.ipynb_checkpoints/PlotGRUWF-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten,Reshape\n",
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "from keras.utils import np_utils\n",
    "from keras.layers import LSTM, LeakyReLU, CuDNNLSTM, CuDNNGRU\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping\n",
    "import h5py\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras import regularizers\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['data/allcoin2015to2017_wf.h5']), 'r') as hf:\n",
    "    test_inputs = hf['test_inputs'].value\n",
    "    test_outputs = hf['test_outputs'].value\n",
    "    test_input_times = hf['test_input_times'].value\n",
    "    test_output_times = hf['test_output_times'].value\n",
    "    original_test_inputs = hf['original_test_inputs'].value\n",
    "    original_test_outputs = hf['original_test_outputs'].value\n",
    "    original_datas= hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_size = test_inputs.shape[1]\n",
    "units= 50\n",
    "second_units = 30\n",
    "batch_size = 8\n",
    "nb_features = test_inputs.shape[2]\n",
    "epochs = 50\n",
    "output_size=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.008878</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.008023</td>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.008973</td>\n",
       "      <td>0.009031</td>\n",
       "      <td>0.010011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.009002</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>0.008756</td>\n",
       "      <td>0.008997</td>\n",
       "      <td>0.009239</td>\n",
       "      <td>0.009600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.008793</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.008407</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>0.008765</td>\n",
       "      <td>0.008888</td>\n",
       "      <td>0.009332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.007489</td>\n",
       "      <td>0.008782</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>0.009595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.008819</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.007530</td>\n",
       "      <td>0.008737</td>\n",
       "      <td>0.008876</td>\n",
       "      <td>0.009004</td>\n",
       "      <td>0.009552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.008591</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.007530</td>\n",
       "      <td>0.008475</td>\n",
       "      <td>0.008593</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>0.009133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.008010</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>0.006154</td>\n",
       "      <td>0.008036</td>\n",
       "      <td>0.008121</td>\n",
       "      <td>0.008212</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.000737</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.004899</td>\n",
       "      <td>0.005140</td>\n",
       "      <td>0.005266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.006485</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>0.005674</td>\n",
       "      <td>0.006392</td>\n",
       "      <td>0.006470</td>\n",
       "      <td>0.006681</td>\n",
       "      <td>0.007069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.007575</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.007119</td>\n",
       "      <td>0.007315</td>\n",
       "      <td>0.007632</td>\n",
       "      <td>0.007691</td>\n",
       "      <td>0.008190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>0.005775</td>\n",
       "      <td>0.006366</td>\n",
       "      <td>0.006655</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>0.007874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.004682</td>\n",
       "      <td>0.005826</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.006450</td>\n",
       "      <td>0.006943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.005019</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.004141</td>\n",
       "      <td>0.004885</td>\n",
       "      <td>0.005125</td>\n",
       "      <td>0.005224</td>\n",
       "      <td>0.005460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.004657</td>\n",
       "      <td>0.005072</td>\n",
       "      <td>0.005265</td>\n",
       "      <td>0.005336</td>\n",
       "      <td>0.005586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.003408</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.003031</td>\n",
       "      <td>0.003299</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>0.003544</td>\n",
       "      <td>0.003746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.003359</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.003876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>0.001805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.002106</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.002006</td>\n",
