This script uses 19 classes of massage therapy to recommend a massage for a client after excluding all other massage modalities based on the contraindications for each massage modality and benefit of each massage modality needed.
modes <- read.csv('MassageModalities2.csv', sep=',', header=TRUE, na.strings=c('',' ','NA'))
colnames(modes)[1] <- 'modality'
head(modes)## modality
## 1 Swedish Massage
## 2 Shiatsu Massage
## 3 Prenatal Massage
## 4 Sports Massage
## 5 Lymphatic Drainage Massage
## 6 Reflexology Massage
## Description
## 1 Traditional spa or clinic massage with hands, palms, elbows, forearms of massage therapist used with glides and varying pressure applied along the muscle fibers of the body with varying amounts of pressure to get up to deeper layers of the body while avoiding causing pain to the client and avoiding discomfort to the client. Avoids massaging fast and avoids choppy short motions that disrupt relaxation, but at the same rhythm to promote relaxation and calm the nervous system.
## 2 Accupressure or small and localized pressure is applied with knuckles, thumbs, or tools to deep muscle layers along the spine and limbs or meridians of the body to promote healing of muscle aches, improve relaxation, calm nervous system, and reduce stress for repetitive cycles of 3-5 seconds with each pressure application, sometimes longer. Helps with muscle tonicity, can be adjusted to the right pressure for each client and can be combined with other massage modalities like hot stone therapy, cold stone therapy, aromatheray, CBD or cannabidiol products, cupping therapy, stretching, massage gun therapy, biofreeze, craniosacral therapy, reflexology, lymphatic drainage, sports , and deep tissue massage
## 3 swedish massage up to medium pressure or middle muscle layers to help relax client going through body changes due to pregnancy and associated aches in the feet, low back, and upper back, avoids the joints and major artery sites of client, client cannot be a high risk pregnancy or within the first trimester unless not a high risk and has had massage regularly for at least one year allowing the clients body to welcome massage. Can be relaxing, helps detox stress from body, improves circulation, and other benefits of massage
## 4 Massage that is focused on sports related discomforts around tendons and tight muscles to improve muscle recovery or improve muscle performance, improve range of motion, can include stretching and percussion and can be combined with other massage modalities like hot stone therapy, cold stone therapy, aromatheray, CBD or cannabidiol products, cupping therapy, stretching, massage gun therapy, biofreeze, craniosacral therapy, reflexology, lymphatic drainage, sports , and deep tissue massage
## 5 Massage focused on superficial muscles and lymphatic system of the body to drain edema or excess fluid retention from the limbs to the torso or heart to be eliminated, it is rhythmic and slower to align with the lymphatic system of lymph nodes that runs along the circulatory system to clean the blood and improve immunity as well as sometimes elevate the limbs to help with drainage of excess water in the limbs from surgery, cancer, or other health related conditions as long as not a contraindication or client has a doctor's approval and then this massage can be combined with other massage modalities like hot stone therapy, cold stone therapy, aromatheray, CBD or cannabidiol products, cupping therapy, stretching, massage gun therapy, biofreeze, craniosacral therapy, reflexology, lymphatic drainage, sports massage, shiatsu massage, instrument assisted soft tissue mobilization or IASTM tools, myofascial massage, trigger point therapy, and deep tissue massage
## 6 Focus of this massage is to stimulate body functions and improve health of clients who find it more relaxing to have their scalp, hands, and/or feet massaged where these areas of the body have alignments in traditional chinese medicine to certain organs in the body reflected in locations on the feet, hands, and scalp to stimulate healing to those areas of the client's body. Historically, this type of massage was the only type of massage allowed for health monitored clients recovering or living with certain health conditions such as cancer. If no contraindications or has a doctor's note, this massage can be combined with other massage modalities like hot stone therapy, cold stone therapy, aromatheray, CBD or cannabidiol products, cupping therapy, stretching, massage gun therapy, biofreeze, craniosacral therapy, reflexology, lymphatic drainage, sports massage, shiatsu massage, instrument assisted soft tissue mobilization or IASTM tools, myofascial massage, trigger point therapy, and deep tissue massage
## benefits
## 1 improves tight muscles, loosens tight muscle fascia, improves circulation, improves relaxation, improves immunity, improves sleep, improves range of motion
## 2 improved circulation, relaxing, detoxes, breaks apart adhesions, improves sleep, improves healing
## 3 improved circulation, better sleep, pain relief, relaxing effect, soothing effect, calming effect, improves range of motion, helps with congestion, helps detox, helps clean old bruises, modified massage if doctor approved for high risk or other health condition
## 4 improves healing from sports or work related muscle and tendon pains and discomforts, relaxing, improves range of motion, improves flexibility, improves immunity, improves sleep, improves workouts, improves musle strength, improves muscle recovery, helps prevent muscle strain, helps prevent tendon inflammation, helps prevent injury, helps muscle performance
## 5 improves circulation,
## 6 relaxing, improves circulation, improves sleep, helps with pain and discomfort, increases immunity, recommended for people with pain sensitivity or people who cannot be touched because it causes discomfort by tickling, itching, or hurting them like some cases of fibromyalgia and neuropathic pain
## contraindications
## 1 dehydration, fever, rashes, infection, some mental disorders, nausea, epilepsy, aneurism history, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing,
## 2 dehydration, fever, rashes, infection, some mental disorders, nausea, epilepsy, aneurism history, cancer, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing,
## 3 dehydration, Be a high risk pregnancy (history of painful menstruation, history of uterine diseases such as fibroids, cysts, endometriosis, history of miscarriages, over the age of 35 and first child, diabetes, blood disorders, heart disorders) fever, rashes, infection, some mental disorders, nausea, epilepsy, aneurism history, cancer, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing,
## 4 dehydration, fever, rashes, infection, some mental disorders, nausea, epilepsy, pregnant, aneurism history, cancer, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing,
## 5 dehydration, fever, rashes, infection, some mental disorders, nausea, epilepsy, pregnant, on wound, pregnant, heart condition, aneurism history, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing,
## 6 dehydration, fever, rashes, infection, some mental disorders, nausea, epilepsy, , aneurism history, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing, not recommended for people who have neuropathic pain or tickling of the feet
## sideEffects
## 1 if no contraindications for massage exist in client, can make client tired
## 2 can cause muscle soreness for a few days from breaking apart adhesions and pressure used
## 3 can make client dizzy or nausious if first massage and not familiar with massage or early stages of pregnancy and a first time pregnancy but not a high risk pregnancy
## 4 can cause muscle soreness for a few days from breaking apart adhesions and pressure used
## 5 client could get dizzy or feel nausious if he or she has an underlying health condition such as a circulatory problem, blood disorder, blockage somewhere in the body
## 6 can make client light headedLets use python 3 to tokenize the contraindications into three adjacent word pairs, and to tokenize the the benefits into two adjacent word pairs using the ngrams tokenization method.
