{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yield Prediction Model - Open Source\n",
    "\n",
    "This notebook demonstrates machine learning models to predict:\n",
    "- **TCH (Tons of Grapes per Hectare)**: Vines yield at harvest\n",
    "\n",
    "The models use remote sensing data (satellite imagery), weather information, soil properties, and agronomic attributes.\n",
    "\n",
    "**Data**: Anonymized observations from vineyards in Portugal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Setup and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "WEATHER_SOIL_EXP_extra = \"Weather + soil features + extra\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Feature Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remote sensing features: 210\n",
      "Agronomic features: 4\n",
      "Weather features: 28\n"
     ]
    }
   ],
   "source": [
    "# Remote sensing features\n",
    "REMOTE_SENSING_FEATURES = (\n",
    "    [f'ndvi_{i}' for i in range(42)] +\n",
    "    [f'evi_{i}' for i in range(42)] +\n",
    "    [f'vari_{i}' for i in range(42)] +\n",
    "    [f'ndre_{i}' for i in range(42)] +\n",
    "    [f'ndwi_{i}' for i in range(42)]\n",
    ")\n",
    "\n",
    "# Agronomic features\n",
    "AGRONOMIC_FEATURES = [\n",
    "    'NCORTE', 'variety', 'IDADE', 'day_of_year'\n",
    "]\n",
    "\n",
    "# Weather time series\n",
    "WEATHER_FEATURES = [\n",
    "    'tp_sum_ts_time_serie_0', 'tp_sum_ts_time_serie_1', 'tp_sum_ts_time_serie_2',\n",
    "    'tp_sum_ts_time_serie_3', 'tp_sum_ts_time_serie_4', 'tp_sum_ts_time_serie_5', 'tp_sum_ts_time_serie_6',\n",
    "    'tp_sum_ts_time_serie_7', 'tp_sum_ts_time_serie_8', 'tp_sum_ts_time_serie_9', 'tp_sum_ts_time_serie_10',\n",
    "    'tp_sum_ts_time_serie_11', 'tp_sum_ts_time_serie_12', 'tp_sum_ts_time_serie_13',\n",
    "    'degree_days_ts_time_serie_0', 'degree_days_ts_time_serie_1', 'degree_days_ts_time_serie_2',\n",
    "    'degree_days_ts_time_serie_3', 'degree_days_ts_time_serie_4', 'degree_days_ts_time_serie_5',\n",
    "    'degree_days_ts_time_serie_6', 'degree_days_ts_time_serie_7', 'degree_days_ts_time_serie_8',\n",
    "    'degree_days_ts_time_serie_9', 'degree_days_ts_time_serie_10', 'degree_days_ts_time_serie_11',\n",
    "    'degree_days_ts_time_serie_12', 'degree_days_ts_time_serie_13'\n",
    "]\n",
    "\n",
    "print(f\"Remote sensing features: {len(REMOTE_SENSING_FEATURES)}\")\n",
    "print(f\"Agronomic features: {len(AGRONOMIC_FEATURES)}\")\n",
    "print(f\"Weather features: {len(WEATHER_FEATURES)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Experiment feature counts:\n",
      "  Weather + soil features + extra: 277 features\n"
     ]
    }
   ],
   "source": [
    "def get_soil_features():\n",
    "    return [\n",
    "        'soil_grids_type',\n",
    "        'clay_0-5cm_mean', 'clay_5-15cm_mean', 'clay_15-30cm_mean', 'clay_30-60cm_mean',\n",
    "        'sand_0-5cm_mean', 'sand_5-15cm_mean', 'sand_15-30cm_mean', 'sand_30-60cm_mean',\n",
    "        'nitrogen_0-5cm_mean', 'nitrogen_5-15cm_mean', 'nitrogen_15-30cm_mean', 'nitrogen_30-60cm_mean',\n",
    "        'bdod_0-5cm_mean', 'bdod_5-15cm_mean', 'bdod_15-30cm_mean', 'bdod_30-60cm_mean',\n",
    "        'silt_0-5cm_mean', 'silt_5-15cm_mean', 'silt_15-30cm_mean', 'silt_30-60cm_mean',\n",
    "        'soc_0-5cm_mean', 'soc_5-15cm_mean', 'soc_15-30cm_mean', 'soc_30-60cm_mean'\n",
    "    ]\n",
    "\n",
    "def get_weather_features():\n",
    "    return (REMOTE_SENSING_FEATURES +\n",
    "            WEATHER_FEATURES +\n",
    "            AGRONOMIC_FEATURES +\n",
    "            ['tp_sum', 'degree_days', 't2m_min_30_days', 'tamp_more13_days_all', 'tamp_more13_days_6m'])\n",
    "\n",
    "def get_weather_soil_features():\n",
    "    return get_weather_features() + get_soil_features()\n",
    "\n",
    "extra_features = [\n",
    "    'rootstock_type',\n",
    "    'tree_spacing',\n",
    "    'row_spacing',\n",
