t81_558_class_02_1_python_pandas.ipynb

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    "<a href=\"https://colab.research.google.com/github/jeffheaton/app_deep_learning/blob/main/t81_558_class_02_1_python_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X0TRjzNmPc-s"
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   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "\n",
    "**Module 2: Python for Machine Learning**\n",
    "\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kJ-2rjbRPc-s"
   },
   "source": [
    "# Module 2 Material\n",
    "\n",
    "Main video lecture:\n",
    "\n",
    "- **Part 2.1: Introduction to Pandas** [[Video]](https://www.youtube.com/watch?v=wixHCvnvnsU&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_02_1_python_pandas.ipynb)\n",
    "- Part 2.2: Categorical Values [[Video]](https://www.youtube.com/watch?v=Fm7Ax23hDP0&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_02_2_pandas_cat.ipynb)\n",
    "- Part 2.3: Grouping, Sorting, and Shuffling in Python Pandas [[Video]](https://www.youtube.com/watch?v=tUhaD8xWd7k&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_02_3_pandas_grouping.ipynb)\n",
    "- Part 2.4: Using Apply and Map in Pandas [[Video]](https://www.youtube.com/watch?v=YNo_mg1RrkM&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_02_4_pandas_functional.ipynb)\n",
    "- Part 2.5: Feature Engineering in Pandas for Deep Learning in PyTorch [[Video]](https://www.youtube.com/watch?v=ezaVtM405Qs&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_02_5_pandas_features.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iZ_8UV9iPc-s"
   },
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running and maps Google Drive if needed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
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    "id": "bW1I-KvlPc-t",
    "outputId": "e2064ab4-b6a6-41bb-c8f1-462b9c2a3d4a"
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "Note: using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    from google.colab import drive\n",
    "\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L40PpDgLPc-t"
   },
   "source": [
    "# Part 2.1: Introduction to Pandas\n",
    "\n",
    "[Pandas](http://pandas.pydata.org/) is an open-source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It is based on the [dataframe](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) concept found in the [R programming language](https://www.r-project.org/about.html). For this class, Pandas will be the primary means by which we manipulate data to be processed by neural networks.\n",
    "\n",
    "The data frame is a crucial component of Pandas. We will use it to access the [auto-mpg dataset](https://archive.ics.uci.edu/ml/datasets/Auto+MPG). You can find this dataset on the UCI machine learning repository. For this class, we will use a version of the Auto MPG dataset, where I added column headers. You can find my [version](https://data.heatonresearch.com/data/t81-558/auto-mpg.csv) at [https://data.heatonresearch.com/](https://data.heatonresearch.com/).\n",
    "\n",
    "UCI took this dataset from the StatLib library, which Carnegie Mellon University maintains. The dataset was used in the 1983 American Statistical Association Exposition. It contains data for 398 cars, including [mpg](https://en.wikipedia.org/wiki/Fuel_economy_in_automobiles), [cylinders](<https://en.wikipedia.org/wiki/Cylinder_(engine)>), [displacement](https://en.wikipedia.org/wiki/Engine_displacement), [horsepower](https://en.wikipedia.org/wiki/Horsepower) , weight, acceleration, model year, origin and the car's name.\n",
    "\n",
    "The following code loads the MPG dataset into a data frame:\n"
   ]
  },
  {
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       "      <td>chevrolet chevelle malibu</td>\n",
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       "      <td>ford torino</td>\n",
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      "text/plain": [
       "    mpg  cylinders  displacement  ... year  origin                       name\n",
       "0  18.0          8         307.0  ...   70       1  chevrolet chevelle malibu\n",
       "1  15.0          8         350.0  ...   70       1          buick skylark 320\n",
       "2  18.0          8         318.0  ...   70       1         plymouth satellite\n",
       "3  16.0          8         304.0  ...   70       1              amc rebel sst\n",
       "4  17.0          8         302.0  ...   70       1                ford torino\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple dataframe\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 7)\n",
    "df = pd.read_csv(\"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\")\n",
    "display(df[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KyOFIsFLPc-u"
   },
   "source": [
    "The **display** function provides a cleaner display than merely printing the data frame. Specifying the maximum rows and columns allows you to achieve greater control over the display.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "height": 236
    },
    "id": "yxcfKTiDPc-u",
    "outputId": "bf369b8d-c567-47e5-a7f0-78c58b5f1e79"
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       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  ... year  origin  \\\n",
       "0    18.0          8         307.0  ...   70       1   \n",
