Modelo definitivo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo Definitivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:13:35.586964Z",
     "start_time": "2021-09-01T16:09:31.505711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11280, 34)\n",
      "3    4083\n",
      "0    3579\n",
      "4     694\n",
      "1     561\n",
      "2     107\n",
      "Name: ORDEN_cat, dtype: int64 3    5104\n",
      "0    4474\n",
      "4     867\n",
      "1     701\n",
      "2     134\n",
      "Name: ORDEN_cat, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jamontanac/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py:68: FutureWarning: Pass classes=[3 0 4 1 2], y=7649     3\n",
      "3961     0\n",
      "10298    3\n",
      "861      0\n",
      "1167     0\n",
      "        ..\n",
      "4812     1\n",
      "8134     3\n",
      "4022     0\n",
      "3060     0\n",
      "2163     0\n",
      "Name: ORDEN_cat, Length: 9024, dtype: int8 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo: {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 90} ✓\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
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      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
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      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 150} ✓\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'gini', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 70} ✓\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 90} ✓\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 21, 'max_features': 'log2', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'auto', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'sqrt', 'n_estimators': 150} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 50} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 70} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 90} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 130} ✓\n",
      "Modelo: {'class_weight': {3: 0.4420279206465834, 0: 0.5042749371332774, 4: 2.6005763688760806, 1: 3.217112299465241, 2: 16.867289719626168}, 'criterion': 'entropy', 'max_depth': 31, 'max_features': 'log2', 'n_estimators': 150} ✓\n"
     ]
    }
   ],
   "source": [
    "# Tratamiento de datos\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Preprocesado y modelado\n",
    "# ==============================================================================\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "import multiprocessing\n",
    "\n",
    "\n",
    "# Configuración warnings\n",
    "# ==============================================================================\n",
    "import warnings\n",
    "warnings.filterwarnings('once')\n",
    "\n",
    "with open('Datos_finales.csv') as f:\n",
    "    df = pd.read_csv(f, delimiter=',')\n",
    "\n",
    "X=df.drop(columns=[\"CLIMA_AMBIENTAL\",\"CODIGO\",\"ORDEN\",\"LATITUD\",\"LONGITUD\",\"PAISAJE\",\"TIPO_RELIEVE\",\"FORMA_TERRENO\",\"MATERIAL_PARENTAL_LITOLOGIA\"])\n",
    "cat_cols = X.select_dtypes(include=['object', 'category','bool']).columns.to_list()\n",
    "cont_cols = numeric_cols = df.select_dtypes(include=['float64', 'int']).columns.to_list()\n",
    "df[\"ORDEN\"] = df[\"ORDEN\"].astype('category')\n",
    "df[\"ORDEN_cat\"] = df[\"ORDEN\"].cat.codes\n",
    "y = df['ORDEN_cat']\n",
    "#CREATING DUMMIES FOR CATEGORICAL VARIABLES IN X MATRIX\n",
    "# ==============================================================================\n",
    "X= pd.get_dummies(X,  columns = cat_cols)\n",
    "#X_cont = X[X[cont_cols]]\n",
    "print(X.shape)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state = 133)\n",
    "print(y_train.value_counts(),y.value_counts())\n",
    "\n",
    "weights= compute_class_weight('balanced',y_train.unique(),y_train)\n",
    "weight_dict={}\n",
    "for i,j in enumerate(y_train.unique()):\n",
    "    weight_dict[j]=weights[i]\n",
    "    \n",
    "param_grid = ParameterGrid(\n",
    "                {'n_estimators': [50,70,90,130,150],\n",
    "                 'class_weight' : ['balanced', 'balanced_subsample', weight_dict],\n",
    "                 'max_features': ['auto', 'sqrt', 'log2'],\n",
    "                 'max_depth'   : [None, 5, 15, 21, 31],\n",
    "                 'criterion'   : ['gini', 'entropy']\n",
    "                }\n",
    "            )\n",
    "\n",
    "# Loop para ajustar un modelo con cada combinación de hiperparámetros\n",
    "# ==============================================================================\n",
    "resultados = {'params': [], 'oob_accuracy': []}\n",
    "\n",
    "for params in param_grid:\n",
    "    \n",
    "    modelo = RandomForestClassifier(\n",
    "                oob_score    = True,\n",
    "                n_jobs       = -1,\n",
    "                random_state = 123,\n",
    "                ** params\n",
    "             )\n",
    "    \n",
    "    modelo.fit(X_train, y_train)\n",
    "    \n",
    "    resultados['params'].append(params)\n",
    "    resultados['oob_accuracy'].append(modelo.oob_score_)\n",
    "    print(f\"Modelo: {params} \\u2713\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:13:36.170160Z",
     "start_time": "2021-09-01T16:13:35.618091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oob_accuracy</th>\n",
       "      <th>class_weight</th>\n",
       "      <th>criterion</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>max_features</th>\n",
       "      <th>n_estimators</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.938608</td>\n",
       "      <td>balanced</td>\n",
       "      <td>gini</td>\n",
       "      <td>31.0</td>\n",
       "      <td>auto</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.938608</td>\n",
       "      <td>balanced</td>\n",
       "      <td>gini</td>\n",