    "    'lat',\n",
    "    'lon',\n",
    "]\n",
    "\n",
    "EXPERIMENTS = {\n",
    "    WEATHER_SOIL_EXP_extra: get_weather_soil_features() + extra_features,\n",
    "}\n",
    "\n",
    "print(\"\\nExperiment feature counts:\")\n",
    "for exp_name, features in EXPERIMENTS.items():\n",
    "    print(f\"  {exp_name}: {len(features)} features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: 926 rows × 589 columns\n"
     ]
    },
    {
     "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>ndvi_0</th>\n",
       "      <th>ndvi_1</th>\n",
       "      <th>ndvi_2</th>\n",
       "      <th>ndvi_3</th>\n",
       "      <th>ndvi_4</th>\n",
       "      <th>ndvi_5</th>\n",
       "      <th>ndvi_6</th>\n",
       "      <th>ndvi_7</th>\n",
       "      <th>ndvi_8</th>\n",
       "      <th>ndvi_9</th>\n",
       "      <th>...</th>\n",
       "      <th>degree_days_recent90d_mean</th>\n",
       "      <th>degree_days_recent90d_std</th>\n",
       "      <th>degree_days_recent90d_slope</th>\n",
       "      <th>tp_sum_3m</th>\n",
       "      <th>tp_sum_6m</th>\n",
       "      <th>tp_sum_12m</th>\n",
       "      <th>degree_days_3m</th>\n",
       "      <th>degree_days_6m</th>\n",
       "      <th>degree_days_12m</th>\n",
       "      <th>tp_sum_delta_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.203887</td>\n",
       "      <td>0.221458</td>\n",
       "      <td>0.239029</td>\n",
       "      <td>0.256600</td>\n",
       "      <td>0.274170</td>\n",
       "      <td>0.291741</td>\n",
       "      <td>0.309312</td>\n",
       "      <td>0.326883</td>\n",
       "      <td>0.344454</td>\n",
       "      <td>0.362024</td>\n",
       "      <td>...</td>\n",
       "      <td>6.905740</td>\n",
       "      <td>1.596763</td>\n",
       "      <td>-0.785736</td>\n",
       "      <td>32.461599</td>\n",
       "      <td>91.227125</td>\n",
       "      <td>413.033505</td>\n",
       "      <td>633.616272</td>\n",
       "      <td>1259.963318</td>\n",
       "      <td>1377.213837</td>\n",
       "      <td>29.399132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183539</td>\n",
       "      <td>0.207914</td>\n",
       "      <td>0.232289</td>\n",
       "      <td>0.256664</td>\n",
       "      <td>0.281039</td>\n",
       "      <td>0.305414</td>\n",
       "      <td>0.329789</td>\n",
       "      <td>0.354164</td>\n",
       "      <td>0.378539</td>\n",
       "      <td>0.402914</td>\n",
       "      <td>...</td>\n",
       "      <td>7.094805</td>\n",
       "      <td>1.734347</td>\n",
       "      <td>-0.842968</td>\n",
       "      <td>35.093367</td>\n",
       "      <td>93.707648</td>\n",
       "      <td>407.216377</td>\n",
       "      <td>650.616119</td>\n",
       "      <td>1307.372314</td>\n",
       "      <td>1427.669952</td>\n",
       "      <td>30.748834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.180039</td>\n",
       "      <td>0.189910</td>\n",
       "      <td>0.199782</td>\n",
       "      <td>0.209654</td>\n",
       "      <td>0.219526</td>\n",
       "      <td>0.229398</td>\n",
       "      <td>0.239270</td>\n",
       "      <td>0.249142</td>\n",
       "      <td>0.259013</td>\n",
       "      <td>0.268885</td>\n",
       "      <td>...</td>\n",
       "      <td>6.840813</td>\n",
       "      <td>1.618880</td>\n",
       "      <td>-0.796780</td>\n",
       "      <td>33.850745</td>\n",
       "      <td>91.376749</td>\n",
       "      <td>398.388898</td>\n",
       "      <td>627.637543</td>\n",
       "      <td>1249.714417</td>\n",
       "      <td>1362.654053</td>\n",
       "      <td>29.573044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.166330</td>\n",
       "      <td>0.171501</td>\n",
       "      <td>0.176672</td>\n",
       "      <td>0.181843</td>\n",
       "      <td>0.187013</td>\n",
       "      <td>0.192184</td>\n",
       "      <td>0.197355</td>\n",
