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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Load modules and define function to load data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"\n", | |
"from matplotlib import pyplot as plt\n", | |
"import pandas as pd\n", | |
"import numpy as np\n", | |
"from sklearn.model_selection import train_test_split\n", | |
"from sklearn.ensemble import RandomForestRegressor\n", | |
"from sklearn.linear_model import LinearRegression\n", | |
"\n", | |
"veg_type_name = {0: \"Combined\", 1: \"Grassland\", 2: \"Shrubsland\", 3: \"Forest\"}\n", | |
"\n", | |
"def generate_train_datasets(veg_type=0, subset_perc=0.15, test_size=0.25):\n", | |
" # 1.- Load\n", | |
" df = pd.read_csv(\"modis_reflectances.csv\")\n", | |
" \n", | |
" # 2.- Remove no data\n", | |
" df.dropna(inplace=True)\n", | |
"\n", | |
" # 2.- Shuffle\n", | |
" df = df.sample(frac=1)\n", | |
"\n", | |
" # 3.- Filter veg_type\n", | |
" if veg_type != 0:\n", | |
" df = df[df['veg_type']==veg_type]\n", | |
"\n", | |
" # 4.- Subset\n", | |
" df = df.sample(frac=subset_perc)\n", | |
"\n", | |
" # 5.- Split train/val\n", | |
" return train_test_split(df, test_size=test_size)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"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>time</th>\n", | |
" <th>latitude</th>\n", | |
" <th>longitude</th>\n", | |
" <th>id</th>\n", | |
" <th>veg_type</th>\n", | |
" <th>1</th>\n", | |
" <th>2</th>\n", | |
" <th>4</th>\n", | |
" <th>6</th>\n", | |
" <th>7</th>\n", | |
" <th>fmc_mean</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>9</th>\n", | |
" <td>9</td>\n", | |
" <td>2015-02-06</td>\n", | |
" <td>-11.875</td>\n", | |
" <td>134.03</td>\n", | |
" <td>1_000</td>\n", | |
" <td>1</td>\n", | |
" <td>0.0518</td>\n", | |
" <td>0.3471</td>\n", | |
" <td>0.0663</td>\n", | |
" <td>0.2538</td>\n", | |
" <td>0.1326</td>\n", | |
" <td>179.8375</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>10</th>\n", | |
" <td>10</td>\n", | |
" <td>2015-02-10</td>\n", | |
" <td>-11.875</td>\n", | |
" <td>134.03</td>\n", | |
" <td>1_000</td>\n", | |
" <td>1</td>\n", | |
" <td>0.0508</td>\n", | |
" <td>0.3436</td>\n", | |
" <td>0.0661</td>\n", | |
" <td>0.2550</td>\n", | |
" <td>0.1336</td>\n", | |
" <td>156.2025</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>17</th>\n", | |
" <td>17</td>\n", | |
" <td>2015-03-10</td>\n", | |
" <td>-11.875</td>\n", | |
" <td>134.03</td>\n", | |
" <td>1_000</td>\n", | |
" <td>1</td>\n", | |
" <td>0.0603</td>\n", | |
" <td>0.3658</td>\n", | |
" <td>0.0705</td>\n", | |
" <td>0.2264</td>\n", | |
" <td>0.0837</td>\n", | |
" <td>239.4425</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>18</th>\n", | |
" <td>18</td>\n", | |
" <td>2015-03-14</td>\n", | |
" <td>-11.875</td>\n", | |
" <td>134.03</td>\n", | |
" <td>1_000</td>\n", | |
" <td>1</td>\n", | |
" <td>0.0596</td>\n", | |
" <td>0.3618</td>\n", | |
" <td>0.0699</td>\n", | |
" <td>0.2327</td>\n", | |
" <td>0.0882</td>\n", | |
