tutorials/demos/demo2_housing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kr9Ut95fpCN5"
   },
   "source": [
    "# Developing Pyreal Applications\n",
    "\n",
    "In this tutorial, we will be going through the process of putting together a RealApp object for future use.\n",
    "\n",
    "If you have not already tried Demonstration 1: Using Pyreal, please do so before trying this demo, to better understand the purpose of RealApp objects.\n",
    "\n",
    "## Scenario Details\n",
    "### Goals for this Scenario\n",
    "1. Learn the components of a Pyreal RealApp, and the steps required to create one for your target application\n",
    "2. Learn about Pyreal's built-in Transformers, and how to make custom, application-specific transformers\n",
    "\n",
    "### User Details\n",
    "\n",
    "- **User Role:** ML Engineer for Real Estate Firm\n",
    "- **Expertise:**\n",
    "    - **Domain (House Pricing):** Some experience\n",
    "    - **Machine Learning:** Expert\n",
    "    - **Explainable ML techniques:** Basic understanding\n",
    "- **User Goals:**\n",
    "    1. Understand what features contribute to average block prices in California\n",
    "\n",
    "### Model and Data Details\n",
    "- 9 Features\n",
    "- Predicts average price of block of houses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading\n",
    "\n",
    "We will begin by loading in our data matrix and targets, and taking a look at the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "n_rows = 500"
   ],
   "metadata": {
    "collapsed": false,
    "nbmake": {
     "mock": {
      "n_rows": 10
     }
    },
    "ExecuteTime": {
     "end_time": "2023-11-09T19:43:54.817958900Z",
     "start_time": "2023-11-09T19:43:54.698337900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-11-09T19:43:54.846959100Z",
     "start_time": "2023-11-09T19:43:54.718959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0      -122.23     37.88                  41          880           129.0   \n1      -122.22     37.86                  21         7099          1106.0   \n2      -122.24     37.85                  52         1467           190.0   \n3      -122.25     37.85                  52         1274           235.0   \n4      -122.25     37.85                  52         1627           280.0   \n..         ...       ...                 ...          ...             ...   \n495    -122.25     37.86                  52         4048           663.0   \n496    -122.26     37.85                  52         3618           768.0   \n497    -122.27     37.85                  52         4076           920.0   \n498    -122.27     37.85                  47         2077           400.0   \n499    -122.27     37.85                  50         1279           300.0   \n\n     population  households  median_income ocean_proximity  \n0           322         126         8.3252        NEAR BAY  \n1          2401        1138         8.3014        NEAR BAY  \n2           496         177         7.2574        NEAR BAY  \n3           558         219         5.6431        NEAR BAY  \n4           565         259         3.8462        NEAR BAY  \n..          ...         ...            ...             ...  \n495        1316         590         5.3794        NEAR BAY  \n496        1508         755         3.2619        NEAR BAY  \n497        1800         815         2.7054        NEAR BAY  \n498         719         326         2.2431        NEAR BAY  \n499         675         255         1.9028        NEAR BAY  \n\n[500 rows x 9 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41</td>\n      <td>880</td>\n      <td>129.0</td>\n      <td>322</td>\n      <td>126</td>\n      <td>8.3252</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21</td>\n      <td>7099</td>\n      <td>1106.0</td>\n      <td>2401</td>\n      <td>1138</td>\n      <td>8.3014</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52</td>\n      <td>1467</td>\n      <td>190.0</td>\n      <td>496</td>\n      <td>177</td>\n      <td>7.2574</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52</td>\n      <td>1274</td>\n      <td>235.0</td>\n      <td>558</td>\n      <td>219</td>\n      <td>5.6431</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52</td>\n      <td>1627</td>\n      <td>280.0</td>\n      <td>565</td>\n      <td>259</td>\n      <td>3.8462</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>-122.25</td>\n      <td>37.86</td>\n      <td>52</td>\n      <td>4048</td>\n      <td>663.0</td>\n      <td>1316</td>\n      <td>590</td>\n      <td>5.3794</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>-122.26</td>\n      <td>37.85</td>\n      <td>52</td>\n      <td>3618</td>\n      <td>768.0</td>\n      <td>1508</td>\n      <td>755</td>\n      <td>3.2619</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>-122.27</td>\n      <td>37.85</td>\n      <td>52</td>\n      <td>4076</td>\n      <td>920.0</td>\n      <td>1800</td>\n      <td>815</td>\n      <td>2.7054</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>-122.27</td>\n      <td>37.85</td>\n      <td>47</td>\n      <td>2077</td>\n      <td>400.0</td>\n      <td>719</td>\n      <td>326</td>\n      <td>2.2431</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>-122.27</td>\n      <td>37.85</td>\n      <td>50</td>\n      <td>1279</td>\n      <td>300.0</td>\n      <td>675</td>\n      <td>255</td>\n      <td>1.9028</td>\n      <td>NEAR BAY</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyreal.sample_applications import california_housing\n",
    "import pandas as pd\n",
    "\n",
    "X, y = california_housing.load_data(n_rows)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Current State: Explaining Without Pyreal\n",
    "Let's start by fitting some transformers, training a model, and trying to generate a global explanation of our model without using Pyreal, so we have a point of comparison for both coding effort and explanation quality over just using ML and explanation algorithms directly.\n",
    "\n",
    "First, we transform the data and train a basic Perceptron..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-11-09T19:43:56.411519100Z",
     "start_time": "2023-11-09T19:43:54.804958100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 Score: 0.506639033530101\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_transform = X.copy()\n",
    "\n",
    "# One-hot encode ocean_proximity to be compatible with model\n",
    "ohe = OneHotEncoder(sparse_output=False)\n",
    "ohe_cols = ohe.fit_transform(X_transform[[\"ocean_proximity\"]])\n",
    "ohe_cols_df = pd.DataFrame(ohe_cols)\n",
    "column_names = ohe.get_feature_names_out([\"ocean_proximity\"])\n",
    "ohe_cols_df.columns = column_names\n",
    "X_transform = X_transform.drop(\"ocean_proximity\", axis=\"columns\")\n",
    "X_transform = pd.concat([X_transform, ohe_cols_df], axis=1)\n",
    "\n",
    "# Impute data to be compatible with model\n",
    "imp = SimpleImputer(strategy=\"mean\")\n",
    "imputed_bedrooms = imp.fit_transform(X_transform[[\"total_bedrooms\"]])\n",
    "X_transform[\"total_bedrooms\"] = imputed_bedrooms\n",
    "\n",
    "# Add additional columns\n",
    "X_transform[\"average_rooms\"] = X_transform[\"total_rooms\"] / X_transform[\"households\"]\n",
    "X_transform[\"average_bedrooms\"] = X_transform[\"total_bedrooms\"] / X_transform[\"households\"]\n",
    "\n",
    "expected_column_order = X_transform.columns\n",
    "\n",
    "# Scale data to improve model performance\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transform, y, random_state=1)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train) # We use DataFrame here to maintain feature names (column names)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "\n",
    "# Train model\n",
    "model = MLPRegressor(hidden_layer_sizes=(50, 50),\n",
    "                     max_iter=500, solver=\"lbfgs\", alpha=0.001).fit(X_train, y_train)\n",
    "print(\"R^2 Score:\", model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... And then we can use SHAP to generate a beeswarm plot explanation of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:49.426928600Z",
     "start_time": "2023-11-09T19:43:56.405651400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x650 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Permutation explainer: 101it [00:11,  2.02it/s]                         \n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x650 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "explainer = shap.Explainer(model.predict, X_train[0:100])\n",
    "shap_values = explainer(X_train[0:100])\n",
    "shap.plots.bar(shap_values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, each point represents a row in our dataset. The farther right a point is, the more that specific feature value in that row *increased* the model prediction. The farther left a point is, the more that specific feature value in that row *decreased* the model prediction. The points are colored by feature value, with blue points representing a higher value.\n",
    "\n",
    "For example, we can see from this plot that higher latitudes and longitudes correspond to higher house prices. Similarly, blocks with fewer total rooms or higher populations are more expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Developing Pyreal Applications: Model Transformers\n",