       "      <td>0.202526</td>\n",
       "      <td>0.207697</td>\n",
       "      <td>0.212868</td>\n",
       "      <td>...</td>\n",
       "      <td>6.840813</td>\n",
       "      <td>1.618880</td>\n",
       "      <td>-0.796780</td>\n",
       "      <td>33.850745</td>\n",
       "      <td>91.376749</td>\n",
       "      <td>398.388898</td>\n",
       "      <td>627.637543</td>\n",
       "      <td>1249.714417</td>\n",
       "      <td>1362.654053</td>\n",
       "      <td>29.573044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.161632</td>\n",
       "      <td>0.186018</td>\n",
       "      <td>0.210404</td>\n",
       "      <td>0.234790</td>\n",
       "      <td>0.259175</td>\n",
       "      <td>0.283561</td>\n",
       "      <td>0.307947</td>\n",
       "      <td>0.332332</td>\n",
       "      <td>0.356718</td>\n",
       "      <td>0.381104</td>\n",
       "      <td>...</td>\n",
       "      <td>7.094805</td>\n",
       "      <td>1.734347</td>\n",
       "      <td>-0.842968</td>\n",
       "      <td>35.093367</td>\n",
       "      <td>93.707648</td>\n",
       "      <td>407.216377</td>\n",
       "      <td>650.616119</td>\n",
       "      <td>1307.372314</td>\n",
       "      <td>1427.669952</td>\n",
       "      <td>30.748834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 589 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ndvi_0    ndvi_1    ndvi_2    ndvi_3    ndvi_4    ndvi_5    ndvi_6  \\\n",
       "0  0.203887  0.221458  0.239029  0.256600  0.274170  0.291741  0.309312   \n",
       "1  0.183539  0.207914  0.232289  0.256664  0.281039  0.305414  0.329789   \n",
       "2  0.180039  0.189910  0.199782  0.209654  0.219526  0.229398  0.239270   \n",
       "3  0.166330  0.171501  0.176672  0.181843  0.187013  0.192184  0.197355   \n",
       "4  0.161632  0.186018  0.210404  0.234790  0.259175  0.283561  0.307947   \n",
       "\n",
       "     ndvi_7    ndvi_8    ndvi_9  ...  degree_days_recent90d_mean  \\\n",
       "0  0.326883  0.344454  0.362024  ...                    6.905740   \n",
       "1  0.354164  0.378539  0.402914  ...                    7.094805   \n",
       "2  0.249142  0.259013  0.268885  ...                    6.840813   \n",
       "3  0.202526  0.207697  0.212868  ...                    6.840813   \n",
       "4  0.332332  0.356718  0.381104  ...                    7.094805   \n",
       "\n",
       "   degree_days_recent90d_std  degree_days_recent90d_slope  tp_sum_3m  \\\n",
       "0                   1.596763                    -0.785736  32.461599   \n",
       "1                   1.734347                    -0.842968  35.093367   \n",
       "2                   1.618880                    -0.796780  33.850745   \n",
       "3                   1.618880                    -0.796780  33.850745   \n",
       "4                   1.734347                    -0.842968  35.093367   \n",
       "\n",
       "   tp_sum_6m  tp_sum_12m  degree_days_3m  degree_days_6m  degree_days_12m  \\\n",
       "0  91.227125  413.033505      633.616272     1259.963318      1377.213837   \n",
       "1  93.707648  407.216377      650.616119     1307.372314      1427.669952   \n",
       "2  91.376749  398.388898      627.637543     1249.714417      1362.654053   \n",
       "3  91.376749  398.388898      627.637543     1249.714417      1362.654053   \n",
       "4  93.707648  407.216377      650.616119     1307.372314      1427.669952   \n",
       "\n",
       "   tp_sum_delta_month  \n",
       "0           29.399132  \n",
       "1           30.748834  \n",
       "2           29.573044  \n",
       "3           29.573044  \n",
       "4           30.748834  \n",
       "\n",
       "[5 rows x 589 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = pd.read_csv('training_features_anonymized.csv')\n",
    "print(f\"Dataset shape: {gdf.shape[0]} rows × {gdf.shape[1]} columns\")\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations per season:\n",
