thaicom-hack-forest-type-classification-pipeline.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":80927,"databundleVersionId":8721443,"sourceType":"competition"}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.pipeline import Pipeline\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\ntrain_df = pd.read_csv('/kaggle/input/forest-type-classification-spai/train.csv')\ntest_df = pd.read_csv('/kaggle/input/forest-type-classification-spai/test.csv')\ndef calculate_vegetation_indices2(df):\n    df['RVI'] = df['b8'] / df['b4']\n    df['PSSRa'] = df['b7'] / df['b4']\n    df['GOSSAN'] = df['b11'] / df['b4']\n\n    SWIRmin = 0.378\n    SWIRmax = 0.397\n    SWIIRmin = 0.027\n    df['NDVI'] = (df['b8'] - df['b4']) / (df['b8'] + df['b4'])\n    df['NDVI_2'] = (df['b8'] - df['b5']) / (df['b8'] + df['b5'])\n    df['NDVIc'] = (df['b8'] - df['b4']) / (df['b8'] + df['b4']) * (1.0 - (df['b12'] - SWIIRmin) / (SWIRmax - SWIRmin))\n\n    df['TNDVI'] = np.sqrt(df['NDVI'] + 0.5)\n    df['NDI45'] = (df['b5'] - df['b4']) / (df['b5'] + df['b4'])\n    df['GNDVI'] = (df['b8'] - df['b3']) / (df['b8'] + df['b3']) # Green Normalized Difference Vegetation Index: General formula: (NIR - [540:570]) / (NIR + [540:570])\n    df['MCARI'] = ((df['b5'] - df['b4']) - 0.2 * (df['b5'] - df['b3'])) * (df['b5'] / df['b4'])\n    df['LCI'] = (df['b8'] - df['b5']) / (df['b8'] + df['b4']) # Leaf Chlorophyll Index\n    df['GBNDVI'] = (df['b8'] - (df['b3'] + df['b2'])) / (df['b8'] + (df['b3']+ df['b2'])) # Green-Blue NDVI\n    df['GRNDVI'] = (df['b8'] - (df['b3'] + df['b4'])) / (df['b8'] + (df['b3'] + df['b4'])) # Green-Red NDVI\n    df['MVI'] = df['b9'] / df['b11'] # Mid-infrared vegetation index\n    df['MGVI'] = -0.386 * df['b3'] - 0.53 * df['b4'] + 0.535 * df['b6'] + 0.532 * df['b9'] # Misra Green Vegetation Index\n    df['MYVI'] = 0.723 * df['b3'] - 0.597 * df['b4'] + 0.206 * df['b6'] - 0.278 * df['b9'] # Misra Yellow Vegetation Index\n    df['mARI'] = (df['b3']**-1.0 - df['b5']**-1.0) * df['b8'] # Modified anthocyanin reflectance index\n    df['BRI'] = ((1.0 / df['b3']) - (1.0 / df['b5'])) / df['b8'] # Browning Reflectance Index\n    df['CCCI'] = ((df['b8'] - df['b5']) / (df['b8'] + df['b5'])) / ((df['b8'] - df['b4']) / (df['b8'] + df['b4'])) # Canopy Chlorophyll Content Index\n    df['CIgreen'] = df['b8'] / df['b3'] - 1.0 # Chlorophyll Index Green\n    df['CIrededge'] = df['b8'] / df['b5'] - 1.0 # Cholorphyll IndexRedEdge\n    df['CIred-edge'] = (df['b7'] / df['b5'])**-1.0 # Chlorophyll Red-Edge\n    df['CVI'] = df['b8'] * df['b4'] / (df['b3'])**2.0 # Chlorophyll Vegetation Index\n    df['CI'] = (df['b4'] - df['b2']) / df['b4'] # Coloration Index\n    df['CTVI'] = (((df['b4'] - df['b3']) / (df['b4'] + df['b3'])) + 0.5) / \\\n                     np.abs(((df['b4'] - df['b3']) / (df['b4'] + df['b3'])) + 0.5) * \\\n                     np.sqrt(np.abs((((df['b4'] - df['b3']) / (df['b4'] + df['b3']))) + 0.5)) # Corrected Transformed Vegetation Index\n\n    df['CARI'] = (df['b5'] / df['b4']) * np.sqrt(np.power(((df['b5'] - df['b3']) / 150.0 * 670.0 + df['b4'] + (df['b3'] - ((df['b5'] - df['b3']) / 150.0 * 550.0))), 2.0)) / \\\n                    np.power(((df['b5'] - df['b3']) / np.power(150.0, 2.0) + 1.0), 0.5) # Chlorophyll Absorption Ratio Index\n    \n    a = 0.496\n    df['CARI_2'] = (np.abs(((df['b5'] - df['b3']) / 150.0 * df['b4'] + df['b4'] + df['b3'] - (a * df['b3']))) / \\\n                    np.sqrt(np.power(a, 2.0) + 1.0)) * (df['b5'] / df['b4']) # Chlorophyll Absorption Ratio Index 2\n    df['Chlgreen'] = (df['b7'] / df['b3'])**-1.0\n\n    df['MCARI'] = ((df['b5'] - df['b4']) - 0.2 * (df['b5'] - df['b3'])) * (df['b5'] / df['b4']) # Modified Chlorophyll Absorption in Reflectance Index\n    df['MCARI_1'] = 1.2 * (2.5 * (df['b8'] - df['b4']) - 1.3 * (df['b8'] - df['b3'])) # Modified Chlorophyll Absorption in Reflectance Index 1\n    df['MCARI_2'] = 1.5 * (2.5 * (df['b8'] - df['b4']) - 1.3 * (df['b8'] - df['b3'])) / \\\n                     np.sqrt(np.power((2.0 * df['b8'] + 1.0), 2.0) - (6.0 * df['b8'] - 5.0 * np.sqrt(df['b4'])) - 0.5) # Modified Chlorophyll Absorption in Reflectance Index 2\n    df['mNDVI'] = (df['b8'] - df['b4']) / (df['b8'] + df['b4'] - 2.0 * df['b1']) # Modified Normalized Difference Vegetation Index\n\n    L = 0\n    df['SAVI'] = (1 + L) * (df['b8'] - df['b4']) / (df['b8'] + df['b4'] + L)\n    df['GSAVI'] = (df['b8'] - df['b3']) / (df['b8'] + df['b3'] + L) * (1.0 + L)\n    df['MSAVI'] = (2 * df['b8'] + 1 - np.sqrt((2 * df['b8'] + 1)**2 - 8 * (df['b8'] - df['b4']))) / 2\n    df['OSAVI'] = (1.0 + 0.16) * (df['b8'] - df['b4']) / (df['b8'] + df['b4'] + 0.16)\n\n    df['EVI'] = 2.5 * (df['b8'] - df['b4']) / (df['b8'] + 6 * df['b4'] - 7.5 * df['b2'] + 1) # General formula: 2.5 * (NIR - RED) / ((NIR + 6*RED - 7.5*BLUE) + 1)\n    df['EVI_2'] = 2.4 * (df['b8'] - df['b4']) / (df['b8'] + df['b4'] + 1) # General formula: 2.4 * (NIR - RED) / (NIR + RED + 1)\n    df['EVI_2_2'] = 2.5 * (df['b8'] - df['b4']) / (df['b8'] + 2.4 * df['b4'] + 1) #General formula: 2.5 * (NIR - RED) / (NIR + 2.4 * RED + 1)\n    df['EVI_8A'] = 2.5 * (df['b8_a'] - df['b4']) / (df['b8_a'] + 6 * df['b4'] - 7.5 * df['b2'] + 1) # General formula: 2.5 * (NIR - RED) / ((NIR + 6*RED - 7.5*BLUE) + 1)\n    df['EVI_8A_2'] = 2.4 * (df['b8_a'] - df['b4']) / (df['b8_a'] + df['b4'] + 1) # General formula: 2.4 * (NIR - RED) / (NIR + RED + 1)\n    df['EVI_8A_2_2'] = 2.5 * (df['b8_a'] - df['b4']) / (df['b8_a'] + 2.4 * df['b4'] + 1) #General formula: 2.5 * (NIR - RED) / (NIR + 2.4 * RED + 1)\n    df['GLI'] = (2.0 * df['b3'] - df['b4'] - df['b2']) / (2.0 * df['b3'] + df['b4'] + df['b2']) # Green Leaf Index: General formula: (2 * GREEN - RED - BLUE)/(2 * GREEN + RED + BLUE)\n    df['GARI'] = (df['b8'] - (df['b3'] - (df['b2'] - df['b4']))) / (df['b8'] - (df['b3'] + (df['b2'] - df['b4']))) # Green atmospherically resistant vegetation index: General formula: ( NIR - (GREEN-(BLUE- RED))) /  ( NIR- (GREEN+(BLUE - RED)))\n\n    ar = 0.374\n    b = 0.735\n    df['PVI'] = (1.0 / np.sqrt(np.power(0.149, 2.0) + 1.0)) * (df['b8'] - ar - b)\n    df['GCI'] = df['b8'] / df['b3'] - 1\n    df['PRI'] = (df['b3'] - df['b5']) / (df['b3'] + df['b5'])\n    df['MSI'] = df['b11'] / df['b8']\n    df['NBR'] = (df['b8'] - df['b12']) / (df['b8'] + df['b12'])\n    df['ARI'] = (1 / df['b3']) - (1 / df['b5'])\n    df['RDVI'] =  (df['b8'] - df['b4']) / np.power((df['b8'] + df['b4']), 0.5)\n    df['NLI'] = (np.power(df['b8'], 2.0) - df['b4']) / (np.power(df['b8'], 2.0) + df['b4']) # Nonlinear Vegetation Index\n    \n    df['DVI'] = df['b8'] - df['b4']\n    df['S2REP'] = 705 + 35 * ((df['b4'] + df['b7']) / 2 - df['b5']) / (df['b6'] - df['b5'])\n    df['IRECI'] = (df['b7'] - df['b4']) / (df['b5'] / df['b6'])\n    df['MOIS'] = ((df['b8_a'] - df['b11']) / (df['b8_a'] + df['b11'])) # Moisture Index ideal for finding water stress in plants. It uses the short-wave and near-infrared to generate an index of moisture content. In general, wetter vegetation has higher values. But lower moisture index values suggest plants are under stress from insufficient moisture.