ML_Lab6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML_Lab6.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO68s7h+0HzvvrcMwxtK1r5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JARACH-209/AI-ML_Lab/blob/master/ML_Lab6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yt_vTzNaDxfR"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn import preprocessing\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQjNhT53EyeA"
      },
      "source": [
        "x,y = load_iris(return_X_y= True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSHa_4GAmvVN"
      },
      "source": [
        "df_x = pd.DataFrame(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ho4ChcfmOZl",
        "outputId": "880d039f-5527-4be6-9b03-89b5661e7511",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "plt.scatter(df_x[0],df_x[2],cmap=\"autumn\",c=y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f610fda30b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "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": {
        "id": "rAqkFSH5gdVF",
        "outputId": "f65e2a39-4b92-407f-e3de-f47a486e0edc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZ-xyWobf1Qy"
      },
      "source": [
        "'''\n",
        "x_1=x[:40]\n",
        "x_2=x[50:90]\n",
        "x_3=x[100:140]\n",
        "y_1=y[:40]\n",
        "y_2=y[50:90]\n",
        "y_3=y[np.where(y==2)[0][0]:140]\n",
        "'''\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.1)\n",
        "scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "x_train = scaler.transform(x_train)\n",
        "x_test = scaler.transform(x_test)\n",
        "\n",
        "y_train = y_train.reshape(-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv1VnPzAnsIN"
      },
      "source": [
        "class SLP():\n",
        "\n",
        "    def __init__(self):\n",
        "        self.weights = None\n",
        "        self.bias = None\n",
        "        self.optimal_w = 0\n",
        "        self.optimal_b = 0 \n",
        "\n",
        "    def activation(self,x):\n",
        "        return 1 if (np.dot(self.weights,x) > self.bias) else 0\n",
        "\n",
        "    def optimal_act(self,x):\n",
        "        return 1 if (np.dot(self.optimal_w,x) > self.optimal_b) else 0\n",
        "\n",
        "    def predict(self,X):\n",
        "        y = []\n",
        "        for x in X:\n",
        "            y.append(self.optimal_act(x))\n",
        "        return np.array(y)\n",
        "        \n",
        "    def predictions(self,X):\n",
        "        y = []\n",
        "        for x in X:\n",
        "            y.append(self.activation(x))\n",
        "        return np.array(y)\n",
        "        \n",
        "    def fit(self,X,Y,learning_rate,iterations):\n",
        "        self.weights = np.zeros(X.shape[1])\n",
        "        self.bias = 0\n",
        "        accuracy = {}\n",
        "        self.optimal_w = np.zeros(X.shape[1])\n",
        "        best_w = np.zeros(X.shape[1])\n",
        "        best_b = 0\n",
        "        best_acc = 0\n",
        "\n",
        "        # for all the iterations\n",
        "        for epoch in range(iterations):\n",
        "            for x,y in zip(X,Y):\n",
        "                y_predicted = self.activation(x)\n",
        "\n",
        "                if y_predicted == 0 and y == 1:\n",
        "                    self.weights = self.weights + learning_rate*x\n",
        "                    self.bias = self.bias - learning_rate\n",
        "                if y_predicted == 1 and y == 0:\n",
        "                    self.weights = self.weights - learning_rate*x\n",
        "                    self.bias = self.bias + learning_rate\n",
        "\n",
        "            accuracy[epoch] = accuracy_score(self.predictions(X), Y)\n",
        "            if accuracy[epoch] > best_acc:\n",
        "                best_acc = accuracy[epoch]\n",
        "                best_w = self.weights\n",
        "                best_b = self.bias\n",
        "        self.optimal_w = best_w\n",
        "        self.optimal_b = best_b\n",
        "        return accuracy\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1CkBQWlcehO"
      },
      "source": [
        "#making the training sets for all the three SLPs\n",
        "y_0 = []\n",
        "y_1 = []\n",
        "y_2 = []\n",
        "\n",
