Day_53_Gradient_regressor.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO47hFf4orxiP5r0mTNiESa",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_53_Gradient_regressor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "_c8gYUhh6xg_"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import make_regression\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split as tts"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = make_regression(n_samples = 100, n_features = 1, n_targets = 1, n_informative = 1, noise = 30, random_state = 42)"
      ],
      "metadata": {
        "id": "LuqlsrBz65sN"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "GMyPN9yg7GLT",
        "outputId": "4a5b6bc8-c0a4-4da4-b7af-ced506c715d4"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fee2da41960>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = tts(X,y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "HXjFc35bFEeO"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(X_train,y_train)\n",
        "model.coef_, model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ozgo0_i77MIb",
        "outputId": "5f01dd51-1e54-4435-a88c-ca568c35a7e1"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([49.25034584]), 0.2976666426776342)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "np.mean(cross_val_score(model, X_test, y_test, scoring='r2', cv = 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vZRoClqsC7t3",
        "outputId": "03e4c3e5-9b07-4a52-b4dd-9cd370428ee6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.023633983891282927"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class gbregressor():\n",
        "  def __init__(self, learning_rate, epochs):\n",
        "    self.b = 0\n",
        "    self.m = 100\n",
        "    self.lr = learning_rate\n",
        "    self.epochs = epochs\n",
        "\n",
        "  def fit(self, X, y):\n",
        "    for i in range(self.epochs):\n",
        "      loss_slope_b = -2*np.sum(y- (self.m*X.ravel())-self.b)\n",
        "      loss_slope_m = -2*np.sum((y- (self.m*X.ravel())-self.b)*X.ravel())\n",
        "      self.b = self.b-self.lr*loss_slope_b\n",
        "      self.m = self.m-self.lr*loss_slope_m\n",
        "    print(self.m,self.b)\n",
        "\n",
        "  def predict(self,X):\n",
        "    return self.m*X+self.b\n",
        "    "
      ],
      "metadata": {
        "id": "XFNRgzFv7c7j"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gd = gbregressor(0.001, 60)"
      ],
      "metadata": {
        "id": "EY3MyFpa8T2b"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gd.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVZP8EYV9EK0",
        "outputId": "2c7f6c0d-fad5-4188-890e-d923720108de"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49.83086175207917 3.496054244006766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "KLKOpEnWE0Io"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(gd.predict(X_test), y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISMJ5Lvo8ZbY",
        "outputId": "675076fa-c8ec-4f37-a5f7-9521914123c4"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5731015877303112"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib notebook\n",
        "from matplotlib.animation import FuncAnimation\n",
        "import matplotlib.animation as animation"
      ],
      "metadata": {
        "id": "MJmKWyg-8bWf"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b = -520\n",
        "m = 600\n",
        "lr = 0.001\n",
        "all_b = []\n",
        "all_m = []\n",
        "all_cost = []\n",
        "\n",
        "epochs = 30\n",
        "\n",
        "for i in range(epochs):\n",
        "    slope_b = 0\n",
        "    slope_m = 0\n",
        "    cost = 0\n",
        "    for j in range(X.shape[0]):\n",
        "        slope_b = slope_b - 2*(y[j] - (m * X[j]) -  b)\n",
        "        slope_m = slope_m - 2*(y[j] - (m * X[j]) -  b)*X[j]\n",
        "        cost = cost + (y[j] - m * X[j] -b) ** 2\n",
        "\n",
        "    b = b - (lr * slope_b)\n",
        "    m = m - (lr * slope_m)\n",
        "    all_b.append(b)\n",
