docs/tutorials/darkgrey_poc_demo_03.ipynb

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     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/compat/pandas.py:65: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import Int64Index as NumericIndex\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:17:38,717 darkgreybox  INFO     Logging enabled...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "\n",
    "from darkgreybox.models import TiTe\n",
    "from darkgreybox.fit import train_models\n",
    "from docs.tutorials.util.plot import plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo Notebook 03 - TiTe Model Wrapper Fit PASS\n",
    "\n",
    "This notebook demonstrates the usage of the `DarkGreyBox` models via fitting them with a wrapper function for a `TiTe` model. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our temporal resolution is 1 hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the duration of a record\n",
    "rec_duration = 1 # hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read some demo data.  \n",
    "* Ph: Heating system power output\n",
    "* Ta: Ambient temperature\n",
    "* Ti: Internal temperature\n",
    "\n",
    "We are also setting a set of custom fields in our input data: `Ti0` and `Te0`. These fields define the initial conditions for `Ti` and `Te` for each record of the input data respectively. E.g. if a sub-model is trained based on the 10-20 records of the training data, the initial conditions for the above params will be set by the 10. record of the input data. \n",
    "\n",
    "Note: This demo data is intentionally far from ideal and particularly challenging to model with low errors. It is taken from a building where there are many factors influencing the heat dynamics of the building that are not accounted for in the modelling (solar gains, passive gains, hot water demand, gas use in the canteen etc.). The time period is relatively short to maintain a reasonable solution time for the demo. However, it includes an also challenging holiday period when the heating system is shut down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input X shape: (792, 4), input y shape: (792,)\n"
     ]
    }
   ],
   "source": [
    "input_df = pd.read_csv('./data/demo_data.csv', index_col=0, parse_dates=True)\n",
    "\n",
    "input_df['Ti0'] = input_df['Ti']\n",
    "input_df['Te0'] = input_df['Ti'] - 2\n",
    "\n",
    "input_X = input_df[['Ph', 'Ta', 'Ti0', 'Te0']]\n",
    "input_y = input_df['Ti']\n",
    "\n",
    "print(f'Input X shape: {input_X.shape}, input y shape: {input_y.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `sklearn.model_selection.train_test_split` function to split the input data into train and test data. (Input data is 33 days long and 5 days of test data is specified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: X shape: (672, 4), y shape: (672,)\n",
      "Test: X shape: (120, 4), y shape: (120,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(input_X, input_y, test_size=5 / 33, shuffle=False)\n",
    "\n",
    "print(f'Train: X shape: {X_train.shape}, y shape: {y_train.shape}')\n",
    "print(f'Test: X shape: {X_test.shape}, y shape: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the model training parameters.  \n",
    "\n",
    "The `Ti0` param is the initial condition for the internal temperature at t=0 - this is set to the first record of `X_train['Ti0']` as described above and is fixed, hence `vary: False`.  \n",
    "\n",
    "The `Te0` param is the initial condition for the building envelope temperature at t=0 - - this is set to the first record of `X_train['Te0']` as described above and is NOT fixed, hence `vary: True`.  \n",
    "\n",
    "`Ci`, `Ce`, `Rie` and `Ria` params are the initial conditions for these thermal parameters. As these will be fit by the model training their default is `vary: True`. The values for these params' initial conditions are set arbitrarily to `1` it is assumed that no estimates have been calculated for them (e.g. based on building physical properties).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_params = {\n",
    "    'Ti0': {'value': X_train.iloc[0]['Ti0'], 'vary': False},\n",
    "    'Te0': {'value': X_train.iloc[0]['Te0'], 'vary': True, 'min': 10, 'max': 25},\n",
    "    'Ci': {'value': 1},\n",
    "    'Ce': {'value': 1},\n",
    "    'Rie': {'value': 1},\n",
    "    'Rea': {'value': 1},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We call the `train_models` wrapper function, passing it:\n",
    "\n",
    "* `models`: An `TiTe` model instantiated with the initial `train_params` as set above\n",
    "* `X_train`: The X of the train set\n",
    "* `y_train`: The y of the train set\n",
    "* `splits`: A list of training data indices specifying sub-sections of `X_train` and `y_train` for the models to be trained on. We use `sklearn.model_selection.KFold` to split the train set into chunks with lenghts of a single day each.\n",