        "for each in y_train:\n",
        "    if each == 0:\n",
        "        y_0.extend([1])\n",
        "    else: \n",
        "        y_0.extend([0])\n",
        "    if each == 1:\n",
        "        y_1.extend([1])\n",
        "    else: \n",
        "        y_1.extend([0])\n",
        "    if each == 2:\n",
        "        y_2.extend([1])\n",
        "    else: \n",
        "        y_2.extend([0])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8NgZqTtmu4l"
      },
      "source": [
        "##Testing our SLP on Linearly Spearable Classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNlgCJ0lg08D"
      },
      "source": [
        "slp_test = SLP()\n",
        "acc = slp_test.fit(x_train,y_0, 0.01, 5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IVbnX_6f7RI",
        "outputId": "b020f289-9c9a-4ceb-b64c-f79ebfee1518",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "plt.plot(list(acc.keys()),list(acc.values()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f610fd84550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "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": {
        "id": "YdAPHrKUnrw2"
      },
      "source": [
        "y_0_test = []\n",
        "for each in y_test:\n",
        "    if each == 0:\n",
        "        y_0_test.extend([1])\n",
        "    else: \n",
        "        y_0_test.extend([0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-L7iG9d5Zmg",
        "outputId": "bfdd361e-d1b4-4dfb-fbb2-7f3494a00baf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "slp_test.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2wmswHd5RZy",
        "outputId": "5ae2bca2-d228-4a26-bf5e-3d8cfe0f0fe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "y_0_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8pjeM5cnWaa",
        "outputId": "0947e150-23b6-4660-d4e5-6b30d4265eac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "print(\"Test Accuracy : %0.2f\"%(accuracy_score(y_0_test,slp_test.predict(x_test))*100),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy : 100.00 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIZBJZUJmz7v"
      },
      "source": [
        "----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQKGpQ4HvnCu"
      },
      "source": [
        "##Testing the SLP on non-linearly separable datasets "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viUhthNMthvb"
      },
      "source": [
        "slp_test2 = SLP()\n",
        "acc = slp_test2.fit(x_train, y_1, 0.01, 100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngVIITZzvvyv",
        "outputId": "947c312b-e275-4149-be41-71d74d1b6b16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "plt.plot(list(acc.keys()), list(acc.values()))      #Graph is noisy probably because of non-lineary separable data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f610f9f2940>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "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": {
        "id": "2ptG8JfGwHH6"
      },
      "source": [
        "y_1_test = []\n",
        "for each in y_test:\n",
        "    if each == 1:\n",
        "        y_1_test.extend([1])\n",
        "    else: \n",
        "        y_1_test.extend([0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evdLzTZHwhoi",
        "outputId": "166af656-1b6e-4a2a-e600-ee1f0e17cdeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 0, 0, 1, 1, 2, 0, 1, 0, 1, 1, 2, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IK3lh7EpwLSd",
        "outputId": "19fb0af5-6f2a-4dc9-8829-f6f0fc4d5c20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "y_1_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mo3DbJrMwYUx",
        "outputId": "9a0624a9-5ed2-4906-ba2b-07b6a6d96c33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "slp_test2.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fgeB_PCwAKS",
        "outputId": "5cd8613f-f92a-40f5-a080-3a63f88df618",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(\"Train Accuracy (Maximum) :  \",100*max(acc.values()),\"%\")\n",
        "print(\"Test Accuracy : %0.2f\"%(accuracy_score(y_1_test,slp_test2.predict(x_test))*100),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy (Maximum) :   68.88888888888889 %\n",
            "Test Accuracy : 60.00 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LpPB9-1AHaU"
      },
      "source": [
