Notebooks/3-ml-models/notebook.ipynb

{
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   "source": [
    "# Import useful libraries\n",
    "import pandas as pd, utils, ipywidgets, numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, MaxAbsScaler, RobustScaler"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Machine Learning Models**\n",
    "\n",
    "The project aim is to create a Machine Learning model capable of detecting the dates when a crop field has been manured, using satellite data. <br>\n",
    "Now is the turn to create and compare different models, considering the previously created features extracted datasets jointly with the results discussed in the [analysis notebook](../2-analysis/notebook.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2_df = pd.read_csv('../../Datasets/main/main-fields-s2-features-extracted.gz', compression='gzip')\n",
    "s1_df = pd.read_csv('../../Datasets/main/main-fields-s1-features-extracted.gz', compression='gzip')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Modify original DataFrames**\n",
    "\n",
    "The purpose of building these `DataFrames` is to detect whether manure has been applied to a particular crop field, by analyzing time series data of Sentinel satellite acquisitions (having for train samples informations about when manure have been applied).\n",
    "\n",
    "The resulting `DataFrames` (one for *Sentinel-1* and one for *Sentinel-2*) consist of rows, each corresponding to a single specific crop field and contains multiple pieces of information. <br>\n",
    "Each column (fixed the row) represents the difference between two consecutive satellites acquisitions (`consequent_xx_acquisitions`), for the considered spectral index. The `y` column, which is a binary indicator variable, indicates whether any manure application date (`manure_dates`) falls within the two consecutive acquisition dates being considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <th>crop_field_name</th>\n",
       "      <th>consequent_s2_acquisitions</th>\n",
       "      <th>B1</th>\n",
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       "    crop_field_name consequent_s2_acquisitions           B1           B2  \\\n",
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       "4             P-BLD   [2022-02-05, 2022-02-10]  1566.938776  1633.841837   \n",
       "..              ...                        ...          ...          ...   \n",
       "821           P-VNS   [2022-09-08, 2022-10-08]  5606.143498  5374.345291   \n",
       "822           P-VNS   [2022-10-08, 2022-10-23] -2735.709417 -2652.242152   \n",
       "823           P-VNS   [2022-10-23, 2022-11-12] -2616.098655 -2423.871749   \n",
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       "825           P-VNS   [2022-11-17, 2022-12-17]  -178.017040  -156.201794   \n",
       "\n",
       "              B3           B4           B5           B6           B7  \\\n",
       "0     -21.562925    -9.930272     6.205782   124.039116   220.292517   \n",
       "1    1057.346939  1060.000000  1126.557823  1147.452381  1167.767007   \n",
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       "3   -1548.894558 -1711.838435 -1013.522109  1331.586735  1878.299320   \n",
       "4    1222.882653  1338.595238   744.566327 -1291.680272 -1764.062925   \n",
       "..           ...          ...          ...          ...          ...   \n",
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       "825  -109.430493  -235.243049  -166.998206   267.694170   347.038565   \n",
       "\n",
       "              B8  ...        CARI2        MCARI       MCARI1    MCARI2  \\\n",
       "0     193.943878  ...   -14.821130   214.161369   275.431837 -0.001536   \n",
       "1    1123.741497  ...  3564.075800 -2129.186864    87.648980 -0.175677   \n",
       "2    -886.345238  ...  2216.904547 -1006.267915 -4114.747959 -0.406936   \n",
       "3    1974.547619  ... -1508.152782  1195.170656  5562.588367  0.420944   \n",
       "4   -1971.335034  ...  1402.703367 -1072.556874 -4946.811224 -0.339269   \n",
       "..           ...  ...          ...          ...          ...       ...   \n",
       "821  1609.651121  ...  5029.973432  -454.213280 -4462.239605 -0.604684   \n",
       "822 -1965.759641  ... -3804.884046    63.383846  1125.412951  0.200635   \n",
       "823  -130.049327  ...  3243.365795   262.395659  1280.547874  0.190679   \n",
       "824    97.081614  ... -1977.622835   150.359500   783.865184  0.098095   \n",
       "825   416.705830  ... -1449.713318   189.153790  1135.073973  0.093841   \n",
       "\n",
       "              BSI       GLI  ALTERATION          SDI    manure_dates    y  \n",
       "0      262.245588  0.004881   -0.003641   151.874150  ['2022-05-26']  0.0  \n",
       "1     4166.625746 -0.391301   -0.629555   163.807823  ['2022-05-26']  0.0  \n",
       "2     3935.251609 -0.118245   -0.277985 -1866.045918  ['2022-05-26']  0.0  \n",
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       "4      371.829866 -0.104554   -0.321017 -2013.052721  ['2022-05-26']  0.0  \n",
       "..            ...       ...         ...          ...             ...  ...  \n",
       "821  14214.068737 -0.097813   -0.469385 -1611.464574  ['2022-04-23']  0.0  \n",
       "822  -9480.670199  0.002424    0.019581   111.947085  ['2022-04-23']  0.0  \n",
       "823  -3448.417006  0.046757    0.294226   185.921973  ['2022-04-23']  0.0  \n",
       "824   -763.198817  0.022308    0.055699   403.480717  ['2022-04-23']  0.0  \n",
       "825   -647.427495  0.036957    0.064493   973.523767  ['2022-04-23']  0.0  \n",
       "\n",
       "[826 rows x 65 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentinel-2\n",
    "s2_df_mod = utils.get_modified_df(s2_df, satellite='s2')\n",
    "s2_df_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "        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>crop_field_name</th>\n",
       "      <th>consequent_s1_acquisitions</th>\n",
       "      <th>VV</th>\n",
       "      <th>VH</th>\n",
       "      <th>AVE</th>\n",
       "      <th>DIF</th>\n",
       "      <th>RAT1</th>\n",
       "      <th>RAT2</th>\n",
       "      <th>NDI</th>\n",
       "      <th>RVI</th>\n",
       "      <th>manure_dates</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-01-08, 2022-01-20]</td>\n",
       "      <td>-1.158187</td>\n",
       "      <td>0.891477</td>\n",
       "      <td>-0.133355</td>\n",
       "      <td>-2.049664</td>\n",
       "      <td>0.089755</td>\n",
       "      <td>-0.183735</td>\n",
       "      <td>0.062131</td>\n",
       "      <td>-0.124261</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-01-20, 2022-02-01]</td>\n",
       "      <td>0.630822</td>\n",
       "      <td>-0.955744</td>\n",
       "      <td>-0.162461</td>\n",
       "      <td>1.586566</td>\n",
       "      <td>-0.065934</td>\n",
       "      <td>0.130239</td>\n",
       "      <td>-0.044994</td>\n",
       "      <td>0.089987</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-02-01, 2022-02-13]</td>\n",
       "      <td>-0.235273</td>\n",
       "      <td>-0.045906</td>\n",
       "      <td>-0.140590</td>\n",
       "      <td>-0.189366</td>\n",
       "      <td>0.009996</td>\n",
       "      <td>-0.021348</td>\n",
       "      <td>0.007047</td>\n",
       "      <td>-0.014095</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-02-13, 2022-02-25]</td>\n",
       "      <td>-0.879335</td>\n",
       "      <td>-0.561385</td>\n",
       "      <td>-0.720360</td>\n",
       "      <td>-0.317949</td>\n",
       "      <td>0.023471</td>\n",
       "      <td>-0.047771</td>\n",
       "      <td>0.016224</td>\n",
       "      <td>-0.032448</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-02-25, 2022-03-09]</td>\n",
       "      <td>0.305355</td>\n",
       "      <td>1.617982</td>\n",
       "      <td>0.961669</td>\n",
       "      <td>-1.312628</td>\n",
       "      <td>0.043798</td>\n",
       "      <td>-0.081217</td>\n",
       "      <td>0.029115</td>\n",
       "      <td>-0.058230</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</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>929</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-10-23, 2022-11-04]</td>\n",
       "      <td>1.302458</td>\n",
       "      <td>0.523352</td>\n",
       "      <td>0.912905</td>\n",
       "      <td>0.779106</td>\n",
       "      <td>-0.056724</td>\n",
       "      <td>0.134730</td>\n",
       "      <td>-0.041709</td>\n",
       "      <td>0.083419</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-11-04, 2022-11-16]</td>\n",
       "      <td>-0.525650</td>\n",
       "      <td>-1.262530</td>\n",
       "      <td>-0.894090</td>\n",
       "      <td>0.736880</td>\n",
       "      <td>-0.014387</td>\n",
       "      <td>0.038177</td>\n",
       "      <td>-0.011047</td>\n",
       "      <td>0.022094</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-11-16, 2022-11-28]</td>\n",
       "      <td>1.401747</td>\n",
       "      <td>1.776336</td>\n",
       "      <td>1.589041</td>\n",
       "      <td>-0.374589</td>\n",
       "      <td>-0.020007</td>\n",
       "      <td>0.056202</td>\n",
       "      <td>-0.015695</td>\n",
       "      <td>0.031391</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-11-28, 2022-12-10]</td>\n",
       "      <td>-1.446950</td>\n",
       "      <td>-2.677388</td>\n",
       "      <td>-2.062169</td>\n",
       "      <td>1.230438</td>\n",
       "      <td>-0.006568</td>\n",
       "      <td>0.019296</td>\n",
       "      <td>-0.005239</td>\n",
       "      <td>0.010479</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-12-10, 2022-12-22]</td>\n",
       "      <td>-0.410894</td>\n",
       "      <td>0.086000</td>\n",
       "      <td>-0.162447</td>\n",
       "      <td>-0.496894</td>\n",
       "      <td>0.024530</td>\n",
       "      <td>-0.069922</td>\n",
       "      <td>0.019348</td>\n",
       "      <td>-0.038696</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>934 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    crop_field_name consequent_s1_acquisitions        VV        VH       AVE  \\\n",
       "0             P-BLD   [2022-01-08, 2022-01-20] -1.158187  0.891477 -0.133355   \n",
       "1             P-BLD   [2022-01-20, 2022-02-01]  0.630822 -0.955744 -0.162461   \n",
       "2             P-BLD   [2022-02-01, 2022-02-13] -0.235273 -0.045906 -0.140590   \n",
       "3             P-BLD   [2022-02-13, 2022-02-25] -0.879335 -0.561385 -0.720360   \n",
       "4             P-BLD   [2022-02-25, 2022-03-09]  0.305355  1.617982  0.961669   \n",
       "..              ...                        ...       ...       ...       ...   \n",
       "929           P-VNS   [2022-10-23, 2022-11-04]  1.302458  0.523352  0.912905   \n",
       "930           P-VNS   [2022-11-04, 2022-11-16] -0.525650 -1.262530 -0.894090   \n",
       "931           P-VNS   [2022-11-16, 2022-11-28]  1.401747  1.776336  1.589041   \n",
       "932           P-VNS   [2022-11-28, 2022-12-10] -1.446950 -2.677388 -2.062169   \n",
       "933           P-VNS   [2022-12-10, 2022-12-22] -0.410894  0.086000 -0.162447   \n",
       "\n",
       "          DIF      RAT1      RAT2       NDI       RVI    manure_dates    y  \n",
       "0   -2.049664  0.089755 -0.183735  0.062131 -0.124261  ['2022-05-26']  0.0  \n",
       "1    1.586566 -0.065934  0.130239 -0.044994  0.089987  ['2022-05-26']  0.0  \n",
       "2   -0.189366  0.009996 -0.021348  0.007047 -0.014095  ['2022-05-26']  0.0  \n",
       "3   -0.317949  0.023471 -0.047771  0.016224 -0.032448  ['2022-05-26']  0.0  \n",
       "4   -1.312628  0.043798 -0.081217  0.029115 -0.058230  ['2022-05-26']  0.0  \n",
       "..        ...       ...       ...       ...       ...             ...  ...  \n",
       "929  0.779106 -0.056724  0.134730 -0.041709  0.083419  ['2022-04-23']  0.0  \n",
       "930  0.736880 -0.014387  0.038177 -0.011047  0.022094  ['2022-04-23']  0.0  \n",
       "931 -0.374589 -0.020007  0.056202 -0.015695  0.031391  ['2022-04-23']  0.0  \n",
       "932  1.230438 -0.006568  0.019296 -0.005239  0.010479  ['2022-04-23']  0.0  \n",
       "933 -0.496894  0.024530 -0.069922  0.019348 -0.038696  ['2022-04-23']  0.0  \n",
       "\n",
       "[934 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentinel-1\n",
    "s1_df_mod = utils.get_modified_df(s1_df, satellite='s1')\n",
    "s1_df_mod"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Balancing the DataFrames**\n",
    "Making a dataset balanced is important when dealing with binary classification problems because it ensures that the model is not biased towards either of the classes.\n",
    "\n",
    "In a binary classification problem (like ours), the goal is to predict the correct label for each sample, which can be either $0$ or $1$. However, if the dataset is imbalanced, meaning that one class has significantly more samples than the other, the model may learn to always predict the majority class, even if the minority class is actually the correct label.\n",
    "\n",
    "This is especially problematic if the minority class is the one that we are more interested in identifying, such as in cases of fraud detection or rare disease diagnosis. In such cases, a model that is biased towards the majority class would be of little use.\n",
    "\n",
    "Therefore, balancing the dataset by increasing the number of samples in the minority class or decreasing the number of samples in the majority class can help improve the accuracy of the model and reduce the chances of bias. This can be done through techniques such as `undersampling` or `oversampling`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crop_field_name</th>\n",
       "      <th>consequent_s2_acquisitions</th>\n",
       "      <th>B1</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>CARI2</th>\n",
       "      <th>MCARI</th>\n",
       "      <th>MCARI1</th>\n",
       "      <th>MCARI2</th>\n",
       "      <th>BSI</th>\n",
       "      <th>GLI</th>\n",
       "      <th>ALTERATION</th>\n",
       "      <th>SDI</th>\n",
       "      <th>manure_dates</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-253.076531</td>\n",
       "      <td>-234.619048</td>\n",
       "      <td>-175.443878</td>\n",
       "      <td>-93.348639</td>\n",
       "      <td>60.715986</td>\n",
       "      <td>-101.731293</td>\n",
       "      <td>-172.363946</td>\n",
       "      <td>-110.688776</td>\n",
       "      <td>...</td>\n",
       "      <td>821.288132</td>\n",
       "      <td>228.081261</td>\n",
       "      <td>-153.038367</td>\n",
       "      <td>-0.005311</td>\n",
       "      <td>-167.126240</td>\n",
       "      <td>0.004451</td>\n",
       "      <td>0.031185</td>\n",
       "      <td>-253.591837</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>289.886054</td>\n",
       "      <td>335.824830</td>\n",
       "      <td>201.591837</td>\n",
       "      <td>735.891156</td>\n",
       "      <td>398.739796</td>\n",
       "      <td>-1703.545918</td>\n",
       "      <td>-2127.702381</td>\n",
       "      <td>-2062.307823</td>\n",
       "      <td>...</td>\n",
       "      <td>5738.416867</td>\n",
       "      <td>-771.599601</td>\n",
       "      <td>-4862.913469</td>\n",
       "      <td>-0.393025</td>\n",
       "      <td>563.443952</td>\n",
       "      <td>-0.112486</td>\n",
       "      <td>-0.159643</td>\n",
       "      <td>-3466.957483</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-BLLT1</td>\n",
       "      <td>[2022-05-06, 2022-05-31]</td>\n",
       "      <td>79.674051</td>\n",
       "      <td>162.807753</td>\n",
       "      <td>104.986551</td>\n",
       "      <td>196.462816</td>\n",
       "      <td>118.330696</td>\n",
       "      <td>-719.818038</td>\n",
       "      <td>-1032.433544</td>\n",
       "      <td>-1109.239715</td>\n",
       "      <td>...</td>\n",
       "      <td>1056.706152</td>\n",
       "      <td>-180.465971</td>\n",
       "      <td>-2022.914620</td>\n",
       "      <td>-0.093796</td>\n",
       "      <td>-98.555306</td>\n",
       "      <td>-0.027062</td>\n",
       "      <td>0.016326</td>\n",
       "      <td>-1542.100475</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-BLLT1</td>\n",
       "      <td>[2022-07-15, 2022-07-20]</td>\n",
       "      <td>160.143196</td>\n",
       "      <td>165.589399</td>\n",
       "      <td>40.116297</td>\n",
       "      <td>20.627373</td>\n",
       "      <td>-51.699367</td>\n",
       "      <td>-158.123418</td>\n",
       "      <td>-149.814082</td>\n",
       "      <td>-188.129747</td>\n",
       "      <td>...</td>\n",
       "      <td>-415.944110</td>\n",
       "      <td>-95.232165</td>\n",
       "      <td>-270.207532</td>\n",
       "      <td>-0.013968</td>\n",
       "      <td>122.433477</td>\n",
       "      <td>-0.017761</td>\n",