    "* `error_metric`: An error metric function that confirms to the `sklearn.metrics` interface. The model results will be evaluated based on this metric. \n",
    "* `method`: Name of the fitting method to use. Valid values are described in: `lmfit.minimize`. We are passing `nelder` here as it is generally quicker than `leastsq`.\n",
    "* `n_jobs`: The number of parallel jobs to be run as described by `joblib.Parallel`. We are passing `-1` here to use all available CPU cores.\n",
    "* `verbose`: The degree of verbosity as described by `joblib.Parallel`. We are passing `10` here for medium verbosity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:18:48,561 darkgreybox  INFO     Training models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:18:49,306 darkgreybox  INFO     Logging enabled...\n",
      "2022-01-28 15:18:49,306 darkgreybox  INFO     Logging enabled...\n",
      "2022-01-28 15:18:49,307 darkgreybox  INFO     Logging enabled...\n",
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      "2022-01-28 15:18:49,311 darkgreybox  INFO     Logging enabled...\n",
      "2022-01-28 15:18:49,318 darkgreybox  INFO     Logging enabled...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    2.8s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    5.0s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  28 | elapsed:    5.7s remaining:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  28 | elapsed:    5.8s remaining:    2.8s\n",
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      "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:    7.3s finished\n"
     ]
    }
   ],
   "source": [
    "prefit_df = train_models(models=[TiTe(train_params, rec_duration=1)],\n",
    "                         X_train=X_train, \n",
    "                         y_train=y_train, \n",
    "                         splits=KFold(n_splits=int(len(X_train) / 24), shuffle=False).split(X_train), \n",
    "                         error_metric=mean_squared_error,\n",
    "                         method='nelder', \n",
    "                         n_jobs=-1, \n",
    "                         verbose=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns a dataframe with a record for each split showing:\n",
    "* `start_date`: the start date of the train sub set for which the model was fit\n",
    "* `end_date`: the end date of the train sub set for which the model was fit\n",
    "* `model`: the fit model object\n",
    "* `model_result`: the model result that was generated by the model's `predict` method for the train sub set\n",
    "* `time`: the time it took for the fitting process to complete\n",
    "* `method`: the fitting method\n",
    "* `error`: the error of the model for the `y` variable (in this case the MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2019-12-23 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b087f0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 17.1...</td>\n",
       "      <td>0.244252</td>\n",
       "      <td>nelder</td>\n",
       "      <td>11.406733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-24 00:00:00+00:00</td>\n",
       "      <td>2019-12-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ad8820&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.3625    , 19.1...</td>\n",
       "      <td>0.325435</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.018890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-25 00:00:00+00:00</td>\n",
       "      <td>2019-12-25 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae69a0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.7875    , 19.6...</td>\n",
       "      <td>2.555123</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.026962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-26 00:00:00+00:00</td>\n",
       "      <td>2019-12-26 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ad8be0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.3125    , 18.2...</td>\n",
       "      <td>0.477023</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.001699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-27 00:00:00+00:00</td>\n",
       "      <td>2019-12-27 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b088b0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.1125    , 17.0...</td>\n",
       "      <td>0.640743</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.001673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-12-28 00:00:00+00:00</td>\n",
       "      <td>2019-12-28 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae6e20&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([16.2875    , 16.2...</td>\n",
       "      <td>2.541821</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-12-29 00:00:00+00:00</td>\n",
       "      <td>2019-12-29 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126affdc0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([15.6       , 15.5...</td>\n",
