demo_notebooks/demo_multiday.ipynb

{
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   "source": [
    "## OASIS - Demonstration notebook - Multiday simulation\n",
    "*V1.0.0 August 2023*\n",
    "\n",
    "This notebook walks through a simulation run using OASIS. The simulator takes as input a set of activities to be scheduled, travel times and utility parameters and outputs optimised schedules. In this notebook, we generate **multiple days** of scheduls.\n",
    "\n",
    "Note that this notebook requires the [docplex library](https://pypi.org/project/docplex/) with a valid [CPLEX license](https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-setting-up-python-api) to run. \n",
    "\n",
    "You can find the full documentation [here](https://oasis-abm.readthedocs.io/en/latest/), and the technical report [here](https://transp-or.epfl.ch/documents/technicalReports/PougHillBier22.pdf).\n",
    "\n",
    "\n",
    "### Main files\n",
    "\n",
    "The following files are used for the simulation.\n",
    "\n",
    "- ``simulation.py``: This script stores information on the ``Simulation`` class and subclasses. These classes are used to set up and solve the optimisation problem. For the multiday problem, we load the ``MultidayMIP`` class.\n",
    "- ``results.py``: The ``Results`` class stores the outputs of the simulation (optimised schedules, runtimes, and objective functions), and handles the visualisation and simulation statistics.\n",
    "- ``input_data.py``: This script handles the input data, and creates ``ActivityData`` objects. These objects contain information on each activity to be scheduled, including preferred start time, duration, and utility parameters.\n",
    "- ``error_terms.py``: The ``ErrorTerms`` class is used to handle the utility function errors -- to be drawn at each new iterations.\n",
    "- ``runner.py``: The main file to run the simulation.\n",
    "\n",
    "In addition, we have provided a small example to illustrate the implemented functions. They are stored in the ``data/`` folder.\n",
    "\n"
   ]
  },
  {
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   "execution_count": 1,
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   "source": [
    "import math\n",
    "import shutil\n",
    "import joblib\n",
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "import sys \n",
    "sys.path.append('simulation/')\n",
    "\n",
    "from input_data import data_reader\n",
    "from error_terms import GaussianError, EVError\n",
    "from simulation import MultidayMIP\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the input\n",
    "\n",
    "There are three main inputs that must be provided for the simulation: \n",
    "\n",
    "- The set of activities (``DATA``)\n",
    "- A mode travel time matrix (``TT``)\n",
    "- The set of activity parameters (``PARAMS``)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:24:15.592264Z",
     "start_time": "2023-08-27T13:24:15.584596Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "DATA = joblib.load('../data/example_data_multiday.joblib')\n",
    "TT = joblib.load('../data/example_tt_multiday.joblib')\n",
    "PARAMS = json.load(open('../data/example_parameters_multiday.json', 'r'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine ``DATA``. \n",
    "\n",
    "This schedule contains *work*, *leisure* and *shopping* activities. The set of activities is provided as a ``pandas DataFrame``, and contains the label of the activities (a unique identifier and the activity type), a feasible start and end time (not mandatory, and will default to the full day if not provided), the location of the activity, the mode of transportation. Note that the location and mode should exist as keys in the travel time matrix.\n",
    "\n",
    "For each activity, we provide information on the desired start time and duration on weekdays and weekends, and the preferred frequency for performing the activity (which must be understood as the number of schedules over one week that contain the activity at least once)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T12:13:44.247267Z",
     "start_time": "2023-08-27T12:13:44.224475Z"
    }
   },
   "outputs": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_id</th>\n",
       "      <th>act_label</th>\n",
       "      <th>label</th>\n",
       "      <th>feasible_start</th>\n",
       "      <th>feasible_end</th>\n",
       "      <th>location</th>\n",
       "      <th>mode_travel</th>\n",
       "      <th>group</th>\n",
       "      <th>desired_duration</th>\n",
       "      <th>desired_start</th>\n",
       "      <th>desired_duration_weekday</th>\n",
       "      <th>desired_start_weekday</th>\n",
       "      <th>desired_duration_weekend</th>\n",
       "      <th>desired_start_weekend</th>\n",
       "      <th>desired_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>home</td>\n",
       "      <td>dawn</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A</td>\n",