       "      <td>-0.012081</td>\n",
       "      <td>-296.157437</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-BLLT2</td>\n",
       "      <td>[2022-05-06, 2022-05-31]</td>\n",
       "      <td>110.491453</td>\n",
       "      <td>231.299145</td>\n",
       "      <td>207.799145</td>\n",
       "      <td>369.918803</td>\n",
       "      <td>315.145299</td>\n",
       "      <td>-470.863248</td>\n",
       "      <td>-767.512821</td>\n",
       "      <td>-827.431624</td>\n",
       "      <td>...</td>\n",
       "      <td>2546.149145</td>\n",
       "      <td>-170.030773</td>\n",
       "      <td>-1977.091282</td>\n",
       "      <td>-0.139965</td>\n",
       "      <td>623.280480</td>\n",
       "      <td>-0.034264</td>\n",
       "      <td>0.015565</td>\n",
       "      <td>-1411.042735</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P-BLLT2</td>\n",
       "      <td>[2022-07-15, 2022-07-20]</td>\n",
       "      <td>144.337607</td>\n",
       "      <td>162.867521</td>\n",
       "      <td>42.529915</td>\n",
       "      <td>4.995726</td>\n",
       "      <td>-39.230769</td>\n",
       "      <td>-89.576923</td>\n",
       "      <td>-63.504274</td>\n",
       "      <td>-87.068376</td>\n",
       "      <td>...</td>\n",
       "      <td>-572.922809</td>\n",
       "      <td>-45.645903</td>\n",
       "      <td>-74.018974</td>\n",
       "      <td>-0.001734</td>\n",
       "      <td>214.956271</td>\n",
       "      <td>-0.012311</td>\n",
       "      <td>-0.021819</td>\n",
       "      <td>-228.722222</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P-CBRCS1</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-380.070981</td>\n",
       "      <td>-412.482255</td>\n",
       "      <td>-307.423800</td>\n",
       "      <td>-403.820459</td>\n",
       "      <td>-116.997912</td>\n",
       "      <td>572.198330</td>\n",
       "      <td>700.480167</td>\n",
       "      <td>756.098121</td>\n",
       "      <td>...</td>\n",
       "      <td>-932.994954</td>\n",
       "      <td>593.991382</td>\n",
       "      <td>1820.661545</td>\n",
       "      <td>0.120347</td>\n",
       "      <td>66.185380</td>\n",
       "      <td>0.052827</td>\n",
       "      <td>0.102936</td>\n",
       "      <td>811.411273</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P-CBRCS1</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>99.931106</td>\n",
       "      <td>106.438413</td>\n",
       "      <td>29.594990</td>\n",
       "      <td>171.755741</td>\n",
       "      <td>62.016701</td>\n",
       "      <td>-767.453027</td>\n",
       "      <td>-961.979123</td>\n",
       "      <td>-953.956159</td>\n",
       "      <td>...</td>\n",
       "      <td>1024.921474</td>\n",
       "      <td>-283.062827</td>\n",
       "      <td>-1842.795908</td>\n",
       "      <td>-0.087669</td>\n",
       "      <td>-252.311687</td>\n",
       "      <td>-0.042444</td>\n",
       "      <td>-0.017196</td>\n",
       "      <td>-1263.488518</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P-CBRCS2</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-401.345982</td>\n",
       "      <td>-385.428571</td>\n",
       "      <td>-324.080357</td>\n",
       "      <td>-289.220982</td>\n",
       "      <td>-181.819196</td>\n",
       "      <td>-58.142857</td>\n",
       "      <td>-34.004464</td>\n",
       "      <td>24.892857</td>\n",
       "      <td>...</td>\n",
       "      <td>-369.197961</td>\n",
       "      <td>161.944643</td>\n",
       "      <td>397.943304</td>\n",
       "      <td>0.057863</td>\n",
       "      <td>-421.515077</td>\n",
       "      <td>0.014136</td>\n",
       "      <td>0.068701</td>\n",
       "      <td>-33.450893</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P-CBRCS2</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>131.178571</td>\n",
       "      <td>99.785714</td>\n",
       "      <td>52.341518</td>\n",
       "      <td>163.837054</td>\n",
       "      <td>63.770089</td>\n",
       "      <td>-449.424107</td>\n",
       "      <td>-560.984375</td>\n",
       "      <td>-548.002232</td>\n",
       "      <td>...</td>\n",
       "      <td>941.666179</td>\n",
       "      <td>-207.597343</td>\n",
       "      <td>-1198.981607</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>217.153036</td>\n",
       "      <td>-0.029580</td>\n",
       "      <td>-0.035077</td>\n",
       "      <td>-956.986607</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P-CLGT</td>\n",
       "      <td>[2022-05-06, 2022-05-31]</td>\n",
       "      <td>45.252033</td>\n",
       "      <td>251.197561</td>\n",
       "      <td>53.341463</td>\n",
       "      <td>-44.653659</td>\n",
       "      <td>-262.644715</td>\n",
       "      <td>-168.608130</td>\n",
       "      <td>-123.317886</td>\n",
       "      <td>-412.474797</td>\n",
       "      <td>...</td>\n",
       "      <td>-2074.587692</td>\n",
       "      <td>-291.204074</td>\n",
       "      <td>-376.790049</td>\n",
       "      <td>0.004877</td>\n",
       "      <td>-589.446583</td>\n",
       "      <td>-0.015945</td>\n",
       "      <td>-0.022645</td>\n",
       "      <td>-184.952033</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>P-CLGT</td>\n",
       "      <td>[2022-07-15, 2022-07-20]</td>\n",
       "      <td>793.839024</td>\n",
       "      <td>906.347154</td>\n",
       "      <td>725.610569</td>\n",
       "      <td>530.953659</td>\n",
       "      <td>607.081301</td>\n",
       "      <td>876.131707</td>\n",
       "      <td>918.872358</td>\n",
       "      <td>1080.114634</td>\n",
       "      <td>...</td>\n",
       "      <td>921.717910</td>\n",
       "      <td>115.350154</td>\n",
       "      <td>1094.456585</td>\n",
       "      <td>0.071434</td>\n",
       "      <td>2857.413849</td>\n",
       "      <td>0.002512</td>\n",
       "      <td>-0.078719</td>\n",
       "      <td>627.694309</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>P-CLMBRS</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>93.134752</td>\n",
       "      <td>107.702128</td>\n",
       "      <td>33.230496</td>\n",
       "      <td>153.446809</td>\n",
       "      <td>6.507092</td>\n",
       "      <td>-307.177305</td>\n",
       "      <td>-338.124113</td>\n",
       "      <td>-428.258865</td>\n",
       "      <td>...</td>\n",
       "      <td>776.432345</td>\n",
       "      <td>-316.538600</td>\n",
       "      <td>-1025.193617</td>\n",
       "      <td>-0.066884</td>\n",
       "      <td>434.895449</td>\n",
       "      <td>-0.034837</td>\n",
       "      <td>0.013449</td>\n",
       "      <td>-809.166667</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P-CLMBRS</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-690.751773</td>\n",
       "      <td>-506.241135</td>\n",
       "      <td>-389.439716</td>\n",
       "      <td>-423.836879</td>\n",
       "      <td>-282.141844</td>\n",
       "      <td>231.046099</td>\n",
       "      <td>427.868794</td>\n",
       "      <td>464.854610</td>\n",
       "      <td>...</td>\n",
       "      <td>-929.496697</td>\n",
       "      <td>204.261356</td>\n",
       "      <td>1333.375319</td>\n",
       "      <td>0.153188</td>\n",
       "      <td>23.586055</td>\n",
       "      <td>0.027862</td>\n",
       "      <td>0.130011</td>\n",
       "      <td>363.124113</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>P-CMNTR</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-286.620321</td>\n",
       "      <td>-336.459893</td>\n",
       "      <td>-233.368984</td>\n",
       "      <td>-267.358289</td>\n",
       "      <td>-22.823529</td>\n",
       "      <td>479.614973</td>\n",
       "      <td>568.235294</td>\n",
       "      <td>664.251337</td>\n",
       "      <td>...</td>\n",
       "      <td>-166.751330</td>\n",
       "      <td>431.693581</td>\n",
       "      <td>1394.541176</td>\n",
       "      <td>0.092449</td>\n",
       "      <td>370.678243</td>\n",
       "      <td>0.034132</td>\n",
       "      <td>0.094812</td>\n",
       "      <td>587.823529</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P-CMNTR</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>207.336898</td>\n",
       "      <td>290.871658</td>\n",
       "      <td>271.871658</td>\n",
       "      <td>614.240642</td>\n",
       "      <td>486.256684</td>\n",
       "      <td>-1143.935829</td>\n",
       "      <td>-1494.534759</td>\n",
       "      <td>-1567.764706</td>\n",
       "      <td>...</td>\n",
       "      <td>4512.450924</td>\n",
       "      <td>-389.621174</td>\n",
       "      <td>-3676.183316</td>\n",
       "      <td>-0.272479</td>\n",
       "      <td>387.605472</td>\n",
       "      <td>-0.070324</td>\n",
       "      <td>-0.130830</td>\n",
       "      <td>-2531.545455</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>P-DR</td>\n",
       "      <td>[2022-03-07, 2022-03-27]</td>\n",
       "      <td>321.150000</td>\n",
       "      <td>355.200000</td>\n",
       "      <td>213.400000</td>\n",
       "      <td>339.916667</td>\n",
       "      <td>26.133333</td>\n",
       "      <td>-921.816667</td>\n",
       "      <td>-1172.900000</td>\n",
       "      <td>-1193.883333</td>\n",
       "      <td>...</td>\n",
       "      <td>700.232978</td>\n",
       "      <td>-574.737753</td>\n",
       "      <td>-2406.038000</td>\n",
       "      <td>-0.149361</td>\n",
       "      <td>-540.355311</td>\n",
       "      <td>-0.054559</td>\n",
       "      <td>-0.092124</td>\n",
       "      <td>-1300.283333</td>\n",
       "      <td>['2022-03-21']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>P-DR</td>\n",
       "      <td>[2022-04-06, 2022-04-11]</td>\n",
       "      <td>-1.100000</td>\n",
       "      <td>96.433333</td>\n",
       "      <td>112.733333</td>\n",
       "      <td>44.300000</td>\n",
       "      <td>83.416667</td>\n",
       "      <td>377.600000</td>\n",
       "      <td>518.633333</td>\n",
       "      <td>711.433333</td>\n",
       "      <td>...</td>\n",
       "      <td>-31.172020</td>\n",
       "      <td>78.461448</td>\n",
       "      <td>1067.428000</td>\n",
       "      <td>0.027446</td>\n",
       "      <td>937.886498</td>\n",
       "      <td>0.006794</td>\n",
       "      <td>0.008158</td>\n",
       "      <td>665.300000</td>\n",
       "      <td>['2022-03-21']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>P-FNFR</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>63.210084</td>\n",
       "      <td>88.274510</td>\n",
       "      <td>-132.718487</td>\n",
       "      <td>652.289916</td>\n",
       "      <td>-37.413165</td>\n",
       "      <td>-1485.815126</td>\n",
       "      <td>-1718.757703</td>\n",
       "      <td>-1716.823529</td>\n",
       "      <td>...</td>\n",
       "      <td>6914.606454</td>\n",
       "      <td>-1212.504584</td>\n",
       "      <td>-4636.136471</td>\n",
       "      <td>-0.438984</td>\n",
       "      <td>553.337905</td>\n",
       "      <td>-0.124530</td>\n",
       "      <td>-0.037182</td>\n",
       "      <td>-2900.495798</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>P-FNFR</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-95.061625</td>\n",
       "      <td>-49.609244</td>\n",
       "      <td>-36.229692</td>\n",
       "      <td>-24.281513</td>\n",
       "      <td>-37.411765</td>\n",
       "      <td>-52.502801</td>\n",
       "      <td>-14.565826</td>\n",
       "      <td>-70.861345</td>\n",
       "      <td>...</td>\n",
       "      <td>-67.052908</td>\n",
       "      <td>-17.567024</td>\n",
       "      <td>-85.714118</td>\n",
       "      <td>-0.004326</td>\n",
       "      <td>-314.410649</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>0.004660</td>\n",
       "      <td>59.815126</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>P-LLT</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-307.268041</td>\n",
       "      <td>-300.285714</td>\n",
       "      <td>-146.440353</td>\n",
       "      <td>-223.683358</td>\n",
       "      <td>158.989691</td>\n",
       "      <td>815.266568</td>\n",
       "      <td>899.045655</td>\n",
       "      <td>1002.251841</td>\n",
       "      <td>...</td>\n",
       "      <td>196.384039</td>\n",
       "      <td>724.336969</td>\n",
       "      <td>1885.845773</td>\n",
       "      <td>0.106060</td>\n",
       "      <td>763.454290</td>\n",
       "      <td>0.047817</td>\n",
       "      <td>0.092775</td>\n",
       "      <td>928.010309</td>\n",
       "      <td>['2022-05-03']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>P-LLT</td>\n",
       "      <td>[2022-04-26, 2022-05-06]</td>\n",
       "      <td>83.313697</td>\n",
       "      <td>149.818851</td>\n",
       "      <td>490.384389</td>\n",
       "      <td>343.898380</td>\n",
       "      <td>993.628866</td>\n",
       "      <td>2928.574374</td>\n",
       "      <td>3439.309278</td>\n",
       "      <td>3743.734904</td>\n",
       "      <td>...</td>\n",
       "      <td>2757.794154</td>\n",
       "      <td>941.613862</td>\n",
       "      <td>5124.282769</td>\n",
       "      <td>0.257979</td>\n",
       "      <td>5799.938957</td>\n",
       "      <td>0.068217</td>\n",
       "      <td>0.323242</td>\n",
       "      <td>2940.132548</td>\n",
       "      <td>['2022-05-03']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>P-LNDRS1</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>0.617117</td>\n",
       "      <td>-35.824324</td>\n",
       "      <td>-233.045045</td>\n",
       "      <td>262.292793</td>\n",
       "      <td>-229.887387</td>\n",
       "      <td>-1008.959459</td>\n",
       "      <td>-1192.968468</td>\n",
       "      <td>-1261.680180</td>\n",
       "      <td>...</td>\n",
       "      <td>3448.102249</td>\n",
       "      <td>-708.820358</td>\n",
       "      <td>-2967.248108</td>\n",
       "      <td>-0.345327</td>\n",
       "      <td>-374.020242</td>\n",
       "      <td>-0.077910</td>\n",
       "      <td>-0.018021</td>\n",
       "      <td>-1840.198198</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>P-LNDRS1</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-129.270270</td>\n",
       "      <td>-90.540541</td>\n",
       "      <td>-85.283784</td>\n",
       "      <td>-57.797297</td>\n",
       "      <td>-79.013514</td>\n",
       "      <td>-473.567568</td>\n",
       "      <td>-634.387387</td>\n",
       "      <td>-673.477477</td>\n",
       "      <td>...</td>\n",
       "      <td>-104.722962</td>\n",
       "      <td>-30.314589</td>\n",
       "      <td>-929.458378</td>\n",
       "      <td>-0.025594</td>\n",
       "      <td>-937.501564</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>-0.007340</td>\n",
       "      <td>-601.851351</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>P-LNDRS2</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>25.596923</td>\n",
       "      <td>-18.218462</td>\n",
       "      <td>-126.606154</td>\n",
       "      <td>103.209231</td>\n",
       "      <td>-130.323077</td>\n",
       "      <td>-262.110769</td>\n",
       "      <td>-239.716923</td>\n",
       "      <td>-243.812308</td>\n",
       "      <td>...</td>\n",
       "      <td>1188.957326</td>\n",
       "      <td>-369.553684</td>\n",
       "      <td>-858.223015</td>\n",
       "      <td>-0.096879</td>\n",
       "      <td>298.356794</td>\n",
       "      <td>-0.043897</td>\n",
       "      <td>-0.006387</td>\n",
       "      <td>-606.609231</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>P-LNDRS2</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-91.560000</td>\n",
       "      <td>-54.240000</td>\n",
       "      <td>-44.864615</td>\n",
       "      <td>7.476923</td>\n",
       "      <td>-9.363077</td>\n",
       "      <td>-360.227692</td>\n",
       "      <td>-539.372308</td>\n",
       "      <td>-557.627692</td>\n",
       "      <td>...</td>\n",
       "      <td>293.366130</td>\n",
       "      <td>-35.261598</td>\n",
       "      <td>-895.403446</td>\n",
       "      <td>-0.048807</td>\n",
       "      <td>-555.519714</td>\n",
       "      <td>-0.006058</td>\n",
       "      <td>-0.017287</td>\n",
       "      <td>-622.529231</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>P-LNDRS3</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>27.773551</td>\n",
       "      <td>67.384058</td>\n",
       "      <td>-107.831522</td>\n",
       "      <td>429.467391</td>\n",
       "      <td>-36.711957</td>\n",
       "      <td>-802.304348</td>\n",
       "      <td>-904.355072</td>\n",
       "      <td>-931.155797</td>\n",
       "      <td>...</td>\n",
       "      <td>4726.127954</td>\n",
       "      <td>-756.450644</td>\n",
       "      <td>-2797.483696</td>\n",
       "      <td>-0.298819</td>\n",
       "      <td>549.249046</td>\n",