    "Often, data needs to be transformed before being passed into a machine learning model, either to improve performance or to format it to be supported by the model architecture. For example, in the code cells above we had to one-hot encode, impute, and standardize our data in order to make model predictions on it.\n",
    "\n",
    "Unfortunately, these kinds of transformation can make explanations hard to parse. For example, in the explanation above, we need to consider every `ocean_proximity` child feature together to get the total contribution of the actual block proximity to the ocean. Additionally, values are given are standardized values, which is difficult to parse.\n",
    "\n",
    "By replacing these transformers with the Pyreal built-in equivalents, our explanations will be automatically presented with these muddling transformations undone. Additionally, the `MultiTypeImputer` transformer automatically imputes all columns with an appropriate strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:49.450523100Z",
     "start_time": "2024-03-20T18:45:49.429907700Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyreal.transformers import OneHotEncoder, MultiTypeImputer, StandardScaler, FeatureSelectTransformer\n",
    "\n",
    "# Initialize and fit transformers using fit_transformers\n",
    "transformers = [MultiTypeImputer(),\n",
    "                OneHotEncoder(columns=\"ocean_proximity\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While Pyreal offers many common transformer types, sometimes our specific domains may require a more specific transformer option. Luckily, Pyreal makes it easy to add new *custom transformers*. Let's start with a basic one.\n",
    "\n",
    "Below, we define the `PerHouseHoldAverager`. Pyreal Transformers extend the base `Transformer` class (line 3). They then define, at minimum, a `data_transform` function. This function acts just like the `.transform()` function of the sklearn transformers we used above - it takes in a dataframe, and returns the dataframe with one or more features transformed. In this case, our transformer adds a new column, with the per-household average of the requested features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:49.458078900Z",
     "start_time": "2024-03-20T18:45:49.445499700Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyreal.transformers import TransformerBase\n",
    "\n",
    "class PerHouseholdAverager(TransformerBase):\n",
    "    def __init__(self, columns, **kwargs):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            columns (list of strings)\n",
    "                The names of the columns to average\n",
    "        \"\"\"\n",
    "        self.columns = columns\n",
    "        super().__init__(**kwargs)\n",
    "\n",
    "    def data_transform(self, x):\n",
    "        \"\"\"\n",
    "        Transform the data by adding a new column from total_[column] called average_[column].\n",
    "        This feature represents the average value of [column] per household.\n",
    "\n",
    "        Args:\n",
    "            x (DataFrame)\n",
    "                The data to transform\n",
    "        \"\"\"\n",
    "        for column in self.columns:\n",
    "            name = column.replace(\"total\", \"average\")\n",
    "            x[name] = x[column] / x[\"households\"]\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go ahead and add that transformer to our list. You may notice the `interpret` flag in our initialization. This flag tells Pyreal that this transformation improves the *interpretability* of the resulting data - or in other words, changes the presentation of the data in such a way that makes it easier to reason about (or at least, not more difficult).\n",
    "\n",
    "In this case, we are adding features (`average_rooms` and `average_bedrooms`) that we expect will be very easy for our users to reason about, so we do not need to undo this transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:49.473720Z",
     "start_time": "2024-03-20T18:45:49.461082300Z"
    }
   },
   "outputs": [],
   "source": [
    "transformers.append(PerHouseholdAverager(columns=[\"total_bedrooms\", \"total_rooms\"], interpret=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, there may be a way to make the data even more interpretable (ie, easy to reason about) than the original data, but that for whatever reason we do not want to or cannot make this change to the data being fed into the model.\n",
    "\n",
