testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa17e90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: matplotlib in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (3.7.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (4.40.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (1.24.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\programdata\\anaconda3\\envs\\dss-env-01\\lib\\site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\karap\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\envs\\dss-env-01\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in c:\\programdata\\anaconda3\\envs\\dss-env-01\\lib\\site-packages (from matplotlib) (5.2.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in c:\\programdata\\anaconda3\\envs\\dss-env-01\\lib\\site-packages (from importlib-resources>=3.2.0->matplotlib) (3.11.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\envs\\dss-env-01\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9ed1dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from expertawrapper import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5947912",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test the generator\n",
    "\n",
    "u = Utils()\n",
    "random_numbers = [u.gaussian(30, 300) for _ in range(1000)]\n",
    "plt.hist(random_numbers, bins=30, alpha=0.5, color='g', edgecolor='black')\n",
    "plt.title('Histogram of 1000 random numbers from a Gaussian distribution')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08bd696f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the global variables\n",
    "\n",
    "successful_tests = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16b6958a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the knowledge engines\n",
    "\n",
    "suspicion_engine = SuspicionInvestigation()\n",
    "tumor_engine = TumorStage()\n",
    "nodes_engine = NodesStage()\n",
    "metastasis_engine = MetastasisStage()\n",
    "cancer_engine = CancerStage()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "406b4348",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define testing methods\n",
    "\n",
    "def test_case(engine, fact, expected, fact_type):\n",
    "    \"\"\"\n",
    "    Test a single case for a given engine.\n",
    "    \n",
    "    Arguments:\n",
    "        engine: KnowledgeEngine instance to test.\n",
    "        fact: Fact instance to assert.\n",
    "        expected: Expected output from the engine.\n",
    "        fact_type: The type of fact.\n",
    "    \"\"\"\n",
    "    # Initialize and reset the engine.\n",
    "    engine.reset()\n",
    "    # Assert the fact to the engine.\n",
    "    engine.declare(fact_type(**fact))\n",
    "    # Run the engine.\n",
    "    engine.run()\n",
    "    \n",
    "    # Compare the expected and actual outputs.\n",
    "    try:\n",
    "        result = engine.get_result()\n",
    "        assert expected == result, f\"Expected: {expected}, but got: {result}\"\n",
    "        global successful_tests\n",
    "        successful_tests += 1  # Increment the counter if the assertion passed\n",
    "    except AssertionError as e:\n",
    "        print(f\"Test failed: {e}\")\n",
    "\n",
    "def run_tests(engine, cases, fact_type):\n",
    "    \"\"\"\n",
    "    Run all test cases for a given engine.\n",
    "    \n",
    "    Arguments:\n",
    "        engine: KnowledgeEngine instance to test.\n",
    "        cases: List of cases to test. Each case is a tuple of a fact and an expected output.\n",
    "        fact_type: The type of fact.\n",
    "    \"\"\"\n",
    "    for fact, expected in cases:\n",
    "        test_case(engine, fact, expected, fact_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc1fd33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test cases for the suspicion engine.\n",
    "suspicion_cases = [\n",
    "    ({'smoking':False, 'asbestos':False, 'radio':False, \n",
    "      'history':False, 'tsymptoms':False, 'nsymptoms':False, 'msymptoms':False}, 'False'),\n",
    "    ({'smoking':True, 'asbestos':False, 'radio':False, \n",
    "      'history':False, 'tsymptoms':False, 'nsymptoms':False, 'msymptoms':False}, 'True'),\n",
    "    ({'smoking':False, 'asbestos':True, 'radio':False, \n",
    "      'history':False, 'tsymptoms':False, 'nsymptoms':False, 'msymptoms':False}, 'True')\n",
    "]\n",
    "\n",
    "# Test cases for the tumor engine.\n",
    "tumor_cases = [\n",
    "    ({\"mass\": False, \"diameter\": 0.0, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": False, \"nearby_organs\": False, \"fna_and_pet_scan\": False}, \"T0\"),\n",
    "    ({\"mass\": False, \"diameter\": 0.0, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": True, \"nearby_organs\": False, \"fna_and_pet_scan\": False}, \"Tx\"),\n",
    "    ({\"mass\": True, \"diameter\": 1.8, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": True, \"nearby_organs\": False, \"fna_and_pet_scan\": True}, \"T1a\"),\n",
    "    ({\"mass\": True, \"diameter\": 2.5, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": True, \"nearby_organs\": False, \"fna_and_pet_scan\": True}, \"T1b\"),\n",
    "    ({\"mass\": True, \"diameter\": 3.1, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": True, \"nearby_organs\": False, \"fna_and_pet_scan\": True}, \"T2a\"),\n",
    "    ({\"mass\": True, \"diameter\": 5.3, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": True, \"nearby_organs\": False, \"fna_and_pet_scan\": True}, \"T2b\"),   \n",
