{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df_samples = pd.read_excel(r'hepatitisBC.xlsx', engine='openpyxl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming you have a DataFrame named 'train' with columns 'prognosis' and 'Symptoms'\n",
    "# 'prognosis' column contains various disease names\n",
    "# 'Symptoms' column contains the symptoms\n",
    "\n",
    "# Calculate the total count of records\n",
    "total_count = len(train)\n",
    "\n",
    "# Get unique disease names from the 'prognosis' column\n",
    "unique_diseases = train['prognosis'].unique()\n",
    "\n",
    "# Create a dictionary to store prior probabilities for each disease\n",
    "disease_prior_probabilities = {}\n",
    "\n",
    "# Calculate the prior probability for each unique disease\n",
    "for disease in unique_diseases:\n",
    "    count_disease = len(train[train['prognosis'] == disease])\n",
    "    prob_disease = count_disease / total_count\n",
    "    disease_prior_probabilities[disease] = prob_disease\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_col(x):\n",
    "    if  any(x)==1:\n",
    "        return x.values.sum()\n",
    "\n",
    "ndf = train.groupby('prognosis',as_index=False).agg(lambda x:(sum_col(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf2=ndf.fillna(0)\n",
    "df=ndf2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "symptom_df=df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Calculate the total count of cases for each symptom in both diseases\n",
    "total_counts = symptom_df.sum(axis=0)[1:]\n",
    "\n",
    "# Create a dictionary to store the conditional probabilities\n",
    "p_Symptom_given_Disease = {}\n",
    "\n",
    "# Calculate the conditional probabilities for each symptom given each disease\n",
    "for disease in symptom_df['prognosis']:\n",
    "    p_Symptom_given_Disease[disease] = {}\n",
    "    for symptom in symptom_df.columns[1:]:\n",
    "        if total_counts[symptom] != 0:\n",
    "            p_Symptom_given_Disease[disease][symptom] = symptom_df[symptom_df['prognosis'] == disease][symptom].sum() / total_counts[symptom]\n",
    "\n",
    "# Convert to the desired dictionary format\n",
    "output_dict = {}\n",
    "for disease in symptom_df['prognosis']:\n",
    "    output_dict[disease] = p_Symptom_given_Disease[disease]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "disease_symptom_graph.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800px\"\n",
       "            src=\"disease_symptom_graph.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2349c2b2548>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "from pyvis.network import Network\n",
    "import random\n",
    "# Create dictionaries for P(Disease) and P(Symptom | Disease)\n",
    "\n",
    "\n",
    "disease_symptom_clusters = output_dict\n",
    "\n",
    "# Create a graph\n",
    "G = nx.Graph()\n",
    "\n",
    "# Define a color mapping function for diseases\n",
    "def get_random_color():\n",
    "    return \"#{:02x}{:02x}{:02x}\".format(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n",
    "\n",
    "# Calculate P(Disease | Symptoms) using Naive Bayes formula for all diseases\n",
    "results = {}\n",
    "sum_prob = 0.0  # To calculate the sum of probabilities for normalization\n",
    "\n",
    "for disease in disease_prior_probabilities:\n",
    "    query_prob = disease_prior_probabilities[disease]\n",
    "    valid_symptoms = {\n",
    "        symptom: disease_symptom_clusters[disease][symptom]\n",
    "        for symptom in disease_symptom_clusters[disease] if disease_symptom_clusters[disease][symptom] != 0\n",
    "    }\n",
    "    \n",
    "    if valid_symptoms:\n",
    "        for symptom, symptom_prob in valid_symptoms.items():\n",
    "            query_prob *= symptom_prob\n",
    "        results[disease] = query_prob\n",
    "        sum_prob += query_prob\n",
    "\n",
    "# Normalize the probabilities for diseases\n",
    "for disease, probability in results.items():\n",
    "    results[disease] = probability / sum_prob\n",
    "\n",
    "# Add disease nodes with prior probabilities and assign colors dynamically\n",
    "disease_colors = {}\n",
    "for disease, probability in disease_prior_probabilities.items():\n",
    "    disease_color = get_random_color()\n",
    "    disease_colors[disease] = disease_color\n",
    "    label = f\"{disease}\\nPrior: {probability:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "    G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add symptom nodes with conditional probabilities and assign colors based on associated disease colors\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    disease_color = disease_colors[disease]\n",
