classification_graphs_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification es el notebook oficial de clasificación\n",
    "\n",
    "### - Limpiamos los datos según conclusiones del notebook main\n",
    "### - Probaremos distintos algorítmos de clasificación y compararemos los resultados\n",
    "### - Utilizamos el dataset de train ya recortado por la cátedra\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TODOS LOS IMPORTS DEL NOTEBOOK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict, validation_curve\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_recall_curve, auc, roc_curve\n",
    "from scripts.auc_curves_data import auc_curves_data\n",
    "from scripts.auc_curves_plot import auc_curves_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LECTURA Y LIMPIEZA DEL DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig = pd.read_csv(r\"DS_G3_HeartFailure_FULL_train.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformaciones necesarias al df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>95.0</td>\n",
       "      <td>371</td>\n",
       "      <td>30</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>40.0</td>\n",
       "      <td>478</td>\n",
       "      <td>30</td>\n",
       "      <td>303000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>70.0</td>\n",
       "      <td>232</td>\n",
       "      <td>30</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2695</td>\n",
       "      <td>40</td>\n",
       "      <td>241000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>59.0</td>\n",
       "      <td>176</td>\n",
       "      <td>25</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  creatinine_phosphokinase  ejection_fraction  platelets  \\\n",
       "55   95.0                       371                 30   461000.0   \n",
       "180  40.0                       478                 30   303000.0   \n",
       "237  70.0                       232                 30   173000.0   \n",
       "280  70.0                      2695                 40   241000.0   \n",
       "181  59.0                       176                 25   221000.0   \n",
       "\n",
       "     serum_creatinine  serum_sodium  DEATH_EVENT  \n",
       "55                2.0           132            1  \n",
       "180               0.9           136            0  \n",
       "237               1.2           132            0  \n",
       "280               1.0           137            0  \n",
       "181               1.0           136            1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned = df_orig.drop([\"time\",\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "#df_cleaned = df_orig.drop([\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "# NOS QUEDAMOS CON AGE, EJECTION_FRACTION, SERUM_CREATININE, SERUM_SODIUM Y DEATH_EVENT\n",
    "df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_train, targ_train = df_cleaned.drop([\"DEATH_EVENT\"],axis=1),df_cleaned[\"DEATH_EVENT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(326, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "feat_train = scaler.fit_transform(feat_train)\n",
    "#APLICAMOS SMOTE PARA AUMENTAR LA CANTIDAD DE SAMPLES Y BALANCEAR EL TARGET\n",
    "feat_train, targ_train = SMOTE(random_state=420).fit_resample(feat_train, targ_train)\n",
    "feat_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ACA TENEMOS EL DATASET RECORTADO Y STANDARIZADO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_metric = \"recall\" # Queremos pocos FN, es decir pacientes q decimos q NO tienen riesgo pero SI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Función útil para graficar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_setup (model):\n",
    "    model.fit(feat_train,targ_train)\n",
    "    scoring = cross_val_score(model, feat_train, targ_train,cv=10, scoring=score_metric)\n",
    "    print(\"Cross Validation Score: {:.3f} +/- {:.3f}\".format(scoring.mean(),scoring.std()))\n",