       "      <td>0.002562</td>\n",
       "      <td>0.004480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.016483</td>\n",
       "      <td>0.027932</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.022846</td>\n",
       "      <td>0.102268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.007786</td>\n",
       "      <td>0.004816</td>\n",
       "      <td>0.002420</td>\n",
       "      <td>0.003905</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.012053</td>\n",
       "      <td>0.014840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.004655</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.002281</td>\n",
       "      <td>0.003213</td>\n",
       "      <td>0.004853</td>\n",
       "      <td>0.014968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.002839</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.003236</td>\n",
       "      <td>0.008123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.042400</td>\n",
       "      <td>0.012514</td>\n",
       "      <td>0.013448</td>\n",
       "      <td>0.037904</td>\n",
       "      <td>0.044248</td>\n",
       "      <td>0.051522</td>\n",
       "      <td>0.054605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.008502</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>0.005097</td>\n",
       "      <td>0.006663</td>\n",
       "      <td>0.008295</td>\n",
       "      <td>0.009276</td>\n",
       "      <td>0.017820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.051656</td>\n",
       "      <td>0.009634</td>\n",
       "      <td>0.037308</td>\n",
       "      <td>0.045824</td>\n",
       "      <td>0.049027</td>\n",
       "      <td>0.053964</td>\n",
       "      <td>0.073210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.128450</td>\n",
       "      <td>0.027377</td>\n",
       "      <td>0.095250</td>\n",
       "      <td>0.107093</td>\n",
       "      <td>0.127077</td>\n",
       "      <td>0.147699</td>\n",
       "      <td>0.180323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      mean       std       min       25%       50%       75%  \\\n",
       "partition                                                                      \n",
       "0           11.0  0.008878  0.000525  0.008023  0.008598  0.008973  0.009031   \n",
       "1           18.0  0.009002  0.000275  0.008617  0.008756  0.008997  0.009239   \n",
       "2           31.0  0.008793  0.000247  0.008407  0.008628  0.008765  0.008888   \n",
       "3           11.0  0.008972  0.000642  0.007489  0.008782  0.009223  0.009355   \n",
       "4           11.0  0.008819  0.000502  0.007530  0.008737  0.008876  0.009004   \n",
       "5           11.0  0.008591  0.000409  0.007530  0.008475  0.008593  0.008810   \n",
       "6           11.0  0.008010  0.000647  0.006154  0.008036  0.008121  0.008212   \n",
       "7           11.0  0.004694  0.000737  0.002744  0.004711  0.004899  0.005140   \n",
       "8           11.0  0.006485  0.000410  0.005674  0.006392  0.006470  0.006681   \n",
       "9           13.0  0.007575  0.000309  0.007119  0.007315  0.007632  0.007691   \n",
       "10          45.0  0.006705  0.000481  0.005775  0.006366  0.006655  0.006925   \n",
       "11          12.0  0.006036  0.000668  0.004682  0.005826  0.006250  0.006450   \n",
       "12          11.0  0.005019  0.000366  0.004141  0.004885  0.005125  0.005224   \n",
       "13          11.0  0.005205  0.000251  0.004657  0.005072  0.005265  0.005336   \n",
       "14          12.0  0.003408  0.000210  0.003031  0.003299  0.003397  0.003544   \n",
       "15          13.0  0.003314  0.000332  0.002766  0.003241  0.003359  0.003431   \n",
       "16          11.0  0.001319  0.000260  0.000820  0.001198  0.001325  0.001446   \n",
       "17          40.0  0.002106  0.001022  0.000480  0.001482  0.002006  0.002562   \n",
       "18          26.0  0.016483  0.027932  0.000777  0.001276  0.001698  0.022846   \n",
       "19          11.0  0.007786  0.004816  0.002420  0.003905  0.006282  0.012053   \n",
       "20          17.0  0.004655  0.003773  0.001222  0.002281  0.003213  0.004853   \n",
       "21          12.0  0.002839  0.002195  0.001049  0.001369  0.001945  0.003236   \n",
       "22          11.0  0.042400  0.012514  0.013448  0.037904  0.044248  0.051522   \n",
       "23          18.0  0.008502  0.003021  0.005097  0.006663  0.008295  0.009276   \n",
       "24          16.0  0.051656  0.009634  0.037308  0.045824  0.049027  0.053964   \n",
       "25          14.0  0.128450  0.027377  0.095250  0.107093  0.127077  0.147699   \n",
       "\n",
       "                max  \n",
       "partition            \n",
       "0          0.010011  \n",
       "1          0.009600  \n",
       "2          0.009332  \n",
       "3          0.009595  \n",
       "4          0.009552  \n",
       "5          0.009133  \n",