    "    valid_symptoms = {\n",
    "        symptom: symptom_prob\n",
    "        for symptom, symptom_prob in symptoms.items() if symptom_prob != 0\n",
    "    }\n",
    "    \n",
    "    for symptom, probability in valid_symptoms.items():\n",
    "        label = f\"{symptom}\\n{probability:.3f}\"\n",
    "        G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add edges between disease nodes and associated symptom nodes\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    for symptom, symptom_prob in symptoms.items():\n",
    "        if symptom_prob != 0:\n",
    "            disease_label = f\"{disease}\\nPrior: {disease_prior_probabilities[disease]:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "            symptom_label = f\"{symptom}\\n{symptom_prob:.3f}\"\n",
    "            G.add_edge(disease_label, symptom_label)\n",
    "\n",
    "# Create a Network object\n",
    "nt = Network(notebook=True, width=\"100%\", height=\"800px\")\n",
    "\n",
    "# Show the graph\n",
    "nt.from_nx(G)\n",
    "nt.show(\"disease_symptom_graph.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "disease_symptom_graph.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800px\"\n",
       "            src=\"disease_symptom_graph.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x234ca866688>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "from pyvis.network import Network\n",
    "import random\n",
    "# Create dictionaries for P(Disease) and P(Symptom | Disease)\n",
    "#disease_prior_probabilities = joint_probabilities\n",
    "#disease_prior_probabilities= disease_prior_probabilities\n",
    "disease_symptom_clusters = output_dict\n",
    "\n",
    "# Symptoms in evidence\n",
    "evidence_symptoms = ['itching', 'yellowing_of_eyes']\n",
    "#evidence_symptoms = ['itching']\n",
    "\n",
    "# Create a graph\n",
    "G = nx.Graph()\n",
    "\n",
    "# Define a color mapping function for diseases\n",
    "def get_random_color():\n",
    "    return \"#{:02x}{:02x}{:02x}\".format(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n",
    "\n",
    "# Calculate P(Disease | Symptoms) using Naive Bayes formula for all diseases based on evidence symptoms\n",
    "results = {}\n",
    "sum_prob = 0.0  # To calculate the sum of probabilities for normalization\n",
    "\n",
    "for disease in disease_prior_probabilities:\n",
    "    query_prob = disease_prior_probabilities[disease]\n",
    "    valid_symptoms = {\n",
    "        symptom: disease_symptom_clusters[disease][symptom]\n",
    "        for symptom in evidence_symptoms if symptom in disease_symptom_clusters[disease] and disease_symptom_clusters[disease][symptom] != 0\n",
    "    }\n",
    "    \n",
    "    if valid_symptoms:\n",
    "        for symptom, symptom_prob in valid_symptoms.items():\n",
    "            query_prob *= symptom_prob\n",
    "        results[disease] = query_prob\n",
    "        sum_prob += query_prob\n",
    "\n",
    "# Normalize the probabilities for diseases\n",
    "for disease, probability in results.items():\n",
    "    results[disease] = probability / sum_prob\n",
    "\n",
    "# Calculate the highest posterior probability\n",
    "highest_posterior = max(results.values())\n",
    "\n",
    "\n",
    "\n",
    "# Add disease nodes with prior probabilities and assign colors dynamically\n",
    "disease_colors = {}\n",
    "for disease, probability in disease_prior_probabilities.items():\n",
    "    disease_color = get_random_color()\n",
    "    disease_colors[disease] = disease_color\n",
    "    label = f\"{disease}\\nPrior: {probability:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "    G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add symptom nodes with conditional probabilities and assign colors based on associated disease colors\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    if any(symptom in evidence_symptoms for symptom in symptoms):\n",
    "        disease_color = disease_colors[disease]\n",
    "        valid_symptoms = {\n",
    "            symptom: symptom_prob\n",
    "            for symptom, symptom_prob in symptoms.items() if symptom in evidence_symptoms and symptom_prob != 0\n",
    "        }\n",
    "\n",
    "        for symptom, probability in valid_symptoms.items():\n",
    "            label = f\"{symptom}\\n{probability:.3f}\"\n",