       "      <td>2.529565</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.010708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-12-30 00:00:00+00:00</td>\n",
       "      <td>2019-12-30 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae69d0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([15.0875    , 12.9...</td>\n",
       "      <td>2.607300</td>\n",
       "      <td>nelder</td>\n",
       "      <td>37.927944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-12-31 00:00:00+00:00</td>\n",
       "      <td>2019-12-31 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae62e0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([15.3125    , 15.2...</td>\n",
       "      <td>0.208122</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.000318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-01 00:00:00+00:00</td>\n",
       "      <td>2020-01-01 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae8670&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([14.625     , 10.4...</td>\n",
       "      <td>2.550822</td>\n",
       "      <td>nelder</td>\n",
       "      <td>8.973590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-01-02 00:00:00+00:00</td>\n",
       "      <td>2020-01-02 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b0cc70&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.0625    , 13.8...</td>\n",
       "      <td>2.627692</td>\n",
       "      <td>nelder</td>\n",
       "      <td>55.576592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-01-03 00:00:00+00:00</td>\n",
       "      <td>2020-01-03 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b180d0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.5375    , 17.7...</td>\n",
       "      <td>2.563974</td>\n",
       "      <td>nelder</td>\n",
       "      <td>64.654774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-01-04 00:00:00+00:00</td>\n",
       "      <td>2020-01-04 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae66d0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.5625    , 19.4...</td>\n",
       "      <td>0.176015</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.008275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-01-05 00:00:00+00:00</td>\n",
       "      <td>2020-01-05 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae84c0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.9875    , 18.9...</td>\n",
       "      <td>2.551741</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.001236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-01-06 00:00:00+00:00</td>\n",
       "      <td>2020-01-06 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b0c550&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.5       , 14.8...</td>\n",
       "      <td>0.539668</td>\n",
       "      <td>nelder</td>\n",
       "      <td>22.395109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-01-07 00:00:00+00:00</td>\n",
       "      <td>2020-01-07 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b0c0d0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.25      , 19.0...</td>\n",
       "      <td>0.404294</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.010387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-01-08 00:00:00+00:00</td>\n",
       "      <td>2020-01-08 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b48f70&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.175     , 20.0...</td>\n",
       "      <td>1.324824</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.066073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-01-09 00:00:00+00:00</td>\n",
       "      <td>2020-01-09 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b18220&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.475     , 18.9...</td>\n",
       "      <td>1.453820</td>\n",
       "      <td>nelder</td>\n",
       "      <td>2.974815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-01-10 00:00:00+00:00</td>\n",
       "      <td>2020-01-10 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b37370&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.6       , 19.5...</td>\n",
       "      <td>0.394029</td>\n",
       "      <td>nelder</td>\n",
       "      <td>1.463338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-01-11 00:00:00+00:00</td>\n",
       "      <td>2020-01-11 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b18dc0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.4125    , 20.2...</td>\n",
       "      <td>0.416629</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.005550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-01-12 00:00:00+00:00</td>\n",
       "      <td>2020-01-12 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b0c310&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.3375    , 19.2...</td>\n",
       "      <td>0.133398</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.007123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-01-13 00:00:00+00:00</td>\n",