\n    df['NIR_RATIO_1'] = df['b8'] / df['b4']\n    df['NIR_RATIO_2'] =df['b7'] / df['b4']\n    df['NDSI'] = (df['b11'] - df['b12']) / (df['b11'] + df['b12']) # Normalized Difference Salinity Index  (abbrv. NDSI)\n    # Global Environment Monitoring Index\n    df['GEMI'] = ((2.0 * (np.power(df['b8'], 2.0) - np.power(df['b4'], 2.0)) + 1.5 * df['b8'] + 0.5 * df['b4']) / (df['b8'] + df['b4'] + 0.5) * (1.0 - 0.25 * ((2.0 * (np.power(df['b8'], 2.0) - np.power(df['b4'], 2.0)) + 1.5 * df['b8'] + 0.5 * df['b4']) / (df['b8'] + df['b4'] + 0.5))) - ((df['b4'] - 0.125) / (1.0 - df['b4'])))\n    df['GEMI_8A'] = ((2.0 * (np.power(df['b8_a'], 2.0) - np.power(df['b4'], 2.0)) + 1.5 * df['b8_a'] + 0.5 * df['b4']) / (df['b8_a'] + df['b4'] + 0.5) * (1.0 - 0.25 * ((2.0 * (np.power(df['b8_a'], 2.0) - np.power(df['b4'], 2.0)) + 1.5 * df['b8_a'] + 0.5 * df['b4']) / (df['b8_a'] + df['b4'] + 0.5))) - ((df['b4'] - 0.125) / (1.0 - df['b4']))) # Self Engineered\n    df['GVMI'] = ((df['b8'] + 0.1) - (df['b12'] + 0.02)) / ((df['b8'] + 0.1) + (df['b12'] + 0.02)) # Global Vegetation Moisture Index\n    df['GVMI_8A'] = ((df['b8_a'] + 0.1) - (df['b12'] + 0.02)) / ((df['b8_a'] + 0.1) + (df['b12'] + 0.02))\n\n    df['b11/b12'] = df['b11'] / df['b12']\n    df['b12/b11'] = df['b12'] / df['b11']\n    df['b11-b12'] = df['b11'] - df['b12']\n    df['b12-b11'] = df['b12'] - df['b11']\n    df['b4-b5'] = df['b4'] - df['b5']\n    df['b11-b5'] = df['b11'] - df['b5']\n    df['b11/b8'] = df['b11'] / df['b8']\n    return df\n\n# Preprocess data\ncalculate_vegetation_indices2(train_df)\ncalculate_vegetation_indices2(test_df)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-14T13:56:16.205824Z","iopub.execute_input":"2024-06-14T13:56:16.206191Z","iopub.status.idle":"2024-06-14T13:56:19.687322Z","shell.execute_reply.started":"2024-06-14T13:56:16.206160Z","shell.execute_reply":"2024-06-14T13:56:19.685933Z"},"trusted":true},"execution_count":1,"outputs":[{"execution_count":1,"output_type":"execute_result","data":{"text/plain":"         id   b1   b11   b12   b2   b3   b4    b5    b6    b7  ...  \\\n0     13467   69  1425   693  312  524  376   847  1821  2356  ...   \n1     12719  242  1514   691  343  522  324   718  1730  2178  ...   \n2      1054  218  2354  1118  292  596  410   965  2586  3226  ...   \n3     13747  350  2013  1134  306  572  475   982  1754  1935  ...   \n4      9453  185  1450   712  293  440  384   673  1487  1965  ...   \n...     ...  ...   ...   ...  ...  ...  ...   ...   ...   ...  ...   \n3995    115  447  1686   811  425  661  441   958  2432  2891  ...   \n3996  10654  252  2694  1503  470  778  753  1294  2334  2656  ...   \n3997   5718  233  1486   618  249  409  260   699  2188  2831  ...   \n3998  13054  221  1840   774  245  441  231   703  2491  3453  ...   \n3999   6539    2  1431   629  140  373  254   596  1740  2436  ...   \n\n           GEMI_8A      GVMI   GVMI_8A   b11/b12   b12/b11  b11-b12  b12-b11  \\\n0    -4.992152e+06  0.548686  0.580512  2.056277  0.486316      732     -732   \n1    -4.138416e+06  0.563077  0.546890  2.191027  0.456407      823     -823   \n2    -1.046070e+07  0.501898  0.530551  2.105546  0.474936     1236    -1236   \n3    -3.291813e+06  0.334714  0.337628  1.775132  0.563338      879     -879   \n4    -3.295400e+06  0.513169  0.510995  2.036517  0.491034      738     -738   \n...            ...       ...       ...       ...       ...      ...      ...   \n3995 -7.205528e+06  0.570562  0.588014  2.078915  0.481020      875     -875   \n3996 -6.043458e+06  0.281216  0.362468  1.792415  0.557906     1191    -1191   \n3997 -7.982255e+06  0.661184  0.666307  2.404531  0.415882      868     -868   \n3998 -1.246287e+07  0.618533  0.658730  2.377261  0.420652     1066    -1066   \n3999 -5.713502e+06  0.541216  0.615762  2.275040  0.439553      802     -802   \n\n      b4-b5  b11-b5    b11/b8  \n0      -471     578  0.599243  \n1      -394     796  0.612460  \n2      -555    1389  0.698309  \n3      -507    1031  0.884835  \n4      -289     777  0.655219  \n...     ...     ...       ...  \n3995   -517     728  0.568442  \n3996   -541    1400  1.005599  \n3997   -439     787  0.490429  \n3998   -472    1137  0.560292  \n3999   -342     835  0.677236  \n\n[4000 rows x 82 columns]","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>id</th>\n      <th>b1</th>\n      <th>b11</th>\n      <th>b12</th>\n      <th>b2</th>\n      <th>b3</th>\n      <th>b4</th>\n      <th>b5</th>\n      <th>b6</th>\n      <th>b7</th>\n      <th>...</th>\n      <th>GEMI_8A</th>\n      <th>GVMI</th>\n      <th>GVMI_8A</th>\n      <th>b11/b12</th>\n      <th>b12/b11</th>\n      <th>b11-b12</th>\n      <th>b12-b11</th>\n      <th>b4-b5</th>\n      <th>b11-b5</th>\n      <th>b11/b8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13467</td>\n      <td>69</td>\n      <td>1425</td>\n      <td>693</td>\n      <td>312</td>\n      <td>524</td>\n      <td>376</td>\n      <td>847</td>\n      <td>1821</td>\n      <td>2356</td>\n      <td>...</td>\n      <td>-4.992152e+06</td>\n      <td>0.548686</td>\n      <td>0.580512</td>\n      <td>2.056277</td>\n      <td>0.486316</td>\n      <td>732</td>\n      <td>-732</td>\n      <td>-471</td>\n      <td>578</td>\n      <td>0.599243</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>12719</td>\n      <td>242</td>\n      <td>1514</td>\n      <td>691</td>\n      <td>343</td>\n      <td>522</td>\n      <td>324</td>\n      <td>718</td>\n      <td>1730</td>\n      <td>2178</td>\n      <td>...