Kniss_HW5_Submission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zh (dBZ)</th>\n",
       "      <th>Zdr (dB)</th>\n",
       "      <th>Ldr (dB)</th>\n",
       "      <th>Kdp (deg km-1)</th>\n",
       "      <th>Ah (dBZ/km)</th>\n",
       "      <th>Adr (dB/km)</th>\n",
       "      <th>R (mm/hr)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>23.144878</td>\n",
       "      <td>0.418637</td>\n",
       "      <td>-41.757733</td>\n",
       "      <td>0.005395</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>2.393520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>22.737156</td>\n",
       "      <td>0.322850</td>\n",
       "      <td>-43.772069</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>3.502699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26.869826</td>\n",
       "      <td>0.330948</td>\n",
       "      <td>-43.577399</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.000903</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>8.627561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>28.540561</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>-42.139731</td>\n",
       "      <td>0.018872</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>8.424447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30.500127</td>\n",
       "      <td>0.543758</td>\n",
       "      <td>-39.763087</td>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.001157</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>8.189291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Zh (dBZ)  Zdr (dB)   Ldr (dB)  Kdp (deg km-1)  Ah (dBZ/km)  \\\n",
       "0           0  23.144878  0.418637 -41.757733        0.005395     0.000290   \n",
       "1           1  22.737156  0.322850 -43.772069        0.005194     0.000360   \n",
       "2           2  26.869826  0.330948 -43.577399        0.013385     0.000903   \n",
       "3           3  28.540561  0.399480 -42.139731        0.018872     0.001036   \n",
       "4           4  30.500127  0.543758 -39.763087        0.027438     0.001157   \n",
       "\n",
       "   Adr (dB/km)  R (mm/hr)  \n",
       "0     0.000012   2.393520  \n",
       "1     0.000012   3.502699  \n",
       "2     0.000030   8.627561  \n",
       "3     0.000043   8.424447  \n",
       "4     0.000064   8.189291  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('radar_parameters.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = (10**data['Zh (dBZ)'])**(1/10) #solve for Z from dBz using dBZ = 10log(Z) \n",
    "R_base = (Z/200)**(1/1.6) #solve for baseline rain rate using Z = 200R**1.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add those columns to dataframe to ensure they line up with the proper index when we split the data later\n",
    "data['R_base'] = R_base\n",
    "data['Z'] = Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zh (dBZ)</th>\n",
       "      <th>Zdr (dB)</th>\n",
       "      <th>Ldr (dB)</th>\n",
       "      <th>Kdp (deg km-1)</th>\n",
       "      <th>Ah (dBZ/km)</th>\n",
       "      <th>Adr (dB/km)</th>\n",
       "      <th>R (mm/hr)</th>\n",
       "      <th>R_base</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>23.144878</td>\n",
       "      <td>0.418637</td>\n",
       "      <td>-41.757733</td>\n",
       "      <td>0.005395</td>\n",
       "      <td>0.00029</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>2.393520</td>\n",
       "      <td>1.019556</td>\n",
       "      <td>206.294563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>22.737156</td>\n",
       "      <td>0.322850</td>\n",
       "      <td>-43.772069</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.00036</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>3.502699</td>\n",
       "      <td>0.961454</td>\n",
       "      <td>187.808651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Zh (dBZ)  Zdr (dB)   Ldr (dB)  Kdp (deg km-1)  Ah (dBZ/km)  \\\n",
       "0           0  23.144878  0.418637 -41.757733        0.005395      0.00029   \n",
       "1           1  22.737156  0.322850 -43.772069        0.005194      0.00036   \n",
       "\n",
       "   Adr (dB/km)  R (mm/hr)    R_base           Z  \n",
       "0     0.000012   2.393520  1.019556  206.294563  \n",
       "1     0.000012   3.502699  0.961454  187.808651  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature matrix and target array\n",
    "features = data.drop('R (mm/hr)', axis=1)\n",
    "\n",
    "target = data['R (mm/hr)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Split the data into 70-30 chunks for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ftrain, Ftest, Ttrain, Ttest = train_test_split(features, target,\n",
    "                                                random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zh (dBZ)</th>\n",
       "      <th>Zdr (dB)</th>\n",
       "      <th>Ldr (dB)</th>\n",
       "      <th>Kdp (deg km-1)</th>\n",
       "      <th>Ah (dBZ/km)</th>\n",
       "      <th>Adr (dB/km)</th>\n",
       "      <th>R_base</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4116</th>\n",
       "      <td>4116</td>\n",
       "      <td>41.956799</td>\n",
       "      <td>1.550639</td>\n",
       "      <td>-31.883942</td>\n",
       "      <td>0.22322</td>\n",
       "      <td>0.003664</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>15.281156</td>\n",
       "      <td>15692.059091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   Zh (dBZ)  Zdr (dB)   Ldr (dB)  Kdp (deg km-1)  Ah (dBZ/km)  \\\n",
       "4116        4116  41.956799  1.550639 -31.883942         0.22322     0.003664   \n",
       "\n",