       "1    15.0          8         350.0  ...   70       1   \n",
       "..    ...        ...           ...  ...  ...     ...   \n",
       "396  28.0          4         120.0  ...   82       1   \n",
       "397  31.0          4         119.0  ...   82       1   \n",
       "\n",
       "                          name  \n",
       "0    chevrolet chevelle malibu  \n",
       "1            buick skylark 320  \n",
       "..                         ...  \n",
       "396                ford ranger  \n",
       "397                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_columns\", 7)\n",
    "pd.set_option(\"display.max_rows\", 5)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ceS8VszPPc-u"
   },
   "source": [
    "It is possible to generate a second data frame to display statistical information about the first data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0tVyccHpPc-u",
    "outputId": "1b7dd999-11cb-4e68-ec75-410af5eac1bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'mpg', 'mean': 23.514572864321607, 'var': 61.089610774274405, 'sdev': 7.815984312565782}\n",
      "{'name': 'cylinders', 'mean': 5.454773869346734, 'var': 2.893415439920003, 'sdev': 1.7010042445332119}\n",
      "{'name': 'displacement', 'mean': 193.42587939698493, 'var': 10872.199152247384, 'sdev': 104.26983817119591}\n",
      "{'name': 'weight', 'mean': 2970.424623115578, 'var': 717140.9905256763, 'sdev': 846.8417741973268}\n",
      "{'name': 'acceleration', 'mean': 15.568090452261307, 'var': 7.604848233611383, 'sdev': 2.757688929812676}\n",
      "{'name': 'year', 'mean': 76.01005025125629, 'var': 13.672442818627143, 'sdev': 3.697626646732623}\n",
      "{'name': 'origin', 'mean': 1.5728643216080402, 'var': 0.6432920268850549, 'sdev': 0.8020548777266148}\n"
     ]
    }
   ],
   "source": [
    "# Strip non-numerics\n",
    "df = df.select_dtypes(include=[\"int\", \"float\"])\n",
    "\n",
    "headers = list(df.columns.values)\n",
    "fields = []\n",
    "\n",
    "for field in headers:\n",
    "    fields.append(\n",
    "        {\n",
    "            \"name\": field,\n",
    "            \"mean\": df[field].mean(),\n",
    "            \"var\": df[field].var(),\n",
    "            \"sdev\": df[field].std(),\n",
    "        }\n",
    "    )\n",
    "\n",
    "for field in fields:\n",
    "    print(field)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dt4tH5P4Pc-u"
   },
   "source": [
    "This code outputs a list of dictionaries that hold this statistical information. This information looks similar to the JSON code seen in Module 1. If proper JSON is needed, the program should add these records to a list and call the Python JSON library's **dumps** command.\n",
    "\n",
    "The Python program can convert this JSON-like information to a data frame for better display.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "W_96J6LJPc-u",
    "outputId": "18d39802-0f56-4b1a-a4a0-d924f50c90f5"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>0</th>\n",
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       "      <td>61.089611</td>\n",
       "      <td>7.815984</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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      ],
      "text/plain": [
       "           name         mean            var        sdev\n",
       "0           mpg    23.514573      61.089611    7.815984\n",
       "1     cylinders     5.454774       2.893415    1.701004\n",
       "2  displacement   193.425879   10872.199152  104.269838\n",
       "3        weight  2970.424623  717140.990526  846.841774\n",
       "4  acceleration    15.568090       7.604848    2.757689\n",
       "5          year    76.010050      13.672443    3.697627\n",
       "6        origin     1.572864       0.643292    0.802055"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_columns\", 0)\n",
    "pd.set_option(\"display.max_rows\", 0)\n",
    "df2 = pd.DataFrame(fields)\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C9_Ee4zQPc-v"
   },
   "source": [
    "## Missing Values\n",
    "\n",
    "Missing values are a reality of machine learning. Ideally, every row of data will have values for all columns. However, this is rarely the case. Most of the values are present in the MPG database. However, there are missing values in the horsepower column. A common practice is to replace missing values with the median value for that column. The program calculates the [median](https://en.wikipedia.org/wiki/Median). The following code replaces any NA values in horsepower with the median:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FIXbcwg4Pc-v",
    "outputId": "df3e1d85-17b8-44bc-cb74-b6313108b247"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "horsepower has na? True\n",
      "Filling missing values...\n",
      "horsepower has na? False\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "print(f\"horsepower has na? {pd.isnull(df['horsepower']).values.any()}\")\n",
    "\n",
    "print(\"Filling missing values...\")\n",
    "med = df[\"horsepower\"].median()\n",
    "df[\"horsepower\"] = df[\"horsepower\"].fillna(med)\n",
    "# df = df.dropna() # you can also simply drop NA values\n",
    "\n",
    "print(f\"horsepower has na? {pd.isnull(df['horsepower']).values.any()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NBzk21ypPc-v"
   },
   "source": [
    "## Dealing with Outliers\n",
    "\n",
