04-create_erosion_distribution_global.ipynb

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   "source": [
    "use_extracted_data = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb711475",
   "metadata": {
    "execution": {
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   "source": [
    "import os\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import dump\n",
    "from scipy.optimize import differential_evolution\n",
    "from scipy.stats import (\n",
    "    fit,\n",
    "    gengamma,\n",
    ")\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "from lib.check_files import check_prepared_data\n",
    "from lib.erodep import clean_outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db65221e",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_jobs = int(os.environ.get(\"N_JOBS\", 8))\n",
    "kde_bandwidth = int(os.environ.get(\"BANDWIDTH\", 1000))\n",
    "random_state = int(os.environ.get(\"RANDOM_STATE\", 1234))\n",
    "outlier_contamination = float(os.environ.get(\"CONTAMINATION\", 0.06))\n",
    "verbose = bool(int(os.environ.get(\"VERBOSE\", 0)))\n",
    "\n",
    "dist_name = \"gengamma\"\n",
    "failure = \"raise\"  # or \"ignore\"\n",
    "\n",
    "gengamma_bounds = {\n",
    "    \"a\": (0, 50),\n",
    "    \"c\": (-50, 50),\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "946bb793",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if use_extracted_data:\n",
    "    data_dir = \"extracted_data\"\n",
    "else:\n",
    "    data_dir = \"prepared_data\"\n",
    "    check_prepared_data(data_dir)\n",
    "data_filename = os.path.join(data_dir, \"training_data_global.csv\")\n",
    "\n",
    "outputs_dir = os.path.join(\"outputs\", \"global\")\n",
    "output_parametric = os.path.join(outputs_dir, \"erodep_dist_parametric.joblib\")\n",
    "output_kde = os.path.join(outputs_dir, \"erodep_dist_kde.joblib\")\n",
    "output_image = os.path.join(outputs_dir, \"erodep_dists.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4513dafb",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_filename)\n",
    "positives = df[df[\"label\"] == \"positive\"]\n",
    "unlabelled = df[df[\"label\"] == \"unlabelled\"]\n",
    "\n",
    "x_positives = np.array(positives[\"erosion (m)\"])\n",
    "x_unlabelled = np.array(unlabelled[\"erosion (m)\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5dbf3dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-16T05:59:08.949719Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_cleaned = clean_outliers(\n",
    "    data=x_positives,\n",
    "    contamination=outlier_contamination,\n",
    "    n_jobs=n_jobs,\n",
    "    random_state=random_state,\n",
    ")\n",
    "x_cleaned = x_cleaned[x_cleaned > 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f91767d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-16T05:59:09.194380Z",
     "iopub.status.busy": "2023-12-16T05:59:09.194119Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parametric (gamma) distribution: NLL = 4899\n",
      "Writing parametric distribution to file: outputs/global/erodep_dist_parametric.joblib\n"
     ]
    }
   ],
   "source": [
    "def optimize_func(*args, **kwargs):\n",
    "    return differential_evolution(*args, seed=random_state, **kwargs)\n",
    "\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
    "    fitted_params = fit(\n",
    "        gengamma,\n",
    "        data=x_cleaned,\n",
    "        bounds=gengamma_bounds,\n",
    "        optimizer=optimize_func,\n",
    "    )\n",
    "if not fitted_params.success:\n",
    "    message = f\"Optimisation failed! {fitted_params.message}\"\n",
    "    if failure == \"raise\":\n",
    "        raise RuntimeError(message)\n",
    "    else:\n",
    "        warnings.warn(message, RuntimeWarning)\n",
    "\n",
    "dist_parametric = gengamma(*(fitted_params.params))\n",
    "nll_parametric = -1 * dist_parametric.logpdf(x_cleaned).sum()\n",
    "\n",
    "dump(dist_parametric, output_parametric, compress=True)\n",
    "if verbose:\n",
    "    print(f\"Parametric (gamma) distribution: NLL = {nll_parametric:0.0f}\")\n",
    "    print(\n",
    "        f\"\"\"Writing parametric distribution to file: {output_parametric}\"\"\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e97d97d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-16T05:59:09.363860Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel density estimate (KDE; bandwidth = 1000): NLL = 4923\n",
      "Writing KDE distribution to file: outputs/global/erodep_dist_kde.joblib\n"
     ]
    }
   ],
   "source": [
    "kde = KernelDensity(\n",
    "    kernel=\"gaussian\",\n",
    "    bandwidth=kde_bandwidth,\n",
    ").fit(x_cleaned.reshape((-1, 1)))\n",
    "nll_kde = -1 * kde.score(x_cleaned.reshape((-1, 1)))\n",
    "\n",
    "dump(kde, output_kde, compress=True)\n",
    "if verbose:\n",
    "    print(\n",
    "        f\"Kernel density estimate (KDE; bandwidth = {kde_bandwidth}): NLL = {nll_kde:0.0f}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"\"\"Writing KDE distribution to file: {output_kde}\"\"\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5c1ef39",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_x = np.linspace(\n",
    "    positives[\"erosion (m)\"].min(),\n",
    "    positives[\"erosion (m)\"].max(),\n",
    "    500,\n",
    ")\n",
    "y_parametric = dist_parametric.pdf(dist_x)\n",
    "y_kde = np.exp(kde.score_samples(dist_x.reshape((-1, 1))))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "nbins = 30\n",
    "_, bins, _ = ax.hist(\n",
    "    unlabelled[\"erosion (m)\"],\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    facecolor=\"blue\",\n",
    "    alpha=0.6,\n",
    "    label=\"random\",\n",
    ")\n",
    "ax.hist(\n",
    "    positives[\"erosion (m)\"],\n",
    "    bins=bins,\n",
    "    density=True,\n",
    "    facecolor=\"red\",\n",
    "    alpha=0.6,\n",
    "    label=\"positive\",\n",
    ")\n",
    "ax.plot(\n",
    "    dist_x,\n",
    "    y_parametric,\n",
    "    linestyle=\"dashed\",\n",
    "    color=\"black\",\n",
    "    zorder=10,\n",
    "    label=f\"positive distribution\\n({dist_name}; NLL = {nll_parametric:0.0f})\",\n",
    ")\n",
    "ax.plot(\n",
    "    dist_x,\n",
    "    y_kde,\n",
    "    linestyle=\"solid\",\n",
    "    color=\"black\",\n",
    "    zorder=10,\n",
    "    label=f\"positive distribution\\n(KDE, bandwidth = {kde_bandwidth}; NLL = {nll_kde:0.0f})\",\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Cumulative erosion (m)\")\n",
    "ax.set_xlim(0, positives[\"erosion (m)\"].quantile(0.99))\n",
    "ax.legend(loc=\"best\")\n",
    "fig.savefig(output_image, dpi=400, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a064e3",
   "metadata": {
    "papermill": {
     "duration": 0.00395,
     "end_time": "2023-12-16T05:59:10.096470",
     "exception": false,
     "start_time": "2023-12-16T05:59:10.092520",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "datamining",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 4.526218,
   "end_time": "2023-12-16T05:59:10.717488",
   "environment_variables": {},
   "exception": null,
   "input_path": "04-create_erosion_distribution_global.ipynb",
   "output_path": "04-create_erosion_distribution_global_output.ipynb",
   "parameters": {},
   "start_time": "2023-12-16T05:59:06.191270",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}