    "    ({\"mass\": True, \"diameter\": 7.8, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": True, \"nearby_organs\": False, \"fna_and_pet_scan\": False}, \"T3\"),\n",
    "    ({\"mass\": True, \"diameter\": 4.1, \"bronchoscopesis\": False, \n",
    "      \"cytologic\": False, \"nearby_organs\": True, \"fna_and_pet_scan\": False}, \"T4\"),   \n",
    "    ({'mass': True, 'diameter': 4.73, 'bronchoscopesis': False, \n",
    "      'cytologic': False, 'nearby_organs': True, 'fna_and_pet_scan': True}, \"T4\")\n",
    "]\n",
    "\n",
    "# Test cases for the nodes engine.\n",
    "nodes_cases = [\n",
    "    ({\"lymph_nodes_size\": 0.4, \"peribronchial_metastasis\": False, \n",
    "      \"mediastinal_metastasis\": False, \"fna_positive\": False}, \"N0\"),\n",
    "    ({\"lymph_nodes_size\": 1.0, \"peribronchial_metastasis\": False, \n",
    "      \"mediastinal_metastasis\": False, \"fna_positive\": False}, \"Nx\"),\n",
    "    ({\"lymph_nodes_size\": 1.6, \"peribronchial_metastasis\": True, \n",
    "      \"mediastinal_metastasis\": False, \"fna_positive\": False}, \"N1\"),\n",
    "    ({\"lymph_nodes_size\": 1.7, \"peribronchial_metastasis\": False, \n",
    "      \"mediastinal_metastasis\": True, \"fna_positive\": False}, \"N2\"),\n",
    "    ({\"lymph_nodes_size\": 0.7, \"peribronchial_metastasis\": False, \n",
    "      \"mediastinal_metastasis\": False, \"fna_positive\": True}, \"N3\"),\n",
    "]\n",
    "\n",
    "# Test cases for the metastasis engine.\n",
    "metastasis_cases = [\n",
    "    ({\"separate_tumor_nodules\": False, \"distant_metastasis\": False}, \"M0\"),\n",
    "    ({\"separate_tumor_nodules\": True, \"distant_metastasis\": False}, \"M1a\"),\n",
    "    ({\"separate_tumor_nodules\": False, \"distant_metastasis\": True}, \"M1b\"),\n",
    "]\n",
    "\n",
    "# Test cases for the cancer engine.\n",
    "cancer_cases = [\n",
    "    ({\"T\": \"Tx\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage Undetermined\"),\n",
    "    ({\"T\": \"T1a\", \"N\": \"Nx\", \"M\": \"M0\"}, \"Stage Undetermined\"),\n",
    "    ({\"T\": \"T1a\", \"N\": \"N0\", \"M\": \"Mx\"}, \"Stage Undetermined\"),\n",
    "    ({\"T\": \"T1a\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage IA2\"),\n",
    "    ({\"T\": \"T1b\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage IA3\"),\n",
    "    ({\"T\": \"T1c\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage IB\"),\n",
    "    ({\"T\": \"T2a\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage IB\"),\n",
    "    ({\"T\": \"T2b\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage IIA\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N0\", \"M\": \"M0\"}, \"Stage IIA\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N1\", \"M\": \"M0\"}, \"Stage IIB\"),\n",
    "    ({\"T\": \"T2\", \"N\": \"N1\", \"M\": \"M0\"}, \"Stage IIB\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N2\", \"M\": \"M0\"}, \"Stage IIIA\"),\n",
    "    ({\"T\": \"T2\", \"N\": \"N2\", \"M\": \"M0\"}, \"Stage IIIA\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N1\", \"M\": \"M0\"}, \"Stage IIIA\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N2\", \"M\": \"M0\"}, \"Stage IIIA\"),\n",
    "    ({\"T\": \"T4\", \"N\": \"N1\", \"M\": \"M0\"}, \"Stage IIIA\"),\n",
    "    ({\"T\": \"T4\", \"N\": \"N2\", \"M\": \"M0\"}, \"Stage IIIA\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N3\", \"M\": \"M0\"}, \"Stage IIIB\"),\n",
    "    ({\"T\": \"T2\", \"N\": \"N3\", \"M\": \"M0\"}, \"Stage IIIB\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N3\", \"M\": \"M0\"}, \"Stage IIIB\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N1\", \"M\": \"M1a\"}, \"Stage IIIC\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N2\", \"M\": \"M1a\"}, \"Stage IIIC\"),\n",
    "    ({\"T\": \"T4\", \"N\": \"N3\", \"M\": \"M1a\"}, \"Stage IIIC\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N2\", \"M\": \"M1b\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T2\", \"N\": \"N2\", \"M\": \"M1b\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N2\", \"M\": \"M1b\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N3\", \"M\": \"M1b\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T4\", \"N\": \"N3\", \"M\": \"M1b\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T1\", \"N\": \"N2\", \"M\": \"M1c\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T2\", \"N\": \"N2\", \"M\": \"M1c\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N2\", \"M\": \"M1c\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T3\", \"N\": \"N3\", \"M\": \"M1c\"}, \"Stage IV\"),\n",
    "    ({\"T\": \"T4\", \"N\": \"N3\", \"M\": \"M1c\"}, \"Stage IV\")\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84c9d533",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total successful test cases:  53\n"
     ]
    }
   ],
   "source": [
    "# Run the tests\n",
    "\n",
    "run_tests(suspicion_engine, suspicion_cases, SUSPICION_FACTS)\n",
    "run_tests(tumor_engine, tumor_cases, TUMOR_FACTS)\n",
    "run_tests(nodes_engine, nodes_cases, NODES_FACTS)\n",
    "run_tests(metastasis_engine, metastasis_cases, METASTASIS_FACTS)\n",
    "run_tests(cancer_engine, cancer_cases, TNM_FACTS)\n",
    "\n",
    "print('Total successful test cases: ', successful_tests)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b49f54da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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