       "      <td>31.0</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0.938608</td>\n",
       "      <td>{3: 0.4420279206465834, 0: 0.5042749371332774,...</td>\n",
       "      <td>gini</td>\n",
       "      <td>31.0</td>\n",
       "      <td>auto</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>0.938608</td>\n",
       "      <td>{3: 0.4420279206465834, 0: 0.5042749371332774,...</td>\n",
       "      <td>gini</td>\n",
       "      <td>31.0</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     oob_accuracy                                       class_weight  \\\n",
       "64       0.938608                                           balanced   \n",
       "69       0.938608                                           balanced   \n",
       "364      0.938608  {3: 0.4420279206465834, 0: 0.5042749371332774,...   \n",
       "369      0.938608  {3: 0.4420279206465834, 0: 0.5042749371332774,...   \n",
       "\n",
       "    criterion  max_depth max_features  n_estimators  \n",
       "64       gini       31.0         auto           150  \n",
       "69       gini       31.0         sqrt           150  \n",
       "364      gini       31.0         auto           150  \n",
       "369      gini       31.0         sqrt           150  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resultados\n",
    "# ==============================================================================\n",
    "resultados = pd.DataFrame(resultados)\n",
    "resultados = pd.concat([resultados, resultados['params'].apply(pd.Series)], axis=1)\n",
    "resultados = resultados.sort_values('oob_accuracy', ascending=False)\n",
    "resultados = resultados.drop(columns = 'params')\n",
    "resultados.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:13:36.190927Z",
     "start_time": "2021-09-01T16:13:36.176473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Mejores hiperparámetros encontrados (oob-accuracy)\n",
      "--------------------------------------------------\n",
      "0.9386081560283688 : 0.9386081560283688 accuracy\n"
     ]
    }
   ],
   "source": [
    "# Mejores hiperparámetros por out-of-bag error\n",
    "# ==============================================================================\n",
    "print(\"--------------------------------------------------\")\n",
    "print(\"Mejores hiperparámetros encontrados (oob-accuracy)\")\n",
    "print(\"--------------------------------------------------\")\n",
    "print(resultados.iloc[0,0], \":\", resultados.iloc[0,:]['oob_accuracy'], \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:15:11.349807Z",
     "start_time": "2021-09-01T16:15:10.502445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight={0: 0.5042749371332774,\n",
       "                                     1: 3.217112299465241,\n",
       "                                     2: 16.867289719626168,\n",
       "                                     3: 0.4420279206465834,\n",
       "                                     4: 2.6005763688760806},\n",
       "                       criterion='entropy', max_depth=31, max_features='log2',\n",
       "                       n_estimators=150, n_jobs=-1, oob_score=True,\n",
       "                       random_state=123)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mejor_modelo = RandomForestClassifier(\n",
    "                oob_score    = True,\n",
    "                n_jobs       = -1,\n",
    "                random_state = 123,\n",
    "                n_estimators = 150,\n",
    "                class_weight = weight_dict,\n",
    "                max_features = 'log2',\n",
    "                max_depth = 31,\n",
    "                criterion = 'entropy'\n",
    "                \n",
    "             )\n",
    "    \n",
    "mejor_modelo.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:15:21.345546Z",
     "start_time": "2021-09-01T16:15:20.816465Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x5040 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mejor_modelo.feature_importances_\n",
    "plt.figure(figsize=(15,70))\n",
    "plt.barh(X.columns, mejor_modelo.feature_importances_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:15:53.490855Z",
     "start_time": "2021-09-01T16:15:53.394500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusión\n",
      "-------------------\n",
      "[[879   1   0  15   0]\n",
      " [  0  63   0  76   1]\n",
      " [  0   0  27   0   0]\n",
      " [  1  19   0 993   8]\n",
      " [  0   1   0  22 150]]\n",
      "\n",
      "El accuracy de test es: 93.61702127659575 %\n"
     ]
    }
   ],
   "source": [
    "pred = mejor_modelo.predict(X = X_test)\n",
    "#Matriz de confusión\n",
    "#===================================================================================\n",
    "\n",
    "mat_confusion = confusion_matrix(\n",
    "                    y_true    = y_test,\n",
    "                    y_pred    = pred\n",
    "                )\n",
    "\n",
    "accuracy = accuracy_score(\n",
    "            y_true    = y_test,\n",
    "            y_pred    = pred,\n",
    "            normalize = True\n",
    "           )\n",
    "\n",
    "print(\"Matriz de confusión\")\n",
    "print(\"-------------------\")\n",
    "print(mat_confusion)\n",
    "print(\"\")\n",
    "print(f\"El accuracy de test es: {100 * accuracy} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:16:42.536152Z",
     "start_time": "2021-09-01T16:16:42.514350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99       895\n",
      "           1       0.75      0.45      0.56       140\n",
      "           2       1.00      1.00      1.00        27\n",
      "           3       0.90      0.97      0.93      1021\n",
      "           4       0.94      0.87      0.90       173\n",
      "\n",
      "    accuracy                           0.94      2256\n",
      "   macro avg       0.92      0.85      0.88      2256\n",
