Piedra Tijeras Papel - LIVE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options = [\"piedra\", \"tijeras\", \"papel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def search_winner(p1, p2):\n",
    "    if p1 == p2:\n",
    "        result = 0\n",
    "    \n",
    "    elif p1 == \"piedra\" and p2 == \"tijeras\":\n",
    "        result = 1\n",
    "    elif p1 == \"piedra\" and p2 == \"papel\":\n",
    "        result = 2\n",
    "    elif p1 == \"tijeras\" and p2 == \"piedra\":\n",
    "        result = 2\n",
    "    elif p1 == \"tijeras\" and p2 == \"papel\":\n",
    "        result = 1\n",
    "    elif p1 == \"papel\" and p2 == \"piedra\":\n",
    "        result = 1\n",
    "    elif p1 == \"papel\" and p2 == \"tijeras\":\n",
    "        result = 2\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_winner(\"papel\", \"tijeras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "player1: piedra player2: piedra Winner: 0 Validation: 0\n",
      "player1: piedra player2: tijeras Winner: 1 Validation: 1\n",
      "player1: piedra player2: papel Winner: 2 Validation: 2\n"
     ]
    }
   ],
   "source": [
    "test = [\n",
    "    [\"piedra\", \"piedra\", 0],\n",
    "    [\"piedra\", \"tijeras\", 1],\n",
    "    [\"piedra\", \"papel\", 2]\n",
    "]\n",
    "\n",
    "for partida in test:\n",
    "    print(\"player1: %s player2: %s Winner: %s Validation: %s\" % (\n",
    "        partida[0], partida[1], search_winner(partida[0], partida[1]), partida[2]\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from random import choice\n",
    "def get_choice():\n",
    "    return choice(options)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "player1: piedra player2: piedra Winner: 0 \n",
      "player1: tijeras player2: tijeras Winner: 0 \n",
      "player1: piedra player2: piedra Winner: 0 \n",
      "player1: papel player2: tijeras Winner: 2 \n",
      "player1: papel player2: papel Winner: 0 \n",
      "player1: piedra player2: piedra Winner: 0 \n",
      "player1: tijeras player2: piedra Winner: 2 \n",
      "player1: piedra player2: tijeras Winner: 1 \n",
      "player1: piedra player2: tijeras Winner: 1 \n",
      "player1: papel player2: papel Winner: 0 \n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    player1 = get_choice()\n",
    "    player2 = get_choice()\n",
    "    print(\"player1: %s player2: %s Winner: %s \" % (\n",
    "        player1, player2, search_winner(player1, player2)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 0, 0], [0, 1, 0], [0, 0, 1]]\n",
      "[[0, 0, 1], [1, 0, 0], [0, 1, 0]]\n"
     ]
    }
   ],
   "source": [
    "def str_to_list(option):\n",
    "    if option==\"piedra\":\n",
    "        res = [1,0,0]\n",
    "    elif option==\"tijeras\":\n",
    "        res = [0,1,0]\n",
    "    else:\n",
    "        res = [0,0,1]\n",
    "    return res\n",
    "\n",
    "data_X = list(map(str_to_list, [\"piedra\", \"tijeras\", \"papel\"]))\n",
    "data_y = list(map(str_to_list, [\"papel\", \"piedra\", \"tijeras\"]))\n",
    "\n",
    "print(data_X)\n",
    "print(data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MLPClassifier(verbose=False, warm_start=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
      "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
      "       verbose=False, warm_start=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/Library/Python/3.6/lib/python/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "model = clf.fit([data_X[0]], [data_y[0]])\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def play_and_learn(iters=10, debug=False):\n",
    "    score = {\"win\": 0, \"loose\": 0}\n",
    "    \n",
    "    data_X = []\n",
    "    data_y = []\n",
    "    \n",
    "    for i in range(iters):\n",
    "        player1 = get_choice()\n",
    "        \n",
    "        predict = model.predict_proba([str_to_list(player1)])[0]\n",
    "        \n",
