ukitttt/prophet_sample.ipynb

## prophet_sample.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Docker&jupyterの起動\n",
    "###  jupyter入ってない方用です\n",
    "```docker run -it --rm -p 8888:8888 jupyter/datascience-notebook\n",
    "http://localhost:8888/?token=xxxxxxxxxxxxxxxxxxxxx\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# データのダウンロード\n",
    "### データセットはPandas経由で取得したNASDAQのFacebookのデータです"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas_datareader\n",
      "  Downloading pandas_datareader-0.5.0-py2.py3-none-any.whl (74kB)\n",
      "\u001b[K    100% |████████████████████████████████| 81kB 345kB/s ta 0:00:01\n",
      "\u001b[?25hCollecting requests-file (from pandas_datareader)\n",
      "  Downloading requests-file-1.4.2.tar.gz\n",
      "Requirement already satisfied: pandas>=0.17.0 in /opt/conda/lib/python3.6/site-packages (from pandas_datareader)\n",
      "Collecting requests-ftp (from pandas_datareader)\n",
      "  Downloading requests-ftp-0.3.1.tar.gz\n",
      "Requirement already satisfied: requests>=2.3.0 in /opt/conda/lib/python3.6/site-packages (from pandas_datareader)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from requests-file->pandas_datareader)\n",
      "Requirement already satisfied: python-dateutil>=2 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.17.0->pandas_datareader)\n",
      "Requirement already satisfied: pytz>=2011k in /opt/conda/lib/python3.6/site-packages (from pandas>=0.17.0->pandas_datareader)\n",
      "Requirement already satisfied: numpy>=1.7.0 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.17.0->pandas_datareader)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests>=2.3.0->pandas_datareader)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests>=2.3.0->pandas_datareader)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests>=2.3.0->pandas_datareader)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests>=2.3.0->pandas_datareader)\n",
      "Building wheels for collected packages: requests-file, requests-ftp\n",
      "  Running setup.py bdist_wheel for requests-file ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jovyan/.cache/pip/wheels/3e/34/3a/c2e634ca7b545510c1b3b7d94dea084e5fdb5f33558f3c3a81\n",
      "  Running setup.py bdist_wheel for requests-ftp ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jovyan/.cache/pip/wheels/76/fb/0d/1026eb562c34a4982dc9d39c9c582a734eefe7f0455f711deb\n",
      "Successfully built requests-file requests-ftp\n",
      "Installing collected packages: requests-file, requests-ftp, pandas-datareader\n",
      "Successfully installed pandas-datareader-0.5.0 requests-file-1.4.2 requests-ftp-0.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas_datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-09-29</th>\n",
       "      <td>129.179993</td>\n",
       "      <td>129.289993</td>\n",
       "      <td>127.550003</td>\n",
       "      <td>128.089996</td>\n",
       "      <td>128.089996</td>\n",
       "      <td>14532200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>128.029999</td>\n",
       "      <td>128.589996</td>\n",
       "      <td>127.449997</td>\n",
       "      <td>128.270004</td>\n",
       "      <td>128.270004</td>\n",
       "      <td>18402900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-03</th>\n",
       "      <td>128.380005</td>\n",
       "      <td>129.089996</td>\n",
       "      <td>127.800003</td>\n",
       "      <td>128.770004</td>\n",
       "      <td>128.770004</td>\n",
       "      <td>13156900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>129.169998</td>\n",
       "      <td>129.279999</td>\n",
       "      <td>127.550003</td>\n",
       "      <td>128.190002</td>\n",
       "      <td>128.190002</td>\n",
       "      <td>14307500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-05</th>\n",
       "      <td>128.250000</td>\n",
       "      <td>128.800003</td>\n",
       "      <td>127.830002</td>\n",
       "      <td>128.470001</td>\n",
       "      <td>128.470001</td>\n",
       "      <td>12386800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2016-09-29  129.179993  129.289993  127.550003  128.089996  128.089996   \n",
       "2016-09-30  128.029999  128.589996  127.449997  128.270004  128.270004   \n",
       "2016-10-03  128.380005  129.089996  127.800003  128.770004  128.770004   \n",
       "2016-10-04  129.169998  129.279999  127.550003  128.190002  128.190002   \n",
       "2016-10-05  128.250000  128.800003  127.830002  128.470001  128.470001   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2016-09-29  14532200  \n",
       "2016-09-30  18402900  \n",
       "2016-10-03  13156900  \n",
       "2016-10-04  14307500  \n",
       "2016-10-05  12386800  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader.data import DataReader\n",
    "from datetime import datetime\n",
    "\n",
    "end = datetime.now()\n",
    "start = datetime(end.year - 1, end.month, end.day)\n",
    "fb = DataReader('FB', 'yahoo', start, end)\n",
    "\n",
    "fb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