      "season\n",
      "2017     26\n",
      "2018     25\n",
      "2019     90\n",
      "2020    141\n",
      "2021    181\n",
      "2022    156\n",
      "2023    194\n",
      "2024    113\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Observations per season:\")\n",
    "print(gdf['season'].value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCH range: 0.00 to 57.06\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "for s in sorted(gdf['season'].unique()):\n",
    "    sdf = gdf[gdf['season'] == s]\n",
    "    sns.kdeplot(sdf['TCH'], label=s, alpha=0.7)\n",
    "plt.xlabel('TCH (Tons of Grapes per Hectare)')\n",
    "plt.ylabel('Density')\n",
    "plt.title('TCH Distribution by Season')\n",
    "plt.legend(title='Season')\n",
    "plt.show()\n",
    "\n",
    "print(f\"TCH range: {gdf['TCH'].min():.2f} to {gdf['TCH'].max():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of varieties: 44\n"
     ]
    }
   ],
   "source": [
    "gdf[\"variety\"] = gdf[\"Variety\"].apply(\n",
    "    lambda x: x.replace(\"-\", \"\").replace(\" \", \"\").strip().lower() if isinstance(x, str) else x\n",
    ")\n",
    "\n",
    "varieties = gdf[\"variety\"].values\n",
    "le_variety = LabelEncoder()\n",
    "le_variety.fit(varieties.astype(str))\n",
    "gdf[\"variety\"] = le_variety.transform(gdf[\"variety\"])\n",
    "\n",
    "print(f\"Number of varieties: {len(le_variety.classes_)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded features: ['rootstock_type']\n"
     ]
    }
   ],
   "source": [
    "encoder_names_array = [\"rootstock_type\"]\n",
    "encoders = {}\n",
    "\n",
    "for col in encoder_names_array:\n",
    "    le = LabelEncoder()\n",
    "    gdf[col] = le.fit_transform(gdf[col].astype(str))\n",
    "    encoders[col.replace(' ', '_')] = le\n",
    "\n",
    "print(f\"Encoded features: {list(encoders.keys())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers_tch(gdf, min_v=0.1, max_v=60.0, two_sigma_filtering=False):\n",
    "    \"\"\"Remove TCH outliers based on value thresholds\"\"\"\n",
    "    gdf = gdf[~gdf['TCH'].isna()]\n",
    "    \n",
    "    input_shape = gdf.shape[0]\n",
    "    print(f\"Input shape: {input_shape}\")\n",
    "    \n",
    "    gdf = gdf[gdf['TCH'] < max_v]\n",
    "    gdf = gdf[gdf['TCH'] > min_v]\n",
    "    print(f\"{int(min_v)} < TCH < {int(max_v)}: {gdf.shape[0]}\\n\")\n",
    "    \n",
    "    gdf = gdf.reset_index(drop=True)\n",
    "    \n",
    "    print(f\"TCH [min,max]: {gdf['TCH'].min()}, {gdf['TCH'].max()}\")\n",
    "    return gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(gdf, features, label_column=\"TCH\", num_leaves=31, n_estimators=100, verbose=True):\n",
    "    \"\"\"\n",
    "    Train LightGBM with Leave-One-Season-Out Cross-Validation.\n",
    "    For each season, train on all other seasons and test on the held-out season.\n",
    "    \"\"\"\n",
    "    years = gdf['season'].unique().tolist()\n",
    "    cv_rmse = []\n",
    "    cv_mae = []\n",
    "    cv_r2 = []\n",
    "    relative_errors = []\n",
    "    \n",
    "    models = {}\n",
    "    \n",
    "    for year in years:\n",
    "        if verbose:\n",
    "            print(f\"Train - {set(years) - set([year])}. Test - {year}\")\n",
    "        \n",
    "        gdf_year = gdf[~gdf[\"season\"].isin([year])]\n",
    "        gdf_year = gdf_year[~gdf_year[label_column].isna()]\n",
    "        \n",
    "        X = gdf_year[features].astype(np.float32)\n",
    "        y = gdf_year[label_column]\n",
    "        \n",
    "        regressor = lgb.LGBMRegressor(\n",
    "            num_leaves=num_leaves,\n",
    "            n_estimators=n_estimators,\n",
    "            random_state=42,\n",
    "            verbose=-1\n",
    "        )\n",
    "        yield_model = regressor.fit(X=X, y=y)\n",
    "        models[year] = yield_model\n",
    "        \n",