    "    predicted = cross_val_predict(model,feat_train,targ_train,cv=10)\n",
    "    print(classification_report(targ_train, predicted))\n",
    "    cm = confusion_matrix(targ_train, predicted)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparación de modelos y scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelos = [\n",
    "    # Perceptron(fit_intercept=False, max_iter=40000),\n",
    "    # Perceptron(fit_intercept=False, max_iter=40000,penalty=\"l2\",random_state=420,alpha=1e-5,eta0=0.05),\n",
    "    # LogisticRegression(fit_intercept=False, dual=False, max_iter=40000,C=2),\n",
    "    # SVC(probability=True,gamma=0.61,C=0.85),\n",
    "    # RandomForestClassifier(n_estimators=400,min_impurity_decrease=2e-4),\n",
    "    # AdaBoostClassifier(base_estimator=DecisionTreeClassifier(),learning_rate=0.2),\n",
    "    # VotingClassifier(estimators=[('lr', LogisticRegression(fit_intercept=False, dual=False, max_iter=40000,C=2)), ('tree', RandomForestClassifier(n_estimators=400,min_impurity_decrease=2e-4)), ('svc', SVC(probability=True,gamma=0.61,C=0.85))],voting='hard'),\n",
    "    VotingClassifier(estimators=[('lr', LogisticRegression(fit_intercept=False, dual=False, max_iter=40000,C=2)), ('tree', RandomForestClassifier(n_estimators=400,min_impurity_decrease=2e-4)), ('svc', SVC(probability=True,gamma=0.61,C=0.85))],voting='soft')\n",
    "]\n",
    "scores = []\n",
    "for model in modelos:\n",
    "    model.fit(feat_train,targ_train)\n",
    "    scores.append(cross_val_score(model, feat_train, targ_train,cv=15, scoring=score_metric))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pd.DataFrame(np.array(scores).T)\n",
    "scores.columns = [\"Per (BL)\",\"Per_op\",\"LR_op\",\"SVC_op\",\"RF_op\",\"ADA_op\",\"VotHard\",\"VotSoft\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "meaned_scores = [0.657,0.711,0.801,0.877,0.865,0.781,0.871,0.862]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 8 artists>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.bar(scores.columns,meaned_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per (BL)</th>\n",
       "      <th>Per_op</th>\n",
       "      <th>LR_op</th>\n",
       "      <th>SVC_op</th>\n",
       "      <th>RF_op</th>\n",
       "      <th>ADA_op</th>\n",
       "      <th>VotHard</th>\n",
       "      <th>VotSoft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Per (BL)    Per_op     LR_op    SVC_op     RF_op    ADA_op   VotHard  \\\n",
       "0   0.545455  0.545455  0.636364  0.818182  0.727273  0.727273  0.818182   \n",
       "1   0.545455  0.363636  0.727273  0.909091  0.727273  0.909091  0.818182   \n",
       "2   0.636364  0.363636  0.727273  0.818182  0.909091  0.636364  0.818182   \n",
       "3   0.545455  0.454545  0.909091  0.909091  0.727273  0.727273  0.818182   \n",
       "4   0.727273  0.727273  0.818182  0.818182  0.818182  0.636364  0.818182   \n",
       "5   0.727273  0.727273  0.818182  0.818182  0.818182  0.727273  0.818182   \n",
       "6   0.636364  0.727273  0.818182  0.818182  0.727273  0.727273  0.818182   \n",
       "7   0.727273  0.818182  0.909091  0.909091  1.000000  0.909091  1.000000   \n",
       "8   0.818182  0.727273  0.818182  0.818182  1.000000  1.000000  0.909091   \n",
       "9   0.636364  0.727273  0.818182  1.000000  1.000000  0.818182  1.000000   \n",
       "10  0.727273  0.636364  0.727273  0.818182  0.818182  0.636364  0.818182   \n",
       "11  0.636364  0.727273  0.636364  0.818182  0.909091  0.818182  0.818182   \n",
       "12  0.818182  0.272727  0.818182  1.000000  0.909091  0.727273  0.909091   \n",
       "13  0.600000  0.600000  0.800000  1.000000  1.000000  1.000000  1.000000   \n",
       "14  0.500000  0.900000  1.000000  1.000000  1.000000  0.900000  1.000000   \n",
       "\n",
       "     VotSoft  \n",
       "0   0.818182  \n",
       "1   0.818182  \n",
       "2   0.818182  \n",
       "3   0.818182  \n",
       "4   0.818182  \n",
       "5   0.818182  \n",
       "6   0.818182  \n",
       "7   1.000000  \n",
