Untitled25.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2017-01-02    236\n",
       "2017-01-03    237\n",
       "2017-01-04    290\n",
       "2017-01-05    221\n",
       "2017-01-07    128\n",
       "Name: unidades, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "plt.style.use('fast')\n",
    " \n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Flatten\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "url = \"https://github.com/jbagnato/machine-learning/master/time_series.csv\"\n",
    "\n",
    "df = pd.read_csv(url, parse_dates=[0], header=None,index_col=0, squeeze=True,names=['fecha','unidades'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-02 00:00:00\n",
      "2018-11-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Por ejemplo, podemos ver de qué fechas tenemos datos con:\n",
    "print(df.index.min())\n",
    "print(df.index.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315\n",
      "289\n"
     ]
    }
   ],
   "source": [
    "# Y ahora veamos cuantas muestras tenemos de cada año:\n",
    "print(len(df['2017']))\n",
    "print(len(df['2018']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    604.000000\n",
       "mean     215.935430\n",
       "std       75.050304\n",
       "min       51.000000\n",
       "25%      171.000000\n",
       "50%      214.000000\n",
       "75%      261.250000\n",
       "max      591.000000\n",
       "Name: unidades, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2017-01-31    203.923077\n",
       "2017-02-28    184.666667\n",
       "2017-03-31    182.964286\n",
       "2017-04-30    198.960000\n",
       "2017-05-31    201.185185\n",
       "2017-06-30    209.518519\n",
       "2017-07-31    278.923077\n",
       "2017-08-31    316.000000\n",
       "2017-09-30    222.925926\n",
       "2017-10-31    207.851852\n",
       "2017-11-30    185.925926\n",
       "2017-12-31    213.200000\n",
       "2018-01-31    201.384615\n",
       "2018-02-28    190.625000\n",
       "2018-03-31    174.846154\n",
       "2018-04-30    186.000000\n",
       "2018-05-31    190.666667\n",
       "2018-06-30    196.037037\n",
       "2018-07-31    289.500000\n",
       "2018-08-31    309.038462\n",
       "2018-09-30    230.518519\n",
       "2018-10-31    209.444444\n",
       "2018-11-30    184.481481\n",
       "Freq: M, Name: unidades, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# De hecho aprovechemos el tener indice de fechas con pandas y saquemos los promedios mensuales:\n",
    "meses =df.resample('M').mean()\n",
    "meses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2888131c668>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Y visualicemos esas medias mensuales:\n",
    "# Azul : 2017\n",
    "# Naraja : 2018\n",
    "plt.plot(meses['2017'].values)\n",
    "plt.plot(meses['2018'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x288812c2d68>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Veamos la gráfica de ventas diarias (en unidades) en junio y julio\n",
    "verano2017 = df['2017-06-01':'2017-09-01']\n",
    "plt.plot(verano2017.values)\n",
    "verano2018 = df['2018-06-01':'2018-09-01']\n",
    "plt.plot(verano2018.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>var1(t-7)</th>\n",
       "      <th>var1(t-6)</th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var1(t-4)</th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.314815</td>\n",
       "      <td>-0.311111</td>\n",
       "      <td>-0.114815</td>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.311111</td>\n",
       "      <td>-0.114815</td>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.114815</td>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "      <td>-0.522222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "      <td>-0.522222</td>\n",
       "      <td>-0.644444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "      <td>-0.522222</td>\n",
