missing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas              as pd\n",
    "import numpy               as np\n",
    "import plotly.offline      as py\n",
    "import joblib              as jb\n",
    "from fbprophet             import Prophet\n",
    "from fbprophet.plot        import plot_plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>ValorAtual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>4.31714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>4.31786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>4.31922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-06-04</td>\n",
       "      <td>4.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>4.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>4.30811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>4.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>4.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>4.20929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>4.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2019-07-02</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2019-07-03</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-07-04</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2019-07-06</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Data ValorAtual\n",
       "25 2019-05-20    4.31714\n",
       "26 2019-05-27    4.31786\n",
       "27 2019-06-03    4.31922\n",
       "28 2019-06-04      4.229\n",
       "29 2019-06-05      4.199\n",
       "30 2019-06-10    4.30811\n",
       "31 2019-06-11      4.229\n",
       "32 2019-06-12      4.199\n",
       "33 2019-06-18    4.20929\n",
       "34 2019-06-26      4.195\n",
       "35 2019-06-27          -\n",
       "36 2019-06-28          -\n",
       "37 2019-06-29          -\n",
       "38 2019-06-30          -\n",
       "39 2019-07-01          -\n",
       "40 2019-07-02          -\n",
       "41 2019-07-03          -\n",
       "42 2019-07-04          -\n",
       "43 2019-07-05          -\n",
       "44 2019-07-06          -"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./QlikProphet/DataSetATUAL.xlsx')\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>4.086143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-04-09</td>\n",
       "      <td>4.126143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>4.299000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-04-16</td>\n",
       "      <td>4.135923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>4.252636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-04-23</td>\n",
       "      <td>4.199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>4.252636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>4.199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>4.263429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>4.263429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>4.317143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>4.317857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>4.319222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-06-04</td>\n",
       "      <td>4.229000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>4.199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>4.308111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>4.229000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>4.199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>4.209286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>4.195000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds         y\n",
       "15 2019-04-02  4.086143\n",
       "16 2019-04-09  4.126143\n",
       "17 2019-04-15  4.299000\n",
       "18 2019-04-16  4.135923\n",
       "19 2019-04-22  4.252636\n",
       "20 2019-04-23  4.199000\n",
       "21 2019-04-29  4.252636\n",
       "22 2019-04-30  4.199000\n",
       "23 2019-05-06  4.263429\n",
       "24 2019-05-13  4.263429\n",
       "25 2019-05-20  4.317143\n",
       "26 2019-05-27  4.317857\n",
       "27 2019-06-03  4.319222\n",
       "28 2019-06-04  4.229000\n",
       "29 2019-06-05  4.199000\n",
       "30 2019-06-10  4.308111\n",
       "31 2019-06-11  4.229000\n",
       "32 2019-06-12  4.199000\n",
       "33 2019-06-18  4.209286\n",
       "34 2019-06-26  4.195000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ValorAtual']        = df['ValorAtual'].apply(lambda x: np.nan if x == '-' else x)\n",
    "df                      = df.dropna()\n",
    "df['Data']              = pd.to_datetime(df['Data'])\n",
    "df                      = df.filter(items=['Data', 'ValorAtual'])\n",
    "df                      = df.set_index('Data').sort_index()\n",
    "df                      = df.reset_index(drop=False)\n",
    "df.columns              = ['ds', 'y']\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    }
   ],
   "source": [
    "# input_qlik\n",
    "# future_qlik\n",
    "prophet2            = Prophet(changepoint_prior_scale=0.5)\n",
    "prophet2.add_country_holidays(country_name='BR')\n",
    "df2                 = df[['ds','y']].dropna()\n",
    "# fit(df, algorithm='Newton')\n",
    "prophet2.fit(df2, algorithm='LBFGS')\n",
    "futuro2             = prophet2.make_future_dataframe(periods=8, freq=\"D\")\n",
    "forecast2           = prophet2.predict(futuro2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>4.250358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>4.275221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>4.294000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>4.288971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>4.268622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-06-04</td>\n",
       "      <td>4.284172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>4.234359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>4.248273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>4.263823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>4.214010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>4.243474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>4.173312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>4.143358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>4.140451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>4.137544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>4.134637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>4.187226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2019-07-02</td>\n",
       "      <td>4.202776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2019-07-03</td>\n",
       "      <td>4.152963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-07-04</td>\n",
       "      <td>4.123009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds      yhat\n",
       "23 2019-05-06  4.250358\n",
       "24 2019-05-13  4.275221\n",
       "25 2019-05-20  4.294000\n",
       "26 2019-05-27  4.288971\n",
       "27 2019-06-03  4.268622\n",
       "28 2019-06-04  4.284172\n",
       "29 2019-06-05  4.234359\n",
       "30 2019-06-10  4.248273\n",
       "31 2019-06-11  4.263823\n",
       "32 2019-06-12  4.214010\n",
       "33 2019-06-18  4.243474\n",
       "34 2019-06-26  4.173312\n",
       "35 2019-06-27  4.143358\n",
       "36 2019-06-28  4.140451\n",
       "37 2019-06-29  4.137544\n",
       "38 2019-06-30  4.134637\n",
       "39 2019-07-01  4.187226\n",
       "40 2019-07-02  4.202776\n",
       "41 2019-07-03  4.152963\n",
       "42 2019-07-04  4.123009"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast2[['ds','yhat']].tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df2            = forecast2.set_index('ds')[['yhat', 'yhat_lower', 'yhat_upper']].join(df2.set_index('ds').y).reset_index()\n",
    "pred_df2.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig6                = plot_plotly(prophet2, forecast2)\n",
    "py.iplot(fig6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['y'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "futuro2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast2[['ds', 'yhat']].set_index('ds').sort_index().reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_qlik              = jb.load(\"./QlikProphet/prev/input_df.pkl.z\")\n",
    "future_qlik             = jb.load(\"./QlikProphet/prev/future_df.pkl.z\")\n",
    "forecast_01_qlik        = jb.load(\"./QlikProphet/prev/forecast_01_df.pkl.z\")\n",
    "forecast_qlik           = jb.load(\"./QlikProphet/prev/forecast_df.pkl.z\")\n",
    "pParam = forecast_qlik  = jb.load(\"./QlikProphet/prev/prophParam.pkl.z\")\n",
    "fParam = forecast_qlik  = jb.load(\"./QlikProphet/prev/fitParam.pkl.z\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fParam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pParam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_qlik['y'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_qlik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_01_qlik[['ds', 'yhat']].set_index('ds').sort_index().reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_qlik[['ds', 'yhat']].set_index('ds').sort_index().reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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