08_xgboost_lightgbm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "catholic-dylan",
   "metadata": {},
   "source": [
    "# XGBoost and LightGBM\n",
    "\n",
    "In this notebook, we try some more advanced tree-based models that are considered state-of-the-art solutions in ML research. Those include:\n",
    "\n",
    "* XGBoost by DMLC (https://github.com/dmlc/xgboost)\n",
    "* LightGBM by Microsoft (https://github.com/microsoft/LightGBM)\n",
    "\n",
    "None of those solutions are natively implemented in Spark, although both XGBoost and LightGBM have a Spark implementation (XGBoost4J-Spark for XGBoost, available for Scala only, and MMLSpark for LightGBM, preinstalled in Azure-based Spark clusters). For simplicity, here we are going to use only the sequential versions of the two algorithms, available as Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "suburban-embassy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n",
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost > /dev/null\n",
    "!pip install lightgbm > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "characteristic-judges",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "trained-pharmaceutical",
   "metadata": {},
   "source": [
    "## Input data parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "funded-waters",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainData = pd.read_csv(\"trainingData.csv\")\n",
    "testData = pd.read_csv(\"testData.csv\")\n",
    "resultsDF = pd.DataFrame(columns=[\"Algorithm\", \"Features\", \"Metric\", \"Value\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organizational-commander",
   "metadata": {},
   "source": [
    "## Solution #1: only numerical features\n",
    "\n",
    "In our first solution, we use only the numerical features exposed by our dataset (`NumBikesDiff-3`, `NumBikesDiff-2`, `NumBikesDiff-1`, `NumBikesDiff` and `NumBikes`) to predict the value of `NumBikesDiff+1`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sought-painting",
   "metadata": {},
   "source": [
    "**XGBoost solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "three-white",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(objective=\"reg:squarederror\", random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alpine-uzbekistan",
   "metadata": {},
   "source": [
    "In terms of feature importance, the XGBoost model appears to behave similarly to a classical decision tree, favoring the `NumBikesDiff` and `NumBikes` attributes. This is coherent with the classical approach for learning the tree followed by the XGBoost algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "circular-rochester",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"],\n",
    "    \"Importance\": xgb.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demographic-exemption",
   "metadata": {},
   "source": [
    "In terms of evaluation metrics, this solution performs similar to the corresponding gradient-boosted tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "serial-intermediate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4315656301471558\n",
      "R2: 0.9668745994207976\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Only numerical\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Only numerical\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supposed-pendant",
   "metadata": {},
   "source": [
    "**LightGBM solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "continent-complaint",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMRegressor(random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stopped-comparison",
   "metadata": {},
   "source": [
    "LightGBM's leaf-wise approach for growing the tree is quite different from the classical, level-wise one. This is reflected in the obtained feature importances, which is much more well-distributed among the different attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "clinical-court",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"],\n",
    "    \"Importance\": lgbm.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stuffed-advancement",
   "metadata": {},
   "source": [
    "In terms of evaluation metrics, this solution performs slightly better than the corresponding XGBoost-based one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "flush-swiss",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.429525399314643\n",
      "R2: 0.9669689510915338\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Only numerical\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Only numerical\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pregnant-check",
   "metadata": {},
   "source": [
    "## Solution #2: simple categorical features\n",
    "\n",
    "In the second solution, we enlarge the feature space by adding the values categorical features (the hour, the day of the week, and the zone of the station) into the model. Note that in this case we do not need to previously encode our features (for example, using one-hot encoding): decision trees indeed do not regress directly on any features; rather, they use them to design proper split values, and in this sense it does not matter if the feature is numerical or categorical."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fitted-semiconductor",
   "metadata": {},
   "source": [
    "**XGBoost solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "changed-silence",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(objective=\"reg:squarederror\", random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exotic-intake",
   "metadata": {},
   "source": [
    "Also in this case, the algorithm seem to favor a few, very informative attributes which consequently get assigned a high importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "egyptian-interval",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "    ],\n",
    "    \"Importance\": xgb.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thorough-broadcasting",
   "metadata": {},
   "source": [
    "Test metrics result in a slightly improvement over the previous model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "silver-glasgow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.428299364720872\n",
      "R2: 0.9670255850518734\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Simple categorical\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Simple categorical\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unavailable-writer",
   "metadata": {},
   "source": [
    "**LightGBM solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "assigned-delivery",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMRegressor(random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "headed-inquiry",
   "metadata": {},
   "source": [
    "Again, LightGBM seems to favor the spread of feature importance on multiple attributes, with `Zone` surprisingly being ranked as the most informative one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "following-convention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "    ],\n",
    "    \"Importance\": lgbm.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "killing-bennett",
   "metadata": {},
   "source": [
