0_0_100p_annual_localTrain_GA_2Class_KNN_logreg_SVC_1varPerYr_bal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "43a6d61b",
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Date Sown</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Date of Cut</th>\n",
       "      <th>Julian Day</th>\n",
       "      <th>Yield (tons/acre)</th>\n",
       "      <th>Time Since Sown (Days)</th>\n",
       "      <th>Time Since Last Harvest (Days)</th>\n",
       "      <th>Total Radiation (MJ/m^2)</th>\n",
       "      <th>Total Rainfall (mm)</th>\n",
       "      <th>Avg Air Temp (C)</th>\n",
       "      <th>Avg Min Temp (C)</th>\n",
       "      <th>Avg Max Temp (C)</th>\n",
       "      <th>Avg Soil Moisture (%)</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Midville</td>\n",
       "      <td>11/1/2007</td>\n",
       "      <td>GA-505</td>\n",
       "      <td>8/14/2009</td>\n",
       "      <td>226</td>\n",
       "      <td>2.40</td>\n",
       "      <td>652</td>\n",
       "      <td>267</td>\n",
       "      <td>3400.10</td>\n",
       "      <td>740.670</td>\n",
       "      <td>21.585771</td>\n",
       "      <td>16.097508</td>\n",
       "      <td>27.881281</td>\n",
       "      <td>0.191288</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Tifton</td>\n",
       "      <td>2/21/2008</td>\n",
       "      <td>Hybri Force 600</td>\n",
       "      <td>11/11/2008</td>\n",
       "      <td>316</td>\n",
       "      <td>3.33</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>3810.06</td>\n",
       "      <td>664.878</td>\n",
       "      <td>23.327679</td>\n",
       "      <td>18.438948</td>\n",
       "      <td>29.262071</td>\n",
       "      <td>0.116674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Athens</td>\n",
       "      <td>10/10/2007</td>\n",
       "      <td>TS 4010</td>\n",
       "      <td>12/5/2008</td>\n",
       "      <td>340</td>\n",
       "      <td>3.35</td>\n",
       "      <td>422</td>\n",
       "      <td>1</td>\n",
       "      <td>3545.32</td>\n",
       "      <td>413.282</td>\n",
       "      <td>18.354044</td>\n",
       "      <td>12.543772</td>\n",
       "      <td>24.636933</td>\n",
       "      <td>0.129465</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Tifton</td>\n",
       "      <td>2/21/2008</td>\n",
       "      <td>Bulldog 805</td>\n",
       "      <td>11/17/2010</td>\n",
       "      <td>321</td>\n",
       "      <td>4.03</td>\n",
       "      <td>1000</td>\n",
       "      <td>345</td>\n",
       "      <td>5022.22</td>\n",
       "      <td>670.614</td>\n",
       "      <td>138.929751</td>\n",
       "      <td>105.988887</td>\n",
       "      <td>176.848279</td>\n",
       "      <td>0.672213</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Midville</td>\n",
       "      <td>11/1/2007</td>\n",
       "      <td>PGI 801</td>\n",
       "      <td>11/20/2008</td>\n",
       "      <td>325</td>\n",
       "      <td>5.20</td>\n",
       "      <td>385</td>\n",
       "      <td>1</td>\n",
       "      <td>4463.92</td>\n",
       "      <td>599.974</td>\n",
       "      <td>21.560558</td>\n",
       "      <td>15.770406</td>\n",
       "      <td>28.373427</td>\n",
       "      <td>0.189270</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "     State      City   Date Sown          Variety Date of Cut  Julian Day  \\\n",
       "0  Georgia  Midville   11/1/2007           GA-505   8/14/2009         226   \n",
       "1  Georgia    Tifton   2/21/2008  Hybri Force 600  11/11/2008         316   \n",
       "2  Georgia    Athens  10/10/2007          TS 4010   12/5/2008         340   \n",
       "3  Georgia    Tifton   2/21/2008      Bulldog 805  11/17/2010         321   \n",
       "4  Georgia  Midville   11/1/2007          PGI 801  11/20/2008         325   \n",
       "\n",
       "   Yield (tons/acre)  Time Since Sown (Days)  Time Since Last Harvest (Days)  \\\n",
       "0               2.40                     652                             267   \n",
       "1               3.33                     264                               1   \n",
       "2               3.35                     422                               1   \n",
       "3               4.03                    1000                             345   \n",
       "4               5.20                     385                               1   \n",
       "\n",
       "   Total Radiation (MJ/m^2)  Total Rainfall (mm)  Avg Air Temp (C)  \\\n",
       "0                   3400.10              740.670         21.585771   \n",
       "1                   3810.06              664.878         23.327679   \n",
       "2                   3545.32              413.282         18.354044   \n",
       "3                   5022.22              670.614        138.929751   \n",
       "4                   4463.92              599.974         21.560558   \n",
       "\n",