        "-----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0GTHUwnAqG0"
      },
      "source": [
        "##Testing SLP on third class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzxCssiAAQQv"
      },
      "source": [
        "slp_test3 = SLP()\n",
        "acc = slp_test3.fit(x_train, y_2, 0.01, 40)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-XZhF8oAXsm",
        "outputId": "779589d3-2dca-4fce-d3ae-81cf55d66c02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "plt.plot(list(acc.keys()),list(acc.values()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f610f9d54e0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "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": {
        "id": "cb3AisuYA5X-",
        "outputId": "7577d450-6307-42de-ba38-1790a4185aea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "plt.plot(list(acc.keys()),list(acc.values()))       #Highest train accuracy after 58 iterations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f610f874940>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        },
        {
          "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": {
        "id": "tjVoc85ZBDAr"
      },
      "source": [
        "y_2_test = []\n",
        "for each in y_test:\n",
        "    if each == 2:\n",
        "        y_2_test.extend([1])\n",
        "    else: \n",
        "        y_2_test.extend([0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_OpKVF3cAYZ",
        "outputId": "c4cb7745-b3ab-47de-91c0-2379ba348fe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "y_2_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qu-cDYhNBt_Q",
        "outputId": "0b19b379-fdae-4760-e08b-9365382b0fa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(\"Train Accuracy (Maximum) :  \",100*max(acc.values()),\"%\")\n",
        "print(\"Test Accuracy : %0.2f\"%(accuracy_score(y_2_test,slp_test3.predict(x_test))*100),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy (Maximum) :   98.51851851851852 %\n",
            "Test Accuracy : 93.33 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUAmIAxwE3Zq"
      },
      "source": [
        "-----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x35MsCO1E5o9"
      },
      "source": [
        "##Building the final Multi-Class Classification Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmdjaN8yZPTs"
      },
      "source": [
        "# we have a SLP and we will train three different SLP for each class of IRIS dataset.\n",
        "# 0 = Setosa , 1 = Versicolor , 2 = Virginica\n",
        "# While training SLP for class 0,1,2 we will make the class label 1 and rest all as 0. ONEvsALL classification\n",
        "# y_0 = Setosa vs All , y_1 = Versicolor vs All, y_2 = Virignica vs All\n",
        "\n",
        "class SLP_Classifier():\n",
        "    def __init__(self,learning_rate, iterations):\n",
        "        self.iterations = iterations\n",
        "        self.learning_rate = learning_rate\n",
        "        self.slp_0 = SLP()\n",
        "        self.slp_1 = SLP()\n",
        "        self.slp_2 = SLP()\n",
        "\n",
        "    def fit(self,x_train,y_0,y_1,y_2):\n",
        "        self.slp_0.fit(x_train,y_0,self.learning_rate,self.iterations)\n",
        "        self.slp_1.fit(x_train,y_1,self.learning_rate,self.iterations)\n",
        "        self.slp_2.fit(x_train,y_2,self.learning_rate,self.iterations)\n",
        "    \n",
        "    def predict(self,x):\n",
        "        y_predictions = []\n",
        "        yp_0 = self.slp_0.predict(x)\n",
        "        yp_1 = self.slp_1.predict(x)\n",
        "        yp_2 = self.slp_2.predict(x)\n",
        "\n",
        "        #since SLP 0>3>2 we will use their classification for final labeling\n",
        "\n",
        "        for y0,y1,y2 in zip(yp_0,yp_1,yp_2):\n",
        "            if y0 == 1:\n",
        "                y_predictions.extend([0])\n",
        "            elif y2 == 1:\n",
        "                y_predictions.extend([2])\n",
        "            elif y1 == 1:\n",
        "                y_predictions.extend([1])\n",
        "            else: y_predictions.extend([1])\n",
        "        return np.array(y_predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84em2gcZOPwO",
        "outputId": "ee0c89a5-133f-4ed0-bb5f-57fcb678129a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "slp_clf = SLP_Classifier(0.1,100)\n",