    "Outliers are values that are unusually high or low. We typically consider outliers to be a value that is several standard deviations from the mean. Sometimes outliers are simply errors; this is a result of [observation error](https://en.wikipedia.org/wiki/Observational_error). Outliers can also be truly large or small values that may be difficult to address. The following function can remove such values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lHl573XqPc-v"
   },
   "outputs": [],
   "source": [
    "# Remove all rows where the specified column is +/- sd standard deviations\n",
    "def remove_outliers(df, name, sd):\n",
    "    drop_rows = df.index[(np.abs(df[name] - df[name].mean())\n",
    "                          >= (sd * df[name].std()))]\n",
    "    df.drop(drop_rows, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXfJZGyqPc-v"
   },
   "source": [
    "The code below will drop every row from the Auto MPG dataset where the horsepower is two standard deviations or more above or below the mean.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 327
    },
    "id": "9ILicqKcPc-v",
    "outputId": "1add0f3f-0933-4a16-ffe5-afb871c866ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length before MPG outliers dropped: 398\n",
      "Length after MPG outliers dropped: 388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-66622ca5b2b3>:13: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  df.drop(\"name\", 1, inplace=True)\n"
     ]
    },
    {
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       "\n",
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      ],
      "text/plain": [
       "      mpg  cylinders  displacement  ...  acceleration  year  origin\n",
       "0    18.0          8         307.0  ...          12.0    70       1\n",
       "1    15.0          8         350.0  ...          11.5    70       1\n",
       "..    ...        ...           ...  ...           ...   ...     ...\n",
       "396  28.0          4         120.0  ...          18.6    82       1\n",
       "397  31.0          4         119.0  ...          19.4    82       1\n",
       "\n",
       "[388 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "# create feature vector\n",
    "med = df[\"horsepower\"].median()\n",
    "df[\"horsepower\"] = df[\"horsepower\"].fillna(med)\n",
    "\n",
    "# Drop the name column\n",
    "df.drop(\"name\", 1, inplace=True)\n",
    "\n",
    "# Drop outliers in horsepower\n",
    "print(\"Length before MPG outliers dropped: {}\".format(len(df)))\n",
    "remove_outliers(df, \"mpg\", 2)\n",
    "print(\"Length after MPG outliers dropped: {}\".format(len(df)))\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 0)\n",
    "pd.set_option(\"display.max_rows\", 5)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D3C0rBLFPc-v"
   },
   "source": [
    "## Dropping Fields\n",
    "\n",
    "You must drop fields that are of no value to the neural network. The following code removes the name column from the MPG dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZAXR_oWxPc-v",
    "outputId": "134f6ad7-77c2-4809-ac9c-bda0fc0ac2d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before drop: ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin', 'name']\n",
      "After drop: ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-4af43aae8a9c>:6: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  df.drop(\"name\", 1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "print(f\"Before drop: {list(df.columns)}\")\n",
    "df.drop(\"name\", 1, inplace=True)\n",
    "print(f\"After drop: {list(df.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JuNpuuT7Pc-w"
   },
   "source": [
    "## Concatenating Rows and Columns\n",
    "\n",
    "Python can concatenate rows and columns together to form new data frames. The code below creates a new data frame from the **name** and **horsepower** columns from the Auto MPG dataset. The program does this by concatenating two columns together.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 236
    },
    "id": "ol6P7SlFPc-w",
    "outputId": "68c3e8cd-e94b-4c91-e6f5-01d10e4ac17e"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    60% {\n",
       "      border-color: transparent;\n",
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       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
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       "\n",
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       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
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       "    </button>\n",
       "    <script>\n",
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       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('result');\n",
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       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                          name  horsepower\n",
       "0    chevrolet chevelle malibu       130.0\n",
       "1            buick skylark 320       165.0\n",
       "..                         ...         ...\n",
       "396                ford ranger        79.0\n",
       "397                 chevy s-10        82.0\n",
       "\n",
       "[398 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new dataframe from name and horsepower\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "col_horsepower = df[\"horsepower\"]\n",
    "col_name = df[\"name\"]\n",