    "For example, the latitude and longitude features above are hard to reason about for most people. Something like a city or neighborhood name would be better, but trying to one-hot encode every neighborhood in California to feed into the model would be a massive memory drain. Instead, we will continue giving the model the `longitude`/`latitude` features, but we will then transform the *explanation* to use this more interpretable feature.\n",
    "\n",
    "To do so, we will use Pyreal's built-in `LatLongToPlace` transformer, which uses the `latitude` and `longitude` columns to get a more descriptive place name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:49.488259200Z",
     "start_time": "2024-03-20T18:45:49.474727Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyreal.transformers import LatLongToPlace\n",
    "\n",
    "transformers.append(LatLongToPlace(result_column=\"neighborhood\", interpret=True, model=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we use the `interpret` flag to indicate that this transformation makes our data more interpretable. We also add the `model=False` flag, to indicate that we should **not** apply this model when making model predictions.\n",
    "\n",
    "We will also add two additional Pyreal built-in transformers, to improve model performance. Pyreal transformers are run in the order they appear in the transformer list, so we need to be careful about our order. The `StandardScaler` standardizes all features (like the `sklearn` equivalent) while maintaining the DataFrame data type and column names. Finally, the `FeatureSelectTransformer` will reorder columns to ensure they are in the order expected by the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "transformers.append(StandardScaler())\n",
    "transformers.append(FeatureSelectTransformer(columns=expected_column_order))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:49.504093300Z",
     "start_time": "2024-03-20T18:45:49.489260900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Transformers: Summary\n",
    "\n",
    "In summary, when selecting Pyreal transformers, we take the following steps:\n",
    "1. Decide what transformations are required to get the data to a model-ready state. For each of these, initialize a transformer with the `model=True` flag.\n",
    "2. For each model-ready transformer, decide if the transformation makes the data more or less interpretable, and set the `interpret` flag accordingly.\n",
    "3. Decide on any additional transformation that may make the data or explanations more interpretable. For these, using the `model=False` and `interpret=True` flags.\n",
    "4. As needed, define custom transformers for additional transformations, extendeding the base `Transformer` class. See the [**custom transformers**](../custom_transformers.ipynb) tutorial for more information on this process."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing RealApps\n",
    "\n",
    "Now that we have our model transformers, we can gather the other components of the application:\n",
    " - [`model`]: We train our machine learning model using the external system of our choice.\n",
    " - [`feature_descriptions`]: This optional parameter is dictionary of feature names (ie, the data columns) to descriptions, which we can use if our feature names are not naturally readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:57.053204300Z",
     "start_time": "2024-03-20T18:45:49.505094800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'longitude': 'Longitude', 'latitude': 'Latitude', 'housing_median_age': 'Median house age', 'total_rooms': 'Total # of Rooms', 'total_bedrooms': 'Total # of Bedrooms', 'population': 'Population', 'households': 'Number of Households', 'median_income': 'Median Income', 'ocean_proximity': 'Proximity to Ocean', 'neighborhood': 'Neighborhood', 'average_bedrooms': 'Average # of Bedrooms Per Household', 'average_rooms': 'Average # of Rooms Per Household'}\n",
      "{'longitude': 'Longitude', 'latitude': 'Latitude', 'housing_median_age': 'Median house age', 'total_rooms': 'Total # of Rooms', 'total_bedrooms': 'Total # of Bedrooms', 'population': 'Population', 'households': 'Number of Households', 'median_income': 'Median Income', 'ocean_proximity': 'Proximity to Ocean', 'neighborhood': 'Neighborhood', 'average_bedrooms': 'Average # of Bedrooms Per Household', 'average_rooms': 'Average # of Rooms Per Household'}\n"
     ]
    }
   ],
   "source": [
    "from pyreal import RealApp\n",
    "\n",
    "feature_descriptions = california_housing.load_feature_descriptions()\n",
    "print(feature_descriptions)\n",
    "\n",
    "real_app = RealApp(model, \n",
    "                   X_train_orig=X, \n",
    "                   y_train=y, \n",
    "                   transformers=transformers,\n",