       "      <td>driving</td>\n",
       "      <td>dawn</td>\n",
       "      <td>9.710772</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.941384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>work</td>\n",
       "      <td>work30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>B</td>\n",
       "      <td>driving</td>\n",
       "      <td>work</td>\n",
       "      <td>0.050157</td>\n",
       "      <td>7.769253</td>\n",
       "      <td>8.166667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>13.50</td>\n",
       "      <td>3.550744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>shopping</td>\n",
       "      <td>shopping17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>driving</td>\n",
       "      <td>shopping</td>\n",
       "      <td>0.102146</td>\n",
       "      <td>14.132230</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>11.25</td>\n",
       "      <td>2.281850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>leisure</td>\n",
       "      <td>leisure87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>D</td>\n",
       "      <td>driving</td>\n",
       "      <td>leisure</td>\n",
       "      <td>1.014532</td>\n",
       "      <td>13.866034</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.486248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>home</td>\n",
       "      <td>dusk</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A</td>\n",
       "      <td>driving</td>\n",
       "      <td>dusk</td>\n",
       "      <td>9.710772</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.941384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "   act_id act_label       label  feasible_start  feasible_end location  \\\n",
       "0     1.0      home        dawn             0.0          24.0        A   \n",
       "1     2.0      work      work30             0.0          24.0        B   \n",
       "3     4.0  shopping  shopping17             0.0          24.0        C   \n",
       "4     5.0   leisure   leisure87             0.0          24.0        D   \n",
       "5     1.0      home        dusk             0.0          24.0        A   \n",
       "\n",
       "  mode_travel     group  desired_duration  desired_start  \\\n",
       "0     driving      dawn          9.710772       0.074515   \n",
       "1     driving      work          0.050157       7.769253   \n",
       "3     driving  shopping          0.102146      14.132230   \n",
       "4     driving   leisure          1.014532      13.866034   \n",
       "5     driving      dusk          9.710772       0.074515   \n",
       "\n",
       "   desired_duration_weekday  desired_start_weekday  desired_duration_weekend  \\\n",
       "0                 10.000000               0.000000                 10.000000   \n",
       "1                  8.166667               6.250000                  0.250000   \n",
       "3                  0.833333               0.216667                  1.650000   \n",
       "4                  0.083333               0.583333                  2.666667   \n",
       "5                 10.000000               0.000000                 10.000000   \n",
       "\n",
       "   desired_start_weekend  desired_frequency  \n",
       "0                   0.00           4.941384  \n",
       "1                  13.50           3.550744  \n",
       "3                  11.25           2.281850  \n",
       "4                   0.00           3.486248  \n",
       "5                   0.00           4.941384  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The travel time matrix is a dictionary formatted as:\n",
    "\n",
    "``{mode: {\n",
    "origin_location: {\n",
    "destination_location1 : travel_time_1,\n",
    "destination_location2 : travel_time_2\n",
    "}}}``\n",
    "\n",
    "The travel time is in hours. In this example, we consider the travel times for the *driving* mode and two different locations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T12:14:12.723674Z",
     "start_time": "2023-08-27T12:14:12.718783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'driving': {'A': {'A': 0,\n",
       "   'B': 0.16666666666666666,\n",
       "   'C': 0.16666666666666666,\n",
       "   'D': 0.16666666666666666},\n",
       "  'B': {'A': 0.16666666666666666,\n",
       "   'B': 0,\n",
       "   'C': 0.16666666666666666,\n",
       "   'D': 0.16666666666666666},\n",
       "  'C': {'A': 0.16666666666666666,\n",
       "   'B': 0.16666666666666666,\n",
       "   'C': 0,\n",
       "   'D': 0.16666666666666666},\n",
       "  'D': {'A': 0.16666666666666666,\n",
       "   'B': 0.16666666666666666,\n",
       "   'C': 0.16666666666666666,\n",
       "   'D': 0}}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the parameters should be a dictionary containing the activity-specific parameters that will be used in the utility function. The dictionary should have the format: \n",
    "\n",
    "``\n",
    "{activity_1: {parameter_1: value, parameter_2: value},\n",
