Some changes.

notes/PipeLine.ipynb

@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4332243f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting Conditional Probability Modeling Pipeline...\n",
+      "\n",
+      "[Task 1] Define a model for conditional probability P(X|Y)\n",
+      "        - Specify assumptions\n",
+      "        - Decide what 'meaningful' means in context\n",
+      "[Task 2] Fit the model to data\n",
+      "        - Implement training procedure\n",
+      "        - Validate model assumptions\n",
+      "[Task 3] Evaluate model performance\n",
+      "        - Use accuracy, log-loss, calibration metrics\n",
+      "        - Consider decision-theoretic criteria\n",
+      "[Task 4] Quantify prediction uncertainty\n",
+      "        - Use Bayesian, ensemble, or bootstrap methods\n",
+      "[Task 5] Interpret model and uncertainty\n",
+      "        - Communicate limitations clearly\n",
+      "        - Visualize relevant insights\n",
+      "[Task 6] Apply model for decision-making\n",
+      "        - Define decision function using P(X|Y)\n",
+      "        - Simulate impact of uncertainty on decisions\n",
+      "\n",
+      "Pipeline complete. Future modules can expand each task.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# condprob_modeling.py\n",
+    "\n",
+    "def define_model():\n",
+    "    print(\"[Task 1] Define a model for conditional probability P(X|Y)\")\n",
+    "    print(\"        - Specify assumptions\")\n",
+    "    print(\"        - Decide what 'meaningful' means in context\")\n",
+    "\n",
+    "def fit_model():\n",
+    "    print(\"[Task 2] Fit the model to data\")\n",
+    "    print(\"        - Implement training procedure\")\n",
+    "    print(\"        - Validate model assumptions\")\n",
+    "\n",
+    "def evaluate_model():\n",
+    "    print(\"[Task 3] Evaluate model performance\")\n",
+    "    print(\"        - Use accuracy, log-loss, calibration metrics\")\n",
+    "    print(\"        - Consider decision-theoretic criteria\")\n",
+    "\n",
+    "def quantify_uncertainty():\n",
+    "    print(\"[Task 4] Quantify prediction uncertainty\")\n",
+    "    print(\"        - Use Bayesian, ensemble, or bootstrap methods\")\n",
+    "\n",
+    "def interpret_results():\n",
+    "    print(\"[Task 5] Interpret model and uncertainty\")\n",
+    "    print(\"        - Communicate limitations clearly\")\n",
+    "    print(\"        - Visualize relevant insights\")\n",
+    "\n",
+    "def use_model():\n",
+    "    print(\"[Task 6] Apply model for decision-making\")\n",
+    "    print(\"        - Define decision function using P(X|Y)\")\n",
+    "    print(\"        - Simulate impact of uncertainty on decisions\")\n",
+    "\n",
+    "def main():\n",
+    "    print(\"Starting Conditional Probability Modeling Pipeline...\\n\")\n",
+    "    define_model()\n",
+    "    fit_model()\n",
+    "    evaluate_model()\n",
+    "    quantify_uncertainty()\n",
+    "    interpret_results()\n",
+    "    use_model()\n",
+    "    print(\"\\nPipeline complete. Future modules can expand each task.\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1a58c592",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=============================================\n",
+      " Conditional Probability Modeling Flowchart\n",
+      "=============================================\n",
+      "\n",
+      "       ┌────────────────────────────┐\n",
+      "       │ Define the Modeling Goal   │\n",
+      "       └────────────┬──────────────┘\n",
+      "                    │\n",
+      "       ┌────────────▼──────────────┐\n",
+      "       │  Choose Meaningful Model  │\n",
+      "       │   and Set Assumptions     │\n",
+      "       └────────────┬──────────────┘\n",
+      "                    │\n",
+      "       ┌────────────▼──────────────┐\n",
+      "       │     Fit Model to Data     │\n",
+      "       └────────────┬──────────────┘\n",
+      "                    │\n",
+      "       ┌────────────▼──────────────┐\n",
+      "       │  Evaluate Model & Loss    │\n",
+      "       └────────────┬──────────────┘\n",
+      "                    │\n",
+      "       ┌────────────▼──────────────┐\n",
+      "       │ Quantify Uncertainty (UQ) │\n",
+      "       └────────────┬──────────────┘\n",
+      "                    │\n",
+      "       ┌────────────▼──────────────┐\n",
+      "       │  Interpret Results & UQ   │\n",
+      "       └────────────┬──────────────┘\n",
+      "                    │\n",
+      "       ┌────────────▼──────────────┐\n",
+      "       │ Apply in Decision Context │\n",
+      "       └────────────────────────────┘\n",
+      "\n",
+      "  ✔ Each block corresponds to one function or module\n",
+      "  ✔ Update each step with your methods and data later\n",
+      "  ✔ Plug in visual tools or UQ techniques where needed\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# condprob_flowchart.py\n",
+    "\n",
+    "def print_flowchart():\n",
+    "    print(\"\\n\" + \"=\"*45)\n",
+    "    print(\" Conditional Probability Modeling Flowchart\")\n",
+    "    print(\"=\"*45 + \"\\n\")\n",
+    "\n",
+    "    print(\"       ┌────────────────────────────┐\")\n",
+    "    print(\"       │ Define the Modeling Goal   │\")\n",
+    "    print(\"       └────────────┬──────────────┘\")\n",
+    "    print(\"                    │\")\n",
+    "    print(\"       ┌────────────▼──────────────┐\")\n",
+    "    print(\"       │  Choose Meaningful Model  │\")\n",
+    "    print(\"       │   and Set Assumptions     │\")\n",
+    "    print(\"       └────────────┬──────────────┘\")\n",
+    "    print(\"                    │\")\n",
+    "    print(\"       ┌────────────▼──────────────┐\")\n",
+    "    print(\"       │     Fit Model to Data     │\")\n",
+    "    print(\"       └────────────┬──────────────┘\")\n",
+    "    print(\"                    │\")\n",
+    "    print(\"       ┌────────────▼──────────────┐\")\n",
+    "    print(\"       │  Evaluate Model & Loss    │\")\n",
+    "    print(\"       └────────────┬──────────────┘\")\n",
+    "    print(\"                    │\")\n",
+    "    print(\"       ┌────────────▼──────────────┐\")\n",
+    "    print(\"       │ Quantify Uncertainty (UQ) │\")\n",
+    "    print(\"       └────────────┬──────────────┘\")\n",
+    "    print(\"                    │\")\n",
+    "    print(\"       ┌────────────▼──────────────┐\")\n",
+    "    print(\"       │  Interpret Results & UQ   │\")\n",
+    "    print(\"       └────────────┬──────────────┘\")\n",
+    "    print(\"                    │\")\n",
+    "    print(\"       ┌────────────▼──────────────┐\")\n",
+    "    print(\"       │ Apply in Decision Context │\")\n",
+    "    print(\"       └────────────────────────────┘\\n\")\n",
+    "\n",
+    "    print(\"  ✔ Each block corresponds to one function or module\")\n",
+    "    print(\"  ✔ Update each step with your methods and data later\")\n",
+    "    print(\"  ✔ Plug in visual tools or UQ techniques where needed\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    print_flowchart()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}