        "    all_m.append(m)\n",
        "    all_cost.append(cost)"
      ],
      "metadata": {
        "id": "aeTV2SokLYyR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(9,5))\n",
        "#fig.set_tight_layout(True)\n",
        "\n",
        "x_i = np.arange(-3, 3, 0.1)\n",
        "y_i = x_i*(-27) -150\n",
        "ax.scatter(X, y)\n",
        "line, = ax.plot(x_i, x_i*50 - 4, 'r-', linewidth=2)\n",
        "\n",
        "def update(i):\n",
        "    label = 'epoch {0}'.format(i + 1)\n",
        "    line.set_ydata(x_i*all_m[i] + all_b[i])\n",
        "    ax.set_xlabel(label)\n",
        "    # return line, ax\n",
        "\n",
        "anim = FuncAnimation(fig, update, repeat=True, frames=epochs, interval=500)"
      ],
      "metadata": {
        "id": "cVZb4_ZzLS8o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cost function\n",
        "# creating a blank window \n",
        "# for the animation \n",
        "num_epochs = list(range(0,30))\n",
        "fig = plt.figure(figsize=(9,5)) \n",
        "axis = plt.axes(xlim =(0, 31), ylim =(0, 4500000)) \n",
        "  \n",
        "line, = axis.plot([], [], lw = 2) \n",
        "  \n",
        "xdata, ydata = [], [] \n",
        "  \n",
        "# animation function \n",
        "def animate(i): \n",
        "    label = 'epoch {0}'.format(i + 1)\n",
        "    xdata.append(num_epochs[i]) \n",
        "    ydata.append(all_cost[i]) \n",
        "    line.set_data(xdata, ydata) \n",
        "    axis.set_xlabel(label)\n",
        "      \n",
        "    return line, \n",
        "  \n",
        "# calling the animation function     \n",
        "anim = animation.FuncAnimation(fig, animate, frames = 30,repeat=False, interval = 500)\n",
        "\n",
        "f = r\"animation5.gif\" \n",
        "writergif = animation.PillowWriter(fps=2) \n",
        "anim.save(f, writer=writergif)"
      ],
      "metadata": {
        "id": "KreHy8PiLdfj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# intercept graph\n",
        "num_epochs = list(range(0,30))\n",
        "fig = plt.figure(figsize=(9,5)) \n",
        "axis = plt.axes(xlim =(0, 31), ylim =(-10, 160)) \n",
        "  \n",
        "line, = axis.plot([], [], lw = 2) \n",
        "  \n",
        "xdata, ydata = [], [] \n",
        "  \n",
        "# animation function \n",
        "def animate(i): \n",
        "    label = 'epoch {0}'.format(i + 1)\n",
        "    xdata.append(num_epochs[i]) \n",
        "    ydata.append(all_b[i]) \n",
        "    line.set_data(xdata, ydata) \n",
        "    axis.set_xlabel(label)\n",
        "      \n",
        "    return line, \n",
        "  \n",
        "# calling the animation function     \n",
        "anim = animation.FuncAnimation(fig, animate, frames = 30,repeat=False, interval = 500)\n",
        "\n",
        "f = r\"animation6.gif\" \n",
        "writergif = animation.PillowWriter(fps=2) \n",
        "anim.save(f, writer=writergif)\n"
      ],
      "metadata": {
        "id": "hOI2DDEvLhyT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# intercept graph\n",
        "num_epochs = list(range(0,30))\n",
        "fig = plt.figure(figsize=(9,5)) \n",
        "axis = plt.axes(xlim =(0, 31), ylim =(-150, 50)) \n",
        "  \n",
        "line, = axis.plot([], [], lw = 2) \n",
        "  \n",
        "xdata, ydata = [], [] \n",
        "  \n",
        "# animation function \n",
        "def animate(i): \n",
        "    label = 'epoch {0}'.format(i + 1)\n",
        "    xdata.append(num_epochs[i]) \n",
        "    ydata.append(all_m[i]) \n",
        "    line.set_data(xdata, ydata) \n",
        "    axis.set_xlabel(label)\n",
        "      \n",
        "    return line, \n",
        "  \n",
        "# calling the animation function     \n",
        "anim = animation.FuncAnimation(fig, animate, frames = 30,repeat=False, interval = 500)\n",
        "\n",
        "f = r\"animation7.gif\" \n",
        "writergif = animation.PillowWriter(fps=2) \n",
        "anim.save(f, writer=writergif)"
      ],
      "metadata": {
        "id": "ui6UqftyLlia"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}