{
 "cells": [
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute this cell to install dependencies\n",
    "%pip install sf-hamilton[visualization] pandas scikit-learn numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MPG Simple Advanced Target [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/hamilton-tutorials/mpg-translation/MPGSimpleAdvancedTarget.ipynb) [![GitHub badge](https://img.shields.io/badge/github-view_source-2b3137?logo=github)](https://github.com/dagworks-inc/hamilton/blob/main/examples/hamilton-tutorials/mpg-translation/MPGSimpleAdvancedTarget.ipynb)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-07-20T17:55:33.133151Z"
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    "jupyter": {
     "is_executing": true
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stefankrawczyk/.pyenv/versions/knowledge_retrieval-py39/lib/python3.9/site-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from hamilton import driver\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T17:32:57.962238Z",
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   "outputs": [],
   "source": [
    "%load_ext hamilton.plugins.jupyter_magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
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    "%%cell_to_module pipeline --display\n",
    "# when done you can write to file and then load it as a module normally\n",
    "# add -w to do so\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "from hamilton.function_modifiers import extract_fields\n",
    "\n",
    "\n",
    "def mpg_df() -> pd.DataFrame:\n",
    "    url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n",
    "    column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
    "                    'Acceleration', 'Model Year', 'Origin']\n",
    "\n",
    "    raw_dataset = pd.read_csv(url, names=column_names,\n",
    "                              na_values='?', comment='\\t',\n",
    "                              sep=' ', skipinitialspace=True)\n",
    "\n",
    "    ## some schema manipulation\n",
    "    _mpg_df = raw_dataset.rename(columns={\"Model Year\": \"ModelYear\"})\n",
    "    return _mpg_df\n",
    "\n",
    "\n",
    "@extract_fields({\"train_dataset\": pd.DataFrame, \"test_dataset\": pd.DataFrame})\n",
    "def data_sets(mpg_df: pd.DataFrame, train_test_split: float = 0.8, seed: int = 123) -> dict:\n",
    "    # Do some feature engineering / data cleaning to create the data sets\n",
    "    # one hot encode -- we know the encoding here.\n",
    "    for value, country in {1: \"USA\", 2: \"Europe\", 3: \"Japan\"}.items():\n",
    "        mpg_df[country] = np.where(mpg_df[\"Origin\"] == value, 1, 0)\n",
    "    raw_dataset = mpg_df.dropna()\n",
    "    # split the pandas dataframe into train and test\n",
    "    train_dataset = raw_dataset.sample(frac=train_test_split, random_state=seed)\n",
    "    test_dataset = raw_dataset.drop(train_dataset.index)\n",
    "\n",
    "    return {\"train_dataset\": train_dataset, \"test_dataset\": test_dataset}\n",
    "\n",
    "\n",
    "def target_column() -> str:\n",
    "    return \"MPG\"\n",
    "\n",
    "\n",
    "@extract_fields({\"lr_model\": LinearRegression, \"scaler\": StandardScaler})\n",
    "def linear_model(train_dataset: pd.DataFrame, target_column: str) -> dict:\n",
    "    # Fit the model\n",
    "    # pull out target\n",
    "    train_labels = train_dataset.pop(target_column)\n",
    "    # Convert boolean columns to integers for the model\n",
    "    bool_columns = train_dataset.select_dtypes(include=[bool]).columns\n",
    "    train_dataset[bool_columns] = train_dataset[bool_columns].astype(int)\n",
    "    # Normalize the features for the model\n",
    "    scaler = StandardScaler()\n",
    "    train_dataset_scaled = scaler.fit_transform(train_dataset)\n",
    "\n",
    "    # Initialize and fit the Linear Regression model\n",
    "    linear_model = LinearRegression()\n",
    "    linear_model.fit(train_dataset_scaled, train_labels)\n",
    "    return {\"lr_model\": linear_model, \"scaler\": scaler}\n",
    "\n",
    "\n",
    "def evaluated_model(lr_model: LinearRegression,\n",
    "                    scaler: StandardScaler,\n",
    "                    test_dataset: pd.DataFrame, target_column: str) -> dict:\n",
    "    # evaluate the model - pull out target\n",
    "    test_labels = test_dataset.pop(target_column)\n",
    "    # Evaluate the model\n",
    "    # convert boolean columns to integers for the model\n",
    "    bool_columns = test_dataset.select_dtypes(include=[bool]).columns\n",
    "    test_dataset[bool_columns] = test_dataset[bool_columns].astype(int)\n",
    "    test_dataset_scaled = scaler.transform(test_dataset)\n",
    "\n",
    "    # Predict and evaluate the model\n",
    "    test_pred = lr_model.predict(test_dataset_scaled)\n",
    "    mae = mean_absolute_error(test_labels, test_pred)\n",
    "    test_results = {\n",
    "        \"linear_model\": mae\n",
    "    }\n",
    "    return test_results\n"
   ]
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       "</svg>\n"
      ],
      "text/plain": [
       "<hamilton.driver.Driver at 0x1516d7100>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr = driver.Builder().with_modules(pipeline).build()\n",
    "dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T17:37:06.029923Z",
     "start_time": "2024-07-20T17:37:05.934165Z"
    },
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     "showTitle": false,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'evaluated_model': {'linear_model': 2.4926580150007007},\n",
       " 'linear_model': {'lr_model': LinearRegression(), 'scaler': StandardScaler()}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = dr.execute([\"evaluated_model\", \"linear_model\"])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T17:37:36.535977Z",
     "start_time": "2024-07-20T17:37:36.241822Z"
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     "showTitle": false,
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   ],
   "source": [
    "# Visualize Overrides\n",
    "dr.visualize_execution([\"evaluated_model\"], \n",
    "                       overrides={\"linear_model\": result[\"linear_model\"]})"
   ]
  },
  {
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     "execution_count": 7,
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   "cell_type": "code",
   "execution_count": null,
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