Skip to content

Commit

Permalink
Lab m04 c02 was added.
Browse files Browse the repository at this point in the history
  • Loading branch information
@aoguedao
aoguedao committed Jan 8, 2020
1 parent 60760d5 commit 893b7b5
Show file tree
Hide file tree
Showing 3 changed files with 403 additions and 5 deletions.
0 ...validation/data/brain_and_body_weight.txt → ...regression/data/brain_and_body_weight.txt
View file
File renamed without changes.
398 changes: 398 additions & 0 deletions m04_machine_learning/m04_c02_linear_regression/m04_c02_lab.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,398 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/4/47/Logo_UTFSM.png\" width=\"200\" alt=\"utfsm-logo\" align=\"left\"/>\n",
"\n",
"# MAT281\n",
"### Aplicaciones de la Matemática en la Ingeniería"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Módulo 04\n",
"## Laboratorio Clase 02: Regresión Lineal"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"### Instrucciones\n",
"\n",
"\n",
"* Completa tus datos personales (nombre y rol USM) en siguiente celda.\n",
"* La escala es de 0 a 4 considerando solo valores enteros.\n",
"* Debes _pushear_ tus cambios a tu repositorio personal del curso.\n",
"* Como respaldo, debes enviar un archivo .zip con el siguiente formato `mXX_cYY_lab_apellido_nombre.zip` a [email protected], debe contener todo lo necesario para que se ejecute correctamente cada celda, ya sea datos, imágenes, scripts, etc.\n",
"* Se evaluará:\n",
" - Soluciones\n",
" - Código\n",
" - Que Binder esté bien configurado.\n",
" - Al presionar `Kernel -> Restart Kernel and Run All Cells` deben ejecutarse todas las celdas sin error."
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"__Nombre__:\n",
"\n",
"__Rol__:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import altair as alt\n",
"\n",
"from sklearn import datasets, linear_model\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"alt.themes.enable('opaque')\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"## Ejercicio 1: Diabetes"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Realizar análisis de regresión a los datos de diabetes disponibles en scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"diabetes = datasets.load_diabetes()\n",
"print(dir(diabetes)) ## Atributos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"print(diabetes.DESCR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"diabetes_df = (\n",
" pd.DataFrame(\n",
" diabetes.data,\n",
" columns=diabetes.feature_names\n",
" )\n",
" .assign(prog=diabetes.target)\n",
")\n",
"\n",
"diabetes_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"#### Pregunta 1 (1 pto):\n",
"\n",
"* ¿Por qué la columna de sexo tiene esos valores?\n",
"* ¿Cuál es la columna a predecir?"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"**---TU RESPUESTA VA AQUÍ--**"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"#### Pregunta 2 (1 pto)\n",
"\n",
"Realiza una regresión lineal con todas las _features_ incluyendo intercepto."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"X = diabetes_df.drop(## FIX ME PLEASE ##).values\n",
"y = diabetes_df[(## FIX ME PLEASE ##].values"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Ajusta el modelo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"regr = ## FIX ME PLEASE ##\n",
"regr.## FIX ME PLEASE ##"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Imprime el intercepto y los coeficientes luego de ajustar el modelo."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"print(f\"Intercept: \\n{## FIX ME PLEASE ##}\\n\")\n",
"print(f\"Coefficients: \\n{## FIX ME PLEASE ##}\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Haz una predicción del modelo con los datos `X`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"y_pred = ## FIX ME PLEASE ##"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Calcula e imprime el error cuadrático medio y el coeficiente de determinación de este modelo ajustado."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Error cuadrático medio\n",
"print(f\"Mean squared error: {## FIX ME PLEASE ##:.2f}\\n\")\n",
"\n",
"# Coeficiente de determinación\n",
"print(f\"Coefficient of determination: {## FIX ME PLEASE ##:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"**Pregunta: ¿Qué tan bueno fue el ajuste del modelo?**"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"**---TU RESPUESTA VA AQUÍ--**"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"### Pregunta 3 (2 ptos).\n",
"\n",
"Realizar multiples regresiones lineales utilizando una sola _feature_ a la vez. \n",
"\n",
"En cada iteración:\n",
"\n",
"- Crea un arreglo `X`con solo una feature filtrando `X`.\n",
"- Crea un modelo de regresión lineal con intercepto.\n",
"- Ajusta el modelo anterior.\n",
"- Genera una predicción con el modelo.\n",
"- Calcula e imprime las métricas de la pregunta anterior."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"for i in range(X.shape[1]):\n",
" X_i = X[:, np.newaxis, i] # Protip! Trata de entender este paso por tu cuenta, es muy clever\n",
" regr_i = ## FIX ME PLEASE ##\n",
" regr_i.## FIX ME PLEASE ##\n",
" y_pred_i = ## FIX ME PLEASE ##\n",
" print(f\"{diabetes_df.columns[i]}:\")\n",
" print(f\"\\tCoefficients: {## FIX ME PLEASE ##}\")\n",
" print(f\"\\tIntercept: {## FIX ME PLEASE ##}\")\n",
" print(f\"\\tMean squared error: {## FIX ME PLEASE ##:.2f}\")\n",
" print(f\"\\tCoefficient of determination: {## FIX ME PLEASE ##:.2f}\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"**Si tuvieras que escoger una sola _feauture_, ¿Cuál sería? ¿Por qué?**"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"**---TU RESPUESTA VA AQUÍ--**"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Con la feature escogida haz el siguiente gráfico:\n",
"\n",
"- Scatter Plot\n",
"- Eje X: Valores de la feature escogida.\n",
"- Eje Y: Valores de la columna a predecir (target).\n",
"- En color rojo dibuja la recta correspondiente a la regresión lineal (utilizando `intercept_`y `coefs_`).\n",
"- Coloca un título adecuado, nombre de los ejes, etc.\n",
"\n",
"Puedes utilizar `matplotlib` o `altair`, el que prefiera."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"## FIX ME PLEASE ##"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python [conda env:ds]",
"language": "python",
"name": "conda-env-ds-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Loading

0 comments on commit 893b7b5

Please sign in to comment.