library(reticulate)## Warning: package 'reticulate' was built under R version 3.6.3library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
library(tm)## Loading required package: NLPlibrary(textstem)## Loading required package: koRpus.lang.en## Loading required package: koRpus## Loading required package: sylly## For information on available language packages for 'koRpus', run
##
## available.koRpus.lang()
##
## and see ?install.koRpus.lang()library(tidytext)## Warning: package 'tidytext' was built under R version 3.6.3library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------ tidyverse 1.3.0 --## v ggplot2 3.2.1 v readr 1.3.1
## v tibble 2.1.3 v purrr 0.3.3
## v tidyr 1.0.0 v forcats 0.4.0## -- Conflicts --------------------------------------------------------------------------- tidyverse_conflicts() --
## x ggplot2::annotate() masks NLP::annotate()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x readr::tokenize() masks koRpus::tokenize()conda_list(conda = "auto") ## name python
## 1 Anaconda2 C:\\Users\\m\\Anaconda2\\python.exe
## 2 djangoenv C:\\Users\\m\\Anaconda2\\envs\\djangoenv\\python.exe
## 3 python36 C:\\Users\\m\\Anaconda2\\envs\\python36\\python.exe
## 4 python37 C:\\Users\\m\\Anaconda2\\envs\\python37\\python.exe
## 5 r-reticulate C:\\Users\\m\\Anaconda2\\envs\\r-reticulate\\python.exeuse_condaenv(condaenv = "python36")import pandas as pd
import matplotlib.pyplot as plt
from textblob import TextBlob
import sklearn
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
import re
import string
import nltk
np.random.seed(47) modes = pd.read_csv('MassageModalities2.csv', encoding = 'unicode_escape')
print(modes.head())## modality ... sideEffects
## 0 Swedish Massage ... if no contraindications for massage exist in c...
## 1 Shiatsu Massage ... can cause muscle soreness for a few days from ...
## 2 Prenatal Massage ... can make client dizzy or nausious if first mas...
## 3 Sports Massage ... can cause muscle soreness for a few days from ...
## 4 Lymphatic Drainage Massage ... client could get dizzy or feel nausious if he ...
##
## [5 rows x 5 columns]print(modes.columns)## Index(['modality', 'Description', 'benefits', 'contraindications',
## 'sideEffects'],
## dtype='object')print(modes['modality'].unique())## ['Swedish Massage' 'Shiatsu Massage' 'Prenatal Massage' 'Sports Massage'
## 'Lymphatic Drainage Massage' 'Reflexology Massage' 'Craniosacral Massage'
## 'Trigger Point Therapy' 'Myofascial Massage' 'Deep tissue Massage'
## 'Hot Stone Therapy Massage' 'Biofreeze Muscle Pain Relief Gel'
## 'Massage Gun Therapy' 'Aromatherapy' 'Cupping Therapy' 'Stretching'
## 'Cold Stone Therapy' 'Cannabidiol (CBD) Massage Balm'
## 'Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage']import numpy as np
modes = modes.reindex(np.random.permutation(modes.index))
print(modes.head())## modality ... sideEffects
## 39 Shiatsu Massage ... can cause muscle soreness for a few days from ...
## 282 Cold Stone Therapy ... if placed directly on the skin it could slight...
## 351 Deep tissue Massage ... can cause muscle soreness for a few days from ...
## 128 Cupping Therapy ... can leave marks on the body that last up to t...
## 416 Cannabidiol (CBD) Massage Balm ... depends on the ingredients and amount of CBD i...
##
## [5 rows x 5 columns]print(modes.tail())## modality ... sideEffects
## 72 Stretching ... Can cause muscle soreness for a few days after...
## 264 Cannabidiol (CBD) Massage Balm ... depends on the ingredients and amount of CBD i...
## 327 Lymphatic Drainage Massage ... client could get dizzy or feel nausious if he ...
## 390 Hot Stone Therapy Massage ... can burn if stones are too hot for the client,...
## 135 Prenatal Massage ... can make client dizzy or nausious if first mas...
##
## [5 rows x 5 columns]modes.groupby('modality').describe()## Description ... sideEffects
## count ... freq
## modality ...
## Aromatherapy 24 ... 24
## Biofreeze Muscle Pain Relief Gel 24 ... 24
## Cannabidiol (CBD) Massage Balm 24 ... 24
## Cold Stone Therapy 24 ... 24
## Craniosacral Massage 24 ... 24
## Cupping Therapy 24 ... 24
## Deep tissue Massage 24 ... 24
## Hot Stone Therapy Massage 24 ... 24
## Instrument Assisted Soft Tissue Mobilization (I... 24 ... 24
## Lymphatic Drainage Massage 24 ... 24
## Massage Gun Therapy 24 ... 24
## Myofascial Massage 24 ... 24
## Prenatal Massage 24 ... 24
## Reflexology Massage 24 ... 24
## Shiatsu Massage 24 ... 24
## Sports Massage 24 ... 24
## Stretching 24 ... 24
## Swedish Massage 24 ... 24
## Trigger Point Therapy 24 ... 24
##
## [19 rows x 16 columns]stopwords = nltk.corpus.stopwords.words('english')
ps=nltk.PorterStemmer()
wn=nltk.WordNetLemmatizer()def lemmatize(text):
text="".join([word.lower() for word in text if word not in string.punctuation])
tokens=re.split('\W+', text)
text=" ".join([wn.lemmatize(word) for word in tokens if word not in stopwords])#unlisted with N-grams vectorization
#text=[wn.lemmatize(word) for word in tokens if word not in stopwords]#when using count Vectorization its a list
#or else single letters returned.
return textmodes['lemmatizedBenefits']=modes['benefits'].apply(lambda x: lemmatize(x))
modes['lemmatizedContraindications']=modes['contraindications'].apply(lambda x: lemmatize(x))modes.columns## Index(['modality', 'Description', 'benefits', 'contraindications',
## 'sideEffects', 'lemmatizedBenefits', 'lemmatizedContraindications'],
## dtype='object')modes.head()## modality ... lemmatizedContraindications
## 39 Shiatsu Massage ... dehydration fever rash infection mental disord...
## 282 Cold Stone Therapy ... dehydration fever rash infection mental disord...
## 351 Deep tissue Massage ... dehydration fever rash infection mental disord...
## 128 Cupping Therapy ... dehydration fever rash infection mental disord...
## 416 Cannabidiol (CBD) Massage Balm ... dehydration fever rash infection mental disord...
##
## [5 rows x 7 columns]from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(modes[['lemmatizedContraindications','lemmatizedBenefits']],modes['modality'],test_size=0.15)X_train.head()## lemmatizedContraindications lemmatizedBenefits
## 350 dehydration local site wound sore sensitive sk... muscle trauma muscle spasm pain trigger point
## 344 dehydration high risk pregnancy history painfu... improved circulation better sleep pain relief ...
## 438 dehydration fever rash infection mental disord... improved circulation relaxing detox break apar...
## 390 dehydration fever rash infection mental disord... relax client reduce stress calming increase h...
## 337 dehydration fever rash infection mental disord... improved circulation better sleep pain relief ...from sklearn.feature_extraction.text import CountVectorizern_gram3_vect=CountVectorizer(ngram_range=(3,3))
n_gram2_vect=CountVectorizer(ngram_range=(2,2))type(X_train['lemmatizedBenefits'])## <class 'pandas.core.series.Series'>X_train['lemmatizedBenefits'].head()## 350 muscle trauma muscle spasm pain trigger point
## 344 improved circulation better sleep pain relief ...
## 438 improved circulation relaxing detox break apar...
## 390 relax client reduce stress calming increase h...
## 337 improved circulation better sleep pain relief ...
## Name: lemmatizedBenefits, dtype: objecttype(X_train['lemmatizedContraindications'])## <class 'pandas.core.series.Series'>X_train['lemmatizedContraindications'].head()## 350 dehydration local site wound sore sensitive sk...
