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‎PPT11 House Prices Numpy.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Copia de Presentacion_11_alumnos.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/ernestcr/DataSciencePython/blob/master/PPT11%20House%20Prices%20Numpy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SnbKAswA5H70",
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"colab_type": "text"
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},
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"source": [
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" ![texto alternativo](https://drive.google.com/uc?export=view&id=14HGkNoRD0Cp4Z0KyPxvrLaj_u3eG97hC)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AFXXyEkA5TO3",
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"colab_type": "text"
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},
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"source": [
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" <img src=\"https://drive.google.com/uc?export=view&id=1dpWC3HxKCInKLhkhLPOyYYyV4b0E_sms\" alt=\"Simply Easy Learning\" width=\"800\" \n",
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" height=\"450\">\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "VdFUEVlOXN6J",
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"colab_type": "text"
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},
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"source": [
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"# **Recordando**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "7gE6I95qDIgA",
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"colab_type": "text"
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},
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"source": [
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" <img src=\"https://drive.google.com/uc?export=view&id=17iqwupvy5Nq2PO59Loj0gaU-e6IUYKSS\" alt=\"Simply Easy Learning\" width=\"800\" \n",
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" height=\"450\">\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "30ClEV3k6oOS",
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"colab_type": "text"
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},
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"source": [
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" <img src=\"https://drive.google.com/uc?export=view&id=12OkMjp-HFxBUrp4CxC4i6GjWuAreunKN\" alt=\"Simply Easy Learning\" width=\"800\" \n",
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" height=\"450\">"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xbJS8J0jJiIR",
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"colab_type": "text"
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},
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"source": [
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" <img src=\"https://intellipaat.com/mediaFiles/2018/12/Python-NumPy-Cheat-Sheet-1.png\" alt=\"Simply Easy Learning\" width=\"850\" \n",
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" height=\"500\">"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "bedLAzr7Kctc",
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"colab_type": "text"
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},
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"source": [
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"# **Ejemplo Práctico 1**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "_FOFEpBBLUhX",
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"colab_type": "text"
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},
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"source": [
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"* Implementaremos el método de regresion lineal simple utilizando NumpPy. Recordemos:\n",
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"\n",
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"<img src=\"https://drive.google.com/uc?export=view&id=1YTC-Fz1FYb2Q7dRWdTFXjA_BWPgfJmtR\" alt=\"Simply Easy Learning\" width=\"500\" \n",
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" height=\"200\">\n",
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"\n",
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"* Esta formulación la podiamos expresar de esta forma:\n",
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"\n",
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"<img src=\"https://drive.google.com/uc?export=view&id=16BQtn-NZA-icSvwSdJSBqp_Qd7n5affs\" alt=\"Simply Easy Learning\" width=\"700\" \n",
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" height=\"200\">"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "m2JnBnpRggR-",
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"colab_type": "text"
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},
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"source": [
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"Formula: Minimizar el error cuadratico Medio (MSE): $W = (X^{T}X)^{-1}X^TY$"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "WMZKGIE9gNtg",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Sirve para calculo numerico, proporciona funcionalidades para trabajar con arrays, matrices y operaciones matriciales\n",
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"import numpy as np \n",
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"# lo mismo que hace numpy lo extiende a herramientas cientificas, lo expande. Proporciona otras funcionalidades como tratamiento de imagenes\n",
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"import scipy as sc \n",
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"# Liberia de visualizacion grafica (SIN EMBARGO, existen otras)\n",
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"import matplotlib.pyplot as plt \n"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "-2BAksYJga83",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Z2Srf3paPTa_",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 125
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},
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"outputId": "9785ddfc-236d-456b-a140-f459caefdae4"
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},
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"source": [
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n",
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"\n",
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"Enter your authorization code:\n",
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"··········\n",
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"Mounted at /content/drive\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1MW7rK78t03N",
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"colab_type": "text"
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},
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"source": [
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"* Ahora mejoremos la solucion para resolver con todas los atributos de la base de datos"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "PFLJrSImnKRo",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"\n"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "sEE_tMhmuFj0",
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"colab_type": "text"
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},
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"source": [
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"* Para medir el error de nuestro Modelo, utilizamos el Error cuadratico médio: \n",
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"\n",
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" $rmse = \\sqrt{(\\frac{1}{n})\\sum_{i=1}^{n}(y_{i} - p_{i})^{2}}$\n",
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"\n",
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"<img src=\"https://iartificial.net/wp-content/uploads/2018/12/error-regresion-lineal2.png\" alt=\"Simply Easy Learning\" width=\"300\" \n",
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" height=\"200\">\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "dFMGCp_6utd1",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Pwg71i0puwpO",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "GS05EA58Rdph",
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"colab_type": "text"
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},
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"source": [
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"# **Ejemplo Práctico 2**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "fpUgpOQiRdcH",
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"colab_type": "text"
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},
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"source": [
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"**El descenso del Gradiente**\n",
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"\n",
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"El ejemplo de regresion lineal nos dio un resultado factible calculando el valor de los pesos con error mínimo de forma ditecta, pero, en la práctica su uso es inviable debido al alto costo de calcular una matriz inversa cuando tenemos demasiado atributos por muestra.\n",
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"\n",
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"Pero siempre existe una solucion!!\n",
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"\n",
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"Exploremos el descenso del gradiente:\n",
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"\n",
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" <img src=\"https://miro.medium.com/max/694/1*7030GXGlVD-u9VyqVJdTyw.png\" alt=\"Simply Easy Learning\" width=\"500\" height=\"250\">\n",
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"\n",
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"Veamos mejor este ejemplo de una forma animada:\n",
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"[An Interactive Tutorial on Numerical Optimization](http://www.benfrederickson.com/numerical-optimization/)\n",
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"\n",
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"\n",
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" <img src=\"https://developers.google.com/machine-learning/crash-course/images/GradientDescentNegativeGradient.svg?hl=es-419\" alt=\"Simply Easy Learning\" width=\"400\">\n",
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"\n",
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"\n",
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" <img src=\"https://developers.google.com/machine-learning/glossary/images/nonconvex_function.svg?hl=es\" alt=\"Simply Easy Learning\" width=\"280\">\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "FljnyMraDScU",
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"colab_type": "text"
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},
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"source": [
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"* Ahora vamos a programarlo"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "YDhbZB60Srax",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import numpy as np \n",
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"import scipy as sc \n",
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"import matplotlib.pyplot as plt"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "nRTJAib6S1pM",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "DlOUjDXlWoeS",
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"colab_type": "text"
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},
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"source": [
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"* Ahora generemos un punto aleatorio en nuestro mapa y comencemos a descender"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "lZengMsnWi0M",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xIoApqs_XSOw",
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"colab_type": "text"
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},
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"source": [
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"* Perfecto, vamos ahora descender hasta una posición minima"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "6urRRhIXXN3i",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "rZNpT2jkexga",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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}
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]
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}

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