter.fit_transform(X_train_norm, y_train)\n",
        "    X_val_filter = bestFeaturesFilter.fit_transform(X_val_norm, y_val)\n",
        "#    X_test_filter = bestFeaturesFilter.fit_transform(X_test_norm, y_test_bin) \n",
        "    \n",
        "    knn = KNeighborsClassifier(n_neighbors=9) \n",
        "    #fit with optimum k to training data\n",
        "    knn.fit(X_train_filter, y_train) \n",
        "    y_pred_val = knn.predict(X_val_filter)\n",
        "\n",
        "#   print('Accuracy on test set: ',metrics.accuracy_score(y_test_bin, y_pred_test))\n",
        "    acc_rate[i] = metrics.accuracy_score(y_val, y_pred_val)\n",
        "#    print('Accuracy on train set: ',metrics.accuracy_score(y_train_bin, y_pred_train))\n",
        "#    print('for  k = %i' %i, acc_rate[i])\n",
        "\n",
        "\n",
        "\n",
        "f, ax = plt.subplots(1)\n",
        "plt.plot(acc_rate,color=\"red\")\n",
        "ax.set_xlim(1, 100)\n",
        "\n",
        "\n",
        "max_value = max(acc_rate)\n",
        "\n",
        "print('highest accuracy on validation at ', max_value)\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "highest accuracy on validation at  0.6538461538461539\n"
          ],
          "name": "stdout"
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51Xn3b7oGYPM",
        "outputId": "1e601c8e-cd16-486e-ed9a-618d518d5fe8"
      },
      "source": [
        "#98 features\n",
        "bestFeaturesFilter = select_n_best_feature(X_train, y_train, 98)\n",
        "X_train_filter = bestFeaturesFilter.fit_transform(X_train_norm, y_train)\n",
        "X_val_filter = bestFeaturesFilter.fit_transform(X_val_norm, y_val)\n",
        "X_test_filter = bestFeaturesFilter.fit_transform(X_test_norm, y_test) \n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=9) \n",
        "\n",
        "#fit with optimum k to training data\n",
        "knn.fit(X_train_filter, y_train) \n",
        "y_pred_val = knn.predict(X_val_filter)\n",
        "y_pred_test = knn.predict(X_test_filter)\n",
        "y_pred_train = knn.predict(X_train_filter)\n",
        "\n",
        "#compare all metrics\n",
        "print('Accuracy on train set: ',metrics.accuracy_score(y_train, y_pred_train))\n",
        "print('Accuracy on validation set: ',metrics.accuracy_score(y_val, y_pred_val))\n",
        "print('Accuracy on test set: ',metrics.accuracy_score(y_test, y_pred_test))\n",
        "\n"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on train set:  0.6558441558441559\n",
            "Accuracy on validation set:  0.6538461538461539\n",
            "Accuracy on test set:  0.4423076923076923\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "TBZoDeCQHRg3",
        "outputId": "f4080598-355f-42f1-ac91-9fd62f778c22"
      },
      "source": [
        "confusion_matrix = pd.crosstab(y_test, y_pred_test, rownames=['Actual'], colnames=['Predicted'])\n",
        "sns.heatmap(confusion_matrix, annot=True)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc28dd0cc88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsLQVmaxICyJ"
      },
      "source": [
        ""
      ],
      "execution_count": 25,
      "outputs": []
    }
  ]
}