## 344 dehydration high risk pregnancy history painfu...
## 438 dehydration fever rash infection mental disord...
## 390 dehydration fever rash infection mental disord...
## 337 dehydration fever rash infection mental disord...
## Name: lemmatizedContraindications, dtype: objectn_gram3_vect_fit=n_gram3_vect.fit(X_train['lemmatizedContraindications'])
n_gram2_vect_fit=n_gram2_vect.fit(X_train['lemmatizedBenefits'])
n_gram3_train=n_gram3_vect_fit.transform(X_train['lemmatizedContraindications'])
n_gram3_test=n_gram3_vect_fit.transform(X_test['lemmatizedContraindications'])
n_gram2_train=n_gram2_vect_fit.transform(X_train['lemmatizedBenefits'])
n_gram2_test=n_gram2_vect_fit.transform(X_test['lemmatizedBenefits'])print(len(n_gram3_vect_fit.get_feature_names()))## 277Ngram3 = n_gram3_vect_fit.get_feature_names()
print(Ngram3)## ['35 first child', 'acute cranium bleeding', 'age 35 first', 'allergy fragrance epilepsy', 'allergy sensitivity pain', 'anemia blood disorder', 'anemia diabetes blood', 'aneurism herniation medulla', 'aneurism history cancer', 'aneurism history psychosis', 'application site fever', 'area sore application', 'arnoldchiari acute cranium', 'artery joint bruise', 'arthritis neuropathic pain', 'asthma allergy fragrance', 'autoimmune disease psychosis', 'bleeding wound area', 'blood clot diabetes', 'blood disorder anemia', 'blood disorder heart', 'blood disorder immune', 'blood disorder leukemia', 'blood pressure diabetes', 'blood pressure disorder', 'blood pressure low', 'blood pressure pregnant', 'blood pressure wound', 'bone bruise wound', 'bone fractured bone', 'bone insensitivity pain', 'bone trauma osteoporosis', 'brain trauma history', 'breast feeding open', 'breathing blood disorder', 'breathing cold sensitive', 'breathing medication could', 'breathing muscle pain', 'breathing people sensitive', 'breathing ragged breathing', 'breathing recommended people', 'breathing spine broken', 'breathing trouble breathing', 'broken bone fractured', 'broken bone insensitivity', 'bruise wound cut', 'burn rash fever', 'cancer autoimmune disease', 'cancer heat sensitive', 'cancer lymphoma psychosis', 'cancer osteoporosis psychosis', 'cancer psychosis numbness', 'cancer sensitivity cold', 'cannabidiol allergy cbd', 'child diabetes blood', 'circulatory disease blood', 'circulatory disorder blood', 'cold circulatory disorder', 'cold cold sensitivity', 'cold sensitive sensitivity', 'cold sensitivity psychosis', 'condition aneurism history', 'condition circulatory disease', 'condition neuropathy pregnant', 'could interact cannabidiol', 'cranium bleeding wound', 'cut burn rash', 'cut rash nerve', 'cyst endometriosis history', 'dehydration asthma allergy', 'dehydration fever rash', 'dehydration high risk', 'dehydration local site', 'dehydration seizure psychological', 'dehydration wound directly', 'diabetes blood disorder', 'diabetes blood pressure', 'diabetes wound anemia', 'difficulty breathing ragged', 'direction massage therapist', 'directly surgical incision', 'disease blood clot', 'disease fibroid cyst', 'disease psychosis numbness', 'disorder anemia diabetes', 'disorder blood clot', 'disorder blood pressure', 'disorder fever rash', 'disorder heart condition', 'disorder heart disorder', 'disorder histerical emotionally', 'disorder immune disorder', 'disorder leukemia sicklecell', 'disorder nausea epilepsy', 'disorder personality disorder', 'disorder pregnant aneurism', 'disturbed unable sit', 'eczema psoriasis sunburn', 'emotionally disturbed unable', 'endangering massage therapist', 'endometriosis history miscarriage', 'epilepsy allergy sensitivity', 'epilepsy aneurism history', 'epilepsy arthritis neuropathic', 'epilepsy bone trauma', 'epilepsy fever infection', 'epilepsy high blood', 'epilepsy pregnant aneurism', 'epilepsy pregnant wound', 'feeding open wound', 'fever infection heart', 'fever infection nausea', 'fever infection skull', 'fever rash infection', 'fibroid cyst endometriosis', 'first child diabetes', 'fracture broken bone', 'fracture fever infection', 'fracture insensitivity pain', 'fractured bone bruise', 'fragrance epilepsy fever', 'heart condition aneurism', 'heart condition circulatory', 'heart condition neuropathy', 'heart disorder fever', 'heat psychosis numbness', 'heat sensitive sensitivity', 'herniation medulla oblongato', 'high blood pressure', 'high risk pregnancy', 'histerical emotionally disturbed', 'history aneurism herniation', 'history cancer autoimmune', 'history cancer heat', 'history cancer lymphoma', 'history cancer osteoporosis', 'history cancer psychosis', 'history cancer sensitivity', 'history miscarriage age', 'history painful menstruation', 'history psychosis numbness', 'history stroke history', 'history uterine disease', 'immune disorder pregnant', 'impaired sensitivity pain', 'incision impaired sensitivity', 'infection heart condition', 'infection mental disorder', 'infection nausea psychosis', 'infection skull trauma', 'injury sprain strain', 'insensitivity pain pregnant', 'interact cannabidiol allergy', 'leukemia sicklecell blood', 'limb limb numbness', 'limb numbness nerve', 'limb spasm direction', 'local site wound', 'low blood pressure', 'lymphoma psychosis numbness', 'massage therapist brain', 'massage therapist endangering', 'medication could interact', 'medulla oblongato arnoldchiari', 'menstruation history uterine', 'mental disorder nausea', 'miscarriage age 35', 'muscle injury sprain', 'muscle pain muscle', 'nausea epilepsy allergy', 'nausea epilepsy aneurism', 'nausea epilepsy arthritis', 'nausea epilepsy bone', 'nausea epilepsy high', 'nausea epilepsy pregnant', 'nausea psychosis numbness', 'nerve artery joint', 'nerve pain difficulty', 'nerve tingling nerve', 'neuropathic pain pregnant', 'neuropathic pain tickling', 'neuropathy pregnant breast', 'numbness limb limb', 'numbness nerve tingling', 'oblongato arnoldchiari acute', 'open wound skin', 'osteoporosis fracture broken', 'osteoporosis fracture insensitivity', 'osteoporosis psychosis numbness', 'pain difficulty breathing', 'pain high blood', 'pain muscle injury', 'pain pregnant aneurism', 'pain psychological disorder', 'pain tickling foot', 'painful menstruation history', 'people neuropathic pain', 'people sensitive pain', 'personality disorder fever', 'pregnancy history painful', 'pregnant aneurism history', 'pregnant breast feeding', 'pregnant heart condition', 'pregnant wound pregnant', 'pressure diabetes wound', 'pressure disorder blood', 'pressure disorder heart', 'pressure low blood', 'pressure pregnant aneurism', 'pressure wound aneurism', 'psoriasis sunburn sensitive', 'psychological disorder histerical', 'psychological disorder personality', 'psychosis numbness limb', 'ragged breathing trouble', 'rash eczema