Meddy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Chatbot - Meddy\n",
    "## Project for course Intelligent Systems at Faculty of Organization and Informatics Varaždin, University of Zagreb\n",
    "\n",
    "(c) 2021 Alagić Aldin, Benkus Maja, Košmerl Igor, Krištofić Miro "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Natural Language Processing (NLP)\n",
    "\n",
    "- this part of the notebook depicts the data preparation, but also the training of the NLP model, as well as the neural network validation \n",
    "- the data for the NLP model is loaded from the _intents_short.json_ file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'intents': [{'tag': 'abdominal_pain',\n",
       "   'patterns': ['abdominal pain',\n",
       "    'abdomen hurts',\n",
       "    'pain in stomach',\n",
       "    'pain in abdomen']},\n",
       "  {'tag': 'abnormal_menstruation',\n",
       "   'patterns': ['abnormal menstruation',\n",
       "    'heavy period',\n",
       "    'Heavy flow',\n",
       "    'Period lasts longer',\n",
       "    'period painful',\n",
       "    'strong menstrual pain',\n",
       "    'Menstrual cramps strong']},\n",
       "  {'tag': 'acidity',\n",
       "   'patterns': ['acid reflux', 'acidity problems', 'heartburn']},\n",
       "  {'tag': 'acute_liver_failure',\n",
       "   'patterns': ['acute liver failure',\n",
       "    'liver hurts',\n",
       "    'pain around liver',\n",
       "    'Upper right abdomen hurts']},\n",
       "  {'tag': 'altered_sensorium',\n",
       "   'patterns': ['altered sensorium',\n",
       "    \"can't think clearly\",\n",
       "    'hard to think',\n",
       "    'unable to concentrate']},\n",
       "  {'tag': 'anxiety',\n",
       "   'patterns': ['anxiety', 'anxious', 'really worried', 'nervous']},\n",
       "  {'tag': 'back_pain',\n",
       "   'patterns': ['back hurts', 'back pain', 'pain in back']},\n",
       "  {'tag': 'belly_pain',\n",
       "   'patterns': ['belly hurts',\n",
       "    'belly pain',\n",
       "    'pain in belly',\n",
       "    'Stomach hurts',\n",
       "    'Stomach pain']},\n",
       "  {'tag': 'blackheads',\n",
       "   'patterns': ['blackheads',\n",
       "    'lot of blackheads',\n",
       "    'Blackheads on face',\n",
       "    'Blackheads on body']},\n",
       "  {'tag': 'bladder_discomfort',\n",
       "   'patterns': ['bladder discomfort',\n",
       "    'bladder hurts',\n",
       "    'discomfort in bladder',\n",
       "    'Pressure in bladder']},\n",
       "  {'tag': 'blister',\n",
       "   'patterns': ['blisters',\n",
       "    'arms are blistered',\n",
       "    'feet are blistered',\n",
       "    'Body is blistered']},\n",
       "  {'tag': 'blood_in_sputum',\n",
       "   'patterns': ['blood in sputum',\n",
       "    'coughing up blood',\n",
       "    'Blood while caughing']},\n",
       "  {'tag': 'bloody_stool',\n",
       "   'patterns': ['blood in stool', 'pooped out blood', 'bloody stool']},\n",
       "  {'tag': 'blurred_and_distorted_vision',\n",
       "   'patterns': ['blurred and distorted vision',\n",
       "    \"can't see clearly\",\n",
       "    \"can't see\",\n",
       "    'blurry vision',\n",
       "    'Vision distorted']},\n",
       "  {'tag': 'breathlessness',\n",
       "   'patterns': ['breathlessness',\n",
       "    'difficulty breathing',\n",
       "    'out of breath',\n",
       "    'feel winded',\n",
       "    'Short of breath']},\n",
       "  {'tag': 'brittle_nails',\n",
       "   'patterns': ['brittle nails',\n",
       "    'nails weak',\n",
       "    'Nails brittle',\n",
       "    'Nails easily broken']},\n",
       "  {'tag': 'bruising',\n",
       "   'patterns': ['bruising', 'bruises', 'easily bruised', 'lot of bruises']},\n",
       "  {'tag': 'burning_micturition',\n",
       "   'patterns': ['burning micturition',\n",
       "    'hurts when pee',\n",
       "    'burns when pee',\n",
       "    'Burning sensation peeing']},\n",
       "  {'tag': 'chest_pain',\n",
       "   'patterns': ['chest hurts', 'chest pain', 'pain in chest']},\n",
       "  {'tag': 'chills', 'patterns': ['chills', 'cold']},\n",
       "  {'tag': 'cold_hands_and_feets',\n",
       "   'patterns': ['cold hands and feets',\n",
       "    'hands are cold',\n",
       "    'feet are cold',\n",
       "    'cold hands',\n",
       "    'cold feet']},\n",
       "  {'tag': 'coma', 'patterns': ['coma', 'experienced coma', 'was in coma']},\n",
       "  {'tag': 'constipation',\n",
       "   'patterns': [\"can't poop\",\n",
       "    'constipated',\n",
       "    'am constipated',\n",
       "    \"haven't pooped in a while\"]},\n",
       "  {'tag': 'continuous_feel_of_urine',\n",
       "   'patterns': ['continuous feel of urine',\n",
       "    'have to pee',\n",
       "    'Urge to pee',\n",
       "    'pee constantly']},\n",
       "  {'tag': 'continuous_sneezing',\n",
       "   'patterns': ['continuous sneezing',\n",
       "    'sneezing all the time',\n",
       "    'sneeze a lot',\n",
       "    'Always sneezing',\n",
       "    'sneeze constantly']},\n",
       "  {'tag': 'cough',\n",
       "   'patterns': ['cough',\n",
       "    'coughing all the time',\n",
       "    'cough a lot',\n",
       "    'Always caughing',\n",
       "    'cough constantly']},\n",
       "  {'tag': 'congestion',\n",
       "   'patterns': ['congestion',\n",
       "    \"can't breathe\",\n",
       "    'nose stuffy',\n",
       "    'nose full',\n",
       "    'nasal congestion']},\n",
       "  {'tag': 'cramps', 'patterns': ['cramps', 'cramping up']},\n",
       "  {'tag': 'dark_urine',\n",
       "   'patterns': ['urine dark', 'Darker color urine', 'pee dark']},\n",
       "  {'tag': 'dehydration',\n",
       "   'patterns': ['dehydrated', 'experiencing dehydration']},\n",
       "  {'tag': 'depression',\n",
       "   'patterns': ['depression', 'have depression', 'feel depressed']},\n",
       "  {'tag': 'diarrhoea',\n",
       "   'patterns': ['diarrhoea', 'always on toilet', 'poop often', 'poo a lot']},\n",
       "  {'tag': 'dischromic_patches',\n",
       "   'patterns': ['dischromic patches',\n",
       "    'lost color on skin',\n",
       "    'patches different color',\n",
       "    'skin patched']},\n",
       "  {'tag': 'distention_of_abdomen',\n",
       "   'patterns': ['distention of abdomen',\n",
       "    'stomach bloated',\n",
       "    'abdomen bloated',\n",
       "    'Abdomen expanded']},\n",
       "  {'tag': 'dizziness',\n",
       "   'patterns': ['dizziness',\n",
       "    'feeling dizzy',\n",
       "    'experiencing dizziness',\n",
       "    'woozy',\n",
       "    'unsteady',\n",
       "    'weak']},\n",
       "  {'tag': 'drying_and_tingling_lips',\n",
       "   'patterns': ['drying and tingling lips',\n",
       "    'lips tingly',\n",
       "    'dry lips',\n",
       "    'Dried lips',\n",
       "    'Tingling lips']},\n",
       "  {'tag': 'enlarged_thyroid',\n",
       "   'patterns': ['thyroid large', 'enlarged thyroid']},\n",
       "  {'tag': 'excessive_hunger',\n",
       "   'patterns': ['excessive hunger',\n",
       "    'really hungry',\n",
       "    'always hungry',\n",
       "    'hunger']},\n",
       "  {'tag': 'extra_marital_contacts',\n",
       "   'patterns': ['extra marital contacts', 'affair', 'sex with stranger']},\n",
       "  {'tag': 'family_history',\n",
       "   'patterns': ['family history',\n",
       "    'family disease history',\n",
       "    'parents also sick',\n",
       "    'genetically at risk',\n",
       "    'genetics']},\n",
       "  {'tag': 'fast_heart_rate',\n",
       "   'patterns': ['fast heart rate',\n",
       "    'heart beating fast',\n",
       "    'high heart rate',\n",
       "    'Heart really pumping']},\n",
       "  {'tag': 'fatigue',\n",
       "   'patterns': ['really tired', 'fatigued', 'lack energy', 'no energy']},\n",
       "  {'tag': 'fluid_overload',\n",
       "   'patterns': ['fluid overload', 'swolen', 'body swelling', 'gained weight']},\n",
       "  {'tag': 'foul_smell_ofurine',\n",
       "   'patterns': ['foul smell ofurine',\n",
       "    'urine smells bad',\n",
       "    'pee smells',\n",
       "    'pee stinks']},\n",
       "  {'tag': 'headache',\n",
       "   'patterns': ['headache', 'have headache', 'head hurts', 'pain in head']},\n",
       "  {'tag': 'high_fever',\n",
       "   'patterns': ['high fever', 'bad fever', 'really high temperature']},\n",
       "  {'tag': 'hip_joint_pain',\n",
       "   'patterns': ['hip joint pain', 'hip hurts', 'pain in hip']},\n",
       "  {'tag': 'history_of_alcohol_consumption',\n",
       "   'patterns': ['history of alcohol consumption',\n",
       "    'drink a lot',\n",
       "    'drinking for long time',\n",
       "    'History drinking']},\n",
       "  {'tag': 'increased_appetite',\n",
       "   'patterns': ['appetite', 'want to eat', 'Appetite increased']},\n",
       "  {'tag': 'indigestion',\n",
       "   'patterns': ['indigestion',\n",
       "    'discomfort in stomach',\n",
       "    'upset stomach',\n",
       "    'Upper abdomen pain']},\n",
       "  {'tag': 'inflammatory_nails',\n",
       "   'patterns': ['inflammatory nails',\n",
       "    'nails inflamed',\n",
       "    'nails infected',\n",
       "    'Inflammation around nails']},\n",
       "  {'tag': 'internal_itching',\n",
       "   'patterns': ['itching on inside', 'internal itching', 'itches inside']},\n",
       "  {'tag': 'irregular_sugar_level',\n",
       "   'patterns': ['irregular sugar level',\n",
       "    'sugar level off',\n",
       "    'Sugar level not normal',\n",
       "    'low sugar',\n",
       "    'high sugar']},\n",
       "  {'tag': 'irritability',\n",
       "   'patterns': ['irritability',\n",
       "    'easily irritable',\n",
       "    'annoyed',\n",
       "    'Easily frustrated']},\n",
       "  {'tag': 'irritation_in_anus',\n",
       "   'patterns': ['irritation in anus',\n",
       "    'butt itchy',\n",
       "    'Anus itchy',\n",
       "    'irritated anus']},\n",
       "  {'tag': 'itching',\n",
       "   'patterns': ['itching', 'itchy everywhere', 'body itching']},\n",
       "  {'tag': 'joint_pain',\n",
       "   'patterns': ['joints hurt',\n",
       "    'ankles hurt',\n",
       "    'Pain joints',\n",
       "    'Pain arm joints']},\n",
       "  {'tag': 'knee_pain', 'patterns': ['knees hurt', 'Pain in knee']},\n",
       "  {'tag': 'lack_of_concentration',\n",
       "   'patterns': ['lack of concentration',\n",
       "    \"can't concentrate\",\n",
       "    'lack concentration',\n",
       "    \"Can't focus\"]},\n",
       "  {'tag': 'lethargy',\n",
       "   'patterns': ['lethargy',\n",
       "    'lack energy',\n",
       "    'enthusiasm low',\n",
       "    'sleepy',\n",
       "    'sluggy']},\n",
       "  {'tag': 'loss_of_appetite',\n",
       "   'patterns': [\"can't eat\",\n",
       "    \"don't have appetite\",\n",
       "    'lost appetite',\n",
       "    'loss of appetite']},\n",
       "  {'tag': 'loss_of_balance',\n",
       "   'patterns': ['loss of balance',\n",
       "    'losing balance',\n",
       "    'unsteady',\n",
       "    'dizzy',\n",
       "    'vertigo']},\n",
       "  {'tag': 'loss_of_smell',\n",
       "   'patterns': [\"can't smell anything\",\n",
       "    \"don't feel sense smell\",\n",
       "    'no smell',\n",
       "    'no sense of smell']},\n",
       "  {'tag': 'malaise',\n",
       "   'patterns': [\"don't feel well\",\n",
       "    \"don't feel good\",\n",
       "    'not comfortable',\n",
       "    'Everything hurts']},\n",
       "  {'tag': 'mild_fever',\n",
       "   'patterns': ['fever',\n",
       "    'small fever',\n",
       "    'little fevered',\n",
       "    'highered temperature']},\n",
       "  {'tag': 'mood_swings',\n",
       "   'patterns': ['changes in mood', 'mood swings', 'Mood changes often']},\n",
       "  {'tag': 'movement_stiffness',\n",
       "   'patterns': ['slow',\n",
       "    \"can't move normally\",\n",
       "    'muscles stiff',\n",
       "    'move stiffly']},\n",
       "  {'tag': 'mucoid_sputum',\n",
       "   'patterns': ['coughing up mucus', 'mucous sputum', 'mucous phlegm']},\n",
       "  {'tag': 'muscle_pain',\n",
       "   'patterns': ['muscles ache', 'Muscles hurt', 'pain muscles']},\n",
       "  {'tag': 'muscle_wasting',\n",
       "   'patterns': ['loosing muscle',\n",
       "    'muscle atrophy',\n",
       "    'muscles getting smaller']},\n",
       "  {'tag': 'muscle_weakness',\n",
       "   'patterns': ['muscles weak', 'weakness in muscles']},\n",
       "  {'tag': 'nausea',\n",
       "   'patterns': ['nausea', 'nauseous', 'like vomit', 'feel sick']},\n",
       "  {'tag': 'neck_pain',\n",
       "   'patterns': ['neck hurts', 'pain in neck', 'neck stiff']},\n",
       "  {'tag': 'nodal_skin_eruptions',\n",
       "   'patterns': ['really bad rash', 'Swollen rash', 'bad skin']},\n",
       "  {'tag': 'obesity', 'patterns': ['high body weight', 'really fat', 'obese']},\n",
       "  {'tag': 'pain_behind_the_eyes',\n",
       "   'patterns': ['eyes hurt', 'Back of eyes hurt', 'hurts behind eyes']},\n",
       "  {'tag': 'pain_during_bowel_movements',\n",
       "   'patterns': ['hurts when go toilet', 'Pooping hurts', 'pain when pooping']},\n",
       "  {'tag': 'pain_in_anal_region',\n",
       "   'patterns': ['anus hurts', 'butt hurts', 'pain anal region', 'pain anus']},\n",
       "  {'tag': 'painful_walking',\n",
       "   'patterns': [\"can't walk\",\n",
       "    'hurts when walk',\n",
       "    'Walking hurts',\n",
       "    'Walking painful']},\n",
       "  {'tag': 'palpitations',\n",
       "   'patterns': ['palpitations',\n",
       "    'heart skipped beat',\n",
       "    'Heart extra beats',\n",
       "    'heart racing',\n",
       "    'Heart pounding']},\n",
       "  {'tag': 'passage_of_gases', 'patterns': ['burp', 'fart', 'gassy']},\n",
       "  {'tag': 'patches_in_throat',\n",
       "   'patterns': ['patchy throat',\n",
       "    'tonsils hurt',\n",
       "    'throat hurts',\n",
       "    'Patches throat']},\n",
       "  {'tag': 'phlegm',\n",
       "   'patterns': ['phlegm',\n",
       "    'phlegm bad',\n",
       "    'cough lot of phlegm',\n",
       "    'coughing up mucus']},\n",
       "  {'tag': 'polyuria',\n",
       "   'patterns': ['pee a lot', 'Pee lot of liquid', 'lot of urine']},\n",
       "  {'tag': 'prominent_veins_on_calf',\n",
       "   'patterns': ['legs show veins',\n",
       "    'Veins on legs prominent',\n",
       "    'Veins showing',\n",
       "    'Veins on legs emphasized']},\n",
       "  {'tag': 'puffy_face_and_eyes',\n",
       "   'patterns': ['puffy face and eyes', 'puffy face', 'Eyes puffy']},\n",
       "  {'tag': 'pus_filled_pimples',\n",
       "   'patterns': ['pus filled pimples',\n",
       "    'acne',\n",
       "    'pimples with pus',\n",
       "    'bad pimples',\n",
       "    'Yellow pus pimples']},\n",
       "  {'tag': 'receiving_blood_transfusion',\n",
       "   'patterns': ['need blood transfusion',\n",
       "    'got transfusion',\n",
       "    'receiving blood transfusion']},\n",
       "  {'tag': 'receiving_unsterile_injections',\n",
       "   'patterns': ['unsterile injection',\n",
       "    'weird injection',\n",
       "    'injected shady needle']},\n",
       "  {'tag': 'red_sore_around_nose',\n",
       "   'patterns': ['red sore around nose',\n",
       "    'rash around nose',\n",
       "    'red bumps nose',\n",
       "    'nose sore red']},\n",
       "  {'tag': 'yellowish_skin',\n",
       "   'patterns': ['yellowish skin',\n",
       "    'skin turned yellow',\n",
       "    'Jaundice',\n",
       "    'yellow color skin',\n",
       "    'yellowish pigmentation',\n",
       "    'greenish pigmentation']},\n",
       "  {'tag': 'yellowing_of_eyes',\n",
       "   'patterns': ['yellowing of eyes',\n",
       "    'eyes turned yellow',\n",
       "    'Jaundice',\n",
       "    'Whites of eyes yellow',\n",
       "    'yellowing eyes']},\n",
       "  {'tag': 'yellow_urine',\n",
       "   'patterns': ['urine yellow',\n",
       "    'urine bright yellow',\n",
       "    'urine dark yellow',\n",
       "    'color of urine deep yellow']},\n",
       "  {'tag': 'yellow_crust_ooze',\n",
       "   'patterns': ['yellow crust ooze',\n",
       "    'yellowish crust',\n",
       "    'Yellow fluid oozed scab',\n",
       "    'Yellow liquid from scab',\n",
       "    'Oozy scabs']},\n",
       "  {'tag': 'weight_loss',\n",
       "   'patterns': ['weight loss',\n",
       "    'lost lot of weight',\n",
       "    'getting thinner',\n",
       "    'skinnier',\n",
       "    'lost weight unintentionally']},\n",
       "  {'tag': 'weight_gain',\n",
       "   'patterns': ['weight gain',\n",
       "    'gained lot of weight',\n",
       "    'getting fatter without eating',\n",
       "    'gained weight significantly']},\n",
       "  {'tag': 'weakness_of_one_body_side',\n",
       "   'patterns': ['weakness on one part of body',\n",
       "    'left side weaker than right side',\n",
       "    'right side weaker than left side',\n",
       "    'Hemiparesis',\n",
       "    'partial weakness']},\n",
       "  {'tag': 'weakness_in_limbs',\n",
       "   'patterns': ['weakness in limbs',\n",
       "    'limbs weak',\n",
       "    'leg weakness',\n",
       "    'arm weakness',\n",
       "    'weakness in arms and hands']},\n",
       "  {'tag': 'watering_from_eyes',\n",
       "   'patterns': ['watering eyes',\n",
       "    'teary eyes',\n",
       "    'excessive tearing',\n",
       "    'watering from eyes']},\n",
       "  {'tag': 'vomiting',\n",
       "   'patterns': ['had vomited',\n",
       "    'vomiting',\n",
       "    'threw up',\n",
       "    'puked',\n",
       "    'puking',\n",
       "    'throwing up']},\n",
       "  {'tag': 'visual_disturbances',\n",
       "   'patterns': ['visual disturbances',\n",
       "    'flashing before eyes',\n",
       "    'double vision',\n",
       "    'brief attacks of blindness',\n",
       "    \"can't see clearly\"]},\n",
       "  {'tag': 'unsteadiness',\n",
       "   'patterns': ['unsteadiness',\n",
       "    'fell lost balance',\n",
       "    'like going to fall',\n",