       "      <td>-0.086645</td>\n",
       "      <td>-0.020925</td>\n",
       "      <td>-1722.449275</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>P-LNDRS3</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-124.333333</td>\n",
       "      <td>-52.293478</td>\n",
       "      <td>-29.648551</td>\n",
       "      <td>20.449275</td>\n",
       "      <td>-20.867754</td>\n",
       "      <td>-417.666667</td>\n",
       "      <td>-654.802536</td>\n",
       "      <td>-672.905797</td>\n",
       "      <td>...</td>\n",
       "      <td>195.609703</td>\n",
       "      <td>-65.742403</td>\n",
       "      <td>-1076.583913</td>\n",
       "      <td>-0.049661</td>\n",
       "      <td>-754.373599</td>\n",
       "      <td>-0.003686</td>\n",
       "      <td>-0.028322</td>\n",
       "      <td>-682.911232</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>P-LNDRS4</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>92.473515</td>\n",
       "      <td>168.844302</td>\n",
       "      <td>48.548957</td>\n",
       "      <td>493.879615</td>\n",
       "      <td>148.097913</td>\n",
       "      <td>-313.956661</td>\n",
       "      <td>-319.089888</td>\n",
       "      <td>-312.531300</td>\n",
       "      <td>...</td>\n",
       "      <td>5331.416165</td>\n",
       "      <td>-521.399809</td>\n",
       "      <td>-1855.947544</td>\n",
       "      <td>-0.270297</td>\n",
       "      <td>1462.977043</td>\n",
       "      <td>-0.070338</td>\n",
       "      <td>0.025704</td>\n",
       "      <td>-1088.133226</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>P-LNDRS4</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-106.775281</td>\n",
       "      <td>-71.335474</td>\n",
       "      <td>-48.057785</td>\n",
       "      <td>-34.873194</td>\n",
       "      <td>-40.184591</td>\n",
       "      <td>-50.515249</td>\n",
       "      <td>-33.650080</td>\n",
       "      <td>-66.349920</td>\n",
       "      <td>...</td>\n",
       "      <td>-44.421278</td>\n",
       "      <td>-7.963485</td>\n",
       "      <td>-65.894446</td>\n",
       "      <td>-0.001209</td>\n",
       "      <td>-310.683496</td>\n",
       "      <td>0.002756</td>\n",
       "      <td>0.004935</td>\n",
       "      <td>42.544141</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>P-MT</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-342.297166</td>\n",
       "      <td>-334.254251</td>\n",
       "      <td>-231.646964</td>\n",
       "      <td>-294.262348</td>\n",
       "      <td>-53.816194</td>\n",
       "      <td>647.244534</td>\n",
       "      <td>808.394332</td>\n",
       "      <td>889.336842</td>\n",
       "      <td>...</td>\n",
       "      <td>-340.299311</td>\n",
       "      <td>425.776510</td>\n",
       "      <td>1802.062834</td>\n",
       "      <td>0.109504</td>\n",
       "      <td>528.776783</td>\n",
       "      <td>0.040626</td>\n",
       "      <td>0.092490</td>\n",
       "      <td>830.800000</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>P-MT</td>\n",
       "      <td>[2022-04-26, 2022-05-06]</td>\n",
       "      <td>-238.952227</td>\n",
       "      <td>-527.768421</td>\n",
       "      <td>-456.244534</td>\n",
       "      <td>-54.612955</td>\n",
       "      <td>-203.283401</td>\n",
       "      <td>-1410.630769</td>\n",
       "      <td>-1653.139271</td>\n",
       "      <td>-1330.940081</td>\n",
       "      <td>...</td>\n",
       "      <td>2204.688100</td>\n",
       "      <td>-293.366621</td>\n",
       "      <td>-2464.456324</td>\n",
       "      <td>-0.185236</td>\n",
       "      <td>-1187.303669</td>\n",
       "      <td>-0.034569</td>\n",
       "      <td>-0.035478</td>\n",
       "      <td>-1909.637247</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>P-NMS</td>\n",
       "      <td>[2022-02-05, 2022-02-10]</td>\n",
       "      <td>2469.476804</td>\n",
       "      <td>2388.721649</td>\n",
       "      <td>2354.182990</td>\n",
       "      <td>2061.925258</td>\n",
       "      <td>2555.012887</td>\n",
       "      <td>3678.884021</td>\n",
       "      <td>4039.056701</td>\n",
       "      <td>4204.389175</td>\n",
       "      <td>...</td>\n",
       "      <td>6675.648809</td>\n",
       "      <td>561.145709</td>\n",
       "      <td>3541.070103</td>\n",
       "      <td>0.009598</td>\n",
       "      <td>10765.071034</td>\n",
       "      <td>-0.020885</td>\n",
       "      <td>0.148088</td>\n",
       "      <td>2745.814433</td>\n",
       "      <td>['2022-02-10']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>P-NMS</td>\n",
       "      <td>[2022-04-06, 2022-04-11]</td>\n",
       "      <td>12.487113</td>\n",
       "      <td>88.046392</td>\n",
       "      <td>60.945876</td>\n",
       "      <td>55.309278</td>\n",
       "      <td>-4.541237</td>\n",
       "      <td>-174.515464</td>\n",
       "      <td>-79.574742</td>\n",
       "      <td>-90.363402</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.717648</td>\n",
       "      <td>-134.688914</td>\n",
       "      <td>-200.975567</td>\n",
       "      <td>-0.008867</td>\n",
       "      <td>12.641877</td>\n",
       "      <td>-0.010386</td>\n",
       "      <td>-0.032610</td>\n",
       "      <td>-101.402062</td>\n",
       "      <td>['2022-02-10']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>P-QNTLS2</td>\n",
       "      <td>[2022-05-09, 2022-05-26]</td>\n",
       "      <td>61.108696</td>\n",
       "      <td>-47.693980</td>\n",
       "      <td>-49.446488</td>\n",
       "      <td>-163.683946</td>\n",
       "      <td>-180.668896</td>\n",
       "      <td>256.071906</td>\n",
       "      <td>188.428094</td>\n",
       "      <td>8.030100</td>\n",
       "      <td>...</td>\n",
       "      <td>-1474.473091</td>\n",
       "      <td>50.372745</td>\n",
       "      <td>425.478662</td>\n",
       "      <td>0.049452</td>\n",
       "      <td>-582.460782</td>\n",
       "      <td>0.021253</td>\n",
       "      <td>0.060969</td>\n",
       "      <td>361.063545</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>P-QNTLS2</td>\n",
       "      <td>[2022-09-08, 2022-09-11]</td>\n",
       "      <td>-291.928094</td>\n",
       "      <td>-257.028428</td>\n",
       "      <td>-250.735786</td>\n",
       "      <td>-197.548495</td>\n",
       "      <td>-255.998328</td>\n",
       "      <td>-466.391304</td>\n",
       "      <td>-539.608696</td>\n",
       "      <td>-554.712375</td>\n",
       "      <td>...</td>\n",
       "      <td>-715.664470</td>\n",
       "      <td>-71.890300</td>\n",
       "      <td>-597.288161</td>\n",
       "      <td>-0.001582</td>\n",
       "      <td>-1362.636350</td>\n",
       "      <td>0.003288</td>\n",
       "      <td>-0.024287</td>\n",
       "      <td>-354.342809</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>P-SNTLLN</td>\n",
       "      <td>[2022-03-07, 2022-03-27]</td>\n",
       "      <td>455.403275</td>\n",
       "      <td>556.715455</td>\n",
       "      <td>512.348004</td>\n",
       "      <td>701.908905</td>\n",
       "      <td>408.728762</td>\n",
       "      <td>-54.480041</td>\n",
       "      <td>-17.447288</td>\n",
       "      <td>5.872057</td>\n",
       "      <td>...</td>\n",
       "      <td>3441.771668</td>\n",
       "      <td>-473.897258</td>\n",
       "      <td>-1298.008066</td>\n",
       "      <td>-0.189540</td>\n",
       "      <td>2144.188231</td>\n",
       "      <td>-0.040417</td>\n",
       "      <td>-0.045884</td>\n",
       "      <td>-748.460594</td>\n",
       "      <td>['2022-03-17']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>P-SNTLLN</td>\n",
       "      <td>[2022-04-06, 2022-04-11]</td>\n",
       "      <td>-66.443193</td>\n",
       "      <td>-163.892528</td>\n",
       "      <td>-287.452405</td>\n",
       "      <td>-388.636643</td>\n",
       "      <td>-497.849539</td>\n",
       "      <td>-538.993859</td>\n",
       "      <td>-566.787103</td>\n",
       "      <td>-671.731832</td>\n",
       "      <td>...</td>\n",
       "      <td>-3425.627965</td>\n",
       "      <td>-80.427483</td>\n",
       "      <td>-249.809662</td>\n",
       "      <td>-0.003305</td>\n",
       "      <td>-2285.027681</td>\n",
       "      <td>-0.002536</td>\n",
       "      <td>-0.055014</td>\n",
       "      <td>104.019447</td>\n",
       "      <td>['2022-03-17']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>P-SNVCNT1</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>47.185990</td>\n",
       "      <td>37.695652</td>\n",
       "      <td>-9.818841</td>\n",
       "      <td>102.524155</td>\n",
       "      <td>-33.333333</td>\n",
       "      <td>-447.074879</td>\n",
       "      <td>-539.574879</td>\n",
       "      <td>-582.299517</td>\n",
       "      <td>...</td>\n",
       "      <td>546.551970</td>\n",
       "      <td>-283.231140</td>\n",
       "      <td>-1161.401159</td>\n",
       "      <td>-0.064282</td>\n",
       "      <td>109.682598</td>\n",
       "      <td>-0.026868</td>\n",
       "      <td>0.016213</td>\n",
       "      <td>-930.118357</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>P-SNVCNT1</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-302.591787</td>\n",
       "      <td>-283.548309</td>\n",
       "      <td>-232.949275</td>\n",
       "      <td>-137.785024</td>\n",
       "      <td>-124.393720</td>\n",
       "      <td>-118.173913</td>\n",
       "      <td>-91.316425</td>\n",
       "      <td>-99.608696</td>\n",
       "      <td>...</td>\n",
       "      <td>447.098358</td>\n",
       "      <td>-4.244031</td>\n",
       "      <td>-93.482319</td>\n",
       "      <td>-0.006762</td>\n",
       "      <td>-99.897904</td>\n",
       "      <td>-0.005074</td>\n",
       "      <td>0.009840</td>\n",
       "      <td>-392.004831</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>P-SNVCNT2</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>-124.439905</td>\n",
       "      <td>-214.482660</td>\n",
       "      <td>-310.209501</td>\n",
       "      <td>-241.193349</td>\n",
       "      <td>-469.130641</td>\n",
       "      <td>-619.256532</td>\n",
       "      <td>-703.848456</td>\n",
       "      <td>-801.123990</td>\n",
       "      <td>...</td>\n",
       "      <td>-1785.121921</td>\n",
       "      <td>-332.677131</td>\n",
       "      <td>-913.965321</td>\n",
       "      <td>-0.017001</td>\n",
       "      <td>-1333.788788</td>\n",
       "      <td>-0.011351</td>\n",
       "      <td>0.041657</td>\n",
       "      <td>-675.477435</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>P-SNVCNT2</td>\n",
       "      <td>[2022-07-30, 2022-08-09]</td>\n",
       "      <td>-156.240380</td>\n",
       "      <td>-118.211876</td>\n",
       "      <td>-98.007126</td>\n",
       "      <td>-63.211876</td>\n",
       "      <td>-69.375772</td>\n",
       "      <td>-36.493112</td>\n",
       "      <td>-10.087886</td>\n",
       "      <td>-33.353444</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.483039</td>\n",
       "      <td>-13.191737</td>\n",
       "      <td>-11.284447</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>-58.568191</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>0.005463</td>\n",
       "      <td>-135.724941</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>P-STBN</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-382.040000</td>\n",
       "      <td>-336.709630</td>\n",
       "      <td>-241.450370</td>\n",
       "      <td>-202.454815</td>\n",
       "      <td>-48.044444</td>\n",
       "      <td>303.154074</td>\n",
       "      <td>368.087407</td>\n",
       "      <td>385.597037</td>\n",
       "      <td>...</td>\n",
       "      <td>380.723326</td>\n",
       "      <td>205.608334</td>\n",
       "      <td>785.961600</td>\n",
       "      <td>0.068818</td>\n",
       "      <td>147.083120</td>\n",
       "      <td>0.015883</td>\n",
       "      <td>0.043514</td>\n",
       "      <td>233.248889</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>P-STBN</td>\n",
       "      <td>[2022-04-26, 2022-05-06]</td>\n",
       "      <td>277.948148</td>\n",
       "      <td>451.005926</td>\n",
       "      <td>747.118519</td>\n",
       "      <td>868.746667</td>\n",
       "      <td>1340.128889</td>\n",
       "      <td>2042.514074</td>\n",
       "      <td>2295.743704</td>\n",
       "      <td>2523.118519</td>\n",
       "      <td>...</td>\n",
       "      <td>6757.824166</td>\n",
       "      <td>471.838505</td>\n",
       "      <td>2192.555556</td>\n",
       "      <td>0.169469</td>\n",
       "      <td>6325.715462</td>\n",
       "      <td>0.015344</td>\n",
       "      <td>0.171402</td>\n",
       "      <td>767.995556</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>P-TGL2</td>\n",
       "      <td>[2022-05-06, 2022-05-31]</td>\n",
       "      <td>147.714441</td>\n",
       "      <td>288.538545</td>\n",
       "      <td>226.954397</td>\n",
       "      <td>337.359392</td>\n",
       "      <td>229.099891</td>\n",
       "      <td>-565.628664</td>\n",
       "      <td>-767.657980</td>\n",
       "      <td>-841.190011</td>\n",
       "      <td>...</td>\n",
       "      <td>1564.671602</td>\n",
       "      <td>-257.712855</td>\n",
       "      <td>-1869.342932</td>\n",
       "      <td>-0.123781</td>\n",
       "      <td>661.769348</td>\n",
       "      <td>-0.037432</td>\n",
       "      <td>-0.050065</td>\n",
       "      <td>-1539.013029</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>P-TGL2</td>\n",
       "      <td>[2022-07-15, 2022-07-20]</td>\n",
       "      <td>263.964169</td>\n",
       "      <td>240.195440</td>\n",
       "      <td>112.519001</td>\n",
       "      <td>45.343105</td>\n",
       "      <td>-30.997828</td>\n",
       "      <td>-13.219327</td>\n",
       "      <td>2.176982</td>\n",
       "      <td>-51.313789</td>\n",
       "      <td>...</td>\n",
       "      <td>-874.281247</td>\n",
       "      <td>-77.251287</td>\n",
       "      <td>-34.391531</td>\n",
       "      <td>-0.000380</td>\n",
       "      <td>260.246126</td>\n",
       "      <td>-0.009145</td>\n",
       "      <td>-0.041794</td>\n",
       "      <td>-163.501629</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>P-TNNS1</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-352.295380</td>\n",
       "      <td>-349.430693</td>\n",
       "      <td>-245.557756</td>\n",
       "      <td>-333.505776</td>\n",
       "      <td>-88.198845</td>\n",
       "      <td>560.451320</td>\n",
       "      <td>731.578383</td>\n",
       "      <td>742.265677</td>\n",
       "      <td>...</td>\n",
       "      <td>-781.951788</td>\n",
       "      <td>492.540873</td>\n",
       "      <td>1686.309802</td>\n",
       "      <td>0.110464</td>\n",
       "      <td>30.433072</td>\n",
       "      <td>0.047709</td>\n",
       "      <td>0.071993</td>\n",
       "      <td>859.867162</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>P-TNNS1</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>201.636964</td>\n",
       "      <td>298.984323</td>\n",
       "      <td>179.517327</td>\n",
       "      <td>634.886139</td>\n",
       "      <td>326.147690</td>\n",
       "      <td>-1049.853960</td>\n",
       "      <td>-1330.286304</td>\n",
       "      <td>-1275.062706</td>\n",
       "      <td>...</td>\n",
       "      <td>4703.209298</td>\n",
       "      <td>-703.669342</td>\n",
       "      <td>-3460.701683</td>\n",
       "      <td>-0.292417</td>\n",
       "      <td>1134.441157</td>\n",
       "      <td>-0.100180</td>\n",
       "      <td>-0.038182</td>\n",
       "      <td>-2365.860561</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>P-TNNS2</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-316.380952</td>\n",
       "      <td>-317.468721</td>\n",
       "      <td>-248.084034</td>\n",
       "      <td>-294.520075</td>\n",
       "      <td>-69.653595</td>\n",
       "      <td>494.329599</td>\n",
       "      <td>630.287582</td>\n",
       "      <td>657.034547</td>\n",
       "      <td>...</td>\n",
       "      <td>-529.273590</td>\n",
       "      <td>447.144797</td>\n",
       "      <td>1442.678880</td>\n",
       "      <td>0.086714</td>\n",
       "      <td>82.552614</td>\n",
       "      <td>0.034506</td>\n",
       "      <td>0.083243</td>\n",
       "      <td>745.814192</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>P-TNNS2</td>\n",
       "      <td>[2022-05-06, 2022-05-26]</td>\n",
       "      <td>112.985994</td>\n",
       "      <td>108.302521</td>\n",
       "      <td>54.428571</td>\n",
       "      <td>128.935574</td>\n",
       "      <td>9.086835</td>\n",
       "      <td>-204.406162</td>\n",
       "      <td>-265.750700</td>\n",
       "      <td>-342.937442</td>\n",
       "      <td>...</td>\n",
       "      <td>588.198542</td>\n",