      "weighted avg       0.93      0.94      0.93      2256\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(\n",
    "        y_true = y_test,\n",
    "        y_pred = pred\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entrenamiento con pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:21:07.968084Z",
     "start_time": "2021-09-01T16:21:05.518965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.936613475177305\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.compose import make_column_selector\n",
    "\n",
    "\n",
    "with open('Datos_finales.csv') as f:\n",
    "    df = pd.read_csv(f, delimiter=',')\n",
    "\n",
    "X=df.drop(columns=[\"CLIMA_AMBIENTAL\",\"CODIGO\",\"ORDEN\",\"LATITUD\",\"LONGITUD\",\"PAISAJE\",\"TIPO_RELIEVE\",\"FORMA_TERRENO\",\"MATERIAL_PARENTAL_LITOLOGIA\"])\n",
    "\n",
    "X=X[sorted(X.columns)]\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "y=le.fit_transform(df.ORDEN)\n",
    "\n",
    "#numeric_features=X.select_dtypes(exclude=['object', 'category','bool']).columns.to_list()\n",
    "#categorical_features=X.select_dtypes(include=['object', 'category','bool']).columns.to_list()\n",
    "numeric_transformer = StandardScaler()\n",
    "categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('categorical', categorical_transformer, make_column_selector(dtype_include=['object', 'category','bool'])),\n",
    "        ('numeric',numeric_transformer,make_column_selector(dtype_exclude=['object', 'category','bool']))])\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state = 133)\n",
    "weights= compute_class_weight('balanced',classes=np.unique(y_train),y=y_train)\n",
    "\n",
    "weight_dict={}\n",
    "for i,j in enumerate(np.unique(y_train)):\n",
    "    weight_dict[j]=weights[i]\n",
    "    \n",
    "    \n",
    "mejor_modelo = RandomForestClassifier(\n",
    "                oob_score    = True,\n",
    "                n_jobs       = -1,\n",
    "                random_state = 123,\n",
    "                n_estimators = 150,\n",
    "                class_weight = weight_dict,\n",
    "                max_features = 'log2',\n",
    "                max_depth = 31,\n",
    "                criterion = 'entropy')\n",
    "clf = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', mejor_modelo)])\n",
    "\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "print(clf.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:21:08.211146Z",
     "start_time": "2021-09-01T16:21:08.014620Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle as pk\n",
    "with open(\"CATS_pipeline.pk\",\"wb\") as f:\n",
    "    pk.dump(clf,f)\n",
    "with open(\"CATS_label_encoder.pk\",\"wb\") as f:\n",
    "    pk.dump(le,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:23:18.583265Z",
     "start_time": "2021-09-01T16:23:18.568750Z"
    }
   },
   "outputs": [],
   "source": [
    "new_df=df.loc[X_test.index].copy()\n",
    "new_df=new_df.drop(columns=[\"ORDEN\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:23:34.334883Z",
     "start_time": "2021-09-01T16:23:34.320556Z"
    }
   },
   "outputs": [],
   "source": [
    "with open(\"test_data.pk\",\"wb\") as f:\n",
    "    pk.dump((new_df,y_test),f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-01T16:32:49.053556Z",
     "start_time": "2021-09-01T16:32:48.966494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'ALTITUD': 463.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Excesivo',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 20.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 70.0},\n",
       " {'ALTITUD': 3553.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Hidrosa',\n",
       "  'H1_ESPESOR': 53.0,\n",
       "  'H1_RESULTADO_ph': 4.4,\n",
       "  'H2_ESPESOR': 32.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 95.0},\n",
       " {'ALTITUD': 1364.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 34.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 6.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 40.0},\n",
       " {'ALTITUD': 3420.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 46.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 4.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 50.0},\n",
       " {'ALTITUD': 3156.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arcillosa',\n",
       "  'H1_ESPESOR': 28.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 36.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 2047.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 14.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 31.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 138.0},\n",
       " {'ALTITUD': 3040.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 32.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 31.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3503.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 18.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 53.0},\n",
       " {'ALTITUD': 3511.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 40.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 65.0},\n",
       " {'ALTITUD': 187.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Molico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 6.8,\n",
       "  'H2_ESPESOR': 42.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 130.0},\n",
       " {'ALTITUD': 2997.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 36.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 1849.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 18.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 32.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 50.0},\n",
       " {'ALTITUD': 3601.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 24.0,\n",
       "  'H1_RESULTADO_ph': 4.6,\n",