    " activity_2: {parameter_1: value, parameter_2:value}}\n",
    "``\n",
    "\n",
    "In this example, we use the default OASIS utility function (see technical report for details), including penalties for early and late start time, short and long duration, travel time, deviation from preferred frequency and deviation from preferred day (e.g. performing *work* on weekends is penalised). The parameters for the *work* activity are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T12:14:21.081584Z",
     "start_time": "2023-08-27T12:14:21.076463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'constant': 13.0838530926,\n",
       " 'early': -0.618657816,\n",
       " 'late': -0.3384943975,\n",
       " 'short': -0.9319615942,\n",
       " 'long': -1.223996357,\n",
       " 'travel_time': -1,\n",
       " 'frequency': -10,\n",
       " 'weekend': -10}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PARAMS['work']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to define the error terms that will be used in the utility function. In the default specification (see technical reports for details), we consider normally distributed error terms for each decision variable, for each day of the time horizon, and an Extreme Value distributed error term. \n",
    "\n",
    "These random components are instances of the ``ErrorTerms`` class, and are defined in a dedicated dictionary ``UTILITY_PARAMS``. During the simulation the ``draw()`` method of these error objects will be called to draw a new value for each iteration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:24:22.778134Z",
     "start_time": "2023-08-27T13:24:22.764054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n"
     ]
    }
   ],
   "source": [
    "UTILITY_PARAMS = {\n",
    "    'error_w': GaussianError(),\n",
    "    'error_x': GaussianError(),\n",
    "    'error_d': GaussianError(),\n",
    "    'error_z': GaussianError(),\n",
    "    'error_ev': EVError(),\n",
    "    'error_day': GaussianError()\n",
    "    }\n",
    "\n",
    "print(UTILITY_PARAMS[\"error_ev\"].loc, UTILITY_PARAMS[\"error_ev\"].scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up and running the model\n",
    "\n",
    "Once the data is imported (with the help of the ``data_reader`` function) we can set up the simulation model. This is done by creating a ``Simulation`` object, and passing the dataset, the utility function parameters and the parameters of the simulation (number of iterations, and progress output) as arguments. The ``verbose`` argument specifies how frequently the model prints its progress.\n",
    "\n",
    "Each new ``Simulation`` object instantiates a ``Model`` from the ``docplex`` library. The method ``initialize()`` adds decision variables and constraints to the model, and can be modified directly in the corresponding script, or using the ``add_constraints()`` function in the runner file. The CPLEX optimisation settings (e.g. time limit) can be specified when creating the ``Simulation``. The default settings are a time limit of 120 seconds per solve call, and the optimality target set to ``global`` (See [IBM's documentation](https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-list-parameters) for an exhaustive list of parameters)\n",
    "\n",
    "The utility and objective functions of the problem can be directly modified in the ``utility_function`` and ``objective_function()`` methods of the ``Simulation`` object. \n",
    "\n",
    "The ``run()`` method solves the optimisation problem, and returns a ``Results`` object containing the optimised schedule(s), the runtime(s) and the value(s) of objective function.\n",
    "\n",
    "Here, we create a simulation with 5 iterations (``N_ITER``) and a Multiday MIP solver. We print the model progress every 2 iterations (as controlled by the ``verbose``argument). We simulate 7 days of schedules (``N_days``), which are consecutive days of the week (Monday to Sunday, as specified by ``DAY_INDEX``). \n",
    "\n",
    "Finally, we slightly increase CPLEX's time limit to 150 seconds per iteration. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:24:47.181521Z",
     "start_time": "2023-08-27T13:24:47.175382Z"
    }
   },
   "outputs": [],
   "source": [
    "N_ITER = 5\n",
    "N_DAYS = 7\n",
    "DAY_INDEX = [*range(1,8)]\n",
    "SETTINGS =  {'optimality_target': 3, 'time_limit': 150}\n",
    "\n",
    "dataset = data_reader(DATA,PARAMS)\n",
    "new_simulation = MultidayMIP(dataset, UTILITY_PARAMS,TT, n_days=N_DAYS, day_index=DAY_INDEX, **SETTINGS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:37:22.834878Z",
     "start_time": "2023-08-27T13:24:50.444368Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation: 5 iterations.\n",
      "-----------------------------------------\n",
      "Starting iteration 2/5.\n",
      "Iteration 2 complete. Iteration runtime: 00:02:30. Time elapsed: 00:05:01.\n",
      "Starting iteration 4/5.\n",
