LSBoost_notebook.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to the LSBoosting Notebook for Folktables!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, you'll be able to import US Census data from Folktables easily via the helper function and then train a LSBoostRegressor using your choice of hyperparameters!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install/Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/home/nikml/.conda/envs/dev/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import LSBoost\n",
    "# from LSBoost import LSBoostingRegressor\n",
    "import helper_functions as hf\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split\n",
    "from helper_functions import MSCE as MSCE\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "import gc\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Control Panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size: 437816\n",
      "Test data size: 109455\n",
      "Max y value: 1.0\n",
      "Min y value: 0.0\n",
      "Mean train label: 0.37863976060090154\n",
      "Mean test label: 0.3793950676955826\n",
      "Std train label: 0.26135886666234426\n",
      "Std test label: 0.26238206917782525\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set random seed\n",
    "random_state = 42\n",
    "np.random.seed(random_state)\n",
    "\n",
    "# set dataset task\n",
    "dataset_task = 'ACSIncome'\n",
    "target = 0\n",
    "states_list = ['CA', 'PA', 'NY', 'TX', 'FL', 'MD']\n",
    "year = '2018'\n",
    "\n",
    "# max income value\n",
    "cutoff = 100_000\n",
    "\n",
    "# import data\n",
    "data, labels = hf.get_data(task = dataset_task, target = target, states_list = states_list, year = year, cutoff = cutoff, random_state = random_state+1)\n",
    "\n",
    "# split train test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size = .2, random_state = random_state)\n",
    "\n",
    "# print out label information\n",
    "hf.print_data_report(x_train, x_test, y_train, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of rounds - (integer)\n",
    "T = 100\n",
    "\n",
    "# level sets - (integer)\n",
    "num_bins = 100\n",
    "\n",
    "# minimum number of points in level set for update to occur - (integer)\n",
    "min_group_size = 5\n",
    "\n",
    "# amount (multiplicative) level set must improve by to accept update if validation data or leniency factor for update requirements - (float)\n",
    "global_gamma = .005\n",
    "\n",
    "# weak learner class for UDT - (ML class with .predict method)\n",
    "weak_learner = DecisionTreeRegressor(max_depth = 1)\n",
    "\n",
    "# determines width of level sets, either 'default' for equal width or 'distribution' for equal number of expected points - (string)\n",
    "bin_type = 'distribution'\n",
    "\n",
    "# multiplicative weight for new update - (float)\n",
    "learning_rate = 1\n",
    "\n",
    "# starting model to boost. Set to None for weak learner to be use as initial model - (fit ML class with .predict method or None)\n",
    "initial_model = None\n",
    "\n",
    "# round final predictions to level set center or use output of weak learner - (bool)\n",
    "final_round = True\n",
    "\n",
    "# center level sets using historical mean - (bool)\n",
    "center_mean = True"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train LSBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LS:  37%|\u001b[32m███████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                                          \u001b[0m 37/100 02:13, Training Error=0.0328056\u001b[0m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early Termination at round: 37\n",
      "Memory released!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "LSBoostReg = LSBoost.LSBoostingRegressor(\n",
    "                                T = T, \n",
    "                                num_bins = num_bins, \n",
    "                                min_group_size = min_group_size, \n",
    "                                global_gamma = global_gamma, \n",
    "                                weak_learner= weak_learner, \n",
    "                                bin_type = bin_type, \n",
    "                                learning_rate = learning_rate, \n",
    "                                initial_model = initial_model,  \n",
    "                                final_round = final_round, \n",
    "                                center_mean=center_mean)\n",
    "LSBoostReg.fit(x_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict on train/test data and print MSE/MSCE on train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of weak learner updates made: 2386 \n",
      "\n",
      "Train Error: 0.032806\n",
      "Test Error: 0.034594\n",
      "\n",