       "      <td>2020-01-13 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b189a0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.225     , 15.9...</td>\n",
       "      <td>1.617867</td>\n",
       "      <td>nelder</td>\n",
       "      <td>16.354067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-01-14 00:00:00+00:00</td>\n",
       "      <td>2020-01-14 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b18f40&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.6125    , 19.4...</td>\n",
       "      <td>0.215751</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.010353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-01-15 00:00:00+00:00</td>\n",
       "      <td>2020-01-15 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b379a0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.2       , 20.0...</td>\n",
       "      <td>0.439717</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.032928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-01-16 00:00:00+00:00</td>\n",
       "      <td>2020-01-16 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b0cbe0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.275     , 20.2...</td>\n",
       "      <td>0.255637</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.038252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-01-17 00:00:00+00:00</td>\n",
       "      <td>2020-01-17 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b376d0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.5125    , 20.0...</td>\n",
       "      <td>1.452425</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.577049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-01-18 00:00:00+00:00</td>\n",
       "      <td>2020-01-18 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b37cd0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([20.2       , 19.9...</td>\n",
       "      <td>0.314696</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.072493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-01-19 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b48ac0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([19.625     , 19.4...</td>\n",
       "      <td>0.201671</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.039016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  start_date                  end_date  \\\n",
       "0  2019-12-23 00:00:00+00:00 2019-12-23 23:00:00+00:00   \n",
       "1  2019-12-24 00:00:00+00:00 2019-12-24 23:00:00+00:00   \n",
       "2  2019-12-25 00:00:00+00:00 2019-12-25 23:00:00+00:00   \n",
       "3  2019-12-26 00:00:00+00:00 2019-12-26 23:00:00+00:00   \n",
       "4  2019-12-27 00:00:00+00:00 2019-12-27 23:00:00+00:00   \n",
       "5  2019-12-28 00:00:00+00:00 2019-12-28 23:00:00+00:00   \n",
       "6  2019-12-29 00:00:00+00:00 2019-12-29 23:00:00+00:00   \n",
       "7  2019-12-30 00:00:00+00:00 2019-12-30 23:00:00+00:00   \n",
       "8  2019-12-31 00:00:00+00:00 2019-12-31 23:00:00+00:00   \n",
       "9  2020-01-01 00:00:00+00:00 2020-01-01 23:00:00+00:00   \n",
       "10 2020-01-02 00:00:00+00:00 2020-01-02 23:00:00+00:00   \n",
       "11 2020-01-03 00:00:00+00:00 2020-01-03 23:00:00+00:00   \n",
       "12 2020-01-04 00:00:00+00:00 2020-01-04 23:00:00+00:00   \n",
       "13 2020-01-05 00:00:00+00:00 2020-01-05 23:00:00+00:00   \n",
       "14 2020-01-06 00:00:00+00:00 2020-01-06 23:00:00+00:00   \n",
       "15 2020-01-07 00:00:00+00:00 2020-01-07 23:00:00+00:00   \n",
       "16 2020-01-08 00:00:00+00:00 2020-01-08 23:00:00+00:00   \n",
       "17 2020-01-09 00:00:00+00:00 2020-01-09 23:00:00+00:00   \n",
       "18 2020-01-10 00:00:00+00:00 2020-01-10 23:00:00+00:00   \n",
       "19 2020-01-11 00:00:00+00:00 2020-01-11 23:00:00+00:00   \n",
       "20 2020-01-12 00:00:00+00:00 2020-01-12 23:00:00+00:00   \n",
       "21 2020-01-13 00:00:00+00:00 2020-01-13 23:00:00+00:00   \n",
       "22 2020-01-14 00:00:00+00:00 2020-01-14 23:00:00+00:00   \n",
       "23 2020-01-15 00:00:00+00:00 2020-01-15 23:00:00+00:00   \n",
       "24 2020-01-16 00:00:00+00:00 2020-01-16 23:00:00+00:00   \n",
       "25 2020-01-17 00:00:00+00:00 2020-01-17 23:00:00+00:00   \n",
       "26 2020-01-18 00:00:00+00:00 2020-01-18 23:00:00+00:00   \n",
       "27 2020-01-19 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "\n",
       "                                              model  \\\n",
       "0   <darkgreybox.models.TiTe object at 0x126b087f0>   \n",
       "1   <darkgreybox.models.TiTe object at 0x126ad8820>   \n",
       "2   <darkgreybox.models.TiTe object at 0x126ae69a0>   \n",
       "3   <darkgreybox.models.TiTe object at 0x126ad8be0>   \n",
       "4   <darkgreybox.models.TiTe object at 0x126b088b0>   \n",
       "5   <darkgreybox.models.TiTe object at 0x126ae6e20>   \n",
       "6   <darkgreybox.models.TiTe object at 0x126affdc0>   \n",
       "7   <darkgreybox.models.TiTe object at 0x126ae69d0>   \n",
       "8   <darkgreybox.models.TiTe object at 0x126ae62e0>   \n",
       "9   <darkgreybox.models.TiTe object at 0x126ae8670>   \n",
