gradient_boosted_forest.ipynb

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    "colab": {
      "name": "gradient_boosted_forest.ipynb",
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      "mount_file_id": "15LRcSBI_mH2z6fXvh3aoxm_bq1UJ7qAS",
      "authorship_tag": "ABX9TyMZf8VHiy6sa7/QyaxTAMDU"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Examine the performance of gradient-boosted random forests. "
      ],
      "metadata": {
        "id": "y_fX2W4KBc59"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAzb5gln3MC3"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import cross_validate\n",
        "import numpy as np\n",
        "\n",
        "from scipy import stats"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLtIK3E93Vpy"
      },
      "source": [
        "TRAIN_DATA_PATH = \"/content/drive/My Drive/cs189 practicum/cs189 code/pollutionTrain.csv\"\n",
        "TEST_DATA_PATH = \"/content/drive/My Drive/cs189 practicum/cs189 code/pollutionTest.csv\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DATA SETUP"
      ],
      "metadata": {
        "id": "2211SRmuiYsq"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPAQ_OwP3XHS"
      },
      "source": [
        "trainPD = pd.read_csv(TRAIN_DATA_PATH)\n",
        "testPD = pd.read_csv(TEST_DATA_PATH)\n",
        "\n",
        "trainPD = trainPD.fillna(0)\n",
        "testPD = testPD.fillna(0)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEHWhN4p3bdZ"
      },
      "source": [
        "label_cols = ['target']\n",
        "\n",
        "# relevant features chosen based on 1) data context and \n",
        "# 2) the SequentialFeatureSelector in regression.ipynb.\n",
        "feature_cols = [\n",
        "                'precipitable_water_entire_atmosphere',\n",
        "                'relative_humidity_2m_above_ground',\n",
        "                'temperature_2m_above_ground',\n",
        "                'u_component_of_wind_10m_above_ground',\n",
        "                'v_component_of_wind_10m_above_ground',\n",
        "                'L3_NO2_NO2_column_number_density',\n",
        "                'L3_NO2_NO2_slant_column_number_density',\n",
        "                'L3_NO2_absorbing_aerosol_index',\n",
        "                'L3_NO2_tropopause_pressure',\n",
        "                'L3_O3_O3_column_number_density',\n",
        "                'L3_CO_CO_column_number_density',\n",
        "                'L3_CO_cloud_height',\n",
        "                'L3_CO_H2O_column_number_density',\n",
        "                'L3_HCHO_tropospheric_HCHO_column_number_density',\n",
        "                'L3_SO2_SO2_column_number_density',\n",
        "]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0q4Us7yY3dtx"
      },
      "source": [
        "# partition features matrix and target matrix \n",
        "X = trainPD[feature_cols]\n",
        "y = trainPD[label_cols]\n",
        "\n",
        "# split training data into training and validation (default 75% and 25% split)\n",
        "Xtrain, Xval, ytrain, yval = train_test_split(X, y, random_state=42)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KejXccOd3hD-"
      },
      "source": [
        "# MODEL TRAINING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBpcsvTn3hfJ"
      },
      "source": [
        "def eval_model(model, data):\n",
        "  Xtrain, ytrain, Xval, yval = data\n",
        "  model.fit(Xtrain, ytrain['target'])\n",
        "  # print(type(model.predict(Xval)))\n",
        "  ypred = model.predict(Xval).flatten()\n",
        "  ypred = pd.Series(ypred, name=\"predicted\")\n",
        "  yval_reindexed = yval.reset_index().drop('index', axis=1)\n",
        "  val_performance = pd.concat([yval_reindexed, ypred], axis=1)\n",
        "  val_performance['error']=val_performance['predicted']-val_performance['target']\n",
        "  RMSE = np.sqrt(mean_squared_error(\n",
        "      val_performance['target'], val_performance['predicted']))\n",
        "  \n",
        "  return val_performance, RMSE\n",
        "\n",
        "data = (Xtrain, ytrain, Xval, yval)\n",
        "\n",
        "def visualize_error(model, data):\n",
        "  val_performance, RMSE = eval_model(model, data)\n",
        "\n",
        "  fig = plt.figure(figsize=(6,6))\n",
        "  ax = fig.add_subplot(111)\n",
        "  plt.scatter(x=val_performance['target'], y=val_performance['target'], alpha=0.2)\n",
        "  plt.scatter(x=val_performance['target'], y=val_performance['predicted'], \n",
        "              alpha=0.2, c='orange')\n",
        "  ax.set_aspect('equal')\n",
        "  ax.set_xlabel('true')\n",
        "  ax.set_ylabel('predicted')\n",
        "  ax.legend(['true', 'predicted'])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avLJLY_n4u-n"
      },
      "source": [
        "### Initial attempt"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "Q8YkBz1U3kUh",
        "outputId": "54b0b34a-38ef-4b6a-aa34-78d755917982"
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      "source": [
        "model = GradientBoostingRegressor(loss='squared_error', learning_rate=0.1, \n",
        "                                  n_estimators=100, subsample=1.0, \n",
        "                                  criterion='friedman_mse')\n",
        "val_performance, RMSE = eval_model(model, data)\n",
