{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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": [
    "#PipleLine for Conditional Probability Modeling\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
}