       "      <td>-178.006926</td>\n",
       "      <td>-795.728067</td>\n",
       "      <td>-0.082315</td>\n",
       "      <td>180.152283</td>\n",
       "      <td>-0.019688</td>\n",
       "      <td>-0.092575</td>\n",
       "      <td>-740.719888</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>P-VG1</td>\n",
       "      <td>[2022-04-06, 2022-04-09]</td>\n",
       "      <td>6205.438657</td>\n",
       "      <td>4044.482522</td>\n",
       "      <td>3897.707334</td>\n",
       "      <td>4290.424263</td>\n",
       "      <td>4081.653187</td>\n",
       "      <td>2484.573681</td>\n",
       "      <td>2126.621659</td>\n",
       "      <td>1306.363948</td>\n",
       "      <td>...</td>\n",
       "      <td>25223.354617</td>\n",
       "      <td>-656.559859</td>\n",
       "      <td>-4909.685264</td>\n",
       "      <td>-0.599642</td>\n",
       "      <td>11772.242742</td>\n",
       "      <td>-0.109800</td>\n",
       "      <td>-0.310905</td>\n",
       "      <td>-1039.524332</td>\n",
       "      <td>['2022-04-09']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>P-VG1</td>\n",
       "      <td>[2022-09-08, 2022-09-11]</td>\n",
       "      <td>-149.202879</td>\n",
       "      <td>-145.343386</td>\n",
       "      <td>-144.688143</td>\n",
       "      <td>-118.567512</td>\n",
       "      <td>-155.986977</td>\n",
       "      <td>-174.477039</td>\n",
       "      <td>-204.949966</td>\n",
       "      <td>-219.895819</td>\n",
       "      <td>...</td>\n",
       "      <td>-353.758320</td>\n",
       "      <td>-46.250105</td>\n",
       "      <td>-186.660946</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>-593.581544</td>\n",
       "      <td>0.001754</td>\n",
       "      <td>-0.002343</td>\n",
       "      <td>-149.614119</td>\n",
       "      <td>['2022-04-09']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>P-VG2</td>\n",
       "      <td>[2022-04-11, 2022-04-14]</td>\n",
       "      <td>27.262295</td>\n",
       "      <td>14.685246</td>\n",
       "      <td>-128.885246</td>\n",
       "      <td>-124.426230</td>\n",
       "      <td>-401.495082</td>\n",
       "      <td>-655.708197</td>\n",
       "      <td>-787.373770</td>\n",
       "      <td>-807.186885</td>\n",
       "      <td>...</td>\n",
       "      <td>-1864.928652</td>\n",
       "      <td>-391.678318</td>\n",
       "      <td>-990.131410</td>\n",
       "      <td>-0.028583</td>\n",
       "      <td>-1570.226567</td>\n",
       "      <td>-0.018065</td>\n",
       "      <td>-0.079463</td>\n",
       "      <td>-467.685246</td>\n",
       "      <td>['2022-04-13']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>P-VG2</td>\n",
       "      <td>[2022-09-08, 2022-09-11]</td>\n",
       "      <td>-164.455738</td>\n",
       "      <td>-153.245902</td>\n",
       "      <td>-167.039344</td>\n",
       "      <td>-124.704918</td>\n",
       "      <td>-182.377049</td>\n",
       "      <td>-247.386885</td>\n",
       "      <td>-261.177049</td>\n",
       "      <td>-300.213115</td>\n",
       "      <td>...</td>\n",
       "      <td>-379.777422</td>\n",
       "      <td>-79.124911</td>\n",
       "      <td>-318.773508</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>-714.367504</td>\n",
       "      <td>-0.001683</td>\n",
       "      <td>-0.014659</td>\n",
       "      <td>-229.662295</td>\n",
       "      <td>['2022-04-13']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>P-VLDMR</td>\n",
       "      <td>[2022-02-05, 2022-02-10]</td>\n",
       "      <td>2372.240095</td>\n",
       "      <td>2498.468304</td>\n",
       "      <td>2277.455626</td>\n",
       "      <td>2114.116482</td>\n",
       "      <td>2163.908875</td>\n",
       "      <td>2062.169572</td>\n",
       "      <td>2120.681458</td>\n",
       "      <td>2254.747227</td>\n",
       "      <td>...</td>\n",
       "      <td>4434.866797</td>\n",
       "      <td>28.419566</td>\n",
       "      <td>457.317338</td>\n",
       "      <td>-0.170553</td>\n",
       "      <td>8375.270056</td>\n",
       "      <td>-0.040809</td>\n",
       "      <td>-0.074039</td>\n",
       "      <td>953.386688</td>\n",
       "      <td>['2022-02-07']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>P-VLDMR</td>\n",
       "      <td>[2022-04-06, 2022-04-11]</td>\n",
       "      <td>44.672742</td>\n",
       "      <td>132.666403</td>\n",
       "      <td>159.402536</td>\n",
       "      <td>119.300317</td>\n",
       "      <td>159.585578</td>\n",
       "      <td>163.436609</td>\n",
       "      <td>269.946910</td>\n",
       "      <td>312.902536</td>\n",
       "      <td>...</td>\n",
       "      <td>414.016524</td>\n",
       "      <td>44.993624</td>\n",
       "      <td>341.346656</td>\n",
       "      <td>-0.003609</td>\n",
       "      <td>872.731294</td>\n",
       "      <td>-0.001521</td>\n",
       "      <td>-0.004834</td>\n",
       "      <td>106.905705</td>\n",
       "      <td>['2022-02-07']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-03-27, 2022-04-06]</td>\n",
       "      <td>-318.926457</td>\n",
       "      <td>-331.351570</td>\n",
       "      <td>-233.149776</td>\n",
       "      <td>-326.513004</td>\n",
       "      <td>-74.084305</td>\n",
       "      <td>845.073543</td>\n",
       "      <td>1120.692377</td>\n",
       "      <td>1187.495964</td>\n",
       "      <td>...</td>\n",
       "      <td>-664.916898</td>\n",
       "      <td>553.865682</td>\n",
       "      <td>2325.819552</td>\n",
       "      <td>0.097909</td>\n",
       "      <td>673.481308</td>\n",
       "      <td>0.053051</td>\n",
       "      <td>0.106713</td>\n",
       "      <td>1226.117489</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-04-16, 2022-04-26]</td>\n",
       "      <td>-650.826009</td>\n",
       "      <td>-693.808969</td>\n",
       "      <td>-707.679821</td>\n",
       "      <td>-833.136323</td>\n",
       "      <td>-859.154260</td>\n",
       "      <td>-687.576682</td>\n",
       "      <td>-677.065471</td>\n",
       "      <td>-731.956054</td>\n",
       "      <td>...</td>\n",
       "      <td>-5609.027149</td>\n",
       "      <td>72.578841</td>\n",
       "      <td>341.411731</td>\n",
       "      <td>0.118290</td>\n",
       "      <td>-2811.743556</td>\n",
       "      <td>0.030338</td>\n",
       "      <td>0.135702</td>\n",
       "      <td>-55.923767</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   crop_field_name consequent_s2_acquisitions           B1           B2  \\\n",
       "0            P-BLD   [2022-03-27, 2022-04-06]  -253.076531  -234.619048   \n",
       "1            P-BLD   [2022-05-06, 2022-05-26]   289.886054   335.824830   \n",
       "2          P-BLLT1   [2022-05-06, 2022-05-31]    79.674051   162.807753   \n",
       "3          P-BLLT1   [2022-07-15, 2022-07-20]   160.143196   165.589399   \n",
       "4          P-BLLT2   [2022-05-06, 2022-05-31]   110.491453   231.299145   \n",
       "5          P-BLLT2   [2022-07-15, 2022-07-20]   144.337607   162.867521   \n",
       "6         P-CBRCS1   [2022-03-27, 2022-04-06]  -380.070981  -412.482255   \n",
       "7         P-CBRCS1   [2022-05-06, 2022-05-26]    99.931106   106.438413   \n",
       "8         P-CBRCS2   [2022-03-27, 2022-04-06]  -401.345982  -385.428571   \n",
       "9         P-CBRCS2   [2022-05-06, 2022-05-26]   131.178571    99.785714   \n",
       "10          P-CLGT   [2022-05-06, 2022-05-31]    45.252033   251.197561   \n",
       "11          P-CLGT   [2022-07-15, 2022-07-20]   793.839024   906.347154   \n",
       "12        P-CLMBRS   [2022-05-06, 2022-05-26]    93.134752   107.702128   \n",
       "13        P-CLMBRS   [2022-07-30, 2022-08-09]  -690.751773  -506.241135   \n",
       "14         P-CMNTR   [2022-03-27, 2022-04-06]  -286.620321  -336.459893   \n",
       "15         P-CMNTR   [2022-05-06, 2022-05-26]   207.336898   290.871658   \n",
       "16            P-DR   [2022-03-07, 2022-03-27]   321.150000   355.200000   \n",
       "17            P-DR   [2022-04-06, 2022-04-11]    -1.100000    96.433333   \n",
       "18          P-FNFR   [2022-05-06, 2022-05-26]    63.210084    88.274510   \n",
       "19          P-FNFR   [2022-07-30, 2022-08-09]   -95.061625   -49.609244   \n",
       "20           P-LLT   [2022-03-27, 2022-04-06]  -307.268041  -300.285714   \n",
       "21           P-LLT   [2022-04-26, 2022-05-06]    83.313697   149.818851   \n",
       "22        P-LNDRS1   [2022-05-06, 2022-05-26]     0.617117   -35.824324   \n",
       "23        P-LNDRS1   [2022-07-30, 2022-08-09]  -129.270270   -90.540541   \n",
       "24        P-LNDRS2   [2022-05-06, 2022-05-26]    25.596923   -18.218462   \n",
       "25        P-LNDRS2   [2022-07-30, 2022-08-09]   -91.560000   -54.240000   \n",
       "26        P-LNDRS3   [2022-05-06, 2022-05-26]    27.773551    67.384058   \n",
       "27        P-LNDRS3   [2022-07-30, 2022-08-09]  -124.333333   -52.293478   \n",
       "28        P-LNDRS4   [2022-05-06, 2022-05-26]    92.473515   168.844302   \n",
       "29        P-LNDRS4   [2022-07-30, 2022-08-09]  -106.775281   -71.335474   \n",
       "30            P-MT   [2022-03-27, 2022-04-06]  -342.297166  -334.254251   \n",
       "31            P-MT   [2022-04-26, 2022-05-06]  -238.952227  -527.768421   \n",
       "32           P-NMS   [2022-02-05, 2022-02-10]  2469.476804  2388.721649   \n",
       "33           P-NMS   [2022-04-06, 2022-04-11]    12.487113    88.046392   \n",
       "34        P-QNTLS2   [2022-05-09, 2022-05-26]    61.108696   -47.693980   \n",
       "35        P-QNTLS2   [2022-09-08, 2022-09-11]  -291.928094  -257.028428   \n",
       "36        P-SNTLLN   [2022-03-07, 2022-03-27]   455.403275   556.715455   \n",
       "37        P-SNTLLN   [2022-04-06, 2022-04-11]   -66.443193  -163.892528   \n",
       "38       P-SNVCNT1   [2022-05-06, 2022-05-26]    47.185990    37.695652   \n",
       "39       P-SNVCNT1   [2022-07-30, 2022-08-09]  -302.591787  -283.548309   \n",
       "40       P-SNVCNT2   [2022-05-06, 2022-05-26]  -124.439905  -214.482660   \n",
       "41       P-SNVCNT2   [2022-07-30, 2022-08-09]  -156.240380  -118.211876   \n",
       "42          P-STBN   [2022-03-27, 2022-04-06]  -382.040000  -336.709630   \n",
       "43          P-STBN   [2022-04-26, 2022-05-06]   277.948148   451.005926   \n",
       "44          P-TGL2   [2022-05-06, 2022-05-31]   147.714441   288.538545   \n",
       "45          P-TGL2   [2022-07-15, 2022-07-20]   263.964169   240.195440   \n",
       "46         P-TNNS1   [2022-03-27, 2022-04-06]  -352.295380  -349.430693   \n",
       "47         P-TNNS1   [2022-05-06, 2022-05-26]   201.636964   298.984323   \n",
       "48         P-TNNS2   [2022-03-27, 2022-04-06]  -316.380952  -317.468721   \n",
       "49         P-TNNS2   [2022-05-06, 2022-05-26]   112.985994   108.302521   \n",
       "50           P-VG1   [2022-04-06, 2022-04-09]  6205.438657  4044.482522   \n",
       "51           P-VG1   [2022-09-08, 2022-09-11]  -149.202879  -145.343386   \n",
       "52           P-VG2   [2022-04-11, 2022-04-14]    27.262295    14.685246   \n",
       "53           P-VG2   [2022-09-08, 2022-09-11]  -164.455738  -153.245902   \n",
       "54         P-VLDMR   [2022-02-05, 2022-02-10]  2372.240095  2498.468304   \n",
       "55         P-VLDMR   [2022-04-06, 2022-04-11]    44.672742   132.666403   \n",
       "56           P-VNS   [2022-03-27, 2022-04-06]  -318.926457  -331.351570   \n",
       "57           P-VNS   [2022-04-16, 2022-04-26]  -650.826009  -693.808969   \n",
       "\n",
       "             B3           B4           B5           B6           B7  \\\n",
       "0   -175.443878   -93.348639    60.715986  -101.731293  -172.363946   \n",
       "1    201.591837   735.891156   398.739796 -1703.545918 -2127.702381   \n",
       "2    104.986551   196.462816   118.330696  -719.818038 -1032.433544   \n",
       "3     40.116297    20.627373   -51.699367  -158.123418  -149.814082   \n",
       "4    207.799145   369.918803   315.145299  -470.863248  -767.512821   \n",
       "5     42.529915     4.995726   -39.230769   -89.576923   -63.504274   \n",
       "6   -307.423800  -403.820459  -116.997912   572.198330   700.480167   \n",
       "7     29.594990   171.755741    62.016701  -767.453027  -961.979123   \n",
       "8   -324.080357  -289.220982  -181.819196   -58.142857   -34.004464   \n",
       "9     52.341518   163.837054    63.770089  -449.424107  -560.984375   \n",
       "10    53.341463   -44.653659  -262.644715  -168.608130  -123.317886   \n",
       "11   725.610569   530.953659   607.081301   876.131707   918.872358   \n",
       "12    33.230496   153.446809     6.507092  -307.177305  -338.124113   \n",
       "13  -389.439716  -423.836879  -282.141844   231.046099   427.868794   \n",
       "14  -233.368984  -267.358289   -22.823529   479.614973   568.235294   \n",
       "15   271.871658   614.240642   486.256684 -1143.935829 -1494.534759   \n",
       "16   213.400000   339.916667    26.133333  -921.816667 -1172.900000   \n",
       "17   112.733333    44.300000    83.416667   377.600000   518.633333   \n",
       "18  -132.718487   652.289916   -37.413165 -1485.815126 -1718.757703   \n",
       "19   -36.229692   -24.281513   -37.411765   -52.502801   -14.565826   \n",
       "20  -146.440353  -223.683358   158.989691   815.266568   899.045655   \n",
       "21   490.384389   343.898380   993.628866  2928.574374  3439.309278   \n",
       "22  -233.045045   262.292793  -229.887387 -1008.959459 -1192.968468   \n",
       "23   -85.283784   -57.797297   -79.013514  -473.567568  -634.387387   \n",
       "24  -126.606154   103.209231  -130.323077  -262.110769  -239.716923   \n",
       "25   -44.864615     7.476923    -9.363077  -360.227692  -539.372308   \n",
       "26  -107.831522   429.467391   -36.711957  -802.304348  -904.355072   \n",
       "27   -29.648551    20.449275   -20.867754  -417.666667  -654.802536   \n",
       "28    48.548957   493.879615   148.097913  -313.956661  -319.089888   \n",
       "29   -48.057785   -34.873194   -40.184591   -50.515249   -33.650080   \n",
       "30  -231.646964  -294.262348   -53.816194   647.244534   808.394332   \n",
       "31  -456.244534   -54.612955  -203.283401 -1410.630769 -1653.139271   \n",
       "32  2354.182990  2061.925258  2555.012887  3678.884021  4039.056701   \n",
       "33    60.945876    55.309278    -4.541237  -174.515464   -79.574742   \n",
       "34   -49.446488  -163.683946  -180.668896   256.071906   188.428094   \n",
       "35  -250.735786  -197.548495  -255.998328  -466.391304  -539.608696   \n",
       "36   512.348004   701.908905   408.728762   -54.480041   -17.447288   \n",
       "37  -287.452405  -388.636643  -497.849539  -538.993859  -566.787103   \n",
       "38    -9.818841   102.524155   -33.333333  -447.074879  -539.574879   \n",
       "39  -232.949275  -137.785024  -124.393720  -118.173913   -91.316425   \n",
       "40  -310.209501  -241.193349  -469.130641  -619.256532  -703.848456   \n",
       "41   -98.007126   -63.211876   -69.375772   -36.493112   -10.087886   \n",
       "42  -241.450370  -202.454815   -48.044444   303.154074   368.087407   \n",
       "43   747.118519   868.746667  1340.128889  2042.514074  2295.743704   \n",
       "44   226.954397   337.359392   229.099891  -565.628664  -767.657980   \n",
       "45   112.519001    45.343105   -30.997828   -13.219327     2.176982   \n",
       "46  -245.557756  -333.505776   -88.198845   560.451320   731.578383   \n",
       "47   179.517327   634.886139   326.147690 -1049.853960 -1330.286304   \n",
       "48  -248.084034  -294.520075   -69.653595   494.329599   630.287582   \n",
       "49    54.428571   128.935574     9.086835  -204.406162  -265.750700   \n",
       "50  3897.707334  4290.424263  4081.653187  2484.573681  2126.621659   \n",
       "51  -144.688143  -118.567512  -155.986977  -174.477039  -204.949966   \n",
       "52  -128.885246  -124.426230  -401.495082  -655.708197  -787.373770   \n",
       "53  -167.039344  -124.704918  -182.377049  -247.386885  -261.177049   \n",
       "54  2277.455626  2114.116482  2163.908875  2062.169572  2120.681458   \n",
       "55   159.402536   119.300317   159.585578   163.436609   269.946910   \n",
       "56  -233.149776  -326.513004   -74.084305   845.073543  1120.692377   \n",
       "57  -707.679821  -833.136323  -859.154260  -687.576682  -677.065471   \n",
       "\n",
       "             B8  ...         CARI2        MCARI       MCARI1    MCARI2  \\\n",
       "0   -110.688776  ...    821.288132   228.081261  -153.038367 -0.005311   \n",
       "1  -2062.307823  ...   5738.416867  -771.599601 -4862.913469 -0.393025   \n",
       "2  -1109.239715  ...   1056.706152  -180.465971 -2022.914620 -0.093796   \n",
       "3   -188.129747  ...   -415.944110   -95.232165  -270.207532 -0.013968   \n",
       "4   -827.431624  ...   2546.149145  -170.030773 -1977.091282 -0.139965   \n",