       "    'unsteady',\n",
       "    'imbalanced']},\n",
       "  {'tag': 'ulcers_on_tongue',\n",
       "   'patterns': ['ulcers on tongue',\n",
       "    'tongue ulcers',\n",
       "    'tongue is sore',\n",
       "    'ulcers in mouth',\n",
       "    'mouth ulcers']},\n",
       "  {'tag': 'toxic_look_(typhos)',\n",
       "   'patterns': ['toxic look typhos', \"don't look so good\"]},\n",
       "  {'tag': 'throat_irritation',\n",
       "   'patterns': ['throat irritation',\n",
       "    'throat feels sore',\n",
       "    'sore throat',\n",
       "    'throat is irritated',\n",
       "    'scratchiness in throat']},\n",
       "  {'tag': 'swollen_legs',\n",
       "   'patterns': ['swollen legs',\n",
       "    'legs swollen',\n",
       "    'feet swollen',\n",
       "    'legs feet swelling']},\n",
       "  {'tag': 'swollen_extremeties',\n",
       "   'patterns': ['swollen extremeties',\n",
       "    'swollen legs',\n",
       "    'swollen arms',\n",
       "    'feet swollen',\n",
       "    'hands swollen',\n",
       "    'swelling legs',\n",
       "    'swelling arms',\n",
       "    'feet swelling',\n",
       "    'hands swelling']},\n",
       "  {'tag': 'swollen_blood_vessels',\n",
       "   'patterns': ['blood vessels swollen', 'blood vessels thicker']},\n",
       "  {'tag': 'swelling_of_stomach',\n",
       "   'patterns': ['stomach swolen',\n",
       "    'swelling of stomach',\n",
       "    'abdomen swollen',\n",
       "    'swelling of abdomen']},\n",
       "  {'tag': 'swelling_joints',\n",
       "   'patterns': ['joints swollen',\n",
       "    'swelling in joints',\n",
       "    'ankles swollen',\n",
       "    'ankles swelling',\n",
       "    'wrists swollen',\n",
       "    'wrists swelling']},\n",
       "  {'tag': 'swelled_lymph_nodes',\n",
       "   'patterns': ['lymph nodes swollen',\n",
       "    'swelling in lymph nodes',\n",
       "    'lymph nodes bigger',\n",
       "    'swollen lymph glands']},\n",
       "  {'tag': 'sweating',\n",
       "   'patterns': ['sweating',\n",
       "    'sweating too much',\n",
       "    'sweating a lot',\n",
       "    'sweating excessively']},\n",
       "  {'tag': 'sunken_eyes',\n",
       "   'patterns': ['sunken eyes',\n",
       "    'eyes tired',\n",
       "    'dark circles under eyes',\n",
       "    'skin around eyes dark',\n",
       "    'skin around eyes thinner',\n",
       "    'bags under eyes']},\n",
       "  {'tag': 'stomach_pain',\n",
       "   'patterns': ['stomach hurts',\n",
       "    'stomach pain',\n",
       "    'abdomen pain',\n",
       "    'abdomen hurts']},\n",
       "  {'tag': 'stomach_bleeding',\n",
       "   'patterns': ['stomach bleeds', 'bleeding in stomach']},\n",
       "  {'tag': 'stiff_neck',\n",
       "   'patterns': ['neck hurts', 'stiff neck', 'neck pain', 'neck stiffness']},\n",
       "  {'tag': 'spotting_urination',\n",
       "   'patterns': ['spotting urination',\n",
       "    'spotted blood urine',\n",
       "    'little blood urine']},\n",
       "  {'tag': 'spinning_movements',\n",
       "   'patterns': ['spinning movements', 'like spinning', 'like moving']},\n",
       "  {'tag': 'small_dents_in_nails',\n",
       "   'patterns': ['dents in nails',\n",
       "    'nail pitting',\n",
       "    'nail pits',\n",
       "    'nails different']},\n",
       "  {'tag': 'slurred_speech',\n",
       "   'patterns': ['slurred speech',\n",
       "    'speech problems',\n",
       "    \"can't pronounce words\",\n",
       "    'mumbling',\n",
       "    'difficulty speaking',\n",
       "    \"don't understand when speaking\"]},\n",
       "  {'tag': 'skin_rash',\n",
       "   'patterns': ['rash',\n",
       "    'lesions on skin',\n",
       "    'skin red and itchy',\n",
       "    'skin rash',\n",
       "    'skin dry']},\n",
       "  {'tag': 'skin_peeling', 'patterns': ['skin peeling']},\n",
       "  {'tag': 'sinus_pressure',\n",
       "   'patterns': ['pressure in sinuses',\n",
       "    'sinus pressure',\n",
       "    'pain in sinuses',\n",
       "    'sinuses congested',\n",
       "    'pain in forehead',\n",
       "    'pain between eyes']},\n",
       "  {'tag': 'silver_like_dusting',\n",
       "   'patterns': ['silver like dusting', 'skin blue', 'skin grey']},\n",
       "  {'tag': 'shivering', 'patterns': ['shivering', 'chills']},\n",
       "  {'tag': 'scurring', 'patterns': ['scurring']},\n",
       "  {'tag': 'rusty_sputum',\n",
       "   'patterns': ['rusty sputum',\n",
       "    'brown phlegm',\n",
       "    'rusty brown sputum',\n",
       "    'brown sputum']},\n",
       "  {'tag': 'runny_nose',\n",
       "   'patterns': ['runny nose',\n",
       "    'watery runny nose',\n",
       "    'watery secretion from nose',\n",
       "    'Fluid from nose']},\n",
       "  {'tag': 'restlessness',\n",
       "   'patterns': ['restlessness',\n",
       "    'need to move around',\n",
       "    'inner tension',\n",
       "    \"can't calm mind\",\n",
       "    'cramps in extremities',\n",
       "    'urge to move']},\n",
       "  {'tag': 'redness_of_eyes',\n",
       "   'patterns': ['redness of eyes',\n",
       "    'red eyes',\n",
       "    'eyes are red',\n",
       "    'eyes are dry']},\n",
       "  {'tag': 'red_spots_over_body',\n",
       "   'patterns': ['red spots over body',\n",
       "    'red spots on body',\n",
       "    'small patches on body']}]}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('intents_short.json', 'r') as f:\n",
    "    intents = json.load(f)\n",
    "    \n",
    "intents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words = []\n",
    "tags = []\n",
    "xy = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "\n",
    "for intent in intents['intents']:\n",
    "    tag = intent['tag']\n",
    "    tags.append(tag)\n",
    "    for pattern in intent['patterns']:\n",
    "        w = nltk.word_tokenize(pattern)\n",
    "        all_words.extend(w)\n",
    "        xy.append( (w, tag) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['abdominal', 'pain'], 'abdominal_pain'),\n",
       " (['abdomen', 'hurts'], 'abdominal_pain'),\n",
       " (['pain', 'in', 'stomach'], 'abdominal_pain'),\n",
       " (['pain', 'in', 'abdomen'], 'abdominal_pain'),\n",
       " (['abnormal', 'menstruation'], 'abnormal_menstruation'),\n",
       " (['heavy', 'period'], 'abnormal_menstruation'),\n",
       " (['Heavy', 'flow'], 'abnormal_menstruation'),\n",
       " (['Period', 'lasts', 'longer'], 'abnormal_menstruation'),\n",
       " (['period', 'painful'], 'abnormal_menstruation'),\n",
       " (['strong', 'menstrual', 'pain'], 'abnormal_menstruation'),\n",
       " (['Menstrual', 'cramps', 'strong'], 'abnormal_menstruation'),\n",
       " (['acid', 'reflux'], 'acidity'),\n",
       " (['acidity', 'problems'], 'acidity'),\n",
       " (['heartburn'], 'acidity'),\n",
       " (['acute', 'liver', 'failure'], 'acute_liver_failure'),\n",
       " (['liver', 'hurts'], 'acute_liver_failure'),\n",
       " (['pain', 'around', 'liver'], 'acute_liver_failure'),\n",
       " (['Upper', 'right', 'abdomen', 'hurts'], 'acute_liver_failure'),\n",
       " (['altered', 'sensorium'], 'altered_sensorium'),\n",
       " (['ca', \"n't\", 'think', 'clearly'], 'altered_sensorium'),\n",
       " (['hard', 'to', 'think'], 'altered_sensorium'),\n",
       " (['unable', 'to', 'concentrate'], 'altered_sensorium'),\n",
       " (['anxiety'], 'anxiety'),\n",
       " (['anxious'], 'anxiety'),\n",
       " (['really', 'worried'], 'anxiety'),\n",
       " (['nervous'], 'anxiety'),\n",
       " (['back', 'hurts'], 'back_pain'),\n",
       " (['back', 'pain'], 'back_pain'),\n",
       " (['pain', 'in', 'back'], 'back_pain'),\n",
       " (['belly', 'hurts'], 'belly_pain'),\n",
       " (['belly', 'pain'], 'belly_pain'),\n",
       " (['pain', 'in', 'belly'], 'belly_pain'),\n",
       " (['Stomach', 'hurts'], 'belly_pain'),\n",
       " (['Stomach', 'pain'], 'belly_pain'),\n",
       " (['blackheads'], 'blackheads'),\n",
       " (['lot', 'of', 'blackheads'], 'blackheads'),\n",
       " (['Blackheads', 'on', 'face'], 'blackheads'),\n",
       " (['Blackheads', 'on', 'body'], 'blackheads'),\n",
       " (['bladder', 'discomfort'], 'bladder_discomfort'),\n",
       " (['bladder', 'hurts'], 'bladder_discomfort'),\n",
       " (['discomfort', 'in', 'bladder'], 'bladder_discomfort'),\n",
       " (['Pressure', 'in', 'bladder'], 'bladder_discomfort'),\n",
       " (['blisters'], 'blister'),\n",
       " (['arms', 'are', 'blistered'], 'blister'),\n",
       " (['feet', 'are', 'blistered'], 'blister'),\n",
       " (['Body', 'is', 'blistered'], 'blister'),\n",
       " (['blood', 'in', 'sputum'], 'blood_in_sputum'),\n",
       " (['coughing', 'up', 'blood'], 'blood_in_sputum'),\n",
       " (['Blood', 'while', 'caughing'], 'blood_in_sputum'),\n",
       " (['blood', 'in', 'stool'], 'bloody_stool'),\n",
       " (['pooped', 'out', 'blood'], 'bloody_stool'),\n",
       " (['bloody', 'stool'], 'bloody_stool'),\n",
       " (['blurred', 'and', 'distorted', 'vision'], 'blurred_and_distorted_vision'),\n",
       " (['ca', \"n't\", 'see', 'clearly'], 'blurred_and_distorted_vision'),\n",
       " (['ca', \"n't\", 'see'], 'blurred_and_distorted_vision'),\n",
       " (['blurry', 'vision'], 'blurred_and_distorted_vision'),\n",
       " (['Vision', 'distorted'], 'blurred_and_distorted_vision'),\n",
       " (['breathlessness'], 'breathlessness'),\n",
       " (['difficulty', 'breathing'], 'breathlessness'),\n",
       " (['out', 'of', 'breath'], 'breathlessness'),\n",
       " (['feel', 'winded'], 'breathlessness'),\n",
       " (['Short', 'of', 'breath'], 'breathlessness'),\n",
       " (['brittle', 'nails'], 'brittle_nails'),\n",
       " (['nails', 'weak'], 'brittle_nails'),\n",
       " (['Nails', 'brittle'], 'brittle_nails'),\n",
       " (['Nails', 'easily', 'broken'], 'brittle_nails'),\n",
       " (['bruising'], 'bruising'),\n",
       " (['bruises'], 'bruising'),\n",
       " (['easily', 'bruised'], 'bruising'),\n",
       " (['lot', 'of', 'bruises'], 'bruising'),\n",
       " (['burning', 'micturition'], 'burning_micturition'),\n",
       " (['hurts', 'when', 'pee'], 'burning_micturition'),\n",
       " (['burns', 'when', 'pee'], 'burning_micturition'),\n",
       " (['Burning', 'sensation', 'peeing'], 'burning_micturition'),\n",
       " (['chest', 'hurts'], 'chest_pain'),\n",
       " (['chest', 'pain'], 'chest_pain'),\n",
       " (['pain', 'in', 'chest'], 'chest_pain'),\n",
       " (['chills'], 'chills'),\n",
       " (['cold'], 'chills'),\n",
       " (['cold', 'hands', 'and', 'feets'], 'cold_hands_and_feets'),\n",
       " (['hands', 'are', 'cold'], 'cold_hands_and_feets'),\n",
       " (['feet', 'are', 'cold'], 'cold_hands_and_feets'),\n",
       " (['cold', 'hands'], 'cold_hands_and_feets'),\n",
       " (['cold', 'feet'], 'cold_hands_and_feets'),\n",
       " (['coma'], 'coma'),\n",
       " (['experienced', 'coma'], 'coma'),\n",
       " (['was', 'in', 'coma'], 'coma'),\n",
       " (['ca', \"n't\", 'poop'], 'constipation'),\n",
       " (['constipated'], 'constipation'),\n",
       " (['am', 'constipated'], 'constipation'),\n",
       " (['have', \"n't\", 'pooped', 'in', 'a', 'while'], 'constipation'),\n",
       " (['continuous', 'feel', 'of', 'urine'], 'continuous_feel_of_urine'),\n",
       " (['have', 'to', 'pee'], 'continuous_feel_of_urine'),\n",
       " (['Urge', 'to', 'pee'], 'continuous_feel_of_urine'),\n",
       " (['pee', 'constantly'], 'continuous_feel_of_urine'),\n",
       " (['continuous', 'sneezing'], 'continuous_sneezing'),\n",
       " (['sneezing', 'all', 'the', 'time'], 'continuous_sneezing'),\n",
       " (['sneeze', 'a', 'lot'], 'continuous_sneezing'),\n",
       " (['Always', 'sneezing'], 'continuous_sneezing'),\n",
       " (['sneeze', 'constantly'], 'continuous_sneezing'),\n",
       " (['cough'], 'cough'),\n",
       " (['coughing', 'all', 'the', 'time'], 'cough'),\n",
       " (['cough', 'a', 'lot'], 'cough'),\n",
       " (['Always', 'caughing'], 'cough'),\n",
       " (['cough', 'constantly'], 'cough'),\n",
       " (['congestion'], 'congestion'),\n",
       " (['ca', \"n't\", 'breathe'], 'congestion'),\n",
       " (['nose', 'stuffy'], 'congestion'),\n",
       " (['nose', 'full'], 'congestion'),\n",
       " (['nasal', 'congestion'], 'congestion'),\n",
       " (['cramps'], 'cramps'),\n",
       " (['cramping', 'up'], 'cramps'),\n",
       " (['urine', 'dark'], 'dark_urine'),\n",
       " (['Darker', 'color', 'urine'], 'dark_urine'),\n",
       " (['pee', 'dark'], 'dark_urine'),\n",
       " (['dehydrated'], 'dehydration'),\n",
       " (['experiencing', 'dehydration'], 'dehydration'),\n",
       " (['depression'], 'depression'),\n",
       " (['have', 'depression'], 'depression'),\n",
       " (['feel', 'depressed'], 'depression'),\n",
       " (['diarrhoea'], 'diarrhoea'),\n",
       " (['always', 'on', 'toilet'], 'diarrhoea'),\n",
       " (['poop', 'often'], 'diarrhoea'),\n",
       " (['poo', 'a', 'lot'], 'diarrhoea'),\n",
       " (['dischromic', 'patches'], 'dischromic_patches'),\n",
       " (['lost', 'color', 'on', 'skin'], 'dischromic_patches'),\n",
       " (['patches', 'different', 'color'], 'dischromic_patches'),\n",
       " (['skin', 'patched'], 'dischromic_patches'),\n",
       " (['distention', 'of', 'abdomen'], 'distention_of_abdomen'),\n",
       " (['stomach', 'bloated'], 'distention_of_abdomen'),\n",
       " (['abdomen', 'bloated'], 'distention_of_abdomen'),\n",
       " (['Abdomen', 'expanded'], 'distention_of_abdomen'),\n",
       " (['dizziness'], 'dizziness'),\n",
       " (['feeling', 'dizzy'], 'dizziness'),\n",
       " (['experiencing', 'dizziness'], 'dizziness'),\n",
       " (['woozy'], 'dizziness'),\n",
       " (['unsteady'], 'dizziness'),\n",
       " (['weak'], 'dizziness'),\n",
       " (['drying', 'and', 'tingling', 'lips'], 'drying_and_tingling_lips'),\n",
       " (['lips', 'tingly'], 'drying_and_tingling_lips'),\n",
       " (['dry', 'lips'], 'drying_and_tingling_lips'),\n",
       " (['Dried', 'lips'], 'drying_and_tingling_lips'),\n",
       " (['Tingling', 'lips'], 'drying_and_tingling_lips'),\n",
       " (['thyroid', 'large'], 'enlarged_thyroid'),\n",
       " (['enlarged', 'thyroid'], 'enlarged_thyroid'),\n",
       " (['excessive', 'hunger'], 'excessive_hunger'),\n",
       " (['really', 'hungry'], 'excessive_hunger'),\n",
       " (['always', 'hungry'], 'excessive_hunger'),\n",
       " (['hunger'], 'excessive_hunger'),\n",
       " (['extra', 'marital', 'contacts'], 'extra_marital_contacts'),\n",
       " (['affair'], 'extra_marital_contacts'),\n",
       " (['sex', 'with', 'stranger'], 'extra_marital_contacts'),\n",
       " (['family', 'history'], 'family_history'),\n",
       " (['family', 'disease', 'history'], 'family_history'),\n",
       " (['parents', 'also', 'sick'], 'family_history'),\n",
       " (['genetically', 'at', 'risk'], 'family_history'),\n",
       " (['genetics'], 'family_history'),\n",
       " (['fast', 'heart', 'rate'], 'fast_heart_rate'),\n",
       " (['heart', 'beating', 'fast'], 'fast_heart_rate'),\n",
       " (['high', 'heart', 'rate'], 'fast_heart_rate'),\n",
       " (['Heart', 'really', 'pumping'], 'fast_heart_rate'),\n",
       " (['really', 'tired'], 'fatigue'),\n",
       " (['fatigued'], 'fatigue'),\n",
       " (['lack', 'energy'], 'fatigue'),\n",
       " (['no', 'energy'], 'fatigue'),\n",
       " (['fluid', 'overload'], 'fluid_overload'),\n",
       " (['swolen'], 'fluid_overload'),\n",
       " (['body', 'swelling'], 'fluid_overload'),\n",
       " (['gained', 'weight'], 'fluid_overload'),\n",
       " (['foul', 'smell', 'ofurine'], 'foul_smell_ofurine'),\n",
       " (['urine', 'smells', 'bad'], 'foul_smell_ofurine'),\n",
       " (['pee', 'smells'], 'foul_smell_ofurine'),\n",
       " (['pee', 'stinks'], 'foul_smell_ofurine'),\n",
       " (['headache'], 'headache'),\n",
       " (['have', 'headache'], 'headache'),\n",
       " (['head', 'hurts'], 'headache'),\n",
       " (['pain', 'in', 'head'], 'headache'),\n",
       " (['high', 'fever'], 'high_fever'),\n",
       " (['bad', 'fever'], 'high_fever'),\n",
       " (['really', 'high', 'temperature'], 'high_fever'),\n",
       " (['hip', 'joint', 'pain'], 'hip_joint_pain'),\n",
       " (['hip', 'hurts'], 'hip_joint_pain'),\n",
       " (['pain', 'in', 'hip'], 'hip_joint_pain'),\n",
       " (['history', 'of', 'alcohol', 'consumption'],\n",
       "  'history_of_alcohol_consumption'),\n",
       " (['drink', 'a', 'lot'], 'history_of_alcohol_consumption'),\n",
       " (['drinking', 'for', 'long', 'time'], 'history_of_alcohol_consumption'),\n",
       " (['History', 'drinking'], 'history_of_alcohol_consumption'),\n",
       " (['appetite'], 'increased_appetite'),\n",
       " (['want', 'to', 'eat'], 'increased_appetite'),\n",
       " (['Appetite', 'increased'], 'increased_appetite'),\n",
       " (['indigestion'], 'indigestion'),\n",
       " (['discomfort', 'in', 'stomach'], 'indigestion'),\n",
       " (['upset', 'stomach'], 'indigestion'),\n",
       " (['Upper', 'abdomen', 'pain'], 'indigestion'),\n",
       " (['inflammatory', 'nails'], 'inflammatory_nails'),\n",
       " (['nails', 'inflamed'], 'inflammatory_nails'),\n",
       " (['nails', 'infected'], 'inflammatory_nails'),\n",
       " (['Inflammation', 'around', 'nails'], 'inflammatory_nails'),\n",
       " (['itching', 'on', 'inside'], 'internal_itching'),\n",
       " (['internal', 'itching'], 'internal_itching'),\n",
       " (['itches', 'inside'], 'internal_itching'),\n",
       " (['irregular', 'sugar', 'level'], 'irregular_sugar_level'),\n",
       " (['sugar', 'level', 'off'], 'irregular_sugar_level'),\n",
       " (['Sugar', 'level', 'not', 'normal'], 'irregular_sugar_level'),\n",
       " (['low', 'sugar'], 'irregular_sugar_level'),\n",
       " (['high', 'sugar'], 'irregular_sugar_level'),\n",
       " (['irritability'], 'irritability'),\n",
       " (['easily', 'irritable'], 'irritability'),\n",
       " (['annoyed'], 'irritability'),\n",
       " (['Easily', 'frustrated'], 'irritability'),\n",
       " (['irritation', 'in', 'anus'], 'irritation_in_anus'),\n",
       " (['butt', 'itchy'], 'irritation_in_anus'),\n",
       " (['Anus', 'itchy'], 'irritation_in_anus'),\n",
       " (['irritated', 'anus'], 'irritation_in_anus'),\n",
       " (['itching'], 'itching'),\n",