</td>\n      <td>-4.138416e+06</td>\n      <td>0.563077</td>\n      <td>0.546890</td>\n      <td>2.191027</td>\n      <td>0.456407</td>\n      <td>823</td>\n      <td>-823</td>\n      <td>-394</td>\n      <td>796</td>\n      <td>0.612460</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1054</td>\n      <td>218</td>\n      <td>2354</td>\n      <td>1118</td>\n      <td>292</td>\n      <td>596</td>\n      <td>410</td>\n      <td>965</td>\n      <td>2586</td>\n      <td>3226</td>\n      <td>...</td>\n      <td>-1.046070e+07</td>\n      <td>0.501898</td>\n      <td>0.530551</td>\n      <td>2.105546</td>\n      <td>0.474936</td>\n      <td>1236</td>\n      <td>-1236</td>\n      <td>-555</td>\n      <td>1389</td>\n      <td>0.698309</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>13747</td>\n      <td>350</td>\n      <td>2013</td>\n      <td>1134</td>\n      <td>306</td>\n      <td>572</td>\n      <td>475</td>\n      <td>982</td>\n      <td>1754</td>\n      <td>1935</td>\n      <td>...</td>\n      <td>-3.291813e+06</td>\n      <td>0.334714</td>\n      <td>0.337628</td>\n      <td>1.775132</td>\n      <td>0.563338</td>\n      <td>879</td>\n      <td>-879</td>\n      <td>-507</td>\n      <td>1031</td>\n      <td>0.884835</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9453</td>\n      <td>185</td>\n      <td>1450</td>\n      <td>712</td>\n      <td>293</td>\n      <td>440</td>\n      <td>384</td>\n      <td>673</td>\n      <td>1487</td>\n      <td>1965</td>\n      <td>...</td>\n      <td>-3.295400e+06</td>\n      <td>0.513169</td>\n      <td>0.510995</td>\n      <td>2.036517</td>\n      <td>0.491034</td>\n      <td>738</td>\n      <td>-738</td>\n      <td>-289</td>\n      <td>777</td>\n      <td>0.655219</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      <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>3995</th>\n      <td>115</td>\n      <td>447</td>\n      <td>1686</td>\n      <td>811</td>\n      <td>425</td>\n      <td>661</td>\n      <td>441</td>\n      <td>958</td>\n      <td>2432</td>\n      <td>2891</td>\n      <td>...</td>\n      <td>-7.205528e+06</td>\n      <td>0.570562</td>\n      <td>0.588014</td>\n      <td>2.078915</td>\n      <td>0.481020</td>\n      <td>875</td>\n      <td>-875</td>\n      <td>-517</td>\n      <td>728</td>\n      <td>0.568442</td>\n    </tr>\n    <tr>\n      <th>3996</th>\n      <td>10654</td>\n      <td>252</td>\n      <td>2694</td>\n      <td>1503</td>\n      <td>470</td>\n      <td>778</td>\n      <td>753</td>\n      <td>1294</td>\n      <td>2334</td>\n      <td>2656</td>\n      <td>...</td>\n      <td>-6.043458e+06</td>\n      <td>0.281216</td>\n      <td>0.362468</td>\n      <td>1.792415</td>\n      <td>0.557906</td>\n      <td>1191</td>\n      <td>-1191</td>\n      <td>-541</td>\n      <td>1400</td>\n      <td>1.005599</td>\n    </tr>\n    <tr>\n      <th>3997</th>\n      <td>5718</td>\n      <td>233</td>\n      <td>1486</td>\n      <td>618</td>\n      <td>249</td>\n      <td>409</td>\n      <td>260</td>\n      <td>699</td>\n      <td>2188</td>\n      <td>2831</td>\n      <td>...</td>\n      <td>-7.982255e+06</td>\n      <td>0.661184</td>\n      <td>0.666307</td>\n      <td>2.404531</td>\n      <td>0.415882</td>\n      <td>868</td>\n      <td>-868</td>\n      <td>-439</td>\n      <td>787</td>\n      <td>0.490429</td>\n    </tr>\n    <tr>\n      <th>3998</th>\n      <td>13054</td>\n      <td>221</td>\n      <td>1840</td>\n      <td>774</td>\n      <td>245</td>\n      <td>441</td>\n      <td>231</td>\n      <td>703</td>\n      <td>2491</td>\n      <td>3453</td>\n      <td>...</td>\n      <td>-1.246287e+07</td>\n      <td>0.618533</td>\n      <td>0.658730</td>\n      <td>2.377261</td>\n      <td>0.420652</td>\n      <td>1066</td>\n      <td>-1066</td>\n      <td>-472</td>\n      <td>1137</td>\n      <td>0.560292</td>\n    </tr>\n    <tr>\n      <th>3999</th>\n      <td>6539</td>\n      <td>2</td>\n      <td>1431</td>\n      <td>629</td>\n      <td>140</td>\n      <td>373</td>\n      <td>254</td>\n      <td>596</td>\n      <td>1740</td>\n      <td>2436</td>\n      <td>...</td>\n      <td>-5.713502e+06</td>\n      <td>0.541216</td>\n      <td>0.615762</td>\n      <td>2.275040</td>\n      <td>0.439553</td>\n      <td>802</td>\n      <td>-802</td>\n      <td>-342</td>\n      <td>835</td>\n      <td>0.677236</td>\n    </tr>\n  </tbody>\n</table>\n<p>4000 rows × 82 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_drop = ['id']\ntrain_df = train_df.drop(columns=columns_to_drop)\ntest_df = test_df.drop(columns=columns_to_drop)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:23.381671Z","iopub.execute_input":"2024-06-06T16:01:23.382210Z","iopub.status.idle":"2024-06-06T16:01:23.404117Z","shell.execute_reply.started":"2024-06-06T16:01:23.382168Z","shell.execute_reply":"2024-06-06T16:01:23.402909Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"X = train_df.drop(columns=['nforest_type'])\ny = train_df['nforest_type']","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:24.364847Z","iopub.execute_input":"2024-06-06T16:01:24.365247Z","iopub.status.idle":"2024-06-06T16:01:24.380822Z","shell.execute_reply.started":"2024-06-06T16:01:24.365218Z","shell.execute_reply":"2024-06-06T16:01:24.379600Z"},"trusted":true},"execution_count":75,"outputs":[]},{"cell_type":"code","source":"# Label encode target labels\nlabel_encoder = LabelEncoder()\ny = label_encoder.fit_transform(y)\n\n# Split data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:24.693225Z","iopub.execute_input":"2024-06-06T16:01:24.693672Z","iopub.status.idle":"2024-06-06T16:01:24.721394Z","shell.execute_reply.started":"2024-06-06T16:01:24.693639Z","shell.execute_reply":"2024-06-06T16:01:24.719839Z"},"trusted":true},"execution_count":76,"outputs":[]},{"cell_type":"code","source":"# Define classifiers\nxgb_clf = XGBClassifier()\nlgbm_clf = LGBMClassifier()\nmlp_clf = MLPClassifier(hidden_layer_sizes=(100, 