       "  'H2_ESPESOR': 21.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 316.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 27.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 70.0},\n",
       " {'ALTITUD': 3126.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 19.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 97.0},\n",
       " {'ALTITUD': 3381.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 43.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 20.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 102.0},\n",
       " {'ALTITUD': 3114.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Arcillosa',\n",
       "  'H1_ESPESOR': 46.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 15.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 85.0},\n",
       " {'ALTITUD': 3048.8,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Pobre',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 39.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 34.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 230.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 12.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 23.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 709.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 27.0,\n",
       "  'H1_RESULTADO_ph': 8.0,\n",
       "  'H2_ESPESOR': 13.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 40.0},\n",
       " {'ALTITUD': 1057.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 18.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 42.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 70.0},\n",
       " {'ALTITUD': 3268.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 50.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 32.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3320.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arenosa',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 4.1,\n",
       "  'H2_ESPESOR': 25.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 3142.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 55.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 65.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 2711.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 31.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 23.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 3330.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 32.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 26.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3040.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 35.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 32.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 115.0},\n",
       " {'ALTITUD': 1141.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 25.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 3440.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Moderado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 40.0,\n",
       "  'H1_RESULTADO_ph': 4.8,\n",
       "  'H2_ESPESOR': 60.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 100.0},\n",
       " {'ALTITUD': 3156.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arcillosa',\n",
       "  'H1_ESPESOR': 27.0,\n",
       "  'H1_RESULTADO_ph': 4.7,\n",
       "  'H2_ESPESOR': 26.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 53.0},\n",
       " {'ALTITUD': 191.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Molico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 18.0,\n",
       "  'H1_RESULTADO_ph': 7.0,\n",
       "  'H2_ESPESOR': 21.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 92.0},\n",
       " {'ALTITUD': 3008.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Pobre',\n",
       "  'EPIPEDON': 'Histico',\n",
       "  'FAMILIA_TEXTURAL': 'Disica',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 40.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 359.5,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 22.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 39.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 82.0},\n",
       " {'ALTITUD': 354.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 7.8,\n",
       "  'H2_ESPESOR': 25.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 135.0},\n",
       " {'ALTITUD': 1806.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 11.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 19.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 104.0},\n",
       " {'ALTITUD': 2659.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 15.0,\n",
       "  'H1_RESULTADO_ph': 3.8,\n",
       "  'H2_ESPESOR': 13.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 40.0},\n",
       " {'ALTITUD': 342.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 17.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 26.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 104.0},\n",
       " {'ALTITUD': 2691.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 21.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 39.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3268.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 41.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 24.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3245.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 50.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 20.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 108.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Molico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 23.0,\n",
       "  'H1_RESULTADO_ph': 7.5,\n",
       "  'H2_ESPESOR': 17.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3192.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 23.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 18.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 50.0},\n",
       " {'ALTITUD': 3333.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Pobre',\n",