       "10  <darkgreybox.models.TiTe object at 0x126b0cc70>   \n",
       "11  <darkgreybox.models.TiTe object at 0x126b180d0>   \n",
       "12  <darkgreybox.models.TiTe object at 0x126ae66d0>   \n",
       "13  <darkgreybox.models.TiTe object at 0x126ae84c0>   \n",
       "14  <darkgreybox.models.TiTe object at 0x126b0c550>   \n",
       "15  <darkgreybox.models.TiTe object at 0x126b0c0d0>   \n",
       "16  <darkgreybox.models.TiTe object at 0x126b48f70>   \n",
       "17  <darkgreybox.models.TiTe object at 0x126b18220>   \n",
       "18  <darkgreybox.models.TiTe object at 0x126b37370>   \n",
       "19  <darkgreybox.models.TiTe object at 0x126b18dc0>   \n",
       "20  <darkgreybox.models.TiTe object at 0x126b0c310>   \n",
       "21  <darkgreybox.models.TiTe object at 0x126b189a0>   \n",
       "22  <darkgreybox.models.TiTe object at 0x126b18f40>   \n",
       "23  <darkgreybox.models.TiTe object at 0x126b379a0>   \n",
       "24  <darkgreybox.models.TiTe object at 0x126b0cbe0>   \n",
       "25  <darkgreybox.models.TiTe object at 0x126b376d0>   \n",
       "26  <darkgreybox.models.TiTe object at 0x126b37cd0>   \n",
       "27  <darkgreybox.models.TiTe object at 0x126b48ac0>   \n",
       "\n",
       "                                         model_result      time  method  \\\n",
       "0   DarkGreyModelResult(Z=array([18.1375    , 17.1...  0.244252  nelder   \n",
       "1   DarkGreyModelResult(Z=array([19.3625    , 19.1...  0.325435  nelder   \n",
       "2   DarkGreyModelResult(Z=array([19.7875    , 19.6...  2.555123  nelder   \n",
       "3   DarkGreyModelResult(Z=array([18.3125    , 18.2...  0.477023  nelder   \n",
       "4   DarkGreyModelResult(Z=array([17.1125    , 17.0...  0.640743  nelder   \n",
       "5   DarkGreyModelResult(Z=array([16.2875    , 16.2...  2.541821  nelder   \n",
       "6   DarkGreyModelResult(Z=array([15.6       , 15.5...  2.529565  nelder   \n",
       "7   DarkGreyModelResult(Z=array([15.0875    , 12.9...  2.607300  nelder   \n",
       "8   DarkGreyModelResult(Z=array([15.3125    , 15.2...  0.208122  nelder   \n",
       "9   DarkGreyModelResult(Z=array([14.625     , 10.4...  2.550822  nelder   \n",
       "10  DarkGreyModelResult(Z=array([20.0625    , 13.8...  2.627692  nelder   \n",
       "11  DarkGreyModelResult(Z=array([19.5375    , 17.7...  2.563974  nelder   \n",
       "12  DarkGreyModelResult(Z=array([19.5625    , 19.4...  0.176015  nelder   \n",
       "13  DarkGreyModelResult(Z=array([18.9875    , 18.9...  2.551741  nelder   \n",
       "14  DarkGreyModelResult(Z=array([17.5       , 14.8...  0.539668  nelder   \n",
       "15  DarkGreyModelResult(Z=array([19.25      , 19.0...  0.404294  nelder   \n",
       "16  DarkGreyModelResult(Z=array([20.175     , 20.0...  1.324824  nelder   \n",
       "17  DarkGreyModelResult(Z=array([20.475     , 18.9...  1.453820  nelder   \n",
       "18  DarkGreyModelResult(Z=array([20.6       , 19.5...  0.394029  nelder   \n",
       "19  DarkGreyModelResult(Z=array([20.4125    , 20.2...  0.416629  nelder   \n",
       "20  DarkGreyModelResult(Z=array([19.3375    , 19.2...  0.133398  nelder   \n",
       "21  DarkGreyModelResult(Z=array([18.225     , 15.9...  1.617867  nelder   \n",
       "22  DarkGreyModelResult(Z=array([19.6125    , 19.4...  0.215751  nelder   \n",
       "23  DarkGreyModelResult(Z=array([20.2       , 20.0...  0.439717  nelder   \n",
       "24  DarkGreyModelResult(Z=array([20.275     , 20.2...  0.255637  nelder   \n",
       "25  DarkGreyModelResult(Z=array([20.5125    , 20.0...  1.452425  nelder   \n",
       "26  DarkGreyModelResult(Z=array([20.2       , 19.9...  0.314696  nelder   \n",
       "27  DarkGreyModelResult(Z=array([19.625     , 19.4...  0.201671  nelder   \n",
       "\n",
       "        error  \n",
       "0   11.406733  \n",
       "1    0.018890  \n",
       "2    0.026962  \n",
       "3    0.001699  \n",
       "4    0.001673  \n",
       "5    0.000211  \n",
       "6    0.010708  \n",
       "7   37.927944  \n",
       "8    0.000318  \n",
       "9    8.973590  \n",
       "10  55.576592  \n",
       "11  64.654774  \n",
       "12   0.008275  \n",
       "13   0.001236  \n",
       "14  22.395109  \n",
       "15   0.010387  \n",
       "16   0.066073  \n",
       "17   2.974815  \n",
       "18   1.463338  \n",
       "19   0.005550  \n",
       "20   0.007123  \n",
       "21  16.354067  \n",
       "22   0.010353  \n",
       "23   0.032928  \n",
       "24   0.038252  \n",
       "25   0.577049  \n",
       "26   0.072493  \n",
       "27   0.039016  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prefit_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, each record is a model fit for a single day. The params of these pre-fit models can be used to train models for the entire duration of the train set. \n",
    "\n",
    "We call the `train_models` wrapper function again, passing it (showing only different arguments:\n",
    "\n",
    "* `models`: a list of the pre-fit models \n",