       "      Adr (dB/km)     R_base             Z  \n",
       "4116     0.000647  15.281156  15692.059091  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ftrain.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separate R baseline and calculated Z values from the feature matrices for training and testing sets\n",
    "#so they are not taken into account for the linear regression\n",
    "R_base_train = Ftrain['R_base']\n",
    "Ftrain_new = Ftrain.drop(['R_base', 'Z'], axis=1)\n",
    "R_base_test = Ftest['R_base']\n",
    "Ftest_new = Ftest.drop(['R_base', 'Z'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zh (dBZ)</th>\n",
       "      <th>Zdr (dB)</th>\n",
       "      <th>Ldr (dB)</th>\n",
       "      <th>Kdp (deg km-1)</th>\n",
       "      <th>Ah (dBZ/km)</th>\n",
       "      <th>Adr (dB/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4116</th>\n",
       "      <td>4116</td>\n",
       "      <td>41.956799</td>\n",
       "      <td>1.550639</td>\n",
       "      <td>-31.883942</td>\n",
       "      <td>0.22322</td>\n",
       "      <td>0.003664</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   Zh (dBZ)  Zdr (dB)   Ldr (dB)  Kdp (deg km-1)  Ah (dBZ/km)  \\\n",
       "4116        4116  41.956799  1.550639 -31.883942         0.22322     0.003664   \n",
       "\n",
       "      Adr (dB/km)  \n",
       "4116     0.000647  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ftrain_new.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Train/validate a linear regression model using data from 1 and compare R2 and RMSE values for the model vs. test target values and baseline rain rate calculation vs. test target values to see which predicts the target values better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression #choose model class\n",
    "\n",
    "model = LinearRegression(fit_intercept=True) #instantiate model\n",
    "\n",
    "model.fit(Ftrain_new, Ttrain) #fit model to data\n",
    "\n",
    "lin_pred = model.predict(Ftest_new) #test using new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model R2 =  0.9905267626588338\n",
      "Baseline prediction calculation R2 =  0.2249639377726802\n",
      "Model RMSE =  0.8817146505998322 (mm/hr)\n",
      "Baseline prediction calculation RMSE =  7.06713305880775 (mm/hr)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "lin_r2 = r2_score(lin_pred, Ttest) #calculate R2 between target test data and model\n",
    "base_r2 = r2_score(R_base_test, Ttest) #calculate R2 between baseline prediction equation and model\n",
    "lin_RMSE = mean_squared_error(lin_pred, Ttest,  squared=False)\n",
    "base_RMSE = mean_squared_error(R_base_test, Ttest, squared=False)\n",
    "\n",
    "print('Model R2 = ',lin_r2)\n",
    "print('Baseline prediction calculation R2 = ', base_r2)\n",
    "print('Model RMSE = ', lin_RMSE, '(mm/hr)') \n",
    "print('Baseline prediction calculation RMSE = ', base_RMSE, '(mm/hr)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the score values, the model did a much better job predicting the target values than a baseline rain rate calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Create best polynomial model using grid search over orders 0-21 and cross-validation of 7 folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "def PolynomialRegression(degree=2, **kwargs):\n",
    "    return make_pipeline(PolynomialFeatures(degree),\n",
    "                         LinearRegression(**kwargs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'polynomialfeatures__degree': np.arange(5)} #ran overnight and did not finish all 21 orders so reducing to the most I can compute\n",
    "\n",
    "grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=7,\n",
       "             estimator=Pipeline(steps=[(&#x27;polynomialfeatures&#x27;,\n",
       "                                        PolynomialFeatures()),\n",
       "                                       (&#x27;linearregression&#x27;,\n",
       "                                        LinearRegression())]),\n",
       "             param_grid={&#x27;polynomialfeatures__degree&#x27;: array([0, 1, 2, 3, 4])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=7,\n",
       "             estimator=Pipeline(steps=[(&#x27;polynomialfeatures&#x27;,\n",
       "                                        PolynomialFeatures()),\n",
       "                                       (&#x27;linearregression&#x27;,\n",
       "                                        LinearRegression())]),\n",
       "             param_grid={&#x27;polynomialfeatures__degree&#x27;: array([0, 1, 2, 3, 4])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures()),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=7,\n",
       "             estimator=Pipeline(steps=[('polynomialfeatures',\n",
       "                                        PolynomialFeatures()),\n",
       "                                       ('linearregression',\n",
       "                                        LinearRegression())]),\n",
       "             param_grid={'polynomialfeatures__degree': array([0, 1, 2, 3, 4])})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(Ftrain_new, Ttrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'polynomialfeatures__degree': 2}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_model = grid.best_estimator_\n",
    "\n",
    "poly_pred = poly_model.predict(Ftest_new) #test using new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model R2 =  0.9994057385976582\n",
      "Model RMSE =  0.21856732463459472 (mm/hr)\n"
     ]
    }
   ],
   "source": [
    "poly_r2 = r2_score(poly_pred, Ttest) #calculate R2 between target test data and model\n",