       "6          0.008614  \n",
       "7          0.005266  \n",
       "8          0.007069  \n",
       "9          0.008190  \n",
       "10         0.007874  \n",
       "11         0.006943  \n",
       "12         0.005460  \n",
       "13         0.005586  \n",
       "14         0.003746  \n",
       "15         0.003876  \n",
       "16         0.001805  \n",
       "17         0.004480  \n",
       "18         0.102268  \n",
       "19         0.014840  \n",
       "20         0.014968  \n",
       "21         0.008123  \n",
       "22         0.054605  \n",
       "23         0.017820  \n",
       "24         0.073210  \n",
       "25         0.180323  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_file = 'allcoin2015to2017_WF_GRU_tanh_leaky.csv'\n",
    "df = pd.read_csv(result_file)\n",
    "zero_indexes = [i for i,e in enumerate(df.epoch) if e==0]\n",
    "zero_indexes\n",
    "df['partition'] = -1\n",
    "for z in zero_indexes:\n",
    "    df.partition += [0]*(z) + [1] * (len(df)-z )\n",
    "\n",
    "df.loc[df.val_loss == df.val_loss.min(), :]\n",
    "df.groupby('partition').describe()['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(CuDNNGRU(units=units, input_shape=(step_size,nb_features),return_sequences=True))\n",
    "model.add(Activation('tanh'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(MaxPooling1D(pool_size=16))\n",
    "model.add(Dense(output_size))\n",
    "model.add(LeakyReLU())\n",
    "model.load_weights('weights/allcoin2015to2017_WF_GRU_tanh_leakypartition_21.hdf5')\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted = model.predict(test_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(original_datas)\n",
    "predicted_inverted = []\n",
    "for i in range(len(predicted)):\n",
    "    predicted_inverted.append(scaler.inverse_transform(predicted[i,:]))\n",
    "predicted_inverted = np.array(predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-16 08:55:00</td>\n",
       "      <td>17860.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-16 09:00:00</td>\n",
       "      <td>17883.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-16 09:05:00</td>\n",
       "      <td>17823.282001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-16 09:10:00</td>\n",
       "      <td>17860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-16 09:15:00</td>\n",
       "      <td>17846.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times         value\n",
       "0 2017-12-16 08:55:00  17860.000001\n",
       "1 2017-12-16 09:00:00  17883.000000\n",
       "2 2017-12-16 09:05:00  17823.282001\n",
       "3 2017-12-16 09:10:00  17860.000000\n",
       "4 2017-12-16 09:15:00  17846.900000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = pd.to_datetime(test_input_times[:,:,0].reshape(-1),unit='s')\n",
    "ground_true_df['value'] = original_test_inputs[:,:,0].reshape(-1)\n",
    "ground_true_df.set_index('times')\n",
    "ground_true_df = ground_true_df.drop_duplicates(subset=['times','value'])\n",
    "ground_true_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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kHYoeXyrpL2bEckmFhmGMkvRBSUtM06wyTbNa0hJJC6LnCkzTfNuMdEn+i6TL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\nF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rB7jkk2fcRPN/3oo3CfpTFjwj7//HPosZ0xI4w0XLIk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woSw/BGRC4IILZKnVjBkizO66q4i2OsoLAH7wA5PWUX+jozJRoNHP4MaN8tfXJo2Pi5i8aJHY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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf6fcb5150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "\n",
    "for i in range(len(test_output_times)):\n",
    "    if i%16 == 0:\n",
    "        prediction_df = pd.DataFrame()\n",
    "        prediction_df['times']= pd.to_datetime(test_output_times[i,:,:].reshape(-1),unit='s')\n",
    "        prediction_df['value']= predicted_inverted[i,:,:].reshape(-1)\n",
    "        plt.plot(prediction_df.times,prediction_df.value,'r', label='Predicted')\n",
    "        \n",
    "# plt.legend(loc='upper left')\n",
    "plt.savefig('result/allcoin2015to2017_WF_GRU_tanh_leaky_scale_on_whole.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-12-17 06:15:00', '2017-12-17 06:20:00',\n",
       "               '2017-12-17 06:25:00', '2017-12-17 06:30:00',\n",
       "               '2017-12-17 06:35:00', '2017-12-17 06:40:00',\n",
       "               '2017-12-17 06:45:00', '2017-12-17 06:50:00',\n",
       "               '2017-12-17 06:55:00', '2017-12-17 07:00:00',\n",
       "               '2017-12-17 07:05:00', '2017-12-17 07:10:00',\n",
       "               '2017-12-17 07:15:00', '2017-12-17 07:20:00',\n",
       "               '2017-12-17 07:25:00', '2017-12-17 07:30:00'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(test_output_times[i,:,:].reshape(-1),unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}