Adam_Batch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Adam Batch",
      "provenance": [],
      "authorship_tag": "ABX9TyMBpVJznspMB6fMCPOTsS7j",
      "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/YousraAshour/machine_learning/blob/main/Adam_Batch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "import pandas as pd\n",
        "from sklearn.metrics import r2_score , mean_squared_error\n"
      ],
      "metadata": {
        "id": "8gR13SyyJspG"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(r'/MultipleLR.csv - MultipleLR.csv.csv',header=None)\n",
        "data=np.asarray(data)\n",
        "x=data[:,:3].reshape(-1,3)\n",
        "x=np.c_[np.ones((len(x),1)),x]\n",
        "xx=np.c_[np.ones((len(x),1)),x]\n",
        "y=data[:,3].reshape(-1,1)\n",
        "y.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gvqhjb9JsZd",
        "outputId": "412a158a-6c3c-41a0-d3fa-af24c0e75917"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **NOTE: I used a higher number of epochs(15000) to test the stopping conditions while the needed epochs are less**"
      ],
      "metadata": {
        "id": "A6pA8SC8W6fg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "nQM31v3EIKVP"
      },
      "outputs": [],
      "source": [
        "m=np.zeros((1,x.shape[1]))\n",
        "v=np.zeros((1,x.shape[1]))\n",
        "Theta=np.zeros((1,x.shape[1])).reshape(1,x.shape[1])\n",
        "epochs= 15000\n",
        "B1=0.9\n",
        "B2=0.999\n",
        "eps=10**-8\n",
        "alpha= 0.009\n",
        "\n",
        "\n",
        "def Adam_Batch (x,y,m,v,eps,alpha,epochs,Theta,B1,B2):\n",
        "  losses=[]\n",
        "  iters=[]\n",
        "  iter=0\n",
        "  Theta0=[]\n",
        "  Theta1=[]\n",
        "  Theta2=[]\n",
        "  Theta3=[]\n",
        "  for e in range (epochs):\n",
        "\n",
        "\n",
        "              h=np.dot(x,np.transpose(Theta))\n",
        "              # print('h',h.shape)\n",
        "              # print('y',Y.shape)\n",
        "\n",
        "              err= h-y\n",
        "              # print('err',err.shape)\n",
        "              loss= (1/(2*x.shape[0]))* np.dot(np.transpose(err),err)\n",
        "              \n",
        "              # print('loss',loss.shape)\n",
        "              grad= 1/x.shape[0] * np.dot(np.transpose(err),x)\n",
        "              losses.append(loss)\n",
        "\n",
        "              if losses[e]-losses[e-1] > 0 or np.linalg.norm(grad) < 0.001 :\n",
        "                \n",
        "                losses.pop()\n",
        "                break\n",
        "              else:  \n",
        "                m=(B1*m)+((1-B1)*(grad))\n",
        "                # print('m',m.shape)\n",
        "                m_cor=(m)/(1-(B1**(e+1)))\n",
        "                \n",
        "                v=(B2*v)+((1-B2)*(grad)**2)\n",
        "\n",
        "                # print('v',v.shape)\n",
        "                v_cor=(v)/(1-(B2**(e+1)))\n",
        "                \n",
        "                Theta= Theta - alpha*(m_cor/(np.sqrt(v_cor)+eps))\n",
        "                Theta= Theta.reshape(1,x.shape[1])\n",
        "                # print('Theta', Theta)\n",
        "                Theta0.append(Theta[:,0])\n",
        "                Theta1.append(Theta[:,1])\n",
        "                Theta2.append(Theta[:,2])\n",
        "                Theta3.append(Theta[:,3])\n",
        "\n",
        "                iters.append(iter)\n",
        "                iter+=1\n",
        "  print('iters', len(iters))\n",
        "  return Theta, losses ,iters,Theta0,Theta1,Theta2,Theta3\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Theta, losses,iters,Theta0,Theta1,Theta2,Theta3 =  Adam_Batch (x,y,m,v,eps,alpha,epochs,Theta,B1,B2)\n",
        "y_pred= np.dot(x,np.transpose(Theta))\n",
        "score=r2_score(y,y_pred)\n",
        "score\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7523-S5J_Qx",
        "outputId": "eadf87ab-6fae-428e-8d2e-4ca1577c04a9"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iters 11956\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9896615227905441"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(losses,iters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "jPrAHU5TKEy6",
        "outputId": "09c8ba1e-959e-4f5d-a9f6-b329671da001"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9046acab10>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses=np.asarray(losses).reshape(-1,1)\n",
        "plt.plot(losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "RkDY5luZKuwg",
        "outputId": "4586adb6-407f-4b0f-d1ed-9c75a467cb85"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f90466d6bd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses,Theta0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "pMexDyG1KymB",
        "outputId": "2a8a194d-f85e-422c-fb4a-8e20fee0d53d"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f904668e850>]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses,Theta1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "fX_VaJYLK2CC",
        "outputId": "b73410ee-3389-4ce9-8249-7738c6c5b037"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9046626ad0>]"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses,Theta2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "f5Kq_iwVK3LD",
        "outputId": "299d16aa-3101-4aee-a879-8414a2d56d2c"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f904654cf50>]"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses,Theta3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "P5_S_sBoK4d7",
        "outputId": "588a1274-596d-4d43-b3ea-b00c3fb917d4"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9046474510>]"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TT1J6z48K52o"
      },
      "execution_count": 120,
      "outputs": []
    }
  ]
}