    "* `splits`: None - as we want to fit for the entire train set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:19:03,590 darkgreybox  INFO     Training models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.1312s.) Setting batch_size=2.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    4.8s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  28 | elapsed:   13.0s remaining:    9.7s\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  28 | elapsed:   47.6s remaining:   22.6s\n",
      "[Parallel(n_jobs=-1)]: Done  22 out of  28 | elapsed:   48.1s remaining:   13.1s\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  28 | elapsed:   49.4s remaining:    5.9s\n",
      "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:   56.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:   56.9s finished\n"
     ]
    }
   ],
   "source": [
    "train_df = train_models(models=prefit_df['model'],\n",
    "                        X_train=X_train, \n",
    "                        y_train=y_train, \n",
    "                        splits=None, \n",
    "                        error_metric=mean_squared_error,\n",
    "                        method='nelder', \n",
    "                        n_jobs=-1, \n",
    "                        verbose=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns a dataframe with a record for each pre-fit model containing the model objects and model results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>model</th>\n",
       "      <th>model_result</th>\n",
       "      <th>time</th>\n",
       "      <th>method</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.188294</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.160120</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081934</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.201751</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450531</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087772</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.226075</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126be8580&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>47.709838</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090002</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126be8640&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>47.371007</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b08ee0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>47.891898</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126be8160&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>47.825314</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b0c250&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>7.089658</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.202379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ad8c40&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>48.063849</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b08d00&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>6.713712</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.202379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b3a2b0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>4.180796</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.202379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126bfda30&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>46.098135</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.283767</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.418770</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.331430</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215265</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b18d30&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>44.043598</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126ae8730&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>4.158240</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.202379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207707</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126be8550&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>6.797514</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.202379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126bab9a0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([ 1.81375000e+001,...</td>\n",
       "      <td>36.832953</td>\n",
       "      <td>nelder</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x126b082b0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>1.740280</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.202379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.110416</td>\n",
       "      <td>nelder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  start_date                  end_date  \\\n",
