tarea4_william_aguilar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageFilter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense, Activation\n",
    "from keras import Sequential\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from tensorflow import keras\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "os.chdir(\"/home/william/Documents\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('MANU.csv',delimiter=\",\",decimal=\".\", index_col=0,parse_dates=['Date'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-500-00bdae0cbc6c>:2: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  testing = dataset['2019'].iloc[:,1:2]\n"
     ]
    }
   ],
   "source": [
    "training = dataset['2015':'2018'].iloc[:,1:2]\n",
    "testing = dataset['2019'].iloc[:,1:2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "outputs": [
    {
     "data": {
      "text/plain": "                 High\nDate                 \n2015-01-02  16.059999\n2015-01-05  15.950000\n2015-01-06  15.740000\n2015-01-07  15.710000\n2015-01-08  15.800000\n...               ...\n2018-12-24  18.600000\n2018-12-26  18.400000\n2018-12-27  18.520000\n2018-12-28  18.840000\n2018-12-31  19.090000\n\n[1006 rows x 1 columns]",
      "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>High</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015-01-02</th>\n      <td>16.059999</td>\n    </tr>\n    <tr>\n      <th>2015-01-05</th>\n      <td>15.950000</td>\n    </tr>\n    <tr>\n      <th>2015-01-06</th>\n      <td>15.740000</td>\n    </tr>\n    <tr>\n      <th>2015-01-07</th>\n      <td>15.710000</td>\n    </tr>\n    <tr>\n      <th>2015-01-08</th>\n      <td>15.800000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-12-24</th>\n      <td>18.600000</td>\n    </tr>\n    <tr>\n      <th>2018-12-26</th>\n      <td>18.400000</td>\n    </tr>\n    <tr>\n      <th>2018-12-27</th>\n      <td>18.520000</td>\n    </tr>\n    <tr>\n      <th>2018-12-28</th>\n      <td>18.840000</td>\n    </tr>\n    <tr>\n      <th>2018-12-31</th>\n      <td>19.090000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1006 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "outputs": [
    {
     "data": {
      "text/plain": "                 High\nDate                 \n2019-01-02  18.820000\n2019-01-03  18.690001\n2019-01-04  19.040001\n2019-01-07  19.559999\n2019-01-08  19.549999\n...               ...\n2019-12-24  19.820000\n2019-12-26  19.639999\n2019-12-27  19.860001\n2019-12-30  19.980000\n2019-12-31  20.000000\n\n[252 rows x 1 columns]",
      "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>High</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2019-01-02</th>\n      <td>18.820000</td>\n    </tr>\n    <tr>\n      <th>2019-01-03</th>\n      <td>18.690001</td>\n    </tr>\n    <tr>\n      <th>2019-01-04</th>\n      <td>19.040001</td>\n    </tr>\n    <tr>\n      <th>2019-01-07</th>\n      <td>19.559999</td>\n    </tr>\n    <tr>\n      <th>2019-01-08</th>\n      <td>19.549999</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2019-12-24</th>\n      <td>19.820000</td>\n    </tr>\n    <tr>\n      <th>2019-12-26</th>\n      <td>19.639999</td>\n    </tr>\n    <tr>\n      <th>2019-12-27</th>\n      <td>19.860001</td>\n    </tr>\n    <tr>\n      <th>2019-12-30</th>\n      <td>19.980000</td>\n    </tr>\n    <tr>\n      <th>2019-12-31</th>\n      <td>20.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>252 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos sin estandarizar:\n",
      "\n",
      "                  High\n",
      "Date                 \n",
      "2015-01-02  16.059999\n",
      "2015-01-05  15.950000\n",
      "2015-01-06  15.740000\n",
      "2015-01-07  15.710000\n",
      "2015-01-08  15.800000\n",
      "...               ...\n",
      "2018-12-24  18.600000\n",
      "2018-12-26  18.400000\n",
      "2018-12-27  18.520000\n",
      "2018-12-28  18.840000\n",
      "2018-12-31  19.090000\n",
      "\n",