        "slp_clf.fit(x_train,y_0,y_1,y_2)\n",
        "y_pred = slp_clf.predict(x_test)\n",
        "print(\"Test accuracy (3 classes together): %0.2f\"%(accuracy_score(y_test,y_pred)*100),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy (3 classes together): 93.33 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3cq3FwLcUAr"
      },
      "source": [
        "###10 different simulations "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axaLaOjjdGKC"
      },
      "source": [
        " def split_testset(y_train):\n",
        "    y_0 = []\n",
        "    y_1 = []\n",
        "    y_2 = []\n",
        "\n",
        "    for each in y_train:\n",
        "        if each == 0:\n",
        "            y_0.extend([1])\n",
        "        else: \n",
        "            y_0.extend([0])\n",
        "        if each == 1:\n",
        "            y_1.extend([1])\n",
        "        else: \n",
        "            y_1.extend([0])\n",
        "        if each == 2:\n",
        "            y_2.extend([1])\n",
        "        else: \n",
        "            y_2.extend([0])\n",
        "    return y_0,y_1,y_2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syOEINO_cXkr"
      },
      "source": [
        "train_accuracy = {}\n",
        "test_accuracy = {}\n",
        "\n",
        "for i in range(10):\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.1)   #test size if 10% of total dataset\n",
        "    scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "    x_train = scaler.transform(x_train)\n",
        "    x_test = scaler.transform(x_test)\n",
        "    y_train = y_train.reshape(-1)\n",
        "\n",
        "    y_0, y_1, y_2 = split_testset(y_train)\n",
        "\n",
        "    slp_clf = SLP_Classifier(0.1,100)\n",
        "    slp_clf.fit(x_train,y_0,y_1,y_2)\n",
        "\n",
        "    test_accuracy[i] = accuracy_score(y_test, slp_clf.predict(x_test))\n",
        "    train_accuracy[i] = accuracy_score(y_train,slp_clf.predict(x_train))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbnBbUPWeMUI"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "temp = []\n",
        "df['Test accuracy'] = list(test_accuracy.values())\n",
        "df['Train_accuracy'] = list(train_accuracy.values())\n",
        "df.to_excel('ML_Lab6_Q1_Part3_Output.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66kVAGTke8D5",
        "outputId": "1d70a8ed-9ed4-44fc-d3e6-ebbdd396c70c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "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>Test accuracy</th>\n",
              "      <th>Train_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.985185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.985185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.985185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.992593</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Test accuracy  Train_accuracy\n",
              "0       0.800000        0.992593\n",
              "1       0.933333        0.992593\n",
              "2       0.933333        0.992593\n",
              "3       0.933333        0.992593\n",
              "4       1.000000        0.985185\n",
              "5       1.000000        0.992593\n",
              "6       0.933333        0.985185\n",
              "7       0.933333        0.985185\n",
              "8       1.000000        0.992593\n",
              "9       0.933333        0.992593"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tfsJRJLLfR5N"
      },
      "source": [
        "###Different Training Size Simulations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTdrQJRzgiw7"
      },
      "source": [
        "# Function runs ten simulations and gives average of train and test accuracies\n",
        "def ten_simulations(x_train,y_train,x_test,y_test,train_size):\n",
        "    for i in range(10):\n",
        "\n",
        "        y_0, y_1, y_2 = split_testset(y_train)\n",
        "\n",
        "        slp_clf = SLP_Classifier(0.1,100)\n",
        "        slp_clf.fit(x_train,y_0,y_1,y_2)\n",
        "\n",
        "        test_accuracy[i] = accuracy_score(y_test, slp_clf.predict(x_test))\n",
        "        train_accuracy[i] = accuracy_score(y_train,slp_clf.predict(x_train))\n",
        "\n",
