tutorials/explainers/LIME/lime_tabular_weather.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"150\" alt=\"Logo_ER10\" src=\"https://user-images.githubusercontent.com/3244249/151994514-b584b984-a148-4ade-80ee-0f88b0aefa45.png\">\n",
    "\n",
    "### Model Interpretation using LIME for weather prediction regressor\n",
    "This notebook demonstrates the use of DIANNA with the LIME tabular method on the weather dataset.\n",
    "\n",
    "LIME (Local Interpretable Model-agnostic Explanations) is an explainable-AI method that aims to create an interpretable model that locally represents the classifier. For more details see the [LIME paper](https://arxiv.org/abs/1602.04938)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colab setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "running_in_colab = 'google.colab' in str(get_ipython())\n",
    "if running_in_colab:\n",
    "    # install dianna\n",
    "    !python3 -m pip install dianna[notebooks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dianna\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from dianna.utils.onnx_runner import SimpleModelRunner\n",
    "from dianna.utils.downloader import download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 - Loading the data\n",
    "Load weather prediction dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(download('weather_prediction_dataset_light.csv', 'data'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the data\n",
    "As the target, the sunshine hours for the next day in the data-set will be used. Therefore, we will remove the last data point as this has no target. A tabular regression model will be trained which does not require time-based data, therefore DATE and MONTH can be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = data.drop(columns=['DATE', 'MONTH'])[:-1]\n",
    "y_data = data.loc[1:][\"BASEL_sunshine\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training, validation, and test data split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_holdout, y_train, y_holdout = train_test_split(X_data, y_data, test_size=0.3, random_state=0)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_holdout, y_holdout, test_size=0.5, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get an instance to explain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get an instance from test data\n",
    "data_instance = X_test.iloc[10].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Loading ONNX model\n",
    "DIANNA supports ONNX models. Here we demonstrate the use of LIME explainer for tabular data with a pre-trained ONNX model, which is a MLP regressor for the weather dataset. <br>\n",
    "\n",
    "The model is trained following this notebook: <br>\n",
    "https://github.com/dianna-ai/dianna-exploration/blob/main/example_data/model_generation/sunshine_prediction/generate_model.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.0719438]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load onnx model and check the prediction with it\n",
    "model_path = download('sunshine_hours_regression_model.onnx', 'model')\n",
    "loaded_model = SimpleModelRunner(model_path)\n",
    "predictions = loaded_model(data_instance.reshape(1,-1).astype(np.float32))\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A runner function is created to prepare data for the ONNX inference session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import onnxruntime as ort\n",
    "\n",
    "def run_model(data):\n",
    "    # get ONNX predictions\n",
    "    sess = ort.InferenceSession(model_path)\n",
    "    input_name = sess.get_inputs()[0].name\n",
    "    output_name = sess.get_outputs()[0].name\n",
    "\n",
    "    onnx_input = {input_name: data.astype(np.float32)}\n",
    "    pred_onnx = sess.run([output_name], onnx_input)[0]\n",
    "    \n",
    "    return pred_onnx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Applying LIME with DIANNA\n",
    "The simplest way to run DIANNA on image data is with `dianna.explain_tabular`.\n",
    "\n",
    "DIANNA requires input in numpy format, so the input data is converted into a numpy array.\n",
    "\n",
    "Note that the training data is also required since LIME needs it to generate proper perturbation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = dianna.explain_tabular(run_model, input_tabular=data_instance, method='lime',\n",
    "                                     mode ='regression', training_data = X_train.to_numpy(),\n",
    "                                     feature_names=X_test.columns, class_names=['sunshine_hours'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Visualization\n",
    "The output can be visualized with the DIANNA built-in visualization function. It shows the top 10 importance of each feature contributing to the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dianna.visualization import plot_tabular\n",
    "\n",
    "_ = plot_tabular(explanation, X_test.columns, num_features=10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}