50), activation='relu', solver='adam', random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:25.653353Z","iopub.execute_input":"2024-06-06T16:01:25.654465Z","iopub.status.idle":"2024-06-06T16:01:25.663516Z","shell.execute_reply.started":"2024-06-06T16:01:25.654395Z","shell.execute_reply":"2024-06-06T16:01:25.662314Z"},"trusted":true},"execution_count":77,"outputs":[]},{"cell_type":"code","source":"# Create ensemble model\nensemble_clf = VotingClassifier(estimators=[('xgb', xgb_clf), ('lgbm', lgbm_clf), ('mlp', mlp_clf)], voting='hard')","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:26.024409Z","iopub.execute_input":"2024-06-06T16:01:26.025405Z","iopub.status.idle":"2024-06-06T16:01:26.031661Z","shell.execute_reply.started":"2024-06-06T16:01:26.025359Z","shell.execute_reply":"2024-06-06T16:01:26.030197Z"},"trusted":true},"execution_count":78,"outputs":[]},{"cell_type":"code","source":"# Create pipeline\npipeline = Pipeline([\n    ('scaler', StandardScaler()),  # Include scaler if needed\n    ('ensemble', ensemble_clf)\n])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:26.676964Z","iopub.execute_input":"2024-06-06T16:01:26.677448Z","iopub.status.idle":"2024-06-06T16:01:26.683685Z","shell.execute_reply.started":"2024-06-06T16:01:26.677411Z","shell.execute_reply":"2024-06-06T16:01:26.682244Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"# Train pipeline\npipeline.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:01:29.368244Z","iopub.execute_input":"2024-06-06T16:01:29.368745Z","iopub.status.idle":"2024-06-06T16:02:33.275415Z","shell.execute_reply.started":"2024-06-06T16:01:29.368709Z","shell.execute_reply":"2024-06-06T16:02:33.274126Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022484 seconds.\nYou can set `force_col_wise=true` to remove the overhead.\n[LightGBM] [Info] Total Bins 20655\n[LightGBM] [Info] Number of data points in the train set: 12922, number of used features: 81\n[LightGBM] [Info] Start training from score -1.042267\n[LightGBM] [Info] Start training from score -1.622054\n[LightGBM] [Info] Start training from score -0.798834\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n  warnings.warn(\n","output_type":"stream"},{"execution_count":80,"output_type":"execute_result","data":{"text/plain":"Pipeline(steps=[('scaler', StandardScaler()),\n                ('ensemble',\n                 VotingClassifier(estimators=[('xgb',\n                                               XGBClassifier(base_score=None,\n                                                             booster=None,\n                                                             callbacks=None,\n                                                             colsample_bylevel=None,\n                                                             colsample_bynode=None,\n                                                             colsample_bytree=None,\n                                                             device=None,\n                                                             early_stopping_rounds=None,\n                                                             enable_categorical=False,\n                                                             eval_metric=None,\n                                                             feature_types=None,\n                                                             gamma=None,\n                                                             grow_policy=None,\n                                                             impor...\n                                                             max_cat_threshold=None,\n                                                             max_cat_to_onehot=None,\n                                                             max_delta_step=None,\n                                                             max_depth=None,\n                                                             max_leaves=None,\n                                                             min_child_weight=None,\n                                                             missing=nan,\n                                                             monotone_constraints=None,\n                                                             multi_strategy=None,\n                                                             n_estimators=None,\n                                                             n_jobs=None,\n                                                             num_parallel_tree=None,\n                                                             random_state=None, ...)),\n                                              ('lgbm', LGBMClassifier()),\n                                              ('mlp',\n                                               MLPClassifier(hidden_layer_sizes=(100,\n                                                                                 50),\n                                                             random_state=42))]))])","text/html":"<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                (&#x27;ensemble&#x27;,\n                 VotingClassifier(estimators=[(&#x27;xgb&#x27;,\n                                               XGBClassifier(base_score=None,\n                                                             booster=None,\n                                                             callbacks=None,\n                                                             colsample_bylevel=None,\n                                                             colsample_bynode=None,\n                                                             colsample_bytree=None,\n                                                             device=None,\n                                                             early_stopping_rounds=None,\n                                                             enable_categorical=False,\n                                                             eval_metric=None,\n                                                             feature_types=None,\n                                                             gamma=None,\n                                                             grow_policy=None,\n                                                             impor...