       "   Avg Min Temp (C)  Avg Max Temp (C)  Avg Soil Moisture (%)  Class  \n",
       "0         16.097508         27.881281               0.191288      1  \n",
       "1         18.438948         29.262071               0.116674      1  \n",
       "2         12.543772         24.636933               0.129465      1  \n",
       "3        105.988887        176.848279               0.672213      1  \n",
       "4         15.770406         28.373427               0.189270      2  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('data/annual_GA_lo_hi_1varPerYear.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e8ed05b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sdv.tabular import TVAE\n",
    "\n",
    "# model = TVAE()\n",
    "# model.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b2fc1d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_data = model.sample(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8fe53469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_data.to_csv('data/VAE_synth200_0722_GA_knn_2cls.csv')\n",
    "# new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d95ff074",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get aggregate data\n",
    "#targetDataLoc = 'data/annual_GA_lo_hi_1varPerYear.csv'\n",
    "#aggDataLoc = 'data/synth1_GA_only_063022.csv'\n",
    "\n",
    "aggDf = data #pd.read_csv(aggDataLoc)\n",
    "#aggDf = aggDf.drop(\"Unnamed: 0\",axis=1)\n",
    "targetDf = data #pd.read_csv(targetDataLoc)\n",
    "#targetDf = targetDf.drop(\"Unnamed: 0\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0f1df971",
   "metadata": {},
   "outputs": [],
   "source": [
    "############## imports\n",
    "# general\n",
    "import statistics\n",
    "import datetime\n",
    "#from sklearn.externals import joblib # save and load models\n",
    "import random\n",
    "# data manipulation and exploration\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "## machine learning stuff\n",
    "# preprocessing\n",
    "from sklearn import preprocessing\n",
    "# feature selection\n",
    "from sklearn.feature_selection import SelectKBest, SelectPercentile\n",
    "from sklearn.feature_selection import f_regression\n",
    "# pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "# train/testing\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV, cross_val_score  \n",
    "# error calculations\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "# models\n",
    "from sklearn.linear_model import LogisticRegression # logistic regression\n",
    "from sklearn.linear_model import BayesianRidge #bayesisan ridge regression\n",
    "from sklearn.svm import SVC  # support vector machines classification\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor # import GaussianProcessRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier # k-nearest neightbors for classification\n",
    "from sklearn.neural_network import MLPRegressor # neural network for regression\n",
    "from sklearn.tree import DecisionTreeRegressor # decision tree regressor\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor  # random forest regression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier # adaboost for classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "# saving models\n",
    "# from sklearn.externals import joblib\n",
    "import joblib\n",
    "\n",
    "# import the API\n",
    "APILoc = 'API/'\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, APILoc)\n",
    "\n",
    "from API import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "079f1aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter out the features that will not be used by the machine learning models\n",
    "\n",
    "# the features to keep:\n",
    "# xColumnsToKeep = [\"Julian Day\", \"Time Since Sown (Days)\", \"Time Since Last Harvest (Days)\", \"Total Radiation (MJ/m^2)\",\n",
    "#                \"Total Rainfall (mm)\", \"Avg Air Temp (C)\", \"Avg Min Temp (C)\", \"Avg Max Temp (C)\",\n",
    "#                  \"Avg Soil Moisture (%)\", \"Day Length (hrs)\"], \"Percent Cover (%)\"]\n",
    "\n",
    "xColumnsToKeep = [\"Julian Day\", \"Time Since Sown (Days)\", \"Total Radiation (MJ/m^2)\",\n",
    "               \"Total Rainfall (mm)\", \"Avg Air Temp (C)\", \"Avg Min Temp (C)\", \"Avg Max Temp (C)\",\n",
    "                 \"Avg Soil Moisture (%)\"]\n",
    "\n",
    "\n",
    "#xColumnsToKeep = [\"Julian Day\", \"Time Since Sown (Days)\", \"Total Radiation (MJ/m^2)\", \"Total Rainfall (mm)\"]\n",
    "\n",
    "# the target to keep\n",
    "yColumnsToKeep = [\"Class\"]\n",
    "\n",
    "# get a dataframe containing the features and the targets\n",
    "xDf = aggDf[xColumnsToKeep]\n",
    "test_xDf = targetDf[xColumnsToKeep]\n",
    "yDf = aggDf[yColumnsToKeep]\n",
    "test_yDf = targetDf[yColumnsToKeep]\n",
    "\n",
    "# reset the index\n",
    "xDf = xDf.reset_index(drop=True)\n",
    "yDf = yDf.reset_index(drop=True)\n",
    "test_xDf = test_xDf.reset_index(drop=True)\n",