        "    return sum(list(test_accuracy.values()))/len(test_accuracy.values()), sum(list(train_accuracy.values()))/len(train_accuracy.values())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJ2SgsPXfRSf"
      },
      "source": [
        "train_acc = {}\n",
        "test_acc = {}\n",
        "training = [0.1,0.2,0.3,0.4,0.5,0.6]\n",
        "\n",
        "for i in training:\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x,y, train_size = i)   #test size if 10% of total dataset\n",
        "    scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "    x_train = scaler.transform(x_train)\n",
        "    x_test = scaler.transform(x_test)\n",
        "    y_train = y_train.reshape(-1)\n",
        "\n",
        "    test,train = ten_simulations(x_train,y_train,x_test,y_test,i)\n",
        "\n",
        "    test_acc[i] = test\n",
        "    train_acc[i] = train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-oT0e9ff0pj",
        "outputId": "781a9334-35c6-4eda-d899-f797962f5a6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "print(\"\\tTrain Accuracies \\t Test Accuracies\")\n",
        "train = list(train_acc.values())\n",
        "test = list(test_acc.values())\n",
        "for i in training:\n",
        "    print(f\"{i}\\t%0.2f\"%(train[int(i*10-1)]*100),\"\\t\\t\\t  %0.2f\"%(test[int(i*10-1)]*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\tTrain Accuracies \t Test Accuracies\n",
            "0.1\t99.94 \t\t\t  86.00\n",
            "0.2\t99.94 \t\t\t  90.64\n",
            "0.3\t99.80 \t\t\t  92.50\n",
            "0.4\t99.53 \t\t\t  93.94\n",
            "0.5\t99.53 \t\t\t  93.21\n",
            "0.6\t99.87 \t\t\t  93.14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oayMM85kVTE",
        "outputId": "609a5c8a-a8dd-44d0-f1b2-a4a1768fb6d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "plt.style.use('ggplot')\n",
        "plt.figure(figsize=(7,5))\n",
        "line1 = plt.plot(training,train,c='black',marker=\"o\")\n",
        "line2 = plt.plot(training,test,c=\"red\",marker='o')\n",
        "plt.legend((line1[0], line2[0]),('Training','Testing'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f610befae10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IseDb_xssnq"
      },
      "source": [
        "------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_pN4JAvsuwZ"
      },
      "source": [
        "#Multi-Layer perceptron"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hqSFW6vxXZI"
      },
      "source": [
        "x,y = load_iris(return_X_y= True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBoyP6ZGxM0B"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2)\n",
        "scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "x_train = scaler.transform(x_train)\n",
        "x_test = scaler.transform(x_test)\n",
        "\n",
        "y_train = y_train.reshape(-1)\n",
        "\n",
        "X= x_train\n",
        "y= y_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2ucRRD-suCj"
      },
      "source": [
        " class MLP():\n",
        "\n",
        "    def __init__(self,X,y,classes):\n",
        "        self.num_examples = len(X) # training set size\n",
        "        self.nn_input_dim = X.shape[1] # input layer dimensionality\n",
        "        self.nn_output_dim = classes # output layer dimensionality\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        # Gradient descent parameters (I picked these by hand)\n",
        "        self.epsilon = 0.01 # learning rate for gradient descent\n",
        "        self.reg_lambda = 0.01 # regularization strength\n",
        "\n",
        "    def calculate_loss(self,model):\n",
        "        W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']\n",
        "        # Forward propagation to calculate our predictions\n",
        "        z1 = self.X.dot(W1) + b1\n",
        "        a1 = np.tanh(z1)\n",
        "        z2 = a1.dot(W2) + b2\n",
        "        exp_scores = np.exp(z2)\n",
        "        probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
        "        # Calculating the loss\n",
        "        corect_logprobs = -np.log(probs[range(self.num_examples), self.y])\n",
        "        data_loss = np.sum(corect_logprobs)\n",
        "        # Add regulatization term to loss (optional)\n",
        "        data_loss += reg_lambda/2 * (np.sum(np.square(W1)) + np.sum(np.square(W2)))\n",
        "        return 1./num_examples * data_loss\n",
        "\n",
