sire_arr129.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "import datetime\n",
    "import warnings\n",
    "from dataset import Dataset\n",
    "from extrapolations import Extrapolations\n",
    "from experiment import Experiment\n",
    "\n",
    "# comment the following when debugging\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae408383",
   "metadata": {},
   "source": [
    "## Load ARR129 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49e8ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the company_id if you want to test a different company\n",
    "company_id = \"5cf74ff84639c79510ef8491d4725967336ad3627216a50553f030f9c51d93bc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c88628d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataset = Dataset()\n",
    "dataset.data[dataset.data.id==company_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa33fdb",
   "metadata": {},
   "source": [
    "## Extrapolate for one company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d8447a",
   "metadata": {},
   "outputs": [],
   "source": [
    "extrapolations = Extrapolations(\n",
    "    dataset=dataset,\n",
    "    org_id=company_id,\n",
    "    latest_known_dt=datetime.datetime(2018, 5, 1),\n",
    "    extrapolate_len=36,\n",
    "    n_trials=10,\n",
    "    method=\"probability_matching\",\n",
    ")\n",
    "extrapolations.run(params={\"filter_type\": \"smooth\"})\n",
    "extrapolations.get_extrapolations_metric_mean_confidence_interval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e280e593",
   "metadata": {},
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dcc59b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NLL\n",
    "extrapolations.calculate_nll()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59fc7d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACC\n",
    "extrapolations.calculate_acc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86325e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMSE\n",
    "extrapolations.calculate_rmse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40f6bdb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAPE\n",
    "extrapolations.calculate_mape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7e01c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCC\n",
    "extrapolations.calculate_pcc()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77c886d",
   "metadata": {},
   "source": [
    "## Extrapolate for all possible dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "359d839c",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = Experiment(\n",
    "    dataset=dataset,\n",
    "    org_id=company_id,\n",
    "    extrapolate_len=36, # months\n",
    "    n_trials=10,\n",
    "    method=\"probability_matching\",\n",
    "    # method=\"simulate:probability_matching\",\n",
    ")\n",
    "experiment.run(params={})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cef8bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize prediction starting from particular date\n",
    "date_to_inspect = datetime.datetime(2018, 5, 1)\n",
    "experiment.plot_extrapolations_start_from(date_to_inspect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d43ba330",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print the extrapolated results from the same date\n",
    "experiment.get_extrapolations_metric_mean_confidence_interval_df_fr(date_to_inspect)"
   ]
  }
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