    "        if predict[0] >= 0.95:\n",
    "            player2 = options[0]\n",
    "        elif predict[1] >= 0.95:\n",
    "            player2 = options[1]\n",
    "        elif predict[2] >= 0.95:\n",
    "            player2 = options[2]\n",
    "        else:\n",
    "            player2 = get_choice()\n",
    "            \n",
    "        if debug==True:\n",
    "            print(\"Player1: %s Player2 (modelo): %s --> %s\" % (player1, predict, player2))\n",
    "        \n",
    "        winner = search_winner(player1, player2)\n",
    "        if debug==True:\n",
    "            print(\"Comprobamos: p1 VS p2: %s\" % winner)\n",
    "        \n",
    "        if winner==2:\n",
    "            data_X.append(str_to_list(player1))\n",
    "            data_y.append(str_to_list(player2))\n",
    "            \n",
    "            score[\"win\"]+=1\n",
    "        else:\n",
    "            score[\"loose\"]+=1\n",
    "        \n",
    "    return score, data_X, data_y\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player1: tijeras Player2 (modelo): [ 0.11859622  0.12092568  0.84820899] --> piedra\n",
      "Comprobamos: p1 VS p2: 2\n",
      "[[0, 1, 0]]\n",
      "[[1, 0, 0]]\n",
      "Score: {'win': 1, 'loose': 0} 100.0 %\n"
     ]
    }
   ],
   "source": [
    "score, data_X, data_y = play_and_learn(1, debug=True)\n",
    "print(data_X)\n",
    "print(data_y)\n",
    "print(\"Score: %s %s %%\" % (score, (score[\"win\"]*100/(score[\"win\"]+score[\"loose\"]))))\n",
    "if len(data_X):\n",
    "    model = model.partial_fit(data_X, data_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Iter: 159 - score: {'win': 538, 'loose': 462} 53.8 %\n",
      "Iter: 160 - score: {'win': 576, 'loose': 424} 57.6 %\n",
      "Iter: 161 - score: {'win': 574, 'loose': 426} 57.4 %\n",
      "Iter: 162 - score: {'win': 549, 'loose': 451} 54.9 %\n",
      "Iter: 163 - score: {'win': 563, 'loose': 437} 56.3 %\n",
      "Iter: 164 - score: {'win': 547, 'loose': 453} 54.7 %\n",
      "Iter: 165 - score: {'win': 558, 'loose': 442} 55.8 %\n",
      "Iter: 166 - score: {'win': 534, 'loose': 466} 53.4 %\n",
      "Iter: 167 - score: {'win': 560, 'loose': 440} 56.0 %\n",
      "Iter: 168 - score: {'win': 547, 'loose': 453} 54.7 %\n",
      "Iter: 169 - score: {'win': 546, 'loose': 454} 54.6 %\n",
      "Iter: 170 - score: {'win': 555, 'loose': 445} 55.5 %\n",
      "Iter: 171 - score: {'win': 563, 'loose': 437} 56.3 %\n",
      "Iter: 172 - score: {'win': 543, 'loose': 457} 54.3 %\n",
      "Iter: 173 - score: {'win': 581, 'loose': 419} 58.1 %\n",
      "Iter: 174 - score: {'win': 554, 'loose': 446} 55.4 %\n",
      "Iter: 175 - score: {'win': 551, 'loose': 449} 55.1 %\n",
      "Iter: 176 - score: {'win': 535, 'loose': 465} 53.5 %\n",
      "Iter: 177 - score: {'win': 563, 'loose': 437} 56.3 %\n",
      "Iter: 178 - score: {'win': 552, 'loose': 448} 55.2 %\n",
      "Iter: 179 - score: {'win': 535, 'loose': 465} 53.5 %\n",
      "Iter: 180 - score: {'win': 560, 'loose': 440} 56.0 %\n",
      "Iter: 181 - score: {'win': 561, 'loose': 439} 56.1 %\n",
      "Iter: 182 - score: {'win': 530, 'loose': 470} 53.0 %\n",
      "Iter: 183 - score: {'win': 565, 'loose': 435} 56.5 %\n",
      "Iter: 184 - score: {'win': 581, 'loose': 419} 58.1 %\n",
      "Iter: 185 - score: {'win': 552, 'loose': 448} 55.2 %\n",
      "Iter: 186 - score: {'win': 778, 'loose': 222} 77.8 %\n",
      "Iter: 187 - score: {'win': 792, 'loose': 208} 79.2 %\n",
      "Iter: 188 - score: {'win': 777, 'loose': 223} 77.7 %\n",
      "Iter: 189 - score: {'win': 780, 'loose': 220} 78.0 %\n",
      "Iter: 190 - score: {'win': 780, 'loose': 220} 78.0 %\n",
      "Iter: 191 - score: {'win': 806, 'loose': 194} 80.6 %\n",
      "Iter: 192 - score: {'win': 793, 'loose': 207} 79.3 %\n",
      "Iter: 193 - score: {'win': 766, 'loose': 234} 76.6 %\n",
      "Iter: 194 - score: {'win': 775, 'loose': 225} 77.5 %\n",
