duckdb/tutorial/07-library-intergration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ce9e837-9280-45fb-a21c-b1d08baa12a1",
   "metadata": {},
   "source": [
    "# Tutorial 7: Beyond Pandas - Third-Party Library Integration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6347215d-c3e1-43e5-925e-6e4488116d41",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"> <b>Before we get started: </b> \n",
    "    <ul style=\"list-style-type: none;margin: 0;padding: 0;\">\n",
    "        <li>✍️ To run this notebook, you need to have Ponder installed and set up on your machine. If you have not done so already, please refer to our <a href=\"https://docs.ponder.io/getting_started/quickstart.html\">Quickstart guide</a> to get started.</li>\n",
    "        <li>📖 Otherwise, if you're just interested in browsing through the tutorial, keep reading below!</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9485aa6a",
   "metadata": {},
   "source": [
    "To support end-to-end data science workflows, Ponder supports integrations with other commonly used data science libraries in Python. In this tutorial, we will show examples of how you can work with other visualization and machine learning libraries in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6593a65b-3a07-4f8e-acd3-ba925e6c17ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install matplotlib seaborn plotly scikit-learn xgboost --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06e6870-3a5e-4b25-b693-74f0702b5527",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"> <b>Note: </b> While Ponder supports pandas and NumPy operations running on the data warehouse, we do not currently yet support other libraries to run the computation on the warehouse directly. Instead, Ponder pulls the data out of the warehouse and operates it in memory. If the table is exceeds 10k rows, we extract a sample of 10k rows to pull into memory. \n",
    "Here we use row_tranfer_limit in ponder.configure() to pull out all the rows in the dataset into memory. The third-party library operations will then be executed locally on this data and not in the warehouse.\n",
    "\n",
    "The focus of this tutorial is to demonstrate how Ponder works well and interoperates with these library. <span>  </span></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d0e78d",
   "metadata": {},
   "source": [
    " ## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc059d7-6cc4-40f2-851c-0534159fd6d9",
   "metadata": {},
   "source": [
    "Visualization is a critical part of any exploratory data analysis workflow for identifying patterns and trends in your data. Ponder works out of the box with popular plotting libraries in the PyData ecosystem, including sklearn and matplotlib. \n",
    "\n",
    "### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bc7d1f3-d20b-4f01-9619-b7d4a1cb323f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-12 10:56:23 - Creating session JohzsEKSY882UqblrjyQo-cmSEYFnOTehxKylHfbtZ\n"
     ]
    }
   ],
   "source": [
    "import ponder; ponder.init()\n",
    "import modin.pandas as pd\n",
    "import duckdb\n",
    "duckdb_con = duckdb.connect()\n",
    "ponder.configure(default_connection=duckdb_con, row_transfer_limit=250_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd66a7cb-cd52-409d-85d1-4afe6dece78e",
   "metadata": {},
   "source": [
    "Here is an example of how to plot a histogram with Ponder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eda22f36-7990-459a-8ea8-9227af25d785",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://github.com/ponder-org/ponder-datasets/blob/main/yellow_tripdata_2015-01.csv?raw=True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7053491e-cdc7-46ba-8dce-6d55280ef376",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"TOTAL_AMOUNT\"].plot.hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941dbe47-ef09-4900-b709-e71849acea8c",
   "metadata": {},
   "source": [
    "Here is an example of how to plot a scatterplot with Ponder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5255118-e13f-44a7-b346-b1f7df715928",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='MilesPerGal', ylabel='Horsepower'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/car.csv\")\n",
    "df.plot(x=\"MilesPerGal\",y=\"Horsepower\",kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f021a25-03b1-497d-89eb-35a07436e0e2",
   "metadata": {},
   "source": [
    "You can also make more elaborate plots by making using of matplotlib's `plt.plot` functionality directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e72ccdfd-071c-4946-8100-5191e5c69799",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ccd3c07-7849-4759-ad9c-bdc5f560ea6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbf2c6f6470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"MilesPerGal\")\n",
    "plt.ylabel(\"Horsepower\")\n",
    "for country in df.Origin.unique(): \n",
    "    cdf = df[df[\"Origin\"]==country]\n",
    "    plt.plot(cdf[\"MilesPerGal\"],cdf[\"Horsepower\"],'o',label=country)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edcf533d",