\n                                                             max_cat_threshold=None,\n                                                             max_cat_to_onehot=None,\n                                                             max_delta_step=None,\n                                                             max_depth=None,\n                                                             max_leaves=None,\n                                                             min_child_weight=None,\n                                                             missing=nan,\n                                                             monotone_constraints=None,\n                                                             multi_strategy=None,\n                                                             n_estimators=None,\n                                                             n_jobs=None,\n                                                             num_parallel_tree=None,\n                                                             random_state=None, ...)),\n                                              (&#x27;lgbm&#x27;, LGBMClassifier()),\n                                              (&#x27;mlp&#x27;,\n                                               MLPClassifier(hidden_layer_sizes=(100,\n                                                                                 50),\n                                                             random_state=42))]))])</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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                (&#x27;ensemble&#x27;,\n                 VotingClassifier(estimators=[(&#x27;xgb&#x27;,\n                                               XGBClassifier(base_score=None,\n                                                             booster=None,\n                                                             callbacks=None,\n                                                             colsample_bylevel=None,\n                                                             colsample_bynode=None,\n                                                             colsample_bytree=None,\n                                                             device=None,\n                                                             early_stopping_rounds=None,\n                                                             enable_categorical=False,\n                                                             eval_metric=None,\n                                                             feature_types=None,\n                                                             gamma=None,\n                                                             grow_policy=None,\n                                                             impor...\n                                                             max_cat_threshold=None,\n                                                             max_cat_to_onehot=None,\n                                                             max_delta_step=None,\n                                                             max_depth=None,\n                                                             max_leaves=None,\n                                                             min_child_weight=None,\n                                                             missing=nan,\n                                                             monotone_constraints=None,\n                                                             multi_strategy=None,\n                                                             n_estimators=None,\n                                                             n_jobs=None,\n                                                             num_parallel_tree=None,\n                                                             random_state=None, ...)),\n                                              (&#x27;lgbm&#x27;, LGBMClassifier()),\n                                              (&#x27;mlp&#x27;,\n                                               MLPClassifier(hidden_layer_sizes=(100,\n                                                                                 50),\n                                                             random_state=42))]))])</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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ensemble: VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;xgb&#x27;,\n                              XGBClassifier(base_score=None, booster=None,\n                                            callbacks=None,\n                                            colsample_bylevel=None,\n                                            colsample_bynode=None,\n                                            colsample_bytree=None, device=None,\n                                            early_stopping_rounds=None,\n                                            enable_categorical=False,\n                                            eval_metric=None,\n                                            feature_types=None, gamma=None,\n                                            grow_policy=None,\n                                            importance_type=None,\n                                            interaction_constraints=None,\n                                            learning_ra...e,\n                                            max_cat_threshold=None,\n                                            max_cat_to_onehot=None,\n                                            max_delta_step=None, max_depth=None,\n                                            max_leaves=None,\n                                            min_child_weight=None, missing=nan,\n                                            monotone_constraints=None,\n                                            multi_strategy=None,\n                                            n_estimators=None, n_jobs=None,\n                                            num_parallel_tree=None,\n                                            random_state=None, ...)),\n                             (&#x27;lgbm&#x27;, LGBMClassifier()),\n                             (&#x27;mlp&#x27;,\n                              MLPClassifier(hidden_layer_sizes=(100, 50),\n                                            random_state=42))])</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\"><label>xgb</label></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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=None, n_jobs=None,\n              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lgbm</label></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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp</label></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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(100, 50), random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# Evaluate pipeline\ny_pred = pipeline.