       "5    -87.068376  ...   -572.922809   -45.645903   -74.018974 -0.001734   \n",
       "6    756.098121  ...   -932.994954   593.991382  1820.661545  0.120347   \n",
       "7   -953.956159  ...   1024.921474  -283.062827 -1842.795908 -0.087669   \n",
       "8     24.892857  ...   -369.197961   161.944643   397.943304  0.057863   \n",
       "9   -548.002232  ...    941.666179  -207.597343 -1198.981607 -0.088839   \n",
       "10  -412.474797  ...  -2074.587692  -291.204074  -376.790049  0.004877   \n",
       "11  1080.114634  ...    921.717910   115.350154  1094.456585  0.071434   \n",
       "12  -428.258865  ...    776.432345  -316.538600 -1025.193617 -0.066884   \n",
       "13   464.854610  ...   -929.496697   204.261356  1333.375319  0.153188   \n",
       "14   664.251337  ...   -166.751330   431.693581  1394.541176  0.092449   \n",
       "15 -1567.764706  ...   4512.450924  -389.621174 -3676.183316 -0.272479   \n",
       "16 -1193.883333  ...    700.232978  -574.737753 -2406.038000 -0.149361   \n",
       "17   711.433333  ...    -31.172020    78.461448  1067.428000  0.027446   \n",
       "18 -1716.823529  ...   6914.606454 -1212.504584 -4636.136471 -0.438984   \n",
       "19   -70.861345  ...    -67.052908   -17.567024   -85.714118 -0.004326   \n",
       "20  1002.251841  ...    196.384039   724.336969  1885.845773  0.106060   \n",
       "21  3743.734904  ...   2757.794154   941.613862  5124.282769  0.257979   \n",
       "22 -1261.680180  ...   3448.102249  -708.820358 -2967.248108 -0.345327   \n",
       "23  -673.477477  ...   -104.722962   -30.314589  -929.458378 -0.025594   \n",
       "24  -243.812308  ...   1188.957326  -369.553684  -858.223015 -0.096879   \n",
       "25  -557.627692  ...    293.366130   -35.261598  -895.403446 -0.048807   \n",
       "26  -931.155797  ...   4726.127954  -756.450644 -2797.483696 -0.298819   \n",
       "27  -672.905797  ...    195.609703   -65.742403 -1076.583913 -0.049661   \n",
       "28  -312.531300  ...   5331.416165  -521.399809 -1855.947544 -0.270297   \n",
       "29   -66.349920  ...    -44.421278    -7.963485   -65.894446 -0.001209   \n",
       "30   889.336842  ...   -340.299311   425.776510  1802.062834  0.109504   \n",
       "31 -1330.940081  ...   2204.688100  -293.366621 -2464.456324 -0.185236   \n",
       "32  4204.389175  ...   6675.648809   561.145709  3541.070103  0.009598   \n",
       "33   -90.363402  ...     -0.717648  -134.688914  -200.975567 -0.008867   \n",
       "34     8.030100  ...  -1474.473091    50.372745   425.478662  0.049452   \n",
       "35  -554.712375  ...   -715.664470   -71.890300  -597.288161 -0.001582   \n",
       "36     5.872057  ...   3441.771668  -473.897258 -1298.008066 -0.189540   \n",
       "37  -671.731832  ...  -3425.627965   -80.427483  -249.809662 -0.003305   \n",
       "38  -582.299517  ...    546.551970  -283.231140 -1161.401159 -0.064282   \n",
       "39   -99.608696  ...    447.098358    -4.244031   -93.482319 -0.006762   \n",
       "40  -801.123990  ...  -1785.121921  -332.677131  -913.965321 -0.017001   \n",
       "41   -33.353444  ...     -2.483039   -13.191737   -11.284447  0.001573   \n",
       "42   385.597037  ...    380.723326   205.608334   785.961600  0.068818   \n",
       "43  2523.118519  ...   6757.824166   471.838505  2192.555556  0.169469   \n",
       "44  -841.190011  ...   1564.671602  -257.712855 -1869.342932 -0.123781   \n",
       "45   -51.313789  ...   -874.281247   -77.251287   -34.391531 -0.000380   \n",
       "46   742.265677  ...   -781.951788   492.540873  1686.309802  0.110464   \n",
       "47 -1275.062706  ...   4703.209298  -703.669342 -3460.701683 -0.292417   \n",
       "48   657.034547  ...   -529.273590   447.144797  1442.678880  0.086714   \n",
       "49  -342.937442  ...    588.198542  -178.006926  -795.728067 -0.082315   \n",
       "50  1306.363948  ...  25223.354617  -656.559859 -4909.685264 -0.599642   \n",
       "51  -219.895819  ...   -353.758320   -46.250105  -186.660946  0.007041   \n",
       "52  -807.186885  ...  -1864.928652  -391.678318  -990.131410 -0.028583   \n",
       "53  -300.213115  ...   -379.777422   -79.124911  -318.773508  0.002646   \n",
       "54  2254.747227  ...   4434.866797    28.419566   457.317338 -0.170553   \n",
       "55   312.902536  ...    414.016524    44.993624   341.346656 -0.003609   \n",
       "56  1187.495964  ...   -664.916898   553.865682  2325.819552  0.097909   \n",
       "57  -731.956054  ...  -5609.027149    72.578841   341.411731  0.118290   \n",
       "\n",
       "             BSI       GLI  ALTERATION          SDI    manure_dates    y  \n",
       "0    -167.126240  0.004451    0.031185  -253.591837  ['2022-05-26']  0.0  \n",
       "1     563.443952 -0.112486   -0.159643 -3466.957483  ['2022-05-26']  1.0  \n",
       "2     -98.555306 -0.027062    0.016326 -1542.100475  ['2022-05-16']  1.0  \n",
       "3     122.433477 -0.017761   -0.012081  -296.157437  ['2022-05-16']  0.0  \n",
       "4     623.280480 -0.034264    0.015565 -1411.042735  ['2022-05-26']  1.0  \n",
       "5     214.956271 -0.012311   -0.021819  -228.722222  ['2022-05-26']  0.0  \n",
       "6      66.185380  0.052827    0.102936   811.411273  ['2022-05-26']  0.0  \n",
       "7    -252.311687 -0.042444   -0.017196 -1263.488518  ['2022-05-26']  1.0  \n",
       "8    -421.515077  0.014136    0.068701   -33.450893  ['2022-05-26']  0.0  \n",
       "9     217.153036 -0.029580   -0.035077  -956.986607  ['2022-05-26']  1.0  \n",
       "10   -589.446583 -0.015945   -0.022645  -184.952033  ['2022-05-16']  1.0  \n",
       "11   2857.413849  0.002512   -0.078719   627.694309  ['2022-05-16']  0.0  \n",
       "12    434.895449 -0.034837    0.013449  -809.166667  ['2022-05-26']  1.0  \n",
       "13     23.586055  0.027862    0.130011   363.124113  ['2022-05-26']  0.0  \n",
       "14    370.678243  0.034132    0.094812   587.823529  ['2022-05-16']  0.0  \n",
       "15    387.605472 -0.070324   -0.130830 -2531.545455  ['2022-05-16']  1.0  \n",
       "16   -540.355311 -0.054559   -0.092124 -1300.283333  ['2022-03-21']  1.0  \n",
       "17    937.886498  0.006794    0.008158   665.300000  ['2022-03-21']  0.0  \n",
       "18    553.337905 -0.124530   -0.037182 -2900.495798  ['2022-05-16']  1.0  \n",
       "19   -314.410649  0.000858    0.004660    59.815126  ['2022-05-16']  0.0  \n",
       "20    763.454290  0.047817    0.092775   928.010309  ['2022-05-03']  0.0  \n",
       "21   5799.938957  0.068217    0.323242  2940.132548  ['2022-05-03']  1.0  \n",
       "22   -374.020242 -0.077910   -0.018021 -1840.198198  ['2022-05-16']  1.0  \n",
       "23   -937.501564 -0.000611   -0.007340  -601.851351  ['2022-05-16']  0.0  \n",
       "24    298.356794 -0.043897   -0.006387  -606.609231  ['2022-05-16']  1.0  \n",
       "25   -555.519714 -0.006058   -0.017287  -622.529231  ['2022-05-16']  0.0  \n",
       "26    549.249046 -0.086645   -0.020925 -1722.449275  ['2022-05-16']  1.0  \n",
       "27   -754.373599 -0.003686   -0.028322  -682.911232  ['2022-05-16']  0.0  \n",
       "28   1462.977043 -0.070338    0.025704 -1088.133226  ['2022-05-16']  1.0  \n",
       "29   -310.683496  0.002756    0.004935    42.544141  ['2022-05-16']  0.0  \n",
       "30    528.776783  0.040626    0.092490   830.800000  ['2022-05-04']  0.0  \n",
       "31  -1187.303669 -0.034569   -0.035478 -1909.637247  ['2022-05-04']  1.0  \n",
       "32  10765.071034 -0.020885    0.148088  2745.814433  ['2022-02-10']  1.0  \n",
       "33     12.641877 -0.010386   -0.032610  -101.402062  ['2022-02-10']  0.0  \n",
       "34   -582.460782  0.021253    0.060969   361.063545  ['2022-05-16']  1.0  \n",
       "35  -1362.636350  0.003288   -0.024287  -354.342809  ['2022-05-16']  0.0  \n",
       "36   2144.188231 -0.040417   -0.045884  -748.460594  ['2022-03-17']  1.0  \n",
       "37  -2285.027681 -0.002536   -0.055014   104.019447  ['2022-03-17']  0.0  \n",
       "38    109.682598 -0.026868    0.016213  -930.118357  ['2022-05-16']  1.0  \n",
       "39    -99.897904 -0.005074    0.009840  -392.004831  ['2022-05-16']  0.0  \n",
       "40  -1333.788788 -0.011351    0.041657  -675.477435  ['2022-05-16']  1.0  \n",
       "41    -58.568191 -0.001250    0.005463  -135.724941  ['2022-05-16']  0.0  \n",
       "42    147.083120  0.015883    0.043514   233.248889  ['2022-05-04']  0.0  \n",
       "43   6325.715462  0.015344    0.171402   767.995556  ['2022-05-04']  1.0  \n",
       "44    661.769348 -0.037432   -0.050065 -1539.013029  ['2022-05-16']  1.0  \n",
       "45    260.246126 -0.009145   -0.041794  -163.501629  ['2022-05-16']  0.0  \n",
       "46     30.433072  0.047709    0.071993   859.867162  ['2022-05-26']  0.0  \n",
       "47   1134.441157 -0.100180   -0.038182 -2365.860561  ['2022-05-26']  1.0  \n",
       "48     82.552614  0.034506    0.083243   745.814192  ['2022-05-26']  0.0  \n",
       "49    180.152283 -0.019688   -0.092575  -740.719888  ['2022-05-26']  1.0  \n",
       "50  11772.242742 -0.109800   -0.310905 -1039.524332  ['2022-04-09']  1.0  \n",
       "51   -593.581544  0.001754   -0.002343  -149.614119  ['2022-04-09']  0.0  \n",
       "52  -1570.226567 -0.018065   -0.079463  -467.685246  ['2022-04-13']  1.0  \n",
       "53   -714.367504 -0.001683   -0.014659  -229.662295  ['2022-04-13']  0.0  \n",
       "54   8375.270056 -0.040809   -0.074039   953.386688  ['2022-02-07']  1.0  \n",
       "55    872.731294 -0.001521   -0.004834   106.905705  ['2022-02-07']  0.0  \n",
       "56    673.481308  0.053051    0.106713  1226.117489  ['2022-04-23']  0.0  \n",
       "57  -2811.743556  0.030338    0.135702   -55.923767  ['2022-04-23']  1.0  \n",
       "\n",
       "[58 rows x 65 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentinel-2\n",
    "s2_df_mod_bal = utils.get_balanced_df(s2_df_mod, method='under', random_state=10)\n",
    "s2_df_mod_bal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crop_field_name</th>\n",
       "      <th>consequent_s1_acquisitions</th>\n",
       "      <th>VV</th>\n",
       "      <th>VH</th>\n",
       "      <th>AVE</th>\n",
       "      <th>DIF</th>\n",
       "      <th>RAT1</th>\n",
       "      <th>RAT2</th>\n",
       "      <th>NDI</th>\n",
       "      <th>RVI</th>\n",
       "      <th>manure_dates</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.115553</td>\n",
       "      <td>-1.778969</td>\n",
       "      <td>-1.447261</td>\n",
       "      <td>0.663417</td>\n",
       "      <td>-0.011032</td>\n",
       "      <td>0.019916</td>\n",
       "      <td>-0.007252</td>\n",
       "      <td>0.014504</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-BLD</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>0.251519</td>\n",
       "      <td>-0.011654</td>\n",
       "      <td>0.119933</td>\n",
       "      <td>0.263173</td>\n",
       "      <td>-0.012674</td>\n",
       "      <td>0.023108</td>\n",
       "      <td>-0.008366</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-BLLT1</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.870915</td>\n",
       "      <td>-0.948468</td>\n",
       "      <td>-0.909692</td>\n",
       "      <td>0.077553</td>\n",
       "      <td>0.015179</td>\n",
       "      <td>-0.037681</td>\n",
       "      <td>0.011360</td>\n",
       "      <td>-0.022721</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-BLLT1</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>1.620705</td>\n",
       "      <td>1.942390</td>\n",
       "      <td>1.781547</td>\n",
       "      <td>-0.321686</td>\n",
       "      <td>-0.012321</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>-0.008309</td>\n",
       "      <td>0.016618</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-BLLT2</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.943051</td>\n",
       "      <td>-0.657796</td>\n",
       "      <td>-0.800423</td>\n",
       "      <td>-0.285255</td>\n",
       "      <td>0.023262</td>\n",
       "      <td>-0.049862</td>\n",
       "      <td>0.016424</td>\n",
       "      <td>-0.032847</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P-BLLT2</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>-0.428368</td>\n",
       "      <td>-0.350908</td>\n",
       "      <td>-0.389638</td>\n",
       "      <td>-0.077460</td>\n",
       "      <td>0.009377</td>\n",
       "      <td>-0.019689</td>\n",
       "      <td>0.006565</td>\n",
       "      <td>-0.013130</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P-CBRCS1</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.558735</td>\n",
       "      <td>-1.164879</td>\n",
       "      <td>-1.361807</td>\n",
       "      <td>-0.393857</td>\n",
       "      <td>0.040925</td>\n",
       "      <td>-0.087761</td>\n",
       "      <td>0.028895</td>\n",
       "      <td>-0.057790</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P-CBRCS1</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>1.579650</td>\n",
       "      <td>1.175115</td>\n",
       "      <td>1.377383</td>\n",
       "      <td>0.404535</td>\n",
       "      <td>-0.038201</td>\n",
       "      <td>0.073015</td>\n",
       "      <td>-0.025721</td>\n",
       "      <td>0.051443</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P-CBRCS2</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.333557</td>\n",
       "      <td>-2.568303</td>\n",
       "      <td>-1.950930</td>\n",
       "      <td>1.234745</td>\n",
       "      <td>-0.015689</td>\n",
       "      <td>0.039544</td>\n",
       "      <td>-0.011811</td>\n",
       "      <td>0.023623</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P-CBRCS2</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>-0.648566</td>\n",
       "      <td>-0.571513</td>\n",
       "      <td>-0.610039</td>\n",
       "      <td>-0.077053</td>\n",
       "      <td>0.014684</td>\n",
       "      <td>-0.032982</td>\n",
       "      <td>0.010565</td>\n",
       "      <td>-0.021130</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P-CLGT</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>0.125504</td>\n",
       "      <td>0.216900</td>\n",
       "      <td>0.171202</td>\n",
       "      <td>-0.091396</td>\n",
       "      <td>0.001644</td>\n",
       "      <td>-0.003240</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>-0.002242</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>P-CLGT</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-0.760434</td>\n",
       "      <td>-0.569523</td>\n",
       "      <td>-0.664978</td>\n",
       "      <td>-0.190911</td>\n",
       "      <td>0.020724</td>\n",
       "      <td>-0.038367</td>\n",
       "      <td>0.013769</td>\n",
       "      <td>-0.027539</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>P-CLMBRS</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.593221</td>\n",
       "      <td>-0.012942</td>\n",
       "      <td>-0.303081</td>\n",
       "      <td>-0.580279</td>\n",
       "      <td>0.031740</td>\n",
       "      <td>-0.063659</td>\n",
       "      <td>0.021806</td>\n",
       "      <td>-0.043611</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P-CLMBRS</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>2.503584</td>\n",
       "      <td>-0.451108</td>\n",
       "      <td>1.026238</td>\n",
       "      <td>2.954692</td>\n",
       "      <td>-0.149608</td>\n",
       "      <td>0.323717</td>\n",
       "      <td>-0.105730</td>\n",
       "      <td>0.211459</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>P-CMNTR</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.141977</td>\n",
       "      <td>0.071445</td>\n",
       "      <td>-0.535266</td>\n",
       "      <td>-1.213422</td>\n",
       "      <td>0.062659</td>\n",
       "      <td>-0.162313</td>\n",
       "      <td>0.047645</td>\n",
       "      <td>-0.095290</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P-CMNTR</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>1.000207</td>\n",
       "      <td>-0.960899</td>\n",
       "      <td>0.019654</td>\n",
       "      <td>1.961105</td>\n",
       "      <td>-0.085665</td>\n",
       "      <td>0.183548</td>\n",
       "      <td>-0.060418</td>\n",
       "      <td>0.120836</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>P-DR</td>\n",
       "      <td>[2022-03-09, 2022-03-21]</td>\n",
       "      <td>1.357863</td>\n",
       "      <td>2.012188</td>\n",
       "      <td>1.685026</td>\n",
       "      <td>-0.654326</td>\n",
       "      <td>-0.000661</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>-0.000475</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>['2022-03-21']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>P-DR</td>\n",
       "      <td>[2022-04-14, 2022-04-26]</td>\n",
       "      <td>0.237871</td>\n",
       "      <td>0.776548</td>\n",
       "      <td>0.507209</td>\n",
       "      <td>-0.538678</td>\n",
       "      <td>0.016985</td>\n",