    "                   fit_transformers=True, \n",
    "                   feature_descriptions=feature_descriptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can now use the `real_app` object's `produce` functions to get explanations."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X has feature names, but MLPRegressor was fitted without feature names\n",
      "X has feature names, but MLPRegressor was fitted without feature names\n"
     ]
    }
   ],
   "source": [
    "house_of_interest = X.iloc[0]\n",
    "prediction = real_app.predict(house_of_interest)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:45:57.098610Z",
     "start_time": "2024-03-20T18:45:57.057734400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:46:00.658991200Z",
     "start_time": "2024-03-20T18:45:57.101606400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c7cab747bd2d4cd5833dd6225c140041"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ea5f3b6016964d3d8e19372501adb656"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation = real_app.produce_feature_contributions(house_of_interest, shap_type=\"kernel\", training_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:46:01.146905600Z",
     "start_time": "2024-03-20T18:46:00.663202800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyreal import visualize\n",
    " \n",
    "visualize.feature_bar_plot(explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can also take a look at similar houses from the dataset, and then predictions"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most similar house from dataset, predicted at $452600.00\n"
     ]
    },
    {
     "data": {
      "text/plain": "   Median house age  Total # of Rooms  Total # of Bedrooms  Population  \\\n0                41               880                129.0         322   \n\n   Number of Households  Median Income Proximity to Ocean  \\\n0                   126         8.3252           NEAR BAY   \n\n   Average # of Bedrooms Per Household  Average # of Rooms Per Household  \\\n0                              1.02381                          6.984127   \n\n  Neighborhood  \n0     Berkeley  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Median house age</th>\n      <th>Total # of Rooms</th>\n      <th>Total # of Bedrooms</th>\n      <th>Population</th>\n      <th>Number of Households</th>\n      <th>Median Income</th>\n      <th>Proximity to Ocean</th>\n      <th>Average # of Bedrooms Per Household</th>\n      <th>Average # of Rooms Per Household</th>\n      <th>Neighborhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>41</td>\n      <td>880</td>\n      <td>129.0</td>\n      <td>322</td>\n      <td>126</td>\n      <td>8.3252</td>\n      <td>NEAR BAY</td>\n      <td>1.02381</td>\n      <td>6.984127</td>\n      <td>Berkeley</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most similar house from dataset, predicted at $452600.00\n"
     ]
    },
    {
     "data": {
      "text/plain": "   Median house age  Total # of Rooms  Total # of Bedrooms  Population  \\\n0                41               880                129.0         322   \n\n   Number of Households  Median Income Proximity to Ocean  \\\n0                   126         8.3252           NEAR BAY   \n\n   Average # of Bedrooms Per Household  Average # of Rooms Per Household  \\\n0                              1.02381                          6.984127   \n\n  Neighborhood  \n0     Berkeley  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Median house age</th>\n      <th>Total # of Rooms</th>\n      <th>Total # of Bedrooms</th>\n      <th>Population</th>\n      <th>Number of Households</th>\n      <th>Median Income</th>\n      <th>Proximity to Ocean</th>\n      <th>Average # of Bedrooms Per Household</th>\n      <th>Average # of Rooms Per Household</th>\n      <th>Neighborhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>41</td>\n      <td>880</td>\n      <td>129.0</td>\n      <td>322</td>\n      <td>126</td>\n      <td>8.3252</td>\n      <td>NEAR BAY</td>\n      <td>1.02381</td>\n      <td>6.984127</td>\n      <td>Berkeley</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_house = real_app.produce_similar_examples(house_of_interest, num_examples=1)\n",
    "print(\"Most similar house from dataset, predicted at $%.2f\" % similar_house[\"y\"].iloc[0])\n",
    "similar_house[\"X\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-03-20T18:46:06.225788100Z",
     "start_time": "2024-03-20T18:46:01.154258Z"
    }
   }
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Pyreal Usability Study",
   "private_outputs": true,
   "provenance": []
  },
  "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}