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   "cell_type": "markdown",
   "id": "8d464cc9-34d1-45fa-bceb-c35fc52e3bfb",
   "metadata": {},
   "source": [
    "## Trend Detector\n",
    "## The trend represents the long-term change in the level of a time series. This change can be either upward (increase in level) or downward (decrease in level).\n",
    "\n",
    "https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c07ec9d2-1ec5-4dad-98d5-a557947203fd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " %load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5553c48-4cd2-46ec-a199-0848fc3cab2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '/root/sagemaker_ml_insights_repo/notebooks/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f5fa78c-eee9-46ca-a86d-648744c412d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " from mlsec.tsat import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efaa0d98-f4a3-47ec-8eb4-34d28e541ea4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " %store -r milo_tss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42472c2-3821-45aa-8b27-5e176c3a3c95",
   "metadata": {
    "tags": []
   },
   "source": [
    "Creating slopes to identify the relationship between x(time) & y(counts).Trend is up when the slope is positive and is down when the slope is negative.\n",
    "\n",
    "If the counts are increasing with time then it is considered as positive slope and vice versa for negative slope. A positive slope is not necessarily a good thing because in an ideal state we would expect counts of a finding type to come down with time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ad90fc0-7287-45db-8055-a71a90d128b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " slopes = [milo_ts.slope for milo_ts in milo_tss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4de5836b-9c41-4e57-9b28-1fb0d4872fe5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " slopes = pd.Series(slopes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3df8e992-d820-4732-8fb8-5931a15829b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "slopes.sort_values().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "317d56b6-8561-4873-8b69-83d61f44b1fe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "slopes.sort_values().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2553b4f-0a8d-4dc6-8b70-220ebffd0d96",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pos_trend_idxs = slopes.sort_values(ascending=False).head(20).index.tolist()\n",
    "pos_trend_slopes = slopes.sort_values(ascending=False).head(20).values.tolist()\n",
    "pos_trend_milo_tss   = [milo_tss[i] for i in pos_trend_idxs]\n",
    "\n",
    "neg_trend_idxs = slopes.sort_values().head(20).index.tolist()\n",
    "neg_trend_slopes = slopes.sort_values().head(20).values.tolist()\n",
    "neg_trend_milo_tss   = [milo_tss[i] for i in neg_trend_idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce6bd8ae-b675-4875-9bfa-dcbbc8480540",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " print('5 MOST POSITIVE TREND')\n",
    "for milo_ts in pos_trend_milo_tss[:5]:\n",
    "    print(f'slope = {milo_ts.slope}')\n",
    "    milo_ts.plot()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fbc6e7f-d584-4daf-b8f4-de66f1270832",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " print('5 MOST NEGATIVE TREND')\n",
    "for milo_ts in neg_trend_milo_tss[:5]:\n",
    "    print(f'slope = {milo_ts.slope}')\n",
    "    milo_ts.plot()\n",
    "    plt.show()"
   ]
  }
 ],
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