classify_images.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# First, you need to install shap module for the TreeExplained model\n",
        "!pip install shap"
      ],
      "metadata": {
        "id": "3R0kfeI0rZv3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72ce076e-7d24-428b-86ce-f190509ca21d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: shap in /usr/local/lib/python3.10/dist-packages (0.41.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shap) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from shap) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from shap) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from shap) (1.5.3)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.10/dist-packages (from shap) (4.65.0)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.10/dist-packages (from shap) (23.1)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.10/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap) (0.56.4)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap) (2.2.1)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba->shap) (67.7.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2022.7.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "zbP_by2ylfAO"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive, files\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import metrics\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.pylab as pl\n",
        "import shap\n",
        "import pickle\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive/', force_remount=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eS6xlr7BlqK7",
        "outputId": "6a12c725-4894-46ff-bbc8-5035382fc993"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load required files"
      ],
      "metadata": {
        "id": "XuiZWaOQvKwR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# you can change the path of the directory based on where you saved the repo.\n",
        "dataset_dir = '/content/drive/My Drive/peak/dataset_dir'\n",
        "pickle_dir = '/content/drive/My Drive/peak/pickle_dir'\n"
      ],
      "metadata": {
        "id": "qyo7AKAels5v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv(dataset_dir +\"/df_train.csv\")\n",
        "df_test = pd.read_csv(dataset_dir +\"/df_test.csv\")\n",
        "\n",
        "train_input = pickle.load(open(pickle_dir+\"/train_input.pickle\", \"rb\"))\n",
        "test_input = pickle.load(open(pickle_dir+\"/test_input.pickle\", \"rb\"))\n",
        "train_label = pickle.load(open(pickle_dir+\"/train_label.pickle\", \"rb\"))\n",
        "test_label = pickle.load(open(pickle_dir+\"/test_label.pickle\", \"rb\"))\n"
      ],
      "metadata": {
        "id": "7JD3bLdgu6np"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train a model using the Random Forest algorithm"
      ],
      "metadata": {
        "id": "lMbh6Jw7nkiA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You can specify your own seed and depth of the algorithm\n",
        "random_state, max_depth = 333, 13\n",
        "classifier_rf = RandomForestClassifier(random_state = random_state, max_depth=max_depth)\n",
        "classifier_rf_fit = classifier_rf.fit(train_input, train_label)\n",
        "classifier_rf_pred = classifier_rf_fit.predict(test_input)\n"
      ],
      "metadata": {
        "id": "OsbXUQYM7cjb"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Performance"
      ],
      "metadata": {
        "id": "roVCLI10ndHu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Random Forest Accuracy (Train): ', metrics.accuracy_score(classifier_rf_fit.predict(train_input), train_label)) \n",
        "print('Random Forest Accuracy (Test): ', metrics.accuracy_score(classifier_rf_pred, test_label)) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sIWog9Sjng6E",
        "outputId": "af1cb05a-0760-4a13-f73b-e11d7fcd5340"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy (Train):  0.8939259259259259\n",
            "Random Forest Accuracy (Test):  0.884\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Show the contributions of each topics using the TreeExplainer model"
      ],
      "metadata": {
        "id": "1tFC-5UEnsNw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "topics = 20\n",
        "my_list = [str(i) for i in np.arange(topics)]\n",
        "input_columns = list(map(lambda orig_string: 'topic ' + orig_string, my_list))\n",
        "\n",
        "train_df_input = pd.DataFrame(train_input, columns = input_columns)\n",
        "train_df_label = pd.DataFrame(train_label, columns = ['label'])\n",
        "len(train_df_input), len(train_df_label)\n",
        "\n",
        "test_df_input = pd.DataFrame(test_input, columns = input_columns)\n",
        "test_df_label = pd.DataFrame(test_label, columns = ['label'])\n"
      ],
      "metadata": {
        "id": "SXpRMeiRoz7c"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def show_topics_contributions(image_idx, demo_df):\n",
        "    # Train a Random Forest model\n",
        "    random_state, max_depth = 333, 13\n",
        "    classifier_rf = RandomForestClassifier(random_state = random_state, max_depth=max_depth)\n",
        "    classifier_rf_fit = classifier_rf.fit(train_input, train_label)\n",
        "    \n",
        "    # Train a TreeExplainer model for the binary (public/private) classification\n",
        "    explainer = shap.TreeExplainer(classifier_rf_fit)\n",
        "    sv = explainer(test_df_input.iloc[[demo_df[image_idx]],:])\n",
        "\n",
        "    if (test_df_label.iloc[[demo_df[image_idx]],:].values == 0):\n",
        "        exp = shap.Explanation(sv.values[:,:,0], \n",
        "                        sv.base_values[:,0], \n",
        "                        data=test_df_input.iloc[[demo_df[image_idx]],:].values, \n",
        "                        feature_names=test_df_input.columns)\n",
        "    else:\n",
        "        exp = shap.Explanation(sv.values[:,:,1], \n",
        "                        sv.base_values[:,1], \n",
        "                        data=test_df_input.iloc[[demo_df[image_idx]],:].values, \n",
        "                        feature_names=test_df_input.columns)\n",
        "\n",
        "    return shap.plots.waterfall(exp[0])\n"
      ],
      "metadata": {
        "id": "9R3qBOQGnSnC"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# You can change an index of image and dataframe (i.e., Train or Test)\n",
        "\n",
        "image_idx = 0\n",
        "show_topics_contributions(image_idx, list(df_test.index))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "bzFjdQMAqPM7",
        "outputId": "d9a2dba9-59ad-4fd3-ab83-d3cdf2ffbdb2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A-A3xKF-tLHO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}