    "test_yDf = test_yDf.reset_index(drop=True)\n",
    "\n",
    "pd.set_option('display.max_rows', 2500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "\n",
    "xCols = list(xDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d48fcfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hide the warnings because training the neural network caues lots of warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# make the parameter grids for sklearn's gridsearchcv\n",
    "rfParamGrid = {\n",
    "        'model__n_estimators': [5, 10, 25, 50, 100], # Number of estimators\n",
    "        'model__max_depth': [5, 10, 15, 20], # Maximum depth of the tree\n",
    "        'model__criterion': [\"gini\"]\n",
    "    }\n",
    "knnParamGrid ={\n",
    "        'model__n_neighbors':[2,5,10],\n",
    "        'model__weights': ['uniform', 'distance'],\n",
    "        'model__leaf_size': [5, 10, 30, 50]    \n",
    "    }\n",
    "svcParamGrid = {\n",
    "        'model__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "        'model__C': [0.1, 1.0, 5.0, 10.0],\n",
    "        'model__gamma': [\"scale\", \"auto\"],\n",
    "        'model__degree': [2,3,4,5]\n",
    "    }\n",
    "nnParamGrid = {\n",
    "        'model__hidden_layer_sizes':[(3), (5), (10), (3,3), (5,5), (7,7)],\n",
    "        'model__solver': ['sgd', 'adam'],\n",
    "        'model__learning_rate' : ['constant', 'invscaling', 'adaptive'],\n",
    "        'model__learning_rate_init': [0.1, 0.01, 0.001]      \n",
    "    }\n",
    "\n",
    "logRegParamGrid = {}\n",
    "\n",
    "bayesParamGrid={\n",
    "        'model__n_iter':[100,300,500]\n",
    "    }\n",
    "\n",
    "dtParamGrid = {\n",
    "    'model__criterion': ['gini'],\n",
    "    'model__max_depth': [5,10,25,50,100]\n",
    "    }\n",
    "\n",
    "aModelList = [#(RandomForestClassifier(), rfParamGrid, \"rfTup.pkl\"),\n",
    "              (KNeighborsClassifier(), knnParamGrid, \"knnTup.pkl\"),\n",
    "              (SVC(), svcParamGrid, \"svrTup.pkl\"),\n",
    "             #(MLPClassifier(), nnParamGrid, \"nnTup.pkl\")]#,\n",
    "             (LogisticRegression(), logRegParamGrid, \"linRegTup.pkl\")]#,\n",
    "             #(BayesianRidge(), bayesParamGrid, \"bayesTup.pkl\"),\n",
    "             #(DecisionTreeClassifier(), dtParamGrid, \"dtTup.pkl\")]\n",
    "\n",
    "N = 6\n",
    "workingDir = 'working_dir'\n",
    "numFeatures = 8 # 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "52261c51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  knnTup\n",
      "Avg MAE:  1166.667\n",
      "Avg R squared:  nan\n",
      "Best MAE:  0.0\n",
      "Best R squared:  1.0\n",
      "Parameters of the best model:  {'model__leaf_size': 5, 'model__n_neighbors': 2, 'model__weights': 'uniform'}\n",
      "Features selected by best model:  ['Julian Day', 'Time Since Sown (Days)', 'Total Radiation (MJ/m^2)', 'Total Rainfall (mm)', 'Avg Air Temp (C)', 'Avg Min Temp (C)', 'Avg Max Temp (C)', 'Avg Soil Moisture (%)']\n",
      "Accuracy:  0.0\n",
      "f1 score:  0.0\n",
      "mcc:  0.0\n",
      " \n",
      "test results on our test data: \n",
      "results:\n",
      "MAE:  0.25\n",
      "R sq:  0.0\n",
      "accuracy score for DA/TDA:  0.75\n",
      "model:  svrTup\n",
      "Avg MAE:  1333.333\n",
      "Avg R squared:  nan\n",
      "Best MAE:  1000.0\n",
      "Best R squared:  -1.0\n",
      "Parameters of the best model:  {'model__C': 5.0, 'model__degree': 3, 'model__gamma': 'scale', 'model__kernel': 'poly'}\n",
      "Features selected by best model:  ['Julian Day', 'Time Since Sown (Days)', 'Total Radiation (MJ/m^2)', 'Total Rainfall (mm)', 'Avg Air Temp (C)', 'Avg Min Temp (C)', 'Avg Max Temp (C)', 'Avg Soil Moisture (%)']\n",
      "Accuracy:  0.0\n",
      "f1 score:  0.0\n",
      "mcc:  0.0\n",
      " \n",
      "test results on our test data: \n",
      "results:\n",
      "MAE:  0.125\n",
      "R sq:  0.5\n",
      "accuracy score for DA/TDA:  0.875\n",
      "model:  linRegTup\n",
      "Avg MAE:  1333.333\n",
      "Avg R squared:  nan\n",
      "Best MAE:  1000.0\n",
      "Best R squared:  -1.0\n",
      "Parameters of the best model:  {}\n",
      "Features selected by best model:  ['Julian Day', 'Time Since Sown (Days)', 'Total Radiation (MJ/m^2)', 'Total Rainfall (mm)', 'Avg Air Temp (C)', 'Avg Min Temp (C)', 'Avg Max Temp (C)', 'Avg Soil Moisture (%)']\n",
      "Accuracy:  1.0\n",
      "f1 score:  1.0\n",
      "mcc:  0.0\n",
      " \n",
      "test results on our test data: \n",
      "results:\n",
      "MAE:  0.125\n",
      "R sq:  0.5\n",
      "accuracy score for DA/TDA:  0.875\n"
     ]
    }
   ],
   "source": [
    "saveMLResults(test_xDf, test_yDf, N, xDf, yDf, aModelList, workingDir, numFeatures, printResults=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71ca6fda",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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