        "    # Helper function to predict an output (0 or 1)\n",
        "    def predict(self,model, x):\n",
        "        W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']\n",
        "        # Forward propagation\n",
        "        z1 = x.dot(W1) + b1\n",
        "        a1 = np.tanh(z1)\n",
        "        z2 = a1.dot(W2) + b2\n",
        "        exp_scores = np.exp(z2)\n",
        "        probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
        "        return np.argmax(probs, axis=1)\n",
        "\n",
        "    # - nn_hdim: Number of nodes in the hidden layer\n",
        "    # - num_passes: Number of passes through the training data for gradient descent\n",
        "    # - print_loss: If True, print the loss every 1000 iterations\n",
        "    def build_model(self,nn_hdim, num_passes=20000, print_loss=False):\n",
        "        \n",
        "        # Initialize the parameters to random values. We need to learn these.\n",
        "        np.random.seed(0)\n",
        "        W1 = np.random.randn(self.nn_input_dim, nn_hdim) / np.sqrt(self.nn_input_dim)\n",
        "        b1 = np.zeros((1, nn_hdim))\n",
        "        W2 = np.random.randn(nn_hdim, self.nn_output_dim) / np.sqrt(nn_hdim)\n",
        "        b2 = np.zeros((1, self.nn_output_dim))\n",
        "    \n",
        "        # This is what we return at the end\n",
        "        model = {}\n",
        "        \n",
        "        # Gradient descent. For each batch...\n",
        "        for i in range(0, num_passes):\n",
        "    \n",
        "            # Forward propagation\n",
        "            z1 = self.X.dot(W1) + b1\n",
        "            a1 = np.tanh(z1)\n",
        "            z2 = a1.dot(W2) + b2\n",
        "            exp_scores = np.exp(z2)\n",
        "            probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
        "    \n",
        "            # Backpropagation\n",
        "            delta3 = probs\n",
        "            delta3[range(self.num_examples), self.y] -= 1\n",
        "            dW2 = (a1.T).dot(delta3)\n",
        "            db2 = np.sum(delta3, axis=0, keepdims=True)\n",
        "            delta2 = delta3.dot(W2.T) * (1 - np.power(a1, 2))\n",
        "            dW1 = np.dot(self.X.T, delta2)\n",
        "            db1 = np.sum(delta2, axis=0)\n",
        "    \n",
        "            # Add regularization terms (b1 and b2 don't have regularization terms)\n",
        "            dW2 += self.reg_lambda * W2\n",
        "            dW1 += self.reg_lambda * W1\n",
        "    \n",
        "            # Gradient descent parameter update\n",
        "            W1 += -self.epsilon * dW1\n",
        "            b1 += -self.epsilon * db1\n",
        "            W2 += -self.epsilon * dW2\n",
        "            b2 += -self.epsilon * db2\n",
        "            \n",
        "            # Assign new parameters to the model\n",
        "            model = { 'W1': W1, 'b1': b1, 'W2': W2, 'b2': b2}\n",
        "            \n",
        "            # Optionally print the loss.\n",
        "            # This is expensive because it uses the whole dataset, so we don't want to do it too often.\n",
        "            if print_loss and i % 1000 == 0:\n",
        "                print(\"Loss after iteration %i: %f\"%(i, self.calculate_loss(model)))\n",
        "\n",
        "        return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUt6dpgu1bUq",
        "outputId": "00fd8f5c-5063-4576-cbf2-660311a946d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Build a model with a 3-dimensional hidden layer\n",
        "mlp = MLP(X,y,3)\n",
        "model = mlp.build_model(3, print_loss=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loss after iteration 0: 1.105687\n",
            "Loss after iteration 1000: 0.009754\n",
            "Loss after iteration 2000: 0.008186\n",
            "Loss after iteration 3000: 0.007843\n",
            "Loss after iteration 4000: 0.007699\n",
            "Loss after iteration 5000: 0.007609\n",
            "Loss after iteration 6000: 0.007541\n",
            "Loss after iteration 7000: 0.007484\n",
            "Loss after iteration 8000: 0.007436\n",
            "Loss after iteration 9000: 0.007394\n",
            "Loss after iteration 10000: 0.007357\n",
            "Loss after iteration 11000: 0.007324\n",
            "Loss after iteration 12000: 0.007295\n",