    "            G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add edges between disease nodes and associated symptom nodes\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    if any(symptom in evidence_symptoms for symptom in symptoms):\n",
    "        for symptom, symptom_prob in symptoms.items():\n",
    "            if symptom in evidence_symptoms and symptom_prob != 0:\n",
    "                disease_label = f\"{disease}\\nPrior: {disease_prior_probabilities[disease]:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "                symptom_label = f\"{symptom}\\n{symptom_prob:.3f}\"\n",
    "                G.add_edge(disease_label, symptom_label)\n",
    "\n",
    "# Create a Network object\n",
    "nt = Network(notebook=True, width=\"100%\", height=\"800px\")\n",
    "\n",
    "# Show the graph\n",
    "nt.from_nx(G)\n",
    "nt.show(\"disease_symptom_graph.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import time\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pgmpy.estimators import BayesianEstimator\n",
    "from pgmpy.models import BayesianModel\n",
    "from pgmpy.inference import VariableElimination\n",
    "from pgmpy.factors.discrete import State\n",
    "from pgmpy.sampling import BayesianModelSampling\n",
    "from pgmpy.sampling import GibbsSampling\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup_nums = {\"prognosis\":     {\"Hepatitis B\": 0, \"Hepatitis C\": 1}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "train2 = train.replace(cleanup_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "architecture= [(\"itching\", \"prognosis\"),\n",
    "                   \n",
    "                      \n",
    "                       (\"fatigue\", \"prognosis\"),\n",
    "                       (\"lethargy\", \"prognosis\"),\n",
    "                    \n",
    "                       (\"yellowish_skin\", \"prognosis\"),\n",
    "                       (\"dark_urine\", \"prognosis\"),\n",
    "                       (\"nausea\", \"prognosis\"),\n",
    "                       (\"loss_of_appetite\", \"prognosis\"),\n",
    "                       (\"abdominal_pain\", \"prognosis\"),\n",
    "                     \n",
    "                     \n",
    "                       (\"yellow_urine\", \"prognosis\"),\n",
    "                       (\"yellowing_of_eyes\", \"prognosis\"),\n",
    "                      \n",
    "                       (\"malaise\", \"prognosis\"),\n",
    "             \n",
    "                       (\"family_history\", \"prognosis\"),\n",
    "                       (\"receiving_blood_transfusion\", \"prognosis\"),\n",
    "                       (\"receiving_unsterile_injections\", \"prognosis\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BayesianModel(architecture)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = list(model.nodes())\n",
    "edges = list(model.edges())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DAG = nx.DiGraph()\n",
    "DAG.add_edges_from(edges)\n",
    "\n",
    "fig = plt.figure(figsize = (16,12))\n",
    "pos = nx.spring_layout(DAG) \n",
    "\n",
    "nx.draw_networkx_nodes(DAG, pos = pos, node_color = 'red')\n",
    "nx.draw_networkx_labels(DAG, pos = pos)\n",
    "nx.draw_networkx_edges(DAG, pos = pos, edge_color = 'black', width = 1.5, arrows = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check model: True\n",
      "\n",
      "CPT of itching:\n",
      "+------------+-------+\n",
      "| itching(0) | 0.524 |\n",
      "+------------+-------+\n",
      "| itching(1) | 0.476 |\n",
      "+------------+-------+ \n",
      "\n",
      "CPT of prognosis:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "\n",
    "# disable text wrapping in output cell\n",
    "display(HTML(\"\"))\n",
    "\n",
    "model.cpds = []\n",
    "model.fit(  data=train2\n",
    "          , estimator=BayesianEstimator\n",
    "          , prior_type=\"BDeu\"\n",
    "          , equivalent_sample_size=10\n",
    "          ,complete_samples_only=False)\n",
    "\n",
    "print(f'Check model: {model.check_model()}\\n')\n",
    "for cpd in model.get_cpds():\n",
    "    print(f'CPT of {cpd.variable}:')\n",
    "    print(cpd, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pgmpy.inference import VariableElimination\n",
    "inference =VariableElimination(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "BMS_inference = BayesianModelSampling(model)\n",
    "GS_inference = GibbsSampling(model)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of the discrete value to let the method access at CPTs\n",
    "value_dict = {\n",
    "    True: 1,\n",
    "    False: 0,\n",
    "    '0-15': 0,\n",
    "    '16-45': 1,\n",
    "    '46-90': 2,\n",