       " (['itchy', 'everywhere'], 'itching'),\n",
       " (['body', 'itching'], 'itching'),\n",
       " (['joints', 'hurt'], 'joint_pain'),\n",
       " (['ankles', 'hurt'], 'joint_pain'),\n",
       " (['Pain', 'joints'], 'joint_pain'),\n",
       " (['Pain', 'arm', 'joints'], 'joint_pain'),\n",
       " (['knees', 'hurt'], 'knee_pain'),\n",
       " (['Pain', 'in', 'knee'], 'knee_pain'),\n",
       " (['lack', 'of', 'concentration'], 'lack_of_concentration'),\n",
       " (['ca', \"n't\", 'concentrate'], 'lack_of_concentration'),\n",
       " (['lack', 'concentration'], 'lack_of_concentration'),\n",
       " (['Ca', \"n't\", 'focus'], 'lack_of_concentration'),\n",
       " (['lethargy'], 'lethargy'),\n",
       " (['lack', 'energy'], 'lethargy'),\n",
       " (['enthusiasm', 'low'], 'lethargy'),\n",
       " (['sleepy'], 'lethargy'),\n",
       " (['sluggy'], 'lethargy'),\n",
       " (['ca', \"n't\", 'eat'], 'loss_of_appetite'),\n",
       " (['do', \"n't\", 'have', 'appetite'], 'loss_of_appetite'),\n",
       " (['lost', 'appetite'], 'loss_of_appetite'),\n",
       " (['loss', 'of', 'appetite'], 'loss_of_appetite'),\n",
       " (['loss', 'of', 'balance'], 'loss_of_balance'),\n",
       " (['losing', 'balance'], 'loss_of_balance'),\n",
       " (['unsteady'], 'loss_of_balance'),\n",
       " (['dizzy'], 'loss_of_balance'),\n",
       " (['vertigo'], 'loss_of_balance'),\n",
       " (['ca', \"n't\", 'smell', 'anything'], 'loss_of_smell'),\n",
       " (['do', \"n't\", 'feel', 'sense', 'smell'], 'loss_of_smell'),\n",
       " (['no', 'smell'], 'loss_of_smell'),\n",
       " (['no', 'sense', 'of', 'smell'], 'loss_of_smell'),\n",
       " (['do', \"n't\", 'feel', 'well'], 'malaise'),\n",
       " (['do', \"n't\", 'feel', 'good'], 'malaise'),\n",
       " (['not', 'comfortable'], 'malaise'),\n",
       " (['Everything', 'hurts'], 'malaise'),\n",
       " (['fever'], 'mild_fever'),\n",
       " (['small', 'fever'], 'mild_fever'),\n",
       " (['little', 'fevered'], 'mild_fever'),\n",
       " (['highered', 'temperature'], 'mild_fever'),\n",
       " (['changes', 'in', 'mood'], 'mood_swings'),\n",
       " (['mood', 'swings'], 'mood_swings'),\n",
       " (['Mood', 'changes', 'often'], 'mood_swings'),\n",
       " (['slow'], 'movement_stiffness'),\n",
       " (['ca', \"n't\", 'move', 'normally'], 'movement_stiffness'),\n",
       " (['muscles', 'stiff'], 'movement_stiffness'),\n",
       " (['move', 'stiffly'], 'movement_stiffness'),\n",
       " (['coughing', 'up', 'mucus'], 'mucoid_sputum'),\n",
       " (['mucous', 'sputum'], 'mucoid_sputum'),\n",
       " (['mucous', 'phlegm'], 'mucoid_sputum'),\n",
       " (['muscles', 'ache'], 'muscle_pain'),\n",
       " (['Muscles', 'hurt'], 'muscle_pain'),\n",
       " (['pain', 'muscles'], 'muscle_pain'),\n",
       " (['loosing', 'muscle'], 'muscle_wasting'),\n",
       " (['muscle', 'atrophy'], 'muscle_wasting'),\n",
       " (['muscles', 'getting', 'smaller'], 'muscle_wasting'),\n",
       " (['muscles', 'weak'], 'muscle_weakness'),\n",
       " (['weakness', 'in', 'muscles'], 'muscle_weakness'),\n",
       " (['nausea'], 'nausea'),\n",
       " (['nauseous'], 'nausea'),\n",
       " (['like', 'vomit'], 'nausea'),\n",
       " (['feel', 'sick'], 'nausea'),\n",
       " (['neck', 'hurts'], 'neck_pain'),\n",
       " (['pain', 'in', 'neck'], 'neck_pain'),\n",
       " (['neck', 'stiff'], 'neck_pain'),\n",
       " (['really', 'bad', 'rash'], 'nodal_skin_eruptions'),\n",
       " (['Swollen', 'rash'], 'nodal_skin_eruptions'),\n",
       " (['bad', 'skin'], 'nodal_skin_eruptions'),\n",
       " (['high', 'body', 'weight'], 'obesity'),\n",
       " (['really', 'fat'], 'obesity'),\n",
       " (['obese'], 'obesity'),\n",
       " (['eyes', 'hurt'], 'pain_behind_the_eyes'),\n",
       " (['Back', 'of', 'eyes', 'hurt'], 'pain_behind_the_eyes'),\n",
       " (['hurts', 'behind', 'eyes'], 'pain_behind_the_eyes'),\n",
       " (['hurts', 'when', 'go', 'toilet'], 'pain_during_bowel_movements'),\n",
       " (['Pooping', 'hurts'], 'pain_during_bowel_movements'),\n",
       " (['pain', 'when', 'pooping'], 'pain_during_bowel_movements'),\n",
       " (['anus', 'hurts'], 'pain_in_anal_region'),\n",
       " (['butt', 'hurts'], 'pain_in_anal_region'),\n",
       " (['pain', 'anal', 'region'], 'pain_in_anal_region'),\n",
       " (['pain', 'anus'], 'pain_in_anal_region'),\n",
       " (['ca', \"n't\", 'walk'], 'painful_walking'),\n",
       " (['hurts', 'when', 'walk'], 'painful_walking'),\n",
       " (['Walking', 'hurts'], 'painful_walking'),\n",
       " (['Walking', 'painful'], 'painful_walking'),\n",
       " (['palpitations'], 'palpitations'),\n",
       " (['heart', 'skipped', 'beat'], 'palpitations'),\n",
       " (['Heart', 'extra', 'beats'], 'palpitations'),\n",
       " (['heart', 'racing'], 'palpitations'),\n",
       " (['Heart', 'pounding'], 'palpitations'),\n",
       " (['burp'], 'passage_of_gases'),\n",
       " (['fart'], 'passage_of_gases'),\n",
       " (['gassy'], 'passage_of_gases'),\n",
       " (['patchy', 'throat'], 'patches_in_throat'),\n",
       " (['tonsils', 'hurt'], 'patches_in_throat'),\n",
       " (['throat', 'hurts'], 'patches_in_throat'),\n",
       " (['Patches', 'throat'], 'patches_in_throat'),\n",
       " (['phlegm'], 'phlegm'),\n",
       " (['phlegm', 'bad'], 'phlegm'),\n",
       " (['cough', 'lot', 'of', 'phlegm'], 'phlegm'),\n",
       " (['coughing', 'up', 'mucus'], 'phlegm'),\n",
       " (['pee', 'a', 'lot'], 'polyuria'),\n",
       " (['Pee', 'lot', 'of', 'liquid'], 'polyuria'),\n",
       " (['lot', 'of', 'urine'], 'polyuria'),\n",
       " (['legs', 'show', 'veins'], 'prominent_veins_on_calf'),\n",
       " (['Veins', 'on', 'legs', 'prominent'], 'prominent_veins_on_calf'),\n",
       " (['Veins', 'showing'], 'prominent_veins_on_calf'),\n",
       " (['Veins', 'on', 'legs', 'emphasized'], 'prominent_veins_on_calf'),\n",
       " (['puffy', 'face', 'and', 'eyes'], 'puffy_face_and_eyes'),\n",
       " (['puffy', 'face'], 'puffy_face_and_eyes'),\n",
       " (['Eyes', 'puffy'], 'puffy_face_and_eyes'),\n",
       " (['pus', 'filled', 'pimples'], 'pus_filled_pimples'),\n",
       " (['acne'], 'pus_filled_pimples'),\n",
       " (['pimples', 'with', 'pus'], 'pus_filled_pimples'),\n",
       " (['bad', 'pimples'], 'pus_filled_pimples'),\n",
       " (['Yellow', 'pus', 'pimples'], 'pus_filled_pimples'),\n",
       " (['need', 'blood', 'transfusion'], 'receiving_blood_transfusion'),\n",
       " (['got', 'transfusion'], 'receiving_blood_transfusion'),\n",
       " (['receiving', 'blood', 'transfusion'], 'receiving_blood_transfusion'),\n",
       " (['unsterile', 'injection'], 'receiving_unsterile_injections'),\n",
       " (['weird', 'injection'], 'receiving_unsterile_injections'),\n",
       " (['injected', 'shady', 'needle'], 'receiving_unsterile_injections'),\n",
       " (['red', 'sore', 'around', 'nose'], 'red_sore_around_nose'),\n",
       " (['rash', 'around', 'nose'], 'red_sore_around_nose'),\n",
       " (['red', 'bumps', 'nose'], 'red_sore_around_nose'),\n",
       " (['nose', 'sore', 'red'], 'red_sore_around_nose'),\n",
       " (['yellowish', 'skin'], 'yellowish_skin'),\n",
       " (['skin', 'turned', 'yellow'], 'yellowish_skin'),\n",
       " (['Jaundice'], 'yellowish_skin'),\n",
       " (['yellow', 'color', 'skin'], 'yellowish_skin'),\n",
       " (['yellowish', 'pigmentation'], 'yellowish_skin'),\n",
       " (['greenish', 'pigmentation'], 'yellowish_skin'),\n",
       " (['yellowing', 'of', 'eyes'], 'yellowing_of_eyes'),\n",
       " (['eyes', 'turned', 'yellow'], 'yellowing_of_eyes'),\n",
       " (['Jaundice'], 'yellowing_of_eyes'),\n",
       " (['Whites', 'of', 'eyes', 'yellow'], 'yellowing_of_eyes'),\n",
       " (['yellowing', 'eyes'], 'yellowing_of_eyes'),\n",
       " (['urine', 'yellow'], 'yellow_urine'),\n",
       " (['urine', 'bright', 'yellow'], 'yellow_urine'),\n",
       " (['urine', 'dark', 'yellow'], 'yellow_urine'),\n",
       " (['color', 'of', 'urine', 'deep', 'yellow'], 'yellow_urine'),\n",
       " (['yellow', 'crust', 'ooze'], 'yellow_crust_ooze'),\n",
       " (['yellowish', 'crust'], 'yellow_crust_ooze'),\n",
       " (['Yellow', 'fluid', 'oozed', 'scab'], 'yellow_crust_ooze'),\n",
       " (['Yellow', 'liquid', 'from', 'scab'], 'yellow_crust_ooze'),\n",
       " (['Oozy', 'scabs'], 'yellow_crust_ooze'),\n",
       " (['weight', 'loss'], 'weight_loss'),\n",
       " (['lost', 'lot', 'of', 'weight'], 'weight_loss'),\n",
       " (['getting', 'thinner'], 'weight_loss'),\n",
       " (['skinnier'], 'weight_loss'),\n",
       " (['lost', 'weight', 'unintentionally'], 'weight_loss'),\n",
       " (['weight', 'gain'], 'weight_gain'),\n",
       " (['gained', 'lot', 'of', 'weight'], 'weight_gain'),\n",
       " (['getting', 'fatter', 'without', 'eating'], 'weight_gain'),\n",
       " (['gained', 'weight', 'significantly'], 'weight_gain'),\n",
       " (['weakness', 'on', 'one', 'part', 'of', 'body'],\n",
       "  'weakness_of_one_body_side'),\n",
       " (['left', 'side', 'weaker', 'than', 'right', 'side'],\n",
       "  'weakness_of_one_body_side'),\n",
       " (['right', 'side', 'weaker', 'than', 'left', 'side'],\n",
       "  'weakness_of_one_body_side'),\n",
       " (['Hemiparesis'], 'weakness_of_one_body_side'),\n",
       " (['partial', 'weakness'], 'weakness_of_one_body_side'),\n",
       " (['weakness', 'in', 'limbs'], 'weakness_in_limbs'),\n",
       " (['limbs', 'weak'], 'weakness_in_limbs'),\n",
       " (['leg', 'weakness'], 'weakness_in_limbs'),\n",
       " (['arm', 'weakness'], 'weakness_in_limbs'),\n",
       " (['weakness', 'in', 'arms', 'and', 'hands'], 'weakness_in_limbs'),\n",
       " (['watering', 'eyes'], 'watering_from_eyes'),\n",
       " (['teary', 'eyes'], 'watering_from_eyes'),\n",
       " (['excessive', 'tearing'], 'watering_from_eyes'),\n",
       " (['watering', 'from', 'eyes'], 'watering_from_eyes'),\n",
       " (['had', 'vomited'], 'vomiting'),\n",
       " (['vomiting'], 'vomiting'),\n",
       " (['threw', 'up'], 'vomiting'),\n",
       " (['puked'], 'vomiting'),\n",
       " (['puking'], 'vomiting'),\n",
       " (['throwing', 'up'], 'vomiting'),\n",
       " (['visual', 'disturbances'], 'visual_disturbances'),\n",
       " (['flashing', 'before', 'eyes'], 'visual_disturbances'),\n",
       " (['double', 'vision'], 'visual_disturbances'),\n",
       " (['brief', 'attacks', 'of', 'blindness'], 'visual_disturbances'),\n",
       " (['ca', \"n't\", 'see', 'clearly'], 'visual_disturbances'),\n",
       " (['unsteadiness'], 'unsteadiness'),\n",
       " (['fell', 'lost', 'balance'], 'unsteadiness'),\n",
       " (['like', 'going', 'to', 'fall'], 'unsteadiness'),\n",
       " (['unsteady'], 'unsteadiness'),\n",
       " (['imbalanced'], 'unsteadiness'),\n",
       " (['ulcers', 'on', 'tongue'], 'ulcers_on_tongue'),\n",
       " (['tongue', 'ulcers'], 'ulcers_on_tongue'),\n",
       " (['tongue', 'is', 'sore'], 'ulcers_on_tongue'),\n",
       " (['ulcers', 'in', 'mouth'], 'ulcers_on_tongue'),\n",
       " (['mouth', 'ulcers'], 'ulcers_on_tongue'),\n",
       " (['toxic', 'look', 'typhos'], 'toxic_look_(typhos)'),\n",
       " (['do', \"n't\", 'look', 'so', 'good'], 'toxic_look_(typhos)'),\n",
       " (['throat', 'irritation'], 'throat_irritation'),\n",
       " (['throat', 'feels', 'sore'], 'throat_irritation'),\n",
       " (['sore', 'throat'], 'throat_irritation'),\n",
       " (['throat', 'is', 'irritated'], 'throat_irritation'),\n",
       " (['scratchiness', 'in', 'throat'], 'throat_irritation'),\n",
       " (['swollen', 'legs'], 'swollen_legs'),\n",
       " (['legs', 'swollen'], 'swollen_legs'),\n",
       " (['feet', 'swollen'], 'swollen_legs'),\n",
       " (['legs', 'feet', 'swelling'], 'swollen_legs'),\n",
       " (['swollen', 'extremeties'], 'swollen_extremeties'),\n",
       " (['swollen', 'legs'], 'swollen_extremeties'),\n",
       " (['swollen', 'arms'], 'swollen_extremeties'),\n",
       " (['feet', 'swollen'], 'swollen_extremeties'),\n",
       " (['hands', 'swollen'], 'swollen_extremeties'),\n",
       " (['swelling', 'legs'], 'swollen_extremeties'),\n",
       " (['swelling', 'arms'], 'swollen_extremeties'),\n",
       " (['feet', 'swelling'], 'swollen_extremeties'),\n",
       " (['hands', 'swelling'], 'swollen_extremeties'),\n",
       " (['blood', 'vessels', 'swollen'], 'swollen_blood_vessels'),\n",
       " (['blood', 'vessels', 'thicker'], 'swollen_blood_vessels'),\n",
       " (['stomach', 'swolen'], 'swelling_of_stomach'),\n",
       " (['swelling', 'of', 'stomach'], 'swelling_of_stomach'),\n",
       " (['abdomen', 'swollen'], 'swelling_of_stomach'),\n",
       " (['swelling', 'of', 'abdomen'], 'swelling_of_stomach'),\n",
       " (['joints', 'swollen'], 'swelling_joints'),\n",
       " (['swelling', 'in', 'joints'], 'swelling_joints'),\n",
       " (['ankles', 'swollen'], 'swelling_joints'),\n",
       " (['ankles', 'swelling'], 'swelling_joints'),\n",
       " (['wrists', 'swollen'], 'swelling_joints'),\n",
       " (['wrists', 'swelling'], 'swelling_joints'),\n",
       " (['lymph', 'nodes', 'swollen'], 'swelled_lymph_nodes'),\n",
       " (['swelling', 'in', 'lymph', 'nodes'], 'swelled_lymph_nodes'),\n",
       " (['lymph', 'nodes', 'bigger'], 'swelled_lymph_nodes'),\n",
       " (['swollen', 'lymph', 'glands'], 'swelled_lymph_nodes'),\n",
       " (['sweating'], 'sweating'),\n",
       " (['sweating', 'too', 'much'], 'sweating'),\n",
       " (['sweating', 'a', 'lot'], 'sweating'),\n",
       " (['sweating', 'excessively'], 'sweating'),\n",
       " (['sunken', 'eyes'], 'sunken_eyes'),\n",
       " (['eyes', 'tired'], 'sunken_eyes'),\n",
       " (['dark', 'circles', 'under', 'eyes'], 'sunken_eyes'),\n",
       " (['skin', 'around', 'eyes', 'dark'], 'sunken_eyes'),\n",
       " (['skin', 'around', 'eyes', 'thinner'], 'sunken_eyes'),\n",
       " (['bags', 'under', 'eyes'], 'sunken_eyes'),\n",
       " (['stomach', 'hurts'], 'stomach_pain'),\n",
       " (['stomach', 'pain'], 'stomach_pain'),\n",
       " (['abdomen', 'pain'], 'stomach_pain'),\n",
       " (['abdomen', 'hurts'], 'stomach_pain'),\n",
       " (['stomach', 'bleeds'], 'stomach_bleeding'),\n",
       " (['bleeding', 'in', 'stomach'], 'stomach_bleeding'),\n",
       " (['neck', 'hurts'], 'stiff_neck'),\n",
       " (['stiff', 'neck'], 'stiff_neck'),\n",
       " (['neck', 'pain'], 'stiff_neck'),\n",
       " (['neck', 'stiffness'], 'stiff_neck'),\n",
       " (['spotting', 'urination'], 'spotting_urination'),\n",
       " (['spotted', 'blood', 'urine'], 'spotting_urination'),\n",
       " (['little', 'blood', 'urine'], 'spotting_urination'),\n",
       " (['spinning', 'movements'], 'spinning_movements'),\n",
       " (['like', 'spinning'], 'spinning_movements'),\n",
       " (['like', 'moving'], 'spinning_movements'),\n",
       " (['dents', 'in', 'nails'], 'small_dents_in_nails'),\n",
       " (['nail', 'pitting'], 'small_dents_in_nails'),\n",
       " (['nail', 'pits'], 'small_dents_in_nails'),\n",
       " (['nails', 'different'], 'small_dents_in_nails'),\n",
       " (['slurred', 'speech'], 'slurred_speech'),\n",
       " (['speech', 'problems'], 'slurred_speech'),\n",
       " (['ca', \"n't\", 'pronounce', 'words'], 'slurred_speech'),\n",
       " (['mumbling'], 'slurred_speech'),\n",
       " (['difficulty', 'speaking'], 'slurred_speech'),\n",
       " (['do', \"n't\", 'understand', 'when', 'speaking'], 'slurred_speech'),\n",
       " (['rash'], 'skin_rash'),\n",
       " (['lesions', 'on', 'skin'], 'skin_rash'),\n",
       " (['skin', 'red', 'and', 'itchy'], 'skin_rash'),\n",
       " (['skin', 'rash'], 'skin_rash'),\n",
       " (['skin', 'dry'], 'skin_rash'),\n",
       " (['skin', 'peeling'], 'skin_peeling'),\n",
       " (['pressure', 'in', 'sinuses'], 'sinus_pressure'),\n",
       " (['sinus', 'pressure'], 'sinus_pressure'),\n",
       " (['pain', 'in', 'sinuses'], 'sinus_pressure'),\n",
       " (['sinuses', 'congested'], 'sinus_pressure'),\n",
       " (['pain', 'in', 'forehead'], 'sinus_pressure'),\n",
       " (['pain', 'between', 'eyes'], 'sinus_pressure'),\n",
       " (['silver', 'like', 'dusting'], 'silver_like_dusting'),\n",
       " (['skin', 'blue'], 'silver_like_dusting'),\n",
       " (['skin', 'grey'], 'silver_like_dusting'),\n",
       " (['shivering'], 'shivering'),\n",
       " (['chills'], 'shivering'),\n",
       " (['scurring'], 'scurring'),\n",
       " (['rusty', 'sputum'], 'rusty_sputum'),\n",
       " (['brown', 'phlegm'], 'rusty_sputum'),\n",
       " (['rusty', 'brown', 'sputum'], 'rusty_sputum'),\n",
       " (['brown', 'sputum'], 'rusty_sputum'),\n",
       " (['runny', 'nose'], 'runny_nose'),\n",
       " (['watery', 'runny', 'nose'], 'runny_nose'),\n",
       " (['watery', 'secretion', 'from', 'nose'], 'runny_nose'),\n",
       " (['Fluid', 'from', 'nose'], 'runny_nose'),\n",
       " (['restlessness'], 'restlessness'),\n",
       " (['need', 'to', 'move', 'around'], 'restlessness'),\n",
       " (['inner', 'tension'], 'restlessness'),\n",
       " (['ca', \"n't\", 'calm', 'mind'], 'restlessness'),\n",
       " (['cramps', 'in', 'extremities'], 'restlessness'),\n",
       " (['urge', 'to', 'move'], 'restlessness'),\n",
       " (['redness', 'of', 'eyes'], 'redness_of_eyes'),\n",
       " (['red', 'eyes'], 'redness_of_eyes'),\n",
       " (['eyes', 'are', 'red'], 'redness_of_eyes'),\n",
       " (['eyes', 'are', 'dry'], 'redness_of_eyes'),\n",
       " (['red', 'spots', 'over', 'body'], 'red_spots_over_body'),\n",
       " (['red', 'spots', 'on', 'body'], 'red_spots_over_body'),\n",
       " (['small', 'patches', 'on', 'body'], 'red_spots_over_body')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# synthetic dataset created for neural network validation\n",
    "xy_test = [\n",
    "    (['ca', \"n't\", 'think', 'straight'], 'altered_sensorium'),\n",
    "    (['suffer', 'from', 'anxeity'], 'anxiety'),\n",
    "    (['suffer', 'from', 'anxeity'], 'anxiety'),\n",
    "    (['bloody', 'poop'], 'bloody_stool'),\n",
    "    (['blurred', 'vision'], 'blurred_and_distorted_vision'),\n",
    "    (['ca', \"n't\", 'breathe'], 'breathlessness'),\n",
    "    (['Yellow', 'liquid', 'pimple'], 'yellow_crust_ooze'),\n",
    "    (['lost', 'weight'], 'weight_loss'),\n",
    "    (['side', 'weaker'], 'weakness_of_one_body_side'),\n",
    "    (['watering', 'eyes'], 'watering_from_eyes'),\n",
    "    (['brief', 'blindness'], 'visual_disturbances'),\n",