       "      <td>-0.644444</td>\n",
       "      <td>-0.344444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-7)  var1(t-6)  var1(t-5)  var1(t-4)  var1(t-3)  var1(t-2)  \\\n",
       "7   -0.314815  -0.311111  -0.114815  -0.370370  -0.714815  -0.103704   \n",
       "8   -0.311111  -0.114815  -0.370370  -0.714815  -0.103704  -0.225926   \n",
       "9   -0.114815  -0.370370  -0.714815  -0.103704  -0.225926  -0.433333   \n",
       "10  -0.370370  -0.714815  -0.103704  -0.225926  -0.433333  -0.607407   \n",
       "11  -0.714815  -0.103704  -0.225926  -0.433333  -0.607407  -0.522222   \n",
       "\n",
       "    var1(t-1)   var1(t)  \n",
       "7   -0.225926 -0.433333  \n",
       "8   -0.433333 -0.607407  \n",
       "9   -0.607407 -0.522222  \n",
       "10  -0.522222 -0.644444  \n",
       "11  -0.644444 -0.344444  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analizamos 7 dias y supondremos el 8\n",
    "PASOS=7\n",
    "\n",
    "# convert series to supervised learning\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    " \n",
    "# load dataset\n",
    "values = df.values\n",
    "# ensure all data is float\n",
    "values = values.astype('float32')\n",
    "# normalize features\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "values=values.reshape(-1, 1) # esto lo hacemos porque tenemos 1 sola dimension\n",
    "scaled = scaler.fit_transform(values)\n",
    "# frame as supervised learning\n",
    "reframed = series_to_supervised(scaled, PASOS, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(567, 1, 7) (567,) (30, 1, 7) (30,)\n"
     ]
    }
   ],
   "source": [
    "# significa algo así como «567 entradas con vectores de 1×7».\n",
    "# split into train and test sets\n",
    "values = reframed.values\n",
    "n_train_days = 315+289 - (30+PASOS)\n",
    "train = values[:n_train_days, :]\n",
    "test = values[n_train_days:, :]\n",
    "# split into input and outputs\n",
    "x_train, y_train = train[:, :-1], train[:, -1]\n",
    "x_val, y_val = test[:, :-1], test[:, -1]\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "x_train = x_train.reshape((x_train.shape[0], 1, x_train.shape[1]))\n",
    "x_val = x_val.reshape((x_val.shape[0], 1, x_val.shape[1]))\n",
    "print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crear_modeloFF():\n",
    "    model = Sequential() \n",
    "    model.add(Dense(PASOS, input_shape=(1,PASOS),activation='tanh'))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(1, activation='tanh'))\n",
    "    model.compile(loss='mean_absolute_error',optimizer='Adam',metrics=[\"mse\"])\n",
    "    model.summary()\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1, 7)              56        \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 7)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 8         \n",
      "=================================================================\n",
      "Total params: 64\n",
      "Trainable params: 64\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 567 samples, validate on 30 samples\n",
      "Epoch 1/40\n",
      "567/567 [==============================] - 1s 1ms/step - loss: 0.2500 - mean_squared_error: 0.1011 - val_loss: 0.1971 - val_mean_squared_error: 0.0704\n",
      "Epoch 2/40\n",
      "567/567 [==============================] - 0s 185us/step - loss: 0.2089 - mean_squared_error: 0.0737 - val_loss: 0.1865 - val_mean_squared_error: 0.0645\n",
      "Epoch 3/40\n",
      "567/567 [==============================] - 0s 178us/step - loss: 0.1992 - mean_squared_error: 0.0687 - val_loss: 0.1751 - val_mean_squared_error: 0.0561\n",
      "Epoch 4/40\n",
      "567/567 [==============================] - 0s 162us/step - loss: 0.1925 - mean_squared_error: 0.0654 - val_loss: 0.1713 - val_mean_squared_error: 0.0537\n",
      "Epoch 5/40\n",