" <td>236.7625</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>19</th>\n", | |
" <td>19</td>\n", | |
" <td>2015-03-18</td>\n", | |
" <td>-11.875</td>\n", | |
" <td>134.03</td>\n", | |
" <td>1_000</td>\n", | |
" <td>1</td>\n", | |
" <td>0.0588</td>\n", | |
" <td>0.3586</td>\n", | |
" <td>0.0693</td>\n", | |
" <td>0.2283</td>\n", | |
" <td>0.0847</td>\n", | |
" <td>232.3300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>...</th>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>899848</th>\n", | |
" <td>99848</td>\n", | |
" <td>2018-12-03</td>\n", | |
" <td>-34.500</td>\n", | |
" <td>143.10</td>\n", | |
" <td>3_716</td>\n", | |
" <td>3</td>\n", | |
" <td>0.0180</td>\n", | |
" <td>0.1040</td>\n", | |
" <td>0.0276</td>\n", | |
" <td>0.0165</td>\n", | |
" <td>0.0223</td>\n", | |
" <td>48.7775</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>899849</th>\n", | |
" <td>99849</td>\n", | |
" <td>2018-12-07</td>\n", | |
" <td>-34.500</td>\n", | |
" <td>143.10</td>\n", | |
" <td>3_716</td>\n", | |
" <td>3</td>\n", | |
" <td>0.0260</td>\n", | |
" <td>0.1077</td>\n", | |
" <td>0.0256</td>\n", | |
" <td>0.0195</td>\n", | |
" <td>0.0457</td>\n", | |
" <td>48.7875</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>899853</th>\n", | |
" <td>99853</td>\n", | |
" <td>2018-12-23</td>\n", | |
" <td>-34.500</td>\n", | |
" <td>143.10</td>\n", | |
" <td>3_716</td>\n", | |
" <td>3</td>\n", | |
" <td>0.0009</td>\n", | |
" <td>0.0031</td>\n", | |
" <td>0.0102</td>\n", | |
" <td>0.0228</td>\n", | |
" <td>0.0086</td>\n", | |
" <td>49.5950</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>899854</th>\n", | |
" <td>99854</td>\n", | |
" <td>2018-12-27</td>\n", | |
" <td>-34.500</td>\n", | |
" <td>143.10</td>\n", | |
" <td>3_716</td>\n", | |
" <td>3</td>\n", | |
" <td>0.0176</td>\n", | |
" <td>0.0513</td>\n", | |
" <td>0.0172</td>\n", | |
" <td>0.0275</td>\n", | |
" <td>0.0144</td>\n", | |
" <td>49.5950</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>899855</th>\n", | |
" <td>99855</td>\n", | |
" <td>2018-12-31</td>\n", | |
" <td>-34.500</td>\n", | |
" <td>143.10</td>\n", | |
" <td>3_716</td>\n", | |
" <td>3</td>\n", | |
" <td>0.0188</td>\n", | |
" <td>0.0500</td>\n", | |
" <td>0.0159</td>\n", | |
" <td>0.0237</td>\n", | |
" <td>0.0124</td>\n", | |
" <td>48.7875</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>703629 rows × 12 columns</p>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" Unnamed: 0 time latitude longitude id veg_type 1 \\\n", | |
"9 9 2015-02-06 -11.875 134.03 1_000 1 0.0518 \n", | |
"10 10 2015-02-10 -11.875 134.03 1_000 1 0.0508 \n", | |
"17 17 2015-03-10 -11.875 134.03 1_000 1 0.0603 \n", | |
"18 18 2015-03-14 -11.875 134.03 1_000 1 0.0596 \n", | |
"19 19 2015-03-18 -11.875 134.03 1_000 1 0.0588 \n", | |
"... ... ... ... ... ... ... ... \n", | |
"899848 99848 2018-12-03 -34.500 143.10 3_716 3 0.0180 \n", | |
"899849 99849 2018-12-07 -34.500 143.10 3_716 3 0.0260 \n", | |
"899853 99853 2018-12-23 -34.500 143.10 3_716 3 0.0009 \n", | |
"899854 99854 2018-12-27 -34.500 143.10 3_716 3 0.0176 \n", | |
