sesion_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "\n",
    "# Separar las variables (X) de las etiquetas (y)\n",
    "X_train = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_train = df[\"ActividadFisica\"]\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "procesador = ColumnTransformer(transformers=[\n",
    "    (\"num\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer()),\n",
    "         (\"std\",StandardScaler())\n",
    "     ]),\n",
    "        ['Peso','Estatura','Edad','PctGrasaCorporal']\n",
    "    ),\n",
    "    (\"cat\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "         (\"std\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "     ]),\n",
    "        ['EstadoCivil']\n",
    "\n",
    "    )\n",
    "])\n",
    "modelo = KNeighborsClassifier()\n",
    "\n",
    "\n",
    "modeloFinal = Pipeline([\n",
    "    (\"Preprocesado\", procesador),\n",
    "    (\"Clasificador\",modelo)\n",
    "])\n",
    "\n",
    "\n",
    "modeloFinal.fit(X_train,y_train)\n",
    "modeloFinal.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "#Dataset completo\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "X_orig = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"ActividadFisica\"]\n",
    "\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X_orig,y_orig,test_size=0.25)\n",
    "\n",
    "modeloFinal.fit(X_train,y_train)\n",
    "\n",
    "y_pred = modeloFinal.predict(X_test)\n",
    "\n",
    "accuracy_score(y_pred,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataset completo\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "X_orig = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"ActividadFisica\"]\n",
    "\n",
    "#stratificar siempre\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X_orig,y_orig,test_size=0.25,stratify=y_orig)\n",
    "\n",
    "modeloFinal.fit(X_train,y_train)\n",
    "\n",
    "y_pred = modeloFinal.predict(X_test)\n",
    "\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#Dataset completo\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "X_orig = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"ActividadFisica\"]\n",
    "\n",
    "\n",
    "accuracies = cross_val_score(modeloFinal,X_orig,y_orig, cv=10)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "print(np.mean(accuracies))\n",
    "print(np.std(accuracies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "#Dataset completo\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "X_orig = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"ActividadFisica\"]\n",
    " \n",
    "cv = StratifiedKFold(n_splits=10,shuffle=True)\n",
    "resultados = cross_validate(modeloFinal,X_orig,y_orig, cv=cv)\n",
    "resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ejercicio 1 clase 2\n",
    "\n",
    "df = pd.read_csv(\"datasets/MathLearningDataset.csv\")\n",
    "\n",
    "X_orig = df.drop(columns=[\"Type of Answer\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"Type of Answer\"]\n",
    "#Hold-out\n",
    "procesador = ColumnTransformer(transformers=[\n",
    "    (\"num\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer()),\n",
    "         (\"std\",StandardScaler())\n",
    "     ]),\n",
    "        ['Student Age','Response Time (s)']\n",
    "    ),\n",
    "    (\"cat\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "         (\"std\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "     ]),\n",
    "        ['Student Country','Question Level','Topic','Subtopic']\n",
    "\n",
    "    )\n",
    "])\n",
    "modelo = KNeighborsClassifier(6)\n",
    "\n",
    "\n",
    "modeloFinal1 = Pipeline([\n",
    "    (\"Preprocesado\", procesador),\n",
    "    (\"Clasificador\",modelo)\n",
    "])\n",
    "\n",
    "\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X_orig,y_orig,test_size=0.25,stratify=y_orig)\n",
    "\n",
    "modeloFinal1.fit(X_train,y_train)\n",
    "\n",
    "y_pred = modeloFinal1.predict(X_test)\n",
    "\n",
    "score = accuracy_score(y_pred,y_test)\n",
    "print(\"Hold-Out=\", score)\n",
    "#Validacion cruzada 10 folds\n",
    "cv = StratifiedKFold(n_splits=10,shuffle=True)\n",
    "accuracies = cross_val_score(modeloFinal1,X_orig,y_orig, cv=cv)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "print(\"Validación cruzada =\",np.mean(accuracies))\n",
    "#print(np.std(accuracies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "#Dataset completo\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "X_orig = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"ActividadFisica\"]\n",
    "\n",
    "\n",
    "\n",
    "procesador = ColumnTransformer(transformers=[\n",
    "    (\"num\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer()),\n",
    "         (\"std\",StandardScaler())\n",
    "     ]),\n",
    "        ['Peso','Estatura','Edad','PctGrasaCorporal']\n",
    "    ),\n",
    "    (\"cat\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "         (\"std\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "     ]),\n",
    "        ['EstadoCivil']\n",
    "\n",
