notebook to calculate replacement values and minor changes

EDA/calculate_replacement_values.ipynb

@@ -0,0 +1,98 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b9144326",
+   "metadata": {},
+   "source": [
+    "### Calculate replacement values for live deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a7b60d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "import yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "197cb8a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: insert path to database\n",
+    "dataset_path = Path(r\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cf26eb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_parquet(dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2c5679e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option(\"display.max_rows\", None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e88981b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# optional: your dataframe filtering code ...\n",
+    "\n",
+    "medians = df.median()\n",
+    "median_dict = medians.to_dict()\n",
+    "\n",
+    "# Wrap in fallback key\n",
+    "output = {'fallback': median_dict}\n",
+    "\n",
+    "# Save to YAML\n",
+    "with open('config.yaml', 'w') as f:\n",
+    "    yaml.dump(output, f, default_flow_style=False, sort_keys=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "310",
+   "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": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

EDA/distribution_plots.ipynb

@@ -606,18 +606,6 @@
    "display_name": "Python 3 (ipykernel)",
    "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": "3.12.10"
   }
  },
  "nbformat": 4,

model_training/CNN/CNN_crossVal_EarlyFusion_Filter.ipynb

@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3e542dfd",
    "metadata": {},
    "outputs": [
@@ -1954,18 +1954,6 @@
    "display_name": "Python 3 (ipykernel)",
    "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": "3.12.10"
   }
  },
  "nbformat": 4,