      "Iter: 195 - score: {'win': 769, 'loose': 231} 76.9 %\n",
      "Iter: 196 - score: {'win': 770, 'loose': 230} 77.0 %\n",
      "Iter: 197 - score: {'win': 785, 'loose': 215} 78.5 %\n",
      "Iter: 198 - score: {'win': 782, 'loose': 218} 78.2 %\n",
      "Iter: 199 - score: {'win': 789, 'loose': 211} 78.9 %\n",
      "Iter: 200 - score: {'win': 791, 'loose': 209} 79.1 %\n",
      "Iter: 201 - score: {'win': 802, 'loose': 198} 80.2 %\n",
      "Iter: 202 - score: {'win': 768, 'loose': 232} 76.8 %\n",
      "Iter: 203 - score: {'win': 762, 'loose': 238} 76.2 %\n",
      "Iter: 204 - score: {'win': 775, 'loose': 225} 77.5 %\n",
      "Iter: 205 - score: {'win': 782, 'loose': 218} 78.2 %\n",
      "Iter: 206 - score: {'win': 788, 'loose': 212} 78.8 %\n",
      "Iter: 207 - score: {'win': 773, 'loose': 227} 77.3 %\n",
      "Iter: 208 - score: {'win': 792, 'loose': 208} 79.2 %\n",
      "Iter: 209 - score: {'win': 771, 'loose': 229} 77.1 %\n",
      "Iter: 210 - score: {'win': 784, 'loose': 216} 78.4 %\n",
      "Iter: 211 - score: {'win': 791, 'loose': 209} 79.1 %\n",
      "Iter: 212 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 213 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 214 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 215 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 216 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 217 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 218 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 219 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 220 - score: {'win': 1000, 'loose': 0} 100.0 %\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "historic_pct = []\n",
    "while True:\n",
    "    i+=1\n",
    "    score, data_X, data_y = play_and_learn(1000, debug=False)\n",
    "    pct = (score[\"win\"]*100/(score[\"win\"]+score[\"loose\"]))\n",
    "    historic_pct.append(pct)\n",
    "    print(\"Iter: %s - score: %s %s %%\" % (i, score, pct))\n",
    "    \n",
    "    if len(data_X):\n",
    "        model = model.partial_fit(data_X, data_y)\n",
    "    \n",
    "    if sum(historic_pct[-9:])==900:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"4709d1a4-93fb-4538-953e-8dad93d64174\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      var el = document.getElementById(\"4709d1a4-93fb-4538-953e-8dad93d64174\");\n",
       "      el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"4709d1a4-93fb-4538-953e-8dad93d64174\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '4709d1a4-93fb-4538-953e-8dad93d64174' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"4709d1a4-93fb-4538-953e-8dad93d64174\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"4709d1a4-93fb-4538-953e-8dad93d64174\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "output_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"9957f49f-6f35-48cb-a8c7-42607a3c1168\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"9957f49f-6f35-48cb-a8c7-42607a3c1168\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"9957f49f-6f35-48cb-a8c7-42607a3c1168\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '9957f49f-6f35-48cb-a8c7-42607a3c1168' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
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       "      load_libs(js_urls, function() {\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(len(historic_pct))\n",
    "y = historic_pct\n",
    "\n",
    "p = figure(\n",
    "    title=\"Porcetaje de aprendizaje en cada iteración\",\n",
    "    x_axis_label=\"Iter\", y_axis_label=\"%\", width=900)\n",
    "\n",
    "p.line(x, y, legend=None, line_width=1)\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00829449,  0.0049859 ,  0.98453702]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba([str_to_list(\"piedra\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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