       "  'EPIPEDON': 'Histico',\n",
       "  'FAMILIA_TEXTURAL': 'Euica',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 47.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 2982.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Pobre',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 22.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3307.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 45.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 2913.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 26.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 24.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 100.0},\n",
       " {'ALTITUD': 3075.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 33.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 37.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3213.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 35.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3014.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 29.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 20.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 55.0},\n",
       " {'ALTITUD': 2432.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 26.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 361.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 218.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 31.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 38.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3071.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 23.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 29.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 124.0},\n",
       " {'ALTITUD': 1967.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Moderadamente_excesivo',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 15.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 23.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 2807.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arenosa',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 23.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 77.0},\n",
       " {'ALTITUD': 570.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arenosa',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 7.0,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 50.0},\n",
       " {'ALTITUD': 3065.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
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       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 17.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 121.0},\n",
       " {'ALTITUD': 3082.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 50.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 336.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 18.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 26.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 3121.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 42.0,\n",
       "  'H1_RESULTADO_ph': 5.1,\n",
       "  'H2_ESPESOR': 33.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 130.0},\n",
       " {'ALTITUD': 1458.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Molico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 17.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 19.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 1209.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arcillosa',\n",
       "  'H1_ESPESOR': 10.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 11.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 80.0},\n",
       " {'ALTITUD': 370.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 23.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 19.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 3309.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 21.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 44.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 112.0},\n",
       " {'ALTITUD': 1310.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
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       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 19.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 100.0},\n",
       " {'ALTITUD': 3055.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 28.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 72.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 100.0},\n",
       " {'ALTITUD': 927.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 22.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 18.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 2286.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 34.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 441.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 22.0,\n",
       "  'H1_RESULTADO_ph': 6.5,\n",
       "  'H2_ESPESOR': 15.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3656.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 45.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 35.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 80.0},\n",
       " {'ALTITUD': 3055.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 65.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 15.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3309.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 40.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 35.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 125.0},\n",
       " {'ALTITUD': 2103.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 23.0,\n",
       "  'H1_RESULTADO_ph': 7.0,\n",
       "  'H2_ESPESOR': 18.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3071.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 10.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 40.0},\n",
       " {'ALTITUD': 3215.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