    "    '1': 0,\n",
    "    '2': 1,\n",
    "    '3': 2,\n",
    "    '4': 3,\n",
    "    '5': 4,\n",
    "    'sv': 0,\n",
    "    '<=5': 1,\n",
    "    '5.5-6.5': 2,\n",
    "    '7-9.5': 3,\n",
    "    '>=10': 4\n",
    "}\n",
    "\n",
    "def discretization(x, value):\n",
    "    '''\n",
    "    Function that helps to discretize for likelihood weighting and rejection sampling\n",
    "    x     : unknown variable\n",
    "    value : value to analyze for the unknown variable based on the value of the query variable\n",
    "    '''\n",
    "    if x == value_dict[value]:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def prob_LW(samples, variable, query_value):\n",
    "    '''\n",
    "    Function that calculates the likelihood weighting probability\n",
    "    samples     : sample from which to extract the probability value\n",
    "    variable    : variable of the query\n",
    "    query_value : value of the query variable\n",
    "    '''\n",
    "    discretize = np.vectorize(discretization)\n",
    "    samples_thresholded = discretize(samples[variable], query_value)\n",
    "    return round(np.sum(np.dot(samples_thresholded, samples['_weight'])) / np.sum(samples['_weight']), 2)\n",
    "\n",
    "def prob_RS(samples, variable, query_value):\n",
    "    '''\n",
    "    Function that calculates the rejection sampling probability\n",
    "    samples     : sample from which to extract the probability value\n",
    "    variable    : variable of the query\n",
    "    query_value : value of the query variable\n",
    "    '''\n",
    "    discretize = np.vectorize(discretization)\n",
    "    samples_rejection = discretize(samples[variable], query_value)\n",
    "    return np.mean(samples_rejection, axis=0)\n",
    "\n",
    "def get_state_index(var, var_val):\n",
    "    '''\n",
    "    Function that returns the index of a given value for a variable\n",
    "    '''\n",
    "    return inference.query([var], show_progress=False).state_names.get(var).index(var_val)\n",
    "\n",
    "def prob_GS(samples, query_variable, query_evidence, query_value):\n",
    "    \"\"\"\n",
    "    Computes the probability of Gibbs Sampling given the samples\n",
    "    it will call a pgmpy query function building the string for the requested query\n",
    "    \"\"\"\n",
    "    gs_query = \"\"\n",
    "    for evidence, value in query_evidence.items():\n",
    "        gs_query += evidence + \" == \" + str(get_state_index(evidence, value))\n",
    "        if evidence != list(query_evidence.keys())[-1]:\n",
    "            # add the '&' except for the last element\n",
    "            gs_query += \" & \"\n",
    "\n",
    "    # check denominator to avoid division by 0\n",
    "    if samples.query(gs_query).shape[0] == 0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return (samples.query(query_variable[0] + \" == \" + str(get_state_index(query_variable[0], query_value)) + \" & \" + gs_query).shape[0] \n",
    "              / samples.query(gs_query).shape[0])\n",
    "\n",
    "#Absolute error function\n",
    "def absolute_error(exact_value, approx_value):\n",
    "    '''\n",
    "    Function that computes the absolute error between the approximated and exact probabilities\n",
    "    exact_value   : reference probability value of the query\n",
    "    approx_value  : approximate probability value of the query\n",
    "    '''\n",
    "    return np.absolute(exact_value - approx_value)\n",
    "\n",
    "#Relative error function\n",
    "def relative_error(exact_value, approx_value):\n",
    "    '''\n",
    "    Function that computes the absolute error between the approximated and exact probabilities\n",
    "    exact_value   : reference probability value of the query\n",
    "    approx_value  : approximate probability value of the query\n",
    "    '''\n",
    "    return np.absolute(((exact_value - approx_value) / approx_value))\n",
    "\n",
    "def query_preprocessing(query_variable, query_evidence, query_value):\n",
    "    '''\n",
    "    Function that returns the processed query variable, evidence to adapt them to the methods\n",
    "    of likelihood weighting and rejection sampling\n",
    "\n",
    "    query_variable: list with one unknown variable of the query\n",
    "    query_evidence: dict representing the evidence of the query\n",
    "    query_value   : value of the query var to find in the query\n",
    "    '''\n",
    "    evidence = []\n",
    "\n",
    "    for (evidence_var, evidence_value) in query_evidence.items():\n",
    "        state = State(evidence_var, evidence_value)\n",