      "[1006 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Datos sin estandarizar:\\n\\n\",training)\n",
    "#escalamos los datos\n",
    "scaler = StandardScaler()\n",
    "sc_training = scaler.fit_transform(training)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Secuencia 1:\n",
      " [-0.7911129  -0.83785323 -0.92708559 -0.93983307 -0.90159063]\n",
      "\n",
      "Secuencia 2:\n",
      " [-0.83785323 -0.92708559 -0.93983307 -0.90159063 -0.95258055]\n"
     ]
    }
   ],
   "source": [
    "time_step = 5\n",
    "X_train = []\n",
    "Y_train = []\n",
    "m = len(sc_training)\n",
    "for i in range(time_step,m):\n",
    "    # X: bloques de \"time_step\" datos: 0->time_step, 1->time_step+1, 2->time_step+2, etc\n",
    "    #print(\"i-time_step: \", i-time_step)\n",
    "    #print(\"i:\",i)\n",
    "    X_train.append(sc_training[i-time_step:i,0])\n",
    "    Y_train.append(sc_training[i,0])\n",
    "X_train, Y_train = np.array(X_train), np.array(Y_train)\n",
    "print(\"Secuencia 1:\\n\",X_train[0][:])\n",
    "print(\"\\nSecuencia 2:\\n\",X_train[1][:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape (1001, 5, 1)\n",
      "X_test.shape (247, 5, 1)\n",
      "\n",
      " [[-0.7911129 ]\n",
      " [-0.83785323]\n",
      " [-0.92708559]\n",
      " [-0.93983307]\n",
      " [-0.90159063]]\n"
     ]
    }
   ],
   "source": [
    "#set de testing normalizamos los datos\n",
    "x_test = testing.values\n",
    "x_test = scaler.transform(x_test)\n",
    "\n",
    "\n",
    "X_test = []\n",
    "for i in range(time_step,len(x_test)):\n",
    "    X_test.append(x_test[i-time_step:i,0])\n",
    "\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
    "\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "\n",
    "print(\"X_train.shape\", X_train.shape)\n",
    "print(\"X_test.shape\", X_test.shape)\n",
    "print(\"\\n\",X_train[0])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_39\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_37 (LSTM)               (None, 124)               62496     \n",
      "_________________________________________________________________\n",
      "dense_75 (Dense)             (None, 62)                7750      \n",
      "_________________________________________________________________\n",
      "dense_76 (Dense)             (None, 1)                 63        \n",
      "=================================================================\n",
      "Total params: 70,309\n",
      "Trainable params: 70,309\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import LSTM\n",
    "\n",
    "dim_entrada = (X_train.shape[1],1)\n",
    "\n",
    "modelo = Sequential()\n",
    "\n",
    "modelo.add(LSTM(units=124, input_shape=dim_entrada))\n",
    "modelo.add(Dense(units=62, activation='sigmoid'))\n",
    "modelo.add(Dense(units=1, activation='relu'))\n",
    "\n",
    "modelo.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "#resumen del modelo\n",
    "modelo.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "outputs": [
    {
     "data": {
      "text/plain": "<keras.callbacks.History at 0x7f23a2992b50>"
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.fit(X_train,Y_train,epochs=32,batch_size=500, verbose = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "outputs": [],
   "source": [
    "prediccion = modelo.predict(X_test)\n",
    "# aplicamos la normalización inversa de dicha predicción\n",
    "prediccion = scaler.inverse_transform(prediccion)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x7f23a21c33d0>"
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 900x600 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico\n",
    "fig, ax = plt.subplots(1,1, figsize = (6,4), dpi = 150)\n",
    "\n",
    "plt.plot(training.index, training.values, label = \" Training\")\n",
    "plt.plot(testing.index, testing.values, label = \" Testing\")\n",
    "plt.plot(testing.index[0:len(X_test)], prediccion, label = \"Prediccion\")\n",
    "plt.legend()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}