"899855 99855 2018-12-31 -34.500 143.10 3_716 3 0.0188 \n", | |
"\n", | |
" 2 4 6 7 fmc_mean \n", | |
"9 0.3471 0.0663 0.2538 0.1326 179.8375 \n", | |
"10 0.3436 0.0661 0.2550 0.1336 156.2025 \n", | |
"17 0.3658 0.0705 0.2264 0.0837 239.4425 \n", | |
"18 0.3618 0.0699 0.2327 0.0882 236.7625 \n", | |
"19 0.3586 0.0693 0.2283 0.0847 232.3300 \n", | |
"... ... ... ... ... ... \n", | |
"899848 0.1040 0.0276 0.0165 0.0223 48.7775 \n", | |
"899849 0.1077 0.0256 0.0195 0.0457 48.7875 \n", | |
"899853 0.0031 0.0102 0.0228 0.0086 49.5950 \n", | |
"899854 0.0513 0.0172 0.0275 0.0144 49.5950 \n", | |
"899855 0.0500 0.0159 0.0237 0.0124 48.7875 \n", | |
"\n", | |
"[703629 rows x 12 columns]" | |
] | |
}, | |
"execution_count": 16, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df = pd.read_csv(\"modis_reflectances.csv\")\n", | |
"df.dropna(inplace=True)\n", | |
" \n", | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Linear regression test" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1152x288 with 4 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, axs = plt.subplots(1,4, figsize=(16, 4), facecolor='w', edgecolor='k')\n", | |
"fig.subplots_adjust(wspace=.3)\n", | |
"axs = axs.ravel()\n", | |
"\n", | |
"for veg_type in [0,1,2,3]:\n", | |
" train, test = generate_train_datasets(veg_type)\n", | |
" lr = LinearRegression()\n", | |
" lr.fit(train[['1','2','4','6','7']], train['fmc_mean'])\n", | |
" y_hat = lr.predict(test[['1','2','4','6','7']])\n", | |
" axs[veg_type].scatter(y_hat, test['fmc_mean'].values)\n", | |
" score = lr.score(test[['1','2','4','6','7']], test['fmc_mean'])\n", | |
" axs[veg_type].set_title(f\"Vegetation type: {veg_type_name[veg_type]}. R2: {score:.2f}\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Random Forest test" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1152x288 with 4 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, axs = plt.subplots(1,4, figsize=(16, 4), facecolor='w', edgecolor='k')\n", | |
"fig.subplots_adjust(wspace=.3)\n", | |
"axs = axs.ravel()\n", | |
"\n", | |
"for veg_type in [0,1,2,3]:\n", | |
" rf = RandomForestRegressor(n_estimators=25, max_depth=10, n_jobs=8)\n", | |
" train, test = generate_train_datasets(veg_type)\n", | |
" rf.fit(train[['1','2','4','6','7']], train['fmc_mean'])\n", | |
" y_hat = rf.predict(test[['1','2','4','6','7']])\n", | |
" axs[veg_type].scatter(y_hat, test['fmc_mean'].values)\n", | |
" score = rf.score(test[['1','2','4','6','7']], test['fmc_mean'])\n", | |
" axs[veg_type].set_title(f\"Vegetation type: {veg_type_name[veg_type]}. R2: {score:.2f}\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### RF configuration R2 comparison" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"------- Vegetation type: Combined --------\n", | |
" Estimators: 10 -- Depth: 05 -- R2: 0.08\n", | |
" Estimators: 10 -- Depth: 10 -- R2: 0.09\n", | |
" Estimators: 10 -- Depth: 25 -- R2: 0.03\n", | |
" Estimators: 10 -- Depth: 50 -- R2: 0.01\n", | |
" Estimators: 25 -- Depth: 05 -- R2: 0.08\n", | |
