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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test if installation was successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check versions of the libraries\n",
    "# they should not be too much behind the ones in the comments...\n",
    "import numpy\n",
    "print(\"numpy\", numpy.__version__)        # >= 1.22.3\n",
    "import pandas\n",
    "print(\"pandas\", pandas.__version__)      # >= 1.4.2\n",
    "import scipy\n",
    "print(\"scipy\", scipy.__version__)        # >= 1.8.0\n",
    "import sklearn\n",
    "print(\"sklearn\", sklearn.__version__)    # >= 1.1.2\n",
    "import matplotlib\n",
    "print(\"matplotlib\", matplotlib.__version__)  # >= 3.5.1\n",
    "import PIL\n",
    "print(\"pillow\", PIL.__version__)         # >= 9.1.0\n",
    "import plotly\n",
    "print(\"plotly\", plotly.__version__)      # >= 5.7.0\n",
    "print(\"Congratulations! Your installation of the basic libraries was successful!\")\n",
    "# the following libraries are needed for the neural network example \n",
    "# (if you're working with the recommended pytorch, not keras/tensorflow)\n",
    "# if you have a computer with a (CUDA-enabled Nvidia) GPU, checkout this site:\n",
    "# https://pytorch.org/get-started/locally/\n",
    "# to install the correct version that can utilize the capabilities of your GPU\n",
    "# (if you're working on a normal laptop and you don't know what GPU means,\n",
    "# don't worry about it and just execute `$ pip install torch torchvision skorch`)\n",
    "import torch\n",
    "print(\"torch\", torch.__version__)        # >= 1.12.1\n",
    "import torchvision\n",
    "print(\"torchvision\", torchvision.__version__)  # >= 0.13.1\n",
    "import skorch\n",
    "print(\"skorch\", skorch.__version__)      # >= 0.11.0\n",
    "print(\"Congratulations! Your installation of the neural network libraries was successful!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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