    "        test_input = gdf[gdf[\"season\"].isin([year])]\n",
    "        test_input[features] = test_input[features].fillna(0)\n",
    "        \n",
    "        if test_input.empty:\n",
    "            continue\n",
    "        \n",
    "        test_features = test_input[features]\n",
    "        test_targets = test_input[label_column]\n",
    "        test_preds = yield_model.predict(test_features)\n",
    "        \n",
    "        if verbose:\n",
    "            print(f\"Train - {X.shape}. Test - {test_features.shape}\")\n",
    "        \n",
    "        relative_error = np.abs(test_targets - test_preds) / test_targets\n",
    "        _mse = mean_squared_error(test_targets, test_preds)\n",
    "        _rmse = np.sqrt(_mse)\n",
    "        _mae = mean_absolute_error(test_targets, test_preds)\n",
    "        _r2 = r2_score(test_targets, test_preds)\n",
    "        \n",
    "        if verbose:\n",
    "            print(_rmse, _mae, _r2)\n",
    "        \n",
    "        cv_rmse.append(_rmse)\n",
    "        cv_mae.append(_mae)\n",
    "        cv_r2.append(_r2)\n",
    "        relative_errors.append(relative_error * 100)\n",
    "        \n",
    "        if verbose:\n",
    "            print(\"MEAN:\", round(np.mean(relative_error * 100), 2))\n",
    "            print(\"STD:\", round(np.std(relative_error * 100), 2))\n",
    "    \n",
    "    if verbose:\n",
    "        print()\n",
    "        print(f\"RMSE: {np.mean(cv_rmse):.2f}\")\n",
    "        print(f\"MAE: {np.mean(cv_mae):.2f}\")\n",
    "        print(f\"R2: {np.mean(cv_r2):.3f}\")\n",
    "        errors = np.concatenate(relative_errors)\n",
    "        print(\"Relative Error\")\n",
    "        print(\"MEAN:\", round(np.mean(errors), 2))\n",
    "        print(\"STD:\", round(np.std(errors), 2))\n",
    "    \n",
    "    test_results = []\n",
    "    for year in years:\n",
    "        if year not in models:\n",
    "            continue\n",
    "        \n",
    "        yield_model = models[year]\n",
    "        test_input = gdf[gdf[\"season\"].isin([year])]\n",
    "        test_input[features] = test_input[features].fillna(0)\n",
    "        \n",
    "        if test_input.empty:\n",
    "            continue\n",
    "        \n",
    "        test_input = test_input.copy()\n",
    "        test_input['predictions'] = yield_model.predict(test_input[features])\n",
    "        test_results.append(test_input)\n",
    "    \n",
    "    res_df = pd.concat(test_results)\n",
    "    return res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Train TCH Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: 923\n",
      "0 < TCH < 60: 919\n",
      "\n",
      "TCH [min,max]: 0.12, 57.06\n"
     ]
    }
   ],
   "source": [
    "gdf_tch = remove_outliers_tch(gdf, min_v=0.1, max_v=60, two_sigma_filtering=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "Training: Weather + soil features + extra\n",
      "Features: 277\n",
      "================================================================================\n",
      "Train - {2018, 2019, 2020, 2021, 2022, 2023, 2024}. Test - 2017\n",
      "Train - (893, 277). Test - (26, 277)\n",
      "4.995195905618707 3.197611527934229 0.3262215038515617\n",
      "MEAN: 36.61\n",
      "STD: 25.63\n",
      "Train - {2017, 2019, 2020, 2021, 2022, 2023, 2024}. Test - 2018\n",
      "Train - (894, 277). Test - (25, 277)\n",
      "8.156240936264465 5.140086721820196 0.27175916440772085\n",
      "MEAN: 508.07\n",
      "STD: 1300.01\n",
      "Train - {2017, 2018, 2020, 2021, 2022, 2023, 2024}. Test - 2019\n",
      "Train - (829, 277). Test - (90, 277)\n",
      "5.302999534230627 3.8817101401365304 0.26181325800222577\n",
      "MEAN: 81.69\n",
      "STD: 296.69\n",
      "Train - {2017, 2018, 2019, 2021, 2022, 2023, 2024}. Test - 2020\n",
      "Train - (778, 277). Test - (141, 277)\n",
      "4.840135485945397 3.295150126679768 0.4640238059513033\n",
      "MEAN: 60.11\n",