    "        evidence.append(state)\n",
    "\n",
    "    default_query_prob = highest_posterior  # Replace this with your desired default value\n",
    "    query_prob = default_query_prob\n",
    "    variables = query_variable[0]\n",
    "\n",
    "    return query_prob, evidence, variables\n",
    "\n",
    "\n",
    "def run_experiment(sample_size,query_variable,query_evidence,query_value):\n",
    "  '''\n",
    "  Function that return the result of a sampling experiment using likelihood weighting,\n",
    "  rejection sampling and Gibbs sampling\n",
    "\n",
    "  sample_size    : size of the sample for the experiment\n",
    "  query_variable : list with one unknown variable of the query\n",
    "  query_evidence : dict representing the evidence of the query\n",
    "  query_value:   : value of the query var to find in the query\n",
    "  '''\n",
    "\n",
    "  #Rounding precision\n",
    "  precision = 3\n",
    "\n",
    "  #Preprocessing for the LW and RS methods\n",
    " \n",
    "  query_prob,evidence,variable = query_preprocessing(query_variable,query_evidence,query_value)\n",
    " \n",
    "\n",
    "  t0_LW = time.time()\n",
    "  samples_LW = BMS_inference.likelihood_weighted_sample(evidence = evidence, size=sample_size, return_type='recarray')\n",
    "  approx_prob_LW = prob_LW(samples_LW,variable,query_value)\n",
    "  time_LW = round(time.time() - t0_LW, precision)\n",
    "\n",
    "  t0_RS = time.time()\n",
    "  samples_RS = BMS_inference.rejection_sample(evidence=evidence, size=sample_size, return_type='recarray',show_progress=False)\n",
    "  approx_prob_RS = prob_RS(samples_RS,variable,query_value)\n",
    "  time_RS = round(time.time() - t0_RS, precision)\n",
    "\n",
    "\n",
    "  t0_GS = time.time()\n",
    "  samples_GS = GS_inference.sample(size=sample_size, seed=37)\n",
    "  approx_prob_GS = prob_GS(samples_GS,query_variable,query_evidence,query_value)\n",
    "  time_GS = round(time.time() - t0_GS, precision)\n",
    "\n",
    "  # Return results\n",
    "  return np.array([(sample_size,query_prob,\n",
    "                        approx_prob_RS, absolute_error(query_prob,approx_prob_RS), relative_error(query_prob,approx_prob_RS),time_RS,\n",
    "                        approx_prob_LW, absolute_error(query_prob,approx_prob_LW), relative_error(query_prob,approx_prob_LW),time_LW,\n",
    "                        approx_prob_GS, absolute_error(query_prob,approx_prob_GS), relative_error(query_prob,approx_prob_GS),time_GS)],\n",
    "                      dtype=[('sample_size', '<i8'),('query_prob', '<f8'),  \n",
    "                            ('approx_prob_RS', '<f8'),('abs_error_RS', '<f8'),('rel_error_RS', '<f8'),('time_RS','<f8'),\n",
    "                            ('approx_prob_LW', '<f8'),('abs_error_LW', '<f8'),('rel_error_LW', '<f8'),('time_LW','<f8'),\n",
    "                            ('approx_prob_GS', '<f8'),('abs_error_GS', '<f8'),('rel_error_GS', '<f8'),('time_GS','<f8')])\n",
    "          \n",
    "#Query in approximate inference\n",
    "def approximate_inference(q,query_var,query_evidence,query_value,starting_size=2.5,final_size=5,experiments=10):  \n",
    "  '''\n",
    "  Function that create the result of the different approximate inference methods using\n",
    "  different sample size values\n",
    "\n",
    "  query_var:     : list with one unknown variable of the query\n",
    "  query_evidence : dict representing the evidence of the query\n",
    "  query_value:   : value of the query var to find in the query\n",
    "  starting_size=2 : exponent of the number of start sample points that are 10^(starting_size)\n",
    "  final_size=5    : exponent of the number of final sample points that are 10^(final_size)\n",
    "  experiments=10 : number of sampling experiments\n",
    "  '''\n",
    "  results = np.array([],dtype=[('sample_size', '<i8'),('query_prob', '<f8'),('approx_prob_RS', '<f8'),\n",
    "                               ('abs_error_RS', '<f8'),('rel_error_RS', '<f8'),('time_RS', '<f8'),\n",
    "                               ('approx_prob_LW', '<f8'),('abs_error_LW', '<f8'),('rel_error_LW', '<f8'),('time_LW','<f8'),\n",
    "                               ('approx_prob_GS', '<f8'),('abs_error_GS', '<f8'),('rel_error_GS', '<f8'),('time_GS','<f8')])\n",
    "\n",
    "  for size in np.logspace(starting_size, final_size, num=experiments, dtype='<i8'):\n",
    "    results=np.append(results,run_experiment(size,query_var,query_evidence,query_value))\n",
    "\n",
    "   \n",
    "\n",
    "  \n",
    "\n",
    "  globals()[f\"query{q}\"]=pd.DataFrame(results)\n",