       "8   0.909091  \n",
       "9   1.000000  \n",
       "10  0.818182  \n",
       "11  0.727273  \n",
       "12  0.909091  \n",
       "13  0.900000  \n",
       "14  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.violinplot(data=scores)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Gráficos AOC de ROC y PR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No se pudo analizar este modelo VotingClassifier(estimators=[('lr',\n",
      "                              LogisticRegression(C=2, fit_intercept=False,\n",
      "                                                 max_iter=40000)),\n",
      "                             ('tree',\n",
      "                              RandomForestClassifier(min_impurity_decrease=0.0002,\n",
      "                                                     n_estimators=400)),\n",
      "                             ('svc',\n",
      "                              SVC(C=0.85, gamma=0.61, probability=True))]) 😅\n"
     ]
    }
   ],
   "source": [
    "AOC_data = []\n",
    "\n",
    "for model in modelos:\n",
    "    try:\n",
    "        (ROC_fpr, ROC_tpr, PR_recalls, PR_precisions) = auc_curves_data(model,feat_train,targ_train)\n",
    "        AOC_data.append((ROC_fpr, ROC_tpr, PR_recalls, PR_precisions, model))        \n",
    "    except:\n",
    "        print(f\"No se pudo analizar este modelo {model} 😅\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(AOC_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc_curves_plot(AOC_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Per (BL)\",\"Per_op\",\"LR_op\",\"SVC_op\",\"RF_op\",\"ADA_op\",\"XGBC_op\",\"VotHard\",\"VotSoft\",\"ANN\"]\n",
    "scores_s_time = np.array([0.542,0.773,0.731,0.895,0.872,0.811,0.813,0.865,0.836,0.954],dtype=\"float32\")\n",
    "scores_c_time = np.array([0.721,0.731,0.811,0.916,0.917,0.837,0.905,0.916,0.910,0.984],dtype=\"float32\")\n",
    "scores_test = np.array([0.538,0.400,0.667,0.586,0.667,0.526,0.579,0.652,0.619,0.736],dtype=\"float32\")\n",
    "ss = np.array([scores_s_time,scores_c_time,scores_test]).T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.542, 0.721, 0.538],\n",
       "       [0.773, 0.731, 0.4  ],\n",
       "       [0.731, 0.811, 0.667],\n",
       "       [0.895, 0.916, 0.586],\n",
       "       [0.872, 0.917, 0.667],\n",
       "       [0.811, 0.837, 0.526],\n",
       "       [0.813, 0.905, 0.579],\n",
       "       [0.865, 0.916, 0.652],\n",
       "       [0.836, 0.91 , 0.619],\n",
       "       [0.954, 0.984, 0.647]], dtype=float32)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scorings = pd.DataFrame(ss,columns=[\"s/time\",\"c/time\",\"test\"])\n",
    "scorings[\"algoritmo\"] = names\n",
    "scorings = scorings.set_index(\"algoritmo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s/time</th>\n",
       "      <th>c/time</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algoritmo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Per (BL)</th>\n",
       "      <td>0.542</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Per_op</th>\n",
       "      <td>0.773</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LR_op</th>\n",
       "      <td>0.731</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC_op</th>\n",
       "      <td>0.895</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_op</th>\n",
       "      <td>0.872</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADA_op</th>\n",
       "      <td>0.811</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBC_op</th>\n",
       "      <td>0.813</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VotHard</th>\n",
       "      <td>0.865</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VotSoft</th>\n",
       "      <td>0.836</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANN</th>\n",
       "      <td>0.954</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           s/time  c/time   test\n",
       "algoritmo                       \n",
       "Per (BL)    0.542   0.721  0.538\n",