psoriasis', 'rash fever rash', 'rash infection mental', 'rash nerve artery', 'recommended people neuropathic', 'risk pregnancy history', 'seizure psychological disorder', 'sensitive sensitivity cold', 'sensitive sensitivity heat', 'sensitive skin sensitive', 'sensitive skin thin', 'sensitive smell pregnant', 'sensitivity cold circulatory', 'sensitivity cold cold', 'sensitivity heat psychosis', 'sensitivity pain high', 'sensitivity pain psychological', 'sensitivity psychosis numbness', 'sicklecell blood disorder', 'sit still limb', 'site fever infection', 'site wound sore', 'skin cut burn', 'skin rash eczema', 'skin sensitive smell', 'skin thin skin', 'skull fracture fever', 'skull trauma skull', 'smell pregnant aneurism', 'sore application site', 'sore sensitive skin', 'spasm direction massage', 'spine broken bone', 'sprain strain injury', 'still limb spasm', 'stroke history aneurism', 'sunburn sensitive skin', 'surgical incision impaired', 'therapist brain trauma', 'therapist endangering massage', 'thin skin cut', 'tingling nerve pain', 'trauma history stroke', 'trauma osteoporosis fracture', 'trauma skull fracture', 'trouble breathing blood', 'trouble breathing cold', 'trouble breathing medication', 'trouble breathing muscle', 'trouble breathing people', 'trouble breathing recommended', 'trouble breathing spine', 'unable sit still', 'uterine disease fibroid', 'wound anemia blood', 'wound aneurism history', 'wound area sore', 'wound cut rash', 'wound directly surgical', 'wound pregnant heart', 'wound skin rash', 'wound sore sensitive']print(len(n_gram2_vect_fit.get_feature_names()))## 245Ngram2 = n_gram2_vect_fit.get_feature_names()
print(type(Ngram2))## <class 'list'>print(Ngram2)## ['ache improve', 'ache relieve', 'acute pain', 'adhesion heal', 'adhesion improve', 'adhesion improves', 'adhesion increase', 'adhesion stress', 'alleviate headache', 'anxiety stress', 'apart adhesion', 'apart muscle', 'approved high', 'arthritis chronic', 'arthritis stress', 'arthritis tendonitis', 'associated nerve', 'auditory disturbancss', 'autonomous disfunction', 'back pain', 'better posture', 'better sleep', 'break adhesion', 'break apart', 'bruise modified', 'calm nervous', 'calming effect', 'calming increase', 'cannot touched', 'case fibromyalgia', 'cause discomfort', 'chronic pain', 'circulation better', 'circulation break', 'circulation good', 'circulation improves', 'circulation pain', 'circulation reduces', 'circulation relaxing', 'clean old', 'client reduce', 'congestion help', 'detox break', 'detox help', 'detox muscle', 'detox recharge', 'discomfort fibromyalgia', 'discomfort increase', 'discomfort relaxing', 'discomfort tickling', 'disfunction autonomous', 'disfunction emotional', 'disorder rheumatoid', 'disturbance auditory', 'disturbancss tmj', 'doctor approved', 'effect calming', 'effect improves', 'effect soothing', 'emotional disorder', 'emotionally upset', 'energize unwind', 'fascia adhesion', 'fascia improves', 'fibromyalgia myofascial', 'fibromyalgia neuropathic', 'fibrosis increased', 'flexibility improved', 'flexibility improves', 'good stretching', 'headache emotionally', 'headache pain', 'headache scoliosis', 'heal sore', 'healing improve', 'healing improved', 'healing improves', 'healing increase', 'healing sport', 'health condition', 'help alleviate', 'help chronic', 'help clean', 'help congestion', 'help detox', 'help muscle', 'help pain', 'help post', 'help prevent', 'help relieve', 'high risk', 'hurting like', 'hypertonicity hypomobility', 'immunity help', 'immunity improves', 'immunity recommended', 'improve circulation', 'improve healing', 'improve range', 'improve sleep', 'improved circulation', 'improved mood', 'improved range', 'improves circulation', 'improves flexibility', 'improves healing', 'improves immunity', 'improves muscle', 'improves musle', 'improves range', 'improves relaxation', 'improves sleep', 'improves tight', 'improves workout', 'increase healing', 'increase immunity', 'increased flexibility', 'increased healing', 'increased range', 'inflammation help', 'injury help', 'injury tight', 'insomnia headache', 'itching hurting', 'joint arthritis', 'joint discomfort', 'like case', 'loosens tight', 'massage doctor', 'modified massage', 'mood muscle', 'motion break', 'motion help', 'motion improve', 'motion improved', 'motion improves', 'motion prevent', 'muscle ache', 'muscle fascia', 'muscle help', 'muscle improve', 'muscle loosens', 'muscle performance', 'muscle recovery', 'muscle relief', 'muscle relieve', 'muscle spasm', 'muscle strain', 'muscle tendon', 'muscle trauma', 'muscular adhesion', 'musle pain', 'musle strength', 'myofascial pain', 'nerve pain', 'nervous system', 'neuromodulated pain', 'neuropathic pain', 'old bruise', 'pain acute', 'pain arthritis', 'pain associated', 'pain calm', 'pain discomfort', 'pain improve', 'pain improves', 'pain insomnia', 'pain point', 'pain relief', 'pain sensitivity', 'pain sensory', 'pain trigger', 'pain visceral', 'people cannot', 'people pain', 'point fibrosis', 'post workout', 'posture increased', 'prevent injury', 'prevent muscle', 'prevent tendon', 'range motion', 'recharge relax', 'recommended people', 'recovery break', 'recovery help', 'reduce stress', 'reduces pain', 'related muscle', 'relax client', 'relax energize', 'relax tight', 'relaxation improves', 'relaxing detox', 'relaxing effect', 'relaxing improves', 'relief better', 'relief muscle', 'relief relaxing', 'relieve muscle', 'relieve musle', 'relieve pain', 'relieve stiff', 'rheumatoid arthritis', 'risk health', 'scoliosis visual', 'sensitivity people', 'sensory stimulation', 'sleep help', 'sleep improves', 'sleep pain', 'soothing effect', 'sore muscle', 'spasm pain', 'sport work', 'stiff muscle', 'stimulation joint', 'strain help', 'strength improves', 'stress back', 'stress calming', 'stretching detox', 'system improves', 'tempromandibular joint', 'tendon inflammation', 'tendon neuromodulated', 'tendon pain', 'tendon working', 'tendonitis hypertonicity', 'tickling itching', 'tight muscle', 'tight tendon', 'tmj tempromandibular', 'touched cause', 'trauma muscle', 'trigger pain', 'trigger point', 'unwind invigorate', 'upset detox', 'visceral disfunction', 'visual disturbance', 'work related', 'working muscular', 'workout ache', 'workout improves']n_gram3_train_df=pd.concat([X_train[['lemmatizedContraindications','lemmatizedBenefits']].reset_index(drop=True),pd.DataFrame(n_gram3_train.toarray())],axis=1)
n_gram3_test_df=pd.concat([X_test[['lemmatizedContraindications','lemmatizedBenefits']].reset_index(drop=True),pd.DataFrame(n_gram3_test.toarray())],axis=1)n_gram3_train_df.head()## lemmatizedContraindications ... 276