      "STD: 117.91\n",
      "Train - {2017, 2018, 2019, 2020, 2022, 2023, 2024}. Test - 2021\n",
      "Train - (739, 277). Test - (180, 277)\n",
      "6.037389767157327 4.22286791328551 0.4740959012286299\n",
      "MEAN: 63.75\n",
      "STD: 103.07\n",
      "Train - {2017, 2018, 2019, 2020, 2021, 2023, 2024}. Test - 2022\n",
      "Train - (763, 277). Test - (156, 277)\n",
      "5.469247789086732 3.705027708552791 0.49822617438222605\n",
      "MEAN: 56.38\n",
      "STD: 140.47\n",
      "Train - {2017, 2018, 2019, 2020, 2021, 2022, 2024}. Test - 2023\n",
      "Train - (729, 277). Test - (190, 277)\n",
      "5.507223104875628 4.239561144306963 0.14330773856904433\n",
      "MEAN: 75.75\n",
      "STD: 130.49\n",
      "Train - {2017, 2018, 2019, 2020, 2021, 2022, 2023}. Test - 2024\n",
      "Train - (808, 277). Test - (111, 277)\n",
      "3.46001520013814 2.707349179480464 0.26223254222031267\n",
      "MEAN: 48.74\n",
      "STD: 54.67\n",
      "\n",
      "RMSE: 5.47\n",
      "MAE: 3.80\n",
      "R2: 0.338\n",
      "Relative Error\n",
      "MEAN: 75.68\n",
      "STD: 267.22\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_column = 'TCH'\n",
    "\n",
    "for experiment_name in EXPERIMENTS:\n",
    "    print(f\"\\n{'='*80}\")\n",
    "    print(f\"Training: {experiment_name}\")\n",
    "    print(f\"Features: {len(EXPERIMENTS[experiment_name])}\")\n",
    "    print(f\"{'='*80}\")\n",
    "    \n",
    "    features = EXPERIMENTS[experiment_name]\n",
    "    res_df = train_model(gdf_tch, features, label_column=label_column)\n",
    "    \n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.scatter(res_df[label_column], res_df['predictions'], alpha=0.5)\n",
    "    plt.plot([res_df[label_column].min(), res_df[label_column].max()],\n",
    "             [res_df[label_column].min(), res_df[label_column].max()], 'r--', lw=2)\n",
    "    plt.xlabel(f'Actual {label_column}')\n",
    "    plt.ylabel(f'Predicted {label_column}')\n",
    "    plt.title(f'{experiment_name}')\n",
    "    plt.grid(alpha=0.3)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Train Final Production Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training final TCH model on all data...\n",
      "TCH model saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Training final TCH model on all data...\")\n",
    "features = EXPERIMENTS[WEATHER_SOIL_EXP_extra]\n",
    "\n",
    "X = gdf_tch[features].astype(np.float32)\n",
    "y = gdf_tch['TCH']\n",
    "\n",
    "final_tch_model = lgb.LGBMRegressor(\n",
    "    num_leaves=31,\n",
    "    n_estimators=100,\n",
    "    random_state=42,\n",
    "    verbose=-1\n",
    ").fit(X=X, y=y)\n",
    "\n",
    "joblib.dump(final_tch_model, 'tch_model.joblib')\n",
    "joblib.dump({'VARIEDE': le_variety}, 'tch_encoders.joblib')\n",
    "print(\"TCH model saved\")\n",
    "\n",
    "_, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "lgb.plot_importance(final_tch_model, max_num_features=15, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook demonstrated machine learning models for predicting vines yield (TCH).\n",
    "\n",
    "### Data Sources:\n",
    "- Remote sensing: 5 spectral indices (NDVI, EVI, VARI, NDRE, NDWI) × 42 time steps\n",
    "- Weather data: Precipitation and temperature time series + aggregates\n",
    "- Water balance: Biomass, evapotranspiration, soil moisture indicators\n",
    "- Soil properties: Clay, sand, nitrogen, organic carbon at 4 depth levels\n",
    "- Agronomic attributes: Variety, age, cut cycle\n",
    "\n",
    "### Model Approach:\n",
    "- Algorithm: LightGBM (gradient boosting decision trees)\n",
    "- Validation: Leave-One-Season-Out Cross-Validation\n",
    "- Best feature set: Weather + Soil features\n",
    "\n",
    "### Files Generated:\n",
    "- `tch_model.joblib` - Final TCH prediction model\n",
    "- `tch_encoders.joblib` - Label encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}