        "print('RMSE: ', RMSE)\n",
        "val_performance.head(10)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE:  35.213592370287884\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "      <th>predicted</th>\n",
              "      <th>error</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>79.0</td>\n",
              "      <td>71.229535</td>\n",
              "      <td>-7.770465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>129.0</td>\n",
              "      <td>88.899205</td>\n",
              "      <td>-40.100795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>72.0</td>\n",
              "      <td>47.570878</td>\n",
              "      <td>-24.429122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34.0</td>\n",
              "      <td>87.037874</td>\n",
              "      <td>53.037874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>52.871565</td>\n",
              "      <td>-8.128435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>198.0</td>\n",
              "      <td>204.257468</td>\n",
              "      <td>6.257468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>26.0</td>\n",
              "      <td>59.409836</td>\n",
              "      <td>33.409836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>124.0</td>\n",
              "      <td>64.135357</td>\n",
              "      <td>-59.864643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25.0</td>\n",
              "      <td>57.786984</td>\n",
              "      <td>32.786984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>27.0</td>\n",
              "      <td>24.384501</td>\n",
              "      <td>-2.615499</td>\n",
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            "text/plain": [
              "   target   predicted      error\n",
              "0    79.0   71.229535  -7.770465\n",
              "1   129.0   88.899205 -40.100795\n",
              "2    72.0   47.570878 -24.429122\n",
              "3    34.0   87.037874  53.037874\n",
              "4    61.0   52.871565  -8.128435\n",
              "5   198.0  204.257468   6.257468\n",
              "6    26.0   59.409836  33.409836\n",
              "7   124.0   64.135357 -59.864643\n",
              "8    25.0   57.786984  32.786984\n",
              "9    27.0   24.384501  -2.615499"
            ]
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          "metadata": {},
          "execution_count": 7
        }
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      "metadata": {
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          "height": 388
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        "id": "6Gd6J66U4I1G",
        "outputId": "7000bc81-456d-4298-c590-c4f151750146"
      },
      "source": [
        "visualize_error(model, data)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZxHSNSF5fKG"
      },
      "source": [
        "### Hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDgepzzd4yeN"
      },
      "source": [
        "param_distributions = {\n",
        "    'learning_rate' : stats.expon(scale=0.5), \n",
        "    'subsample' : stats.uniform(0.5, 0.5), # (lower bound, range)\n",
        "    'criterion' : ['friedman_mse', 'squared_error'], \n",
        "    'max_depth' : stats.randint(2, 8), #important to tune\n",
        "    }\n",
        "\n",
        "search = RandomizedSearchCV(GradientBoostingRegressor(n_estimators=250), \n",
        "                            param_distributions, n_iter=20, cv=5)\n",
        "\n",
        "search.fit(Xtrain, ytrain['target']);"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-tzfwQdEMvB",
        "outputId": "37a2ad21-61c9-4b80-fb12-88d4148c1525"
      },
      "source": [
        "search.best_params_"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'squared_error',\n",
              " 'learning_rate': 0.059282826738596174,\n",
              " 'loss': 'squared_error',\n",
              " 'max_depth': 7,\n",
              " 'subsample': 0.9726121953368219}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbVa7m384zJa"
      },
      "source": [
        "### Cross Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2tZlv5k4mhw",
        "outputId": "d6b89af3-2413-4035-ea69-f470bcb44e0c"
      },
      "source": [
        "model = GradientBoostingRegressor(loss='squared_error', learning_rate=0.6, \n",
        "                                  n_estimators=250, subsample=1, max_depth=7,\n",
        "                                  criterion='squared_error')\n",
        "\n",
        "scoring = {'negRMSE' : 'neg_root_mean_squared_error',\n",
        "           'R2' : 'r2',\n",
        "           'max_error' : 'max_error'}\n",
        "\n",
        "scores = cross_validate(model, X, y['target'], cv=5, scoring=scoring)\n",
        "\n",
        "print('-RMSE: ', scores['test_negRMSE'])\n",
        "print('R2: ', scores['test_R2'])\n",
        "print('max error: ', scores['test_max_error'])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-RMSE:  [-43.0569746  -40.4949696  -41.36364515 -42.76175093 -40.98783753]\n",
            "R2:  [0.17489139 0.07929042 0.03997657 0.3257627  0.26708371]\n",
            "max error:  [-772.02313569 -534.2310329  -452.43087397 -473.46028486 -383.22757388]\n"
          ]
        }
      ]
    }
  ]
}