## 0 dehydration local site wound sore sensitive sk... ... 1
## 1 dehydration high risk pregnancy history painfu... ... 0
## 2 dehydration fever rash infection mental disord... ... 0
## 3 dehydration fever rash infection mental disord... ... 0
## 4 dehydration fever rash infection mental disord... ... 0
##
## [5 rows x 279 columns]n_gram2_train_df=pd.concat([X_train[['lemmatizedContraindications','lemmatizedBenefits']].reset_index(drop=True),pd.DataFrame(n_gram2_train.toarray())],axis=1)
n_gram2_test_df=pd.concat([X_test[['lemmatizedContraindications','lemmatizedBenefits']].reset_index(drop=True),pd.DataFrame(n_gram2_test.toarray())],axis=1)n_gram2_train_df.head()## lemmatizedContraindications ... 244
## 0 dehydration local site wound sore sensitive sk... ... 0
## 1 dehydration high risk pregnancy history painfu... ... 0
## 2 dehydration fever rash infection mental disord... ... 0
## 3 dehydration fever rash infection mental disord... ... 0
## 4 dehydration fever rash infection mental disord... ... 0
##
## [5 rows x 247 columns]
n_gram2_train2=pd.DataFrame(n_gram2_train.toarray())
n_gram3_train3=pd.DataFrame(n_gram3_train.toarray())
n_gram2_test2=pd.DataFrame(n_gram2_test.toarray())
n_gram3_test3=pd.DataFrame(n_gram3_test.toarray())
n_gram3_train3.columns=Ngram3
n_gram2_train2.columns=Ngram2
n_gram3_test3.columns=Ngram3
n_gram2_test2.columns=Ngram2
n_gram_2_3_train_df=pd.concat([X_train[['lemmatizedContraindications','lemmatizedBenefits']].reset_index(drop=True),n_gram2_train2,n_gram3_train3],axis=1)
n_gram_2_3_test_df=pd.concat([X_test[['lemmatizedContraindications','lemmatizedBenefits']].reset_index(drop=True),n_gram2_test2,n_gram3_test3],axis=1)ngram23Train = pd.concat([n_gram2_train2,n_gram3_train3],axis=1)
ngram23Test = pd.concat([n_gram2_test2,n_gram3_test3],axis=1)
ngram23Train.head()## ache improve ache relieve ... wound skin rash wound sore sensitive
## 0 0 0 ... 0 1
## 1 0 0 ... 0 0
## 2 0 0 ... 0 0
## 3 0 0 ... 0 0
## 4 0 0 ... 0 0
##
## [5 rows x 522 columns]n_gram_2_3_train_df.head()## lemmatizedContraindications ... wound sore sensitive
## 0 dehydration local site wound sore sensitive sk... ... 1
## 1 dehydration high risk pregnancy history painfu... ... 0
## 2 dehydration fever rash infection mental disord... ... 0
## 3 dehydration fever rash infection mental disord... ... 0
## 4 dehydration fever rash infection mental disord... ... 0
##
## [5 rows x 524 columns]Write this table of ngram tokens out to csv.
n_gram_2_3_test_df.to_csv('ngrams2_3_test.csv',index=False)
n_gram_2_3_train_df.to_csv('ngrams2_3_train.csv', index=False)
y_train.to_csv('y_train_ngrams23.csv', index=False)## C:/Users/m/Anaconda2/envs/python36/python.exe:1: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.y_test.to_csv('y_test_ngrams23.csv', index=False)Lets read in this large file in RStudio, and combine the data into one table.
ngrams23train <- read.csv('ngrams2_3_train.csv', sep=',', header=TRUE,
na.strings=c('',' ','NA'))
ngrams23test <- read.csv('ngrams2_3_test.csv', sep=',', header=TRUE,
na.strings=c('',' ','NA'))
ytrain <- read.csv('y_train_ngrams23.csv', sep=',', header=FALSE,
na.strings=c('',' ','NA'))
colnames(ytrain) <- 'modality'
ytest <- read.csv('y_test_ngrams23.csv', sep=',', header=FALSE,
na.strings=c('',' ','NA'))
colnames(ytest) <- 'modality'
train <- cbind(ytrain,ngrams23train)
test <- cbind(ytest,ngrams23test)
ngrams23All <- rbind(train,test)
write.csv(ngrams23All,'lemmNgramsBenefits2Contraindications3.csv', row.names=FALSE)We now have the lemmatized ngram tokens of 2 adjacent word pairs for our benefits and three adjacent word pairs for our contraindications saved to csv to use later or as needed for building our recommender system for a specific massage modality.
Lets get back to python, for machine learning using our previous models for the random forest classifier and the gradient boosted trees classifier. Lets use the combined tokens for the benefits and contraindications to see how well these trees do in classifying our massage modalities.
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import precision_recall_fscore_support as score
import timerf=RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)
start=time.time()
rf_model=rf.fit(ngram23Train,y_train)
end=time.time()
fit_time=(end-start)
fit_time## 0.584841251373291start=time.time()
y_pred=rf_model.predict(ngram23Test)
end=time.time()
pred_time=(end-start)
pred_time## 0.1760413646697998
prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=y_test.index
pred=pd.concat([pd.DataFrame(prd),y_test],axis=1)
print(pred)## Predicted modality
## 30 Biofreeze Muscle Pain Relief Gel Biofreeze Muscle Pain Relief Gel
## 164 Massage Gun Therapy Massage Gun Therapy
## 43 Reflexology Massage Reflexology Massage
## 215 Craniosacral Massage Craniosacral Massage
## 233 Reflexology Massage Reflexology Massage
## .. ... ...
## 169 Cannabidiol (CBD) Massage Balm Cannabidiol (CBD) Massage Balm
## 175 Lymphatic Drainage Massage Lymphatic Drainage Massage
## 316 Massage Gun Therapy Massage Gun Therapy
## 351 Deep tissue Massage Deep tissue Massage
## 271 Reflexology Massage Reflexology Massage
##
## [69 rows x 2 columns]from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(y_test, y_pred))## accuracy 1.0print('confusion matrix\n', confusion_matrix(y_test, y_pred))## confusion matrix
## [[3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2]]print('(row=expected, col=predicted)')## (row=expected, col=predicted)print(classification_report(y_test, y_pred))## precision recall f1-score support
##
## Aromatherapy 1.00 1.00 1.00 3
## Biofreeze Muscle Pain Relief Gel 1.00 1.00 1.00 6
## Cannabidiol (CBD) Massage Balm 1.00 1.00 1.00 5
## Cold Stone Therapy 1.00 1.00 1.00 5
## Craniosacral Massage 1.00 1.00 1.00 3
## Cupping Therapy 1.00 1.00 1.00 5
## Deep tissue Massage 1.00 1.00 1.00 5
## Hot Stone Therapy Massage 1.00 1.00 1.00 5
## Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage 1.00 1.00 1.00 7
## Lymphatic Drainage Massage 1.00 1.00 1.00 1
## Massage Gun Therapy 1.00 1.00 1.00 4
## Myofascial Massage 1.00 1.00 1.00 4
## Prenatal Massage 1.00 1.00 1.00 1
## Reflexology Massage 1.00 1.00 1.00 5
## Shiatsu Massage 1.00 1.00 1.00 1
## Sports Massage 1.00 1.00 1.00 3
## Swedish Massage 1.00 1.00 1.00 4
## Trigger Point Therapy 1.00 1.00 1.00 2
##
## accuracy 1.00 69
## macro avg 1.00 1.00 1.00 69
## weighted avg 1.00 1.00 1.00 69gb=GradientBoostingClassifier(n_estimators=150,max_depth=11)
start=time.time()
gb_model=gb.fit(ngram23Train,y_train)
end=time.time()
fit_time=(end-start)
fit_time## 8.200702667236328start=time.time()
y_pred=gb_model.predict(ngram23Test)
end=time.time()
pred_time=(end-start)
pred_time## 0.08502626419067383prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=y_test.index
pred=pd.concat([pd.DataFrame(prd),y_test],axis=1)