       "      <td>-0.032316</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>-0.022833</td>\n",
       "      <td>['2022-03-21']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>P-FNFR</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.706521</td>\n",
       "      <td>-0.709543</td>\n",
       "      <td>-0.708032</td>\n",
       "      <td>0.003022</td>\n",
       "      <td>0.009693</td>\n",
       "      <td>-0.017536</td>\n",
       "      <td>0.006378</td>\n",
       "      <td>-0.012756</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>P-FNFR</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-0.540933</td>\n",
       "      <td>-0.585093</td>\n",
       "      <td>-0.563013</td>\n",
       "      <td>0.044160</td>\n",
       "      <td>0.006044</td>\n",
       "      <td>-0.011754</td>\n",
       "      <td>0.004100</td>\n",
       "      <td>-0.008200</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>P-LLT</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.046799</td>\n",
       "      <td>-1.516100</td>\n",
       "      <td>-0.781449</td>\n",
       "      <td>1.469301</td>\n",
       "      <td>-0.057994</td>\n",
       "      <td>0.132613</td>\n",
       "      <td>-0.042006</td>\n",
       "      <td>0.084013</td>\n",
       "      <td>['2022-05-03']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>P-LLT</td>\n",
       "      <td>[2022-04-26, 2022-05-08]</td>\n",
       "      <td>0.122826</td>\n",
       "      <td>0.182352</td>\n",
       "      <td>0.152589</td>\n",
       "      <td>-0.059525</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>-0.000914</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>-0.000637</td>\n",
       "      <td>['2022-05-03']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>P-LNDRS1</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.277670</td>\n",
       "      <td>-3.064989</td>\n",
       "      <td>-2.171330</td>\n",
       "      <td>1.787319</td>\n",
       "      <td>-0.061497</td>\n",
       "      <td>0.104003</td>\n",
       "      <td>-0.039290</td>\n",
       "      <td>0.078579</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>P-LNDRS1</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-0.928625</td>\n",
       "      <td>-1.600201</td>\n",
       "      <td>-1.264413</td>\n",
       "      <td>0.671576</td>\n",
       "      <td>-0.000757</td>\n",
       "      <td>0.002165</td>\n",
       "      <td>-0.000598</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>P-LNDRS2</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.182680</td>\n",
       "      <td>-2.524879</td>\n",
       "      <td>-1.853779</td>\n",
       "      <td>1.342199</td>\n",
       "      <td>-0.027679</td>\n",
       "      <td>0.063065</td>\n",
       "      <td>-0.020027</td>\n",
       "      <td>0.040054</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>P-LNDRS2</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-0.730043</td>\n",
       "      <td>-3.035945</td>\n",
       "      <td>-1.882994</td>\n",
       "      <td>2.305903</td>\n",
       "      <td>-0.066755</td>\n",
       "      <td>0.138298</td>\n",
       "      <td>-0.046457</td>\n",
       "      <td>0.092915</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>P-LNDRS3</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.599114</td>\n",
       "      <td>-1.187353</td>\n",
       "      <td>-1.393234</td>\n",
       "      <td>-0.411761</td>\n",
       "      <td>0.047182</td>\n",
       "      <td>-0.107589</td>\n",
       "      <td>0.034142</td>\n",
       "      <td>-0.068285</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>P-LNDRS3</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-1.886422</td>\n",
       "      <td>-1.817239</td>\n",
       "      <td>-1.851831</td>\n",
       "      <td>-0.069183</td>\n",
       "      <td>0.035654</td>\n",
       "      <td>-0.084929</td>\n",
       "      <td>0.026253</td>\n",
       "      <td>-0.052506</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>P-LNDRS4</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.417689</td>\n",
       "      <td>-1.044122</td>\n",
       "      <td>-1.230905</td>\n",
       "      <td>-0.373567</td>\n",
       "      <td>0.038445</td>\n",
       "      <td>-0.087757</td>\n",
       "      <td>0.027835</td>\n",
       "      <td>-0.055669</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>P-LNDRS4</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-1.091805</td>\n",
       "      <td>-4.920665</td>\n",
       "      <td>-3.006235</td>\n",
       "      <td>3.828860</td>\n",
       "      <td>-0.097053</td>\n",
       "      <td>0.219154</td>\n",
       "      <td>-0.069889</td>\n",
       "      <td>0.139778</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>P-MT</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>0.233439</td>\n",
       "      <td>0.670460</td>\n",
       "      <td>0.451950</td>\n",
       "      <td>-0.437021</td>\n",
       "      <td>0.012040</td>\n",
       "      <td>-0.025017</td>\n",
       "      <td>0.008394</td>\n",
       "      <td>-0.016787</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>P-MT</td>\n",
       "      <td>[2022-04-26, 2022-05-08]</td>\n",
       "      <td>1.324789</td>\n",
       "      <td>1.172469</td>\n",
       "      <td>1.248629</td>\n",
       "      <td>0.152320</td>\n",
       "      <td>-0.027329</td>\n",
       "      <td>0.053972</td>\n",
       "      <td>-0.018656</td>\n",
       "      <td>0.037311</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>P-NMS</td>\n",
       "      <td>[2022-02-01, 2022-02-13]</td>\n",
       "      <td>-0.208391</td>\n",
       "      <td>-0.397787</td>\n",
       "      <td>-0.303089</td>\n",
       "      <td>0.189396</td>\n",
       "      <td>-0.002363</td>\n",
       "      <td>0.005857</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>0.003535</td>\n",
       "      <td>['2022-02-10']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>P-NMS</td>\n",
       "      <td>[2022-04-14, 2022-04-26]</td>\n",
       "      <td>-0.766308</td>\n",
       "      <td>-1.364623</td>\n",
       "      <td>-1.065466</td>\n",
       "      <td>0.598315</td>\n",
       "      <td>-0.008508</td>\n",
       "      <td>0.018252</td>\n",
       "      <td>-0.006009</td>\n",
       "      <td>0.012018</td>\n",
       "      <td>['2022-02-10']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>P-QNTLS2</td>\n",
       "      <td>[2022-05-13, 2022-05-20]</td>\n",
       "      <td>1.453882</td>\n",
       "      <td>-1.698040</td>\n",
       "      <td>-0.122079</td>\n",
       "      <td>3.151921</td>\n",
       "      <td>-0.156539</td>\n",
       "      <td>0.326074</td>\n",
       "      <td>-0.108911</td>\n",
       "      <td>0.217821</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>P-QNTLS2</td>\n",
       "      <td>[2022-07-12, 2022-07-19]</td>\n",
       "      <td>1.423832</td>\n",
       "      <td>1.419204</td>\n",
       "      <td>1.421518</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>-0.024773</td>\n",
       "      <td>0.048554</td>\n",
       "      <td>-0.016858</td>\n",
       "      <td>0.033716</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>P-SNTLLN</td>\n",
       "      <td>[2022-03-09, 2022-03-21]</td>\n",
       "      <td>-1.868089</td>\n",
       "      <td>-0.908963</td>\n",
       "      <td>-1.388526</td>\n",
       "      <td>-0.959126</td>\n",
       "      <td>0.073091</td>\n",
       "      <td>-0.181845</td>\n",
       "      <td>0.054708</td>\n",
       "      <td>-0.109417</td>\n",
       "      <td>['2022-03-17']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>P-SNTLLN</td>\n",
       "      <td>[2022-04-14, 2022-04-26]</td>\n",
       "      <td>0.347733</td>\n",
       "      <td>-1.220868</td>\n",
       "      <td>-0.436567</td>\n",
       "      <td>1.568600</td>\n",
       "      <td>-0.053718</td>\n",
       "      <td>0.161580</td>\n",
       "      <td>-0.043201</td>\n",
       "      <td>0.086402</td>\n",
       "      <td>['2022-03-17']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>P-SNVCNT1</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.776342</td>\n",
       "      <td>-0.281233</td>\n",
       "      <td>-1.028788</td>\n",
       "      <td>-1.495109</td>\n",
       "      <td>0.075628</td>\n",
       "      <td>-0.167282</td>\n",
       "      <td>0.054040</td>\n",
       "      <td>-0.108079</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>P-SNVCNT1</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>0.052351</td>\n",
       "      <td>-0.518415</td>\n",
       "      <td>-0.233032</td>\n",
       "      <td>0.570766</td>\n",
       "      <td>-0.022122</td>\n",
       "      <td>0.042519</td>\n",
       "      <td>-0.014932</td>\n",
       "      <td>0.029863</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>P-SNVCNT2</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.670216</td>\n",
       "      <td>-0.680815</td>\n",
       "      <td>-0.675515</td>\n",
       "      <td>0.010599</td>\n",
       "      <td>0.009333</td>\n",
       "      <td>-0.019114</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>-0.012937</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>P-SNVCNT2</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>-0.519734</td>\n",
       "      <td>-1.169996</td>\n",
       "      <td>-0.844865</td>\n",
       "      <td>0.650262</td>\n",
       "      <td>-0.016424</td>\n",
       "      <td>0.032030</td>\n",
       "      <td>-0.011154</td>\n",
       "      <td>0.022307</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>P-STBN</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.709278</td>\n",
       "      <td>-0.471898</td>\n",
       "      <td>-0.590588</td>\n",
       "      <td>-0.237380</td>\n",
       "      <td>0.022147</td>\n",
       "      <td>-0.047958</td>\n",
       "      <td>0.015701</td>\n",
       "      <td>-0.031402</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>P-STBN</td>\n",
       "      <td>[2022-04-26, 2022-05-08]</td>\n",
       "      <td>-0.102179</td>\n",
       "      <td>-0.046946</td>\n",
       "      <td>-0.074563</td>\n",
       "      <td>-0.055233</td>\n",
       "      <td>0.003921</td>\n",
       "      <td>-0.007468</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>-0.005273</td>\n",
       "      <td>['2022-05-04']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>P-TGL2</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-0.817114</td>\n",
       "      <td>-0.052753</td>\n",
       "      <td>-0.434934</td>\n",
       "      <td>-0.764361</td>\n",
       "      <td>0.042043</td>\n",
       "      <td>-0.094101</td>\n",
       "      <td>0.030200</td>\n",
       "      <td>-0.060400</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>P-TGL2</td>\n",
       "      <td>[2022-05-08, 2022-05-20]</td>\n",
       "      <td>1.172942</td>\n",
       "      <td>1.050218</td>\n",
       "      <td>1.111580</td>\n",
       "      <td>0.122724</td>\n",
       "      <td>-0.020961</td>\n",
       "      <td>0.038473</td>\n",
       "      <td>-0.013876</td>\n",
       "      <td>0.027752</td>\n",
       "      <td>['2022-05-16']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>P-TNNS1</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.729251</td>\n",
       "      <td>-2.579826</td>\n",
       "      <td>-2.154538</td>\n",
       "      <td>0.850574</td>\n",
       "      <td>-0.009095</td>\n",
       "      <td>0.016785</td>\n",
       "      <td>-0.006035</td>\n",
       "      <td>0.012070</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>P-TNNS1</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>-0.300308</td>\n",
       "      <td>-4.782761</td>\n",
       "      <td>-2.541535</td>\n",
       "      <td>4.482453</td>\n",
       "      <td>-0.125656</td>\n",
       "      <td>0.330887</td>\n",
       "      <td>-0.095971</td>\n",
       "      <td>0.191942</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>P-TNNS2</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>-1.738262</td>\n",
       "      <td>-0.241261</td>\n",
       "      <td>-0.989761</td>\n",
       "      <td>-1.497001</td>\n",
       "      <td>0.087156</td>\n",
       "      <td>-0.190860</td>\n",
       "      <td>0.062011</td>\n",
       "      <td>-0.124023</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>P-TNNS2</td>\n",
       "      <td>[2022-05-20, 2022-06-01]</td>\n",
       "      <td>-0.075190</td>\n",
       "      <td>-0.885237</td>\n",
       "      <td>-0.480213</td>\n",
       "      <td>0.810047</td>\n",
       "      <td>-0.025776</td>\n",
       "      <td>0.051789</td>\n",
       "      <td>-0.017722</td>\n",
       "      <td>0.035444</td>\n",
       "      <td>['2022-05-26']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>P-VG1</td>\n",
       "      <td>[2022-04-07, 2022-04-14]</td>\n",
       "      <td>0.276720</td>\n",
       "      <td>1.022237</td>\n",
       "      <td>0.649478</td>\n",
       "      <td>-0.745517</td>\n",
       "      <td>0.027329</td>\n",
       "      <td>-0.052872</td>\n",
       "      <td>0.018496</td>\n",
       "      <td>-0.036993</td>\n",
       "      <td>['2022-04-09']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>P-VG1</td>\n",
       "      <td>[2022-07-12, 2022-07-19]</td>\n",
       "      <td>2.354669</td>\n",
       "      <td>0.569833</td>\n",
       "      <td>1.462251</td>\n",
       "      <td>1.784836</td>\n",
       "      <td>-0.103404</td>\n",
       "      <td>0.252743</td>\n",
       "      <td>-0.076807</td>\n",
       "      <td>0.153614</td>\n",
       "      <td>['2022-04-09']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>P-VG2</td>\n",
       "      <td>[2022-04-07, 2022-04-14]</td>\n",
       "      <td>1.959829</td>\n",
       "      <td>0.919831</td>\n",
       "      <td>1.439830</td>\n",
       "      <td>1.039999</td>\n",
       "      <td>-0.091460</td>\n",
       "      <td>0.270908</td>\n",
       "      <td>-0.073072</td>\n",
       "      <td>0.146145</td>\n",
       "      <td>['2022-04-13']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>P-VG2</td>\n",
       "      <td>[2022-07-12, 2022-07-19]</td>\n",
       "      <td>-0.800472</td>\n",
       "      <td>0.363599</td>\n",
       "      <td>-0.218437</td>\n",
       "      <td>-1.164071</td>\n",
       "      <td>0.058835</td>\n",
       "      <td>-0.207522</td>\n",
       "      <td>0.050071</td>\n",
       "      <td>-0.100142</td>\n",
       "      <td>['2022-04-13']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>P-VLDMR</td>\n",
       "      <td>[2022-02-01, 2022-02-13]</td>\n",
       "      <td>0.141535</td>\n",
       "      <td>-0.153090</td>\n",
       "      <td>-0.005777</td>\n",
       "      <td>0.294625</td>\n",
       "      <td>-0.012239</td>\n",
       "      <td>0.036568</td>\n",
       "      <td>-0.009823</td>\n",
       "      <td>0.019647</td>\n",
       "      <td>['2022-02-07']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>P-VLDMR</td>\n",
       "      <td>[2022-04-14, 2022-04-26]</td>\n",
       "      <td>1.404173</td>\n",
       "      <td>0.535078</td>\n",
       "      <td>0.969626</td>\n",
       "      <td>0.869095</td>\n",
       "      <td>-0.057199</td>\n",
       "      <td>0.120535</td>\n",
       "      <td>-0.040091</td>\n",
       "      <td>0.080182</td>\n",
       "      <td>['2022-02-07']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-04-02, 2022-04-14]</td>\n",
       "      <td>1.683667</td>\n",
       "      <td>0.784324</td>\n",
       "      <td>1.233995</td>\n",
       "      <td>0.899343</td>\n",
       "      <td>-0.061932</td>\n",
       "      <td>0.114326</td>\n",
       "      <td>-0.041082</td>\n",
       "      <td>0.082164</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>P-VNS</td>\n",
       "      <td>[2022-04-14, 2022-04-26]</td>\n",
       "      <td>-0.219109</td>\n",
       "      <td>0.102404</td>\n",
       "      <td>-0.058353</td>\n",
       "      <td>-0.321513</td>\n",
       "      <td>0.016783</td>\n",
       "      <td>-0.032916</td>\n",
       "      <td>0.011424</td>\n",
       "      <td>-0.022849</td>\n",
       "      <td>['2022-04-23']</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   crop_field_name consequent_s1_acquisitions        VV        VH       AVE  \\\n",
       "0            P-BLD   [2022-04-02, 2022-04-14] -1.115553 -1.778969 -1.447261   \n",
       "1            P-BLD   [2022-05-20, 2022-06-01]  0.251519 -0.011654  0.119933   \n",
       "2          P-BLLT1   [2022-04-02, 2022-04-14] -0.870915 -0.948468 -0.909692   \n",
       "3          P-BLLT1   [2022-05-08, 2022-05-20]  1.620705  1.942390  1.781547   \n",
       "4          P-BLLT2   [2022-04-02, 2022-04-14] -0.943051 -0.657796 -0.800423   \n",
       "5          P-BLLT2   [2022-05-20, 2022-06-01] -0.428368 -0.350908 -0.389638   \n",