    "  globals()[f\"query{q}\"].to_csv(f\"query{q}\"+'.csv')\n",
    "  # display table\n",
    "  print(\"\\nResults during sampling:\")\n",
    "  display(pd.DataFrame(results))\n",
    "  print(\"\\n\")\n",
    "  # show plots\n",
    "  plot_results(results,query_evidence,query_var,query_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set the desired figure size\n",
    "figure_width = 15  # You can adjust this value based on your needs\n",
    "figure_height = 10  # You can adjust this value based on your needs\n",
    "\n",
    "# Adjust font sizes globally\n",
    "plt.rcParams.update({'font.size': 14, 'legend.fontsize': 12, 'xtick.labelsize': 12, 'ytick.labelsize': 12})\n",
    "\n",
    "def plot_results(results, query_evidence, query_variable, query_value):\n",
    "    '''\n",
    "    Function to plot the result of the approximate inference\n",
    "    '''\n",
    "    query_prob, evidence, var = query_preprocessing(query_variable, query_evidence, query_value)\n",
    "    \n",
    "    plt.figure(figsize=(figure_width, figure_height))  # Set the figure size here\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Approximate probability values\", fontsize=16)\n",
    "    LWCplot, = plt.semilogx(results['sample_size'], results['approx_prob_LW'], 'go--', label=\"Likelihood Weighting\", markersize=8)\n",
    "    RSCplot, = plt.semilogx(results['sample_size'], results['approx_prob_RS'], 'bo--', label=\"Rejection Sampling\", markersize=8)\n",
    "    GSCplot, = plt.semilogx(results['sample_size'], results['approx_prob_GS'], 'mo--', label=\"Gibbs Sampling\", markersize=8)\n",
    "    VECplot, = plt.semilogx(results['sample_size'], query_prob * np.ones(results.size), 'r', label=\"reference value\")\n",
    "    plt.xlabel(\"Sample size\")\n",
    "    plt.ylabel(\"Approximate probability value\")\n",
    "    plt.legend(handles=[LWCplot, RSCplot, GSCplot, VECplot], loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the plot with desired dimensions\n",
    "    plt.savefig('output_plot1.png', dpi=300)  # You can adjust the filename and dpi as needed\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(figure_width, figure_height))  # Set the figure size here\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Trend of absolute error\", fontsize=16)\n",
    "    ELWCplot, = plt.semilogx(results['sample_size'], results['abs_error_LW'], 'go--', label=\"Likelihood Weighting\", markersize=8)\n",
    "    ERSCplot, = plt.semilogx(results['sample_size'], results['abs_error_RS'], 'bo--', label=\"Rejection Sampling\", markersize=8)\n",
    "    EGSCplot, = plt.semilogx(results['sample_size'], results['abs_error_GS'], 'mo--', label=\"Gibbs Sampling\", markersize=8)\n",
    "    plt.xlabel(\"Sample size\")\n",
    "    plt.ylabel(\"Absolute error value\")\n",
    "    plt.legend(handles=[ELWCplot, ERSCplot, EGSCplot], loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the plot with desired dimensions\n",
    "    plt.savefig('output_plot2.png', dpi=300)  # You can adjust the filename and dpi as needed\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(figure_width, figure_height))  # Set the figure size here\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Trend of relative error\", fontsize=16)\n",
    "    ELWCplot, = plt.semilogx(results['sample_size'], results['rel_error_LW'], 'go--', label=\"Likelihood Weighting\", markersize=8)\n",
    "    ERSCplot, = plt.semilogx(results['sample_size'], results['rel_error_RS'], 'bo--', label=\"Rejection Sampling\", markersize=8)\n",
    "    EGSCplot, = plt.semilogx(results['sample_size'], results['rel_error_GS'], 'mo--', label=\"Gibbs Sampling\", markersize=8)\n",
    "    plt.xlabel(\"Sample size\")\n",
    "    plt.ylabel(\"Relative error value\")\n",
    "    plt.legend(handles=[ELWCplot, ERSCplot, EGSCplot], loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the plot with desired dimensions\n",
    "    plt.savefig('output_plot3.png', dpi=300)  # You can adjust the filename and dpi as needed\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(figure_width, figure_height))  # Set the figure size here\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Trend of the sampling time\", fontsize=16)\n",
    "    TLWplot, = plt.semilogx(results['sample_size'], results['time_LW'], 'go--', label=\"Likelihood Weighting\", markersize=8)\n",
    "    TRSplot, = plt.semilogx(results['sample_size'], results['time_RS'], 'bo--', label=\"Rejection Sampling\", markersize=8)\n",
    "    TGSplot, = plt.semilogx(results['sample_size'], results['time_GS'], 'mo--', label=\"Gibbs Sampling\", markersize=8)\n",