    "    (['throat', 'hurts'], 'throat_irritation'),\n",
    "    (['extremities', 'swelling'], 'swollen_extremeties'),\n",
    "    (['swollen', 'lymph', 'nodes'], 'swelled_lymph_nodes'),\n",
    "    (['dark', 'under', 'eyes'], 'sunken_eyes'),\n",
    "    (['stomach', 'blood'], 'stomach_bleeding'),\n",
    "    (['blood', 'urine'], 'spotting_urination'),\n",
    "    (['sinuses', 'hurt'], 'sinus_pressure'),\n",
    "    (['watery', 'from', 'nose'], 'runny_nose'),\n",
    "    (['have', 'to', 'move'], 'restlessness'),\n",
    "    (['red', 'patches', 'body'], 'red_spots_over_body'),\n",
    "    (['sneeze'], 'continuous_sneezing'),\n",
    "    (['coughing'], 'cough'),\n",
    "    (['skin', 'patches'], 'dischromic_patches'),\n",
    "    (['skin', 'bruised'], 'bruising'),\n",
    "    (['burning', 'pee'], 'burning_micturition'),\n",
    "    (['hurts', 'pee'], 'burning_micturition'),\n",
    "    (['Burning', 'sensation'], 'burning_micturition'),\n",
    "    (['chest', 'pressure'], 'chest_pain'),\n",
    "    (['pain', 'butt'], 'pain_in_anal_region'),\n",
    "    (['heart', 'bad', 'beat'], 'palpitations'),\n",
    "    (['fart', 'lot'], 'passage_of_gases'),\n",
    "    (['cough', 'phlegm'], 'phlegm'),\n",
    "    (['lot', 'urine'], 'polyuria'),\n",
    "    (['Veins', 'bigger'], 'prominent_veins_on_calf'),\n",
    "    (['Veins', 'emphasized'], 'prominent_veins_on_calf'),\n",
    "    (['yellow', 'pimples'], 'pus_filled_pimples'),\n",
    "    (['red', 'nose'], 'red_sore_around_nose'),\n",
    "    (['skin', 'yellow'], 'yellowish_skin'),\n",
    "    (['eyes', 'yellow'], 'yellowing_of_eyes'),\n",
    "    (['large', 'thyroid'], 'enlarged_thyroid'),\n",
    "    (['really', 'hunger'], 'excessive_hunger'),\n",
    "    (['always', 'hungry'], 'excessive_hunger'),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(xy_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'abdomen',\n",
       " 'abdomin',\n",
       " 'abnorm',\n",
       " 'ach',\n",
       " 'acid',\n",
       " 'acn',\n",
       " 'acut',\n",
       " 'affair',\n",
       " 'alcohol',\n",
       " 'all',\n",
       " 'also',\n",
       " 'alter',\n",
       " 'alway',\n",
       " 'am',\n",
       " 'anal',\n",
       " 'and',\n",
       " 'ankl',\n",
       " 'annoy',\n",
       " 'anu',\n",
       " 'anxieti',\n",
       " 'anxiou',\n",
       " 'anyth',\n",
       " 'appetit',\n",
       " 'are',\n",
       " 'arm',\n",
       " 'around',\n",
       " 'at',\n",
       " 'atrophi',\n",
       " 'attack',\n",
       " 'back',\n",
       " 'bad',\n",
       " 'bag',\n",
       " 'balanc',\n",
       " 'beat',\n",
       " 'befor',\n",
       " 'behind',\n",
       " 'belli',\n",
       " 'between',\n",
       " 'bigger',\n",
       " 'blackhead',\n",
       " 'bladder',\n",
       " 'bleed',\n",
       " 'blind',\n",
       " 'blister',\n",
       " 'bloat',\n",
       " 'blood',\n",
       " 'bloodi',\n",
       " 'blue',\n",
       " 'blur',\n",
       " 'blurri',\n",
       " 'bodi',\n",
       " 'breath',\n",
       " 'breathless',\n",
       " 'brief',\n",
       " 'bright',\n",
       " 'brittl',\n",
       " 'broken',\n",
       " 'brown',\n",
       " 'bruis',\n",
       " 'bump',\n",
       " 'burn',\n",
       " 'burp',\n",
       " 'butt',\n",
       " 'ca',\n",
       " 'calm',\n",
       " 'caugh',\n",
       " 'chang',\n",
       " 'chest',\n",
       " 'chill',\n",
       " 'circl',\n",
       " 'clearli',\n",
       " 'cold',\n",
       " 'color',\n",
       " 'coma',\n",
       " 'comfort',\n",
       " 'concentr',\n",
       " 'congest',\n",
       " 'constantli',\n",
       " 'constip',\n",
       " 'consumpt',\n",
       " 'contact',\n",
       " 'continu',\n",
       " 'cough',\n",
       " 'cramp',\n",
       " 'crust',\n",
       " 'dark',\n",
       " 'darker',\n",
       " 'deep',\n",
       " 'dehydr',\n",
       " 'dent',\n",
       " 'depress',\n",
       " 'diarrhoea',\n",
       " 'differ',\n",
       " 'difficulti',\n",
       " 'dischrom',\n",
       " 'discomfort',\n",
       " 'diseas',\n",
       " 'distent',\n",
       " 'distort',\n",
       " 'disturb',\n",
       " 'dizzi',\n",
       " 'do',\n",
       " 'doubl',\n",
       " 'dri',\n",
       " 'drink',\n",
       " 'dust',\n",
       " 'easili',\n",
       " 'eat',\n",
       " 'emphas',\n",
       " 'energi',\n",
       " 'enlarg',\n",
       " 'enthusiasm',\n",
       " 'everyth',\n",
       " 'everywher',\n",
       " 'excess',\n",
       " 'expand',\n",
       " 'experienc',\n",
       " 'extra',\n",
       " 'extrem',\n",
       " 'extremeti',\n",
       " 'eye',\n",
       " 'face',\n",
       " 'failur',\n",
       " 'fall',\n",
       " 'famili',\n",
       " 'fart',\n",
       " 'fast',\n",
       " 'fat',\n",
       " 'fatigu',\n",
       " 'fatter',\n",
       " 'feel',\n",
       " 'feet',\n",
       " 'fell',\n",
       " 'fever',\n",
       " 'fill',\n",
       " 'flash',\n",
       " 'flow',\n",
       " 'fluid',\n",
       " 'focu',\n",
       " 'for',\n",
       " 'forehead',\n",
       " 'foul',\n",
       " 'from',\n",
       " 'frustrat',\n",
       " 'full',\n",
       " 'gain',\n",
       " 'gassi',\n",
       " 'genet',\n",
       " 'get',\n",
       " 'gland',\n",
       " 'go',\n",
       " 'good',\n",
       " 'got',\n",
       " 'greenish',\n",
       " 'grey',\n",
       " 'had',\n",
       " 'hand',\n",
       " 'hard',\n",
       " 'have',\n",
       " 'head',\n",
       " 'headach',\n",
       " 'heart',\n",
       " 'heartburn',\n",
       " 'heavi',\n",
       " 'hemiparesi',\n",
       " 'high',\n",
       " 'higher',\n",
       " 'hip',\n",
       " 'histori',\n",
       " 'hunger',\n",
       " 'hungri',\n",
       " 'hurt',\n",
       " 'imbalanc',\n",
       " 'in',\n",
       " 'increas',\n",
       " 'indigest',\n",
       " 'infect',\n",
       " 'inflam',\n",
       " 'inflamm',\n",
       " 'inflammatori',\n",
       " 'inject',\n",
       " 'inner',\n",
       " 'insid',\n",
       " 'intern',\n",
       " 'irregular',\n",
       " 'irrit',\n",
       " 'is',\n",
       " 'itch',\n",
       " 'itchi',\n",
       " 'jaundic',\n",
       " 'joint',\n",
       " 'knee',\n",
       " 'lack',\n",
       " 'larg',\n",
       " 'last',\n",
       " 'left',\n",
       " 'leg',\n",
       " 'lesion',\n",
       " 'lethargi',\n",
       " 'level',\n",
       " 'like',\n",
       " 'limb',\n",
       " 'lip',\n",
       " 'liquid',\n",
       " 'littl',\n",
       " 'liver',\n",
       " 'long',\n",
       " 'longer',\n",
       " 'look',\n",
       " 'loos',\n",
       " 'lose',\n",
       " 'loss',\n",
       " 'lost',\n",
       " 'lot',\n",
       " 'low',\n",
       " 'lymph',\n",
       " 'marit',\n",
       " 'menstrual',\n",
       " 'menstruat',\n",
       " 'micturit',\n",
       " 'mind',\n",
       " 'mood',\n",
       " 'mouth',\n",
       " 'move',\n",
       " 'movement',\n",
       " 'much',\n",
       " 'mucou',\n",
       " 'mucu',\n",
       " 'mumbl',\n",
       " 'muscl',\n",
       " \"n't\",\n",
       " 'nail',\n",
       " 'nasal',\n",
       " 'nausea',\n",
       " 'nauseou',\n",
       " 'neck',\n",
       " 'need',\n",
       " 'needl',\n",
       " 'nervou',\n",
       " 'no',\n",
       " 'node',\n",
       " 'normal',\n",
       " 'nose',\n",
       " 'not',\n",
       " 'obes',\n",
       " 'of',\n",
       " 'off',\n",
       " 'often',\n",
       " 'ofurin',\n",
       " 'on',\n",
       " 'one',\n",
       " 'ooz',\n",
       " 'oozi',\n",
       " 'out',\n",
       " 'over',\n",
       " 'overload',\n",
       " 'pain',\n",
       " 'palpit',\n",
       " 'parent',\n",
       " 'part',\n",
       " 'partial',\n",
       " 'patch',\n",
       " 'patchi',\n",
       " 'pee',\n",
       " 'peel',\n",
       " 'period',\n",
       " 'phlegm',\n",
       " 'pigment',\n",
       " 'pimpl',\n",
       " 'pit',\n",
       " 'poo',\n",
       " 'poop',\n",
       " 'pound',\n",
       " 'pressur',\n",
       " 'problem',\n",
       " 'promin',\n",
       " 'pronounc',\n",
       " 'pu',\n",
       " 'puffi',\n",
       " 'puke',\n",
       " 'pump',\n",
       " 'race',\n",
       " 'rash',\n",
       " 'rate',\n",
       " 'realli',\n",
       " 'receiv',\n",
       " 'red',\n",
       " 'reflux',\n",
       " 'region',\n",
       " 'restless',\n",
       " 'right',\n",
       " 'risk',\n",
       " 'runni',\n",
       " 'rusti',\n",
       " 'scab',\n",
       " 'scratchi',\n",
       " 'scur',\n",
       " 'secret',\n",
       " 'see',\n",
       " 'sens',\n",
       " 'sensat',\n",
       " 'sensorium',\n",
       " 'sex',\n",
       " 'shadi',\n",
       " 'shiver',\n",
       " 'short',\n",
       " 'show',\n",
       " 'sick',\n",
       " 'side',\n",
       " 'significantli',\n",
       " 'silver',\n",
       " 'sinu',\n",
       " 'sinus',\n",
       " 'skin',\n",
       " 'skinnier',\n",
       " 'skip',\n",
       " 'sleepi',\n",
       " 'slow',\n",
       " 'sluggi',\n",
       " 'slur',\n",
       " 'small',\n",
       " 'smaller',\n",
       " 'smell',\n",
       " 'sneez',\n",
       " 'so',\n",
       " 'sore',\n",
       " 'speak',\n",
       " 'speech',\n",
       " 'spin',\n",
       " 'spot',\n",
       " 'sputum',\n",
       " 'stiff',\n",
       " 'stiffli',\n",
       " 'stink',\n",
       " 'stomach',\n",
       " 'stool',\n",
       " 'stranger',\n",
       " 'strong',\n",
       " 'stuffi',\n",
       " 'sugar',\n",
       " 'sunken',\n",
       " 'sweat',\n",
       " 'swell',\n",
       " 'swing',\n",
       " 'swolen',\n",
       " 'swollen',\n",
       " 'tear',\n",
       " 'teari',\n",
       " 'temperatur',\n",
       " 'tension',\n",
       " 'than',\n",
       " 'the',\n",
       " 'thicker',\n",
       " 'think',\n",
       " 'thinner',\n",
       " 'threw',\n",
       " 'throat',\n",
       " 'throw',\n",
       " 'thyroid',\n",
       " 'time',\n",
       " 'tingl',\n",
       " 'tingli',\n",
       " 'tire',\n",
       " 'to',\n",
       " 'toilet',\n",
       " 'tongu',\n",
       " 'tonsil',\n",
       " 'too',\n",
       " 'toxic',\n",
       " 'transfus',\n",
       " 'turn',\n",
       " 'typho',\n",
       " 'ulcer',\n",
       " 'unabl',\n",
       " 'under',\n",
       " 'understand',\n",
       " 'unintent',\n",
       " 'unsteadi',\n",
       " 'unsteril',\n",
       " 'up',\n",
       " 'upper',\n",
       " 'upset',\n",
       " 'urg',\n",
       " 'urin',\n",
       " 'vein',\n",
       " 'vertigo',\n",
       " 'vessel',\n",
       " 'vision',\n",
       " 'visual',\n",
       " 'vomit',\n",
       " 'wa',\n",
       " 'walk',\n",
       " 'want',\n",
       " 'water',\n",
       " 'wateri',\n",
       " 'weak',\n",
       " 'weaker',\n",
       " 'weight',\n",
       " 'weird',\n",
       " 'well',\n",
       " 'when',\n",
       " 'while',\n",
       " 'white',\n",
       " 'wind',\n",
       " 'with',\n",
       " 'without',\n",
       " 'woozi',\n",
       " 'word',\n",
       " 'worri',\n",
       " 'wrist',\n",
       " 'yellow',\n",
       " 'yellowish']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "ignore_words = ['?', '!', '.', ',']\n",
    "all_words = [stemmer.stem(w.lower()) for w in all_words if w not in ignore_words]\n",
    "all_words = sorted(set(all_words))\n",
    "all_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abdominal_pain',\n",
       " 'abnormal_menstruation',\n",
       " 'acidity',\n",
       " 'acute_liver_failure',\n",
       " 'altered_sensorium',\n",
       " 'anxiety',\n",
       " 'back_pain',\n",
       " 'belly_pain',\n",
       " 'blackheads',\n",
       " 'bladder_discomfort',\n",
       " 'blister',\n",
       " 'blood_in_sputum',\n",
       " 'bloody_stool',\n",
       " 'blurred_and_distorted_vision',\n",
       " 'breathlessness',\n",
       " 'brittle_nails',\n",
       " 'bruising',\n",
       " 'burning_micturition',\n",
       " 'chest_pain',\n",
       " 'chills',\n",
       " 'cold_hands_and_feets',\n",
       " 'coma',\n",
       " 'congestion',\n",
       " 'constipation',\n",
       " 'continuous_feel_of_urine',\n",
       " 'continuous_sneezing',\n",
       " 'cough',\n",
       " 'cramps',\n",
       " 'dark_urine',\n",
       " 'dehydration',\n",
       " 'depression',\n",
       " 'diarrhoea',\n",
       " 'dischromic_patches',\n",
       " 'distention_of_abdomen',\n",
       " 'dizziness',\n",
       " 'drying_and_tingling_lips',\n",
       " 'enlarged_thyroid',\n",
       " 'excessive_hunger',\n",
       " 'extra_marital_contacts',\n",
       " 'family_history',\n",
       " 'fast_heart_rate',\n",
       " 'fatigue',\n",
       " 'fluid_overload',\n",
       " 'foul_smell_ofurine',\n",
       " 'headache',\n",
       " 'high_fever',\n",
       " 'hip_joint_pain',\n",
       " 'history_of_alcohol_consumption',\n",
       " 'increased_appetite',\n",
       " 'indigestion',\n",
       " 'inflammatory_nails',\n",
       " 'internal_itching',\n",
       " 'irregular_sugar_level',\n",
       " 'irritability',\n",
       " 'irritation_in_anus',\n",
       " 'itching',\n",
       " 'joint_pain',\n",
       " 'knee_pain',\n",
       " 'lack_of_concentration',\n",
       " 'lethargy',\n",
       " 'loss_of_appetite',\n",
       " 'loss_of_balance',\n",
       " 'loss_of_smell',\n",
       " 'malaise',\n",
       " 'mild_fever',\n",
       " 'mood_swings',\n",
       " 'movement_stiffness',\n",
       " 'mucoid_sputum',\n",
       " 'muscle_pain',\n",
       " 'muscle_wasting',\n",
       " 'muscle_weakness',\n",
       " 'nausea',\n",
       " 'neck_pain',\n",
       " 'nodal_skin_eruptions',\n",
       " 'obesity',\n",
       " 'pain_behind_the_eyes',\n",
       " 'pain_during_bowel_movements',\n",
       " 'pain_in_anal_region',\n",
       " 'painful_walking',\n",
       " 'palpitations',\n",
       " 'passage_of_gases',\n",
       " 'patches_in_throat',\n",
       " 'phlegm',\n",
       " 'polyuria',\n",
       " 'prominent_veins_on_calf',\n",
       " 'puffy_face_and_eyes',\n",
       " 'pus_filled_pimples',\n",
       " 'receiving_blood_transfusion',\n",
       " 'receiving_unsterile_injections',\n",
       " 'red_sore_around_nose',\n",
       " 'red_spots_over_body',\n",
       " 'redness_of_eyes',\n",
       " 'restlessness',\n",
       " 'runny_nose',\n",
       " 'rusty_sputum',\n",
       " 'scurring',\n",
       " 'shivering',\n",
       " 'silver_like_dusting',\n",
       " 'sinus_pressure',\n",
       " 'skin_peeling',\n",
       " 'skin_rash',\n",
       " 'slurred_speech',\n",
       " 'small_dents_in_nails',\n",
       " 'spinning_movements',\n",
       " 'spotting_urination',\n",
       " 'stiff_neck',\n",
       " 'stomach_bleeding',\n",
       " 'stomach_pain',\n",
       " 'sunken_eyes',\n",
       " 'sweating',\n",
       " 'swelled_lymph_nodes',\n",
       " 'swelling_joints',\n",
       " 'swelling_of_stomach',\n",
       " 'swollen_blood_vessels',\n",
       " 'swollen_extremeties',\n",
       " 'swollen_legs',\n",
       " 'throat_irritation',\n",
       " 'toxic_look_(typhos)',\n",
       " 'ulcers_on_tongue',\n",
       " 'unsteadiness',\n",
       " 'visual_disturbances',\n",
       " 'vomiting',\n",
       " 'watering_from_eyes',\n",
       " 'weakness_in_limbs',\n",
       " 'weakness_of_one_body_side',\n",
       " 'weight_gain',\n",
       " 'weight_loss',\n",
       " 'yellow_crust_ooze',\n",
       " 'yellow_urine',\n",
       " 'yellowing_of_eyes',\n",
       " 'yellowish_skin']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = sorted(set(tags))\n",
    "tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- for better understanding, an example is given:\n",
    "     - set of all words is defined as (i, you, he, she, we, they)\n",
    "     - the sentence is given: \"I am pondering while they are sleeping\"\n",
    "     - from that sencence, a bag of words in generated: (1, 0, 0, 0, 0, 1)\n",
    "         - in that sentence, there are two words from the set of all words - _i_ and _they_\n",
    "         - a 1 is put in the place of those words in the set of all words and a 0 everywhere else\n",
    "- in case of the _bag\\_of\\_words_ method, a bag is initially filled with zeros (the length of the bag is always equal to the length of the set of all words)\n",
    "- in the initialized bag of words, the 1's are placeon the indexes of those the words from set of all words that appear in the given sentence \n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "def bag_of_words(tokenized_sentence, all_words):\n",
    "    tokenized_sentence = [stemmer.stem(w.lower()) for w in tokenized_sentence]\n",
    "    \n",
    "    bag = np.zeros(len(all_words), dtype=np.float32)\n",
    "    for idx, w in enumerate(all_words):\n",
    "        if w in tokenized_sentence:\n",
    "            bag[idx] = 1.0\n",
    "    \n",
    "    return bag\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 1., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example of method bag_of_words\n",
    "\n",
    "import numpy as np\n",
    "from nltk_utils import bag_of_words\n",
    "\n",
    "sentence = ['hello', 'how', 'are', 'you']\n",
    "words = ['hi', 'hello', 'I', 'you', 'bye', 'thanks', 'cool']\n",
    "bag_of_words(sentence, words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "for (pattern, tag) in xy:\n",
    "    bag = bag_of_words(pattern, all_words)\n",
    "    X_train.append(bag)\n",
    "    \n",
    "    label = tags.index(tag)\n",
    "    y_train.append(label) \n",
    "    \n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   0,   0,   0,   1,   1,   1,   1,   1,   1,   1,   2,   2,\n",
       "         2,   3,   3,   3,   3,   4,   4,   4,   4,   5,   5,   5,   5,\n",
       "         6,   6,   6,   7,   7,   7,   7,   7,   8,   8,   8,   8,   9,\n",
       "         9,   9,   9,  10,  10,  10,  10,  11,  11,  11,  12,  12,  12,\n",
       "        13,  13,  13,  13,  13,  14,  14,  14,  14,  14,  15,  15,  15,\n",
       "        15,  16,  16,  16,  16,  17,  17,  17,  17,  18,  18,  18,  19,\n",
       "        19,  20,  20,  20,  20,  20,  21,  21,  21,  23,  23,  23,  23,\n",
       "        24,  24,  24,  24,  25,  25,  25,  25,  25,  26,  26,  26,  26,\n",
       "        26,  22,  22,  22,  22,  22,  27,  27,  28,  28,  28,  29,  29,\n",
       "        30,  30,  30,  31,  31,  31,  31,  32,  32,  32,  32,  33,  33,\n",
       "        33,  33,  34,  34,  34,  34,  34,  34,  35,  35,  35,  35,  35,\n",
