assignments/assignment_yourname_t81_558_class8.ipynb

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    "<a href=\"https://colab.research.google.com/github/jeffheaton/app_deep_learning/blob/main/assignments/assignment_yourname_t81_558_class8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    "# T81-558: Applications of Deep Neural Networks\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/).\n",
    "\n",
    "**Module 8 Assignment: Feature Engineering**\n",
    "\n",
    "**Student Name: Your Name**"
   ]
  },
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   "source": [
    "# Assignment Instructions\n",
    "\n",
    "This assignment is similar to assignment 5, except that you must use feature engineering to solve it.  I provide you with a dataset that contains dimensions and the quality of items of specific shapes.  With the values of 'height', 'width', 'depth'. 'shape', and 'quality' you should try to predict the cost of these items.  You should be able to match very close to solution file, if you feature engineer correctly.  To get full credit your average cost should not be more than 50 off from the solution.  The autocorrector will let you know if you are in this range.\n",
    "\n",
    "You can find all of the needed CSV files here:\n",
    "\n",
    "* [Shapes - Training](https://data.heatonresearch.com/data/t81-558/datasets/shapes-train.csv)\n",
    "* [Shapes - Submit](https://data.heatonresearch.com/data/t81-558/datasets/shapes-test.csv)\n",
    "\n",
    "Use the training file to train your neural network and submit results for for the data contained in the test/submit file."
   ]
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   "source": [
    "try:\n",
    "  from google.colab import drive, userdata\n",
    "  drive.mount('/content/drive', force_remount=True)\n",
    "  COLAB = True\n",
    "  print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "  print(\"Note: not using Google CoLab\")\n",
    "  COLAB = False\n",
    "\n",
    "# Assignment Submission Key - Was sent you first week of class.\n",
    "# If you are in both classes, this is the same key.\n",
    "if COLAB:\n",
    "  # For Colab, add to your \"Secrets\" (key icon at the left)\n",
    "  key = userdata.get('T81_558_KEY')\n",
    "else:\n",
    "  # If not colab, enter your key here, or use an environment variable.\n",
    "  # (this is only an example key, use yours)\n",
    "  key = \"Gx5en9cEVvaZnjhdaushddhuhhO4PsI32sgldAXj\""
   ]
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   "metadata": {
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   "source": [
    "# Assignment Submit Function\n",
    "\n",
    "You will submit the 10 programming assignments electronically.  The following submit function can be used to do this.  My server will perform a basic check of each assignment and let you know if it sees any basic problems.\n",
    "\n",
    "**It is unlikely that should need to modify this function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "import base64\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import PIL\n",
    "import PIL.Image\n",
    "import io\n",
    "from typing import List, Union\n",
    "\n",
    "# This function submits an assignment.  You can submit an assignment as much as you like, only the final\n",
    "# submission counts.  The paramaters are as follows:\n",
    "# data - List of pandas dataframes or images.\n",
    "# key - Your student key that was emailed to you.\n",
    "# course - The course that you are in, currently t81-558 or t81-559.\n",
    "# no - The assignment class number, should be 1 through 10.\n",
    "# source_file - The full path to your Python or IPYNB file.  This must have \"_class1\" as part of its name.\n",
    "# .             The number must match your assignment number.  For example \"_class2\" for class assignment #2.\n",
    "\n",
    "def submit(\n",
    "    data: List[Union[pd.DataFrame, PIL.Image.Image]],\n",
    "    key: str,\n",
    "    course: str,\n",
    "    no: int,\n",
    "    source_file: str = None\n",
    ") -> None:\n",
    "    if source_file is None and '__file__' not in globals():\n",
    "        raise Exception(\"Must specify a filename when in a Jupyter notebook.\")\n",
    "    if source_file is None:\n",
    "        source_file = __file__\n",
    "\n",
    "    suffix = f'_class{no}'\n",
    "    if suffix not in source_file:\n",
    "        raise Exception(f\"{suffix} must be part of the filename.\")\n",
    "\n",
    "    ext = os.path.splitext(source_file)[-1].lower()\n",
    "    if ext not in ['.ipynb', '.py']:\n",
    "        raise Exception(f\"Source file is {ext}; must be .py or .ipynb\")\n",
    "\n",
    "    with open(source_file, \"rb\") as file:\n",
    "        encoded_python = base64.b64encode(file.read()).decode('ascii')\n",
    "\n",
    "    payload = []\n",
    "    for item in data:\n",
    "        if isinstance(item, PIL.Image.Image):\n",
    "            buffered = io.BytesIO()\n",
    "            item.save(buffered, format=\"PNG\")\n",
    "            payload.append({'PNG': base64.b64encode(buffered.getvalue()).decode('ascii')})\n",
    "        elif isinstance(item, pd.DataFrame):\n",
    "            payload.append({'CSV': base64.b64encode(item.to_csv(index=False).encode('ascii')).decode(\"ascii\")})\n",
    "        else:\n",
    "            raise ValueError(f\"Unsupported data type: {type(item)}\")\n",
    "\n",
    "    response = requests.post(\n",
    "        \"https://api.heatonresearch.com/wu/submit\",\n",
    "        headers={'x-api-key': key},\n",
    "        json={\n",
    "            'payload': payload,\n",
    "            'assignment': no,\n",
    "            'course': course,\n",
    "            'ext': ext,\n",
    "            'py': encoded_python\n",
    "        }\n",
    "    )\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        print(f\"Success: {response.text}\")\n",
    "    else:\n",
    "        print(f\"Failure: {response.text}\")"
   ]
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   },
   "source": [
    "# Assignment #8 Sample Code\n",
    "\n",
    "The following code provides a starting point for this assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XxG1Q-4806C-",
    "outputId": "4ae95bb1-1905-4c3a-8bd0-f7c5c096eb25",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "# You must identify your source file.  (modify for your local setup)\n",
    "file=\"/content/drive/My Drive/Colab Notebooks/assignment_yourname_t81_558_class8.ipynb\"  # Google CoLab\n",
    "# file='C:\\\\Users\\\\jeffh\\\\projects\\\\t81_558_deep_learning\\\\assignments\\\\assignment_yourname_class8.ipynb'  # Windows\n",
    "# file='/Users/jheaton/projects/t81_558_deep_learning/assignments/assignment_yourname_class8.ipynb'  # Mac/Linux\n",
    "\n",
    "\n",
    "# Reading data\n",
    "df_train = pd.read_csv(\"https://data.heatonresearch.com/data/t81-558/datasets/shapes-train.csv\")\n",
    "df_submit = pd.read_csv(\"https://data.heatonresearch.com/data/t81-558/datasets/shapes-test.csv\")\n",
    "\n",
    "## ... continue your code...\n",
    "\n",
    "## Submit assignment\n",
    "\n",
    "submit(source_file=file, data=[submit_df], key=key, no=8, course='t81-558')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "welI_CXa06C-"
   },
   "outputs": [],
   "source": []
  }
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