      "567/567 [==============================] - 0s 183us/step - loss: 0.1885 - mean_squared_error: 0.0633 - val_loss: 0.1669 - val_mean_squared_error: 0.0500\n",
      "Epoch 6/40\n",
      "567/567 [==============================] - 0s 155us/step - loss: 0.1854 - mean_squared_error: 0.0616 - val_loss: 0.1635 - val_mean_squared_error: 0.0487\n",
      "Epoch 7/40\n",
      "567/567 [==============================] - 0s 155us/step - loss: 0.1823 - mean_squared_error: 0.0610 - val_loss: 0.1593 - val_mean_squared_error: 0.0458\n",
      "Epoch 8/40\n",
      "567/567 [==============================] - 0s 157us/step - loss: 0.1797 - mean_squared_error: 0.0595 - val_loss: 0.1569 - val_mean_squared_error: 0.0448\n",
      "Epoch 9/40\n",
      "567/567 [==============================] - 0s 144us/step - loss: 0.1782 - mean_squared_error: 0.0588 - val_loss: 0.1564 - val_mean_squared_error: 0.0452\n",
      "Epoch 10/40\n",
      "567/567 [==============================] - 0s 142us/step - loss: 0.1767 - mean_squared_error: 0.0586 - val_loss: 0.1538 - val_mean_squared_error: 0.0409\n",
      "Epoch 11/40\n",
      "567/567 [==============================] - 0s 137us/step - loss: 0.1756 - mean_squared_error: 0.0577 - val_loss: 0.1508 - val_mean_squared_error: 0.0404\n",
      "Epoch 12/40\n",
      "567/567 [==============================] - 0s 160us/step - loss: 0.1739 - mean_squared_error: 0.0571 - val_loss: 0.1515 - val_mean_squared_error: 0.0389\n",
      "Epoch 13/40\n",
      "567/567 [==============================] - 0s 206us/step - loss: 0.1739 - mean_squared_error: 0.0567 - val_loss: 0.1484 - val_mean_squared_error: 0.0392\n",
      "Epoch 14/40\n",
      "567/567 [==============================] - 0s 195us/step - loss: 0.1733 - mean_squared_error: 0.0567 - val_loss: 0.1490 - val_mean_squared_error: 0.0394\n",
      "Epoch 15/40\n",
      "567/567 [==============================] - 0s 179us/step - loss: 0.1724 - mean_squared_error: 0.0563 - val_loss: 0.1474 - val_mean_squared_error: 0.0385\n",
      "Epoch 16/40\n",
      "567/567 [==============================] - 0s 181us/step - loss: 0.1734 - mean_squared_error: 0.0573 - val_loss: 0.1466 - val_mean_squared_error: 0.0379\n",
      "Epoch 17/40\n",
      "567/567 [==============================] - 0s 157us/step - loss: 0.1717 - mean_squared_error: 0.0562 - val_loss: 0.1448 - val_mean_squared_error: 0.0367\n",
      "Epoch 18/40\n",
      "567/567 [==============================] - 0s 155us/step - loss: 0.1712 - mean_squared_error: 0.0561 - val_loss: 0.1445 - val_mean_squared_error: 0.0368\n",
      "Epoch 19/40\n",
      "567/567 [==============================] - 0s 145us/step - loss: 0.1712 - mean_squared_error: 0.0560 - val_loss: 0.1450 - val_mean_squared_error: 0.0370\n",
      "Epoch 20/40\n",
      "567/567 [==============================] - 0s 140us/step - loss: 0.1712 - mean_squared_error: 0.0561 - val_loss: 0.1446 - val_mean_squared_error: 0.0359\n",
      "Epoch 21/40\n",
      "567/567 [==============================] - 0s 173us/step - loss: 0.1714 - mean_squared_error: 0.0559 - val_loss: 0.1433 - val_mean_squared_error: 0.0356\n",
      "Epoch 22/40\n",
      "567/567 [==============================] - 0s 208us/step - loss: 0.1704 - mean_squared_error: 0.0557 - val_loss: 0.1434 - val_mean_squared_error: 0.0363\n",
      "Epoch 23/40\n",
      "567/567 [==============================] - 0s 185us/step - loss: 0.1703 - mean_squared_error: 0.0557 - val_loss: 0.1430 - val_mean_squared_error: 0.0361\n",
      "Epoch 24/40\n",
      "567/567 [==============================] - 0s 178us/step - loss: 0.1697 - mean_squared_error: 0.0557 - val_loss: 0.1456 - val_mean_squared_error: 0.0373\n",
      "Epoch 25/40\n",
      "567/567 [==============================] - 0s 174us/step - loss: 0.1702 - mean_squared_error: 0.0556 - val_loss: 0.1428 - val_mean_squared_error: 0.0360\n",
      "Epoch 26/40\n",