       "        36,  36,  37,  37,  37,  37,  38,  38,  38,  39,  39,  39,  39,\n",
       "        39,  40,  40,  40,  40,  41,  41,  41,  41,  42,  42,  42,  42,\n",
       "        43,  43,  43,  43,  44,  44,  44,  44,  45,  45,  45,  46,  46,\n",
       "        46,  47,  47,  47,  47,  48,  48,  48,  49,  49,  49,  49,  50,\n",
       "        50,  50,  50,  51,  51,  51,  52,  52,  52,  52,  52,  53,  53,\n",
       "        53,  53,  54,  54,  54,  54,  55,  55,  55,  56,  56,  56,  56,\n",
       "        57,  57,  58,  58,  58,  58,  59,  59,  59,  59,  59,  60,  60,\n",
       "        60,  60,  61,  61,  61,  61,  61,  62,  62,  62,  62,  63,  63,\n",
       "        63,  63,  64,  64,  64,  64,  65,  65,  65,  66,  66,  66,  66,\n",
       "        67,  67,  67,  68,  68,  68,  69,  69,  69,  70,  70,  71,  71,\n",
       "        71,  71,  72,  72,  72,  73,  73,  73,  74,  74,  74,  75,  75,\n",
       "        75,  76,  76,  76,  77,  77,  77,  77,  78,  78,  78,  78,  79,\n",
       "        79,  79,  79,  79,  80,  80,  80,  81,  81,  81,  81,  82,  82,\n",
       "        82,  82,  83,  83,  83,  84,  84,  84,  84,  85,  85,  85,  86,\n",
       "        86,  86,  86,  86,  87,  87,  87,  88,  88,  88,  89,  89,  89,\n",
       "        89, 130, 130, 130, 130, 130, 130, 129, 129, 129, 129, 129, 128,\n",
       "       128, 128, 128, 127, 127, 127, 127, 127, 126, 126, 126, 126, 126,\n",
       "       125, 125, 125, 125, 124, 124, 124, 124, 124, 123, 123, 123, 123,\n",
       "       123, 122, 122, 122, 122, 121, 121, 121, 121, 121, 121, 120, 120,\n",
       "       120, 120, 120, 119, 119, 119, 119, 119, 118, 118, 118, 118, 118,\n",
       "       117, 117, 116, 116, 116, 116, 116, 115, 115, 115, 115, 114, 114,\n",
       "       114, 114, 114, 114, 114, 114, 114, 113, 113, 112, 112, 112, 112,\n",
       "       111, 111, 111, 111, 111, 111, 110, 110, 110, 110, 109, 109, 109,\n",
       "       109, 108, 108, 108, 108, 108, 108, 107, 107, 107, 107, 106, 106,\n",
       "       105, 105, 105, 105, 104, 104, 104, 103, 103, 103, 102, 102, 102,\n",
       "       102, 101, 101, 101, 101, 101, 101, 100, 100, 100, 100, 100,  99,\n",
       "        98,  98,  98,  98,  98,  98,  97,  97,  97,  96,  96,  95,  94,\n",
       "        94,  94,  94,  93,  93,  93,  93,  92,  92,  92,  92,  92,  92,\n",
       "        91,  91,  91,  91,  90,  90,  90])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = []\n",
    "y_test = []\n",
    "\n",
    "for (pattern, tag) in xy_test:\n",
    "    bag = bag_of_words(pattern, all_words)\n",
    "    X_test.append(bag)\n",
    "    \n",
    "    label = tags.index(tag)\n",
    "    y_test.append(label) \n",
    "    \n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,   5,   5,  12,  13,  14, 127, 126, 124, 122, 120, 116, 114,\n",
       "       110, 108, 106, 104,  98,  93,  92,  90,  25,  26,  32,  16,  17,\n",
       "        17,  17,  18,  77,  79,  80,  82,  83,  84,  84,  86,  89, 130,\n",
       "       129,  36,  37,  37])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ChatDataset(Dataset):\n",
    "    def __init__(self, X_data, y_data):\n",
    "        self.n_samples = len(X_data)\n",
    "        self.X_data = X_data\n",
    "        self.y_data = y_data\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        return self.X_data[index], self.y_data[index]\n",
    "    \n",
    "    def __len__(self):\n",
    "        return  self.n_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "import torch.nn as nn\n",
    "\n",
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_classes):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.l1 = nn.Linear(input_size, hidden_size)\n",
    "        self.l2 = nn.Linear(hidden_size, hidden_size)\n",
    "        self.l3 = nn.Linear(hidden_size, num_classes)\n",
    "        self.relu = nn.ReLU() \n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.l1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.l2(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.l3(out)\n",
    "        return out\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nnet import NeuralNet\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "batch_size = 8\n",
    "hidden_size = 8\n",
    "output_size = len(tags) \n",
    "input_size = len(all_words)\n",
    "learning_rates = [0.01, 0.05, 0.1, 0.15]\n",
    "num_epochs = 1000\n",
    "\n",
    "def nn_validation():\n",
    "    dataset_train = ChatDataset(X_train, y_train)\n",
    "    \n",
    "    train_loader = DataLoader(dataset=dataset_train, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "    \n",
    "    device = torch.device('cpu') \n",
    "    model = NeuralNet(input_size, hidden_size, output_size).to(device)\n",
    "\n",
    "    loss_train = []\n",
    "    loss_test = []\n",
    "    \n",
    "    for lr in learning_rates:\n",
    "        # loss and optimizer\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        optimizer = torch.optim.ASGD(model.parameters(), lr=lr)\n",
    "\n",
    "        print(f\"lr: {lr}, train\")\n",
    "        for epoch in range(num_epochs):\n",
    "            for (words, labels) in train_loader:\n",
    "                words = words.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                outputs = model(words)\n",
    "                loss = criterion(outputs, labels)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "            if (epoch + 1) % (num_epochs / 2) == 0:\n",
    "                print(f'epoch {epoch + 1}/{num_epochs}, loss = {loss.item():.4f}')\n",
    "\n",
    "        print(f'final loss = {loss.item():.4f}')\n",
    "        loss_train.append(loss.item())\n",
    "        \n",
    "        y_predicted = []\n",
    "        \n",
    "        for x in X_test:\n",
    "            \n",
    "            x = x.reshape(1, x.shape[0])\n",
    "            x = torch.from_numpy(x)\n",
    "\n",
    "            output = model(x)\n",
    "            _, predicted = torch.max(output, dim=1)\n",
    "            y_pred = predicted.item()\n",
    "            y_predicted.append(y_pred)\n",
    "            \n",
    "        \n",
    "        print(\"y_predicted:\", y_predicted)\n",
    "        y_predicted = np.array(y_predicted)\n",
    "        \n",
    "        loss_test.append(accuracy_score(y_test, y_predicted))\n",
    "        print()\n",
    "    \n",
    "    return loss_train, loss_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.01, train\n",
      "epoch 500/1000, loss = 0.2861\n",
      "epoch 1000/1000, loss = 0.0233\n",
      "final loss = 0.0233\n",
      "y_predicted: [4, 96, 96, 96, 120, 22, 10, 126, 34, 122, 23, 81, 114, 110, 108, 36, 17, 98, 93, 92, 90, 25, 26, 32, 80, 17, 56, 29, 80, 77, 79, 79, 82, 83, 125, 78, 13, 89, 130, 129, 36, 37, 37]\n",
      "\n",
      "lr: 0.05, train\n",
      "epoch 500/1000, loss = 0.0278\n",
      "epoch 1000/1000, loss = 0.0057\n",
      "final loss = 0.0057\n",
      "y_predicted: [4, 99, 99, 99, 13, 22, 130, 126, 34, 122, 120, 81, 114, 110, 108, 45, 105, 98, 93, 92, 100, 25, 26, 32, 80, 17, 17, 29, 18, 77, 26, 26, 82, 83, 126, 87, 130, 89, 130, 129, 36, 37, 37]\n",
      "\n",
      "lr: 0.1, train\n",
      "epoch 500/1000, loss = 0.0038\n",
      "epoch 1000/1000, loss = 0.0211\n",
      "final loss = 0.0211\n",
      "y_predicted: [13, 74, 74, 3, 13, 22, 1, 126, 124, 122, 59, 81, 114, 110, 108, 45, 104, 98, 93, 92, 100, 25, 26, 32, 9, 17, 17, 76, 18, 77, 26, 80, 82, 83, 87, 84, 13, 89, 130, 129, 36, 37, 37]\n",
      "\n",
      "lr: 0.15, train\n",
      "epoch 500/1000, loss = 0.0010\n",
      "epoch 1000/1000, loss = 0.0080\n",
      "final loss = 0.0080\n",
      "y_predicted: [4, 31, 31, 3, 13, 22, 1, 126, 124, 122, 120, 81, 114, 110, 108, 45, 104, 98, 93, 92, 100, 25, 26, 32, 47, 17, 17, 76, 18, 77, 26, 80, 82, 83, 84, 84, 13, 89, 130, 129, 36, 37, 37]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_errors, test_errors = nn_validation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "plt.title(\"Train and test errors for different learning rates\")\n",
    "plt.plot(learning_rates, train_errors, c='purple', label='Training error')\n",
    "plt.plot(learning_rates, test_errors, c='orange', label='Testing error')\n",
    "plt.legend()\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 100/1000, loss = 4.7768\n",
      "epoch 200/1000, loss = 4.6867\n",
      "epoch 300/1000, loss = 4.3395\n",
      "epoch 400/1000, loss = 2.6974\n",
      "epoch 500/1000, loss = 2.9778\n",
      "epoch 600/1000, loss = 2.3033\n",
      "epoch 700/1000, loss = 1.1747\n",
      "epoch 800/1000, loss = 1.4283\n",
      "epoch 900/1000, loss = 0.0406\n",
      "epoch 1000/1000, loss = 0.1783\n",
      "final loss = 0.1783\n"
     ]
    }
   ],
   "source": [
    "from nnet import NeuralNet\n",
    "\n",
    "batch_size = 8\n",
    "hidden_size = 8\n",
    "output_size = len(tags) \n",
    "input_size = len(all_words)\n",
    "learning_rate = 0.01\n",
    "num_epochs = 1000\n",
    "\n",
    "dataset = ChatDataset(X_train, y_train)\n",
    "train_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "device = torch.device('cpu') \n",
    "model = NeuralNet(input_size, hidden_size, output_size).to(device)\n",
    "\n",
    "# loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.ASGD(model.parameters(), lr=learning_rate)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for (words, labels) in train_loader:\n",
    "        words = words.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        outputs = model(words)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "    if (epoch + 1) % (num_epochs / 10) == 0:\n",
    "        print(f'epoch {epoch + 1}/{num_epochs}, loss = {loss.item():.4f}')\n",
    "\n",
    "print(f'final loss = {loss.item():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"model_state\": model.state_dict(),\n",
    "    \"input_size\": input_size,\n",
    "    \"output_size\": output_size,\n",
    "    \"hidden_size\": hidden_size,\n",
    "    \"all_words\": all_words,\n",
    "    \"tags\": tags\n",
    "}\n",
    "\n",
    "FILE = \"models/data.pth\"\n",
    "torch.save(data, FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symptom prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prob: tensor(0.9788, grad_fn=<SelectBackward>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'headache'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"My head hurts\"\n",
    "sentence = nltk.word_tokenize(sentence)\n",
    "X = bag_of_words(sentence, all_words)\n",
    "X = X.reshape(1, X.shape[0])\n",
    "X = torch.from_numpy(X)\n",
    "\n",
    "output = model(X)\n",
    "_, predicted = torch.max(output, dim=1)\n",
    "tag = tags[predicted.item()]\n",
    "\n",
    "probs = torch.softmax(output, dim=1)\n",
    "prob = probs[0][predicted.item()]\n",
    "\n",
    "print(\"prob:\", prob)\n",
    "\n",
    "tag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming disease data\n",
    "\n",
    "This part of the notebook shows the transformations over data related to diseases and symptoms. For each unique symptom, a new column was created in the dataframe. If symptom is typical for a paticular disease, then the column related to that symptom is set to 1, and otherwise to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4920 entries, 0 to 4919\n",
      "Data columns (total 18 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Disease     4920 non-null   object\n",
      " 1   Symptom_1   4920 non-null   object\n",
      " 2   Symptom_2   4920 non-null   object\n",
      " 3   Symptom_3   4920 non-null   object\n",
      " 4   Symptom_4   4572 non-null   object\n",
      " 5   Symptom_5   3714 non-null   object\n",
      " 6   Symptom_6   2934 non-null   object\n",
      " 7   Symptom_7   2268 non-null   object\n",
      " 8   Symptom_8   1944 non-null   object\n",
      " 9   Symptom_9   1692 non-null   object\n",
      " 10  Symptom_10  1512 non-null   object\n",
      " 11  Symptom_11  1194 non-null   object\n",
      " 12  Symptom_12  744 non-null    object\n",
      " 13  Symptom_13  504 non-null    object\n",
      " 14  Symptom_14  306 non-null    object\n",
      " 15  Symptom_15  240 non-null    object\n",
      " 16  Symptom_16  192 non-null    object\n",
      " 17  Symptom_17  72 non-null     object\n",
      "dtypes: object(18)\n",
      "memory usage: 692.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>Disease</th>\n",
       "      <th>Symptom_1</th>\n",
       "      <th>Symptom_2</th>\n",
       "      <th>Symptom_3</th>\n",
       "      <th>Symptom_4</th>\n",
       "      <th>Symptom_5</th>\n",
       "      <th>Symptom_6</th>\n",
       "      <th>Symptom_7</th>\n",
       "      <th>Symptom_8</th>\n",
       "      <th>Symptom_9</th>\n",
       "      <th>Symptom_10</th>\n",
       "      <th>Symptom_11</th>\n",
       "      <th>Symptom_12</th>\n",
       "      <th>Symptom_13</th>\n",
       "      <th>Symptom_14</th>\n",
       "      <th>Symptom_15</th>\n",
       "      <th>Symptom_16</th>\n",
       "      <th>Symptom_17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Impetigo</td>\n",
       "      <td>high_fever</td>\n",
       "      <td>blister</td>\n",
       "      <td>red_sore_around_nose</td>\n",
       "      <td>yellow_crust_ooze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Impetigo</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>blister</td>\n",
       "      <td>red_sore_around_nose</td>\n",
       "      <td>yellow_crust_ooze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Impetigo</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>high_fever</td>\n",
       "      <td>red_sore_around_nose</td>\n",
       "      <td>yellow_crust_ooze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Impetigo</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>high_fever</td>\n",
       "      <td>blister</td>\n",
       "      <td>yellow_crust_ooze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Impetigo</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>high_fever</td>\n",
       "      <td>blister</td>\n",
       "      <td>red_sore_around_nose</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Disease    Symptom_1              Symptom_2  \\\n",
       "0    Fungal infection      itching              skin_rash   \n",
       "1    Fungal infection    skin_rash   nodal_skin_eruptions   \n",
       "2    Fungal infection      itching   nodal_skin_eruptions   \n",
       "3    Fungal infection      itching              skin_rash   \n",
       "4    Fungal infection      itching              skin_rash   \n",
       "..                ...          ...                    ...   \n",
       "402          Impetigo   high_fever                blister   \n",
       "403          Impetigo    skin_rash                blister   \n",
       "405          Impetigo    skin_rash             high_fever   \n",
       "406          Impetigo    skin_rash             high_fever   \n",
       "407          Impetigo    skin_rash             high_fever   \n",
       "\n",
       "                 Symptom_3              Symptom_4 Symptom_5 Symptom_6  \\\n",
       "0     nodal_skin_eruptions    dischromic _patches       NaN       NaN   \n",
       "1      dischromic _patches                    NaN       NaN       NaN   \n",
       "2      dischromic _patches                    NaN       NaN       NaN   \n",
       "3      dischromic _patches                    NaN       NaN       NaN   \n",
       "4     nodal_skin_eruptions                    NaN       NaN       NaN   \n",
       "..                     ...                    ...       ...       ...   \n",
       "402   red_sore_around_nose      yellow_crust_ooze       NaN       NaN   \n",
       "403   red_sore_around_nose      yellow_crust_ooze       NaN       NaN   \n",
       "405   red_sore_around_nose      yellow_crust_ooze       NaN       NaN   \n",
       "406                blister      yellow_crust_ooze       NaN       NaN   \n",
       "407                blister   red_sore_around_nose       NaN       NaN   \n",
       "\n",
       "    Symptom_7 Symptom_8 Symptom_9 Symptom_10 Symptom_11 Symptom_12 Symptom_13  \\\n",
       "0         NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "1         NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "2         NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "3         NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "4         NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "..        ...       ...       ...        ...        ...        ...        ...   \n",
       "402       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "403       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "405       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "406       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "407       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "    Symptom_14 Symptom_15 Symptom_16 Symptom_17  \n",
       "0          NaN        NaN        NaN        NaN  \n",
       "1          NaN        NaN        NaN        NaN  \n",
       "2          NaN        NaN        NaN        NaN  \n",
       "3          NaN        NaN        NaN        NaN  \n",
       "4          NaN        NaN        NaN        NaN  \n",
       "..         ...        ...        ...        ...  \n",
       "402        NaN        NaN        NaN        NaN  \n",
       "403        NaN        NaN        NaN        NaN  \n",
       "405        NaN        NaN        NaN        NaN  \n",
       "406        NaN        NaN        NaN        NaN  \n",
       "407        NaN        NaN        NaN        NaN  \n",
       "\n",
       "[304 rows x 18 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "symptoms = np.concatenate((df.Symptom_1.unique(), df.Symptom_2.unique(), df.Symptom_3.unique(), df.Symptom_4.unique(), \n",
    "                          df.Symptom_5.unique(), df.Symptom_6.unique(), df.Symptom_7.unique(), df.Symptom_8.unique(), \n",
    "                          df.Symptom_9.unique(), df.Symptom_10.unique(), df.Symptom_11.unique(), df.Symptom_12.unique(), \n",
    "                          df.Symptom_13.unique(), df.Symptom_14.unique(), df.Symptom_15.unique(), df.Symptom_16.unique(), \n",