    "This solution already achieves quite a significant improvement of our test metrics, basically in line with the full (but unoptimized) version of the GBT model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "arbitrary-syria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4168959078165833\n",
      "R2: 0.9675500146766053\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Simple categorical\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Simple categorical\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proprietary-coaching",
   "metadata": {},
   "source": [
    "## Solution #3: disjoint, effect-encoded categorical features\n",
    "\n",
    "In the third solution, we use the effect-encoded version of our categorical features, considering only the \"disjoint\" ones (i.e., we consider separately the effect of the hour, day of the week, and zone of the station)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governmental-updating",
   "metadata": {},
   "source": [
    "**XGBoost solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "least-columbia",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(objective=\"reg:squarederror\", random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flexible-jackson",
   "metadata": {},
   "source": [
    "In this model, the averaged features seem to capture some importance, but still they are very behind `NumBikesDiff` and `NumBikes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dimensional-tuesday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S8hT8LVmyJBx00EHxc2bsqlSpElq0aBEWL14cA8FsYj0hASVppr9USUnVrB6LJEnYZJNqvhEJZxtJUpaCv0svvXRtv/UH6tWrF7eHWLZsWZz145G9Anl9dW+88UY477zzwogRI8LWW2/9i3/XvHlfheXLV/zqY5WkYmAnOPvmzl2U1Z9nGyW/jSSpvKlYscJPTob9on3+vv322/DJJ5+Eb7755gcbwP+UkpKS0KBBgzBhwoRY8IVHnq+e8smawu7du4drrrkmVhaVJEmSJGXHWgd/rMEbMGBArPa5cvXNChUqhGeeeeZn/32/fv3ithDM6LFP4JAhQ+LrXbp0Cd26dYvbRvTv3z989913sRJoxtChQ2PxF0mSJElSHoK/YcOGheHDh4d99tnnV/2ibbbZJowdO/YHr48cObLs83Hjxv2qny1JkiRJytJWD8z4NW3adG2/XZIkSZJUHoM/tnC47LLLfrA3nyRJkiSpiNI+t9xyy1iI5a677ip7jU3YWfP33nvv5eTgJEmSJEl5Dv7OP//8WKmzdevWqxR8kSRJkiQVUfC3YMGCmPrJTJ8kSZIkqUjX/LVv3z6MHz8+l8ciSZIkSSr0zB8bsI8ZMyZcf/31oVatWqt87c4778z6gUmSJEmSChD8HXXUUfFDkiRJklSEwd9LL70UH+vWrZvzg5EkSZIkFSj46927909+nQIwEydOzNoBSZIkSZIKEPw99dRT2f+tkiRJkqRkVvuUJEmSJJVfBn+SJEmSlAIGf5IkSZKUAhULfQCSJEmSpNwz+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFDD4kyRJkqQUMPiTJEmSpBQw+JMkSZKkFMhb8Ddt2rTQoUOH0LJly/g4ffr0H3zPpEmTQvv27cNOO+0UhgwZkq9DkyRJkqSil7fgr2/fvqFjx47hsccei499+vT5wff87ne/C4MGDQonnXRSvg5LkiRJklIhL8HfvHnzwtSpU0ObNm3icx55Pn/+/FW+b4sttgg77rhjqFy5cj4OS5IkSZJSIy9R1qxZs0KdOnVCpUqV4nMea9euHV+vWbNmVn9XSUnVrP48SZKwySbVfCMSzjaSpJ9WdFNs8+Z9FZYvX1How5CkgrITnH1z5y7K6s+zjZLfRpJU3lSsWOEnJ8PykvZZr169MHv27LBs2bL4nMc5c+bE1yVJkiRJuZeX4K+kpCQ0aNAgTJgwIT7nkefZTvmUJEmSJBW42me/fv3CmDFj4lYPPPbv3z++3qVLl/D222/Hz1977bXQrFmzMGrUqHD33XfHz59//vl8HaIkSZIkFa28rfnbZpttwtixY3/w+siRI8s+b9KkSXjuuefydUiSJEmSlBp5m/mTJEmSJBWOwZ8kSZIkpYDBnyRJkiSlgMGfJEmSJKWAwZ8kSZIkpYDBnyRJkiSlgMGfJEmSJKWAwZ8kSZIkpYDBnyRJkiSlgMGfJEmSJKWAwZ8kSZIkpYDBnyRJkiSlgMGfJEmSJKWAwZ8kSZIkpYDBnyRJkiSlQOVCH4AkSZKk4lO9+nqhSpV1Cn0YRWPJkqXhyy+/+00/w+BPkiRJUtYR+P3tb3/znc2SHj16lP73twV/pn1KkiRJUgoY/EmSJElSChj8SZIkSVIKVC70AUgqnzaqvm5Yt0qVQh9GUVi8ZElY+OXiQh+GJEkqcgZ/kn4VAr8TRp3lu5cFt3W+uvS/Bn9S0lTfqEqosu66hT6MorBk8eLw5cIlhT4MKfUM/iRJktaAwO+KC071vcmCcy69sfS/Bn9SobnmT5IkSZJSwOBPkiRJklLA4E+SJEmSUsDgT5IkSZJSwOBPkiRJklLAap9KpI2rVwmVq1heO1u+X7I4fPGlVdYkSZLSzOBPiUTg98+hJxf6MIrGbuffXPpfgz9JkqQ0M+1TkiRJklLA4E+SJEmSUsDgT5IkSZJSwOBPkiRJklLA4E+SJEmSUsDgT5IkSZJSwOBPkiRJklLAff4kSZJU7mxcff1QuYpd2Wz5fsn34Ysvv83az1MypfaMqbbRemG9ddcp9GEUhe8WLw2LFn5X6MOQJEkpQuA3ZcQzhT6MorHL6fsV+hCUB6kN/gj8Op5/Z6EPoyjcNfQvYVEw+JMkSZKSzDV/kiRJkpQCBn+SJEmSlAIGf5IkSZKUAgZ/kiRJkpQCBn+SJEmSlAIGf5IkSZKUAgZ/kiRJkpQCBn+SJEmSlAJ5C/6mTZsWOnToEFq2bBkfp0+f/oPvWbZsWejfv3848MADQ4sWLcLYsWPzdXiSJEmSVNTyFvz17ds3dOzYMTz22GPxsU+fPj/4noceeijMmDEjPP744+Gee+4Jw4cPD//5z3/ydYiSJEmSVLQq5+OXzJs3L0ydOjWMGjUqPm/Tpk0YOHBgmD9/fqhZs2bZ9z3yyCPhyCOPDBUrVoyvMwP46KOPhpNPPnmtf1fFihXW+ntrbbzh2v9PKGvv+9qqslGJ73rC26hW1f9//ip57bN+Lc+hpLdR9RobZP1nplku2mijGp5HSW6fdaqtl/WfmWY5OYc22ijrPzPNKv5MG/3c1yusKJXNA1qTd955J/Ts2TM8/PDDZa+1bt06DBs2LDRs2LDstbZt24bBgweHRo0axecjR44Ms2fPDhdddFGuD1GSJEmSipoFXyRJkiQpBfIS/NWrVy/O4FHQBTzOmTMnvr76982cObPs+axZs0LdunXzcYiSJEmSVNTyEvyVlJSEBg0ahAkTJsTnPPJ85fV+aNWqVazwuXz58rge8Mknn4zVQSVJkiRJv01e1vzho48+Cr169QoLFy6MCz+HDBkStt5669ClS5fQrVu3sPPOO8cZwQEDBoQXXngh/hu+xrYQkiRJkqRyEvxJkiRJkgrHgi+SJEmSlAIGf5IkSZKUAgZ/kiRJkpQCBn+SJEmSlAIGf5IkSZKUAgZ/kqSi88UXX4SlS5cW+jCkcsci8FJxM/hTxB6LSjZvyNLaeeKJJ8Kll14avv32W88daS199tln8bFChQq+Z+Wc/QXf/59i8JdiM2bMCB9++GFYvHhxqFSpkgFgAs2dOzcsWrQofP/99/GGvHz58kIfklbioEkyZ/xuvPHG0KZNmzBr1qzYRnZmC9v5nD9/fgGOQL+knV599dU4YDJ79mzfuHKMPh285hXmPPrqq6/KxfvvJu8p9dxzz4WhQ4eGrbfeOkyePDk8++yzYb311ot/wEn/o02L559/PlxzzTVh0003LevQ0kZKhkmTJoXp06eH1q1bh5o1axb6cFTq008/DRUrVgxjxowJc+bMCf/85z/DhAkTQtWqVX1/8mjl+8j//M//xCC8S5cusR28vyTPlClTwuuvvx522WWXsOuuuxb6cPQrEXice+654ZhjjgnNmze3P5fn693TTz8dHnzwwbDhhhuGP/zhD+Gggw4KG220Ub4O4xdx5i+FXnjhhXD55ZeHiy66KAYXjRs3jp0mA79kBedDhgwJ3bt3Dz169AglJSXh5ptvjl8znaPwGCWnM3vHHXeEJ598MgbnKiza4YILLoiDJeuuu2545plnQrt27cI666xj0+RZJsC78847wwMPPBA6dOgQqlWr5hrMhGFWnI+zzz473HDDDaF69eplr6v8IYOrVq1aYdq0afG5Ay35UaH0eke/+tprrw3nnHNOTJ/m/lO5cuU8HcEvZ/CXMqyBmThxYuwk7bnnnjHoY3T81ltvDX/5y1/Ca6+9Fr/P9MLC+eabb8K9994bunbtGvbee+/wu9/9LjRq1Cj897//LbvQqHCWLFkS3nnnnTB8+PDQu3fvMH78+PCPf/xjlQDQAD2/vvvuuzjDR6onM+bMkHONI4Vt9OjRYebMmXk+IvHeMwrev3//GPjdfffdMcgYNWqUb05CfP755zFgoJ1q1KgRRowYEV/nNfsA5avPwH1p/fXXjwNenGv/+te/Cn1YqfJsafbcgAEDYjYQM7AXXnhh2GCDDcJ//vOfQh/aGlXqV6rQB6H8YRR8t912C9tss038A73ssstCixYtwnnnnRc7SMwEHnHEEXHkXIVro5122inOYGTSpBYuXBhefPHF0LZt2/g93JgNAguDjtGWW24Z6tWrF3bYYYfYsSVYx2abbRZvwNyIkzzqV2w4H0gtfOqpp8LDDz8cLr744tCkSZPYDqS1c63bZJNNEpuCUwxWzxwh/fbtt98Ojz76aHj55ZfjbBLXNa5j++23X6hSpUoBjzbdaCvuKX/84x9jOzDISNAwbNiwGDQceOCB3l/KiQ8++CCu1XzllVdC3bp1w3bbbRfmzZsX6tSpE/sQ9hXyF/y9UtoGXN8GDhwYNt988/D444+H++67L+y1116Jy0Cxd5JCmfUvPJ5yyilh++23j8+7desWpk6dGm8KrpEpbAeK2b7VX//yyy/j54zq0akaPHhwIQ5RpTbeeOOy94G8fm6wrDPj9U8++SS88cYb4frrr4+BonKbHs25stVWW8WUNYod0KFlFpb0J9Zj0gYE5wTjrIUxKM9t4MffPoHf73//+9CpU6fw7rvvxuCifv364bHHHouztJ4XhUVbcb6Q8XPaaafFAPCEE04IjzzySFwrxqDJFVdcUeCj1Nqg/0ZaNes2yUTh+vf3v/89BoUs6Ula0FFM17sFCxbEwV+uZ0yqsEznuuuui7U03nrrrfg52Q4MCCeNwV8K0BEimFv5IkBnlRt0JvAD6WukFibxDzWNbbRyO7GAmM4Uo0vjxo2LI0tKTvu0atUqBhtnnXVWnDVnBt0Obu6RUsMs7Ndffx2aNWsWb7p0YO+5557YJsz+tWzZMrbFzjvvbOCXIyuv8XvooYdCw4YNYxou1yoyScAIOGtkWcvsPaZwSFlnBhYE5SNHjoyBHwFgx44d41olAnYlO/CgQA9FrRhw3GeffeIH10AGV/7973/HKuHvvfdeXDJiPYfsqlD6/rN86rbbbovrmAnwDj744FjVuG/fvrEvTSYd/YH9998/ke+/aZ8p2O/q9ttvjxd5OqWZsud0WFn/R2eWUT5mk+gwkQZKqoAK30Z8ZNqIWb8+ffrEzi4zfisH7Spc+3CjzQSE3HCZ2eCGQDqoco+AjrVl3GBJiaZaITNMrF1m0T1BHzODBIVmM+QWQQMzrDfddFPZuhdm/jhPmA0nHZflBQxiKf8y6X/HH398bItMUM79nrVJl1xySQwkdt9993jOJLHDqv8NPDjXSPVk1okBlo8//jgGf7Vr147XP66HpFqzjRczUrZjdqz4v3OCez195Z49e8ZtuDifWGfevn37mNLO7CvLqZh5zfybpLHgS8r2uyLo4w+RFAG+xog5o0eMYnDx33bbbQt92Knyc21ER4oAg+fM/hH4kdOvZLQPX6OTy3lEhTVmPjyHcosiVYxos56Cc4Ng4owzzgjHHXdcnLHg/DjxxBNjMYuXXnopfo+yj07NyhgBJ6Cguifv+y233BLPlfvvvz92SFmH6bWrcO1EW4DiVAz4co5kMBNI27FmOSOJHda0Wrn6KjN+pBNSOIlBLYq9fPTRRzEYySCY33HHHeM9ieBE2VGh9JwgnZPK6wcccEAM7kj1JFWayROCQNZaMnhCRkrm3ySRM39F3EHipGfhLxcLgoijjjoqzlyQ9sF0NNU96ahSBIEUKf5olbw2ojgPKYV8zoJuJat96NCSMsUsFO2k3GHEm0Eq1pVRSOSuu+6KN1rWvNBBosIaI68EhMyOk/aZKV+v7KNjwzpxZoxIeWL7IAYTqbDKrCuBIJ2iP/3pTxbbKYDMrAOFKMhgYIsa7vnMyNJGBIIED1T5PP/88+Mef0mdqUgrluIceeSRcTaPvhr