ARIMA/7_model_deployment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "source": [
    "import requests\r\n",
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "import pickle\r\n",
    "import pmdarima as pm\r\n",
    "from sklearn.preprocessing import StandardScaler\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "def encode_query(queryDate=None, queryHour=None):\r\n",
    "    if queryDate is None:\r\n",
    "        queryTimestamp = pd.Timestamp.now()\r\n",
    "        queryTimestamp = queryTimestamp.tz_localize(None)\r\n",
    "    else:\r\n",
    "        queryDateTime = queryDate +' '+ queryHour\r\n",
    "        queryTimestamp = pd.Timestamp(queryDateTime,freq='h')\r\n",
    "    return queryTimestamp\r\n",
    "\r\n",
    "def get_timestamps_training_data(queryTimestamp, timespan=24*7*4*2, unit='h'):\r\n",
    "        nowTimestamp = pd.Timestamp.now()\r\n",
    "        nowTimestamp = nowTimestamp.tz_localize(None)\r\n",
    "        if (queryTimestamp < nowTimestamp):\r\n",
    "            startTimestamp=queryTimestamp-pd.to_timedelta(timespan,unit=unit)\r\n",
    "            endTimestamp=queryTimestamp-pd.to_timedelta(1,unit=unit)\r\n",
    "        else:\r\n",
    "            startTimestamp=nowTimestamp-pd.to_timedelta(timespan,unit=unit)\r\n",
    "            endTimestamp=nowTimestamp-pd.to_timedelta(1,unit=unit)\r\n",
    "        startTimestamp = startTimestamp.tz_localize(None)\r\n",
    "        endTimestamp = endTimestamp.tz_localize(None)\r\n",
    "        return startTimestamp, endTimestamp\r\n",
    "\r\n",
    "def get_spot_market_training_data(startTimestamp, endTimestamp, tokenfile=\"ami/spotcasting/montel_bearer.json\"):\r\n",
    "    r = requests.get('https://coop.eikon.tum.de/mbt/mbt.json')\r\n",
    "    token_data = r.json()\r\n",
    "    startDateTime=startTimestamp.strftime('%Y-%m-%d')\r\n",
    "    endDateTime=endTimestamp.strftime('%Y-%m-%d')\r\n",
    "\r\n",
    "    url = 'http://api.montelnews.com/spot/getprices'\r\n",
    "    headers = {\"Authorization\": \"Bearer \" + token_data['access_token'] }\r\n",
    "    params = {'spotKey': '14',\r\n",
    "            'fields': ['Base', 'Peak','Hours'],\r\n",
    "            'fromDate': startDateTime ,\r\n",
    "            'toDate': endDateTime,\r\n",
    "            'currency': 'eur',\r\n",
    "            'sortType': 'Ascending'}\r\n",
    "    response = requests.get(url, headers=headers, params=params)\r\n",
    "\r\n",
    "def get_top_feature_names():\r\n",
    "    feature_names = [\r\n",
    "            'value__cwt_coefficients__coeff_2__w_2__widths_(2, 5, 10, 20)',\r\n",
    "            'value__root_mean_square',\r\n",
    "            'value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0',\r\n",
    "            'value__energy_ratio_by_chunks__num_segments_10__segment_focus_9',\r\n",
    "            'value__mean_second_derivative_central',\r\n",
    "            'value__fft_coefficient__attr_\"imag\"__coeff_3',\r\n",
    "            'value__cwt_coefficients__coeff_0__w_10__widths_(2, 5, 10, 20)',\r\n",
    "            'value__quantile__q_0.8', 'value__index_mass_quantile__q_0.9',\r\n",
    "            'value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0',\r\n",
    "            'value__mean_change', 'value__fft_coefficient__attr_\"real\"__coeff_8',\r\n",
    "            'value__fft_coefficient__attr_\"real\"__coeff_9',\r\n",
    "            'value__fft_aggregated__aggtype_\"centroid\"',\r\n",
    "            'value__fft_coefficient__attr_\"real\"__coeff_10',\r\n",
    "            'value__fft_coefficient__attr_\"real\"__coeff_12',\r\n",
    "            'value__fft_coefficient__attr_\"real\"__coeff_11'\r\n",
    "        ]\r\n",
    "    return feature_names\r\n",
    "\r\n",
    "def get_exogenous_features(queryTimestamp, prediction_length, features, label, reference_label):\r\n",
    "    \r\n",
    "    curr_time = pd.Timestamp('2021-07-03 00:00:00')\r\n",
    "    # get date indeces\r\n",
    "    train_start, train_end = get_timestamps_training_data(queryTimestamp)\r\n",
    "    test_end = train_end + pd.to_timedelta(prediction_length-1,unit='h')\r\n",
    "\r\n",
    "    # get label\r\n",
    "    # label = get_spot_market_training_data(train_start, train_end)\r\n",
    "    label = label[train_start:train_end]\r\n",
    "\r\n",
    "    # get mask\r\n",
    "    date_range = pd.date_range(start=train_start,end=test_end,freq='h')\r\n",
    "    normal_mask = (date_range >= reference_label.index[0]) & (date_range <= reference_label.index[-1]) # label + features\r\n",