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lDSDOnDkT11xzTRw6dGjJ5cePH49f+ZVfWfz+yJEjsWvXrti9e3c89NBDKYcE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\nmwTGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe41abb3748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.finance import candlestick2_ohlc\n",
    "\n",
    "# ローソクだけをプロット\n",
    "fig = plt.figure(figsize=(18, 9))\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "# candlestick2を使って描画\n",
    "candlestick2_ohlc(ax, fb[\"Open\"], fb[\"High\"], fb[\"Low\"], fb[\"Close\"], width=0.9, colorup=\"b\", colordown=\"r\")\n",
    "\n",
    "# 軸メモリやラベルを整える\n",
    "ax.set_ylabel(\"Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Prophetによる時系列予測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pystan\n",
      "  Downloading pystan-2.16.0.0-cp36-cp36m-manylinux1_x86_64.whl (52.9MB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.6/site-packages (from pystan)\n",
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      "Collecting fbprophet\n",
      "  Downloading fbprophet-0.2.tar.gz\n",
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      "Building wheels for collected packages: fbprophet\n",
      "  Running setup.py bdist_wheel for fbprophet ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jovyan/.cache/pip/wheels/ae/90/23/ef753de5757d77bb1b5a6bb9846c1d36689355bdb5ddf67862\n",
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      "Installing collected packages: fbprophet\n",
      "Successfully installed fbprophet-0.2\n"
     ]
    }
   ],
   "source": [
    "!pip install pystan\n",
    "!pip install fbprophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read dataset\n",
    "df = fb\n",
    "df['y'] = df['Close']\n",
    "df.reset_index(level='Date', inplace=True)\n",
    "df = df.rename(columns = {'Date':'ds'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split dataset for train-test\n",
    "df = df.sort_values(by='ds')\n",
    "df_test = df.tail(30)\n",
    "df_train = df.head(df.ds.count() -30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet.forecaster:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet.forecaster:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7fe44c1dbba8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction\n",
    "m = Prophet()\n",
    "m.fit(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "future = m.make_future_dataframe(periods=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = m.predict(future)\n",
    "verification = pd.merge(df_test, forecast[['ds', 'yhat']].tail(30), on= 'ds')\n",
    "\n",
    "verification['diff'] = abs(1-verification.yhat/verification.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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DMbnqaixl8Zf/dZxXmnvZsjTI56+vxrRs+uIpSgNuHnYYHGkNEbgQnldOsk3CPYEqdf44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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4143b0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot(forecast)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zG+/uq/ZNuDhY9+XtywYkRjDh8O3LpvSKx2bRwsciEvp0pw0R6fK8hkFeab1v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\n9erVY82aNQQFBRESEkLDhg1Zs2YNaWlpPPHEE6xduxaA6OhonnrqKSZ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uo0ePpn///ly8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\nBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe41747be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# トレンドと曜日成分の寄与度\n",
    "m.plot_components(forecast)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference\n",
      "0     0.026438\n",
      "1     0.030386\n",
      "2     0.020956\n",
      "3     0.028662\n",
      "4     0.033473\n",
      "5     0.046684\n",
      "6     0.047157\n",
      "7     0.043996\n",
      "8     0.034569\n",
      "9     0.021127\n",
      "10    0.025072\n",
      "11    0.040838\n",
      "12    0.034589\n",
      "13    0.026797\n",
      "14    0.044641\n",
      "15    0.035233\n",
      "16    0.040358\n",
      "17    0.041842\n",
      "18    0.053463\n",
      "19    0.053541\n",
      "Name: diff, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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i0hEZuriERvmKiIgcZ2hBtvJtQyIiIoFk+PKLmn5QRETEBAVZo3zFCJoYRUTM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\nS2PevHns37+fqqoqKisrqai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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe41749f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BKioqOrNkvfzyyyouLlbXrl319ttva9q0aVq6dKn69OnTqYOhr1q1SuvWrVNt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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe417387ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直近30日を予測してみた\n",
    "print('difference')\n",
    "print(verification['diff'])\n",
    "\n",
    "verification.plot(x= 'ds', y= ['y','yhat'], style='.')\n",
    "verification.plot(x= 'ds', y= 'diff', kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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