Correciones para reentrenamiento

corrector/i/correcciones.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-04-24T08:44:10.320385Z",
+     "start_time": "2025-04-24T08:44:10.302500Z"
+    }
+   },
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Cargar el archivo CSV\n",
+    "file_path = 'samples_con_predicciones_20250421_183556.csv'\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "# Mostrar las primeras filas para entender la estructura\n",
+    "df.head()\n",
+    "# Excluir la columna 'i' si no es numérica\n",
+    "df_numeric = df.drop(columns=['i'], errors='ignore')\n",
+    "\n",
+    "# Calcular medidas de tendencia central\n",
+    "means = df_numeric.mean()\n",
+    "medians = df_numeric.median()\n",
+    "modes = df_numeric.mode().iloc[0]  # Tomar la primera moda en caso de múltiples\n",
+    "\n",
+    "# Crear un DataFrame resumen\n",
+    "summary_df = pd.DataFrame({\n",
+    "    'Media': means,\n",
+    "    'Mediana': medians,\n",
+    "    'Moda': modes\n",
+    "})\n",
+    "\n",
+    "print(summary_df)\n",
+    "\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                    Media     Mediana        Moda\n",
+      "51226.555    52433.122823   52451.027   53030.723\n",
+      "34661.008    34671.744242   34683.582   34659.220\n",
+      "-46453.117  -46255.689251  -46241.650  -46557.766\n",
+      "-34479.934  -34483.822386  -34540.957  -34778.710\n",
+      "-54979.523  -53310.700648  -53318.120  -53400.220\n",
+      "187500.0    187500.000000  187500.000  187500.000\n",
+      "-42187.754  -41231.398199  -41227.590  -42124.812\n",
+      "-38942.484  -38493.926292  -38475.242  -38937.766\n",
+      "-13639.772  -12337.295411  -12108.163  -13484.159\n",
+      "-19195.746  -18393.630795  -18439.430  -18445.710\n",
+      "-44561.867  -43576.375646  -43575.040  -43121.746\n",
+      "15900.853    15775.570330   15771.972   15587.816\n",
+      "-8673.84     -7640.306663   -7818.551   -8171.530\n",
+      "-13790.78   -11946.194669  -11769.981  -12878.539\n",
+      "-34718.676  -35074.907533  -35091.234  -35262.270\n",
+      "-42390.598  -41837.181986  -41881.670  -43301.970\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-24T10:21:58.452969Z",
+     "start_time": "2025-04-24T10:21:58.418323Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "!python --version",
+   "id": "881d3baa4281633b",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.11.11\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

corrector/i/resumen_tendencia.csv

@@ -0,0 +1,17 @@
+edia,Mediana,Moda
+52433.12282348221,52451.027,53030.723
+34671.74424150843,34683.582,34659.22
+-46255.68925086922,-46241.65,-46557.766
+-34483.822386199514,-34540.957,-34778.71
+-53310.70064830168,-53318.12,-53400.22
+187500.0,187500.0,187500.0
+-41231.39819871623,-41227.59,-42124.812
+-38493.926292056705,-38475.242,-38937.766
+-12337.295410671302,-12108.163,-13484.159
+-18393.630794597488,-18439.43,-18445.71
+-43576.37564642952,-43575.04,-43121.746
+15775.570329767317,15771.972,15587.816
+-7640.306662530087,-7818.551,-8171.53
+-11946.194668627977,-11769.981,-12878.539
+-35074.90753329767,-35091.234,-35262.27
+-41837.18198635999,-41881.67,-43301.97