HW3/Practical/HW3_Practical_Questions_Codes.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jf6eTCn2H29H"
      },
      "source": [
        "Pouya Khani 99210283\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tl5RbNDu2Gve"
      },
      "source": [
        "Importing essential Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--sDOYWq_S3Q"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly.express as pl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwZ-Na0C2NFv"
      },
      "source": [
        "Importing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30-Hd5Kc_cHY"
      },
      "source": [
        "train_data = pd.read_csv(\"./train.csv\")\n",
        "test_data = pd.read_csv(\"./test.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfVGtYFN2Pnk"
      },
      "source": [
        "Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzBjXKxh_wJa"
      },
      "source": [
        "del train_data['Id']\n",
        "del test_data['Id']\n",
        "train_data = train_data.dropna(axis=1, how='any')\n",
        "test_data = test_data.dropna(axis=1, how='any')\n",
        "for col in train_data.select_dtypes(include=['object']):\n",
        "    train_data[col] = train_data[col].astype('category')\n",
        "    train_data[col] = train_data[col].cat.codes\n",
        "for col in test_data.select_dtypes(include=['object']):\n",
        "    test_data[col] = test_data[col].astype('category')\n",
        "    test_data[col] = test_data[col].cat.codes\n",
        "trainX = train_data.iloc[:,0:60]\n",
        "trainY = train_data.iloc[:,60]\n",
        "testX = test_data.iloc[:,:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfwOcado2SKe"
      },
      "source": [
        "Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qQrq0Kt_yVr"
      },
      "source": [
        "for column in trainX:\n",
        "    trainX[column] = (trainX[column] - trainX[column].min()) / (trainX[column].max() - trainX[column].min())\n",
        "\n",
        "for column in testX:\n",
        "    testX[column] = (testX[column] - testX[column].min()) / (testX[column].max() - testX[column].min())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKKrrndP2UVP"
      },
      "source": [
        "Split train data for validation data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19tVSIdTjtEu"
      },
      "source": [
        "cut_index_x = int(0.85 * len(trainX))\n",
        "cut_index_y = int(0.85 * len(trainY))\n",
        "\n",
        "validX , validY = trainX[cut_index_x:] , trainY[cut_index_y:]\n",
        "trainX , trainY = trainX[:cut_index_x] , trainY[:cut_index_y]\n",
        "validY = validY.to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ZbrCQ3F2Zbp"
      },
      "source": [
        "box and gaussian kernel function - weight calculation function - prediction function using weight vector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejH8AEHb_3UE"
      },
      "source": [
        "def kernel_function(h, x, xi,kernel_type):\n",
        "  if kernel_type == 2:\n",
        "    output = (1/( np.sqrt(np.pi * 2))*h) * np.exp(-1*((0.5*np.power(np.linalg.norm(xi-x),2))/h))\n",
        "    return output\n",
        "  else:\n",
        "    if np.linalg.norm(xi-x) <= h:\n",
        "      return 1\n",
        "    else:\n",
        "      return 0\n",
        "\n",
        "def weights( h, input_vector, test_vector,kernel_type): \n",
        "    w = np.zeros(shape=(len(input_vector),1))\n",
        "    k = np.zeros(shape=(len(input_vector),1))\n",
        "    k_sum = 0\n",
        "    i=0\n",
        "    while i < len(input_vector):\n",
        "        k[i] = kernel_function(h, input_vector.iloc[i,:] , test_vector,kernel_type)\n",
        "        k_sum = np.add(k_sum , k[i])\n",
        "        i = np.add(i,1)\n",
        "    j=0\n",
        "    while j < len(input_vector):\n",
        "      if k_sum == 0:\n",
        "        w[j] = 0\n",
        "      else:\n",
        "        w[j] = np.divide(k[j],k_sum)\n",
        "      j = np.add(j,1)\n",
        "    return w\n",
        "\n",
        "def y_pred(h, trainX, trainY , x_i,kernel_type): \n",
        "    w = weights(h, trainX, x_i,kernel_type)\n",
        "    y = np.sum(np.dot(trainY,w))\n",
        "    return y\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bea2TUA_2lJD"
      },
      "source": [
        "checking predict value for preventing NaN outputs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-goelq6R_5Kw"
      },
      "source": [
        "def prediction(h,test,kernel_type):\n",
        "    y = np.zeros(shape=(len(test),1))\n",
        "    counter = 0\n",
        "    while counter < len(test):\n",
        "        temp = y_pred(h,trainX,trainY, test.iloc[counter,:],kernel_type)\n",
        "        if temp == 0:\n",
        "          y[counter] = np.mean(trainY)\n",
        "        else:\n",
        "          y[counter] = temp\n",
        "        counter = np.add(counter,1)\n",
        "    return np.array(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdqC4CEh2zkI"
      },
      "source": [
        "RMSE calculation function based on predict labels and real labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwIdnNAsB5AU"
      },
      "source": [
        "def mseCalculator(predict_labels,validY):\n",
        "  rows,cols = predict_labels.shape\n",
        "  temp = np.empty((cols,rows))\n",
        "  i=0\n",
        "  while i < cols:\n",
        "    temp[i,:] = np.square(np.subtract(predict_labels[:,i],validY))\n",
        "    i = np.add(i,1)\n",
        "  mse = np.sqrt(np.divide(np.sum(temp,axis=1),rows))\n",
        "  return mse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRu5IPSh26hD"
      },
      "source": [
        "predict labels using gaussian kernel and plot result"
      ]
    },
    {
      "cell_type": "code",
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          "height": 542
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        "outputId": "935b6d74-b666-40d4-8e54-ca022b0e8ad6"
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      "source": [
        "predict_labels_gaussian = np.empty((len(validX),0))\n",
        "for i in np.arange(0.02,1,0.03):\n",
        "  predict_labels_gaussian = np.append(predict_labels_gaussian,prediction(i,validX,2),axis=1)\n",
        "\n",
        "rows,cols = predict_labels_gaussian.shape\n",
        "mse_gauss = np.empty((cols,1))\n",
        "mse_gauss = mseCalculator(predict_labels_gaussian,validY)\n",
        "\n",
        "bandwitch1 = np.arange(0.02,1,0.03)\n",
        "temp = np.vstack((bandwitch1,mse_gauss))\n",
        "\n",
        "temp2 = pd.DataFrame({'parameter':np.array(temp)[0,:],'rmse':np.array(temp)[1,:]})\n",
        "\n",
        "fig = pl.line(temp2,x='parameter',y='rmse',title='gaussian kernel mse')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
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      "metadata": {
        "id": "Tsd0U6bw3DjI"
      },
      "source": [
        "predict labels using box kernel and plot result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
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          "height": 542
        },
        "outputId": "3eb2b9ac-c268-4630-9a47-2a891057c8ae"
      },
      "source": [
        "predict_labels_box = np.empty((len(validX),0))\n",
        "for i in np.arange(1,3,0.1):\n",
        "  predict_labels_box = np.append(predict_labels_box,prediction(i,validX,1),axis=1)\n",
        "\n",
        "rows,cols = predict_labels_box.shape\n",
        "mse_box = np.empty((cols,1))\n",
        "mse_box = mseCalculator(predict_labels_box,validY)\n",
        "\n",
        "bandwitch2 = np.arange(1,3,0.1)\n",
        "temp = np.vstack((bandwitch2,mse_box))\n",
        "\n",
        "temp2 = pd.DataFrame({'parameter':np.array(temp)[0,:],'rmse':np.array(temp)[1,:]})\n",
        "\n",
        "fig = pl.line(temp2,x='parameter',y='rmse',title='box kernel mse')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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