      "567/567 [==============================] - 0s 165us/step - loss: 0.1699 - mean_squared_error: 0.0556 - val_loss: 0.1415 - val_mean_squared_error: 0.0355\n",
      "Epoch 27/40\n",
      "567/567 [==============================] - 0s 132us/step - loss: 0.1702 - mean_squared_error: 0.0552 - val_loss: 0.1452 - val_mean_squared_error: 0.0373\n",
      "Epoch 28/40\n",
      "567/567 [==============================] - 0s 135us/step - loss: 0.1702 - mean_squared_error: 0.0559 - val_loss: 0.1413 - val_mean_squared_error: 0.0352\n",
      "Epoch 29/40\n",
      "567/567 [==============================] - 0s 157us/step - loss: 0.1695 - mean_squared_error: 0.0557 - val_loss: 0.1426 - val_mean_squared_error: 0.0359\n",
      "Epoch 30/40\n",
      "567/567 [==============================] - 0s 174us/step - loss: 0.1704 - mean_squared_error: 0.0557 - val_loss: 0.1410 - val_mean_squared_error: 0.0352\n",
      "Epoch 31/40\n",
      "567/567 [==============================] - 0s 172us/step - loss: 0.1693 - mean_squared_error: 0.0556 - val_loss: 0.1412 - val_mean_squared_error: 0.0346\n",
      "Epoch 32/40\n",
      "567/567 [==============================] - 0s 135us/step - loss: 0.1691 - mean_squared_error: 0.0549 - val_loss: 0.1423 - val_mean_squared_error: 0.0358\n",
      "Epoch 33/40\n",
      "567/567 [==============================] - 0s 142us/step - loss: 0.1694 - mean_squared_error: 0.0555 - val_loss: 0.1414 - val_mean_squared_error: 0.0351\n",
      "Epoch 34/40\n",
      "567/567 [==============================] - 0s 139us/step - loss: 0.1689 - mean_squared_error: 0.0554 - val_loss: 0.1410 - val_mean_squared_error: 0.0345\n",
      "Epoch 35/40\n",
      "567/567 [==============================] - 0s 134us/step - loss: 0.1702 - mean_squared_error: 0.0558 - val_loss: 0.1407 - val_mean_squared_error: 0.0347\n",
      "Epoch 36/40\n",
      "567/567 [==============================] - 0s 142us/step - loss: 0.1689 - mean_squared_error: 0.0554 - val_loss: 0.1402 - val_mean_squared_error: 0.0345\n",
      "Epoch 37/40\n",
      "567/567 [==============================] - 0s 155us/step - loss: 0.1694 - mean_squared_error: 0.0552 - val_loss: 0.1401 - val_mean_squared_error: 0.0351\n",
      "Epoch 38/40\n",
      "567/567 [==============================] - 0s 142us/step - loss: 0.1695 - mean_squared_error: 0.0552 - val_loss: 0.1421 - val_mean_squared_error: 0.0358\n",
      "Epoch 39/40\n",
      "567/567 [==============================] - 0s 137us/step - loss: 0.1694 - mean_squared_error: 0.0551 - val_loss: 0.1415 - val_mean_squared_error: 0.0356\n",
      "Epoch 40/40\n",
      "567/567 [==============================] - 0s 125us/step - loss: 0.1692 - mean_squared_error: 0.0554 - val_loss: 0.1413 - val_mean_squared_error: 0.0355\n"
     ]
    }
   ],
   "source": [
    "# Veamos cómo se comporta nuestra máquina al cabo de 40 épocas.\n",
    "EPOCHS=40\n",
    "\n",
    "model = crear_modeloFF()\n",
    "\n",
    "history=model.fit(x_train,y_train,epochs=EPOCHS,validation_data=(x_val,y_val),batch_size=PASOS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results=model.predict(x_val)\n",
    "plt.scatter(range(len(y_val)),y_val,c='g')\n",
    "plt.scatter(range(len(results)),results,c='r')\n",
    "plt.title('validate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2018-11-16    152\n",
       "2018-11-17    111\n",
       "2018-11-19    207\n",
       "2018-11-20    206\n",
       "2018-11-21    183\n",
       "2018-11-22    200\n",
       "2018-11-23    187\n",
       "2018-11-24    189\n",
       "2018-11-25     76\n",
       "2018-11-26    276\n",
       "2018-11-27    220\n",
       "2018-11-28    183\n",
       "2018-11-29    251\n",
       "2018-11-30    189\n",
       "Name: unidades, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ultimosDias = df['2018-11-16':'2018-11-30']\n",
