{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reconocimiento de digitos usando KNN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importamos las librerías necesarias"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import sklearn\n",
"import sklearn.datasets as ds\n",
"import sklearn.model_selection as cv\n",
"import sklearn.neighbors as nb\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (12, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"cargamos los digitos "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.0, 16.0)\n",
"(1797, 64)\n"
]
}
],
"source": [
"digits = ds.load_digits()\n",
"X = digits.data\n",
"y = digits.target\n",
"print((X.min(), X.max()))\n",
"print(X.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Más infomacion acerca de esta data en \n",
"http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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7SjSWF42FStLJkyeLtUcffbRYi/bBpjvZj8U24wwZAJIgkAEgCQIZAJIgkAEgCQIZAJIg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6d147427f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nrows, ncols = 2, 5\n",
"plt.figure(figsize=(6,3));\n",
"plt.gray()\n",
"for i in range(ncols * nrows):\n",
" ax = plt.subplot(nrows, ncols, i + 1)\n",
" ax.matshow(digits.images[i,...])\n",
" plt.xticks([]); plt.yticks([]);\n",
" plt.title(digits.target[i]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Separamos nuestra data de entrenamiento y nuestra data de prueba:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"(X_train, X_test, \n",
" y_train, y_test) = cv.train_test_split(X, y, test_size=.25)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Usamos el clasificador de KNN"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"knc = nb.KNeighborsClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"knc.fit(X_train, y_train);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nos da un margen de acierto de un 98.2%"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.98666666666666669"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knc.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 16., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dibujamos un 1 \n",
"one = np.zeros((8, 8))\n",
"one[1:-1, 4] = 16 # The image values are in [0, 16].\n",
"one[2, 3] = 16\n",
"one"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIsAAACcCAYAAABPyljcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6d147424a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(2,2));\n",
"plt.imshow(one, interpolation='none');\n",
"plt.grid(False);\n",
"plt.xticks(); plt.yticks();\n",
"plt.title(\"One\");"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"array([1])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knc.predict(one.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 16., 16., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 16., 16., 16., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 16., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dibujamos un 7 \n",
"seven = np.zeros((8, 8))\n",
"seven[1:-1, 4] = 16 # The image values are in [0, 16].\n",
"seven[1, 2:5] = 16\n",
"seven[4, 3:6] = 16\n",
"seven"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f6d11bd9710>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f6d11c37240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(2,2));\n",
"plt.imshow(seven, interpolation='none');\n",
"plt.grid(False);\n",
"plt.xticks(); plt.yticks();\n",
"plt.title(\"Seven\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"array([1])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knc.predict(seven.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"/* This template is inspired in the one used by Lorena Barba\n",
"in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc\n",
"We thank her work and hope you also enjoy the look of the notobooks with this style */\n",
"\n",
"<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>\n",
"Estilo aplicado\n",
"<style>\n",
"\n",
"\n",
"\n",
"#notebook_panel { /* main background */\n",
" background: #f7f7f7;\n",
"}\n",
"\n",
"div.cell { /* set cell width */\n",
" width: 1000px;\n",
"}\n",
"\n",
"div #notebook { /* centre the content */\n",
" background: #fff; /* white background for content */\n",
" width: 1100px;\n",
" margin: auto;\n",
" padding-left: 0em;\n",
"}\n",
"\n",
"#notebook li { /* More space between bullet points */\n",
" margin-top:0.6em;\n",
"}\n",
"\n",
"/* draw border around running cells */\n",
"div.cell.border-box-sizing.code_cell.running { \n",
" border: 1px solid #111;\n",
"}\n",
"\n",
"/* Put a solid color box around each cell and its output, visually linking them*/\n",
"div.cell.code_cell {\n",
" font-family: 'Source Sans Pro', sans-serif;\n",
" background-color: rgb(256,256,256);\n",
" font-size: 110%;\n",
" border-radius: 0px; \n",
" padding: 0.5em;\n",
" margin-left:1em;\n",
" margin-top: 1em;\n",
"}\n",
"\n",
"div.text_cell_render{\n",
" font-family: 'Josefin Sans', serif;\n",
" line-height: 145%;\n",
" font-size: 125%;\n",
" font-weight: 500;\n",
" width:850px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
"}\n",
"\n",
"\n",
"/* Formatting for header cells */\n",
".text_cell_render h1, .text_cell_render h2, .text_cell_render h3,\n",
".text_cell_render h4, .text_cell_render h5 {\n",
" font-family: 'Ubuntu Condensed', sans-serif;\n",
"}\n",
"/*\n",
".text_cell_render h1 {\n",
" font-family: Flux, 'Ubuntu Condensed', serif;\n",
" font-style:regular;\n",
" font-weight: 400; \n",
" font-size: 30pt;\n",
" text-align: center;\n",
" line-height: 100%;\n",
" color: #335082;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
"}\n",
"*/\n",
".text_cell_render h1 {\n",
"\tfont-family: Flux, 'Ubuntu Condensed', serif;\n",
" font-style:regular;\n",
" font-weight: 600;\n",
" font-size: 35pt;\n",
"\ttext-align: center;\n",
" line-height: 100%;\n",
" color: #2c7638;\n",
" margin-bottom: 0.2em;\n",
" margin-top: 0.3em;\n",
" display: block;\n",
"}\n",
"\n",
".text_cell_render h2 {\n",
" margin-top:16px;\n",
" font-size: 27pt;\n",
" font-weight: 550;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #498550\t;\n",
"}\t\n",
"\n",
".text_cell_render h3 {\n",
" font-size: 20pt;\n",
" font-weight: 550\n",
" text-align: left;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #1a66ab;\n",
"}\n",
"\n",
".text_cell_render h4 { /*Use this for captions*/\n",
" font-size: 18pt;\n",
" font-weight: 450\n",
" text-align: left;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #578cb4;\n",
"}\n",
"\n",
".text_cell_render h5 { /*Use this for small titles*/\n",
" font-size: 18pt;\n",
" font-weight: 550;\n",
" color: rgb(163,0,0);\n",
" font-style: italic;\n",
" margin-bottom: .1em;\n",
" margin-top: 0.8em;\n",
" display: block;\n",
" color: #b21c0d;\n",
"}\n",
"\n",
".text_cell_render h6 { /*use this for copyright note*/\n",
" font-family: 'Ubuntu Condensed', sans-serif;\n",
" font-weight: 300;\n",
" font-size: 14pt;\n",
" line-height: 100%;\n",
" color: #252525;\n",
" text-align: right;\n",
" margin-bottom: 1px;\n",
" margin-top: 1px;\n",
"}\n",
"\n",
".CodeMirror{\n",
" font-family: 'Duru Sans', sans-serif;\n",
" font-size: 100%;\n",
"}\n",
"\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"],\n",
" equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Esta celda da el estilo al notebook\n",
"from IPython.core.display import HTML\n",
"css_file = '../styles/StyleCursoPython.css'\n",
"HTML(open(css_file, \"r\").read())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}