    "    plt.xlabel(\"Sample size\")\n",
    "    plt.ylabel(\"Seconds\")\n",
    "    plt.legend(handles=[TLWplot, TRSplot, TGSplot], loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the plot with desired dimensions\n",
    "    plt.savefig('output_plot4.png', dpi=300)  # You can adjust the filename and dpi as needed\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:03<00:00,  3.83it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████| 315/315 [00:00<00:00, 495.78it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:04<00:00,  3.60it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 598/598 [00:00<00:00, 1538.58it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:03<00:00,  3.85it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 1135/1135 [00:01<00:00, 803.16it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:04<00:00,  3.40it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 2153/2153 [00:02<00:00, 767.40it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.76it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 4083/4083 [00:04<00:00, 857.36it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.94it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 7741/7741 [00:09<00:00, 855.48it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:03<00:00,  3.90it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 14676/14676 [00:18<00:00, 812.47it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:04<00:00,  3.48it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 27824/27824 [00:35<00:00, 794.25it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:04<00:00,  3.43it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 52748/52748 [01:05<00:00, 800.42it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.90it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 99999/99999 [02:07<00:00, 782.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results during sampling:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_size</th>\n",
       "      <th>query_prob</th>\n",
       "      <th>approx_prob_RS</th>\n",
       "      <th>abs_error_RS</th>\n",
       "      <th>rel_error_RS</th>\n",
       "      <th>time_RS</th>\n",
       "      <th>approx_prob_LW</th>\n",
       "      <th>abs_error_LW</th>\n",
       "      <th>rel_error_LW</th>\n",
       "      <th>time_LW</th>\n",
       "      <th>approx_prob_GS</th>\n",
       "      <th>abs_error_GS</th>\n",
       "      <th>rel_error_GS</th>\n",
       "      <th>time_GS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>316</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.531646</td>\n",
       "      <td>0.005330</td>\n",
       "      <td>0.010025</td>\n",
       "      <td>3.927</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.006316</td>\n",
       "      <td>0.012146</td>\n",
       "      <td>3.553</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>27.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>599</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.517529</td>\n",
       "      <td>0.008787</td>\n",
       "      <td>0.016978</td>\n",
       "      <td>4.172</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>4.202</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>26.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1136</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.529930</td>\n",
       "      <td>0.003614</td>\n",
       "      <td>0.006819</td>\n",
       "      <td>3.907</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.036316</td>\n",
       "      <td>0.074114</td>\n",
       "      <td>3.880</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>32.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2154</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.489786</td>\n",
       "      <td>0.036529</td>\n",
       "      <td>0.074582</td>\n",
       "      <td>4.415</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.006316</td>\n",
       "      <td>0.012146</td>\n",
       "      <td>4.206</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>30.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4084</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.488002</td>\n",
       "      <td>0.038314</td>\n",
       "      <td>0.078512</td>\n",
       "      <td>5.455</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.016316</td>\n",
       "      <td>0.031992</td>\n",
       "      <td>5.036</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>32.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7742</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.502454</td>\n",