    "ultimosDias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>var1(t-7)</th>\n",
       "      <th>var1(t-6)</th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var1(t-4)</th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-7)  var1(t-6)  var1(t-5)  var1(t-4)  var1(t-3)  var1(t-2)  \\\n",
       "7       -0.24      -0.65       0.31       0.30       0.07       0.24   \n",
       "8       -0.65       0.31       0.30       0.07       0.24       0.11   \n",
       "9        0.31       0.30       0.07       0.24       0.11       0.13   \n",
       "10       0.30       0.07       0.24       0.11       0.13      -1.00   \n",
       "11       0.07       0.24       0.11       0.13      -1.00       1.00   \n",
       "12       0.24       0.11       0.13      -1.00       1.00       0.44   \n",
       "13       0.11       0.13      -1.00       1.00       0.44       0.07   \n",
       "\n",
       "    var1(t-1)  \n",
       "7        0.11  \n",
       "8        0.13  \n",
       "9       -1.00  \n",
       "10       1.00  \n",
       "11       0.44  \n",
       "12       0.07  \n",
       "13       0.75  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = ultimosDias.values\n",
    "values = values.astype('float32')\n",
    "# normalize features\n",
    "values=values.reshape(-1, 1) # esto lo hacemos porque tenemos 1 sola dimension\n",
    "scaled = scaler.fit_transform(values)\n",
    "reframed = series_to_supervised(scaled, PASOS, 1)\n",
    "reframed.drop(reframed.columns[[7]], axis=1, inplace=True)\n",
    "reframed.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.11000001,  0.13      , -1.        ,  1.        ,\n",
       "          0.44000006,  0.06999993,  0.75      ]]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "x_test = values[6:, :]\n",
    "x_test = x_test.reshape((x_test.shape[0], 1, x_test.shape[1]))\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.11000001  0.13       -1.          1.          0.44000006\n",
      "    0.06999993  0.75      ]]]\n",
      "[[[ 0.13       -1.          1.          0.44000006  0.06999993\n",
      "    0.75       -0.03744889]]]\n",
      "[[[-1.          1.          0.44000006  0.06999993  0.75\n",
      "   -0.03744889 -0.50365824]]]\n",
      "[[[ 1.          0.44000006  0.06999993  0.75       -0.03744889\n",
      "   -0.50365824  0.43663496]]]\n",
      "[[[ 0.44000006  0.06999993  0.75       -0.03744889 -0.50365824\n",
      "    0.43663496  0.31845507]]]\n",
      "[[[ 0.06999993  0.75       -0.03744889 -0.50365824  0.43663496\n",
      "    0.31845507 -0.01693732]]]\n",
      "[[[ 0.75       -0.03744889 -0.50365824  0.43663496  0.31845507\n",
      "   -0.01693732  0.30122426]]]\n"
     ]
    }
   ],
   "source": [
    "def agregarNuevoValor(x_test,nuevoValor):\n",
    "    for i in range(x_test.shape[2]-1):\n",
    "        x_test[0][0][i] = x_test[0][0][i+1]\n",
    "    x_test[0][0][x_test.shape[2]-1]=nuevoValor\n",
    "    return x_test\n",
    "\n",
    "results=[]\n",
    "for i in range(7):\n",
    "    parcial=model.predict(x_test)\n",
    "    results.append(parcial[0])\n",
    "    print(x_test)\n",
    "    x_test=agregarNuevoValor(x_test,parcial[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[172.25511385],\n",
       "       [125.63417835],\n",
       "       [219.66349974],\n",
       "       [207.84551072],\n",
       "       [174.30627102],\n",
       "       [206.12242981],\n",
       "       [170.47341102]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adimen = [x for x in results]    \n",
    "inverted = scaler.inverse_transform(adimen)\n",
    "inverted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediccion1SemanaDiciembre = pd.DataFrame(inverted)\n",
    "prediccion1SemanaDiciembre.columns = ['pronostico']\n",
    "prediccion1SemanaDiciembre.plot()\n",
    "prediccion1SemanaDiciembre.to_csv('pronostico.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}