            "Loss after iteration 13000: 0.007270\n",
            "Loss after iteration 14000: 0.007247\n",
            "Loss after iteration 15000: 0.007228\n",
            "Loss after iteration 16000: 0.007211\n",
            "Loss after iteration 17000: 0.007196\n",
            "Loss after iteration 18000: 0.007183\n",
            "Loss after iteration 19000: 0.007172\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBbgDSOJyX63",
        "outputId": "8b310b4d-055e-41ac-fcca-95acbe2b8854",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(\"Test Accuracy : %0.2f\"%accuracy_score(y_test,predict(model,x_test)))\n",
        "print(\"Train Accuracy : %0.2f\"%accuracy_score(y_train,predict(model,x_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy : 0.93\n",
            "Train Accuracy : 1.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6zkvR8bzXps"
      },
      "source": [
        "train_accuracy = {}\n",
        "test_accuracy = {}\n",
        "\n",
        "for i in range(10):\n",
        "    x,y = load_iris(return_X_y= True)\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2)   #test size if 10% of total dataset\n",
        "    scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "    x_train = scaler.transform(x_train)\n",
        "    x_test = scaler.transform(x_test)\n",
        "    y_train = y_train.reshape(-1)\n",
        "\n",
        "    mlp = MLP(x_train,y_train,3)\n",
        "    model = mlp.build_model(3, print_loss=False)\n",
        "\n",
        "    test_accuracy[i] = accuracy_score(y_test, mlp.predict(model,x_test))\n",
        "    train_accuracy[i] = accuracy_score(y_train,mlp.predict(model,x_train))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9HZDA74z4Y7"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "temp = []\n",
        "df['Test accuracy'] = list(test_accuracy.values())\n",
        "df['Train_accuracy'] = list(train_accuracy.values())\n",
        "df.to_excel('ML_Lab6_Q2_Part3_Output.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JX8fFjgB3jdk"
      },
      "source": [
        "Different Train Size"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRTIO_Yi3ilO"
      },
      "source": [
        "train_acc = {}\n",
        "test_acc = {}\n",
        "training = [0.1,0.2,0.3,0.4,0.5,0.6]\n",
        "\n",
        "for i in training:\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x,y, train_size = i)   #test size if 10% of total dataset\n",
        "    scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "    x_train = scaler.transform(x_train)\n",
        "    x_test = scaler.transform(x_test)\n",
        "    y_train = y_train.reshape(-1)\n",
        "\n",
        "    mlp = MLP(x_train,y_train,3)\n",
        "    model = mlp.build_model(3, print_loss=False)\n",
        "\n",
        "    test_acc[i] = accuracy_score(y_test, mlp.predict(model,x_test))\n",
        "    train_acc[i] = accuracy_score(y_train,mlp.predict(model,x_train))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aF_R__Q13yTb",
        "outputId": "f9665919-db7e-4e5f-f1ea-2397d2287070",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "print(\"\\tTrain Accuracies \\t Test Accuracies\")\n",
        "train = list(train_acc.values())\n",
        "test = list(test_acc.values())\n",
        "for i in training:\n",
        "    print(f\"{i}\\t%0.2f\"%(train[int(i*10-1)]*100),\"\\t\\t\\t  %0.2f\"%(test[int(i*10-1)]*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\tTrain Accuracies \t Test Accuracies\n",
            "0.1\t100.00 \t\t\t  74.81\n",
            "0.2\t100.00 \t\t\t  91.67\n",
            "0.3\t100.00 \t\t\t  96.19\n",
            "0.4\t100.00 \t\t\t  95.56\n",
            "0.5\t100.00 \t\t\t  93.33\n",
            "0.6\t100.00 \t\t\t  95.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36gyFJw24DVg",
        "outputId": "1183929f-7663-4590-d2dc-1aa0be2683ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "plt.style.use('ggplot')\n",
        "plt.figure(figsize=(7,5))\n",
        "line1 = plt.plot(training,train,c='black',marker=\"o\")\n",
        "line2 = plt.plot(training,test,c=\"red\",marker='o')\n",
        "plt.legend((line1[0], line2[0]),('Training','Testing'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f610bdb94a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 283
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}