       "6         P-CBRCS1   [2022-04-02, 2022-04-14] -1.558735 -1.164879 -1.361807   \n",
       "7         P-CBRCS1   [2022-05-20, 2022-06-01]  1.579650  1.175115  1.377383   \n",
       "8         P-CBRCS2   [2022-04-02, 2022-04-14] -1.333557 -2.568303 -1.950930   \n",
       "9         P-CBRCS2   [2022-05-20, 2022-06-01] -0.648566 -0.571513 -0.610039   \n",
       "10          P-CLGT   [2022-04-02, 2022-04-14]  0.125504  0.216900  0.171202   \n",
       "11          P-CLGT   [2022-05-08, 2022-05-20] -0.760434 -0.569523 -0.664978   \n",
       "12        P-CLMBRS   [2022-04-02, 2022-04-14] -0.593221 -0.012942 -0.303081   \n",
       "13        P-CLMBRS   [2022-05-20, 2022-06-01]  2.503584 -0.451108  1.026238   \n",
       "14         P-CMNTR   [2022-04-02, 2022-04-14] -1.141977  0.071445 -0.535266   \n",
       "15         P-CMNTR   [2022-05-08, 2022-05-20]  1.000207 -0.960899  0.019654   \n",
       "16            P-DR   [2022-03-09, 2022-03-21]  1.357863  2.012188  1.685026   \n",
       "17            P-DR   [2022-04-14, 2022-04-26]  0.237871  0.776548  0.507209   \n",
       "18          P-FNFR   [2022-04-02, 2022-04-14] -0.706521 -0.709543 -0.708032   \n",
       "19          P-FNFR   [2022-05-08, 2022-05-20] -0.540933 -0.585093 -0.563013   \n",
       "20           P-LLT   [2022-04-02, 2022-04-14] -0.046799 -1.516100 -0.781449   \n",
       "21           P-LLT   [2022-04-26, 2022-05-08]  0.122826  0.182352  0.152589   \n",
       "22        P-LNDRS1   [2022-04-02, 2022-04-14] -1.277670 -3.064989 -2.171330   \n",
       "23        P-LNDRS1   [2022-05-08, 2022-05-20] -0.928625 -1.600201 -1.264413   \n",
       "24        P-LNDRS2   [2022-04-02, 2022-04-14] -1.182680 -2.524879 -1.853779   \n",
       "25        P-LNDRS2   [2022-05-08, 2022-05-20] -0.730043 -3.035945 -1.882994   \n",
       "26        P-LNDRS3   [2022-04-02, 2022-04-14] -1.599114 -1.187353 -1.393234   \n",
       "27        P-LNDRS3   [2022-05-08, 2022-05-20] -1.886422 -1.817239 -1.851831   \n",
       "28        P-LNDRS4   [2022-04-02, 2022-04-14] -1.417689 -1.044122 -1.230905   \n",
       "29        P-LNDRS4   [2022-05-08, 2022-05-20] -1.091805 -4.920665 -3.006235   \n",
       "30            P-MT   [2022-04-02, 2022-04-14]  0.233439  0.670460  0.451950   \n",
       "31            P-MT   [2022-04-26, 2022-05-08]  1.324789  1.172469  1.248629   \n",
       "32           P-NMS   [2022-02-01, 2022-02-13] -0.208391 -0.397787 -0.303089   \n",
       "33           P-NMS   [2022-04-14, 2022-04-26] -0.766308 -1.364623 -1.065466   \n",
       "34        P-QNTLS2   [2022-05-13, 2022-05-20]  1.453882 -1.698040 -0.122079   \n",
       "35        P-QNTLS2   [2022-07-12, 2022-07-19]  1.423832  1.419204  1.421518   \n",
       "36        P-SNTLLN   [2022-03-09, 2022-03-21] -1.868089 -0.908963 -1.388526   \n",
       "37        P-SNTLLN   [2022-04-14, 2022-04-26]  0.347733 -1.220868 -0.436567   \n",
       "38       P-SNVCNT1   [2022-04-02, 2022-04-14] -1.776342 -0.281233 -1.028788   \n",
       "39       P-SNVCNT1   [2022-05-08, 2022-05-20]  0.052351 -0.518415 -0.233032   \n",
       "40       P-SNVCNT2   [2022-04-02, 2022-04-14] -0.670216 -0.680815 -0.675515   \n",
       "41       P-SNVCNT2   [2022-05-08, 2022-05-20] -0.519734 -1.169996 -0.844865   \n",
       "42          P-STBN   [2022-04-02, 2022-04-14] -0.709278 -0.471898 -0.590588   \n",
       "43          P-STBN   [2022-04-26, 2022-05-08] -0.102179 -0.046946 -0.074563   \n",
       "44          P-TGL2   [2022-04-02, 2022-04-14] -0.817114 -0.052753 -0.434934   \n",
       "45          P-TGL2   [2022-05-08, 2022-05-20]  1.172942  1.050218  1.111580   \n",
       "46         P-TNNS1   [2022-04-02, 2022-04-14] -1.729251 -2.579826 -2.154538   \n",
       "47         P-TNNS1   [2022-05-20, 2022-06-01] -0.300308 -4.782761 -2.541535   \n",
       "48         P-TNNS2   [2022-04-02, 2022-04-14] -1.738262 -0.241261 -0.989761   \n",
       "49         P-TNNS2   [2022-05-20, 2022-06-01] -0.075190 -0.885237 -0.480213   \n",
       "50           P-VG1   [2022-04-07, 2022-04-14]  0.276720  1.022237  0.649478   \n",
       "51           P-VG1   [2022-07-12, 2022-07-19]  2.354669  0.569833  1.462251   \n",
       "52           P-VG2   [2022-04-07, 2022-04-14]  1.959829  0.919831  1.439830   \n",
       "53           P-VG2   [2022-07-12, 2022-07-19] -0.800472  0.363599 -0.218437   \n",
       "54         P-VLDMR   [2022-02-01, 2022-02-13]  0.141535 -0.153090 -0.005777   \n",
       "55         P-VLDMR   [2022-04-14, 2022-04-26]  1.404173  0.535078  0.969626   \n",
       "56           P-VNS   [2022-04-02, 2022-04-14]  1.683667  0.784324  1.233995   \n",
       "57           P-VNS   [2022-04-14, 2022-04-26] -0.219109  0.102404 -0.058353   \n",
       "\n",
       "         DIF      RAT1      RAT2       NDI       RVI    manure_dates    y  \n",
       "0   0.663417 -0.011032  0.019916 -0.007252  0.014504  ['2022-05-26']  0.0  \n",
       "1   0.263173 -0.012674  0.023108 -0.008366  0.016733  ['2022-05-26']  1.0  \n",
       "2   0.077553  0.015179 -0.037681  0.011360 -0.022721  ['2022-05-16']  0.0  \n",
       "3  -0.321686 -0.012321  0.023630 -0.008309  0.016618  ['2022-05-16']  1.0  \n",
       "4  -0.285255  0.023262 -0.049862  0.016424 -0.032847  ['2022-05-26']  0.0  \n",
       "5  -0.077460  0.009377 -0.019689  0.006565 -0.013130  ['2022-05-26']  1.0  \n",
       "6  -0.393857  0.040925 -0.087761  0.028895 -0.057790  ['2022-05-26']  0.0  \n",
       "7   0.404535 -0.038201  0.073015 -0.025721  0.051443  ['2022-05-26']  1.0  \n",
       "8   1.234745 -0.015689  0.039544 -0.011811  0.023623  ['2022-05-26']  0.0  \n",
       "9  -0.077053  0.014684 -0.032982  0.010565 -0.021130  ['2022-05-26']  1.0  \n",
       "10 -0.091396  0.001644 -0.003240  0.001121 -0.002242  ['2022-05-16']  0.0  \n",
       "11 -0.190911  0.020724 -0.038367  0.013769 -0.027539  ['2022-05-16']  1.0  \n",
       "12 -0.580279  0.031740 -0.063659  0.021806 -0.043611  ['2022-05-26']  0.0  \n",
       "13  2.954692 -0.149608  0.323717 -0.105730  0.211459  ['2022-05-26']  1.0  \n",
       "14 -1.213422  0.062659 -0.162313  0.047645 -0.095290  ['2022-05-16']  0.0  \n",
       "15  1.961105 -0.085665  0.183548 -0.060418  0.120836  ['2022-05-16']  1.0  \n",
       "16 -0.654326 -0.000661  0.001478 -0.000475  0.000949  ['2022-03-21']  1.0  \n",
       "17 -0.538678  0.016985 -0.032316  0.011416 -0.022833  ['2022-03-21']  0.0  \n",
       "18  0.003022  0.009693 -0.017536  0.006378 -0.012756  ['2022-05-16']  0.0  \n",
       "19  0.044160  0.006044 -0.011754  0.004100 -0.008200  ['2022-05-16']  1.0  \n",
       "20  1.469301 -0.057994  0.132613 -0.042006  0.084013  ['2022-05-03']  0.0  \n",
       "21 -0.059525  0.000469 -0.000914  0.000319 -0.000637  ['2022-05-03']  1.0  \n",
       "22  1.787319 -0.061497  0.104003 -0.039290  0.078579  ['2022-05-16']  0.0  \n",
       "23  0.671576 -0.000757  0.002165 -0.000598  0.001195  ['2022-05-16']  1.0  \n",
       "24  1.342199 -0.027679  0.063065 -0.020027  0.040054  ['2022-05-16']  0.0  \n",
       "25  2.305903 -0.066755  0.138298 -0.046457  0.092915  ['2022-05-16']  1.0  \n",
       "26 -0.411761  0.047182 -0.107589  0.034142 -0.068285  ['2022-05-16']  0.0  \n",
       "27 -0.069183  0.035654 -0.084929  0.026253 -0.052506  ['2022-05-16']  1.0  \n",
       "28 -0.373567  0.038445 -0.087757  0.027835 -0.055669  ['2022-05-16']  0.0  \n",
       "29  3.828860 -0.097053  0.219154 -0.069889  0.139778  ['2022-05-16']  1.0  \n",
       "30 -0.437021  0.012040 -0.025017  0.008394 -0.016787  ['2022-05-04']  0.0  \n",
       "31  0.152320 -0.027329  0.053972 -0.018656  0.037311  ['2022-05-04']  1.0  \n",
       "32  0.189396 -0.002363  0.005857 -0.001768  0.003535  ['2022-02-10']  1.0  \n",
       "33  0.598315 -0.008508  0.018252 -0.006009  0.012018  ['2022-02-10']  0.0  \n",
       "34  3.151921 -0.156539  0.326074 -0.108911  0.217821  ['2022-05-16']  1.0  \n",
       "35  0.004628 -0.024773  0.048554 -0.016858  0.033716  ['2022-05-16']  0.0  \n",
       "36 -0.959126  0.073091 -0.181845  0.054708 -0.109417  ['2022-03-17']  1.0  \n",
       "37  1.568600 -0.053718  0.161580 -0.043201  0.086402  ['2022-03-17']  0.0  \n",
       "38 -1.495109  0.075628 -0.167282  0.054040 -0.108079  ['2022-05-16']  0.0  \n",
       "39  0.570766 -0.022122  0.042519 -0.014932  0.029863  ['2022-05-16']  1.0  \n",
       "40  0.010599  0.009333 -0.019114  0.006468 -0.012937  ['2022-05-16']  0.0  \n",
       "41  0.650262 -0.016424  0.032030 -0.011154  0.022307  ['2022-05-16']  1.0  \n",
       "42 -0.237380  0.022147 -0.047958  0.015701 -0.031402  ['2022-05-04']  0.0  \n",
       "43 -0.055233  0.003921 -0.007468  0.002637 -0.005273  ['2022-05-04']  1.0  \n",
       "44 -0.764361  0.042043 -0.094101  0.030200 -0.060400  ['2022-05-16']  0.0  \n",
       "45  0.122724 -0.020961  0.038473 -0.013876  0.027752  ['2022-05-16']  1.0  \n",
       "46  0.850574 -0.009095  0.016785 -0.006035  0.012070  ['2022-05-26']  0.0  \n",
       "47  4.482453 -0.125656  0.330887 -0.095971  0.191942  ['2022-05-26']  1.0  \n",
       "48 -1.497001  0.087156 -0.190860  0.062011 -0.124023  ['2022-05-26']  0.0  \n",
       "49  0.810047 -0.025776  0.051789 -0.017722  0.035444  ['2022-05-26']  1.0  \n",
       "50 -0.745517  0.027329 -0.052872  0.018496 -0.036993  ['2022-04-09']  1.0  \n",
       "51  1.784836 -0.103404  0.252743 -0.076807  0.153614  ['2022-04-09']  0.0  \n",
       "52  1.039999 -0.091460  0.270908 -0.073072  0.146145  ['2022-04-13']  1.0  \n",
       "53 -1.164071  0.058835 -0.207522  0.050071 -0.100142  ['2022-04-13']  0.0  \n",
       "54  0.294625 -0.012239  0.036568 -0.009823  0.019647  ['2022-02-07']  1.0  \n",
       "55  0.869095 -0.057199  0.120535 -0.040091  0.080182  ['2022-02-07']  0.0  \n",
       "56  0.899343 -0.061932  0.114326 -0.041082  0.082164  ['2022-04-23']  0.0  \n",
       "57 -0.321513  0.016783 -0.032916  0.011424 -0.022849  ['2022-04-23']  1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentinel-1\n",
    "s1_df_mod_bal = utils.get_balanced_df(s1_df_mod, method='under', random_state=10)\n",
    "s1_df_mod_bal"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Models (for now just using Sentinel-2 data)**\n",
    "\n",
    "Now you must select which set of `optical indexes` will be used in the models, the number of `KFolds` and finally the `Scaler` to use.\n",
    "\n",
    "### Optical features subset selection\n",
    "**[Wrapper methods](https://towardsdatascience.com/feature-selection-for-machine-learning-in-python-wrapper-methods-2b5e27d2db31)**. <br>\n",
    "> *\"Evaluate multiple models using procedures that add and/or remove predictors to find the optimal combination that maximizes model performance. These procedures are normally built after the concept of Greedy Search technique (or algorithm). A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage.\"* - Cit. Jack Yee Tan\n",
    "\n",
    "We already ranked in terms of importance, which are the optical features most impacted by manure application in the analysis notebook.<br>\n",
    "The objective now is to consider a subset of them (and also modify this subset), in order to see which are the ones improving most the overall performances (without incurring into neither [overfitting nor overfitting](https://www.v7labs.com/blog/overfitting-vs-underfitting) issues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bc8d6b4bd454b3cb1cad4d63f87ca94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "SelectMultiple(description='Features', index=(40, 48, 38, 60), options=('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2_features_widget = ipywidgets.SelectMultiple(options=s2_df.select_dtypes(include=np.number).columns, value=['EOMI3', 'SCI', 'EOMI1', 'SDI'], description='Features')\n",
    "s2_features_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features list: ['EOMI3', 'SCI', 'EOMI1', 'SDI']\n"
     ]
    }
   ],
   "source": [
    "print('Selected features list: ' + str(list(s2_features_widget.value)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K selection (for KFold Cross-Validation)\n",
    "KFold cross validation is a method for evaluating the performance of a machine learning model by dividing the data into `K` subsets or folds. The algorithm is trained on `K-1` folds and tested on the remaining fold, and this process is repeated `K` times, with each fold used as the test set exactly once. The performance metric is then averaged across all `K` folds to provide an estimate of the model's performance on unseen data.\n",
    "\n",
    "KFold cross validation is used instead of the classical train and test split because it provides a more reliable estimate of the model's performance. The classical train and test split can be biased depending on the way the data is divided, which can result in overfitting or underfitting. KFold cross validation helps to **mitigate this bias** by using all data for both training and testing.\n",
    "\n",
    "Choosing the right `K` value is important because it can affect the reliability of the performance estimate. A value that is too low can result in a high variance estimate, meaning the estimate can be sensitive to the particular way the data is split into folds. Whereas, a value that is too high can result in a high bias estimate, meaning the estimate may be less accurate due to the larger training sets used in each fold.\n",
    "\n",
    "In general, a `K` value of $5$ or $10$ is often used for KFold cross validation, although the optimal value may depend on the size and complexity of the dataset. It is important to experiment with different values and evaluate the performance of the model on different folds to choose the optimal one.\n",
    "\n",
    "**Why opting for Stratified KFold?**\n",
    "Standard KFold randomly splits the data into `K` folds without considering the class distribution. This can result in a fold that contains a disproportionate number of instances from one class, which can lead to overfitting or underfitting of the model. In contrast, Stratified KFold ensures that each fold contains a proportional number of instances from each class. This is achieved by dividing the data into folds while preserving the percentage of samples for each class.\n",
    "\n",
    "If you choose `K` as the maximum pickable value this will mean using [*Leave One Out Cross-Validation*](https://www.baeldung.com/cs/cross-validation-k-fold-loo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3cb4e7a348949328ef4f281b4401b2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=5, description='KFolds', max=29, min=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kfolds_widget = ipywidgets.IntSlider(value=5, min=1, max=len(s2_df.crop_field_name.unique()), step=1, description='KFolds')\n",
    "kfolds_widget"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization method selection\n",
    "Normalization is typically needed before building machine learning models because many machine learning algorithms are sensitive to the scale of the input features. Normalization can help to improve the performance of machine learning models by ensuring that the input features have a similar scale.\n",
    "**It is very important to keep the statistics computed on the training data and reuse them also to normalize test and validation data.**\n",
    "\n",
    "Here are some reasons why normalization is important in machine learning:\n",
    "* **Better performances:** Normalization can help to improve the performance of machine learning algorithms. Some machine learning algorithms are based on distance metrics that are affected by the scale of the input features. If the input features have different scales, the algorithm may be biased towards features with larger scales, leading to suboptimal performance.\n",
    "* **Faster convergence:** Normalization can help machine learning algorithms to converge more quickly. Some optimization algorithms, such as gradient descent, converge faster when the input features have a similar scale.\n",
    "* **Improved interpretability:** Normalization can improve the interpretability of machine learning models. When the input features have vastly different scales, it can be difficult to interpret the coefficients of the model or the importance of the features.\n",
    "\n",
    "There are several normalization techniques that can be used, each one with its own advantages and drawbacks.\n",
    "\n",
    "#### Min-Max scaling\n",
    "One of the simplest normalization technique consists in scaling all the data in such a way that all features have values in the same range, typically between $0$ and $1$ (using a different range is trivial). <br>\n",
    "This techniques is called min-max scaling and it is based on the computation of the minimum $m_j$ and the maximum $M_j$ values for each feature ($j=0,1,...,n−1$). Then each feature of an input feature vector $x$ are normalized by applying the following linear scaling:\n",
    " * $$\\bar{x}_j = \\frac{x_j − m_j}{M_j − m_j}$$\n",
    "\n",