print(pred)## Predicted modality
## 30 Biofreeze Muscle Pain Relief Gel Biofreeze Muscle Pain Relief Gel
## 164 Massage Gun Therapy Massage Gun Therapy
## 43 Reflexology Massage Reflexology Massage
## 215 Craniosacral Massage Craniosacral Massage
## 233 Reflexology Massage Reflexology Massage
## .. ... ...
## 169 Cannabidiol (CBD) Massage Balm Cannabidiol (CBD) Massage Balm
## 175 Lymphatic Drainage Massage Lymphatic Drainage Massage
## 316 Massage Gun Therapy Massage Gun Therapy
## 351 Deep tissue Massage Deep tissue Massage
## 271 Reflexology Massage Reflexology Massage
##
## [69 rows x 2 columns]from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(y_test, y_pred))## accuracy 1.0print('confusion matrix\n', confusion_matrix(y_test, y_pred))## confusion matrix
## [[3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0]
## [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2]]print('(row=expected, col=predicted)')## (row=expected, col=predicted)print(classification_report(y_test, y_pred))## precision recall f1-score support
##
## Aromatherapy 1.00 1.00 1.00 3
## Biofreeze Muscle Pain Relief Gel 1.00 1.00 1.00 6
## Cannabidiol (CBD) Massage Balm 1.00 1.00 1.00 5
## Cold Stone Therapy 1.00 1.00 1.00 5
## Craniosacral Massage 1.00 1.00 1.00 3
## Cupping Therapy 1.00 1.00 1.00 5
## Deep tissue Massage 1.00 1.00 1.00 5
## Hot Stone Therapy Massage 1.00 1.00 1.00 5
## Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage 1.00 1.00 1.00 7
## Lymphatic Drainage Massage 1.00 1.00 1.00 1
## Massage Gun Therapy 1.00 1.00 1.00 4
## Myofascial Massage 1.00 1.00 1.00 4
## Prenatal Massage 1.00 1.00 1.00 1
## Reflexology Massage 1.00 1.00 1.00 5
## Shiatsu Massage 1.00 1.00 1.00 1
## Sports Massage 1.00 1.00 1.00 3
## Swedish Massage 1.00 1.00 1.00 4
## Trigger Point Therapy 1.00 1.00 1.00 2
##
## accuracy 1.00 69
## macro avg 1.00 1.00 1.00 69
## weighted avg 1.00 1.00 1.00 69It is great that these two produced the same results of 100%, as they should because each class of modality is a duplicate up to 23 duplicates, or 24 samples of each modality that are all identical. I ran a previous script on the same data and used 1-4 ngrams and the hot stone therapy observations were all getting misclassified as deep tissue recommendations for benefits and the same for contraindications of each type.
Lets try user inputs using this data after we make the above into a function for both models.
def lemmatize(text):
text="".join([word.lower() for word in text if word not in string.punctuation])
tokens=re.split('\W+', text)
text=" ".join([wn.lemmatize(word) for word in tokens if word not in stopwords])
return text
def predict_ngramRFC_lemma(new_review):
nr=pd.DataFrame([new_review])
nr.columns=['newReview']
nr['lemma']=nr['newReview'].apply(lambda x: lemmatize(x))
rf=RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)
n_gram2_vect=CountVectorizer(ngram_range=(2,2))
n_gram3_vect=CountVectorizer(ngram_range=(3,3))
n_gram2_vect_fit=n_gram2_vect.fit(X_train['lemmatizedBenefits'])
n_gram3_vect_fit=n_gram3_vect.fit(X_train['lemmatizedContraindications'])
n_gram2_train=n_gram2_vect_fit.transform(X_train['lemmatizedBenefits'])
n_gram3_train=n_gram3_vect_fit.transform(X_train['lemmatizedContraindications'])
Ngram2 = n_gram2_vect_fit.get_feature_names()
Ngram3 = n_gram3_vect_fit.get_feature_names()
n_gram2_train2=pd.DataFrame(n_gram2_train.toarray())
n_gram3_train3=pd.DataFrame(n_gram3_train.toarray())
n_gram2_train2.columns=Ngram2
n_gram3_train3.columns=Ngram3
ngram23Train = pd.concat([n_gram2_train2,n_gram3_train3],axis=1)
nr_gram2_test=n_gram2_vect_fit.transform(nr['lemma'])
nr_gram3_test=n_gram3_vect_fit.transform(nr['lemma'])
nr_test2=pd.DataFrame(nr_gram2_test.toarray())
nr_test3=pd.DataFrame(nr_gram3_test.toarray())
nr_test2.columns=Ngram2
nr_test3.columns=Ngram3
nrTest = pd.concat([nr_test2,nr_test3],axis=1)
model = rf.fit(ngram23Train,y_train)
pred=pd.DataFrame(model.predict(nrTest))
pred.columns=['Recommended Healthcare Service:']
pred.index= ['lemmatized_2ngram3_RFC_85-15:']
print('\n\n',pred)np.random.seed(12345)
predict_ngramRFC_lemma('I need a massage!') ##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Biofreeze Muscle Pain Relief Gelnp.random.seed(12345)
predict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Biofreeze Muscle Pain Relief Gel
def lemmatize(text):
text="".join([word.lower() for word in text if word not in string.punctuation])
tokens=re.split('\W+', text)
text=" ".join([wn.lemmatize(word) for word in tokens if word not in stopwords])
return text
def predict_ngramGBC_lemma(new_review):
nr=pd.DataFrame([new_review])
nr.columns=['newReview']
nr['lemma']=nr['newReview'].apply(lambda x: lemmatize(x))
gb=GradientBoostingClassifier(n_estimators=150,max_depth=11)
n_gram2_vect=CountVectorizer(ngram_range=(2,2))
n_gram3_vect=CountVectorizer(ngram_range=(3,3))
n_gram2_vect_fit=n_gram2_vect.fit(X_train['lemmatizedBenefits'])
n_gram3_vect_fit=n_gram3_vect.fit(X_train['lemmatizedContraindications'])
n_gram2_train=n_gram2_vect_fit.transform(X_train['lemmatizedBenefits'])
n_gram3_train=n_gram3_vect_fit.transform(X_train['lemmatizedContraindications'])
Ngram2 = n_gram2_vect_fit.get_feature_names()
Ngram3 = n_gram3_vect_fit.get_feature_names()
n_gram2_train2=pd.DataFrame(n_gram2_train.toarray())
n_gram3_train3=pd.DataFrame(n_gram3_train.toarray())
n_gram2_train2.columns=Ngram2
n_gram3_train3.columns=Ngram3
ngram23Train = pd.concat([n_gram2_train2,n_gram3_train3],axis=1)
nr_gram2_test=n_gram2_vect_fit.transform(nr['lemma'])
nr_gram3_test=n_gram3_vect_fit.transform(nr['lemma'])
nr_test2=pd.DataFrame(nr_gram2_test.toarray())
nr_test3=pd.DataFrame(nr_gram3_test.toarray())
nr_test2.columns=Ngram2
nr_test3.columns=Ngram3
nrTest = pd.concat([nr_test2,nr_test3],axis=1)
model = gb.fit(ngram23Train,y_train)
pred=pd.DataFrame(model.predict(nrTest))
pred.columns=['Recommended Healthcare Service:']
pred.index= ['lemmatized_2ngram3_GBC_85-15:']
print('\n\n',pred)np.random.seed(12345)
predict_ngramGBC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingnp.random.seed(12345)
predict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: StretchingThat was pretty interesting, to see the different recommendations. Since many of the contraindications and benefits are the same between modalities, these simple user inputs produced the same results with the seed set. If I remove the seed or starting point to randomize within the operating system, then lets see how this knits.