       "Per_op      0.773   0.731  0.400\n",
       "LR_op       0.731   0.811  0.667\n",
       "SVC_op      0.895   0.916  0.586\n",
       "RF_op       0.872   0.917  0.667\n",
       "ADA_op      0.811   0.837  0.526\n",
       "XGBC_op     0.813   0.905  0.579\n",
       "VotHard     0.865   0.916  0.652\n",
       "VotSoft     0.836   0.910  0.619\n",
       "ANN         0.954   0.984  0.736"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Scorings'}, xlabel='Algoritmos', ylabel='Recall'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorings.plot(kind=\"bar\",figsize=(15,5),title=\"Scorings\",ylabel=\"Recall\",xlabel=\"Algoritmos\",grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algoritmo</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Per (BL)</td>\n",
       "      <td>s</td>\n",
       "      <td>0.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Per_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LR_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RF_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ADA_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBC_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VotHard</td>\n",
       "      <td>s</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VotSoft</td>\n",
       "      <td>s</td>\n",
       "      <td>0.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANN</td>\n",
       "      <td>s</td>\n",
       "      <td>0.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Per (BL)</td>\n",
       "      <td>c</td>\n",
       "      <td>0.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Per_op</td>\n",
       "      <td>c</td>\n",
       "      <td>0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LR_op</td>\n",
       "      <td>c</td>\n",
       "      <td>0.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SVC_op</td>\n",
       "      <td>c</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RF_op</td>\n",
       "      <td>c</td>\n",
       "      <td>0.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ADA_op</td>\n",
       "      <td>c</td>\n",
       "      <td>0.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>XGBC_op</td>\n",
       "      <td>c</td>\n",
       "      <td>0.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>VotHard</td>\n",
       "      <td>c</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VotSoft</td>\n",
       "      <td>c</td>\n",
       "      <td>0.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ANN</td>\n",
       "      <td>c</td>\n",
       "      <td>0.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Per (BL)</td>\n",
       "      <td>test</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Per_op</td>\n",
       "      <td>test</td>\n",
       "      <td>0.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LR_op</td>\n",
       "      <td>test</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SVC_op</td>\n",
       "      <td>test</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RF_op</td>\n",
       "      <td>test</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ADA_op</td>\n",
       "      <td>test</td>\n",
       "      <td>0.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>XGBC_op</td>\n",
       "      <td>test</td>\n",
       "      <td>0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>VotHard</td>\n",
       "      <td>test</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>VotSoft</td>\n",
       "      <td>test</td>\n",
       "      <td>0.619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ANN</td>\n",
       "      <td>test</td>\n",
       "      <td>0.647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   algoritmo variable  value\n",
       "0   Per (BL)        s  0.542\n",
       "1     Per_op        s  0.773\n",
       "2      LR_op        s  0.731\n",
       "3     SVC_op        s  0.895\n",
       "4      RF_op        s  0.872\n",
       "5     ADA_op        s  0.811\n",
       "6    XGBC_op        s  0.813\n",