    "This ensures that all the training features assume values between $0$ and $1$. Note that for data outside the training set normalized values may still fall outside the $[0, 1]$ range.\n",
    "Sometimes the transformation performed by min-max scaling degenerates due to the presence of a few unrepresentative outliers in the training data. A single very large (or very small) value causes the compression of all the others. <br>\n",
    "*This scaling algorithm works very well in cases where the standard deviation is very small, or in cases which don’t have Gaussian distribution.*\n",
    "\n",
    "#### Mean-Var scaling\n",
    "Mean-var scaling is another normalization techniques that does not present the same drawback. In mean-var scaling each feature is linearly scaled to have zero mean and unit variance. <br>\n",
    "Training data is used to compute the mean and the standard deviation of each feature. Then the components of a given feature vector $x$ are normalized accordingly:\n",
    " * $$\\bar{x}_j = \\frac{x_j − μ_j}{σ_j}$$\n",
    "\n",
    "*It assumes a normal distribution for data within each feature.*\n",
    "\n",
    "#### Max-Abs scaling\n",
    "In some applications sparseness of features is a very important property. A feature is sparse when its value is most of the times exactly zero. Both min-max scaling and mean-var scaling do not preserve sparsity. <br>\n",
    "A normalization scheme that is suitable for sparse data is max abs scaling. It consists in dividing each feature by the largest absolute value found in the training set:\n",
    " * $$\\bar{x}_j = \\frac{x_j}{V_j}$$\n",
    "\n",
    "Where $V_j$ is the maximum absolute value element of the $j$-th feature considered.\n",
    "\n",
    "#### Robust scaling\n",
    "Robust Scaler algorithms scale features that are robust to outliers. The method it follows is almost similar to the MinMax Scaler but it uses the interquartile range (rather than the min-max used in MinMax Scaler). The median and scales of the data are removed by this scaling algorithm according to the quantile range. <br>\n",
    "It, thus, follows the following formula:\n",
    " * $$\\bar{x}_j = \\frac{x_j - Q_1(x_j)}{Q_3(x_j) - Q_1(x_j)}$$\n",
    " \n",
    "Where $Q_1$ is the first quartile, while $Q_3$ is the third quartile."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note that there is also the possibility to avoid applying a normalization technique (selecting **`No`** option in the following slider, which is the `default` method)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "549df45eacce4f4995a0b0777ac4b4e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Scaler', options=(('No', None), ('Min-Max', MinMaxScaler()), ('Mean-Var', StandardScaler…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler_widet = ipywidgets.Dropdown(options=[('No', None), ('Min-Max', MinMaxScaler()), ('Mean-Var', StandardScaler()), ('Max-Abs', MaxAbsScaler()), ('Robust', RobustScaler())], description='Scaler')\n",
    "scaler_widet"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new DataFrame (`X`) that contains the selected optical features, from the balanced one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EOMI3</th>\n",
       "      <th>SCI</th>\n",
       "      <th>EOMI1</th>\n",
       "      <th>SDI</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.050282</td>\n",
       "      <td>0.052243</td>\n",
       "      <td>0.047235</td>\n",
       "      <td>-253.591837</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.319567</td>\n",
       "      <td>0.414346</td>\n",
       "      <td>0.391562</td>\n",
       "      <td>-3466.957483</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.134599</td>\n",
       "      <td>0.178288</td>\n",
       "      <td>0.160978</td>\n",
       "      <td>-1542.100475</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.030946</td>\n",
       "      <td>0.038442</td>\n",
       "      <td>0.034784</td>\n",
       "      <td>-296.157437</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.136649</td>\n",
       "      <td>0.189385</td>\n",
       "      <td>0.174058</td>\n",
       "      <td>-1411.042735</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.025078</td>\n",
       "      <td>0.027322</td>\n",
       "      <td>0.024694</td>\n",
       "      <td>-228.722222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.020467</td>\n",
       "      <td>-0.047984</td>\n",
       "      <td>-0.049732</td>\n",
       "      <td>811.411273</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.117342</td>\n",
       "      <td>0.145191</td>\n",
       "      <td>0.133610</td>\n",
       "      <td>-1263.488518</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      EOMI3       SCI     EOMI1          SDI    y\n",
       "0  0.050282  0.052243  0.047235  -253.591837  0.0\n",
       "1  0.319567  0.414346  0.391562 -3466.957483  1.0\n",
       "2  0.134599  0.178288  0.160978 -1542.100475  1.0\n",
       "3  0.030946  0.038442  0.034784  -296.157437  0.0\n",
       "4  0.136649  0.189385  0.174058 -1411.042735  1.0\n",
       "5  0.025078  0.027322  0.024694  -228.722222  0.0\n",
       "6 -0.020467 -0.047984 -0.049732   811.411273  0.0\n",
       "7  0.117342  0.145191  0.133610 -1263.488518  1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the features and target variables\n",
    "X = s2_df_mod_bal[list(s2_features_widget.value)]\n",
    "y = s2_df_mod_bal.iloc[:, -1]\n",
    "pd.concat([X, y], axis=1).head(8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model performances evaluation (ASIDE)\n",
    "In order to evaluate the performance of each considered model, some considerations have been taken ([code](utils.py)).\n",
    "\n",
    "Stratified Cross-Validation have been used, in order to have a more reliable estimate of the different performance metrics.\n",
    "For each repeated split of the dataset into train and test, **mean** (with respect to the two considered classes, where the number of samples per each class have been balanced a-priori) [accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html), [precision](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html), [recall](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) and [F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) metrics have been calculated for both the train and test sets, normalizing the data accordingly (if asked).\n",
    "\n",
    "Then, each metric list has been collapsed, with a *mean reduce function*, to a unique value. In so doing, the output is basically the mean accuracy, precision, recall and f1 over different \"experiments\" (actually trainings).\n",
    "\n",
    "#### Formulae (source: [confusion-matrix-guide](https://www.v7labs.com/blog/confusion-matrix-guide))\n",
    "* *Accuracy*: the number of samples correctly classified out of all the samples present in the test set. $$Accuracy = \\frac{TP + TN}{TP + FP + TN + FN}$$\n",
    "* *Precision* (for the positive class): The number of samples actually belonging to the positive class out of all the samples that were predicted to be of the positive class by the model. $$Precision = \\frac{TP}{TP + FP}$$\n",
    "* *Recall* (for the positive class): The number of samples predicted correctly to be belonging to the positive class out of all the samples that actually belong to the positive class. $$Recall=\\frac{TP}{TP+FN}$$\n",
    "* *F1_score*: The harmonic mean of the precision and recall scores obtained for the positive, or negative, class. $$F1\\_score=\\frac{2*precision*recall}{precision+recall}$$\n",
    "\n",
    "Where:\n",
    "* *True Positive (TP)* refers to a sample belonging to the positive class being classified correctly.\n",
    "* *True Negative (TN)* refers to a sample belonging to the negative class being classified correctly.\n",
    "* *False Positive (FP)* refers to a sample belonging to the negative class but being classified wrongly as belonging to the positive class.\n",
    "* *False Negative (FN)* refers to a sample belonging to the positive class but being classified wrongly as belonging to the negative class.\n",
    "\n",
    "For this particular classification task, the positive class is formed by the manured observations ($y=1$), whereas the negative class is the not manured ones ($y=0$)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "\n",
    "[Logistic Regression](https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148) is a simple and powerful linear classification algorithm used for binary classification. It estimates the probability of an event occurring based on a given dataset of independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: LogisticRegression(), MaxAbsScaler(), 5 KFolds, 0.097s\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Mean Precision</th>\n",
       "      <th>Mean Recall</th>\n",
       "      <th>Mean F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean Accuracy  Mean Precision  Mean Recall  Mean F1\n",
       "Dataset                                                     \n",
       "Train             0.81            0.82         0.81     0.81\n",
       "Test              0.79            0.80         0.79     0.79"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measure the performances using k-fold cross validation\n",
    "utils.measure_scv_performances(X, y, LogisticRegression(), scaler=scaler_widet.value, n_folds=kfolds_widget.value, random_state=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Discriminant Analysis\n",
    "\n",
    "[Linear Discriminant Analysis](https://towardsdatascience.com/linear-discriminant-analysis-explained-f88be6c1e00b) is another linear classification algorithm that is used to find a linear combination of features that characterizes or separates two or more classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: LinearDiscriminantAnalysis(), MaxAbsScaler(), 5 KFolds, 0.121s\n",
      "\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Mean Precision</th>\n",
       "      <th>Mean Recall</th>\n",
       "      <th>Mean F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean Accuracy  Mean Precision  Mean Recall  Mean F1\n",
       "Dataset                                                     \n",
       "Train             0.88            0.90         0.88     0.87\n",
       "Test              0.85            0.89         0.85     0.84"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measure the performances using k-fold cross validation\n",
    "utils.measure_scv_performances(X, y, LinearDiscriminantAnalysis(), scaler=scaler_widet.value, n_folds=kfolds_widget.value, random_state=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Classifier (**saved**)\n",
    "[Support Vector Classifier](https://towardsdatascience.com/everything-about-svm-classification-above-and-beyond-cc665bfd993e) is a non-linear classification algorithm that finds the best boundary between classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: SVC(), MaxAbsScaler(), 5 KFolds, 0.095s\n",
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Mean Precision</th>\n",
       "      <th>Mean Recall</th>\n",
       "      <th>Mean F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean Accuracy  Mean Precision  Mean Recall  Mean F1\n",
       "Dataset                                                     \n",
       "Train             0.89            0.91         0.89     0.89\n",
       "Test              0.88            0.90         0.88     0.88"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measure the performances using k-fold cross validation\n",
    "utils.measure_scv_performances(X, y, SVC(), scaler=scaler_widet.value, n_folds=kfolds_widget.value, random_state=3, save=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors Classifier\n",
    "\n",
    "[K-Nearest Neighbors (KNN) Classifier](https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761) is a non-parametric classification algorithm that classifies new data points based on the k number of nearest data points in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: KNeighborsClassifier(n_neighbors=9), MaxAbsScaler(), 5 KFolds, 0.101s\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Mean Precision</th>\n",
       "      <th>Mean Recall</th>\n",
       "      <th>Mean F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean Accuracy  Mean Precision  Mean Recall  Mean F1\n",
       "Dataset                                                     \n",
       "Train             0.84            0.88         0.84     0.84\n",
       "Test              0.81            0.86         0.81     0.80"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measure the performances using k-fold cross validation\n",
    "utils.measure_scv_performances(X, y, KNeighborsClassifier(n_neighbors=9), scaler=scaler_widet.value, n_folds=kfolds_widget.value, random_state=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier\n",
    "\n",
    "[Random Forest Classifier](https://towardsdatascience.com/random-forest-classification-678e551462f5) is an ensemble learning method that constructs a multitude of decision trees at training time and outputs the class that is the mode of the classes, of the individual trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: RandomForestClassifier(max_depth=1, random_state=30), MaxAbsScaler(), 5 KFolds, 0.82s\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Mean Precision</th>\n",
       "      <th>Mean Recall</th>\n",
       "      <th>Mean F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean Accuracy  Mean Precision  Mean Recall  Mean F1\n",
       "Dataset                                                     \n",
       "Train             0.86            0.89         0.86     0.86\n",
       "Test              0.83            0.86         0.83     0.82"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measure the performances using k-fold cross validation\n",
    "utils.measure_scv_performances(X, y, RandomForestClassifier(max_depth=1, random_state=30), scaler=scaler_widet.value, n_folds=kfolds_widget.value, random_state=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Models (using Sentinel-1 data instead)**\n",
    "\n",
    "Please note that now **radar indexes** will be used. <br>\n",
    "The value of `K` (for KFold Cross-Validation), as well as the `normalization method` will be the same as the one previously selected. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Radar features subset selection\n",
    "\n",
    "We already ranked in terms of importance, which are the radar features most impacted by manure application in the analysis notebook.<br>\n",
    "The objective now is to consider a subset of them (and also modify this subset), in order to see which are the ones improving most the overall performances (without incurring into neither overfitting nor overfitting issues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14e2a33ba4d04076b476ebd637d06325",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "SelectMultiple(description='Features', index=(3,), options=('VV', 'VH', 'AVE', 'DIF', 'RAT1', 'RAT2', 'NDI', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sentinel-1 features\n",
    "s1_features_widget = ipywidgets.SelectMultiple(options=s1_df.select_dtypes(include=np.number).columns, value=['DIF'], description='Features')\n",
    "s1_features_widget"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new DataFrame (`X`) that contains the selected radar features, from the balanced one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "        DIF    y\n",
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       "5 -0.077460  1.0\n",
       "6 -0.393857  0.0\n",
       "7  0.404535  1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the features and target variables\n",
    "X = s1_df_mod_bal[list(s1_features_widget.value)]\n",
    "y = s1_df_mod_bal.iloc[:, -1]\n",
    "pd.concat([X, y], axis=1).head(8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Classifier\n",
    "\n",
    "Different models have been tested, the one that works better in this case is SVC. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: SVC(), MaxAbsScaler(), 5 KFolds, 0.099s\n",
      "\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Mean Precision</th>\n",
       "      <th>Mean Recall</th>\n",
       "      <th>Mean F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         Mean Accuracy  Mean Precision  Mean Recall  Mean F1\n",
       "Dataset                                                     \n",
       "Train             0.66            0.68         0.66     0.66\n",
       "Test              0.60            0.65         0.60     0.57"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measure the performances using k-fold cross validation\n",
    "utils.measure_scv_performances(X, y, SVC(), scaler=scaler_widet.value, n_folds=kfolds_widget.value, random_state=3)"
   ]
  }
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