Chapter 5 - ScikitLearn - Datasets - 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducción al uso de Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En los capitulos previos hemos usado tanto numpy como Pandas para realizar un manejo de datos, tanto operacionalmente como una visualización de estos para su análisis.\n",
    "\n",
    "Para hacer Machine Learning necesitamos un alto conocimiento matemático en algoritmos de machine learning que no es tan sencillo de abarcar.\n",
    "\n",
    "Pero en vez de implementar estos algoritmos manualmente, existe el paquete Scikit-Learn que posee estos algoritmos ya implementos listos para ser usados.\n",
    "\n",
    "Algunos algoritmos que incluye Scikit-Learn son:\n",
    "   * Classificación\n",
    "   * Regresión\n",
    "   * Clustering\n",
    "   * Árboles de decisión\n",
    "\n",
    "Ahora implementar Machine Learning con Scikit-learn es simplemente llamar a una función con los datos apropiados para ajustar y entrenar un modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obteniendo datasets\n",
    "\n",
    "Es usual que uno de los retos del machine learning es obtener datatsets para experimentar con ellos.\n",
    "\n",
    "Para comenzar tendremos pequeños datasets que pueden ser creado fácilmente por nosotros mismos para testear que un algoritmo de machine learning esta trabajado correctamente de acuerdo a nuestros conocimientos matemáticos. Luego de esto trabajaremos con datasets más extensos que tendremos que buscar en lugares como:\n",
    "\n",
    "   * Kaggle\n",
    "   * UCI (University of California) Repository\n",
    "   * Scikit-learn dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset de Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris() # datos crudos o raw data\n",
    "print(iris.DESCR)\n",
    "# print(iris.data) # mostrar los datos numéricos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='|S10')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names # labels names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target # labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convirtiendolo a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
       "1  4.9  3.0  1.4  0.2\n",
       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(iris.data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Más interesantes datasets ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data sobre cáncer de mama\n",
    "breast_cancer = datasets.load_breast_cancer()\n",
    "\n",
    "# data sobre diabetes\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "# data de 1797 imágenes de 8x8 de digitos escritos a mano\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.gray()\n",
    "plt.matshow(digits.images[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando dataset de Kaggle\n",
    "\n",
    "Kaggle es la comunidad más grande de científicos de datos y machine learners, que es propiedad de Google desde el 2007.\n",
    "[Datasets de kaggle](https://www.kaggle.com/datasets/):\n",
    "\n",
    "   * [Precio de zapato de mujeres](https://www.kaggle.com/datafiniti/womens-shoes-prices)\n",
    "   * [Venta de propiedades de NYC](https://www.kaggle.com/new-york-city/nyc-property-sales#nyc-rolling-sales.csv)\n",
    "   * [Retraso de Vuelo de USA](https://www.kaggle.com/niranjan0272/us-flight-delay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando dataset de UCI (University of California, Irvine) Repository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los datasets de UCI los puedes encontrar [aquí](https://archive.ics.uci.edu/ml/datasets.php).\n",
    "\n",
    "Estos son una colección de bases de datos, teorías de dominio y generadores de datos que utiliza la comunidad de aprendizaje automático para el análisis empírico de los algoritmos de machine learning:\n",
    "\n",
    "   * Dataset de Autos MPG: Colección de datos sobre la eficiencia de combustible en distintos tipos de carros [link](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n",
    "   * Rendimiento de estudiantes: Predecir el rendimiento en secundaria [link](https://archive.ics.uci.edu/ml/datasets/Student+Performance)\n",
    "   * Conjunto de datos de Censo: [link here](https://archive.ics.uci.edu/ml/datasets/census+income)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Generando tu propio dataset\n",
    "\n",
    "Scikit-learn nos proporciona la herramienta para generar nuestro propio conjunto de datos, esta herramienta es \"sklearn.datasets.samples_generator\" que contiene diferentes funciones para este fin.\n",
    "\n",
    "Puedes generar datasets de diferentes distribuciones:\n",
    "   * Conjunto de datos distribuidos linealmente.\n",
    "   * Conjunto de datos agrupados (clusters).\n",
    "   * Clustered data distribuidos en circular fashion (Conjunto de datos agrupados distribuidos en forma circular)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset distribuido linealmente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_regression(n_samples=100, n_features=1, noise=5.4)\n",
    "plt.plot(X,y, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noise es la desviación estandar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape # n_samples, n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape # n_samples "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustered datasets o Datos Agrupados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fccaa04a410>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,y = make_blobs(500, centers = 3)\n",
    "\n",
    "rgb = np.array(['r','g','b'])\n",
    "plt.scatter(X[:,0], X[:,1], color = rgb[y])\n",
    "            # x , y       , # colores y puede ser 0 , 1 o 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datos agrupados distribuidos en forma circular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcca83aa390>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "X,y = make_circles(n_samples = 100, noise = 0.09)\n",
    "\n",
    "rgb = np.array(['r','g'])\n",
    "plt.scatter(X[:,0],X[:,1], color=rgb[y])\n",
    "            # x, y  , color -> y puede ser 0 o 1  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}