3I9eaPaoAACAASURBVGb5GIyk3TJrM9lPkxl2vo9zkWsl9ydmA5U9s0rfd9I6GfAlpZb7C0EgGSgsOeBaR/CddM78pXC/Ky4mpAwwNZ0p52zlteS2UWbk1rS1ZLePa/xyiy0crrzyyriJMcV0CP5Y48L+ZHzt9NNPj7MZhx12WHj//fdjERg7PtnHjCoIEHifmdEjvYxgmxlyZvkIKuiMEmBQ/txBkcLI7D9GGzHzx3WNip4MZpEuSHo6KboMZGVmZQ38koVzh3R2AjnOPWZp+aBdeY3ZP8450t4ZnETNmjVjMO/ykN9uxf/d31l6w0AJm7ezbpmMH7ZIYXkBTj311DgwWV6udc78FRlSnBgNYkqalDU+KHhAFTxGhRglZzE+6R38UWdSQbzgJ7eNbJv8sn2Sh71ISe28+uqr4+wEKYZU7ST4I9WTGUAqeVJSm4EsZjfoACn7o97MNNAJpUPK4CGzSqx1Yc0L22lwzWLtKwFhjx49XOOXZ2QrZO7rdFAJ9hgUOfzww+OsBOuVaDO2c9h3333jLBLZJd5rkofrHIOKe+yxRxxQYRad6tLMNnHdo1YDX6doT+fOnWNgklnb6WBkdlQofS/ZQuiqq64KY8eOjdc+UmoZ+OX9ZyCYPhuzsuUpy8TgL0X7XbEAmG0cGJngj5Q/atlG8hxK+jXtzTffjGk1XLsYGCHw45pGB4c0T6pIMgLO3koUODDwyz5Gvck+oIode8ExaJVJQ6MYEgj+SIViFoJ2KSkpycGR6MdwjgwfPjyeB2zazkAIM0ScM1tssUWsssqMOGuWaB8KhmT6AfYHkofrG2ubL7/88nDIIYfEgm8sP2CGnVRDBlgI5Lt06VJ2DtqO2fVm6b2HWguktHOP4RybNGlSbAMGGSlyxUA+51t5YvBXRPtd0Umic8TiX0aJ9txzz9CgQYN4Ayalg4sCI7KM8HEzyIwOyjaS51ASZbITSGsi0CBtjQ4QAR4dIwJAZjooMMLNuLzdgMsL0ju5d/C+c08h4GOvUdKhaRPagK0caAdep2PqzENhzhcCuxkzZsSOKYO8tAlrkQjIGTSh80qa9KGHHlou1ialPQtl6NChcS9GthKgKA+VPSn4khlgYQaXtnbmNjs+LM3Aov/MIBaYSCGrgXWXvM+sb2Y7G+5JTZs2jVU9y2O9DIO/IsHoHtWFuLjXq1cvpnKQy09qABd4/lD5w+VzRojoSMk2kudQUq3cmSH9ifUrPKczSzGeTADIfnJUYGNNpvvH5QbrXUjzJKgglZB7yNy5c+M9htkjikww28q9h1TC8rLupZgw+EsnlQCBNGjOCYJwggYCdNZgUqF4zJgxcX1SZq8/JfO6x8AWA1+sm2WwPlNFmhRd2pcURNb8sQ49U4Fav8300usZgTZVUgn2eE/5nEwHrnnMpJPdwAAX9xrahfe/PL73Bn9Fwv2uks82SjbbJ5mBHx2dp59+Oo6ysg6Wmy2DXQQhBCO33nprLCrCugtlF1UD6Vhm1vJR0IB1yaQQ0hmiDQjMGQ0n6GBWkM+V3zbiXKGdMtk8VCMkXS0zSEJHloCcwIFgkLZzpiiZMgNcZHOxvpkqxsy6c+2j2jcBCgP9J5xwQnzNDK7s+Lh0RpV9SBkw4ZyiDgPF3pgwYW0f1zrahkEv7jlHH310bI/yGPjB4K8c48LO+gvKnlOVkKlnLuhsBs7oK5W8uEgwbb1o0aL4R83MoGwjeQ4lWeaGyr6JVFSjwidBBTdjtnGgw8M6P9JzKJ7kxuHZR8BNVTsCCdJpmXmls0mlSJYW8DkFdrgPUUSEjiozT8ofAoNTTjklzuiRfsbsHzMXzEiwNoyKnvQPCMpJY6M/kNkuqLx2WosR26MwM8s5BK5rpOxSWITrHrUaSP+kv8eaTra22X333Qt81MXjo9LrGdsEEdBRWIxZVdJquZ7RDhTSoU04z+hPn3nmmfHaV55VWLH6Tq0qF1hrQblz/jAZ/eain9njis4Qm08zWsFFn2lrNgi37LltJM+h8oK1Sd26dQv9+/ePaesUsHr22WdjlTXSndi3jAEuZ/yyj5lVOpsMJBLskTZIsE3aEyPkbK/BWnKqfBJQUAymPFW6KyYE32zVwOAuZecp6NKzZ8/4NWYw+BqBOm1pWnRyB1rIcCC1msCCbYRoR4KRDPZoXLBgQZzBpa2VHQtK31PuM6yBJXuBkIjtgsgmIfimb811j2scWScUuqIvXd5nzp35K4dYrE257V69esUqT2zYSioOi7qZ/eMPmHxxRstZDEyxF4I/2UbyHEqq1W+mzO6xtoL9/KhUzCwHqbmkQ5GSw3XNgCM3gd9f//rXGOAxW0RHh9k/1o8T+DEDSOl57jcUfiEd18IhhcM5wH1+1KhRsSN77bXXxtfpA1Cdle0dGCAhAFQyMRtLHQbSPZndI9ggm4v2I9ggwGcGkBRe1tUqe74pDep4X9k2iBR2zhsKunDtIwBnXfnkyZPjdZGgm2sd96nyHPjB4K+ccb+r5LONks32SXbgR5oaaVCMcLO2jE4PI+CsVWIbG9YytW7dOo7CKvvYt4qsEQIKAj/SCvmcWdapU6fGgJxRcjpGBH4WDyscggIGgjkfKPJGp5XXSFNjlo9ZCx7LYzXCNF33uJYR7JFmSFohlXO5zlFohIJWzLyTZmg7Zt/6pecHAR6PIPDmvSfIZoCRzAdmAbkGstVDeQ/6Mgz+yhH3u0o+2yjZbJ/kr/FjMT2j3aSxH3fccXHLGipK0iHKFHfJrFtS9s8PUmwZ4SboYx0Ss4C0A0sImI0lRY1AnM4RMxMqHAZJWAPGGj+Cc2Yv2OqBcvWcN3RklezAj+CCNbNsYcN+cQSAnGcEHhRYIgWR9ZwMhCn7Pi0dLGF9LME1QSBtQrCdWb/MsoMePXrEtinvqZ4rc6O3crbfVfPmzcMxxxwT177wBwsu8ExVs+En6y9cA2MbyXOoPK5jpqrdLbfcEgMLirgQiIC0djpJrEOzuEtugj5wj+FewywSJeYzFe5WTpEC9xsVrp0yCBAIzNkInLYhtfOaa64Jr732WkwfVHJxXjGQwmDW559/HguNMMBFsT5mmajlcNVVV8XvZfBLudtL8anS2dZ33nmnrHIq9xjWmLP28uyzz4797kybFQtn/soB97tKPtso2Wyf5Fl9FJXRb2667E/GumYGt6hOfP/995etybBoVW5k2oE1RxTaYQSctmBGiYIhpDsRTFBkjJFwUtSU36Avs86IwI6ibqSpsQUHjwR/rVq1iumDpOGy7p+vKdkzTpdddlm8zjGDy/nFbDuVdRnEZ20ZM/C0o9s5ZP9cyqBwFevJqeTJWmaufaydZT/Mfv36lQV+xcbgL+Hc7yr5bKNks32Si5sw68hY10dlwosuuih2hJh14ib8wAMPhHvuuScWrXBtWW7xPg8aNCim3I4cOTLO/rF2jMIHVCCk6A5BB7NNyh+qdfO+M/vNDBApzy+++GIcLKGdKPlPpUjOF7YK4HqXSV9TcrFfHNc6Mra43g0bNizO3LK1DcEIabsG8PkdQDnooIPiAArfSyXjJk2aFFWq58oM/hLO/a6SzzZKNtsnWUhxYq0Y7fL+++/HgI8CIgSAX375ZZzt4+bLDZo1Z4MHDzaVPccj4BR5IQWN9/rwww+PbXT55ZfH2VYCQFLR2FuMTcKVP1RXZeaV9Xy0D2m4rD1iMIQPUtYIDgkEaU8KvxRDJcJitHoQwXIdZpdoVyq0br755vGaRwGf/fff3+IuBRhAqVia9kmWSc2aNVcJvIvxfHLH73KANBxGg0i34Q+XDtHK+10xyud+V7aRPIeSjmqddG7YsoEtajI32cxWNIy2Uo2VVE/W/bHJuDNNuUFHB7zXzECw0TEVVgki2CaIDg+BIF8nAFR+0R7nnXdeOP7442MaJ+XmKebCuth111037unLOURQQQGeE044IUyaNCkG6kpm4EdaNYVdGOQ6+eSTY0rhe++9F26//fZ4TaR9u3fvHmfbld0BFAZLWMfHQFf30veYYjpg1pWgm2CcJQerK8bAD878JZD7XSWfbZRstk/yUJCCGT/WtlDCnPRCUjnZK27vvfeO30MgSMenbdu2sXPLegzl7tz4xz/+EYtKkC7IoCIBIeteQJtQ2IV9/ViHpPy68sor48wEAyCkARIIkp7GWjE6q6QN0m4E65wnBBXMnlsQKZnnGwWt/va3v8U9Si+44IIYbLCFDQNf06dPj3vMEeQTEBZrqmGhBlAoinTiiSfGDJLM4Ml/Ss8X1lbusssuceaPQRP2NuX8YRa22Dnzl/D9rrgZ88fICB+b7HJDpgIef8DkinNTsJyzbSTPoSQjvZM1Y8xOdOzYsWwfK6qsUcWT4gZUt+ORDuypp57qPn45krm/sEk7e/bR4WF2lVLnRx11VPwaa/zQrVu3XB2GfgZVIJkdOu2002IQTqB3/vnnx9RP0gVJYQOZP3RyWTtLJpCSYdGiRfFcY8CL9cx33HFHTDGkqiQBR5s2beLXGOTiY+W+n4Ffdtx2222xeAtBNX1lBlSqV68e1/ctXLgwnjcnnXRSHEAhpZ0tNfj+NHCrhwSvT2LEj3RPLgyke/IHTOBHx4kbNvtgZTamlG0kz6GkYhE9s3kEgYy4HnnkkWHHHXeMN10CD268dG5JfXID99ygc7kyOkDcS5hJ+v777+MM39///ve4/iizjZAK1070BUgD5NxhzRL7LYKAgZRoZv5Y8wf2gKMSq2syk4HAgmU6XO/ArB6zswza33jjjbHKZ6YvRyqicjeAstVWW8UBFCqpco/h/e9f2jas7VvTAAr7maaBwV8Cud9V8j3jnmSJZvskAyPeZCjQKWXTXAILqkoSADLLREEROq6sgWFw689//nPcVkC53c6BoJt0T/YTY3SctZgEHQSAEyZMiOtjVLh2YquT6667Ln7Ovn2kp7EOkyCdmXKCQlIGOadoS4okMWOuZCCdPTPThDp16sR2ot2oqEt70o7saZopLOJsX/Y4gPLzKqxYfThQebd6fvcTTzwRvv3225g2QFoUI3qkdrLwnlQBLiLkLMs2kudQUlHFjsCC61Xnzp1jlUIW1LO2jNQbZv9Yg0E6FCmf55xzTnxdubvPkD5IZVXWVQ4dOjTed3r06BHXXbKnFYG4ndDC9gOYLRo3blw8LyhUwawFSAGlnQgACQRdG5Y89NtWzsY65phj4lIdZm0p0scaWwZaqOZ57733xnOPz5V9DKBwvznjjDPic9b80TZXX311fL1v375l5xH3qEwBrLSw4EtCuN9V8tlGyWb7JMcLL7wQixuwRomBq7Fjx4Z27drFVCdm9rgxU+SA59x82U/JwC/35wczRKTVUtacmQdm/ihAQaeUNqNqZNo6QUlqHwIEzhnW+JMCPXz48JjayXpMgnbaiE4sS0Ey/0bJQBruIYccEgMJPri2kaLL2mZS2xlw2XbbbWPwTgElrofspWlxl9wMoDz66KNxjSUpnRR04f1mAoXKqk+VTqqsPICSxmuewV8Bud9V8tlGyWb7JA8dVEa6WZdMh6d27dph/PjxsWAVZbdJLaSiJAVHmMlo2rRpLPSi7KIiJO83levoCLHuiK0ceK8pbMDaF7bVIPhjPSYdJNLVVDisuaSTSvuwvQad0549e8YBFNYoEbizhsmAIXkoxPPqq6/GtpkyZUr47LPP4mDKAw88ECsdc50jNZd2ZL3zpptuGv+dAXz2OICy9gz+CrzfFZ1XKj8xUvTKK6/Efa7I4ycPnItIpqon6QGWcLaN5DmUdOyjdPfdd8dZJQI/FtrzOXuRsqCeCpPMYtCJpUNkwJEbBHzDhg2LATj3EWb7KHhAB5QAkPsJRcW4v5CaxiyFCoM1sLz/FOEhcGAGCfQD2BqF2QrOpYYNG8bXDRiSg1RqZpdoP7avoU/Xu3fvOOtE0EcbUkCJYH6zzTYr9OEWPQdQ1o7BXwG431Xy2UbJZvskUybdqXHjxjHoY30fs4Dss0QVNdJrWAvIiDgVCw04coc2oD3oeLJlA+cMASCDjcwAZjZ179Chgym3BTR79uxYfIf2om1efPHFOGPLwAhrkwgMSfNkXSwFkpSsgS7WkFGwiowGBleo4MksINXYGexicIVsCCqAsu6Z6q3KPgdQfhmDvzwjF5lRWKb+6QyR9sRIONUJn3zyyZgPzsWDimy8zg2BUSXZRvIcSiqCDGYj+CBjgVRDUjsZhWXWj44R6AyR/kmqoUWrcpPq+eGHH8aZPTaPpjNK55OKkaeffnrspNImkydPjgUnmKFIw4bGScb9neIuBAeke9Ju9AFoH9YoUQiJdmONLFVYnfVLDrbgYKsUZmYJzgne99xzz5jFxcAKwR7XPq6JBx10UFz7p+xzAOWXs9pnnjHaymJfFgdz0ae6GsUQnnvuufD111/Hhd5c6BkdIl3Hsuf5Zxslm+2TXAxace2iiiflzSlg0bVr11j8hU4to+SXXnppWTCo7KE6NAE3gTgBN5UHqTZIMR3WXLKFw8CBA2NaGrOApOF6f8m/NVUWZONvZv8uvvji2CdgmwCCePaGYxaQdmOZiMFD8pDqyUzfeeedF9uONE8GX/g8k76b4VrN3OBaRyEkCuiceeaZ8b5DlfzM7CzbppBOzRpn7j8VU1jgZXXO/OVxvyv+4DJr+S655JK4wSQXCNZbcPFgQT7rMdjvillB9+3JL9so2WyfZGP/PvawojPEehduxKxzYRCLG/LLL78cRowYYQc2R5hJ5V5CJdWTTjopFgn597//HTs7rO8j+CPrhOIulJf3/lKYtWHM3BHsdenSJc4OEQxyv2cAmAET9oCjr8DX5syZE8aMGRMrgLrmP3mY9eOaRpEeUnMJQv71r3+FO++8M6Z6MvNOGnyGs7bZzTTJYMCRwSyK6zD7ysdhhx0WH5lQYcCLyrkXXHBBPK9k8JcXrHFhYT2jr8z6MerNwl9KbTOLweeUcuZCws2aCwZFX5Q/tlGy2T7JvgEzukobDR48OBx++OHxZnv55ZfHNUoEgPvuu29cW8Zm78od1lESSJDmecIJJ4SjjjoqzvzVrFkzziYRgNApMvDLL2bv+vfvH5d50CGleAvpuCwDYU0m7cHgFllAzBZl7v/0F0gjXDmAUHIQnBNU7LTTTnEbBwIQKrRynhEEtmrVqqyqp347B1Cyx7TPHGP0h810+/TpE4M91vWR38+oLDcEyqGT2kGVT0b8SMdxv6v8so2SzfZJNtYl0QkipYb0GmY3mLkg1ZNZC77OtU35Tb8lVXDAgAEx+AMZJ+wZt/Im1Mpf5VXSArm3U8iFjB+KIBGUMyNOIR6C84kTJ8Y+AgPCpgiWj5