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:02:37.001295Z","iopub.execute_input":"2024-06-06T16:02:37.002707Z","iopub.status.idle":"2024-06-06T16:02:37.039942Z","shell.execute_reply.started":"2024-06-06T16:02:37.002648Z","shell.execute_reply":"2024-06-06T16:02:37.035722Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Accuracy: 0.7633587786259542\n","output_type":"stream"}]},{"cell_type":"code","source":"test_df","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:02:38.290129Z","iopub.execute_input":"2024-06-06T16:02:38.290598Z","iopub.status.idle":"2024-06-06T16:02:38.325598Z","shell.execute_reply.started":"2024-06-06T16:02:38.290562Z","shell.execute_reply":"2024-06-06T16:02:38.324173Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"       b1   b11   b12   b2   b3   b4    b5    b6    b7    b8  ...  \\\n0      69  1425   693  312  524  376   847  1821  2356  2378  ...   \n1     242  1514   691  343  522  324   718  1730  2178  2472  ...   \n2     218  2354  1118  292  596  410   965  2586  3226  3371  ...   \n3     350  2013  1134  306  572  475   982  1754  1935  2275  ...   \n4     185  1450   712  293  440  384   673  1487  1965  2213  ...   \n...   ...   ...   ...  ...  ...  ...   ...   ...   ...   ...  ...   \n3995  447  1686   811  425  661  441   958  2432  2891  2966  ...   \n3996  252  2694  1503  470  778  753  1294  2334  2656  2679  ...   \n3997  233  1486   618  249  409  260   699  2188  2831  3030  ...   \n3998  221  1840   774  245  441  231   703  2491  3453  3284  ...   \n3999    2  1431   629  140  373  254   596  1740  2436  2113  ...   \n\n           GEMI_8A      GVMI   GVMI_8A   b11/b12   b12/b11  b11-b12  b12-b11  \\\n0    -4.992152e+06  0.548686  0.580512  2.056277  0.486316      732     -732   \n1    -4.138416e+06  0.563077  0.546890  2.191027  0.456407      823     -823   \n2    -1.046070e+07  0.501898  0.530551  2.105546  0.474936     1236    -1236   \n3    -3.291813e+06  0.334714  0.337628  1.775132  0.563338      879     -879   \n4    -3.295400e+06  0.513169  0.510995  2.036517  0.491034      738     -738   \n...            ...       ...       ...       ...       ...      ...      ...   \n3995 -7.205528e+06  0.570562  0.588014  2.078915  0.481020      875     -875   \n3996 -6.043458e+06  0.281216  0.362468  1.792415  0.557906     1191    -1191   \n3997 -7.982255e+06  0.661184  0.666307  2.404531  0.415882      868     -868   \n3998 -1.246287e+07  0.618533  0.658730  2.377261  0.420652     1066    -1066   \n3999 -5.713502e+06  0.541216  0.615762  2.275040  0.439553      802     -802   \n\n      b4-b5  b11-b5    b11/b8  \n0      -471     578  0.599243  \n1      -394     796  0.612460  \n2      -555    1389  0.698309  \n3      -507    1031  0.884835  \n4      -289     777  0.655219  \n...     ...     ...       ...  \n3995   -517     728  0.568442  \n3996   -541    1400  1.005599  \n3997   -439     787  0.490429  \n3998   -472    1137  0.560292  \n3999   -342     835  0.677236  \n\n[4000 rows x 81 columns]","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>b1</th>\n      <th>b11</th>\n      <th>b12</th>\n      <th>b2</th>\n      <th>b3</th>\n      <th>b4</th>\n      <th>b5</th>\n      <th>b6</th>\n      <th>b7</th>\n      <th>b8</th>\n      <th>...</th>\n      <th>GEMI_8A</th>\n      <th>GVMI</th>\n      <th>GVMI_8A</th>\n      <th>b11/b12</th>\n      <th>b12/b11</th>\n      <th>b11-b12</th>\n      <th>b12-b11</th>\n      <th>b4-b5</th>\n      <th>b11-b5</th>\n      <th>b11/b8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>69</td>\n      <td>1425</td>\n      <td>693</td>\n      <td>312</td>\n      <td>524</td>\n      <td>376</td>\n      <td>847</td>\n      <td>1821</td>\n      <td>2356</td>\n      <td>2378</td>\n      <td>...</td>\n      <td>-4.992152e+06</td>\n      <td>0.548686</td>\n      <td>0.580512</td>\n      <td>2.056277</td>\n      <td>0.486316</td>\n      <td>732</td>\n      <td>-732</td>\n      <td>-471</td>\n      <td>578</td>\n      <td>0.599243</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>242</td>\n      <td>1514</td>\n      <td>691</td>\n      <td>343</td>\n      <td>522</td>\n      <td>324</td>\n      <td>718</td>\n      <td>1730</td>\n      <td>2178</td>\n      <td>2472</td>\n      <td>...</td>\n      <td>-4.138416e+06</td>\n      <td>0.563077</td>\n      <td>0.546890</td>\n      <td>2.191027</td>\n      <td>0.456407</td>\n      <td>823</td>\n      <td>-823</td>\n      <td>-394</td>\n      <td>796</td>\n      <td>0.612460</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>218</td>\n      <td>2354</td>\n      <td>1118</td>\n      <td>292</td>\n      <td>596</td>\n      <td>410</td>\n      <td>965</td>\n      <td>2586</td>\n      <td>3226</td>\n      <td>3371</td>\n      <td>...</td>\n      <td>-1.046070e+07</td>\n      <td>0.501898</td>\n      <td>0.530551</td>\n      <td>2.105546</td>\n      <td>0.474936</td>\n      <td>1236</td>\n      <td>-1236</td>\n      <td>-555</td>\n      <td>1389</td>\n      <td>0.698309</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>350</td>\n      <td>2013</td>\n      <td>1134</td>\n      <td>306</td>\n      <td>572</td>\n      <td>475</td>\n      <td>982</td>\n      <td>1754</td>\n      <td>1935</td>\n      <td>2275</td>\n      <td>...</td>\n      <td>-3.291813e+06</td>\n      <td>0.334714</td>\n      <td>0.337628</td>\n      <td>1.775132</td>\n      <td>0.563338</td>\n      <td>879</td>\n      <td>-879</td>\n      <td>-507</td>\n      <td>1031</td>\n      <td>0.884835</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>185</td>\n      <td>1450</td>\n      <td>712</td>\n      <td>293</td>\n      <td>440</td>\n      <td>384</td>\n      <td>673</td>\n      <td>1487</td>\n      <td>1965</td>\n      <td>2213</td>\n      <td>...