       "0  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "1  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "2  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "3  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "4  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "5  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "6  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "7  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "8  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "9  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "10 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "11 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "12 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "13 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "14 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "15 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "16 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "17 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "18 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "19 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "20 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "21 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "22 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "23 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "24 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "25 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "26 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "27 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "\n",
       "                                              model  \\\n",
       "0                                               NaN   \n",
       "1                                               NaN   \n",
       "2                                               NaN   \n",
       "3                                               NaN   \n",
       "4                                               NaN   \n",
       "5                                               NaN   \n",
       "6                                               NaN   \n",
       "7   <darkgreybox.models.TiTe object at 0x126be8580>   \n",
       "8                                               NaN   \n",
       "9   <darkgreybox.models.TiTe object at 0x126be8640>   \n",
       "10  <darkgreybox.models.TiTe object at 0x126b08ee0>   \n",
       "11  <darkgreybox.models.TiTe object at 0x126be8160>   \n",
       "12  <darkgreybox.models.TiTe object at 0x126b0c250>   \n",
       "13  <darkgreybox.models.TiTe object at 0x126ad8c40>   \n",
       "14  <darkgreybox.models.TiTe object at 0x126b08d00>   \n",
       "15  <darkgreybox.models.TiTe object at 0x126b3a2b0>   \n",
       "16  <darkgreybox.models.TiTe object at 0x126bfda30>   \n",
       "17                                              NaN   \n",
       "18                                              NaN   \n",
       "19                                              NaN   \n",
       "20                                              NaN   \n",
       "21  <darkgreybox.models.TiTe object at 0x126b18d30>   \n",
       "22  <darkgreybox.models.TiTe object at 0x126ae8730>   \n",
       "23                                              NaN   \n",
       "24  <darkgreybox.models.TiTe object at 0x126be8550>   \n",
       "25  <darkgreybox.models.TiTe object at 0x126bab9a0>   \n",
       "26  <darkgreybox.models.TiTe object at 0x126b082b0>   \n",
       "27                                              NaN   \n",
       "\n",
       "                                         model_result       time  method  \\\n",
       "0                                                 NaN   0.188294  nelder   \n",
       "1                                                 NaN   0.160120  nelder   \n",
       "2                                                 NaN   0.081934  nelder   \n",
       "3                                                 NaN   0.201751  nelder   \n",
       "4                                                 NaN   0.450531  nelder   \n",
       "5                                                 NaN   0.087772  nelder   \n",
       "6                                                 NaN   0.226075  nelder   \n",
       "7   DarkGreyModelResult(Z=array([ 1.81375000e+001,...  47.709838  nelder   \n",
       "8                                                 NaN   0.090002  nelder   \n",
       "9   DarkGreyModelResult(Z=array([ 1.81375000e+001,...  47.371007  nelder   \n",
       "10  DarkGreyModelResult(Z=array([ 1.81375000e+001,...  47.891898  nelder   \n",
       "11  DarkGreyModelResult(Z=array([ 1.81375000e+001,...  47.825314  nelder   \n",
       "12  DarkGreyModelResult(Z=array([18.1375    , 18.0...   7.089658  nelder   \n",
       "13  DarkGreyModelResult(Z=array([ 1.81375000e+001,...  48.063849  nelder   \n",
       "14  DarkGreyModelResult(Z=array([18.1375    , 18.0...   6.713712  nelder   \n",
       "15  DarkGreyModelResult(Z=array([18.1375    , 18.0...   4.180796  nelder   \n",
       "16  DarkGreyModelResult(Z=array([ 1.81375000e+001,...  46.098135  nelder   \n",
       "17                                                NaN   0.283767  nelder   \n",