TdW265JQ52sW8c+2hmajSwDo2ZXNsxO+gvX3nllfH84dzBbbfdFrfUoI9Adgn3INZbct9ZuR9NZp3FxVZl2mcOud9V8tlGyWb7JM/KnRk2B2etEtkKVPakY8S6M5CCyEgtmQ7MYCh/WHPJTARpuMy2MvtAm1lpsDAI8uiA0kFlIIQO60UXXRQHe9nzrWPHjjFIp+ALs0cwRTCZBa0oqkSqJ4MoLOPhkS06mOVjUJ+ifVwHM8GG7ZgdFKliOQEp03wwa05mA8XDuMYxyMi6Z9rn6KOPjlsIZe5VtItWZfCXQ+53lXy2UbLZPsmT6cywSTsViZnRoAohRUZYU8EMBqOzIBA08CsMZo8IAimy06ZNmzgrocIgQKCiN4Uo2HaDVE4CCNI5aR8qebL9Ce0FZ4uSVdGTtqJGQ7Vq1ULdunXjdhws0SEY2X333ePMLQEfg2AWE8kNB1Cyy7TPHKcHMHPBwm5GIdj3hc2NwfoYpqxvvPFG1/cViG2UbLZPsqzeIT377LNjeiEFRVjbR3DxwQcfxOIHlKdnnz8VHkUoTPUs/HnDzB/VPBkQoSAI58nJJ58c9ycjbZrAQclLNTzrrLPi4BbpubRd9+7dy74+evTouEUHs07M4LL+T7nTr1+/MHPmzLhOlv4BW6a1bt069qcpYtW3b9+yWT4HUH6a9U6zjD/I+MaWBn788VHxjgsEF/6VO06ke4C0KOWXbZRstk8yZa5fBHy0EemezPYxiDVr1qx4vSPFk6qSdJaUDAZ+yThvGBxhEJiiSJSlJ/DjPGKG1sAveSjGxzq+448/Pg50EfSxTykVc5nFBZUkzzjjjBiMvPnmm2HSpEkFPurixL0FtAdbBt19991x8IT3nvOJvUspNLZyeqfptj/NtM8cdpBY8MviX0o0N2zYMI4gsa8SIxfsQUL1LytB5Z9tlGy2T3JvwAxWnXrqqTHdk/VJBHl0eB5++OHQuHHjuMieAlak6Ej6/wj+Nt988xg4EDRQct7ZieSiuAipnVRqJ5Wd9HYGUqjKzroy9momaCeA55rHDC5VW92SI3d9At7rV199NQbbZJf85S9/ia9x36HSsX7Be7oiE1Irq/tdMf1PigAjReSLb7XVVnEfH1KhuOjffvvtlj0vINso2Wyf5KJc/emnnx4Hskj5BAEhN+hrr73WtWXST6DyN7N9bMVhIYrkomvM7CxtlClcxX6LZHFRTZJBfKrpZmYJe/fuHWeh3M4mt3ivu3btGvvYFEdaUwVW/TyDvyxY+Y+PAhWU/mVkj5EI9rpiVJzX6CyRGsCiYS8Q+WUbJZvtkzyMbrM1AHv0Uaqe8vTcbAnyFixYEMttM8rNlg4rlzaX9OMYEOa8MtUzmVaejeVzMraoLkk/LpNCzZ7NXBOZHWRPRip8UmnS/TPzwwGU387yX1mQCfwy+10xMsHsHh1acpS5kJCPzNfZ8Fj5Zxslm+2TPG+99Va49dZb49q+559/Pqaqs2cpWwhQwZPF9ccee2ysWNijRw8DP2ktUBhEyUV/jesd1z/WlF1zzTVxjSZ9OSqz8vrNN98cnxP40c8jpdfAL3/YyoEBFGfOfz3X/P0G7neVfLZRstk+ycUaPjo2rK+gPD1Fqihpvssuu8TBrcwgV4cOHVbZUFeSyuu9iP3jmNVjGwdqNrCVA8VFGLxnuc5TTz0VA7/mzZvHf2PKYf6xv6Jr/H4b0z6zILPfVfv27cN2220Xy52z+SRbPLCpqwrPNko22yc5qZ4EeQ0aNIg3WFDNc+zYsXF934MPPhhToNiPjGseHaTM3mSSVJ6xDyOF+MhqYO/FK664Iq7NzGzv8Ne//jX289hWQCrPTPv8FVav0PX444/H6p6MEFHYhYXBlJ5ltIhpafe7yj/bKNlsn+RhzQprlZntYwsH9oc75phjwp///Od4vRs4cGD8YA0gAeJpp51m4CepaEyePDmmsDOrxwd9OpbssL0A/bjMPn5WaVV5Z4mcX8H9rpLPNko22yd5KETF5tO1a9cObdq0ieuTX3zxxRj8ff3113HGr3PnzrHoARvrUsBKkso7tmlg0ItKnhR2WXl95mGHHRbX+pH9kOEecirvXPP3K7jfVfLZRslm+yQT6yhI+aTIAelOpK83adIk7tvHXleUPKczZHEDScWAKsUUtWKNM+uZGfD6+OOPQ9OmTWNxFwLD/fbbL8yYMSM0a9as0IcrZYVr/n4D97tKPtso2WyfZCKNnRRP9rEi+MsE7FRYy5Q7l6TyjvR2qnnus88+sUYD6/6obJzZuuu6664LU6ZMCf/85z/jumcLvKgYGPytBfe7Sj7bKNlsn/K5/oVNiy+66KI4Ci5J5d2aNgUnpZ3Zv4svvjjuZ0qWw4cffhhKSkriLCADYRR/oaCfVAxM+1wLlP0dNmxYaNSoUXjsscdiuXM2aCX1aYMNNogbHQ8ZMiQsXrw47L333qFq1aq5bjfZRuWK51D5s9lmm8WCLqx3YQ0ge1lJUnlEyjpbN7Bej2CPauyZ/ZhJdX/uuefiXqVbbrllDA75Gvs2jxkzJpx//vmxnycVC4O/teB+V8lnGyWb7VM+bb755uGQQw4x1VNSucXsHVs4sMyAdX0UtWKwnj392MeUgfz58+fHoi5c79i8HTVq1Ah77rlnqF+/foH/D6TsMu3zR7jfVfLZRslm+0iSkpB5ct5554Xq1avHFHZSO4877rhYyOrll1+O2Vykek6cODE8+eSTMevB7RxUzAz+fmS/qz322OMH+11R+GD8+PFhwoQJMQf8888/j/td7bDDDpY9zzPbKNlsH0lSEjCrxx59Bx98cNzO4fXXXw/jxo2L/boWLVrEtXxz584Nn332WVmBK6mYuc/fGrjfVfLZRslm+0iSkoAZPmbybr755nDAAQeEc845J1YuZpuHk046KfTt2zcWd1m5srFUzFzz9yPc7yr5bKNks30kSYWUSd8ksJs0aVJYsGBBuPDCC2PaJ6mgLVu2jGmgpHpmuIm7ip1pnz/D/a6SzzZKNttHklRIzPSxT999990X9y897LDD1rjtg5QGzvz9DEqdb7vttmHQoEFhm222CZtuumkcFVpnnXXy0T5aC7ZRstk+kqRCYqsaqhc/88wzoVOnTnErB4u6KK0M/taC+10ln22UbLaPJKnQa//YvP2LL74IO++8s3uXKrVM+/wFqPq5/vrr56otlAW2UbLZPpKkQpk6dWpMAd11111tBKWWwZ8kSZIkpYArXSVJkiQpBQz+JEmSJCkFDP4kSZIkKQUM/iRJkiQpBQz+JEn6BSZPnhyaNWvmeyZJKncqF/oAJEkqlIceeiiMGjUqTJs2LWy44YZhhx12CF27dg1NmjSxUSRJRcfgT5KUSgR9N910U+jfv3/Yd999wzrrrBOef/75MHHiRIM/SVJRMu1TkpQ6ixYtCtdcc03o06dPOOigg8IGG2wQg78DDjgg9OzZMyxZsiQMHjw4BoV88Dmvrcn2228fPvnkk7LnvXr1CldeeeUqKaIjR44Me+21V/xZTz75ZHj22WdDy5YtQ9OmTcMNN9xQ9m+HDx8ezjrrrHD++eeHxo0bh0MOOSS8/fbbuX0zJEmpYfAnSUqdN954IyxevDi0WkEG+AAAAhZJREFUaNFijV+//vrrw5QpU8L48ePDgw8+GAOwESNG/Krf9d///jf+rueeey5069YtXHTRRfFnjhs3Ltx5553huuuuC59++mnZ9z/11FMx6HvttddiMDpw4MBf9XslSVqdwZ8kKXUWLFgQNt5441C5cuUfXQt4xhlnhJKSklCzZs34OQHbr8HvOO200+LMYuvWrcMXX3wROnXqFKpWrRq22267+PHBBx+Uff9uu+0WmjdvHipVqhTatWsX3n///V/1eyVJWp3BnyQpdWrUqBGDsO+//36NX58zZ06oX79+2XM+57Vf+7sI5LDeeuvFR4LKjHXXXTd8/fXXZc9r1apV9jnfz6zhjx2nJEm/hMGfJCl1WE9H0MX6uzWpXbt2mDlzZtnzWbNmxdfWZP311w/ffvtt2fO5c+dm92AlScoSgz9JUupUq1Ytrr8bMGBADAAJ3pYuXRoLsQwdOjSuuWPd3/z58+MH6/Latm27xp/F9hATJkwIy5Yti+v6Xn311Tz/30iStHbc6kGSlEqdO3eO6ZcUcjn33HPjPn8NGzaM+/zxSCrmoYceGr+3VatW4fTTT1/jz+ndu3es8EnxlgMPPDB+SJKURBVWlCr0QUiSJEmScsu0T0mSJElKAYM/SZIkSUoBgz9JkiRJSgGDP0mSJElKAYM/SZIkSUoBgz9JkiRJSgGDP0mSJElKAYM/SZIkSUqB/wfHNseFIj/SrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "    ],\n",
    "    \"Importance\": xgb.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equivalent-stupid",
   "metadata": {},
   "source": [
    "The performance of this model is quite similar to the one fitted by the algorithm on the previous set of features, with only a slight improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "acute-advice",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4262452115870505\n",
      "R2: 0.9671203632086612\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Disjoint effect\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Disjoint effect\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confused-throw",
   "metadata": {},
   "source": [
    "**LightGBM solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "vanilla-train",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMRegressor(random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "south-passing",
   "metadata": {},
   "source": [
    "As opposed to XGBoost, LightGBM seems to favor also in this case the information carried by categorical attributes, with the most informative ones being `AvgByZone` and `AvgByHour`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "golden-carrier",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "    ],\n",
    "    \"Importance\": lgbm.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "black-cartoon",
   "metadata": {},
   "source": [
    "Still, this feature combination results in lower performance with respect to the previous LightGBM solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aware-australian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4192976338399796\n",
      "R2: 0.9674399119839188\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Disjoint effect\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Disjoint effect\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "designing-reverse",
   "metadata": {},
   "source": [
    "## Solution #4: combined, effect-encoded categorical features\n",
    "\n",
    "Here, we consider a solution in which we use all the available numerical features, plus the joint average computed considering all the categorical values at once."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artistic-reconstruction",
   "metadata": {},
   "source": [
    "**XGBoost solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "known-bicycle",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(objective=\"reg:squarederror\", random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fallen-mineral",
   "metadata": {},
   "source": [
    "In terms of feature importance, this models still favors only a few attributes, with a notable information gain obtained by `AvgCombined` (second highest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "confidential-salem",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"],\n",