    "    )\n",
    "])\n",
    "modelo = KNeighborsClassifier()\n",
    "\n",
    "\n",
    "modeloFinal = Pipeline([\n",
    "    (\"Preprocesado\", procesador),\n",
    "    (\"Clasificador\",modelo)\n",
    "])\n",
    "\n",
    "\n",
    "#stratificar siempre\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X_orig,y_orig,test_size=0.25,stratify=y_orig)\n",
    "\n",
    "parametros = {\n",
    "    \"Clasificador__n_neighbors\":[1,3,5,7,9,11]\n",
    "}\n",
    "\n",
    "\n",
    "gridSearch =  GridSearchCV(modeloFinal, param_grid=parametros)\n",
    "gridSearch.fit(X_train,y_train)\n",
    "gridSearch.best_params_\n",
    "\n",
    "mejorModelo = gridSearch.best_estimator_\n",
    "\n",
    "y_pred =  mejorModelo.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "df = pd.read_csv(\"datasets/datos05_train.csv\")\n",
    "X_orig = df.drop(columns=[\"ActividadFisica\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"ActividadFisica\"]\n",
    "\n",
    "\n",
    "\n",
    "procesador = ColumnTransformer(transformers=[\n",
    "    (\"num\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer()),\n",
    "         (\"std\",StandardScaler())\n",
    "     ]),\n",
    "        ['Peso','Estatura','Edad','PctGrasaCorporal']\n",
    "    ),\n",
    "    (\"cat\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "         (\"std\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "     ]),\n",
    "        ['EstadoCivil']\n",
    "\n",
    "    )\n",
    "])\n",
    "\n",
    "#Se cambia el modelo de vecinos cercanod  a tree\n",
    "modelo = DecisionTreeClassifier()\n",
    "\n",
    "\n",
    "modeloFinal = Pipeline([\n",
    "    (\"Preprocesado\", procesador),\n",
    "    (\"Clasificador\",modelo)\n",
    "])\n",
    "modeloFinal.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_text\n",
    "\n",
    "r = export_text(modeloFinal.named_steps[\"Clasificador\"],\n",
    "                    feature_names=modeloFinal.named_steps[\"Preprocesado\"].get_feature_names_out(),\n",
    "                    class_names=[\"Alta\", \"Baja\"])\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plot_tree(modeloFinal.named_steps[\"Clasificador\"],\n",
    "                    feature_names=modeloFinal.named_steps[\"Preprocesado\"].get_feature_names_out(),\n",
    "                    class_names=[\"Alta\", \"Baja\"], filled=True, rounded=True, label=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ejercicio 2\n",
    "#Ejercicio 1 clase 2\n",
    "\n",
    "df = pd.read_csv(\"datasets/MathLearningDataset.csv\")\n",
    "\n",
    "X_orig = df.drop(columns=[\"Type of Answer\"]) # Quitar la columna de clase\n",
    "y_orig = df[\"Type of Answer\"]\n",
    "#Hold-out\n",
    "procesador = ColumnTransformer(transformers=[\n",
    "    (\"num\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer()),\n",
    "         (\"std\",StandardScaler())\n",
    "     ]),\n",
    "        ['Student Age','Response Time (s)']\n",
    "    ),\n",
    "    (\"cat\",\n",
    "     Pipeline([\n",
    "         (\"imputador\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "         (\"std\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "     ]),\n",
    "        ['Student Country','Question Level','Topic','Subtopic']\n",
    "\n",
    "    )\n",
    "])\n",
    "modelo = KNeighborsClassifier()\n",
    "\n",
    "\n",
    "modeloFinal1 = Pipeline([\n",
    "    (\"Preprocesado\", procesador),\n",
    "    (\"Clasificador\",modelo)\n",
    "])\n",
    "\n",
    "\n",
    "modelo1 = DecisionTreeClassifier()\n",
    "\n",
    "modeloFinal2= Pipeline([\n",
    "    (\"Preprocesado\", procesador),\n",
    "    (\"Clasificador\",modelo1)\n",
    "])\n",
    "\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X_orig,y_orig,test_size=0.25,stratify=y_orig)\n",
    "\n",
    "parametros1 = {\n",
    "    \"Clasificador__n_neighbors\":[1,3,5,7,9,11]\n",
    "}\n",
    "\n",
    "parametros2 = {\n",
    "    \"Clasificador__max_depth\":[3,5,10,None],\n",
    "    \"Clasificador__min_samples_split\":[5,10,None],\n",
    "    \"Clasificador__min_samples_leaf\":[5,10,None],\n",
    "    \"Clasificador__max_leaf_nodes\":[5,10,None]\n",
    "}\n",
    "#Validacion cruzada 10 folds\n",
    "cv = StratifiedKFold(n_splits=5,shuffle=True)\n",
    "\n",
    "\n",
    "gridSearch =  GridSearchCV(modeloFinal1, param_grid=parametros1,cv=cv)\n",
    "\n",
    "\n",
    "gridSearch2 = GridSearchCV(modeloFinal2,param_grid=parametros2,cv=cv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridSearch.fit(X_train,y_train)\n",
    "\n",
    "gridSearch2.fit(X_train,y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mejorModelo1 = gridSearch.best_estimator_\n",
    "mejorModelo2 = gridSearch2.best_estimator_\n",
    "\n",
    "predicionKnn =  mejorModelo1.predict(X_test)\n",
    "\n",
    "predicionArbol = mejorModelo2.predict(X_test)\n",
    "\n",
    "\n",
    "print(accuracy_score(predicionKnn,y_test))\n",
    "print(accuracy_score(predicionArbol,y_test))"
   ]
  }
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