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       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 26.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 55.0},\n",
       " {'ALTITUD': 3103.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 80.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 40.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3444.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 48.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 25.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 746.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 20.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 15.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 78.0},\n",
       " {'ALTITUD': 3639.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 12.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 42.0},\n",
       " {'ALTITUD': 2960.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 38.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 12.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 235.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 30.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 625.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 6.0,\n",
       "  'H2_ESPESOR': 17.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 90.0},\n",
       " {'ALTITUD': 207.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Pobre',\n",
       "  'EPIPEDON': 'Molico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 32.0,\n",
       "  'H1_RESULTADO_ph': 7.0,\n",
       "  'H2_ESPESOR': 38.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 3167.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 38.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 32.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 75.0},\n",
       " {'ALTITUD': 3085.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 26.0,\n",
       "  'H1_RESULTADO_ph': 4.0,\n",
       "  'H2_ESPESOR': 52.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 121.0},\n",
       " {'ALTITUD': 1457.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arcillosa',\n",
       "  'H1_ESPESOR': 16.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 13.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3337.2,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 65.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 55.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3120.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 30.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 15.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 63.0},\n",
       " {'ALTITUD': 2683.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Pobre',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Arcillosa',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 5.2,\n",
       "  'H2_ESPESOR': 40.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 1249.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 22.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 16.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 104.0},\n",
       " {'ALTITUD': 1068.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Fina',\n",
       "  'H1_ESPESOR': 12.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 24.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 93.0},\n",
       " {'ALTITUD': 3302.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 60.0,\n",
       "  'H1_RESULTADO_ph': 5.8,\n",
       "  'H2_ESPESOR': 15.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 100.0},\n",
       " {'ALTITUD': 3109.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 60.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 35.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 120.0},\n",
       " {'ALTITUD': 3138.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 25.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 29.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 115.0},\n",
       " {'ALTITUD': 3459.817,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 32.0,\n",
       "  'H1_RESULTADO_ph': 5.5,\n",
       "  'H2_ESPESOR': 24.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 90.0},\n",
       " {'ALTITUD': 203.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Molico',\n",
       "  'FAMILIA_TEXTURAL': 'Limosa',\n",
       "  'H1_ESPESOR': 23.0,\n",
       "  'H1_RESULTADO_ph': 8.0,\n",
       "  'H2_ESPESOR': 27.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 110.0},\n",
       " {'ALTITUD': 2995.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Umbrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 18.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 11.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 100.0},\n",
       " {'ALTITUD': 3407.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 26.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 38.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 108.0},\n",
       " {'ALTITUD': 892.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': False,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Ocrico',\n",
       "  'FAMILIA_TEXTURAL': 'Franca',\n",
       "  'H1_ESPESOR': 28.0,\n",
       "  'H1_RESULTADO_ph': 4.5,\n",
       "  'H2_ESPESOR': 41.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 101.0},\n",
       " {'ALTITUD': 3185.0,\n",
       "  'CONTENIDO_CENIZA_VOLCANICA': True,\n",
       "  'DRENAJE_NATURAL': 'Bien_drenado',\n",
       "  'EPIPEDON': 'Melanico',\n",
       "  'FAMILIA_TEXTURAL': 'Medial',\n",
       "  'H1_ESPESOR': 19.0,\n",
       "  'H1_RESULTADO_ph': 5.0,\n",
       "  'H2_ESPESOR': 34.0,\n",
       "  'PROFUNDIDAD_MAXIMA': 121.0}]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.iloc[:100].to_dict(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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