Comparison _of_prediction_algorithms_with_actual_hash.ipynb

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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>facebook_prophet</th>\n",
       "      <th>excel</th>\n",
       "      <th>calc</th>\n",
       "      <th>fact</th>\n",
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       "      <th>2017-07-29</th>\n",
       "      <td>2017-07-29</td>\n",
       "      <td>30.126481</td>\n",
       "      <td>30.297378</td>\n",
       "      <td>30.126481</td>\n",
       "      <td>30.111644</td>\n",
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       "      <th>2017-07-30</th>\n",
       "      <td>2017-07-30</td>\n",
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       "      <th>2017-07-31</th>\n",
       "      <td>2017-07-31</td>\n",
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       "      <td>30.277179</td>\n",
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       "      <th>2017-08-01</th>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>30.161138</td>\n",
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       "      <td>30.348650</td>\n",
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       "      <th>2017-08-02</th>\n",
       "      <td>2017-08-02</td>\n",
       "      <td>30.171779</td>\n",
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       "                 date  facebook_prophet      excel       calc       fact\n",
       "date                                                                    \n",
       "2017-07-29 2017-07-29         30.126481  30.297378  30.126481  30.111644\n",
       "2017-07-30 2017-07-30         30.139244  30.297591  30.139244  30.316897\n",
       "2017-07-31 2017-07-31         30.150905  30.297804  30.150905  30.277179\n",
       "2017-08-01 2017-08-01         30.161138  30.298017  30.161138  30.348650\n",
       "2017-08-02 2017-08-02         30.171779  30.298230  30.171779  30.311933"
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   "source": [
    "#!/usr/local/bin/python\n",
    "# -*- coding: utf-8 -*-\n",
    "# Copyright (c) 2017 Mikhail Shilov\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.plotly as py\n",
    "from plotly import graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected = True)\n",
    "cnt_day =  55 # количество дней для просмотра\n",
    "\n",
    "path = \"comparative_analysis.csv\"  \n",
    "data = pd.read_csv(path, delimiter=\";\")\n",
    "data['date'] =  pd.to_datetime(data.date, format=\"%d.%m.%Y\")\n",
    "data['facebook_prophet'] = np.log(data['facebook_prophet'])\n",
    "data['excel'] = np.log(data['excel'])\n",
    "data['calc'] = np.log(data['calc'])\n",
    "data['fact'] = np.log(data['fact'])\n",
    "\n",
    "data = data[0:cnt_day][['date', 'facebook_prophet', 'excel','calc','fact']]\n",
    "data.set_index(data['date'],inplace=True)\n",
    "data.head()"
   ]
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\\u0441\\u043e\\u0432\\u043f\\u0430\\u0434\\u0430\\u0435\\u0442 \\u0441 Facebook Prophet \", \"x\": \"2017-08-21\", \"y\": 30.35}, {\"text\": \"\\u0417\\u0434\\u0435\\u0441\\u044c \\u0437\\u0430\\u0436\\u0436\\u0443\\u0436\\u0430\\u0442 \\u043f\\u0435\\u0440\\u0432\\u044b\\u0435 \\u0430\\u0441\\u0438\\u043a\\u0438 \", \"x\": \"2017-09-10\", \"y\": 30.49}]}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "\n",
    "\n",
    "def show_forecast(data):\n",
    "\n",
    "    # Facebook Prophet\n",
    "    facebook_prophet = go.Scatter(\n",
    "        name='Facebook Prophet',\n",
    "        x=data.index,\n",
    "        y=data.facebook_prophet,\n",
    "        mode='lines',\n",
    "        line=dict(color='#17BECF'),\n",
    "        opacity = 0.8,\n",
    "    )\n",
    "    \n",
    "      # excel\n",
    "    excel = go.Scatter(\n",
    "        name='Excel',\n",
    "        x=data.index,\n",
    "        y=data.excel,\n",
    "        marker=dict(color=\"#7F7F7F\"),\n",
    "        mode='lines',\n",
    "        opacity = 0.8,\n",
    "    )\n",
    "    \n",
    "    \n",
    "     # calc\n",
    "    calc = go.Scatter(\n",
    "        name='Calc',\n",
    "        x=data.index,\n",
    "        y=data.calc,\n",
    "        marker=dict(color=\"#71B33C\"),\n",
    "        mode='lines',\n",
    "        opacity = 0.8,\n",
    "    )\n",
    "    \n",
    "  \n",
    "    # fact\n",
    "    fact = go.Scatter(\n",
    "        name='Fact',\n",
    "        x=data.index,\n",
    "        y=data.fact,\n",
    "        marker=dict(color=\"8080F0\"),\n",
    "        mode='lines',\n",
    "         opacity = 0.8,\n",
    "    )\n",
    "\n",
    "    \n",
    "    data = [facebook_prophet, fact, excel, calc]\n",
    "\n",
    "    layout = go.Layout(\n",
    "       \n",
    "        yaxis=dict(title='Хэшрейт сети DASH'),\n",
    "        title='Сравнение алгоритмов прогнозирования с фактическим хэшрейтом ',\n",
    "        showlegend = True,\n",
    "        )\n",
    "    \n",
    "    \n",
    "    layout.update(dict(annotations=[\n",
    "        go.Annotation(text=\"Прогноз калькулятора пока <br> совпадает с Facebook Prophet \", x='2017-08-21', y=30.35),\n",
    "        go.Annotation(text=\"Здесь зажжужат первые асики \", x='2017-09-10', y=30.49)\n",
    "    ]))\n",
    "    \n",
    "    \n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "    iplot(fig, show_link=False)\n",
    "\n",
    "show_forecast(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3807851181797815, 0.11540633402049849)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Оценка качества Prophet\n",
    "data['e_prophet'] = data['fact'] - data['facebook_prophet']\n",
    "data['p_prophet'] = 100*data['e_prophet']/data['fact']\n",
    "np.mean(abs(data[0:cnt_day]['p_prophet'])), np.mean(abs(data[0:cnt_day]['e_prophet']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13341491280742304, 0.040303466536180506)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Оценка качества Excel\n",
    "data['e_excel'] = data['fact'] - data['excel']\n",
    "data['p_excel'] = 100*data['e_excel']/data['fact']\n",
    "np.mean(abs(data[0:cnt_day]['p_excel'])), np.mean(abs(data[0:cnt_day]['e_excel']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3807851181797815, 0.11540633402049849)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Оценка качества calc\n",
    "data['e_calc'] = data['fact'] - data['calc']\n",
    "data['p_calc'] = 100*data['e_calc']/data['fact']\n",
    "np.mean(abs(data[0:cnt_day]['p_calc'])), np.mean(abs(data[0:cnt_day]['e_calc']))"
   ]
  }
 ],
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