predict_ngramGBC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramGBC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: Stretchingpredict_ngramRFC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Aromatherapypredict_ngramRFC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Cannabidiol (CBD) Massage Balmpredict_ngramRFC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Lymphatic Drainage Massagepredict_ngramRFC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Biofreeze Muscle Pain Relief Gelpredict_ngramRFC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Lymphatic Drainage Massagepredict_ngramRFC_lemma('I need a massage!')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Aromatherapypredict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Biofreeze Muscle Pain Relief Gelpredict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Cannabidiol (CBD) Massage Balmpredict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Lymphatic Drainage Massagepredict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Aromatherapypredict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Lymphatic Drainage Massagepredict_ngramRFC_lemma('I have been working out a lot more than normal and am sore all over. Feels like a car hit me. I can\'t touch my toes to tie my shoes and my neck won\'t turn to the right. Help me.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_RFC_85-15: Lymphatic Drainage MassageWonderful! But now lets try to get some of the modalities other than CBD, biofreeze, aromatherapy, stretching, and lymphatic drainage massage. Those are more additional therapeutics for massage therapy.
predict_ngramGBC_lemma('I want either a Swedish or Deep Tissue massage, I want a lot of pressure, and need to fall asleep, I workout, have stress at work, alright with some hot stones or cold stones, or added cups.')##
##
## Recommended Healthcare Service:
## lemmatized_2ngram3_GBC_85-15: StretchingOur next model will use ngrams on the modality description to better capture the tokenized words for each modality, and keep the bigrams on benefits and trigrams on contraindications.Also, there should be a filter system so that those contraindicated massage modalities are excluded from the next run using the benefits or expectations of a user. These choices are selecting both benefits and those massages contraindicated with the current design. This did make the prediction on the testing set 100% accurate as it should, because only using benefits or contraindications tokenized produced a precision error on classifying hot stone therapy as deep tissue massage from a previous script done in Jupyter Notebook for python.
Lets switch to R.
ngrams23All <- read.csv('lemmNgramsBenefits2Contraindications3.csv', sep=',', header=TRUE,
na.strings=c('',' ','NA'))unique(ngrams23All$modality)## [1] Myofascial Massage
## [2] Prenatal Massage
## [3] Shiatsu Massage
## [4] Hot Stone Therapy Massage
## [5] Cupping Therapy
## [6] Sports Massage
## [7] Biofreeze Muscle Pain Relief Gel
## [8] Cold Stone Therapy
## [9] Stretching
## [10] Aromatherapy
## [11] Swedish Massage
## [12] Deep tissue Massage
## [13] Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage
## [14] Trigger Point Therapy
## [15] Massage Gun Therapy
## [16] Lymphatic Drainage Massage
## [17] Reflexology Massage
## [18] Craniosacral Massage
## [19] Cannabidiol (CBD) Massage Balm
## 19 Levels: Aromatherapy ... Trigger Point Therapymyofascial <- subset(ngrams23All, ngrams23All$modality=='Myofascial Massage')
dim(myofascial)## [1] 24 525myofascial1 <- myofascial[,-c(1:3)]
myofascial2 <- subset(myofascial1, colSums(myofascial1)!=0)
dim(myofascial2)## [1] 42 522myofascial2 <- myofascial1[,colSums(myofascial1) >= 1]
colSums(myofascial2)## muscle.spasm muscle.trauma
## 24 24
## pain.trigger spasm.pain
## 24 24
## trauma.muscle trigger.point
## 24 24
## aneurism.history.cancer breathing.ragged.breathing
## 24 24
## breathing.trouble.breathing burn.rash.fever
## 24 24
## cancer.psychosis.numbness cut.burn.rash
## 24 24
## dehydration.local.site difficulty.breathing.ragged
## 24 24
## disorder.nausea.epilepsy epilepsy.pregnant.aneurism
## 24 24
## fever.rash.infection history.cancer.psychosis
## 24 24
## infection.mental.disorder limb.limb.numbness
## 24 24
## limb.numbness.nerve local.site.wound
## 24 24
## mental.disorder.nausea nausea.epilepsy.pregnant
## 24 24
## nerve.pain.difficulty nerve.tingling.nerve
## 24 24
## numbness.limb.limb numbness.nerve.tingling
## 24 24
## pain.difficulty.breathing pregnant.aneurism.history
## 24 24
## psychosis.numbness.limb ragged.breathing.trouble
## 24 24
## rash.fever.rash rash.infection.mental
## 24 24
## sensitive.skin.thin site.wound.sore
## 24 24
## skin.cut.burn skin.thin.skin
## 24 24
## sore.sensitive.skin thin.skin.cut
## 24 24
## tingling.nerve.pain wound.sore.sensitive
## 24 24contraMyo <- grep('[.].*[.].*',colnames(myofascial2))
myoContra <- myofascial2[,contraMyo]
myoBenefit <- myofascial2[,-contraMyo]We now have the benefits as bigrams and contraindications as trigrams to exclude this as a list of the myofascial therapy contraindications, and to recommend the list of myofascial benefits. We just have to make these column names into lists for benefits and contraindications.
benefits_myofascial <- gsub('[.]',' ', colnames(myoBenefit), perl=TRUE)
contra_myofascial <- gsub('[.]',' ', colnames(myoContra), perl=TRUE)We will make the lists of the other 18 categories of massage modalities’ benefits and contraindications to use in building our recommender system for massage modalities for each user.