      "Calibration Error: 0.000000\n",
      "Calibration Error: 0.000079\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of weak learner updates made: {LSBoostReg.updates} \\n')\n",
    "\n",
    "training_predictions = LSBoostReg.predict(x_train)\n",
    "test_predictions = LSBoostReg.predict(x_test)\n",
    "\n",
    "print(f'Train Error: {MSE(y_train, training_predictions):.6f}')\n",
    "print(f'Test Error: {MSE(y_test, test_predictions):.6f}\\n')\n",
    "\n",
    "print(f'Calibration Error: {MSCE(y_train, training_predictions):.6f}')\n",
    "print(f'Calibration Error: {MSCE(y_test, test_predictions):.6f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Track MSE/MSCE for each round on train/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_errors_mse = LSBoostReg.track(x_train, y_train, MSE)\n",
    "test_errors_mse = LSBoostReg.track(x_test, y_test, MSE)\n",
    "training_errors_msce = LSBoostReg.track(x_train, y_train, MSCE)\n",
    "test_errors_msce = LSBoostReg.track(x_test, y_test, MSCE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hf.test_train_graph({'train error': training_errors_mse, 'test error':test_errors_mse, }, title='MSE of LSBoost per Round', ref_line = LSBoostReg.round_count, dotted = 'test', y_axis = 'MSE')\n",
    "hf.test_train_graph({'train error': training_errors_msce, 'test error':test_errors_msce, }, title='MSCE of LSBoost per Round', ref_line = LSBoostReg.round_count, dotted = 'test', y_axis = 'MSCE')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train LSBoost Regressor with validation hueristic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LS:  14%|\u001b[32m██████████████████████████████▉                                                                                                                                                                                              \u001b[0m 14/100 02:18, Training Error=0.0356598, Val Error=0.0359374, Lowest Val Error=0.0359374\u001b[0m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early Termination at round: 14\n",
      "Memory released!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "x_train_no_val, x_val, y_train_no_val, y_val = train_test_split(x_train, y_train, test_size = .2, random_state=42)\n",
    "\n",
    "LSBoostRegValidation = LSBoost.LSBoostingRegressor(\n",
    "                                T = T, \n",
    "                                num_bins = num_bins, \n",
    "                                min_group_size = min_group_size, \n",
    "                                global_gamma = global_gamma, \n",
    "                                weak_learner= weak_learner, \n",
    "                                bin_type = bin_type, \n",
    "                                learning_rate = learning_rate, \n",
    "                                initial_model = initial_model,  \n",
    "                                final_round = final_round, \n",
    "                                center_mean=center_mean)\n",
    "LSBoostRegValidation.fit_validation(x_train_no_val, y_train_no_val, x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of weak learner updates made: 275 \n",
      "\n",
      "Train Error: 0.035715\n",
      "Test Error: 0.036173\n",
      "\n",
      "Calibration Error: 0.000000\n",
      "Calibration Error: 0.000022\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of weak learner updates made: {LSBoostRegValidation.updates} \\n')\n",
    "\n",
    "training_predictions = LSBoostRegValidation.predict(x_train)\n",
    "test_predictions = LSBoostRegValidation.predict(x_test)\n",
    "\n",
    "print(f'Train Error: {MSE(y_train, training_predictions):.6f}')\n",
    "print(f'Test Error: {MSE(y_test, test_predictions):.6f}\\n')\n",
    "\n",
    "print(f'Calibration Error: {MSCE(y_train, training_predictions):.6f}')\n",
    "print(f'Calibration Error: {MSCE(y_test, test_predictions):.6f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_errors_mse = LSBoostRegValidation.track(x_train, y_train, MSE)\n",
    "test_errors_mse = LSBoostRegValidation.track(x_test, y_test, MSE)\n",
    "training_errors_msce = LSBoostRegValidation.track(x_train, y_train, MSCE)\n",
    "test_errors_msce = LSBoostRegValidation.track(x_test, y_test, MSCE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hf.test_train_graph({'train error': training_errors_mse, 'test error':test_errors_mse, }, title='MSE of LSBoost per Round', ref_line = LSBoostReg.round_count, dotted = 'test', y_axis = 'MSE')\n",
    "hf.test_train_graph({'train error': training_errors_msce, 'test error':test_errors_msce, }, title='MSCE of LSBoost per Round', ref_line = LSBoostReg.round_count, dotted = 'test', y_axis = 'MSCE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.11.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}