       "18                                                NaN   0.418770  nelder   \n",
       "19                                                NaN   1.331430  nelder   \n",
       "20                                                NaN   0.215265  nelder   \n",
       "21  DarkGreyModelResult(Z=array([ 1.81375000e+001,...  44.043598  nelder   \n",
       "22  DarkGreyModelResult(Z=array([18.1375    , 18.0...   4.158240  nelder   \n",
       "23                                                NaN   0.207707  nelder   \n",
       "24  DarkGreyModelResult(Z=array([18.1375    , 18.0...   6.797514  nelder   \n",
       "25  DarkGreyModelResult(Z=array([ 1.81375000e+001,...  36.832953  nelder   \n",
       "26  DarkGreyModelResult(Z=array([18.1375    , 18.0...   1.740280  nelder   \n",
       "27                                                NaN   0.110416  nelder   \n",
       "\n",
       "       error  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "5        NaN  \n",
       "6        NaN  \n",
       "7        inf  \n",
       "8        NaN  \n",
       "9        inf  \n",
       "10       inf  \n",
       "11       inf  \n",
       "12  0.202379  \n",
       "13       inf  \n",
       "14  0.202379  \n",
       "15  0.202379  \n",
       "16       inf  \n",
       "17       NaN  \n",
       "18       NaN  \n",
       "19       NaN  \n",
       "20       NaN  \n",
       "21       inf  \n",
       "22  0.202379  \n",
       "23       NaN  \n",
       "24  0.202379  \n",
       "25       inf  \n",
       "26  0.202379  \n",
       "27       NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select the model with the lowest error and display its params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> Ti0 </td><td>  18.1375000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 18.1375 </td><td>        -inf </td><td>         inf </td><td> False </td></tr><tr><td> Te0 </td><td>  16.8310431 </td><td>  0.11157696 </td><td> (0.66%) </td><td> 16.1375 </td><td>  10.0000000 </td><td>  25.0000000 </td><td> True </td></tr><tr><td> Ci </td><td>  184.890748 </td><td>  5.25904113 </td><td> (2.84%) </td><td> 14.07835533256204 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Ce </td><td>  483.231457 </td><td>  15.3675848 </td><td> (3.18%) </td><td> 9.423834996894758 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Rie </td><td>  0.11119564 </td><td>  0.00454589 </td><td> (4.09%) </td><td> 0.19972994685761403 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Rea </td><td>  0.52298050 </td><td>  0.00503848 </td><td> (0.96%) </td><td> 0.05342883055090569 </td><td>        -inf </td><td>         inf </td><td> True </td></tr></table>"
      ],
      "text/plain": [
       "Parameters([('Ti0', <Parameter 'Ti0', value=18.1375 (fixed), bounds=[-inf:inf]>), ('Te0', <Parameter 'Te0', value=16.831043119528786 +/- 0.112, bounds=[10:25]>), ('Ci', <Parameter 'Ci', value=184.89074762976924 +/- 5.26, bounds=[-inf:inf]>), ('Ce', <Parameter 'Ce', value=483.2314569601738 +/- 15.4, bounds=[-inf:inf]>), ('Rie', <Parameter 'Rie', value=0.11119564067991965 +/- 0.00455, bounds=[-inf:inf]>), ('Rea', <Parameter 'Rea', value=0.5229804979028708 +/- 0.00504, bounds=[-inf:inf]>)])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_idx = train_df['error'].argmin()\n",
    "\n",
    "model = train_df.loc[select_idx, 'model']\n",
    "train_results = train_df.loc[select_idx, 'model_result']\n",
    "\n",
    "model.result.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the modelled and measured data for the train set.  \n",
    "\n",
    "This yields a much better fit than the `Ti` model in `Demo 01`. (Bear in mind the complexity of the input data e.g. the building overheating during the second week of January.) We can also see the building envelope temperature `Te` on the plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(y_train, train_results, 'TiTe (Train)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the test results, the model takes `X_test` as the input data. Also, as the initial conditions for `Ti0` and `Te0` params at t=0 are different at the start of the test data to what the model params currently holds (which is `Ti0` and `Te0` at t=0 of the *training data*), we need to update those variables. For `Ti0` this is the same as before - we set it at the first value of `Ti` in the test set. We do not have a measured value for `Te0` though, so we take the last predicted value of `Te` of the model on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results = model.predict(X=X_test, ic_params={'Ti0': y_test.iloc[0], 'Te0': train_results.var['Te'][-1]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the modelled and measured data for the test set.  \n",
    "\n",
    "As expected, the `TiTe` model generalises much better than a `Ti` model on data it has not seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(y_test, test_results, 'TiTe (Test)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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