    "result = pd.concat([col_name, col_horsepower], axis=1)\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 0)\n",
    "pd.set_option(\"display.max_rows\", 5)\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "23K7Fc2KPc-w"
   },
   "source": [
    "The **concat** function can also concatenate rows together. This code concatenates the first two rows and the last two rows of the Auto MPG dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "1Ggw-tuRPc-w",
    "outputId": "1ca5d601-04c8-45e0-ec50-883c2e943cb9"
   },
   "outputs": [
    {
     "data": {
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       "      <th>mpg</th>\n",
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       "      <td>chevrolet chevelle malibu</td>\n",
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       "\n",
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      ],
      "text/plain": [
       "      mpg  cylinders  displacement  ...  year  origin  \\\n",
       "0    18.0          8         307.0  ...    70       1   \n",
       "1    15.0          8         350.0  ...    70       1   \n",
       "396  28.0          4         120.0  ...    82       1   \n",
       "397  31.0          4         119.0  ...    82       1   \n",
       "\n",
       "                          name  \n",
       "0    chevrolet chevelle malibu  \n",
       "1            buick skylark 320  \n",
       "396                ford ranger  \n",
       "397                 chevy s-10  \n",
       "\n",
       "[4 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new dataframe from first 2 rows and last 2 rows\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "result = pd.concat([df[0:2], df[-2:]], axis=0)\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 7)\n",
    "pd.set_option(\"display.max_rows\", 0)\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pieLh51kPc-w"
   },
   "source": [
    "## Training and Validation\n",
    "\n",
    "We must evaluate a machine learning model based on its ability to predict values that it has never seen before. Because of this, we often divide the training data into a validation and training set. The machine learning model will learn from the training data but ultimately be evaluated based on the validation data.\n",
    "\n",
    "- **Training Data** - **In Sample Data** - The data that the neural network used to train.\n",
    "- **Validation Data** - **Out of Sample Data** - The data that the machine learning model is evaluated upon after it is fit to the training data.\n",
    "\n",
    "There are two effective means of dealing with training and validation data:\n",
    "\n",
    "- **Training/Validation Split** - The program splits the data according to some ratio between a training and validation (hold-out) set. Typical rates are 80% training and 20% validation.\n",
    "- **K-Fold Cross Validation** - The program splits the data into several folds and models. Because the program creates the same number of models as folds, the program can generate out-of-sample predictions for the entire dataset.\n",
    "\n",
    "The code below splits the MPG data into a training and validation set. The training set uses 80% of the data, and the validation set uses 20%. Figure 2.TRN-VAL shows how we train a model on 80% of the data and then validated against the remaining 20%.\n",
    "\n",
    "**Figure 2.TRN-VAL: Training and Validation**\n",
    "![Training and Validation](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/class_1_train_val.png \"Training and Validation\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LjPVxQipPc-w",
    "outputId": "21bdda20-5f67-4787-d2f8-b4fab981e64d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DF: 314\n",
      "Validation DF: 84\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "# Usually a good idea to shuffle\n",
    "df = df.reindex(np.random.permutation(df.index))\n",
    "\n",
    "mask = np.random.rand(len(df)) < 0.8\n",
    "trainDF = pd.DataFrame(df[mask])\n",
    "validationDF = pd.DataFrame(df[~mask])\n",
    "\n",
    "print(f\"Training DF: {len(trainDF)}\")\n",
    "print(f\"Validation DF: {len(validationDF)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RT_4yOcuPc-w"
   },
   "source": [
    "## Converting a Dataframe to a Matrix\n",
    "\n",
    "Neural networks do not directly operate on Python data frames. A neural network requires a numeric matrix. The program uses a data frame's **values** property to convert the data to a matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kOP2e28LPc-w",
    "outputId": "2ca85af1-f3b8-4df5-bd0e-b70a20e2c238"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15.0, 8, 429.0, ..., 70, 1, 'ford galaxie 500'],\n",
       "       [25.8, 4, 156.0, ..., 81, 1, 'dodge aries wagon (sw)'],\n",
       "       [13.0, 8, 350.0, ..., 76, 1, 'chevy c10'],\n",
       "       ...,\n",
       "       [17.5, 6, 258.0, ..., 76, 1, 'amc pacer d/l'],\n",
       "       [15.0, 8, 350.0, ..., 75, 1, 'chevrolet bel air'],\n",
       "       [30.0, 4, 146.0, ..., 80, 2, 'mercedes-benz 240d']], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VRb-CDKYPc-x"
   },
   "source": [
    "You might wish only to convert some of the columns, to leave out the name column, use the following code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tLi9PbzzPc-x",
    "outputId": "8fb1efa4-e53e-475d-c9f6-daf80a4f3922"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15. ,   8. , 429. , ...,  10. ,  70. ,   1. ],\n",