    "    # match_feature_mask = (date_range <= pd.Timestamp.now()) & ((date_range < reference_label.index[0])|(date_range > reference_label.index[-1]))\r\n",
    "    # predict_feature_mask = date_range > pd.Timestamp.now()\r\n",
    "    match_feature_mask = (date_range < curr_time) & ((date_range < reference_label.index[0])|(date_range > reference_label.index[-1]))\r\n",
    "    predict_feature_mask = date_range >= curr_time\r\n",
    "    # get the features\r\n",
    "    feature_names = get_top_feature_names()\r\n",
    "    # normal (we have corresponding features in cache)\r\n",
    "    total_features = pd.DataFrame(columns=feature_names,index=date_range)\r\n",
    "    total_features[normal_mask] = np.array(features.loc[date_range[normal_mask]])\r\n",
    "\r\n",
    "    # match features (we have label data, we can find approximate feature part using spot price)\r\n",
    "    target_label = label.loc[date_range[match_feature_mask]]\r\n",
    "    best_mae = float('inf')\r\n",
    "    best_index = 0\r\n",
    "    for index in reference_label.loc['2021-05-01 00:00:00':].index:\r\n",
    "        ref_label = reference_label[index:index+pd.Timedelta(len(target_label)-1, unit='h')]\r\n",
    "        if len(ref_label) == len(target_label):\r\n",
    "            mae = sum(abs(np.array(ref_label.value) - np.array(target_label.value)))\r\n",
    "            if mae < best_mae:\r\n",
    "                best_mae = mae\r\n",
    "                best_index = index\r\n",
    "        else:\r\n",
    "            break\r\n",
    "    total_features[match_feature_mask] = np.array(features[best_index:best_index+pd.Timedelta(len(target_label)-1, unit='h')])\r\n",
    "\r\n",
    "    # predict features (we do not have label and feature, we need to predict features)\r\n",
    "    total_features[predict_feature_mask] = np.array(total_features[curr_time-pd.Timedelta(predict_feature_mask.sum(), unit='h'):curr_time-pd.Timedelta('1h')])\r\n",
    "\r\n",
    "    # split\r\n",
    "    train_features = total_features[train_start:train_end]\r\n",
    "    test_features = total_features[train_end:test_end]\r\n",
    "\r\n",
    "    return label, train_features, test_features\r\n",
    "\r\n",
    "def AMI_ARIMA(train_label, train_features, test_features):\r\n",
    "    model = pm.ARIMA(order=(2,1,2))\r\n",
    "    train_X = StandardScaler().fit_transform(np.array(train_features))\r\n",
    "    test_X = StandardScaler().fit_transform(np.array(test_features))\r\n",
    "    model.fit(train_label, X=train_X)\r\n",
    "    predict_values = model.predict(n_periods=len(test_X), X=test_X)\r\n",
    "    predict_values = pd.DataFrame(predict_values, index=test_features.index)\r\n",
    "    return predict_values\r\n",
    "\r\n",
    "def Visualization(train_label, predict_values, label):\r\n",
    "    _, ax = plt.subplots(1,1,figsize=(12,8))\r\n",
    "    train_label.iloc[-24*7:].plot(ax=ax)\r\n",
    "    test_label = label.loc[predict_values.index]\r\n",
    "    test_label.plot(ax=ax)\r\n",
    "    predict_values.plot(ax=ax)\r\n",
    "    ax.legend(['actual spot price', 'real test spot price', 'predicted spot price'])\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "source": [
    "# get from user\r\n",
    "queryDate = '2021-07-15'\r\n",
    "queryHour='00'\r\n",
    "prediction_length = 24\r\n",
    "\r\n",
    "# data in server\r\n",
    "# with open('ARIMA.pkl', 'rb') as pkl:\r\n",
    "#     model = pickle.load(pkl)\r\n",
    "label = pd.read_pickle('label.pkl')\r\n",
    "reference_label = label\r\n",
    "\r\n",
    "features = pd.read_pickle('2021_features.pkl')\r\n",
    "feature_names = get_top_feature_names()\r\n",
    "features = features[feature_names]\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "queryTimestamp = encode_query(queryDate=queryDate, queryHour=queryHour)\r\n",
    "train_label, train_features, test_features = get_exogenous_features(queryTimestamp, prediction_length, features, label, reference_label)\r\n",
    "predict_values = AMI_ARIMA(train_label, train_features, test_features)\r\n",
    "Visualization(train_label, predict_values, label)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\ZYH7ABT\\AppData\\Local\\conda\\conda\\envs\\machineL\\Lib\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.6.8",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit"
  },
  "interpreter": {
   "hash": "69556a9b218a438d843cf826c443dfec2a545d3a90ab41b1e3d6b92a703fb1e2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}