    "                          df.Symptom_17.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "symptoms_unique = list((set(symptoms)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(symptoms_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 18\n",
    "for each in symptoms_unique:\n",
    "    df.insert(i, each, 0)\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df.iterrows(): \n",
    "    disease_symptoms = [symptom for symptom in list(row)[1:] if symptom != 0]\n",
    "    for each in disease_symptoms: \n",
    "        df.at[index, each] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = ['Symptom_1', 'Symptom_2', 'Symptom_3', 'Symptom_4', 'Symptom_5', 'Symptom_6', 'Symptom_7', 'Symptom_8', \n",
    "             'Symptom_9', 'Symptom_10', 'Symptom_11', 'Symptom_12', 'Symptom_13', 'Symptom_14', 'Symptom_15', 'Symptom_16'\n",
    "             , 'Symptom_17'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:, df.columns.notnull()]\n",
    "df.columns = df.columns.str.replace(' ', '')\n",
    "df = df.reindex(sorted(df.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Disease',\n",
       " 'abdominal_pain',\n",
       " 'abnormal_menstruation',\n",
       " 'acidity',\n",
       " 'acute_liver_failure',\n",
       " 'altered_sensorium',\n",
       " 'anxiety',\n",
       " 'back_pain',\n",
       " 'belly_pain',\n",
       " 'blackheads',\n",
       " 'bladder_discomfort',\n",
       " 'blister',\n",
       " 'blood_in_sputum',\n",
       " 'bloody_stool',\n",
       " 'blurred_and_distorted_vision',\n",
       " 'breathlessness',\n",
       " 'brittle_nails',\n",
       " 'bruising',\n",
       " 'burning_micturition',\n",
       " 'chest_pain',\n",
       " 'chills',\n",
       " 'cold_hands_and_feets',\n",
       " 'coma',\n",
       " 'congestion',\n",
       " 'constipation',\n",
       " 'continuous_feel_of_urine',\n",
       " 'continuous_sneezing',\n",
       " 'cough',\n",
       " 'cramps',\n",
       " 'dark_urine',\n",
       " 'dehydration',\n",
       " 'depression',\n",
       " 'diarrhoea',\n",
       " 'dischromic_patches',\n",
       " 'distention_of_abdomen',\n",
       " 'dizziness',\n",
       " 'drying_and_tingling_lips',\n",
       " 'enlarged_thyroid',\n",
       " 'excessive_hunger',\n",
       " 'extra_marital_contacts',\n",
       " 'family_history',\n",
       " 'fast_heart_rate',\n",
       " 'fatigue',\n",
       " 'fluid_overload',\n",
       " 'foul_smell_ofurine',\n",
       " 'headache',\n",
       " 'high_fever',\n",
       " 'hip_joint_pain',\n",
       " 'history_of_alcohol_consumption',\n",
       " 'increased_appetite',\n",
       " 'indigestion',\n",
       " 'inflammatory_nails',\n",
       " 'internal_itching',\n",
       " 'irregular_sugar_level',\n",
       " 'irritability',\n",
       " 'irritation_in_anus',\n",
       " 'itching',\n",
       " 'joint_pain',\n",
       " 'knee_pain',\n",
       " 'lack_of_concentration',\n",
       " 'lethargy',\n",
       " 'loss_of_appetite',\n",
       " 'loss_of_balance',\n",
       " 'loss_of_smell',\n",
       " 'malaise',\n",
       " 'mild_fever',\n",
       " 'mood_swings',\n",
       " 'movement_stiffness',\n",
       " 'mucoid_sputum',\n",
       " 'muscle_pain',\n",
       " 'muscle_wasting',\n",
       " 'muscle_weakness',\n",
       " 'nausea',\n",
       " 'neck_pain',\n",
       " 'nodal_skin_eruptions',\n",
       " 'obesity',\n",
       " 'pain_behind_the_eyes',\n",
       " 'pain_during_bowel_movements',\n",
       " 'pain_in_anal_region',\n",
       " 'painful_walking',\n",
       " 'palpitations',\n",
       " 'passage_of_gases',\n",
       " 'patches_in_throat',\n",
       " 'phlegm',\n",
       " 'polyuria',\n",
       " 'prominent_veins_on_calf',\n",
       " 'puffy_face_and_eyes',\n",
       " 'pus_filled_pimples',\n",
       " 'receiving_blood_transfusion',\n",
       " 'receiving_unsterile_injections',\n",
       " 'red_sore_around_nose',\n",
       " 'red_spots_over_body',\n",
       " 'redness_of_eyes',\n",
       " 'restlessness',\n",
       " 'runny_nose',\n",
       " 'rusty_sputum',\n",
       " 'scurring',\n",
       " 'shivering',\n",
       " 'silver_like_dusting',\n",
       " 'sinus_pressure',\n",
       " 'skin_peeling',\n",
       " 'skin_rash',\n",
       " 'slurred_speech',\n",
       " 'small_dents_in_nails',\n",
       " 'spinning_movements',\n",
       " 'spotting_urination',\n",
       " 'stiff_neck',\n",
       " 'stomach_bleeding',\n",
       " 'stomach_pain',\n",
       " 'sunken_eyes',\n",
       " 'sweating',\n",
       " 'swelled_lymph_nodes',\n",
       " 'swelling_joints',\n",
       " 'swelling_of_stomach',\n",
       " 'swollen_blood_vessels',\n",
       " 'swollen_extremeties',\n",
       " 'swollen_legs',\n",
       " 'throat_irritation',\n",
       " 'toxic_look_(typhos)',\n",
       " 'ulcers_on_tongue',\n",
       " 'unsteadiness',\n",
       " 'visual_disturbances',\n",
       " 'vomiting',\n",
       " 'watering_from_eyes',\n",
       " 'weakness_in_limbs',\n",
       " 'weakness_of_one_body_side',\n",
       " 'weight_gain',\n",
       " 'weight_loss',\n",
       " 'yellow_crust_ooze',\n",
       " 'yellow_urine',\n",
       " 'yellowing_of_eyes',\n",
       " 'yellowish_skin']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description, Precaution and Symptom Severity data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "diseases_description = pd.read_csv(\"data/symptom_Description.csv\")\n",
    "diseases_description['Disease'] = diseases_description['Disease'].apply(lambda x: x.lower().strip(\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease_precaution = pd.read_csv(\"data/symptom_precaution.csv\")\n",
    "disease_precaution['Disease'] = disease_precaution['Disease'].apply(lambda x: x.lower().strip(\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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>Disease</th>\n",
       "      <th>Precaution_1</th>\n",
       "      <th>Precaution_2</th>\n",
       "      <th>Precaution_3</th>\n",
       "      <th>Precaution_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drug reaction</td>\n",
       "      <td>stop irritation</td>\n",
       "      <td>consult nearest hospital</td>\n",
       "      <td>stop taking drug</td>\n",
       "      <td>follow up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>malaria</td>\n",
       "      <td>Consult nearest hospital</td>\n",
       "      <td>avoid oily food</td>\n",
       "      <td>avoid non veg food</td>\n",
       "      <td>keep mosquitos out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>allergy</td>\n",
       "      <td>apply calamine</td>\n",
       "      <td>cover area with bandage</td>\n",
       "      <td>keep a diary</td>\n",
       "      <td>use ice to compress itching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hypothyroidism</td>\n",
       "      <td>reduce stress</td>\n",
       "      <td>exercise</td>\n",
       "      <td>eat healthy</td>\n",
       "      <td>get proper sleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>psoriasis</td>\n",
       "      <td>wash hands with warm soapy water</td>\n",
       "      <td>stop bleeding using pressure</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>salt baths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gerd</td>\n",
       "      <td>avoid fatty spicy food</td>\n",
       "      <td>avoid lying down after eating</td>\n",
       "      <td>maintain healthy weight</td>\n",
       "      <td>exercise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chronic cholestasis</td>\n",
       "      <td>cold baths</td>\n",
       "      <td>anti itch medicine</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>eat healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hepatitis a</td>\n",
       "      <td>Consult nearest hospital</td>\n",
       "      <td>wash hands through</td>\n",
       "      <td>avoid fatty spicy food</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>osteoarthristis</td>\n",
       "      <td>acetaminophen</td>\n",
       "      <td>consult nearest hospital</td>\n",
       "      <td>follow up</td>\n",
       "      <td>salt baths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(vertigo) paroymsal  positional vertigo</td>\n",
       "      <td>lie down</td>\n",
       "      <td>avoid sudden change in body</td>\n",
       "      <td>avoid abrupt head movment</td>\n",
       "      <td>relax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hypoglycemia</td>\n",
       "      <td>lie down on side</td>\n",
       "      <td>check in pulse</td>\n",
       "      <td>drink sugary drinks</td>\n",
       "      <td>consult doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>acne</td>\n",
       "      <td>bath twice</td>\n",
       "      <td>avoid fatty spicy food</td>\n",
       "      <td>drink plenty of water</td>\n",
       "      <td>avoid too many products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>diabetes</td>\n",
       "      <td>have balanced diet</td>\n",
       "      <td>exercise</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>follow up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>impetigo</td>\n",
       "      <td>soak affected area in warm water</td>\n",
       "      <td>use antibiotics</td>\n",
       "      <td>remove scabs with wet compressed cloth</td>\n",
       "      <td>consult doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hypertension</td>\n",
       "      <td>meditation</td>\n",
       "      <td>salt baths</td>\n",
       "      <td>reduce stress</td>\n",
       "      <td>get proper sleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>peptic ulcer diseae</td>\n",
       "      <td>avoid fatty spicy food</td>\n",
       "      <td>consume probiotic food</td>\n",
       "      <td>eliminate milk</td>\n",
       "      <td>limit alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>dimorphic hemmorhoids(piles)</td>\n",
       "      <td>avoid fatty spicy food</td>\n",
       "      <td>consume witch hazel</td>\n",
       "      <td>warm bath with epsom salt</td>\n",
       "      <td>consume alovera juice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>common cold</td>\n",
       "      <td>drink vitamin c rich drinks</td>\n",
       "      <td>take vapour</td>\n",
       "      <td>avoid cold food</td>\n",
       "      <td>keep fever in check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>chicken pox</td>\n",
       "      <td>use neem in bathing</td>\n",
       "      <td>consume neem leaves</td>\n",
       "      <td>take vaccine</td>\n",
       "      <td>avoid public places</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cervical spondylosis</td>\n",
       "      <td>use heating pad or cold pack</td>\n",
       "      <td>exercise</td>\n",
       "      <td>take otc pain reliver</td>\n",
       "      <td>consult doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>hyperthyroidism</td>\n",
       "      <td>eat healthy</td>\n",
       "      <td>massage</td>\n",
       "      <td>use lemon balm</td>\n",
       "      <td>take radioactive iodine treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>urinary tract infection</td>\n",
       "      <td>drink plenty of water</td>\n",
       "      <td>increase vitamin c intake</td>\n",
       "      <td>drink cranberry juice</td>\n",
       "      <td>take probiotics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>varicose veins</td>\n",
       "      <td>lie down flat and raise the leg high</td>\n",
       "      <td>use oinments</td>\n",
       "      <td>use vein compression</td>\n",
       "      <td>dont stand still for long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>aids</td>\n",
       "      <td>avoid open cuts</td>\n",
       "      <td>wear ppe if possible</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>follow up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>paralysis (brain hemorrhage)</td>\n",
       "      <td>massage</td>\n",
       "      <td>eat healthy</td>\n",
       "      <td>exercise</td>\n",
       "      <td>consult doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>typhoid</td>\n",
       "      <td>eat high calorie vegitables</td>\n",
       "      <td>antiboitic therapy</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hepatitis b</td>\n",
       "      <td>consult nearest hospital</td>\n",
       "      <td>vaccination</td>\n",
       "      <td>eat healthy</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fungal infection</td>\n",
       "      <td>bath twice</td>\n",
       "      <td>use detol or neem in bathing water</td>\n",
       "      <td>keep infected area dry</td>\n",
       "      <td>use clean cloths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hepatitis c</td>\n",
       "      <td>Consult nearest hospital</td>\n",
       "      <td>vaccination</td>\n",
       "      <td>eat healthy</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>migraine</td>\n",
       "      <td>meditation</td>\n",
       "      <td>reduce stress</td>\n",
       "      <td>use poloroid glasses in sun</td>\n",
       "      <td>consult doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>bronchial asthma</td>\n",
       "      <td>switch to loose cloothing</td>\n",
       "      <td>take deep breaths</td>\n",
       "      <td>get away from trigger</td>\n",
       "      <td>seek help</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>alcoholic hepatitis</td>\n",
       "      <td>stop alcohol consumption</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>medication</td>\n",
       "      <td>follow up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>jaundice</td>\n",
       "      <td>drink plenty of water</td>\n",
       "      <td>consume milk thistle</td>\n",
       "      <td>eat fruits and high fiberous food</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>hepatitis e</td>\n",
       "      <td>stop alcohol consumption</td>\n",
       "      <td>rest</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>dengue</td>\n",
       "      <td>drink papaya leaf juice</td>\n",
       "      <td>avoid fatty spicy food</td>\n",
       "      <td>keep mosquitos away</td>\n",
       "      <td>keep hydrated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>hepatitis d</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>medication</td>\n",
       "      <td>eat healthy</td>\n",
       "      <td>follow up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>heart attack</td>\n",
       "      <td>call ambulance</td>\n",
       "      <td>chew or swallow asprin</td>\n",
       "      <td>keep calm</td>\n",
       "      <td>lie down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>pneumonia</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>medication</td>\n",
       "      <td>rest</td>\n",
       "      <td>follow up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>arthritis</td>\n",
       "      <td>exercise</td>\n",
       "      <td>use hot and cold therapy</td>\n",
       "      <td>try acupuncture</td>\n",
       "      <td>massage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gastroenteritis</td>\n",
       "      <td>stop eating solid food for while</td>\n",
       "      <td>try taking small sips of water</td>\n",
       "      <td>rest</td>\n",
       "      <td>ease back into eating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>tuberculosis</td>\n",
       "      <td>cover mouth</td>\n",
       "      <td>consult doctor</td>\n",
       "      <td>medication</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Disease  \\\n",
       "0                             drug reaction   \n",
       "1                                   malaria   \n",
       "2                                   allergy   \n",
       "3                            hypothyroidism   \n",
       "4                                 psoriasis   \n",
       "5                                      gerd   \n",
       "6                       chronic cholestasis   \n",
       "7                               hepatitis a   \n",
       "8                           osteoarthristis   \n",
       "9   (vertigo) paroymsal  positional vertigo   \n",
       "10                             hypoglycemia   \n",
       "11                                     acne   \n",
       "12                                 diabetes   \n",
       "13                                 impetigo   \n",
       "14                             hypertension   \n",
       "15                      peptic ulcer diseae   \n",
       "16             dimorphic hemmorhoids(piles)   \n",
       "17                              common cold   \n",
       "18                              chicken pox   \n",
       "19                     cervical spondylosis   \n",
       "20                          hyperthyroidism   \n",
       "21                  urinary tract infection   \n",
       "22                           varicose veins   \n",
       "23                                     aids   \n",
       "24             paralysis (brain hemorrhage)   \n",
       "25                                  typhoid   \n",
       "26                              hepatitis b   \n",
       "27                         fungal infection   \n",
       "28                              hepatitis c   \n",
       "29                                 migraine   \n",
       "30                         bronchial asthma   \n",
       "31                      alcoholic hepatitis   \n",
       "32                                 jaundice   \n",
       "33                              hepatitis e   \n",
       "34                                   dengue   \n",
       "35                              hepatitis d   \n",
       "36                             heart attack   \n",
       "37                                pneumonia   \n",
       "38                                arthritis   \n",
       "39                          gastroenteritis   \n",
       "40                             tuberculosis   \n",
       "\n",
       "                            Precaution_1                        Precaution_2  \\\n",
       "0                        stop irritation            consult nearest hospital   \n",
       "1               Consult nearest hospital                     avoid oily food   \n",
       "2                         apply calamine             cover area with bandage   \n",
       "3                          reduce stress                            exercise   \n",