   "metadata": {},
   "source": [
    "### seaborn\n",
    "Here is an example of how to plot a scatterplot with marginal distributions with Ponder and seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fac66788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='MilesPerGal', ylabel='Horsepower'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/car.csv\")\n",
    "sns.scatterplot(data=df, x=\"MilesPerGal\",y=\"Horsepower\")\n",
    "sns.rugplot(data=df, x=\"MilesPerGal\",y=\"Horsepower\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549b6bf6",
   "metadata": {},
   "source": [
    "### plotly\n",
    "\n",
    "Here is an example of how to plot a histogram with Ponder and plotly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85a087ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/car.csv\")\n",
    "df = df[['MilesPerGal']]\n",
    "fig = px.histogram(df, opacity=0.5, nbins=10)\n",
    "fig.update_layout(xaxis_range=[0,60])\n",
    "fig.update_xaxes(title_text='MilesPerGal')\n",
    "fig.update_yaxes(title_text='No. of Cars')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ced6c0-d579-4e51-ab76-6e94afd42373",
   "metadata": {},
   "source": [
    "### Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44f64dd-606b-4961-b29b-2a15b5681a0b",
   "metadata": {},
   "source": [
    "Ponder integrates with popular libraries used for machine learning including sci-kit learn, XGBoost, HuggingFace, Tensorflow, and more. Ponder also provides the ability to run NumPy on your data warehouse directly, which is the foundation of machine learning training in Python.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d28bec20-6fba-420a-8633-37f2b6bb595e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://github.com/ponder-org/ponder-datasets/blob/main/USA_Housing.csv?raw=True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29a4712e-b95c-496c-8b55-810d988fb856",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.drop(columns=['PRICE', 'ADDRESS'])\n",
    "y = df[['PRICE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962ae214-1a99-4d08-9c6c-9fd66272766b",
   "metadata": {},
   "source": [
    "#### Sci-kit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ac72c1",
   "metadata": {},
   "source": [
    "The example here uses scikit-learn to predict housing prices. We replace missing values using SimpleImputer, train a LinearRegression model, and calculate the regression score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5787bd4-e3cd-41cc-a3ea-502011b1ee2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.25)\n",
    "\n",
    "my_imputer = SimpleImputer()\n",
    "train_X = my_imputer.fit_transform(train_X)\n",
    "test_X = my_imputer.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eaa22d0c-2946-4b04-ab48-31ce4c7e9a05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8083a1c9-2836-40a5-866b-f33d4fcfaf80",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(train_X, train_y)\n",
    "pred_y = lr.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cde9db6e-086c-4d7d-914b-bd9d00eb5bba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.52208618721653\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(pred_y,test_y)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84878ed4-4b1a-48a0-b105-0c8f4c7fcdd8",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af67d7e2",
   "metadata": {},
   "source": [
    "Here we train an XGBoost regression model on the same housing data above and calculate metrics using scikit-learn mean_absolute_error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07f8fe26-b718-4a4a-be6a-e1777a704bae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/labanyamukhopadhyay/opt/anaconda3/envs/ponder-local/lib/python3.10/site-packages/xgboost/sklearn.py:835: UserWarning:\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "my_model = XGBRegressor(n_estimators=1000, learning_rate=0.05)\n",
    "my_model.fit(train_X, train_y, early_stopping_rounds=5, \n",
    "             eval_set=[(test_X, test_y)], verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e9d842d-6a47-4b6b-b423-b42d83e72953",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error : 90730.19818035843\n",
      "88.66960051056105\n"
     ]
    }
   ],
   "source": [
    "predictions = my_model.predict(test_X)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(predictions, test_y)))\n",
    "print(r2_score(predictions,test_y)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6153b053-ed9d-4f4a-8ea2-9d8219c96ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb_con.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca60831",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "In this tutorial, we learned how you can use popular Python libraries such as matplotlib, seaborn, sci-kit learn and XGBoost alongside Ponder.\n",
    "\n",
    "In our [next tutorial](https://github.com/ponder-org/ponder-notebooks/blob/main/bigquery/tutorial/08-numpy.ipynb), we will demonstrate how you can use Ponder to run NumPy operations directly on your database."
   ]
  }
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