    "poly_RMSE = mean_squared_error(poly_pred, Ttest,  squared=False)\n",
    "\n",
    "print('Model R2 = ',poly_r2)\n",
    "print('Model RMSE = ', poly_RMSE, '(mm/hr)') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Repeat with a Random Forest Regressor, performing a grid search utilizing provided parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Ran this one overnight and on Google Colab as well and wasn't able to complete, so reducing the gridsearch parameters to try to get something to complete\n",
    "forest_parameters = {'bootstrap': [True, False],  \n",
    "               'max_depth': [10, 20, 30,  None], # 40, 50, 60, 70, 80, 90, 100,\n",
    "               'max_features': ['auto', 'sqrt'],  \n",
    "               'min_samples_leaf': [1, 2, 4],  \n",
    "               'min_samples_split': [2, 5, 10],  \n",
    "               'n_estimators': [200, 400]} # 600, 800, 1000, 1200, 1400, 1600, 1800, 2000\n",
    "\n",
    "forest_grid = GridSearchCV(RandomForestRegressor(), forest_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shelbyfatcheric/anaconda3/envs/ATMS_523/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n",
      "720 fits failed out of a total of 1440.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "720 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/shelbyfatcheric/anaconda3/envs/ATMS_523/lib/python3.12/site-packages/sklearn/model_selection/_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/shelbyfatcheric/anaconda3/envs/ATMS_523/lib/python3.12/site-packages/sklearn/base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"/Users/shelbyfatcheric/anaconda3/envs/ATMS_523/lib/python3.12/site-packages/sklearn/base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"/Users/shelbyfatcheric/anaconda3/envs/ATMS_523/lib/python3.12/site-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/Users/shelbyfatcheric/anaconda3/envs/ATMS_523/lib/python3.12/site-packages/sklearn/model_selection/_search.py:976: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97352149 0.9716786  0.97021945 0.97109754 0.96467763 0.96390107\n",
      " 0.97003923 0.96890199 0.96845637 0.96899596 0.96204199 0.96146717\n",
      " 0.96007668 0.95952745 0.95993869 0.95946665 0.95709267 0.95748864\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97355925 0.97394083 0.97196434 0.97118815 0.96505364 0.965939\n",
      " 0.97118147 0.97074211 0.96970329 0.97052931 0.96424016 0.96420774\n",
      " 0.95998196 0.96025137 0.9602598  0.96047837 0.95709928 0.95848127\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97356017 0.97360494 0.97091484 0.97241358 0.96498899 0.96470664\n",
      " 0.96985454 0.97106474 0.97079572 0.97031857 0.96251098 0.96386668\n",
      " 0.96089467 0.9601812  0.96060512 0.95966396 0.95894099 0.95809318\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97376033 0.9732147  0.97151442 0.97188249 0.96463584 0.96456774\n",
      " 0.97058954 0.9711465  0.97027248 0.97027888 0.96290697 0.96304365\n",
      " 0.96101104 0.96062981 0.96105819 0.9607839  0.95841103 0.95867326\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97648681 0.9766941  0.9751868  0.97548005 0.97025416 0.97110264\n",
      " 0.97506862 0.9751795  0.974392   0.97444816 0.96979093 0.96969779\n",
      " 0.96966136 0.96976756 0.96964661 0.96964543 0.96762974 0.96685123\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97796359 0.97858102 0.97675716 0.97712598 0.97141239 0.97289953\n",
      " 0.97635884 0.97711782 0.97620043 0.9764151  0.97208966 0.97081085\n",
      " 0.97084149 0.97073573 0.97046804 0.97096621 0.96821507 0.96829089\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97898982 0.97807239 0.97652949 0.9771666  0.97241462 0.97242348\n",
      " 0.97723334 0.97684919 0.97618569 0.9765701  0.97163687 0.97145731\n",
      " 0.97070265 0.97068324 0.97087349 0.97110946 0.96744898 0.96861154\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97857889 0.97852394 0.97726573 0.97710332 0.97304268 0.97259\n",
      " 0.97715017 0.97743879 0.9758699  0.9762948  0.97091874 0.97129434\n",
      " 0.97120343 0.97099458 0.97051666 0.97080136 0.96866858 0.96862788]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;bootstrap&#x27;: [True, False],\n",
       "                         &#x27;max_depth&#x27;: [10, 20, 30, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [200, 400]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;bootstrap&#x27;: [True, False],\n",
       "                         &#x27;max_depth&#x27;: [10, 20, 30, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [200, 400]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={'bootstrap': [True, False],\n",
       "                         'max_depth': [10, 20, 30, None],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [200, 400]})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_grid.fit(Ftrain_new, Ttrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_model = forest_grid.best_estimator_\n",
    "\n",
    "forest_pred = forest_model.predict(Ftest_new) #test using new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model R2 =  0.9838020333650129\n",
      "Model RMSE =  1.1101228814583326 (mm/hr)\n"
     ]
    }
   ],
   "source": [
    "forest_r2 = r2_score(forest_pred, Ttest) #calculate R2 between target test data and model\n",
    "forest_RMSE = mean_squared_error(forest_pred, Ttest,  squared=False)\n",
    "\n",
    "print('Model R2 = ',forest_r2)\n",
    "print('Model RMSE = ', forest_RMSE, '(mm/hr)') "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ATMS_523",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}