       "4       wash hands with warm soapy water        stop bleeding using pressure   \n",
       "5                 avoid fatty spicy food       avoid lying down after eating   \n",
       "6                             cold baths                  anti itch medicine   \n",
       "7               Consult nearest hospital                  wash hands through   \n",
       "8                          acetaminophen            consult nearest hospital   \n",
       "9                               lie down         avoid sudden change in body   \n",
       "10                      lie down on side                      check in pulse   \n",
       "11                            bath twice              avoid fatty spicy food   \n",
       "12                    have balanced diet                            exercise   \n",
       "13      soak affected area in warm water                     use antibiotics   \n",
       "14                            meditation                          salt baths   \n",
       "15                avoid fatty spicy food              consume probiotic food   \n",
       "16                avoid fatty spicy food                 consume witch hazel   \n",
       "17           drink vitamin c rich drinks                         take vapour   \n",
       "18                  use neem in bathing                  consume neem leaves   \n",
       "19          use heating pad or cold pack                            exercise   \n",
       "20                           eat healthy                             massage   \n",
       "21                 drink plenty of water           increase vitamin c intake   \n",
       "22  lie down flat and raise the leg high                        use oinments   \n",
       "23                       avoid open cuts                wear ppe if possible   \n",
       "24                               massage                         eat healthy   \n",
       "25           eat high calorie vegitables                  antiboitic therapy   \n",
       "26              consult nearest hospital                         vaccination   \n",
       "27                            bath twice  use detol or neem in bathing water   \n",
       "28              Consult nearest hospital                         vaccination   \n",
       "29                            meditation                       reduce stress   \n",
       "30             switch to loose cloothing                   take deep breaths   \n",
       "31              stop alcohol consumption                      consult doctor   \n",
       "32                 drink plenty of water                consume milk thistle   \n",
       "33              stop alcohol consumption                                rest   \n",
       "34               drink papaya leaf juice              avoid fatty spicy food   \n",
       "35                        consult doctor                          medication   \n",
       "36                        call ambulance              chew or swallow asprin   \n",
       "37                        consult doctor                          medication   \n",
       "38                              exercise            use hot and cold therapy   \n",
       "39      stop eating solid food for while      try taking small sips of water   \n",
       "40                           cover mouth                      consult doctor   \n",
       "\n",
       "                              Precaution_3                       Precaution_4  \n",
       "0                         stop taking drug                          follow up  \n",
       "1                       avoid non veg food                 keep mosquitos out  \n",
       "2                             keep a diary        use ice to compress itching  \n",
       "3                              eat healthy                   get proper sleep  \n",
       "4                           consult doctor                         salt baths  \n",
       "5                  maintain healthy weight                           exercise  \n",
       "6                           consult doctor                        eat healthy  \n",
       "7                   avoid fatty spicy food                         medication  \n",
       "8                                follow up                         salt baths  \n",
       "9                avoid abrupt head movment                              relax  \n",
       "10                     drink sugary drinks                     consult doctor  \n",
       "11                   drink plenty of water            avoid too many products  \n",
       "12                          consult doctor                          follow up  \n",
       "13  remove scabs with wet compressed cloth                     consult doctor  \n",
       "14                           reduce stress                   get proper sleep  \n",
       "15                          eliminate milk                      limit alcohol  \n",
       "16               warm bath with epsom salt              consume alovera juice  \n",
       "17                         avoid cold food                keep fever in check  \n",
       "18                            take vaccine                avoid public places  \n",
       "19                   take otc pain reliver                     consult doctor  \n",
       "20                          use lemon balm  take radioactive iodine treatment  \n",
       "21                   drink cranberry juice                    take probiotics  \n",
       "22                    use vein compression          dont stand still for long  \n",
       "23                          consult doctor                          follow up  \n",
       "24                                exercise                     consult doctor  \n",
       "25                          consult doctor                         medication  \n",
       "26                             eat healthy                         medication  \n",
       "27                  keep infected area dry                   use clean cloths  \n",
       "28                             eat healthy                         medication  \n",
       "29             use poloroid glasses in sun                     consult doctor  \n",
       "30                   get away from trigger                          seek help  \n",
       "31                              medication                          follow up  \n",
       "32       eat fruits and high fiberous food                         medication  \n",
       "33                          consult doctor                         medication  \n",
       "34                     keep mosquitos away                      keep hydrated  \n",
       "35                             eat healthy                          follow up  \n",
       "36                               keep calm                           lie down  \n",
       "37                                    rest                          follow up  \n",
       "38                         try acupuncture                            massage  \n",
       "39                                    rest              ease back into eating  \n",
       "40                              medication                               rest  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_precaution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "symptom_severity = pd.read_csv(\"data/Symptom-severity.csv\")\n",
    "symptom_severity = symptom_severity.applymap(lambda s: s.lower().strip(\" \").replace(\" \", \"\") if type(s) == str else s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "304\n",
      "304\n",
      "131\n"
     ]
    }
   ],
   "source": [
    "labels = df.to_numpy()[:, :1]\n",
    "examples = df.to_numpy()[:, 1:]\n",
    "list_of_symptoms = list(df.columns)[1:]\n",
    "\n",
    "with open('data/list_of_symptoms.pickle', 'wb') as data_file: \n",
    "    pickle.dump(list_of_symptoms, data_file)\n",
    "\n",
    "print(len(labels))\n",
    "print(len(examples))\n",
    "print(len(list_of_symptoms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test example\n",
    "\n",
    "symptoms = ['stomach_pain', 'headache'] \n",
    "x_test = []\n",
    "\n",
    "with open('data/list_of_symptoms.pickle', 'rb') as data_file:\n",
    "    symptoms_list = pickle.load(data_file)\n",
    "\n",
    "for each in symptoms_list: \n",
    "    if each in symptoms:\n",
    "        x_test.append(1)\n",
    "    else: \n",
    "        x_test.append(0)\n",
    "\n",
    "x_test = np.asarray(x_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross validation function\n",
    "\n",
    "def cross_validation(X_train, y_train, X_test, y_test, model_name, parameter_range=50): \n",
    "    train_errors = []\n",
    "    test_errors = []\n",
    "    \n",
    "    parameters = np.linspace(1, parameter_range, parameter_range, dtype=int)\n",
    "    \n",
    "    for parameter in parameters: \n",
    "        if model_name == 'knn':            \n",
    "            model = KNeighborsClassifier(n_neighbors=parameter, metric='cosine')\n",
    "        elif model_name == 'logreg':\n",
    "            model = LogisticRegression(solver='liblinear', C=1/(parameter*20))\n",
    "        elif model_name == 'dctree':\n",
    "            model = DecisionTreeClassifier(splitter='random', max_depth=parameter)\n",
    "        elif model_name =='svm': \n",
    "            model = SVC(C=1/(parameter*10))\n",
    "            \n",
    "        model.fit(X_train, y_train)\n",
    "        learning_error = 1 - model.score(X_train, y_train) \n",
    "        testing_error = 1 - model.score(X_test, y_test)\n",
    "        train_errors.append(learning_error)\n",
    "        test_errors.append(testing_error)\n",
    "    \n",
    "    if model_name == 'logreg':\n",
    "        best_parameter_value = 1/(parameters[np.argmin(test_errors)]*20)\n",
    "    elif model_name == 'svm': \n",
    "        best_parameter_value = 1/(parameters[np.argmin(test_errors)]*10)\n",
    "    else: \n",
    "        best_parameter_value = parameters[np.argmin(test_errors)]\n",
    "    \n",
    "    return parameters, best_parameter_value, train_errors, test_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='cosine', n_neighbors=6)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#kNN algorithm\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=6, metric='cosine')\n",
    "X_train, X_test, y_train, y_test = train_test_split(examples, labels.ravel(), test_size=0.2)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hypertension \n"
     ]
    }
   ],
   "source": [
    "disease = knn.predict(x_test.reshape(1,-1))[0]\n",
    "print(disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9508196721311475"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hypertension (HTN or HT), also known as high blood pressure (HBP), is a long-term medical condition in which the blood pressure in the arteries is persistently elevated. High blood pressure typically does not cause symptoms.\n",
      "Precautions: meditation, salt baths, reduce stress, get proper sleep\n"
     ]
    }
   ],
   "source": [
    "#Additional information about disease and precautions\n",
    "\n",
    "description = diseases_description.loc[diseases_description['Disease'] == disease.strip(\" \").lower(), 'Description'].iloc[0]\n",
    "print(description)\n",
    "\n",
    "precaution = disease_precaution[disease_precaution['Disease'] == disease.strip(\" \").lower()]\n",
    "sentence = 'Precautions: ' + precaution.Precaution_1.iloc[0] + \", \" + precaution.Precaution_2.iloc[0] + \", \" + precaution.Precaution_3.iloc[0] + \", \" + precaution.Precaution_4.iloc[0]\n",
    "print(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering your symptoms are severe, and Meddy isn't a real doctor, you should consider talking to one. :)\n"
     ]
    }
   ],
   "source": [
    "#Giving the advice about seeing the doctor if any of the symptoms has severity larger than 5 or if mean of all the symptoms is larger than 4\n",
    "\n",
    "symptoms = ['stomach_pain', 'headache', 'coma'] \n",
    "severity = []\n",
    "\n",
    "for each in symptoms: \n",
    "    severity.append(symptom_severity.loc[symptom_severity['Symptom'] == each.lower().strip(\" \").replace(\" \", \"\"), 'weight'].iloc[0])\n",
    "\n",
    "if np.mean(severity) > 4 or np.max(severity) > 5:\n",
    "    print(\"Considering your symptoms are severe, and Meddy isn't a real doctor, you should consider talking to one. :)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_range = 20\n",
    "\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "\n",
    "k, best_k, train_errors, test_errors = cross_validation(X_train, y_train, X_test, y_test, model_name='knn')\n",
    "plt.title(f\"Optimal k = {best_k}\")\n",
    "plt.plot(k, train_errors, c='purple', label='Training error')\n",
    "plt.plot(k, test_errors, c='orange', label='Testing error')\n",
    "plt.legend()\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cervical spondylosis'], dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Decision Tree\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(splitter='random', max_depth=100)\n",
    "\n",
    "dc_tree = clf.fit(X_train, y_train)\n",
    "dc_tree.predict(x_test.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6885245901639344"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ICODhhx8u8fXz5s0jICCAvn37AjB+/PhyqfNUcnNz8fLyOuXrU8nLyyvce7CsVPuRqaKCWwRz7bfXEv1JNEnxSUzpMYWf/vwTuZln7qJaaRjjbKbc+SmIfx8WXwd5Z/4vCBERkTNZsWIFAwcOpEePHlx88cXs27cPgFdffZUOHTrQpUsXxo4dy44dO3jjjTf497//TVRUFAsWLGDixIm8+OKLAAwaNIhHH32U3r170759exYsWABAeno6V199NV26dOGaa67hvPPOo7g2SaeqY9CgQTz++OMMHDiQV1555Xev58yZQ7du3ejcuTO33norWVlZALRs2ZKnnnqK/v3788UXX5T5n1uNG9IwxtD52s60GdaGWQ/NYsGzC9j4xUZGvzWaFhe0cLu8kjEGOv/FGaFa9RDkZsCAL8DTz+3KRETkXKy4H5JWl+0960ZBj5dLfLq1lnvvvZdvvvmG0NBQPvvsM5544gneffddnnvuOeLj4/H19SU5OZng4GDGjx9/wmjWnDlzTrhfbm4uS5cuZebMmTz55JPMnj2bSZMmUbduXdauXcv69euJior6XR05OTmnrAMgOTmZn3/+GYAZM2YUvs7MzKRdu3bMmTOH9u3b84c//IHJkydz//33A+Dn58fChQvP6Y/yTGrUyFRRtUJqcfnUy7lh1g3k5eQxdeBUZtw5g8zkTLdLK7nIB53+VHu/g3mXQE6a2xWJiEgVlZWVxfr16xk6dChRUVE888wzJCQkANClSxeuv/56PvrooxJNpQFER0cD0KNHj8KNjBcuXMjYsWMB6NSpE126dPnddZs2bTplHQDXXHPNCecff71p0yZatWpF+/btAbjpppuYP3/+Ka8rSzVuZOpkbYa24a51dzFv4jx++dcvbJ6xmZGvjyQyuop0pW13J3jVgl9uhrkXw6CZ4PP7TSJFRKQSO4sRpPJiraVjx44sWbLkd8e+++475s+fz/Tp03n66afZsGHDGe93fENhT0/Pwk2JS9Ki6HR1ANSuXbvY12e698nXlaUaOzJVlE9tH4b9cxi3Lb2NgIYBfH7F53w25jOO7jnqdmkl0+pG6Pc5HFkGcy6EzErc+V1ERColX19fEhMTC0NMTk4OGzZsID8/n927dzN48GBeeOEFkpOTSUtLIzAwkNTU1LP6jP79+/P5558DsHHjRtatW/e7c8LDw4ut40wiIiLYsWMHW7duBeDDDz9k4MCBZ1XfuVKYKqJJjybctvQ2Lnr+Irb+sJVJHSax/I3l2Pwq0NOp+RUw4GtI2QBzBkHiErVOEBGREvPw8ODLL7/k0UcfpWvXrkRFRbF48WLy8vK44YYb6Ny5M926deOBBx4gODiY0aNHM23atMIF6CUxYcIEEhMT6dKlC88//zxdunShTp0TZ1N8fHyKreNM/Pz8eO+997jqqqvo3LkzHh4eFfaEoTY6PoUjW4/w7Z3fEv9TPM37N2f0W6OpH1Hf7bLO7MBc+PkyyE0F/8YQdrmzcXKDgU6vKhERqRRq4kbHeXl55OTk4Ofnx7Zt2xgyZAibN2/Gx8fH7dJOcLYbHdf4NVOnUq9tPW6cfSOrp65m1kOzeKPrGwz48wD6P9ofT5+y7U9RphoOhst3O4vSd8c4+/ttmQw+daHpaAgbA42HOeusREREKlB6ejqDBw8mJycHay2TJ0+udEHqXGhkqgTSDqTxw//9wIbPNhDaMZTRb42mWZ9mbpdVMrkZsH8W7J4Ge6ZDdhJ41oImwyEsGpqOcvYDFBGRClUTR6aqirMdmdKaqRIIaBjAlf+9kmtnXEtWShbv9nuX7+/7nqzULLdLO7OT9/e78Ednj79DS2DJDfBVKMwdDlvehIz9blcrIlKjuDWgIad2Ln8nGpk6S1mpWcx5fA7L/rOMoLAgRk0eRftR7d0u6+zZfDi81Bmx2h0DaVsBA6F9nanAZmMgoLXbVYqIVFvx8fEEBgYSEhKCMcbtcgQnSB0+fJjU1FRatWp1wrHTjUwpTJ2j3Ut2M+O2GSRuTKTT2E5c/PLFBDQMcLusc2Ot8xTg7hhImPZbF97grs7i9WZjoE4np/O6iIiUiZycHBISEsjMrELNomsAPz8/wsLC8PY+8aEthalykpedx8LnFrLg2QV41/Zm2EvDiLo5qur/F0ZavDNilTANEhcBFoIioPu/oMkIt6sTERGpcApT5SwxNpEZt89g96LdtBrSikvevIR6beq5XVbZyNjvLFyPexmOxkKLsdDjFfBr4HZlIiIiFUYL0MtZaGQot8y/hZGTRrJn6R4md57MohcWkZeT53ZppeffCNreASNWQeeJzlTgtxGw7T1nelBERKSG08hUGTuacJSZ98xk0zebCAkPYeg/h9L+kvZVf+rvuJRYWHq7M/3XcAj0fgMC27pdlYiISLnSyFQFCgoL4ppp1zD2m7Fg4b+X/pcPhnzAvlX73C6tbNSJhIvmQ6/Jzl6AMzvDhucgP8ftykRERFxRojBljBlujNlkjNlqjHmsmON1jDEzjDFrjDEbjDG3lH2pVYcxhvBLw7lr/V2MeG0EB9YeYEqPKXx989dVZ/Pk0zEe0G48jNoIjUfAmj/BD73g8DK3KxMREalwZ5zmM8Z4ApuBoUACsAy41lq7scg5jwN1rLWPGmNCgU1AI2tt9qnuW12n+YqTmZzJ/Gfns/TVpRhPQ9+H+9LvkX74BFT9FvqA8+Tf8rsh8wC0vw+6PA3eVbRNhIiISDFKO83XG9hqrd1eEI7+C1x20jkWCDTOwqAA4AiQW4qaqxW/YD+G/XMYd8fdTfil4cx/ej6vtXuNle+sJD8v3+3ySq/ZGBgVC23ugE0vw8xOsPd7t6sSERGpECUJU02B3UVeJxS8V9TrQCSwF1gH/J+19ncpwRhzhzFmuTFmeWJi4jmWXHXVbVWXK/97JeOWjCO4VTAzbpvBlO5T2PbjNrdLKz2fOtB7Mgxd6Oz9N28kLLoOMg+6XZmIiEi5KkmYKu4xtJPnBi8GVgNNgCjgdWNM0O8usnaKtbantbZnaGjoWRdbXYSdH8ati27lys+vJCs1i4+GfcQnoz4hcWM1CJih/Yq0UfhSbRRERKTaK0mYSgCaFXkdhjMCVdQtQIx1bAXigYiyKbF6MsbQ8aqO3B17N0P/OZRdi3Yxuctkvr3rW44dPOZ2eaXj6Qud/wYj1kCdDvDrrfDTUEjd6nZlIiIiZa4kYWoZ0M4Y08oY4wOMBaafdM4uYAiAMaYhEA5sL8tCqysvXy/6PtyX+7beR68JvVj19ipebfsqC/6xgJyMKt5uoLg2ChufVxsFERGpVkrUtNMYMxJ4GfAE3rXWPmuMGQ9grX3DGNMEmAo0xpkWfM5a+9Hp7lmTnuY7G4c2HWL2I7PZNH0TdZrXYcg/htBpbCeMRxVv+pm+B5bf6+z3F9wVznsbQop9KEJERKTS0d58VVD83HhmPTSL/av206RXEy7+18U079/c7bJKr2gbhcbDwdPvzNd4+EGji6DpaPCrX/41ioiInERhqoqy+Za1H61lzuNzSN2TSmR0JBc9fxH12lbxTZSzU2Dtn+HAvBKefxgy9oHxhAYXQNgYCLscajc787UiIiJlQGGqistJz2HxS4tZ9Pwi8rLz6HV3Lwb+ZSD+9fzdLq1iWAtJK51RrYRpkFLQL7ZeL2gW7fS5Cgp3t0YREanWFKaqidR9qcz9y1xWvbsKv2A/Bv51IL0m9MLTx9Pt0irW0U1OsNod4yxsBwiKdIJV2zugdjWYDhURkUpFYaqaObD2ALMensX2H7dTr209Lnr+IiLGROA0oK9hju2GhG8gIQYOznfWYHV5BtrfCx41LGSKiEi5UZiqhqy1bP1hKz8+/COJGxNpPqA5w14aRtNeJzenr0GO7YRlE2DvTGcK8Ly3oG5Xt6sSEZFqoLR780klZIyh3Yh2jF8znlFvjOLwpsO83fttYq6PIWVXitvluaN2Cxj4LfT9FNJ3wg89YPWfIDfD7cpERKQa08hUNZF1NIuFzy3kl3//grWWPg/2of9j/fEN8nW7NHdkHYFVD8P29yCgLfR+Expd6HZVIiJSRWmarwZJ2ZXCnMfnsO7jddRuUJtBTw6i+23d8fCqoYOQ+3+CpXdA2jZofQt0exF8q3hrCRERqXAKUzXQnmV7mPXQLHYt2EVoh1CG/nMobUe0rZmL1HMzYP1TEPtP8A2B7q9Ai2ugJv5ZiIjIOVGYqqGstcRNi2P2o7M5svUIrS9qzbCXhtGwS0O3S3NH0hr49XannUJQJHjVPvM1nv7Q4RFoekn51yciIpWWwlQNl5edx7JJy/j5qZ/JTM6k263dGPz0YAIbB7pdWsXLz4Mtk2DfDyU7P3WL89P8aujxCvg3Kt/6RESkUlKYEgAyjmQw/5n5LH19KZ4+nvR7pB99HuqDT20ft0urvPKyIfYFWP80eNaCbv+ENuM0RSgiUsMoTMkJjmw9wuxHZxMbE0tgk0AufPZCuv6hK8ZDAeGUjm5yFrIfnA8NBkLvKRDU3u2qRESkgqjPlJygXtt6XP3V1dw8/2YCmwbyzS3fMKXHFOJ/ine7tMorKByGzHVCVNJqmNkF1j/rjFyJiEiNpjBVg7UY0ILbfrmN6I+jyTiSwQdDPuDTSz/lUNwht0urnIwHtL0dLomFpqNh7Z+dxqCHfnW7MhERcZHCVA1nPAydr+vM3XF3M+QfQ9gxbweTOk1i5j0zOZZ4zO3yKif/xjDgC7jgG8hOgll9YPl9zu9FRKTG0ZopOcGxg8eYN3EeK6aswKe2DwOeGMB5952Hl5+X26VVTjlHYfXjzhOCxhMaDYGwMRB2mZ78ExGpRrQAXc5a4sZEfnzkR7Z8t4XglsEM+ccQOl7TsWY2/SyJpNWw4xPYHeN0W8dAaF8Ii4ZmYyCgldsViohIKShMyTnbPns7sx6axYG1Bwg7P4xh/xpGsz7N3C6r8rIWUtbD7mlOsEpe47xfN8oZsWoWDXU6qrWCiEgVozAlpZKfl8+a99fw059/Im1fGh2u6sBFz11E3dZ13S6t8kvb7gSrhGmQuBiwENwFek2C0H5uVyciIiWkMCVlIjstm0X/XMTify7G5ll639ubC/58AX7Bfm6XVjVk7IeEr2Hjc3BsJ7QdD1HPgU8dtysTEZEzUJiSMnV0z1Hm/nkuq99fjX89fwb+bSA9x/fE09vT7dKqhpw0WPtX2PwK+DWCnq8766pERKTSUtNOKVNBTYO47L3LuHPlnTTq2ogf7vuByZ0mE/dNHG6F8yrFOwB6/AuG/QK+obAgGuZHQ/petysTEZFzoDAl56xRVCNunH0j1864FuNh+Ozyz/jgwg/Yt3Kf26VVDSG9YPgyZ6pv3/fwXSRseQNsvtuViYjIWdA0n5SJvJw8Vr61knl/m0f64XS63tiVC5+9kKCwILdLqxpSt8LSO+HATxDa39m2pk6k21WJiEgBrZmSCpOZksmCvy/g15d/xXga+jzUh/6P9scnwMft0io/ayH+fVj5IOQeg46PQ4fHwNPX7cpERGo8hSmpcMk7kpnzpzms/+96AhoFMPjpwUTdEoWHp2aWzyjzIKy4H3Z+CkGRcN5baqMgIuIyLUCXChfcMpgrPr2CcUvGEdwqmBm3z+DNbm+ybdY2t0ur/PwaQL9PYNBMZ4Tqx/6w9C7ITnG7MhERKYbClJSrsPPDuHXRrVz5+ZVkp2Xz0cUf8fHIjzm44aDbpVV+TUbAqA0Qfj9smwLfdXAagIqISKWiMCXlzhhDx6s6cnfs3Qx9cSi7F+/mjS5v8O34b0k7kOZ2eZWbdwD0+HdBG4X6aqMgIlIJac2UVLj0w+n8/NTPLJ+0HC9/L/o/1p/zHzgfb39vt0ur3PJzIO5fsG4iePhA1PPQ9g4w+m8iEZHyVuo1U8aY4caYTcaYrcaYx05xziBjzGpjzAZjzM+lKViqt1ohtRjxyggmbJhA6yGt+emJn3g9/HXWfrQWm6+mn6fk4Q0dHoWR66BeT1h2F8weCCmxblcmIlKjnXFkyhjjCWwGhgIJwDLgWmvtxiLnBAOLgeHW2l3GmAbW2tMuitHIlBy34+cdzHpwFvtW7qNJzyYM+9cwWgxo4XZZldvJbRR6vwmtb3a7KhGRaqu0I9Q/EAUAACAASURBVFO9ga3W2u3W2mzgv8BlJ51zHRBjrd0FcKYgJVJUy4EtuX3Z7Vz+weWk7U9j6gVT+Sz6Mw5vOex2aZWXMU54uiQOQs5zWilkHXG7KhGRGqkkYaopsLvI64SC94pqD9Q1xswzxqwwxvyhuBsZY+4wxiw3xixPTEw8t4qlWjIehq43duWeTfcw+JnBbJu1jUkdJvHD/T+QcSTD7fIqL78G0Os/kHMUYl9wuxoRkRqpJGHKFPPeyXODXkAPYBRwMfAXY0z7311k7RRrbU9rbc/Q0NCzLlaqP+9a3lzwxAXct/U+om6JYulrS3m1zass+dcScrNy3S6vcgruDC2vg02vQob2RRQRqWglCVMJQLMir8OAk5/LTgB+sNYes9YeAuYDXcumRKmJAhoFMHrKaMavGU/T3k2Z9dAsJnWYxMavNuLWE6iVWueJztN+6591uxIRkRqnJGFqGdDOGNPKGOMDjAWmn3TON8AAY4yXMaYWcB6gR4yk1Bp0asAN/7uB67+/Hi9/L7648gumXjCVPUv3uF1a5RLYFtqMc5p7psW7XY2ISI1yxjBlrc0F7gH+hxOQPrfWbjDGjDfGjC84Jxb4AVgLLAXettauL7+ypaZpO7wt41eP55I3L+Hw5sO8fd7bfHXdVyTvTHa7tMqj018AD1j3pNuViIjUKGraKVVOVmoWi55fxJKXlmCt5fz7z6f/n/rjV8fP7dLct/Jh2PRvpxdVnQ5uVyMiUm1oo2OpVnwDfbnwmQu5Z/M9dLy6I4ueX8Rr7V5j2eRl5Ofmu12euzo8Bp61YO1f3a5ERKTGUJiSKqtOszqM+WAMty+7ndDIUGZOmMnkLpPZ/N3mmrtI3a8+RDwEu7+Cwxr5FRGpCApTUuU16dmEm+bdxDXTriE/N59PL/mUD4d+yP41+90uzR2RD4JvCKz9s9uViIjUCApTUi0YY4i4PIIJ6ycw/JXh7F+1nze7vck3474hdW+q2+VVLO8g6PAn2Pc/OKBtMkVEypsWoEu1lJGUwYJnF/Drq7/i6e1J30f60vfhvvjU9nG7tIqRmwEz2kJAK7hogbP9jIiInDMtQJcax7+uP8NeHMbdsXfTbmQ7fp74M6+3f51V760iP68GLFL38odOf4XERbD3e7erERGp1hSmpFqr16YeV31xFbcsvIWgZkFMv3U6b/V8i53zd7pdWvlrcysEtIa1T4CtAQFSRMQlClNSIzTv15xxS8ZxxadXkJGUwfsXvs+6T9e5XVb58vCGzk9B0mrY9YXb1YiIVFsKU1JjGGPoNLYTd627ixYDWhBzfQyr3l3ldlnlq8VYqNMJ1v4F8rVRtIhIeVCYkhrHN9CX6767jjbD2jB93HSWvr7U7ZLKj4cndH0GUrdA/PtuVyMiUi0pTEmN5F3Lm7HfjCXi8gi+v/d7Fr2wyO2Syk/TSyGkt7NnX16m29WIiFQ7ClNSY3n5enHl51fSaWwnZj86m3kT51XPzunGQNe/Q/pu2PKm29WIiFQ7Xm4XIOImT29Pxnw0Bq9aXvz85M9kH8tm6AtDMdWtL1OjIdBwCKx8ANY8dubzjSfU7wfNoiHsMvBvVP41iohUUQpTUuN5eHpw6VuX4l3LmyUvLiEnPYeRr43EeFSzQNX7Ddj2TsnaJOQeczqoLxsPy+6C0L4QNgaajXHaLYiISCGFKRHAeBhGvDoC71reLH5hMbnpuYx+ezQentVoJjywLUT9o+TnWwspG2B3DCRMg1UPOz/BXZ0Rq2ZjnCcFq9sonojIWdJ2MiJFWGuZ//R85v1tHh2v6ciYD8fg6e3pdlmVQ1o87J7mBKvERYCFgLZOqGoW7SxyN9UofIqIFHG67WQUpkSKsfilxfz48I+EXxrOpe9eSq2QWm6XVLlk7Ic9051RqwM/QX4O+Df+bSqwwUCnaaiISDWhMCVyDpZNXsbMu2fiXcubnuN70uehPgQ2DnS7rMonOxn2znSC1d7vIS8dfOpC09FOuGo8DLwURkWkalOYEjlHBzccZNFzi1j36To8vDzodms3+j3Sj+CWwW6XVjnlZsD+WU6w2jMDspPAsxY0Ge4Eq6aXgI/+7ESk6lGYEimlpO1JLHx+IWumriE/L58u13eh32P9CI0Mdbu0yis/Bw7+/Ns6q4x9YLyg4YVquSAiVY7ClEgZObrnKEteWsKKN1eQk5FDZHQkAx4fQOPujd0urXKz+XDoVydU7Z4GaVsBo5YLIlJlKEyJlLFjicf49ZVfWfr6UrJSsmg7vC0DnhhA8/7N3S6t8rMWUtb/NmKVtNp5P7irE6rCLgf/JiW7l09d8FCHFxEpfwpTIuUkMyWTZZOW8cu/fyE9MZ0WF7RgwBMDaD20dfXrol5e0rbD7q8hIQYSFwNn8f9JtVtAr8nQZES5lSciAgpTIuUuJz2HFW+tYPE/F5O6J5UmPZvQ//H+RFwWUf06qZenjP2wbxbkpp753Pxc2PomHI2FFtdCj5fBr0H51ygiNZLClEgFyc3KZe2Ha1n43EKStiUR2iGU/n/qT6exnfDwUkPLMpeXBRufgw3PglcgdH8JWt2kruwiUuYUpkQqWH5uPhu+2MDCvy/k4PqD1G1dl36P9qPrTV3x8tUanzKXEgtLb3c6szccAr3fhMA2Jb/+2E5nqnHf/yDifqc3lohIEQpTIi6x+ZbN325mwbML2LN0D4FNAjn/gfOJuiVKXdXLms2HrVNg9aOQnw2dJ0LEg6fuxJ4S6/TD2h0DSSud9zz9nUXtozaCT50KK11EKj+FKRGXWWuJnxPPgmcXsGPeDjx9PelwZQd63NGD5gOaa7F6WUrfA8vvdZ4UDO4K570NIT2dpwiPLP9t4+ajm5zzQ84veIpwDOQkw6zzoe1d0Ot1d7+HiFQqClMilciBtQdY8dYK1n64lqyULOpH1Kf7Hd3p+oeuGq0qS7tjYPk9kHkAml7qBKn0BDCe0HCwE57CLoNaTU+8bvn/webXYNhiqH++O7WLSKWjMCVSCeWk57Dh8w2smLKChCUJePo4o1Xd7+hOiwtaaLSqLGSnwOrHYNfn0GAAhEU7W9r41jv1NTmp8G2kc87wFdqwWUQAhSmRSu/AugOsmPLbaFVIeAg97uxBrwm9tGDdDbu/hgVjIOo56PCo29WISCVwujBVome1jTHDjTGbjDFbjTGPnea8XsaYPGPMledarEhN1LBzQ0a+NpKH9j7EZVMvw7+eP7MenMVHwz4i40iG2+XVPM0udzqxr3vSaSp6rqx1emfZ/LKrTUQqnTOGKWOMJ/AfYATQAbjWGNPhFOc9D/yvrIsUqSm8a3kTdVMU4xaP44pPryDhlwTe7f8uyTuT3S6t5un5mrMx89K7nFB0tmw+LLsLpjWGr5vDsntg/09Os1ERqVZKMjLVG9hqrd1urc0G/gtcVsx59wJfAQfLsD6RGqvT2E7cMOsG0val8c7577Bv5T63S6pZaoVB12dh/yzY+enZXZufC0tucjq0t74VQnrD9nfhpyEQ0xCW3AwJ0yFXo44i1UFJwlRTYHeR1wkF7xUyxjQFxgBvlF1pItJyYEtuXXQrnj6evHfBe2z5fovbJdUs7SY4QWjF/ZB1pGTX5GXDomtgx0dOGDv/HbggBq44BANioMkoSPgG5l8GMaGw4ErY8YmzWF5EqqSShKniHik6ecz7ZeBRa23eaW9kzB3GmOXGmOWJiYklrVGkRgvtEMq4JeMIaR/Cp6M/ZeXbK90uqebw8ITeUyD7iNMM9ExyM2D+5U5bhu7/ho6P/3bMq5bTz6rvB3DFQRg8C1re6HRtX3y9E6yWTVCoEqmCShKmEoBmRV6HAXtPOqcn8F9jzA7gSmCSMebyk29krZ1ire1pre0ZGhp6jiWL1DyBTQK5+eebaTO0DTNun8Hcv87FrSdxa5y6XZ1O6tvehoMLTn1eTirMGwn7fnACWMT9pz7XwxsaD4Xek2HMHhi6GFqPc6YFv+sAu6eV/fcQkXJTkjC1DGhnjGlljPEBxgLTi55grW1lrW1prW0JfAlMsNZ+XebVitRgvoG+jJ0+lm7jujH/6fl8c/M35GWfdjBYykrnv0HtlrD0Dmdz5ZNlJ8FPwyBxAfT5ENreXvJ7Gw8I7eMEq2G/gG8oLIiG+dGQfvJ/t4pIZXTGMGWtzQXuwXlKLxb43Fq7wRgz3hgzvrwLFJHfeHp7Mvqt0Qx6ahBrPljDJ6M+ITMl0+2yqj+v2tBrEhyNg43Pn3gsMxHmXAhJK6D/F9Dq+nP/nJBeMHyZ099q3/fwXSRseUOtFUQqOTXtFKmiVr+/mhm3zSC0QyjXfXcdQWFBbpdU/S0c6+zrN3ItBIU7I0c/XQTH4mHANGgyvOw+K3UrLL0TDvwEof2cqcM6v+tKIyIVpNRNO0Wk8om6KYrrZl5HUnwS7/R5hwPrDrhdUvXX42XwrAVLx0NaPMweAOm7YdAPZRukAALbwoWz4fz3IGUjfB8FaycWP80oIq5SmBKpwtoMbcMtC27B5lve6/8e2+eUolu3nJl/I+j2PBycBzO7Ou0SLpwNDQeWz+cZA61vhkvioNmVsP5JJ1TtmwX5Wi8nUlkoTIlUcY26NmLcL+Oo07wOH4/4mDUfrnG7pOqtzW3QYCB4+sFFc6H+eeX/mX4NoN8nMPA7yE2HuRc7ndV/vR32fq/RKhGXac2USDWRmZzJ51d8TvxP8Qx+ZjADHh+AMcW1iZNSy8uE/BzwDqz4z85Nhz0znPYJe7+D3DTwDnKagTaLhsbDwTug4usSqeZOt2ZKYUqkGsnLzmP6uOms/Wgt3W/vzqhJo/Dw0gB0tZWXCfvnOE1C90yHrEPg4QuNhznBqsko8Al2qTgPp+mpVG3WOtPNctow5VXRxYhI+fH08eTyDy4nqHkQC/++kKMJR7nq86vwCfBxuzQpD55+0HSU85Of63RT3x3jPHG4Z4a7tRkvaH8vdHlKI2VV1abXYe1foMMfIfKPTrNZKZZGpkSqqRVTVvDdhO9o1LUR1313HQGN9A9ajWEtHFkBB+ZCfrY7NaRugfj3oXYL6DUZmoxwpw45NxuegzV/gtqtnNYfwZ2h91sVs0awktI0n0gNtWXmFr646gtqhdbi+u+vJzRS2zhJBTq40OkafzQWWlzrtJbwa+B2VXI61jqjURuehRbXQZ+pzkMOyyZAxl5ntLHrM+6sF3SZwpRIDbZ3+V4+GfUJeTl5jP16LC0uaOF2SVKT5GXBxuecf5y9AqH7S9DqJvfX4eSkwvqnIamMn341HlD/fAgb44zmuP09z4a1sPIB2PQKtLndGVE8vu4t5yisfhy2TIJaYc6OAE0vcbfeCqYwJVLDJcUn8fGIjzm8+TAdr+pI/8f706hrI7fLkpokJRaW3u6s62o4BHq/CYFt3KklYQYsnwDpe6BeTzBluFA+LwOS1wIWAtpAszEQFu1Mj5lK/DBIfh4sG+9s6B3+f9D938UHwcQlzt9jygZofg30eAX8G1Z8vS5QmBIRMpIyWPTCIpb9ZxnZqdm0G9WOAU8MoFmfZm6XJjWFzYetU2D1o85ars4TIeLBilvYnLEPVvwf7PoC6nSC895yRpHK/HMOwJ5vnPYVB+Y4bTT8G0PY5c6IVcNBlWsxd34uLLkJdn4CHZ+ALk+ffkQtL9vZo3LDM86+ld1ehNa3VK1RuHOgMCUihTKSMlj6+lJ+fflXMo5k0HJwSwY8MYBWF7ZSXyqpGOl7YPm9zlOHwV2dLu8lGbXxDna27TnbdVc2H7a9A6v+6LST6PxXiHgYPCvgKdfsFKcf2O6Yggar6c73aDraGbVqfDF41Sr952QcgP0/OqNhZzMKlpcFi651/i66/gM6Plbyz0yJc9bEJS6ABoOc/SOD2p1T+VWBwpSI/E52WjYrpqxg8YuLSduXRtPeTRnwxADaX9Ie46FQJRVg9zRYfo+zsLmkjAeE9ndGeJqNcZ4WPJ2jm5x/8A/OL/gH/00Ial+qss9Zbgbsn+V87z3TITsJPP2dRqvNop01SGfTFywt3rlXQgwkLgYK/j0/PgrWLNrp1n+qUbDcdFhwBez7AXq8CuH3nv13svnO1OCqRwqC6t8g8uHKNfJWRhSmROSUcjNzWf3+ahY9v4jk+GQadG5A/z/1p+NVHdXwU8pffq6zuLkkju2EhK+dUZTkdc57dbs7oaHZGAiK/G2qKS8bYl+A9c84gaX7i9D61sozFZWf4wS83dOc75Ox1+nN1fDCgnVWlzt7QRZlrbNW6XgvsaTVzvvBXQuatI6E1M0njoL51HVGwcLGOM1cj4+C5aTCz6OdGs57G9rcWrrvk7EPlt8Hu7+stm0UFKZE5Izyc/NZ9+k6Fv5jIYdiDxHQOIBu47rRfVx3glu61UVb5BRStzpBZHcMHP7FeS8o3AkN9XrAuichZT00v7pgkXQlfuDC5sPhZb+FpNQtgIH6fZyQVLcb7Pufczxtq3MstO9vo3MBrX9/z2JHwWo506RNL3OeyjuyHPp8BC3Hlt13SZheNm0UrIX03U7oLAnvIPAr39YvClMiUmI237L5u82seHMFW2ZuAaDtxW3pcWcP2o1qh6e3tgiRSiZ9b8GC7xinUanNK3h8f3LVe3zfWkjZWGT0aZXzfuGoVTSEXXZ24bBwFCzGGdnL2AsePtD/c+deZe13bRQmO136z8Tmw6FffvvuadtL/pnt7nLaNZQjhSkROScpu1JY+c5KVr2zitQ9qc5o1a3d6H6bRqukkso6AoeXQmi/6tFYMi0ektdDgwFls8+izYfDy53pv/JeLJ642Fmvdro2Cvk5TgDePc0Jepn7nfVWDYcU7C1Zp2SfFdi+3KcVFaZEpFTyc/PZMnMLK6acOFrV/Y7utL+kvUarRKR4xbVRaHEN7Ds+BTkDcpILpiBHOtOWZxOiKpDClIiUmZNHq/zr+RN+aTgR0RG0GdoGLz/tny4iJynaRsF4OlOxPvUKWkREQ6Oh4OXvdpWnpTAlImUuPzefLd9vYePnG9k0YxNZKVn4BPjQbmQ7IsZE0G5kO3yDfN0uU0QqC5sP2993HgxoMhIaXFClWigoTIlIucrLziN+bjxx0+KI+zqOYweO4enjSeuhrYmMjiT80nBq1S+DxoQiIi5RmBKRCpOfl0/CkgRip8USFxNH8o5kjIeh5aCWdBvXjcjoSE0FikiVozAlIq6w1rJ/9X7ipsWx7uN1JG1Pwj/En643daXH7T2oH1Hf7RJFREpEYUpEXGfzLfE/xbNiygripsWRn5tPiwta0OPOHhqtEpFKT2FKRCqVtANprJ66mpVvrSRpWxL+9ZzRqu63dyc0sny7GIuInAuFKRGplGy+JX5uPCunrCR2Wiz5Ofk0H9CcC/58AW2GtXG7PBGRQgpTIlLpHTt4jNVTV7N88nKSdyTTdnhbhr44lAYdG7hdmoiIwpSIVB25Wbks+88y5j89n6yjWXS7rRuDnxpMQMMAt0sTkRrsdGHKo6KLERE5HS9fL/o82Id7t95Lr3t6sfrd1bzW9jXmPzufnIwS7iAvIlKBFKZEpFKqFVKLEa+MYMKGCbS+qDVz/zyX19u/zpoP12Dz3RlRFxEpjsKUiFRqIe1DuGbaNdw07yZqN6zN13/4mrd6v8XO+TvdLk1EBFCYEpEqouXAlty+9HbGfDiGYweOMXXgVD4a/hFx3zg9q0RE3FKiMGWMGW6M2WSM2WqMeayY49cbY9YW/Cw2xnQt+1JFpKYzHoYuN3Thnk33cOHfL+TA2gN8dvlnvNziZeb+dS7JO5PdLlFEaqAzPs1njPEENgNDgQRgGXCttXZjkXP6ArHW2iRjzAhgorX2vNPdV0/ziUhp5efms/m7zaycspIt328BoO3wtvS4swftR7XHw0uD7yJSNk73NF9J9m/oDWy11m4vuNl/gcuAwjBlrV1c5PxfgLBzL1dEpGQ8vDyIuCyCiMsiSN6ZzKp3VrHqnVV8dvlnBDQOoNu4bnQf153glsFulyoi1VhJRqauBIZba28reH0jcJ619p5TnP8wEHH8/JOO3QHcAdC8efMeO3dqAamIlK3iRqvaDGtDx6s7En5pOLXq13K5QhGpiko7MmWKea/YBGaMGQyMA/oXd9xaOwWYAs40Xwk+W0TkrPxutOrdVax5fw3Tx03HeBhaDGxBxJgIIsdEEhQW5Ha5IlINlGRkqg/OGqiLC17/CcBa+4+TzusCTANGWGs3n+mDtWZKRCqKtZb9q/cTGxNLXEwciRsTAWjauykR0U6wCmkf4nKVIlKZlWo7GWOMF84C9CHAHpwF6NdZazcUOac58BPwh5PWT52SwpSIuOVQ3CFipznBau/yvQCEdgwlMjqSyOhIGnZtiDHFDcqLSE1V6r35jDEjgZcBT+Bda+2zxpjxANbaN4wxbwNXAMcXQeWe6gOPU5gSkcogZVcKcV/HERsTy64Fu7D5luBWwYXBKuz8MIyHgpVITaeNjkVESuDYwWNsmrGJuJg4tv24jfycfAIaBRB+eTiR0ZG0HNQST29Pt8sUERcoTImInKXMlEy2zNxC3LQ4tszcQs6xHPyC/Qi/NJyIMRG0GdYG71rebpcpIhVEYUpEpBRyMnLY/uN2YmNi2TR9E5lJmXjX8qbtiLZERkfSblQ7/Or4uV2miJQjhSkRkTKSl5PHzp93OgvYp8WRti8ND28PWl/UmogxTkuG2g1qu12miJQxhSkRkXJg8y17lu4hNiaW2JhYkrYlYTwMzfs3L2y5UKd5HbfLFJEyoDAlIlLOrLUcXHewsOXCgbUHAGjco3Hhk4H1I+q7XKWInCuFKRGRCnZk65HCYJXwSwIA9SPqOyNW0ZE07t5YvaxEqhCFKRERFx3dc5RN32wiNiaWHfN2YPMsdZrXcba1iY6kWb9meHh6uF2miJyGwpSISCWRfjidzTM2ExsTy7ZZ28jLyqNWaC3CL3N6WbW6sBVeviXZNlVEKpLClIhIJZSVmsXWH7YSFxPH5m83k52WjW+QL+0vaU9EdARth7fFp7aP22WKCApTIiKVXm5mLtvnFPSy+mYTGYcz8PLzos3FbYiMjqT96Pb41/V3u0yRGkthSkSkCsnPzWfXwl2FLRdS96Ti4eVBy8EtC4NVUNMgt8sUqVEUpkREqiibb9m7fC+x02KJ/SqWI1uOAFC3dV1aXNCC5gOa03xAc+q1raenA0XKkcKUiEg1YK0lcWMi22ZtY9f8XexcsJOMwxkABDQKoPmA5oUBq0GnBnpCUKQMKUyJiFRD1loOxR5i54Kd7Fqwi53zd3J091EAfOv40rx/c8IvDSf8snACGga4XK1I1aYwJSJSQyTvTHaC1YKdxM+OJ2l7Ehho3u+3LW6CWwa7XaZIlaMwJSJSA1lrObj+ILExJ21x071xYcPQ+pH1tdZKpAQUpkREhCPbjhA3LY7YmFgSljhb3ISEhxTuHdi4h7a4ETkVhSkRETlB6t5U4r6JIy4mjvi58dg8S1CzoMIRq+b9m2sBu0gRClMiInJKGUcy2DRjE3ExcWz931ZtcSNSDIUpEREpkey0bLZ8v4W4aQVb3KRm4xPoQ/tL2hMZHelscROgLW6k5lGYEhGRs5ablUv8nPjCLW7SD6U7W9wMa0NEdATho8Pxr6ctbqRmUJgSEZFSKdziZprzZODRhKMYT0Orwa2IiI4g4vIIAhsHul2mSLlRmBIRkTJjbcEWNwUtFw5vPgwGmvVpVtjLqm7rum6XKVKmFKZERKRcHO/CfnxT5v2r9gPQoHMDmvRsQv3I+oRGhlI/sj7BLYP1hKBUWQpTIiJSIZLik4ibFseWmVs4uP4gxw4cKzzm6etJSPuQwnB1PGiFtA/By09PC0rlpjAlIiKuyEjK4FDcIQ7FHiIxNpFDsc7vk+KToOCfH+NhCG4VTP2I+ieMZIVGhuIX7OfuFxApcLowpf8UEBGRcuNf159mfZrRrE+zE97Pycjh8ObDTrgqEra2z95OXlZe4XkBjQKcUayTglZgk0B1a5dKQ2FKREQqnLe/N426NqJR10YnvJ+fl09yfLIzilUQsg7FHmLdJ+vISskqPM83yLcwYBWGrIj61G1dFw8vrcuSiqVpPhERqfSstaTtT/vddGFibCJp+9IKz/P08aRe23oENgmkVv1a1AqtdcKvtUNr//Y6pJaCl5SYpvlERKRKM8YQ2DiQwMaBtLqw1QnHMpMznVGsOCdcHd50mGMHjpG8I5ljicdOGNE6WWCTwGKnEQMaBZzVNKK1luzUbDKOZOBd2xv/ev56crEGUZgSEZEqzS/Yj7Dzwwg7P6zY43nZeaQfTif9UDrpic6vxxKPkZ6YXjiluOaDNWSnZhde41vH94SnDoPCgshMynSuK3Kfwl8PpZOX/dtaLwz41/M/cSTspBGyoLAg6kfWp3aD2lr/VcWVaJrPGDMceAXwBN621j530nFTcHwkkA7cbK1debp7appPREQqC2stqXtTi51GLNreAZzwVjQU+df/LTT51/Mn+1h2sYHreBCzeSf+u+tX1++E4FY/whkhC24ZjPFQyKosSjXNZ4zxBP4DDAUSgGXGmOnW2o1FThsBtCv4OQ+YXPCriIhIpWeMIahpEEFNg2h9UesTjmUkZZC2Lw3/ev74h/jj6e15zp9jrSUzOdMZFduRXDg1eSj2EJtnbGbVO6sKz/Xy8yIkPISQ9iHU/v/27i7EqioM4/j/YVKccQJt/GTGSQuDNMpuRDDQJMRKmm4Cg0i6kcDAoAgLIgq8jW4CkZKMPkQoS0IosaJuKrUMFY1ELGXUMcM+GC0/3i721s58aCfPOGsdz/OD4ey9zpnZLzwM55291qwzcfTANV8Xmrkaa7LaVTPNNxvYHxEHACStB7qAymaqC3gzittcX0kaI2lyRBwZ8orNzMyGUfPYZprHDs0HOku6+PPabmnj5oU393m+90TvgH25ju48Su/xXk6fPH3JnztqzChawaXzugAABHxJREFUxrXQNLIxm6qZS2Yy7/l5ya5fTTPVDhyqOD/MwLtOg72mHejTTElaBiwD6Ozs/L+1mpmZXdNa2lronNtJ59yB75Hnzpzj1IlTfaYM+6/bOn/mfIKq02ud1Jr0+tU0U4NN2PZfaFXNa4iINcAaKNZMVXFtMzMzA5pGNNE6qTV542ADVfN/m4eByq1rO4DuK3iNmZmZ2TWnmmZqGzBd0jRJI4ElwKZ+r9kEPKrCHOA3r5cyMzOzRvCf03wRcVbSE8DHFFsjrI2IPZIeL59fDWym2BZhP8XWCI9dvZLNzMzM8lHVpp0RsZmiYaocW11xHMDyoS3NzMzMLH/e697MzMysBm6mzMzMzGrgZsrMzMysBm6mzMzMzGpQ1QcdX5ULS8eBn67w28cBvwxhOTa0nE++nE3enE++nE3ehiOfGyNi/GBPJGumaiFp+6U+udnScz75cjZ5cz75cjZ5S52Pp/nMzMzMauBmyszMzKwG9dpMrUldgF2W88mXs8mb88mXs8lb0nzqcs2UmZmZWS7q9c6UmZmZWRbcTJmZmZnVoO6aKUmLJP0gab+klanraWSS1krqkbS7YuwGSVsk/Vg+jk1ZYyOTNEXSZ5L2StojaUU57owSkzRK0jeSvi+zebEcdzaZkNQk6TtJH5XnziYTkg5K2iVpp6Tt5VjSfOqqmZLUBLwK3AvMAB6WNCNtVQ3tDWBRv7GVwNaImA5sLc8tjbPAUxFxKzAHWF7+vjij9P4CFkTEHcAsYJGkOTibnKwA9lacO5u83B0Rsyr2lkqaT101U8BsYH9EHIiIv4H1QFfimhpWRHwB/NpvuAtYVx6vAx4c1qLsoog4EhHflsd/ULwxtOOMkovCn+XpiPIrcDZZkNQB3A+8VjHsbPKWNJ96a6bagUMV54fLMcvHxIg4AsWbOTAhcT0GSJoK3Al8jTPKQjmNtBPoAbZEhLPJxyvAM8D5ijFnk48APpG0Q9KycixpPtcN58WGgAYZ894OZpchqRV4D3gyIn6XBvs1suEWEeeAWZLGABsl3Za6JgNJi4GeiNghaX7qemxQcyOiW9IEYIukfakLqrc7U4eBKRXnHUB3olpscMckTQYoH3sS19PQJI2gaKTejoj3y2FnlJGIOAl8TrH+0NmkNxd4QNJBiqUkCyS9hbPJRkR0l489wEaKJUBJ86m3ZmobMF3SNEkjgSXApsQ1WV+bgKXl8VLgw4S1NDQVt6BeB/ZGxMsVTzmjxCSNL+9IIakZuAfYh7NJLiKejYiOiJhK8R7zaUQ8grPJgqTRkq6/cAwsBHaTOJ+62wFd0n0U89lNwNqIWJW4pIYl6V1gPjAOOAa8AHwAbAA6gZ+BhyKi/yJ1GwaS7gK+BHbx79qP5yjWTTmjhCTdTrFItonij9oNEfGSpDacTTbKab6nI2Kxs8mDpJso7kZBsVTpnYhYlTqfumumzMzMzHJSb9N8ZmZmZllxM2VmZmZWAzdTZmZmZjVwM2VmZmZWAzdTZmZmZjVwM2VmZmZWAzdTZmZmZjX4B3bBbOInEYUXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "depth, best_depth, train_errors, test_errors = cross_validation(X_train, y_train, X_test, y_test, model_name='dctree')\n",
    "plt.title(f\"Optimal depth = {best_depth}\")\n",
    "plt.plot(depth, train_errors, c='purple', label='Training error')\n",
    "plt.plot(depth, test_errors, c='orange', label='Testing error')\n",
    "plt.legend()\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GERD'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', C=0.05)\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "logreg.predict(x_test.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9508196721311475\n"
     ]
    }
   ],
   "source": [
    "print(logreg.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "c, best_c, train_errors, test_errors = cross_validation(X_train, y_train, X_test, y_test, model_name='logreg')\n",
    "c_reg = []\n",
    "for each in c: \n",
    "    c_reg.append(1/(each*20))\n",
    "plt.title(f\"Optimal C = {best_c}\")\n",
    "plt.plot(c_reg, train_errors, c='purple', label='Training error')\n",
    "plt.plot(c_reg, test_errors, c='orange', label='Testing error')\n",
    "plt.legend()\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GERD'], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector Machine (SVM)\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(C=0.3)\n",
    "svm.fit(X_train, y_train)\n",
    "svm.predict(x_test.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7868852459016393\n"
     ]
    }
   ],
   "source": [
    "print(svm.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "c, best_c, train_errors, test_errors = cross_validation(X_train, y_train, X_test, y_test, model_name='svm')\n",
    "c_reg = []\n",
    "for each in c: \n",
    "    c_reg.append(1/(each*10))\n",
    "plt.title(f\"Optimal C = {best_c}\")\n",
    "plt.plot(c_reg, train_errors, c='purple', label='Training error')\n",
    "plt.plot(c_reg, test_errors, c='orange', label='Testing error')\n",
    "plt.legend()\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "\n",
    "def evaluate_model(model, X, y):\n",
    "    cv = RepeatedStratifiedKFold(n_repeats=3, random_state=1)\n",
    "    scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
    "    return scores\n",
    "    \n",
    "def get_stacking():\n",
    "    level0 = list()\n",
    "    level0.append(('lr', LogisticRegression(solver='liblinear', C=0.03)))\n",
    "    level0.append(('knn', KNeighborsClassifier(n_neighbors=6, metric='cosine')))\n",
    "    level0.append(('dctree', DecisionTreeClassifier(splitter='random', max_depth=34)))\n",
    "    level0.append(('svm', SVC(C=0.1)))\n",
    "\n",
    "    level1 = LogisticRegression()\n",
    "\n",
    "    model = StackingClassifier(estimators=level0, final_estimator=level1, cv=5)\n",
    "    return level0, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 0.962714 (0.014065)\n",
      "knn 1.000000 (0.000000)\n",
      "dctree 0.603024 (0.040069)\n",
      "svm 0.185246 (0.018835)\n",
      "ensemble 0.787158 (0.067706)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models, model = get_stacking()\n",
    "results, names = list(), list()\n",
    "\n",
    "for each in models:\n",
    "    scores = evaluate_model(each[1], examples, labels)\n",
    "    results.append(scores)\n",
    "    names.append(each[0])\n",
    "    print('%s %f (%f)' % (each[0], np.mean(scores), np.std(scores)))\n",
    "\n",
    "ensemble_score = evaluate_model(model, examples, labels)\n",
    "results.append(ensemble_score)\n",
    "names.append('ensemble')\n",
    "print('%s %f (%f)' % ('ensemble', np.mean(ensemble_score), np.std(ensemble_score)))\n",
    "\n",
    "plt.boxplot(results, labels=names, showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class: Migraine\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\majab\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "#Using pickle for dumping the model\n",
    "\n",
    "model.fit(examples, labels.ravel())\n",
    "\n",
    "with open('models/fitted_model.pickle', 'wb') as modelFile:\n",
    "    model_final = pickle.dump(model, modelFile)\n",
    "\n",
    "with open('models/fitted_model.pickle', 'rb') as modelFile:\n",
    "    model_final = pickle.load(modelFile)\n",
    "\n",
    "predicted = model_final.predict(x_test.reshape(1, -1))[0]\n",
    "print(f'Predicted Class: {predicted}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Additional information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9752040670466473e+18\n"
     ]
    }
   ],
   "source": [
    "#Condition number of X_train \n",
    "\n",
    "print(np.linalg.cond(examples.astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation matrix\n",
    "\n",
    "plt.matshow(df.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}