       "7    VotHard        s  0.865\n",
       "8    VotSoft        s  0.836\n",
       "9        ANN        s  0.954\n",
       "10  Per (BL)        c  0.721\n",
       "11    Per_op        c  0.731\n",
       "12     LR_op        c  0.811\n",
       "13    SVC_op        c  0.916\n",
       "14     RF_op        c  0.917\n",
       "15    ADA_op        c  0.837\n",
       "16   XGBC_op        c  0.905\n",
       "17   VotHard        c  0.916\n",
       "18   VotSoft        c  0.910\n",
       "19       ANN        c  0.984\n",
       "20  Per (BL)     test  0.538\n",
       "21    Per_op     test  0.400\n",
       "22     LR_op     test  0.667\n",
       "23    SVC_op     test  0.586\n",
       "24     RF_op     test  0.667\n",
       "25    ADA_op     test  0.526\n",
       "26   XGBC_op     test  0.579\n",
       "27   VotHard     test  0.652\n",
       "28   VotSoft     test  0.619\n",
       "29       ANN     test  0.647"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorings = scorings.melt(\"algoritmo\",(\"s\",\"c\",\"test\"))\n",
    "scorings.astype({\"value\":float})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algoritmo</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Per (BL)</td>\n",
       "      <td>s</td>\n",
       "      <td>0.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Per_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LR_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RF_op</td>\n",
       "      <td>s</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  algoritmo variable  value\n",
       "0  Per (BL)        s  0.542\n",
       "1    Per_op        s  0.773\n",
       "2     LR_op        s  0.731\n",
       "3    SVC_op        s  0.895\n",
       "4     RF_op        s  0.872"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ba8a461ee0>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAP2klEQVR4nO3df6zdd13H8edr7crPOTN2cbBubpFiKD/UcR3iRIf8SEd0M4KymWX+ICyoA+TXMgKZs2JkHYrBTMNEw8DgGERNgdKiMBAnw5YAG+1SbCawFhq6jZEhga307R/nWzi7dLun62nf7enzkdz0fL/nc895352c5773e+85N1WFJOnQO6Z7AEk6WhlgSWpigCWpiQGWpCYGWJKaLO2641WrVtX69eu77l6SDqXsa2fbEfAdd9zRddeSdFjwFIQkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNWl7O0rpULv00kvZuXMnJ510EmvWrOkeRzLAOnrs3LmTHTt2dI8hfZ+nICSpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpie+GNiW+1aGk/WWAp8S3OpS0vwywpuIrq5/aPcKidt91ArCU3Xd9+bCf99TLb+keQYeA54AlqYkBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpyUQBTrIqydYk25Jcto/rT01yQ5LPJrk5yQumP6okzZZF/yRRkiXA1cDzgO3AxiRrq2rL2LI3AtdX1d8mWQmsA06b1pBPf927pnVTB81xd9zDEuArd9xz2M/7masu6h5BEpMdAZ8JbKuq26rqXuA64LwFawr4keHy8cBXpzeiNB0nPnwPP/aI3Zz48D3do0jAZH+U82Tg9rHt7cAzFqy5AvhIkpcDjwKeO5XppCl67dPu7h5Bup9p/RDuAuCdVbUceAHw7iQ/dNtJLk6yKcmmXbt2TemuJenINEmAdwCnjG0vH/aNewlwPUBVfQp4OHDiwhuqqmuqar6q5ufm5h7axJI0IyYJ8EZgRZLTkywDzgfWLljzFeA5AEmexCjAHuJK0oNYNMBVtRu4BNgA3Mrotx02J1md5Nxh2WuAlyb5PPBPwO9UVR2soSVpFkzyQziqah2jXy0b33f52OUtwFnTHU2SZpuvhJOkJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmkz0J4m0uD3LHnW/fyVpMQZ4Sv5vxfO7R5B0hPEUhCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDVZ2j2AJO116aWXsnPnTk466STWrFnTPc5BZ4AlHTZ27tzJjh07usc4ZDwFIUlNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1GSiACdZlWRrkm1JLnuANb+ZZEuSzUneM90xJWn2LPpCjCRLgKuB5wHbgY1J1lbVlrE1K4DXA2dV1TeSPPZgDSxJs2KSI+AzgW1VdVtV3QtcB5y3YM1Lgaur6hsAVfX16Y4pSbNnkgCfDNw+tr192DfuicATk9yY5KYkq6Y1oCTNqmm9F8RSYAVwNrAc+I8kT62qu8cXJbkYuBjg1FNPndJdS9KRaZIj4B3AKWPby4d947YDa6vqvqr6X+CLjIJ8P1V1TVXNV9X83NzcQ51ZkmbCJAHeCKxIcnqSZcD5wNoFa/6V0dEvSU5kdEritumNKUmzZ9EAV9Vu4BJgA3ArcH1VbU6yOsm5w7INwJ1JtgA3AK+rqjsP1tCSNAsmOgdcVeuAdQv2XT52uYBXDx+SDkNn/fVZ3SMsatndyziGY7j97tsP+3lvfPmNB3wbvhJOkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpyUR/E06SDoV6ZLGHPdQjq3uUQ8IASzps3HfWfd0jHFKegpCkJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGBJamKAJamJAZakJgZYkppMFOAkq5JsTbItyWUPsu6FSSrJ/PRGlKTZtGiAkywBrgbOAVYCFyRZuY91xwGvBD497SElaRZNcgR8JrCtqm6rqnuB64Dz9rHuT4Erge9McT5JmlmTBPhk4Pax7e3Dvu9LcgZwSlV96MFuKMnFSTYl2bRr1679HlaSZskB/xAuyTHAXwKvWWxtVV1TVfNVNT83N3egdy1