       "       [ 25.8,   4. , 156. , ...,  14.4,  81. ,   1. ],\n",
       "       [ 13. ,   8. , 350. , ...,  12. ,  76. ,   1. ],\n",
       "       ...,\n",
       "       [ 17.5,   6. , 258. , ...,  17.8,  76. ,   1. ],\n",
       "       [ 15. ,   8. , 350. , ...,  14. ,  75. ,   1. ],\n",
       "       [ 30. ,   4. , 146. , ...,  21.8,  80. ,   2. ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    [\n",
    "        \"mpg\",\n",
    "        \"cylinders\",\n",
    "        \"displacement\",\n",
    "        \"horsepower\",\n",
    "        \"weight\",\n",
    "        \"acceleration\",\n",
    "        \"year\",\n",
    "        \"origin\",\n",
    "    ]\n",
    "].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e_fDxfxzPc-x"
   },
   "source": [
    "## Saving a Dataframe to CSV\n",
    "\n",
    "Many of the assignments in this course will require that you save a data frame to submit to the instructor. The following code performs a shuffle and then saves a new copy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "gdxj7en3Pc-x"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "path = \".\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "filename_write = os.path.join(path, \"auto-mpg-shuffle.csv\")\n",
    "df = df.reindex(np.random.permutation(df.index))\n",
    "# Specify index = false to not write row numbers\n",
    "df.to_csv(filename_write, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mbiNd8LkPc-x"
   },
   "source": [
    "## Saving a Dataframe to Pickle\n",
    "\n",
    "A variety of software programs can use text files stored as CSV. However, they take longer to generate and can sometimes lose small amounts of precision in the conversion. Generally, you will output to CSV because it is very compatible, even outside of Python. Another format is [Pickle](https://docs.python.org/3/library/pickle.html). The code below stores the Dataframe to Pickle. Pickle stores data in the exact binary representation used by Python. The benefit is that there is no loss of data going to CSV format. The disadvantage is that generally, only Python programs can read Pickle files.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "Yndp62B1Pc-x"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "path = \".\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "filename_write = os.path.join(path, \"auto-mpg-shuffle.pkl\")\n",
    "df = df.reindex(np.random.permutation(df.index))\n",
    "\n",
    "with open(filename_write, \"wb\") as fp:\n",
    "    pickle.dump(df, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gqyjYGiKPc-x"
   },
   "source": [
    "Loading the pickle file back into memory is accomplished by the following lines of code. Notice that the index numbers are still jumbled from the previous shuffle? Loading the CSV rebuilt (in the last step) did not preserve these values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 236
    },
    "id": "EEqdoNSMPc-x",
    "outputId": "ce815a34-a7d6-4064-9d2e-f12a09067c98"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
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       "      <td>225.0</td>\n",
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       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth valiant</td>\n",
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       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>16.5</td>\n",
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       "      <td>79</td>\n",
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       "      <td>mercury grand marquis</td>\n",
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       "<p>398 rows × 9 columns</p>\n",
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       "\n",
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       "      .colab-df-generate {\n",
       "        background-color: #E8F0FE;\n",
       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_5ee75274-d6df-4176-b6d8-c8aa60d763d6 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('df');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  ...  year  origin                    name\n",
       "191  22.0          6         225.0  ...    76       1        plymouth valiant\n",
       "287  16.5          8         351.0  ...    79       1   mercury grand marquis\n",
       "..    ...        ...           ...  ...   ...     ...                     ...\n",
       "70   13.0          8         400.0  ...    72       1  chrysler newport royal\n",
       "337  32.4          4         107.0  ...    80       3            honda accord\n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "path = \".\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv\", na_values=[\"NA\", \"?\"]\n",
    ")\n",
    "\n",
    "filename_read = os.path.join(path, \"auto-mpg-shuffle.pkl\")\n",
    "\n",
    "with open(filename_write, \"rb\") as fp:\n",
    "    df = pickle.load(fp)\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 7)\n",
    "pd.set_option(\"display.max_rows\", 5)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jVG4vxSiPc-x"
   },
   "source": [
    "# Module 2 Assignment\n",
    "\n",
    "You can find the second assignment here: [assignment 2](https://github.com/jeffheaton/app_deep_learning/blob/main/assignments/assignment_yourname_t81_558_class2.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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