       "      <td>0.023862</td>\n",
       "      <td>0.047490</td>\n",
       "      <td>5.120</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>3.741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>38.361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14677</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.500307</td>\n",
       "      <td>0.026009</td>\n",
       "      <td>0.051986</td>\n",
       "      <td>3.863</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.016316</td>\n",
       "      <td>0.031992</td>\n",
       "      <td>3.771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>47.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27825</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.497969</td>\n",
       "      <td>0.028346</td>\n",
       "      <td>0.056924</td>\n",
       "      <td>4.337</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>4.322</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>67.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52749</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.498531</td>\n",
       "      <td>0.027785</td>\n",
       "      <td>0.055734</td>\n",
       "      <td>4.422</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>5.054</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>96.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100000</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.498570</td>\n",
       "      <td>0.027746</td>\n",
       "      <td>0.055651</td>\n",
       "      <td>5.236</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>5.054</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>154.831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_size  query_prob  approx_prob_RS  abs_error_RS  rel_error_RS  \\\n",
       "0          316    0.526316        0.531646      0.005330      0.010025   \n",
       "1          599    0.526316        0.517529      0.008787      0.016978   \n",
       "2         1136    0.526316        0.529930      0.003614      0.006819   \n",
       "3         2154    0.526316        0.489786      0.036529      0.074582   \n",
       "4         4084    0.526316        0.488002      0.038314      0.078512   \n",
       "5         7742    0.526316        0.502454      0.023862      0.047490   \n",
       "6        14677    0.526316        0.500307      0.026009      0.051986   \n",
       "7        27825    0.526316        0.497969      0.028346      0.056924   \n",
       "8        52749    0.526316        0.498531      0.027785      0.055734   \n",
       "9       100000    0.526316        0.498570      0.027746      0.055651   \n",
       "\n",
       "   time_RS  approx_prob_LW  abs_error_LW  rel_error_LW  time_LW  \\\n",
       "0    3.927            0.52      0.006316      0.012146    3.553   \n",
       "1    4.172            0.50      0.026316      0.052632    4.202   \n",
       "2    3.907            0.49      0.036316      0.074114    3.880   \n",
       "3    4.415            0.52      0.006316      0.012146    4.206   \n",
       "4    5.455            0.51      0.016316      0.031992    5.036   \n",
       "5    5.120            0.50      0.026316      0.052632    3.741   \n",
       "6    3.863            0.51      0.016316      0.031992    3.771   \n",
       "7    4.337            0.50      0.026316      0.052632    4.322   \n",
       "8    4.422            0.50      0.026316      0.052632    5.054   \n",
       "9    5.236            0.50      0.026316      0.052632    5.054   \n",
       "\n",
       "   approx_prob_GS  abs_error_GS  rel_error_GS  time_GS  \n",
       "0             1.0      0.473684      0.473684   27.101  \n",
       "1             1.0      0.473684      0.473684   26.266  \n",
       "2             1.0      0.473684      0.473684   32.663  \n",
       "3             1.0      0.473684      0.473684   30.888  \n",
       "4             1.0      0.473684      0.473684   32.061  \n",
       "5             1.0      0.473684      0.473684   38.361  \n",
       "6             1.0      0.473684      0.473684   47.785  \n",
       "7             1.0      0.473684      0.473684   67.236  \n",
       "8             1.0      0.473684      0.473684   96.443  \n",
       "9             1.0      0.473684      0.473684  154.831  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "approximate_inference(1,['prognosis'],{'fatigue':1},True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "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": "3.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}