JR7RJArwDOGVse/mwb6/jgKcAH0/yJeDngLX+IE6SHtwkAd4IrEhyepJlwPnA2r1XVtU3q+rEqjqtqk4DbgLOrapNB2ViSZoRiwa4qnYDlwAbgFuB66tqc5LVSc492ANK0qxaOsmiqloHrFuw7/IHWHv2gY8lSbPPV8JJUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTSYKcJJVSbYm2Zbksn1c/+okW5LcnOSjSX58+qNK0mxZNMBJlgBXA+cAK4ELkqxcsOyzwHxVPQ14P7Bm2oNK0qyZ5Aj4TGBbVd1WVfcC1wHnjS+oqhuq6tvD5k3A8umOKUmzZ5IAnwzcPra9fdj3QF4CfHhfVyS5OMmmJJt27do1+ZSSNIOm+kO4JBcC88BV+7q+qq6pqvmqmp+bm5vmXUvSEWfpBGt2AKeMbS8f9t1PkucCbwB+qaq+O53xJGl2TXIEvBFYkeT0JMuA84G14wuS/AzwduDcqvr69MeUpNmzaICrajdwCbABuBW4vqo2J1md5Nxh2VXAo4H3JflckrUPcHOSpMEkpyCoqnXAugX7Lh+7/NwpzyVJM89XwklSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNDLAkNTHAktTEAEtSEwMsSU0MsCQ1McCS1MQAS1ITAyxJTQywJDUxwJLUxABLUhMDLElNJgpwklVJtibZluSyfVz/sCTvHa7/dJLTpj6pJM2YRQOcZAlwNXAOsBK4IMnKBcteAnyjqp4AvBW4ctqDStKsmeQI+ExgW1XdVlX3AtcB5y1Ycx5w7XD5/cBzkmR6Y0rS7Fk6wZqTgdvHtrcDz3igNVW1O8k3gccAd4wvSnIxcPGw+a0kWx/K0IexE1nwNR+O8pbf7h6h0xHxGPHHR/XxyxHxGOUV+/UYra+qVQt3ThLgqamqa4BrDuV9HkpJNlXVfPccemA+Roe/o+kxmuQUxA7glLHt5cO+fa5JshQ4HrhzGgNK0qyaJMAbgRVJTk+yDDgfWLtgzVpg7/e1LwI+VlU1vTElafYsegpiOKd7CbABWAL8Q1VtTrIa2FRVa4G/B96dZBtwF6NIH41m9vTKDPExOvwdNY9RPFCVpB6+Ek6SmhhgSWpigCeU5Pwkb0hydpKfH9v/siQXdc6mkSSPS/KRJKcl+a2x/fNJ3tY5m0aS/GiSP3iIn/tHSR457Zk6eQ54QkmuBd4G/Crwrap6S/NIWiDJ7wInAJ8BXltVv9I8khYY3ifmg1X1lIfwuV8C5qvqsH+RxqQ8Al4gyaOSfCjJ55N8IcmLh5dV/zSj3/B4GfCqJJ9L8qwkVyR57fC5H0/y1iSbktya5GeT/HOS/0nyprH7uDDJfw+38fbh/Ta0H5JclOTm4XF697B7FfBh4M3As4b/vq8avmv54PB5VyS5Nsknk3w5ya8nWZPkliTrkxw7rHt6kk8k+UySDUke1/OVzpw3Az8xPDZXJXldko3DY/kn8IDPwVcAjwduSHJD61cwTVXlx9gH8ELg78a2jwfOAN41bF/B6OiKhdvAx4Erh8uvBL4KPA54GKOXcD8GeBLwAeDYYd3fABd1f91H0gfwZOCLwInD9gmMfkXyc8P22YyOsli4PTxe/wkcC/wU8G3gnOG6fwF+bbjuv4C5Yf+LGf36ZfvXfqR/AKcBXxguP5/Rr5yF0cHgB4Ff3NdzcPj3S3sf81n5OKQvRT5C3AL8RZIrGT1pP5lk75HVJPa+SOUWYHNVfQ0gyW2MXi34C8DTgY3D+xU9Avj6FOc/Gvwy8L4avhWtqruG8/KfnvDzP1xV9yW5hVG41w/7b2EUiJ8EngL82/AYLQG+Nr3xNXj+8PHZYfvRwArgkyx4DjbNd9AZ4AWq6otJzgBeALwpyUcZPeFfOOFNfHf4d8/Y5b3bSxn93/7aqnr9lEbWyDn8IKSL+S5AVe1Jcl8Nh1fc/zHaXFXPnP6YGhPgz6vq7T90xYLnYFWtPuTTHQKeA14gyeOBb1fVPwJXAc8GllbV3ve2uAc47gDu4qPAi5I8dri/E5L8+IHMfBT6GPAbSR4Do/+GwHOAfx+uP9DHaCswl+SZw+0fm+TJB3B7+oHxx2YD8HtJHg2Q5OQkj93Hc/CMfXzuTPAI+Ic9FbgqyR7gPuC9jL412usDwPuTnAe8fH9vvKq2JHkj8JEkxwz38YfAlw948qNEjV4K/2fAJ5J8j9H59e9U1T3DkpuB7yX5PPBOfvAt7qS3f2+SFwFvS3I8o+fJXwGbp/QlHLWq6s4kNyb5AqPTeu8BPjWc6vkWcCHwBO7/HPz94dOvAdYn+WpVPfvQTz99/hraIpK8A3hHVd3UPYv2LcmFwPKqenP3LNL+MMCS1MRzwJLUxABLUhMDLElNDLAkNTHAktTEAEtSk/8HVjStQsviMboAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=scorings,kind=\"bar\",hue=)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15960/1203878885.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcatplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscorings\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"algoritmo\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"variable\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mcatplot\u001b[1;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, seed, order, hue_order, row_order, col_order, kind, height, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)\u001b[0m\n\u001b[0;32m   3860\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3861\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlegend\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhue\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhue\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3862\u001b[1;33m         \u001b[0mhue_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_utf8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3863\u001b[0m         \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel_order\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3864\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not iterable"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=scorings,x=\"algoritmo\",hue=\"variable\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d9579fd63862c719ce91d1dd1fb1c44b2ef43bdaa2f86d843d9fd8562746d35b"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}