</td>\n      <td>-3.295400e+06</td>\n      <td>0.513169</td>\n      <td>0.510995</td>\n      <td>2.036517</td>\n      <td>0.491034</td>\n      <td>738</td>\n      <td>-738</td>\n      <td>-289</td>\n      <td>777</td>\n      <td>0.655219</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      <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>3995</th>\n      <td>447</td>\n      <td>1686</td>\n      <td>811</td>\n      <td>425</td>\n      <td>661</td>\n      <td>441</td>\n      <td>958</td>\n      <td>2432</td>\n      <td>2891</td>\n      <td>2966</td>\n      <td>...</td>\n      <td>-7.205528e+06</td>\n      <td>0.570562</td>\n      <td>0.588014</td>\n      <td>2.078915</td>\n      <td>0.481020</td>\n      <td>875</td>\n      <td>-875</td>\n      <td>-517</td>\n      <td>728</td>\n      <td>0.568442</td>\n    </tr>\n    <tr>\n      <th>3996</th>\n      <td>252</td>\n      <td>2694</td>\n      <td>1503</td>\n      <td>470</td>\n      <td>778</td>\n      <td>753</td>\n      <td>1294</td>\n      <td>2334</td>\n      <td>2656</td>\n      <td>2679</td>\n      <td>...</td>\n      <td>-6.043458e+06</td>\n      <td>0.281216</td>\n      <td>0.362468</td>\n      <td>1.792415</td>\n      <td>0.557906</td>\n      <td>1191</td>\n      <td>-1191</td>\n      <td>-541</td>\n      <td>1400</td>\n      <td>1.005599</td>\n    </tr>\n    <tr>\n      <th>3997</th>\n      <td>233</td>\n      <td>1486</td>\n      <td>618</td>\n      <td>249</td>\n      <td>409</td>\n      <td>260</td>\n      <td>699</td>\n      <td>2188</td>\n      <td>2831</td>\n      <td>3030</td>\n      <td>...</td>\n      <td>-7.982255e+06</td>\n      <td>0.661184</td>\n      <td>0.666307</td>\n      <td>2.404531</td>\n      <td>0.415882</td>\n      <td>868</td>\n      <td>-868</td>\n      <td>-439</td>\n      <td>787</td>\n      <td>0.490429</td>\n    </tr>\n    <tr>\n      <th>3998</th>\n      <td>221</td>\n      <td>1840</td>\n      <td>774</td>\n      <td>245</td>\n      <td>441</td>\n      <td>231</td>\n      <td>703</td>\n      <td>2491</td>\n      <td>3453</td>\n      <td>3284</td>\n      <td>...</td>\n      <td>-1.246287e+07</td>\n      <td>0.618533</td>\n      <td>0.658730</td>\n      <td>2.377261</td>\n      <td>0.420652</td>\n      <td>1066</td>\n      <td>-1066</td>\n      <td>-472</td>\n      <td>1137</td>\n      <td>0.560292</td>\n    </tr>\n    <tr>\n      <th>3999</th>\n      <td>2</td>\n      <td>1431</td>\n      <td>629</td>\n      <td>140</td>\n      <td>373</td>\n      <td>254</td>\n      <td>596</td>\n      <td>1740</td>\n      <td>2436</td>\n      <td>2113</td>\n      <td>...</td>\n      <td>-5.713502e+06</td>\n      <td>0.541216</td>\n      <td>0.615762</td>\n      <td>2.275040</td>\n      <td>0.439553</td>\n      <td>802</td>\n      <td>-802</td>\n      <td>-342</td>\n      <td>835</td>\n      <td>0.677236</td>\n    </tr>\n  </tbody>\n</table>\n<p>4000 rows × 81 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = pipeline.predict(test_df)\ny_pred_labels = label_encoder.inverse_transform(y_pred)\nresult_df = test_df.copy()\nresult_df['nforest_type'] = y_pred_labels","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:02:41.127469Z","iopub.execute_input":"2024-06-06T16:02:41.127989Z","iopub.status.idle":"2024-06-06T16:02:41.388557Z","shell.execute_reply.started":"2024-06-06T16:02:41.127954Z","shell.execute_reply":"2024-06-06T16:02:41.386726Z"},"trusted":true},"execution_count":83,"outputs":[]},{"cell_type":"code","source":"result_df.to_csv('NAMOTUSSA2.csv', index=True)\n\nprint(\"Prediction complete. The results have been saved to 'predicted_forest_type.csv'.\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:02:42.136132Z","iopub.execute_input":"2024-06-06T16:02:42.136624Z","iopub.status.idle":"2024-06-06T16:02:42.830840Z","shell.execute_reply.started":"2024-06-06T16:02:42.136587Z","shell.execute_reply":"2024-06-06T16:02:42.829459Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"Prediction complete. The results have been saved to 'predicted_forest_type.csv'.\n","output_type":"stream"}]},{"cell_type":"code","source":"result_df","metadata":{"execution":{"iopub.status.busy":"2024-06-06T16:02:43.482465Z","iopub.execute_input":"2024-06-06T16:02:43.482948Z","iopub.status.idle":"2024-06-06T16:02:43.516181Z","shell.execute_reply.started":"2024-06-06T16:02:43.482911Z","shell.execute_reply":"2024-06-06T16:02:43.514685Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"       b1   b11   b12   b2   b3   b4    b5    b6    b7    b8  ...      GVMI  \\\n0      69  1425   693  312  524  376   847  1821  2356  2378  ...  0.548686   \n1     242  1514   691  343  522  324   718  1730  2178  2472  ...  0.563077   \n2     218  2354  1118  292  596  410   965  2586  3226  3371  ...  0.501898   \n3     350  2013  1134  306  572  475   982  1754  1935  2275  ...  0.334714   \n4     185  1450   712  293  440  384   673  1487  1965  2213  ...  0.513169   \n...   ...   ...   ...  ...  ...  ...   ...   ...   ...   ...  ...       ...   \n3995  447  1686   811  425  661  441   958  2432  2891  2966  ...  0.570562   \n3996  252  2694  1503  470  778  753  1294  2334  2656  2679  ...  0.281216   \n3997  233  1486   618  249  409  260   699  2188  2831  3030  ...  0.661184   \n3998  221  1840   774  245  441  231   703  2491  3453  3284  ...  0.618533   \n3999    2  1431   629  140  373  254   596  1740  2436  2113  ...  0.541216   \n\n       GVMI_8A   b11/b12   b12/b11  b11-b12  b12-b11  b4-b5  b11-b5    b11/b8  \\\n0     0.580512  2.056277  0.486316      732     -732   -471     578  0.599243   \n1     0.546890  2.191027  0.456407      823     -823   -394     796  0.612460   \n2     0.530551  2.105546  0.474936     1236    -1236   -555    1389  0.698309   \n3     0.337628  1.775132  0.563338      879     -879   -507    1031  0.884835   \n4     0.510995  2.036517  0.491034      738     -738   -289     777  0.655219   \n...        ...       ...       ...      ...      ...    ...     ...       ...   \n3995  0.588014  2.078915  0.481020      875     -875   -517     728  0.568442   \n3996  0.362468  1.792415  0.557906     1191    -1191   -541    1400  1.005599   \n3997  0.666307  2.404531  0.415882      868     -868   -439     787  0.490429   \n3998  0.658730  2.377261  0.420652     1066    -1066   -472    1137  0.560292   \n3999  0.615762  2.275040  0.439553      802     -802   -342     835  0.677236   \n\n      nforest_type  \n0              DEF  \n1              DDF  \n2              MDF  \n3              DDF  \n4              DDF  \n...            ...  \n3995           DEF  \n3996           MDF  \n3997           DDF  \n3998           MDF  \n3999           DEF  \n\n[4000 rows x 82 columns]","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>b1</th>\n      <th>b11</th>\n      <th>b12</th>\n      <th>b2</th>\n      <th>b3</th>\n      <th>b4</th>\n      <th>b5</th>\n      <th>b6</th>\n      <th>b7</th>\n      <th>b8</th>\n      <th>...</th>\n      <th>GVMI</th>\n      <th>GVMI_8A</th>\n      <th>b11/b12</th>\n      <th>b12/b11</th>\n      <th>b11-b12</th>\n      <th>b12-b11</th>\n      <th>b4-b5</th>\n      <th>b11-b5</th>\n      <th>b11/b8</th>\n      <th>nforest_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>69</td>\n      <td>1425</td>\n      <td>693</td>\n      <td>312</td>\n      <td>524</td>\n      <td>376</td>\n      <td>847</td>\n      <td>1821</td>\n      <td>2356</td>\n      <td>2378</td>\n      <td>...</td>\n      <td>0.548686</td>\n      <td>0.580512</td>\n      <td>2.056277</td>\n      <td>0.486316</td>\n      <td>732</td>\n      <td>-732</td>\n      <td>-471</td>\n      <td>578</td>\n      <td>0.599243</td>\n      <td>DEF</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>242</td>\n      <td>1514</td>\n      <td>691</td>\n      <td>343</td>\n      <td>522</td>\n      <td>324</td>\n      <td>718</td>\n      <td>1730</td>\n      <td>2178</td>\n      <td>2472</td>\n      <td>...</td>\n      <td>0.563077</td>\n      <td>0.546890</td>\n      <td>2.191027</td>\n      <td>0.456407</td>\n      <td>823</td>\n      <td>-823</td>\n      <td>-394</td>\n      <td>796</td>\n      <td>0.612460</td>\n      <td>DDF</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>218</td>\n      <td>2354</td>\n      <td>1118</td>\n      <td>292</td>\n      <td>596</td>\n      <td>410</td>\n      <td>965</td>\n      <td>2586</td>\n      <td>3226</td>\n      <td>3371</td>\n      <td>...</td>\n      <td>0.501898</td>\n      <td>0.530551</td>\n      <td>2.105546</td>\n      <td>0.474936</td>\n      <td>1236</td>\n      <td>-1236</td>\n      <td>-555</td>\n      <td>1389</td>\n      <td>0.698309</td>\n      <td>MDF</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>350</td>\n      <td>2013</td>\n      <td>1134</td>\n      <td>306</td>\n      <td>572</td>\n      <td>475</td>\n      <td>982</td>\n      <td>1754</td>\n      <td>1935</td>\n      <td>2275</td>\n      <td>...</td>\n      <td>0.334714</td>\n      <td>0.337628</td>\n      <td>1.775132</td>\n      <td>0.563338</td>\n      <td>879</td>\n      <td>-879</td>\n      <td>-507</td>\n      <td>1031</td>\n      <td>0.884835</td>\n      <td>DDF</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>185</td>\n      <td>1450</td>\n      <td>712</td>\n      <td>293</td>\n      <td>440</td>\n      <td>384</td>\n      <td>673</td>\n      <td>1487</td>\n      <td>1965</td>\n      <td>2213</td>\n      <td>...</td>\n      <td>0.513169</td>\n      <td>0.510995</td>\n      <td>2.036517</td>\n      <td>0.491034</td>\n      <td>738</td>\n      <td>-738</td>\n      <td>-289</td>\n      <td>777</td>\n      <td>0.655219</td>\n      <td>DDF</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      <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>3995</th>\n      <td>447</td>\n      <td>1686</td>\n      <td>811</td>\n      <td>425</td>\n      <td>661</td>\n      <td>441</td>\n      <td>958</td>\n      <td>2432</td>\n      <td>2891</td>\n      <td>2966</td>\n      <td>...</td>\n      <td>0.570562</td>\n      <td>0.588014</td>\n      <td>2.078915</td>\n      <td>0.481020</td>\n      <td>875</td>\n      <td>-875</td>\n      <td>-517</td>\n      <td>728</td>\n      <td>0.568442</td>\n      <td>DEF</td>\n    </tr>\n    <tr>\n      <th>3996</th>\n      <td>252</td>\n      <td>2694</td>\n      <td>1503</td>\n      <td>470</td>\n      <td>778</td>\n      <td>753</td>\n      <td>1294</td>\n      <td>2334</td>\n      <td>2656</td>\n      <td>2679</td>\n      <td>...</td>\n      <td>0.281216</td>\n      <td>0.362468</td>\n      <td>1.792415</td>\n      <td>0.557906</td>\n      <td>1191</td>\n      <td>-1191</td>\n      <td>-541</td>\n      <td>1400</td>\n      <td>1.005599</td>\n      <td>MDF</td>\n    </tr>\n    <tr>\n      <th>3997</th>\n      <td>233</td>\n      <td>1486</td>\n      <td>618</td>\n      <td>249</td>\n      <td>409</td>\n      <td>260</td>\n      <td>699</td>\n      <td>2188</td>\n      <td>2831</td>\n      <td>3030</td>\n      <td>...</td>\n      <td>0.661184</td>\n      <td>0.666307</td>\n      <td>2.404531</td>\n      <td>0.415882</td>\n      <td>868</td>\n      <td>-868</td>\n      <td>-439</td>\n      <td>787</td>\n      <td>0.490429</td>\n      <td>DDF</td>\n    </tr>\n    <tr>\n      <th>3998</th>\n      <td>221</td>\n      <td>1840</td>\n      <td>774</td>\n      <td>245</td>\n      <td>441</td>\n      <td>231</td>\n      <td>703</td>\n      <td>2491</td>\n      <td>3453</td>\n      <td>3284</td>\n      <td>...</td>\n      <td>0.618533</td>\n      <td>0.658730</td>\n      <td>2.377261</td>\n      <td>0.420652</td>\n      <td>1066</td>\n      <td>-1066</td>\n      <td>-472</td>\n      <td>1137</td>\n      <td>0.560292</td>\n      <td>MDF</td>\n    </tr>\n    <tr>\n      <th>3999</th>\n      <td>2</td>\n      <td>1431</td>\n      <td>629</td>\n      <td>140</td>\n      <td>373</td>\n      <td>254</td>\n      <td>596</td>\n      <td>1740</td>\n      <td>2436</td>\n      <td>2113</td>\n      <td>...</td>\n      <td>0.541216</td>\n      <td>0.615762</td>\n      <td>2.275040</td>\n      <td>0.439553</td>\n      <td>802</td>\n      <td>-802</td>\n      <td>-342</td>\n      <td>835</td>\n      <td>0.677236</td>\n      <td>DEF</td>\n    </tr>\n  </tbody>\n</table>\n<p>4000 rows × 82 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}