ia/ml-labs/Laboratorul_3_Naive_Bayes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Laboratorul 3 - Naive Bayes",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTAdmwdb4A3O",
        "colab_type": "code",
        "outputId": "42656551-63df-4470-b0d1-d85ba990ee28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "!wget https://fmi-unibuc-ia.github.io/ia/Data/data_MNIST.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-23 07:12:08--  https://fmi-unibuc-ia.github.io/ia/Data/data_MNIST.zip\n",
            "Resolving fmi-unibuc-ia.github.io (fmi-unibuc-ia.github.io)... 185.199.111.153, 185.199.109.153, 185.199.110.153, ...\n",
            "Connecting to fmi-unibuc-ia.github.io (fmi-unibuc-ia.github.io)|185.199.111.153|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 629644 (615K) [application/zip]\n",
            "Saving to: ‘data_MNIST.zip’\n",
            "\n",
            "data_MNIST.zip      100%[===================>] 614.89K  --.-KB/s    in 0.06s   \n",
            "\n",
            "2020-04-23 07:12:09 (9.30 MB/s) - ‘data_MNIST.zip’ saved [629644/629644]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5Zs7rw64PxT",
        "colab_type": "code",
        "outputId": "7b1c4ff1-af4a-429d-a3de-08ca85ab9693",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "!unzip data_MNIST.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  data_MNIST.zip\n",
            "  inflating: data/test_images.txt    \n",
            "  inflating: data/test_labels.txt    \n",
            "  inflating: data/train_images.txt   \n",
            "  inflating: data/train_labels.txt   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FtCzft94RMg",
        "colab_type": "code",
        "outputId": "8d0655a2-c4f2-4dcf-c0a4-987148151af3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "train_images = np.loadtxt('data/train_images.txt')\n",
        "train_labels = np.loadtxt('data/train_labels.txt').astype(int)\n",
        "test_images = np.loadtxt('data/test_images.txt')\n",
        "test_labels = np.loadtxt('data/test_labels.txt').astype(int)\n",
        "\n",
        "image = train_images[0, :]\n",
        "image = np.reshape(image, (28, 28))\n",
        "\n",
        "plt.imshow(image.astype(np.uint8), cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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": "GY1LebHH7lj1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "2. Știind că valoarea minimă a unui pixel este 0, iar valoarea maximă este 255,\n",
        "calculați capetele a num_bins intervale (utilizați funcția linspace). Definiți\n",
        "metoda values_to_bins care primește o matrice de dimensiune (n_samples,\n",
        "n_features) și capetele intervalelor calculate anterior, iar pentru fiecare \n",
        "\n",
        "exemplu și fiecare atribut calculează indexul intervalului corespunzător\n",
        "(utilizați funcția np.digitize).\n",
        "Folosiți funcția definită pentru a discretiza mulțimea de antrenare și cea de\n",
        "testare."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFU0sgW84joG",
        "colab_type": "code",
        "outputId": "e87b19b9-d7bd-4822-8265-9ebea1c83362",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "def values_to_bins(X, num_bins):\n",
        "    bins = np.linspace(start=0, stop=255, num=num_bins)\n",
        "    return np.digitize(X, bins) \n",
        "\n",
        "values_to_bins(train_images, 5).mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3956326530612244"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agn0TcjY5Bbf",
        "colab_type": "code",
        "outputId": "0a706cea-498d-4819-8f1a-8360cd29ca69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "clf = MultinomialNB()\n",
        "\n",
        "X_train = values_to_bins(train_images, 5)\n",
        "X_test = values_to_bins(test_images, 5)\n",
        "\n",
        "clf.fit(X_train, train_labels)\n",
        "\n",
        "clf.score(X_test, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.806"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWDfi_5x-Ebd",
        "colab_type": "code",
        "outputId": "a37ad026-cf78-4e60-9c99-fc7ca05cf127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "for num_bins in [3, 5, 7, 9, 11]:\n",
        "    clf = MultinomialNB()\n",
        "\n",
        "    X_train = values_to_bins(train_images, num_bins)\n",
        "    X_test = values_to_bins(test_images, num_bins)\n",
        "\n",
        "    clf.fit(X_train, train_labels)\n",
        "\n",
        "    accuracy = clf.score(X_test, test_labels)\n",
        "    \n",
        "    print(num_bins, accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3 0.802\n",
            "5 0.806\n",
            "7 0.81\n",
            "9 0.81\n",
            "11 0.814\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZiTJyMe-hDr",
        "colab_type": "code",
        "outputId": "0f6b5aa8-27dc-466d-ef9c-afea76fb9e70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "clf = MultinomialNB()\n",
        "\n",
        "X_train = values_to_bins(train_images, 11)\n",
        "X_test = values_to_bins(test_images, 11)\n",
        "\n",
        "clf.fit(X_train, train_labels)\n",
        "pred_labels = clf.predict(X_test)\n",
        "\n",
        "wrongs = np.argwhere(pred_labels != test_labels)\n",
        "for index in wrongs[:10]:\n",
        "    image = test_images[index, :]\n",
        "    image = np.reshape(image, (28, 28))\n",
        "\n",
        "    plt.title(\"Aceasta imagine a fost clasificata ca \" + str(pred_labels[index][0]))\n",
        "    plt.imshow(image.astype(np.uint8), cmap='gray')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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": "DEfK0i55-pXz",
        "colab_type": "code",
        "outputId": "637cc998-5d84-48bb-d3e6-745763d97551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "def confusion_matrix(y_true, y_pred):\n",
        "    num_classes = len(np.unique(y_true))\n",
        "    \n",
        "    return [[np.sum((y_true == row) & (y_pred == column))\n",
        "             for column in range(num_classes)\n",
        "             ] for row in range(num_classes)]\n",
        "\n",
        "confusion_matrix(test_labels, pred_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[49, 0, 0, 0, 0, 0, 3, 0, 1, 0],\n",
              " [0, 49, 0, 0, 0, 0, 0, 0, 3, 0],\n",
              " [2, 4, 43, 2, 2, 0, 3, 1, 1, 0],\n",
              " [0, 0, 1, 49, 0, 1, 0, 0, 0, 2],\n",
              " [0, 0, 0, 0, 36, 0, 0, 0, 1, 9],\n",
              " [1, 1, 2, 9, 0, 32, 0, 0, 5, 2],\n",
              " [2, 3, 0, 0, 1, 0, 41, 0, 1, 0],\n",
              " [1, 2, 0, 0, 1, 0, 0, 41, 0, 5],\n",
              " [0, 1, 2, 4, 0, 2, 1, 1, 34, 1],\n",
              " [0, 0, 0, 1, 7, 0, 0, 0, 1, 33]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW1aRG9zAjGH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}