      "Iteration 4 complete. Iteration runtime: 00:02:30. Time elapsed: 00:10:01.\n",
      "-----------------------------------------\n",
      "Simulation complete. Total runtime: 00:12:32\n"
     ]
    }
   ],
   "source": [
    "results = new_simulation.run(N_ITER,verbose = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing the outputs\n",
    "\n",
    "\n",
    "The simulation outputs a ```Results``` object, that stores the optimal schedules, runtimes and objective values for all iterations. Several methods can be helpful to visualise the results: \n",
    "\n",
    "- ``plot()``: plots the optimal schedules. The argument ``plot_iter`` specifies which iteration to plot, but all or a selection of schedules can be plotted by modifying the ``plot_every`` argument. By default, the figure is saved as a ``png`` file, but this can be modified with the ``save_fig`` argument (either saving with a different extension or not saving the figure). The plotting function can be further customised by specifying for example the size, title, the legend or the activity colors.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:54:28.227202Z",
     "start_time": "2023-08-27T13:54:26.889125Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x2300 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot(plot_iter = 2, save_fig=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``plot_distribution():`` plots the time of day distribution across all iterations. By default, the figure is saved as a ``png`` file, but this can be modified with the ``save_fig`` argument (either saving with a different extension or not saving the figure). We can use the ``day`` argument to specify how to aggregate the days in the horizon. Here, we decide to plot the distributions for weekdays and weekends separately. \n",
    "\n",
    "The plotting function can be further customised by specifying for example the size, title, the legend or the activity colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:58:35.795209Z",
     "start_time": "2023-08-27T13:58:35.287121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_distribution(days = [*range(1,6)], figure_size= [7,4], save_fig= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:58:39.473043Z",
     "start_time": "2023-08-27T13:58:39.165592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_distribution(days = [6,7], figure_size= [7,4], save_fig= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- ``compute_statistics()``: computes simulation statistics (e.g. proportion of out-of-home schedules, average time spent in a given activity) for a given list of activities. These statistics can be directly printed or saved to a file. This function provides bootsrap confidence intervals for each indicator. The settings for the bootstrapping can be specified in the arguments (e.g. number of samples, level of confidence). Similarly to the time of day distribution, we can decide how to aggregate days with the ``days`` argument. Here, we compute the statistics for weekdays and weekends separately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T13:52:23.468470Z",
     "start_time": "2023-08-27T13:52:23.308935Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of collected statistics (Monday to Friday)\n",
      "------------------------------------------------\n",
      "\n",
      "Total number of schedules: 15\n",
      "Proportion of out-of-home schedules: 100.00 %\n",
      "Average time spent out-of-home: 5.50, CI: [4.695,6.553] hours\n",
      "Average number of out-of-home activities: 2.20, CI: [2.133, 2.267]\n",
      "------------------------------------------------\n",
      "\n",
      "Average duration of each activity:\n",
      "Home: 17.97, CI: [17.256, 18.767] hours\n",
      "Work: 2.25, CI: [1.636, 2.773] hours\n",
      "Leisure: 3.71, CI: [2.644, 4.423] hours\n",
      "------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results.compute_statistics(['home', 'work', 'leisure'], days = [*range(1,6)], save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-27T14:01:27.170762Z",
     "start_time": "2023-08-27T14:01:27.087341Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of collected statistics (Saturday and Sunday)\n",
      "------------------------------------------------\n",
      "\n",
      "Total number of schedules: 6\n",
      "Proportion of out-of-home schedules: 50.00 %\n",
      "Average time spent out-of-home: 6.27, CI: [2.917,9.866] hours\n",
      "Average number of out-of-home activities: 1.67, CI: [1.333, 2.000]\n",
      "------------------------------------------------\n",
      "\n",
      "Average duration of each activity:\n",
      "Home: 17.29, CI: [17.011, 20.667] hours\n",
      "Leisure: 5.60, CI: [2.333, 6.916] hours\n",
      "------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results.compute_statistics(['home', 'leisure'], days = [6,7], save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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