" Estimators: 25 -- Depth: 10 -- R2: 0.10\n", | |
" Estimators: 25 -- Depth: 25 -- R2: 0.07\n", | |
" Estimators: 25 -- Depth: 50 -- R2: 0.05\n", | |
" Estimators: 50 -- Depth: 05 -- R2: 0.08\n", | |
" Estimators: 50 -- Depth: 10 -- R2: 0.11\n", | |
" Estimators: 50 -- Depth: 25 -- R2: 0.09\n", | |
" Estimators: 50 -- Depth: 50 -- R2: 0.08\n", | |
"------- Vegetation type: Grassland --------\n", | |
" Estimators: 10 -- Depth: 05 -- R2: 0.19\n", | |
" Estimators: 10 -- Depth: 10 -- R2: 0.19\n", | |
" Estimators: 10 -- Depth: 25 -- R2: 0.12\n", | |
" Estimators: 10 -- Depth: 50 -- R2: 0.13\n", | |
" Estimators: 25 -- Depth: 05 -- R2: 0.20\n", | |
" Estimators: 25 -- Depth: 10 -- R2: 0.20\n", | |
" Estimators: 25 -- Depth: 25 -- R2: 0.18\n", | |
" Estimators: 25 -- Depth: 50 -- R2: 0.17\n", | |
" Estimators: 50 -- Depth: 05 -- R2: 0.18\n", | |
" Estimators: 50 -- Depth: 10 -- R2: 0.21\n", | |
" Estimators: 50 -- Depth: 25 -- R2: 0.20\n", | |
" Estimators: 50 -- Depth: 50 -- R2: 0.22\n", | |
"------- Vegetation type: Shrubsland --------\n", | |
" Estimators: 10 -- Depth: 05 -- R2: 0.03\n", | |
" Estimators: 10 -- Depth: 10 -- R2: 0.05\n", | |
" Estimators: 10 -- Depth: 25 -- R2: -0.05\n", | |
" Estimators: 10 -- Depth: 50 -- R2: -0.03\n", | |
" Estimators: 25 -- Depth: 05 -- R2: 0.04\n", | |
" Estimators: 25 -- Depth: 10 -- R2: 0.06\n", | |
" Estimators: 25 -- Depth: 25 -- R2: 0.04\n", | |
" Estimators: 25 -- Depth: 50 -- R2: 0.03\n", | |
" Estimators: 50 -- Depth: 05 -- R2: 0.04\n", | |
" Estimators: 50 -- Depth: 10 -- R2: 0.07\n", | |
" Estimators: 50 -- Depth: 25 -- R2: 0.04\n", | |
" Estimators: 50 -- Depth: 50 -- R2: 0.06\n", | |
"------- Vegetation type: Forest --------\n", | |
" Estimators: 10 -- Depth: 05 -- R2: 0.07\n", | |
" Estimators: 10 -- Depth: 10 -- R2: 0.10\n", | |
" Estimators: 10 -- Depth: 25 -- R2: 0.06\n", | |
" Estimators: 10 -- Depth: 50 -- R2: 0.06\n", | |
" Estimators: 25 -- Depth: 05 -- R2: 0.07\n", | |
" Estimators: 25 -- Depth: 10 -- R2: 0.10\n", | |
" Estimators: 25 -- Depth: 25 -- R2: 0.11\n", | |
" Estimators: 25 -- Depth: 50 -- R2: 0.10\n", | |
" Estimators: 50 -- Depth: 05 -- R2: 0.07\n", | |
" Estimators: 50 -- Depth: 10 -- R2: 0.11\n", | |
" Estimators: 50 -- Depth: 25 -- R2: 0.13\n", | |
" Estimators: 50 -- Depth: 50 -- R2: 0.11\n" | |
] | |
} | |
], | |
"source": [ | |
"for veg_type in [0,1,2,3]:\n", | |
" print(f\"------- Vegetation type: {veg_type_name[veg_type]} --------\")\n", | |
" for n_estimators in [10,25,50]:\n", | |
" for max_depth in [5,10,25,50]:\n", | |
" rf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, n_jobs=8)\n", | |
" train, test = generate_train_datasets(veg_type)\n", | |
" rf.fit(train[['1','2','4','6','7']], train['fmc_mean'])\n", | |
" score = rf.score(test[['1','2','4','6','7']], test['fmc_mean'])\n", | |
" print(f\" Estimators: {n_estimators} -- Depth: {max_depth:02} -- R2: {score:.2f}\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.8.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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