    "    \"Importance\": xgb.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recovered-bangkok",
   "metadata": {},
   "source": [
    "This solution achieves another significant improvement over the last XGBoost-based one, performing on par with the last LightGBM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "falling-edward",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.419869932549417\n",
      "R2: 0.967413648495854\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Combined effect\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"Combined effect\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "western-tyler",
   "metadata": {},
   "source": [
    "**LightGBM solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "settled-force",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMRegressor(random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "structured-found",
   "metadata": {},
   "source": [
    "This LightGBM-based model spreads feature importance very evenly across the different attributes, with the largest contribution given again by `AvgCombined`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "narrow-stack",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09HTLy8urzGsBAAAAAFSBcud5Hnrooda/f38744wzrGXLlr5GMEIbzAMAAAAAEiwQfPfdd22PPfawBQsWbHdcASGBIAAAAAAkYCCoojEAAAAAgOpvp0qArlq1yl555RVbtmyZtWjRwo4//nhr3LhxZV0bAAAAACCWxWLee+8969q1qz3++OP22Wef+aOe6zgAAAAAIAFnBMeOHWvDhg2zU089teTYc889Z6NHj7bp06dXysUBAAAAAGI4I7ho0SI75ZRTtjvWrVs3++abb6J+UQAAAACAOAgE9957b3v22We3O/b8889b69ato35RAAAAAIA4SA0dMmSIDRgwwKuHtmrVyr777jtbvHixTZkypfKuDgAAAAAQu0DwkEMOsRdffNFeffVVKygo8IqhnTt3tuTk5KhfFAAAAAAgTraP0FYRhx9+eMn2EQSBAAAAAJDAgeDSpUtt0KBB9sEHH1ijRo1s9erV1r59e7v11lttjz32qMxrBAAAAADEIhC84YYbrF27dnbvvfdavXr1bN26dTZhwgTLzMz0dYOJpGGjula3zm6xvgxUwE8bfrY1q3/ivQMAAACiEQh+/PHHdv/999tuu/03QKpfv77PEB522GHl+v3LL7/cvv32W0tKSvJA8qabbrL09HTLy8vzYHLlypWeapqdnW1t2rTx3ynrXGVSENj7+qmV/jqIvkfH/dnWGIEgAAAAEJXtIw4++GD78MMPtzu2cOFC69ChQ7l+X0HcrFmzbMaMGdavXz+vQirapL537942Z84cf8zKyir5nbLOAQAAAAAqeUZQ+wVecskldtxxx1nLli3t+++/t3nz5llGRoaniEYMHDhwh7/fsGHDku/Xrl1rNWrUsMLCQsvNzbUHHnjAj+u/NWrUKCsqKrItW7aUei4lJaVC/2cBAAAAADsRCG7cuNFOOukk/17BWO3ata1r1662YcMGDwrLY+jQoTZ//nwP8rTWMD8/36uP1qxZ08/rsXnz5n5cP1PauZ0JBFNTG/DvHJhmzf7foANAewH3FsQCn0WgrSDe7y3lDgRvvvnmXX6xMWPG+KPSQ8eNG1fq7GE0FRautc2bt+zU73Dzrt6WL19TZa9FW6n+aC+grYB7C2KJzyFUZntJSqpR6sTYTu0juH79elu8eLH9+OOPv9psfmecfvrpvt5PKabak7C4uNhn/PSozerT0tJ8RrC0cwAAAACAiit3IKhZvJEjR3rV0Lp165Yc11q/V199tczf1VYT2ncwEsTNnTvXN6dPTU31yqE5OTnWs2dPf9TzSOpnWecAAAAAAJUcCI4fP97uuOMOO+qoo3b6RTSTqDRQPWr7CAWBU6ZM8SBy+PDhvkXE5MmTfaN6VReNKOscAAAAAKCSA0HNBB566KEVepGmTZvak08+ucNzbdu2tWnTpu30OQAAAABAJe8jqBm9W265xSuGAgAAAAACmBFs06aNTZw40R599NGSYyroovTOTz75pFIuDgAAAAAQw0Dw+uuv96It3bt3365YDAAAAAAgQQPBlStXenqoZgABAAAAAAGsETzzzDNt5syZlXktAAAAAIB4mhH88MMP7ZFHHrG77rrLq4Bua+rUqVG/MAAAAABAjAPBc845x78AAAAAAAkeCP773//2x5YtW1b6xQAAAAAA4iAQHDp0aJnnVTzm5ZdfjtoFAQAAAABiHAjOnTu3cq8AAAAAABCfVUMBAAAAAImBQBAAAAAAAkMgCAAAAACBIRAEAAAAgMAQCAIAAABAYAgEAQAAACAwBIIAAAAAEBgCQQAAAAAIDIEgAAAAAASGQBAAAAAAAkMgCAAAAACBIRAEAAAAgMAQCAIAAABAYAgEAQAAACAwBIIAAAAAEBgCQQAAAAAIDIEgAAAAAASGQBAAAAAAAkMgCAAAAACBqZJAcMWKFda/f3/r1q2b9ejRw6688korKiryc3l5edarVy8/p8dFixaV/F5Z5wAAAAAAcRwI1qhRwy6++GKbM2eOzZ4921q3bm233nqrnxs2bJj17t3bz+kxKyur5PfKOgcAAAAAiONAMDk52Q477LCS5wcffLAtXbrUCgsLLTc31zIyMvy4HvVcs4VlnQMAAAAAVFytiv9qxWzevNkee+wx69Kli+Xn51uLFi2sZs2afk6PzZs39+Nbtmwp9VxKSkq5Xy81tUGl/P9A/GrWrGGsLwHVCO0FtBVwb0Es8TmEWLWXKg8ER40aZfXq1bPzzz/fZ/gqW2Hh2q3B55ad+h3+IKu35cvXVNlr0VaqP9oLaCvg3oJY4nMIldlekpJqlDoxVqWBYHZ2ti1evNimTJmy9aKSLC0tzZYtW2bFxcU+46fHgoICP64ZwdLOAQAAAACqwfYRt912my1cuNDuvPNOq127th9LTU219PR0y8nJ8ed61HOlfpZ1DgAAAABQcVUyI/jFF1/4LGCbNm3s3HPP9WN77rmnB4XDhw+3zMxMmzx5sjVq1MhnDSPKOgcAAAAAiONAcL/99rPPPvtsh+fatm1r06ZN2+lzAAAAAIA4Tw0FAAAAAMQHAkEAAAAACAyBIAAAAAAEhkAQAAAAAAJDIAgAAAAAgSEQBAAAAIDAEAgCAAAAQGAIBAEAAAAgMASCAAAAABAYAkEAAAAACAyBIAAAAAAEhkAQAAAAAAJDIAgAAAAAgSEQBAAAAIDAEAgCAAAAQGAIBAEAAAAgMASCAAAAABAYAkEAAAAACAyBIAAAAAAEhkAQAAAAAAJDIAgAAAAAgSEQBAAAAIDAEAgCAAAAQGAIBAEAAAAgMASCAAAAABAYAkEAAAAACAyBIAAAAAAEhkAQAAAAAAJDIAgAAAAAgamSQDA7O9u6dOli+++/v33++eclx/Py8qxXr17WrVs3f1y0aFG5zgEAAAAA4jwQPOGEE2zq1Km2xx57bHd82LBh1rt3b5szZ44/ZmVllescAAAAACDOA8FOnTpZWlradscKCwstNzfXMjIy/Lke9byoqKjMcwAAAACAXVNr13694vLz861FixZWs2ZNf67H5s2b+/EtW7aUei4lJWWnXic1tUHUrx3xrVmzhrG+BFQjtBfQVsC9BbHE5xBi1V5iFghWlcLCtbZ585ad+h3+IKu35cvXVNlr0VaqP9oLaCvg3oJY4nMIldlekpJqlDoxFrNAUKmiy5Yts+LiYp/x02NBQYEf14xgaecAAAAAANV0+4jU1FRLT0+3nJwcf65HPVfqZ1nnAAAAAAC7pkpmBEePHm0vvPCC/fDDD9a3b19LTk62Z5991oYPH26ZmZk2efJka9SokW8zEVHWOQAAAABAnAeCN954o3/9Utu2bW3atGk7/J2yzgEAAAAAqmFqKAAAAAAgNggEAQAAACAwBIIAAAAAEBgCQQAAAAAIDIEgAAAAAASGQBAAAAAAAkMgCAAAAACBIRAEAAAAgMAQCAIAAABAYAgEAQAAACAwBIIAAAAAEBgCQQAAAAAIDIEgAAAAAASGQBAAAAAAAkMgCAAAAACBIRAEAAAAgMAQCAIAAABAYAgEAQAAACAwBIIAAAAAEBgCQQAAAAAIDIEgAAAAAASGQBAAAAAAAkMgCAAAAACBIRAEAAAAgMAQCAIAAABAYAgEAQAAACAwBIIAAAAAEBgCQQAAAAAIDIEgAAAAAAQm7gPBvLw869Wrl3Xr1s0fFy1aFOtLAgAAAIBqLe4DwWHDhlnv3r1tzpw5/piVlRXrSwIAAACAaq1WrC+gLIWFhZabm2sPPPCAP8/IyLBRo0ZZUVGRpaSklOu/kZRUo0Kv3bRJ/Qr9HmKvov/mFVW7UWqVvh6qd3tp2qB89y7En6puK7s35d5SnVV1e2mcXK9KXw/Vt600SubeElJ7SSrj52ts2WpXL6iyLFy40G644QZ79tlnS451797dxo8fb+3atYvhlQEAAABA9RX3qaEAAAAAgIACwbS0NFu2bJkVFxf7cz0WFBT4cQAAAABAAgaCqamplp6ebjk5Of5cj3pe3vWBAAAAAIBqtkZQvvrqK8vMzLTVq1dbo0aNLDs72/bZZ59YXxYAAAAAVFtxHwgCAAAAAAJKDQUAAAAARB+BIAAAAAAEhkAQAAAAAAJDIAgAAAAAgSEQBAAAAIDAEAgCAICEsGLFCvv5559jfRkAEsjmzZtjfQmVhkAQlaa4uJh3F+XGTjYAdsWLL75oN998s61fv557CoCoyM/Pt5tuusmKiooS8h0lEERUffPNN/bll1/ahg0brGbNmgSDKNPy5cttzZo1tmnTJqtRo0ZCj7ph1zCwhN+aCfzHP/5hGRkZ3nFTe9E9BWHass0W2YnagUfVSE1NtU8++cTGjRtnhYWFCfe2s6E8oua1117zP5R99tnH3nrrLZs3b57VrVvXb8h8IOOX/vWvf9nEiRNtjz32KOnEqb0Av/T666/bokWLrHv37paSksIbhO0sWbLEkpKS7JFHHrGCggL7z3/+Yzk5OdagQQPeqQBt2+d47LHHfGCgf//+3h7oi2Bn/Lw1zXy33XbzQPCqq66yAw880EaOHGnJyckJ80YyI4iomD9/vt1666124403eue+Q4cO/uFMEIjSBg2ys7Ptmmuuseuuu85H3O69995fjeQCb7/9tnfiHn74YXvppZd80ACIUJsYPHiwDyjVqVPHXn31VevZs6d33hCmSLA3depUmzFjhvXq1csaNmzI2lHstN223kd0j7nrrrvsrLPOsvfff98yMzMT6nOIQBC7TOsxXn75Zf8wPvzwwz0A1Ijs/fffb3/+85/tnXfe8Z8j7Q/y448/2lNPPWUDBgywI4880lq3bm3t27e3H374wc8zYouIjRs32sKFC+2OO+6woUOH2syZM+3//u//tvsQZuAgXD/99JPP/CkdVBkGyijQ59CyZcvsoYcesqVLl8b6EhEjagOzZs2yESNGeBD4+OOP21/+8hd74IEH+DdBua1evdqDwPPOO88uu+wyH8RWqrHuM4mSclxz+FaxvghU/xGTjh07Wtu2bW3t2rV2yy23WNeuXe2vf/2rfxBrhvDss8/20VpA7eUPf/iDj+BHUnV0s33jjTesR48eJYMGBITQOuM2bdpYWlqaHXDAAd6h0yCC7Lnnnrb77rt7sFirVi3erADpPqG0v7lz59qzzz7rBR06derkbULLE/R51KxZM2vUqFGsLxWV7JfZR0oV/uijj+z555+3N99809eM6nNHnzPHHXec1a5dm38TlGui47nnnvPZwMaNG/uxdu3aeQacBq+PPfZY/5yqzvj0RFRE1mLo8ZJLLrH999/fn1999dWWm5vrHX3WayDyYa1ZwF8eX7VqlX+vkVt9gI8ZM4Y3DNakSZOSd+Gkk07yzr/Wgun44sWL7b333vMR2+r+YYzy06i87iG/+93vvHOmAmXHHHOMzxQ3bdrU15KqPWjQQIMEGs1nsCCMIFD3AwWBv//9761Pnz728ccfe+ZJq1atbM6cOT6LzL0Cv9WWftyauVSvXj1fC6h1gRpkitQx0LpBta0TTjghIQYUCARRIfrAVWCn2Z0IddB0A44EgaJULo2aaOQe4dpRe9m2zdSvX98/uFVgaPr06TZq1KgYXSniva2cfPLJ3tkfOHCgZxko44COXVi+/fZbnylet26dj8irQJlG7Z944glvH5oV7Natm7eLP/7xjwSBAa0JnD17ts/YKGVYnyXKRpKnn37a1xlrbTr9EZQVBL766qu+BEGB4GGHHWZ9+/b1wUZlLGlm8Mknn7Tx48d7Jlwi1MFgjSB2ea8mpVzoj0GdtMgxpebopqwbr1JFtx3VR1hKay+i0VlRIKibq262Y8eO9TRAhKestqItaSIUAGjLkbvvvtvTvRCW3r17+71DHTENNOrx3HPP9Xajwg7//ve//edOPPFEa9GiRYyvFlVBnXfN+N13332eSq6BRc0CijIH3n33Xa9qruPAjtTYGtCpQrUGFy+99FLPZtNnUvPmzf1z6aKLLvJUc2Ur6Z4T+Z3qjkAQu7xXkwJA/TF88MEHfk6dNJXwVgEZder33Xdf3uVA/VZ7UUdeHTo9VzCoG+x+++0X68tGnN5btOZL95e8vDwfaOLeEg4VIVMJ9xdeeMHvGerQX3HFFXbBBRd4+p/uG/369cStlt4AABtQSURBVLPvv//eA8HIIBMS0y+LRCldT7N/qhKqf38FhLp/PPPMMx4QKrWPzxaUZfPWrBPdX9Rv1X1Ez1XVXOmg2vNYg02aETziiCMS6o1kH0FEZa8mVfbTHivaX0VrNSIj+BSICdfOthd18llHGqadbSsKEkkHDWu2R9kCWg/4zTffeAdtyJAhXvRj8uTJPkigtcXp6ek+SKBBJY3iI3FFUvI0a6NBAd0zVBVUKcPaO1AUFKpdTJo0ydPJgd8yevRo/3zRfUSDByqCqIJDWnt67bXXJmSflhlBRGWvJqXnaOpcHTV9SEsiLKJF5beXyMguQWCYKtJWCALDoW0hbrvtNhs0aJAHg+qUaV3glClT/Nzll1/uM4FnnHGGffrpp15AhiAwcWmmRhQE6t9bnXUNOqs2gbIJNPun9V0KBrWFiAaQCAKxI5HPkzVr1pQc03pjtR0VOlQQqIFIDTZpa7REDAKF7SPwm5Rioz+ELl26ePqWvrQQX5XalHuv6m1anK1y7pG1gomSO43Kby+0k3DRVlAWzfIo/XPChAl2yCGHePqfqn8qEFQ66KOPPuoVQZWqpYFHpQqnpKTwpiYopYz//e9/92DwoIMO8kHnBQsW2Jlnnunpe1q/pc8UrRVUcKi0PtYEojTqe8ydO9cLv6jIkO4f6rdo/fntt99uH374oc8qKyjU8UTtrxAI4jeVtVeT9ufR1hAacVPaTiL+kWDn0F5AW0E07iPvv/++bwyuzxcNHikI1OeOZoSVFqpiZBrB32uvvbx4A0Fg4lI5f2WNaG/izz//3AcYtT9kYWGhbw8hCgTbt2/v1R3VPlJTU2N81YhHkYBOM8pjx471QE99XBWGUWHDXr16eR9Xs4AaxE6U6qClIRBEmXs16cNYH8JfffWVp1voD0PrMHSD1cJr/WFo9E1/JPqgjswGIjy0F9BWEA2RzBKl+amzr/RhbRmhDpmCQAWDGrVXURClA2qvLyQuddjVz9C/v/ofCv6016zSyNU21Ba0PYTag4536NCB9HH8SmQQSf1WzfZp/ejhW/u0GjjQjJ/WBao96Z6jGWdVHI4MLiVqECgEgijV/PnzfeG1RmFVjvmoo46yRYsWeUqO9lfRh7Sm0vW9RuT0x4Nw0V5AW8Gu2nbkXamgWvul5+rkq6hQJBhUMSF15rSelH3hEtuqVau8Y65CQSoqpv6GqjiqP9KwYUM7/vjjvcOufsrRRx/NmkD8igYPhg0b5u1DaeRaqvLUU095YRi1Lc02KyhUZWJVvFebCuW+QiCIUmkjXqXl6A9CIyb6Y1Hw984779h3333nH8Za76XUHAp9gPaC8qKt4LeCQK3/e+WVV+zQQw/19cQqHqTBJgUCCgjuv/9+LwSizyAkpqKiIp8Zjqz9Uxqf1pnvvffeHgyqLWiwQOl82jpCs4XsWYwd0YSF2oe2H9L9409/+pNPZMydO9fblgYR1I/VhIfSiyN7UIaAQBDb0YircvBVKldV/DQ1rj+SrKwsH0nRRt+aJdQfjyotKQVDM4YIE+0FtBVESyQI1B6Rs2bN8kqh6tirWp+Kfigo0LpALVNQQSoKgSQuDQJkZ2fbzJkzPfVXM8MaENDG8Fqqou9VJEifQV988YUXE1LBGOCXtMRJ9xbVsVC1YW0noswCtZlmWwcZpk+f7mtQ1aYUDIY2mMA+gijBXk3YGbQX0FYQbcpCUfGGESNG+Ki8CpTNmzfPtw/585//7AUdNCDJTGDi0szvuHHjfABagZ/WbWkAQCl9X3/9tXfmVadA1UI1MK1OvDr5QGlZBpo5jmxJpCrE2jhe2xK13zr7p76M2tStt97qwWBomBGE035MKsucmZlp/fv3tz59+ngKxnPPPeezgkq70EJbjdCeeOKJXihGm/YiTLQX0FYQDb+sxqdZPxX80H6BqkqtNTxKJVYxKlXw02cPnf7EDgKvuuoq75irk649ITUrqNoECgI1M3jYYYd530TrvpQ6rLQ/YFvr16/3wE/3Fg0kaU9JFRxq2bKlnXbaaV519r777vNBJa0NVLEY9XVDRCAI9mrCTmFvL9BWEO0gUMsRCgoKvNCH1oApPevcc8+1U045xUu7ayuJ7t27l4zqIzH98MMPXs1RA84KAi+55BL/Xh323NxcHyRQR/53v/udB4EUqcOOigv169fPswa0jnT48OE+e6wUYm0Qr3WCmuxQwZgHH3zQ21PI258RCAaOvZpAewH3FsR6TaCKv2iUfsiQIXbBBRf4KL0qQmpbgEhhGI3mI7H7I0oHVu0BBYCPPfaYzw6qPajQh2aLtXZQgwOR4h7AL2mt6Pfff+8zfqploQGkU0891TMKlHqu2WQ9VyHEw7feZ3RfCTUIFDZ9C1hkr6bOnTvbeeed59Pnd999t59TLr5SQTds2OA5+KzHAO0F3FsQbVqfo5QtddrUuVcBmEjFPi1P0BoxrRejMExiB4Ci/og+Z1S5Ual8ms3ZtoOutYCivglQVlvSAMI555xjs2fP9plkrRGU888/39NGtfWM7LPPPsG/kcwIBoq9mkB7AfcWxPKzR5SupSBPHTOtPdZgpCpRP/PMM17Z79hjj/UUQSSuSHvQXpGasdHWVGoT7dq1s4EDB3pnXdXM77rrLi8ipIrmQGmD1RowUAr5wQcf7JMaCgbVhpRhoHak/QMVJCr9vEbAM4ERBIIBYq8m0F7AvQWxos6XRunVEdNecTfeeKOvD9QskIKAGTNm2BNPPGEnnHACa8ACoX/v0aNHe3rwPffc47OC6sirYMyVV17phYO0ZYiKBQGl3Ve0tdm9997rW8xoyzPtF6gZ5Ntvv92zCxYtWuQZcBpkwv//vm0NCrb891uEJrJXk6qFbjvCpmpt+kNSjrUW0mrTTYD2Au4tqCh9nkTW+H366ac2dOhQD/yUsnXnnXd6QKiiICtXrrRp06bZLbfcQjpogqfwafYmUiBGqcEq4KH0YJXx17YhOqaZHBUK0mxO27ZtY3zViGdKJb/00ku9uv2jjz7qS5u0F6W2hPjnP//p/Vr1Y/baa69fZSaEjJ3AA6X0CwWBSrPQDVaLsrfdq0mjsuzVBNoLuLdgV6nqpwYctQ2EOvspKSm+kXNkCyJV9FM1YqWDKhBQ542Zn8QWCQL1b67ZYG0Sr4qxChDVkVcn/ayzzvLzmhkEdiQS0KlPq7TPK664wgeU9HXdddfZ4MGDbezYsfa///u/nmEQ2SeQIPD/ITU0EOzVBNoLuLegqmm9jqo7qpP2+eefe+qfSv5rD7gjjzzSf0ZBoVIAe/ToYccdd5xvFo7E74sofU8pe4cccogPRis41B6BorahlD7tG5icnBzLS0YcU1t66aWXfIuInJwc3xpC7Un3HFUJffnllz3VXFtEsCZwx5gRDHCvJt1stRBbI7PapFU3XFVpU+U2TaVrA18tsEWYaC+grSAalAKqtV0XXnih9e7d27eCUJqf1vFovY42Av/mm2/8UZ00pXWxT2Bii/RFVMJfewJq43jN/h5xxBFewEO0JlCuvvrqmF0nqgftBah0T6WSL1myxJcz6f6iGUENMCnt/JNPPrE6derE+lLjFoFgQDfeqVv/WFQ9SZW4NHIyffp0O/vss/2cPqAffvhhT8nZfffdY3m5iDHaC2griAYFdZrlU0Co5Qb/8z//4+v/XnvtNe/8a82X0gH1c5oRJAgMJyvphRde8CqhKuahzeE18/fkk09az549fSBa+wgCZVE68aRJk3zGWJMb+lJqsfqymtTQ/oGqOKw9KFE69hEMBHs1gfYC7i2oCir8oo6YAj1t7qzOmqpCfvvttz7roy0h9t57b1u1apUPRp5++uns55XgIkGggj911pUSqlkbzeBoDakCRQWDGqTWWi7gtyiLQLN+Wh/4yiuveBvq2rWrnXvuuV70cNOmTbyJ5fnbpGpoGKNvL774om+iuWbNGp821348GnXTQuyMjAy/MTN1Hi7aC2griAZ1yNS512dK3759rUuXLr5HoNaANW7c2GcFW7du7aP2Sgu99tpr/TgS/zNGa/60Vkuzv+PGjfM+igp6aL2o1nhpcIAiHiirDal9aEBJ3yubQJkGGlTQoNJJJ51knTt3LqlEq/P4bRSLSWDs1QTaC7i3oKrMnz/f/va3v9n111/vA41KA1Wqn9agaxsAbRivfbz0XB22Tp06EQQG1B+pVauWp+uptL/WiGpGUAU9VDRGbeeUU04pqSYK7KgNqfiL1gNqQuONN97wAacLLrjAPvjgA29TSi9XqrEyERhUKB8CwQTcq0nVkvQHoHUZ2qg3Ui1JIya6EesP55133vER2jFjxvjoLMJEewFtBdGgjvxVV13lxT8046O1OTNnzvSCZF9//bWn/akipIqEaAbo0EMP9SIxSEzqY+jfXXu2KU1P+0Vqhkb/5krf+8c//uFbhigQ1DpSrRVUNVmgNKo6PHr0aBs/frwPKGjrM6USax9szQYqGFTRIVUdJggsPwLBBNyrSZ37gw46yAO+BQsW+B5NGh1RLrWm0yPVQZWSocW1CBPtBbQVRItSsR5//HGf5VEQqGIf+l770ebm5nqFSA1KarReQSCd/sSm4E8ddg0KqM+hWUCV9degtIJB9T1UnE59EVUu1wA28Ms+yttvv+2DCZop/uKLLzwY1J6ACgLVp1XaubaoURtSEKh7D3YOgWCCYK8m0F7AvQWxoEFHpXt26NDBA0Blm2h2UClbqtinTpzWDir1TxvG0+lPfGoLahd33323bwOhPoqCQQ1Sa2YwsoF8r169SA/Gr6h9aBsRrQFU0NeyZUtbu3atB4aaAdSsX/369X2d8fLly+2oo47y3yO1eOexfUQCYK8m0F7AvQVVTR19dbz0pWwTdcYeeugh3zdw29QsBQGiYiHKTkHipoPq31rBv2ZrLr74Yl+OMnjwYLv55pu9vagojJajaIb4nnvuYWkKfiUvL88HD/r37+8VhSO0zljtStudqa1pVlmDC2pTut8oaMTOo2pogoycaB8VjYroD0GVtyJ7Na1bt843jtf6wMheTfpjQrhoL6CtIJq0JYA+X9TBb9Gihc2bN88GDBjghWM0aj9hwgQPBLROEIlJFcm1BlTBnlKCVaX8vPPO84JAWiuqtVyjRo3ypSsKFpUyTF8EO6oKetttt3n68KWXXlpyXFtB6B6jdqWtaNSPUTtSunmkUigqhtTQar5Xk0ZiI2v/xo4da4sXL/ZAUPnSmkbXAm3l5GuvJo3S6XuEqYj2AtoKokydMhVwWLFihc/waFbw4IMP9kFHpXa9+eabNnnyZN9AHolL20+p36HKsBdddJFXjdWaLg0AaOZGgaDWCqowzPHHH09fBL8SySJ46aWXfK2f7iHqw2qCIzLbt3TpUm9nKjh0zDHHcF+JAgLBakrrLbTQWiNtmg3USOuee+7p5XM1UqLvtXB2yZIlfjPWIm1ScsJFewFtBdGgGZ9Ih00FYnRvUfXps846y2d7br31Vt8wXsHg0Ucf7WvAtLE8Ep/Wf2rAeeLEiZ4efM455/iMYEpKihUXF3tq8BlnnEEQiDJpOxFtM6M1xQoAIxvD677zyCOP+KCTBpZ0juqgu45AsBpirybQXsC9BbEQ6Xg988wzvk5H20Nok3gd12ygtobQtkUnn3yyp/8pCEA4NAitKo9ZWVk+QK0BAaUMawZHe0pS1RFlpYWKUoa19k8BnyoMK/NN57Q9hLYd0bpBFY8hCIwOAsFqhr2aQHsB9xbEsqOmEfvbb7/dM01UIVQdNa0PE6VtaeZHQYCyVRAeBX777ruvpwxrNlhVHtV2tMYL2BG1D6UVa9sRDRpojeD06dN9sEk+++wzzzz461//6tluiB4CwWqGvZpAewH3FlS1SBCoDeFV8VEdshNOOMELg6gqpFL/NHovCgoJAsOmmUEFhCoUlJGR4dVDgdIGmFT9XkGgZvy0x6hSyrUFiYJDpYmq76vCh1pfuu2gFHYdf5nVbG2GRmDvu+8+L6urP5YhQ4Z41SRRIZgHH3zQfvrpJ8/TR9hoL6CtYFf9stP1wgsveJXQP/3pT745vGb+nnzySU/7U4EQ7SMIiNaIduzYkfoEKJXuLaoyPGLECBs2bJjPHmud8erVq73Y1JQpU/znNNCkNYEEgdGXFP3/JCqjQy+/3KtJVUFL26sJ4aK9gLaCaIl8xij4071FKaGaBdSgY35+vn8mKRhUVUjNEALb2n333XlDUKa33nrLrrvuOt8GQkWGHn/8cbvzzju9CnGE7jPb3o8QPaSGVrMP4mXLlvmoiMoxt2vXzgYOHOgLa1VS96677vJRFY2oIFy0F9BWEC3qgGlwUXt6KSVU63cU8L3++uv27LPPevpW48aNLTU1lcIwAMrt22+/tQYNGniWgQrBnHrqqX5cW6KpT/vPf/7T9yU98MADfSIElYMN5avRXk133HGHr73Izc31fZmUlqNN45WK07RpU/+joUw3aC/g3oJoUzXQyy+/3AcetTecKDjUwNOkSZNYAwag3DSp8be//c3XGms5k7ZDU5VhpYN++OGHvl5Q64wLCgq8CjEqD4FgnFIKTmQERItktS5QC2W1T4/ypzUSq2P6UH7//fetYcOGBIEBo72AtoJoUJU+dcy0vkudsb333ts7aAr4Vq5caRdccIFnpKgTF+nQadQeAMpr/fr11q9fP1/qpOBP6wSV1Rbp8yo1VLOE//nPf3zgiRnBykOxmDgVafTaq0kjItokXrN+6vAPGjTIP5S1ga/Oa58ehI32AtoKokGj8ffff7+vBVTFPnXOxo0b59sBaIReBR3OP/98a9Wqla/rIQgEsDOD1ZG1o0OHDvX7zOLFi319oAafvvzyS08z//rrr+3hhx+2v//97wSBlYw1gnGGvZpAewH3FsSK1vyp03b33Xfb1Vdf7UXItLnzQQcd5IORkUFJlXfX2kAAKI3WF6uuhSYvFi5c6BXvI5Maqm6v5U0aTGrTpo0HfDqnyY9HHnnErr/+es8+QOUiNTRORfZqOvPMM22//fbzzTTPOecc/yPSNDpAewH3FkQrHVQBnzpmKtQgqgo6bdo0T8uaNWuWd+K0L5w+l1TNT98DQGk0q3fbbbf5/qJKKY/cV7777jubP3++92WV+rlgwQIP/LYdWFJVfBWSQeUjNTROsFcTaC/g3oKqtmbNGl9/rhF6bQuhtTvnnXeenX766T6KP2rUKP9S2paCxcsuu4wgEMBv+vzzz+3VV1+19957z7+03lgBYUpKir355pueaaD9Ab/44gu/DykQjPSFCQKrDjOCcUZbRKgyqKbIlT+9ZMkSGzNmjO25557+x6H8aT1SHRS0F3BvQTSok6atiIYMGeKdshUrVtjcuXM9C0XVqrU2UGt59NkEAOVRVFTk949TTjnFZ/jeffddmz59ug80aRsaZbstX77cZwg7derEmxojBIJxtlfTaaedZu3bt/fF+Qr4tBhfZbuHDx/uoylspgnaC7i3INo0Qq/PHX3W6DNIqaAapX/66ad9AFJVQlWlGgDKS/cT7QmoNcfKOujYsaN1797dXnnlFTv++OO9+FTt2rV3mBmHqkEgGGfYqwm0F3BvQawyUpQGOnLkyJIRenXOtJ2EqvwBQHlEgjrNBN50002eTTB16lTr2bOnXXzxxb6ZvIrCHHLIIbyhMUYgGCPs1QTaC7i3IN689dZbvhxBmziryAMAVJQGkVRwSpkFGmA644wzfrWVBGKL7SNiRBv1jh8/3lNw5syZ44tmNTLSpEkTq1evnpfMzc7Otg0bNtiRRx7JwtnA0V5AW0FV0Hp0VQSdMGGCZWRkWK1a1JQDUDG6f+y1115eNEZFqbQ9BCmg8YVAMEbYqwm0F3BvQTxSx+3UU08lHRTALlOVUK0zVhGqP/7xjwwuxRlSQ6sQezWB9gLuLQCAkOTm5nqaKGsC4w+BYBVR9TUtlv3lXk1akD9z5kzLycnxRfrff/+979V0wAEHUKEtYLQX0FYAAEBlYrVmFWnYsKFXTGrevLmvuzj44IPtjTfe8E17161b56W6+/bt66k4Kq1Lme6w0V5AWwEAAJWJNYJVKC0tzdLT023ixIl24YUX+ma9mhFU/nRxcbHvI6iKSioYA9BewL0FAABUFlJDY4C9mkB7AfcWAAAQS8wIxoBKc++77742evRoa9u2re2xxx6+8eZuu+0Wi8tBnKO9gLYCAACijRnBGHr99ddt0qRJ9uCDD1rdunVjeSmoBmgvoK0AAIBoIRCMMVUPVYEYgPYC7i0AAKCqEAgCAAAAQGDYPgIAAAAAAkMgCAAAAACBIRAEAAAAgMAQCAIAsAveeustO/bYY3kPAQDVSq1YXwAAAPFi9uzZ9sADD1heXp7Vr1/fDjjgABswYIB16tQp1pcGAEBUEQgCALCVAsC7777bRowYYUcffbTttttu9q9//ctefvllAkEAQMIhNRQAELw1a9bYxIkTLSsry0466SSrV6+eB4JdunSxG264wTZu3GhjxozxAFFf+l7HdmT//fe3xYsXlzzPzMy02267bbs00nvuuceOOOII/2+99NJLNm/ePOvWrZsdeuihNmXKlJLfveOOO2zgwIF2/fXXW4cOHezUU0+1jz76KPh/LwAAgSAAALvsvffesw0bNljXrl13eP6uu+6yDz74wGbOnGmzZs3yYGzy5MkVeq0ffvjBX+u1116zq6++2m688Ub/b06fPt2mTp1qd955py1ZsqTk5+fOnesB4DvvvOOB6ahRoyr0ugAAbIsZQQBA8FauXGlNmjSxWrVqlbp28IorrrDU1FRLSUnx7xW8VYRe47LLLvMZx+7du9uKFSusT58+1qBBA9tvv/3867PPPiv5+Y4dO1rnzp2tZs2a1rNnT/v000+D//cCAOw6AkEAQPCSk5M9INu0adMO34uCggJr1apVyXN9r2MVfS0FdVK3bl1/VIAZUadOHVu3bl3J86ZNm5Z8r5/XbGJp1wkAQHkRCAIAgqf1dwrAtF5vR5o3b25Lly4teZ6fn+/HdmT33Xe39evXlzxfvnx58O8vACD+EAgCAILXsGFDX683cuRIDwYVyP38889exGXcuHG+Rk/rBIuKivxL6/h69Oixw/dNW07k5ORYcXGxrwN8++23g39/AQDxh+0jAADYqm/fvp6iqSIwgwYN8n0E27Vr5/sI6lHpmqeddpq/VyeffLJdfvnlO3zfhg4d6pVCVfjlxBNP9C8AAOJNjS1bxfoiAAAAAABVh9RQAAAAAAgMgSAAAAAABIZAEAAAAAACQyAIAAAAAIEhEAQAAACAwBAIAgAAAEBgCAQBAAAAIDAEggAAAAAQGAJBAAAAAAjM/wcl4CoNliznPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"],\n",
    "    \"Importance\": lgbm.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "joined-moral",
   "metadata": {},
   "source": [
    "As for its performance, this solution achieves only a slight improvement over the previous one if we consider our test metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "contrary-cause",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.417430290129749\n",
      "R2: 0.9675255330359344\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\", \"AvgCombined\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Combined effect\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"Combined effect\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fossil-brown",
   "metadata": {},
   "source": [
    "## Solution #5: putting all together\n",
    "\n",
    "Finally, we test a solution in which all the aforementioned features are fed to the learning algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assumed-start",
   "metadata": {},
   "source": [
    "**XGBoost solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "alert-floor",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(objective=\"reg:squarederror\", random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "provincial-carol",
   "metadata": {},
   "source": [
    "The addition of the other variables in the model does not seem to affect much the distribution of feature importance, with `NumBikesDiff`, `AvgCombined` and `NumBikes` accounting for 70% of the total information gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "every-eating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "        \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "    ],\n",
    "    \"Importance\": xgb.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complicated-ready",
   "metadata": {},
   "source": [
    "Consequently, only a small performance improvement is recorded by this model with respect to the previous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "earned-calculator",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.417406934959475\n",
      "R2: 0.9675266031985551\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgb.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"All\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"All\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "damaged-significance",
   "metadata": {},
   "source": [
    "**LightGBM solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "reserved-leisure",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMRegressor(random_state=42, n_jobs=-1).fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "simplified-soldier",
   "metadata": {},
   "source": [
    "As opposed to XGBoost, the LightGBM solution actually seems to favor the usage of more features, with importance distributed almost evenly across many of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "organized-philip",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "        \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "    ],\n",
    "    \"Importance\": lgbm.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interim-scoop",
   "metadata": {},
   "source": [
    "Consequently, this model achieves a good improvement in test set metrics over the previous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "flexible-renaissance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4113938899575804\n",
      "R2: 0.9678015416212588\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbm.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"All\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"All\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confident-ottawa",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization\n",
    "\n",
    "For solution #5, we run an optimization procedure to determine the best values for the parameters of the regression models, so to increase even further their performance. To do so, we used the parameter grid defined in the cells below, in which we tested different values of some of the most important parameters of the two algorithms using scikit-learn's `GridSearchCV`. For uniformity with Spark, also here we used a 3-fold cross-validation.\n",
    "\n",
    "**Warning**: running this cross-validation procedure requires ~3 hours. Do it only if necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roman-stress",
   "metadata": {},
   "source": [
    "**XGBoost solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "hearing-somerset",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 128 candidates, totalling 384 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  96 tasks      | elapsed: 54.9min\n",
      "[Parallel(n_jobs=-1)]: Done 256 tasks      | elapsed: 132.1min\n",
      "[Parallel(n_jobs=-1)]: Done 384 out of 384 | elapsed: 197.0min finished\n"
     ]
    }
   ],
   "source": [
    "xgbCV = GridSearchCV(\n",
    "    estimator=XGBRegressor(objective=\"reg:squarederror\", random_state=42, n_jobs=-1),\n",
    "    param_grid={\n",
    "        \"n_estimators\": [100, 300],\n",
    "        \"max_depth\": [5, 10],\n",
    "        \"learning_rate\": [0.1, 0.25],\n",
    "        \"gamma\": [0, 0.5],\n",
    "        \"min_child_weight\": [1, 2],\n",
