tutorials/explainers/KernelSHAP/kernelshap_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 KernelSHAP for weather prediction regressor\n",
    "This notebook demonstrates the use of DIANNA with the [KernelSHAP](https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf) explainer method for next day temperature predictor (regressor) on a [weather dataset](https://zenodo.org/records/10580833) containing tabular data of the temperatures from several locations in Europe."
   ]
  },
  {
   "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": [
    "#### 0 - 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\n",
    "\n",
    "from numba.core.errors import NumbaDeprecationWarning\n",
    "import warnings\n",
    "# silence the Numba deprecation warnings in shap\n",
    "warnings.simplefilter('ignore', category=NumbaDeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 - Loading the data\n",
    "Load weather prediction dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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 KernelSHAP explainer for tabular data with a pre-trained ONNX model, which is a MLP regressor for the weather dataset. The model is trained following this [notebook](https://github.com/dianna-ai/dianna-exploration/blob/main/example_data/model_generation/sunshine_prediction/generate_model.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.0719438]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "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 KernelSHAP with DIANNA\n",
    "The simplest way to run DIANNA on tabular 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 KernelSHAP needs it to generate proper perturbation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = dianna.explain_tabular(run_model, input_tabular=data_instance, method='kernelshap',\n",
    "                                     mode ='regression', training_data = X_train, \n",
    "                                     training_data_kmeans = 5, feature_names=X_test.columns)"
   ]
  },
  {
   "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": 10,
   "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",
    "fig, _ = plot_tabular(explanation, X_test.columns, num_features=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}