    "        \"subsample\": [1, 0.75],\n",
    "        \"colsample_bytree\": [1, 0.75]\n",
    "    },\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    cv=3,\n",
    "    n_jobs=-1,\n",
    "    verbose=3\n",
    ").fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "furnished-respect",
   "metadata": {},
   "source": [
    "The overall best model is characterized by the parameters below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "confirmed-mechanism",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model params:\n",
      "- colsample_bytree: 0.75\n",
      "- gamma: 0.5\n",
      "- learning_rate: 0.1\n",
      "- max_depth: 10\n",
      "- min_child_weight: 2\n",
      "- n_estimators: 100\n",
      "- subsample: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Best model params:\")\n",
    "print(\"\\n\".join([f\"- {x[0]}: {x[1]}\" for x in xgbCV.best_params_.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collective-spirituality",
   "metadata": {},
   "source": [
    "With respect to the unoptimized case, this model seems to penalize the `AvgCombined` and `NumBikes` attributes, favoring instead `NumBikesDiff`. The other features all provide almost the same contribution to the information gain, around 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fifteen-situation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "        \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "    ],\n",
    "    \"Importance\": xgbCV.best_estimator_.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animal-radius",
   "metadata": {},
   "source": [
    "In terms of evaluation metrics, this solution present a good improvement over the unoptimized case, and outperforms the best Spark GBT-based model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "selective-burlington",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4075712428545188\n",
      "R2: 0.967975719310193\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgbCV.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + xgbCV.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"All (CV)\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"XGBoost\", \"Features\": \"All (CV)\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sonic-header",
   "metadata": {},
   "source": [
    "**LightGBM solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "sensitive-train",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 128 candidates, totalling 384 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py:691: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "[Parallel(n_jobs=-1)]: Done  96 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 256 tasks      | elapsed:  5.7min\n",
      "[Parallel(n_jobs=-1)]: Done 384 out of 384 | elapsed:  8.5min finished\n"
     ]
    }
   ],
   "source": [
    "lgbmCV = GridSearchCV(\n",
    "    estimator=LGBMRegressor(random_state=42, n_jobs=-1),\n",
    "    param_grid={\n",
    "        \"n_estimators\": [100, 300],\n",
    "        \"learning_rate\": [0.1, 0.25],\n",
    "        \"num_leaves\": [31, 127],\n",
    "        \"class_weight\": [None, \"balanced\"],\n",
    "        \"min_child_samples\": [20, 100],\n",
    "        \"subsample\": [1, 0.75],\n",
    "        \"colsample_bytree\": [1, 0.75]\n",
    "    },\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    cv=3,\n",
    "    n_jobs=-1,\n",
    "    verbose=3\n",
    ").fit(trainData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]], trainData[\"NumBikesDiff+1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protective-style",
   "metadata": {},
   "source": [
    "The overall best model is characterized by the parameters below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "intimate-onion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model params:\n",
      "- class_weight: None\n",
      "- colsample_bytree: 0.75\n",
      "- learning_rate: 0.1\n",
      "- min_child_samples: 100\n",
      "- n_estimators: 300\n",
      "- num_leaves: 127\n",
      "- subsample: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Best model params:\")\n",
    "print(\"\\n\".join([f\"- {x[0]}: {x[1]}\" for x in lgbmCV.best_params_.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respiratory-value",
   "metadata": {},
   "source": [
    "With respect to XGBoost's case, the optimization procedure for LightGBM requires an impressively reduced amount of time (8.5 vs 195.8 minutes) and produces a model where the information gain is very well-distributed among the different features, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bacterial-heath",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importanceDF = pd.DataFrame({\n",
    "    \"Column\": [\n",
    "        \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "        \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "        \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "    ],\n",
    "    \"Importance\": lgbmCV.best_estimator_.feature_importances_\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(data=importanceDF, x=\"Column\", y=\"Importance\", ax=ax)\n",
    "ax.set_xticklabels(labels=importanceDF[\"Column\"], rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assured-macintosh",
   "metadata": {},
   "source": [
    "The resulting model results to be the best overall, improving ever so slightly on XGBoost's performance (but at a very reduced computational cost)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "loaded-switch",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4070748685379177\n",
      "R2: 0.9679983017942942\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbmCV.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]), squared=False)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(testData[\"NumBikes+1\"], testData[\"NumBikes\"] + lgbmCV.predict(testData[[\n",
    "    \"NumBikesDiff-3\", \"NumBikesDiff-2\", \"NumBikesDiff-1\", \"NumBikesDiff\", \"NumBikes\",\n",
    "    \"Hour\", \"DayOfWeek\", \"Zone\",\n",
    "    \"AvgByHour\", \"AvgByWeekday\", \"AvgByZone\", \"AvgCombined\"\n",
    "]]))\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "resultsDF = resultsDF.append([\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"All (CV)\", \"Metric\": \"RMSE\", \"Value\": rmse},\n",
    "    {\"Algorithm\": \"LightGBM\", \"Features\": \"All (CV)\", \"Metric\": \"R2\", \"Value\": r2}\n",
    "], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unusual-vegetable",
   "metadata": {},
   "source": [
    "## Comparison of the results\n",
    "\n",
    "The two graphs below summarize the evolution of the RMSE and R2 metrics for the different sets of features tested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "absolute-ghost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "sns.lineplot(data=resultsDF[resultsDF[\"Metric\"] == \"RMSE\"], x=\"Features\", y=\"Value\", hue=\"Algorithm\", ax=ax[0])\n",
    "sns.lineplot(data=resultsDF[resultsDF[\"Metric\"] == \"R2\"], x=\"Features\", y=\"Value\", hue=\"Algorithm\", ax=ax[1])\n",
    "ax[0].set_xticklabels(labels=resultsDF[\"Features\"].unique(), rotation=45)\n",
    "ax[1].set_xticklabels(labels=resultsDF[\"Features\"].unique(), rotation=45);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}