Prenatal Massage
prenatal <- subset(ngrams23All, ngrams23All$modality=='Prenatal Massage')
prenatal1 <- prenatal[,-c(1:3)]
prenatal2 <- subset(prenatal1, colSums(prenatal1)!=0)
prenatal2 <- prenatal1[,colSums(prenatal1) >= 1]
contraPre <- grep('[.].*[.].*',colnames(prenatal2))
PreContra <- prenatal2[,contraPre]
PreBenefit <- prenatal2[,-contraPre]
benefits_prenatal <- gsub('[.]',' ', colnames(PreBenefit), perl=TRUE)
contra_prenatal <- gsub('[.]',' ', colnames(PreContra), perl=TRUE)Shiatsu Massage
shiatsu <- subset(ngrams23All, ngrams23All$modality=='Shiatsu Massage')
shiatsu1 <- shiatsu[,-c(1:3)]
shiatsu2 <- subset(shiatsu1, colSums(shiatsu1)!=0)
shiatsu2 <- shiatsu1[,colSums(shiatsu1) >= 1]
contrashi <- grep('[.].*[.].*',colnames(shiatsu2))
shiContra <- shiatsu2[,contrashi]
shiBenefit <- shiatsu2[,-contrashi]
benefits_shiatsu <- gsub('[.]',' ', colnames(shiBenefit), perl=TRUE)
contra_shiatsu <- gsub('[.]',' ', colnames(shiContra), perl=TRUE)Hot Stone Therapy Massage
hotStone <- subset(ngrams23All, ngrams23All$modality=='Hot Stone Therapy Massage')
hotStone1 <- hotStone[,-c(1:3)]
hotStone2 <- subset(hotStone1, colSums(hotStone1)!=0)
hotStone2 <- hotStone1[,colSums(hotStone1) >= 1]
contrahs <- grep('[.].*[.].*',colnames(hotStone2))
hsContra <- hotStone2[,contrahs]
hsBenefit <- hotStone2[,-contrahs]
benefits_hs <- gsub('[.]',' ', colnames(hsBenefit), perl=TRUE)
contra_hs <- gsub('[.]',' ', colnames(hsContra), perl=TRUE)Cupping Therapy
Cupping <- subset(ngrams23All, ngrams23All$modality=='Cupping Therapy')
Cupping1 <- Cupping[,-c(1:3)]
Cupping2 <- subset(Cupping1, colSums(Cupping1)!=0)
Cupping2 <- Cupping1[,colSums(Cupping1) >= 1]
contracup <- grep('[.].*[.].*',colnames(Cupping2))
cupContra <- Cupping2[,contracup]
cupBenefit <- Cupping2[,-contracup]
benefits_cup <- gsub('[.]',' ', colnames(cupBenefit), perl=TRUE)
contra_cup <- gsub('[.]',' ', colnames(cupContra), perl=TRUE)Sports Massage
Sports <- subset(ngrams23All, ngrams23All$modality=='Sports Massage')
Sports1 <- Sports[,-c(1:3)]
Sports2 <- subset(Sports1, colSums(Sports1)!=0)
Sports2 <- Sports1[,colSums(Sports1) >= 1]
contrasports <- grep('[.].*[.].*',colnames(Sports2))
sportsContra <- Sports2[,contrasports]
sportsBenefit <- Sports2[,-contrasports]
benefits_sports <- gsub('[.]',' ', colnames(sportsBenefit), perl=TRUE)
contra_sports <- gsub('[.]',' ', colnames(sportsContra), perl=TRUE)Biofreeze Muscle Pain Relief Gel
Freeze <- subset(ngrams23All, ngrams23All$modality=='Biofreeze Muscle Pain Relief Gel')
Freeze1 <- Freeze[,-c(1:3)]
Freeze2 <- subset(Freeze1, colSums(Freeze1)!=0)
Freeze2 <- Freeze1[,colSums(Freeze1) >= 1]
contrafreeze <- grep('[.].*[.].*',colnames(Freeze2))
freezeContra <- Freeze2[,contrafreeze]
freezeBenefit <- Freeze2[,-contrafreeze]
benefits_freeze <- gsub('[.]',' ', colnames(freezeBenefit), perl=TRUE)
contra_freeze <- gsub('[.]',' ', colnames(freezeContra), perl=TRUE)Cold Stone Therapy
ColdStone <- subset(ngrams23All, ngrams23All$modality=='Cold Stone Therapy')
ColdStone1 <- ColdStone[,-c(1:3)]
ColdStone2 <- subset(ColdStone1, colSums(ColdStone1)!=0)
ColdStone2 <- ColdStone1[,colSums(ColdStone1) >= 1]
contracold <- grep('[.].*[.].*',colnames(ColdStone2))
coldContra <- ColdStone2[,contracold]
coldBenefit <- ColdStone2[,-contracold]
benefits_cold <- gsub('[.]',' ', colnames(coldBenefit), perl=TRUE)
contra_cold <- gsub('[.]',' ', colnames(coldContra), perl=TRUE)Stretching
Stretch <- subset(ngrams23All, ngrams23All$modality=='Stretching')
Stretch1 <- Stretch[,-c(1:3)]
Stretch2 <- subset(Stretch1, colSums(Stretch1)!=0)
Stretch2 <- Stretch1[,colSums(Stretch1) >= 1]
contrastretch <- grep('[.].*[.].*',colnames(Stretch2))
stretchContra <- Stretch2[,contrastretch]
stretchBenefit <- Stretch2[,-contrastretch]
benefits_stretch <- gsub('[.]',' ', colnames(stretchBenefit), perl=TRUE)
contra_stretch <- gsub('[.]',' ', colnames(stretchContra), perl=TRUE)Aromatherapy
AromaTherapy <- subset(ngrams23All, ngrams23All$modality=='Aromatherapy')
AromaTherapy1 <- AromaTherapy[,-c(1:3)]
AromaTherapy2 <- subset(AromaTherapy1, colSums(AromaTherapy1)!=0)
AromaTherapy2 <- AromaTherapy1[,colSums(AromaTherapy1) >= 1]
contraaroma <- grep('[.].*[.].*',colnames(AromaTherapy2))
aromaContra <- AromaTherapy2[,contraaroma]
aromaBenefit <- AromaTherapy2[,-contraaroma]
benefits_aroma <- gsub('[.]',' ', colnames(aromaBenefit), perl=TRUE)
contra_aroma <- gsub('[.]',' ', colnames(aromaContra), perl=TRUE)Swedish Massage
Swedish <- subset(ngrams23All, ngrams23All$modality=='Swedish Massage')
Swedish1 <- Swedish[,-c(1:3)]
Swedish2 <- subset(Swedish1, colSums(Swedish1)!=0)
Swedish2 <- Swedish1[,colSums(Swedish1) >= 1]
contraswedish <- grep('[.].*[.].*',colnames(Swedish2))
swedishContra <- Swedish2[,contraswedish]
swedishBenefit <- Swedish2[,-contraswedish]
benefits_swedish <- gsub('[.]',' ', colnames(swedishBenefit), perl=TRUE)
contra_swedish <- gsub('[.]',' ', colnames(swedishContra), perl=TRUE)Deep tissue Massage
DTmassage <- subset(ngrams23All, ngrams23All$modality=='Deep tissue Massage')
DTmassage1 <- DTmassage[,-c(1:3)]
DTmassage2 <- subset(DTmassage1, colSums(DTmassage1)!=0)
DTmassage2 <- DTmassage1[,colSums(DTmassage1) >= 1]
contraDT <- grep('[.].*[.].*',colnames(DTmassage2))
DTContra <- DTmassage2[,contraDT]
DTBenefit <- DTmassage2[,-contraDT]
benefits_DT <- gsub('[.]',' ', colnames(DTBenefit), perl=TRUE)
contra_DT <- gsub('[.]',' ', colnames(DTContra), perl=TRUE)Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage
IASTM <- subset(ngrams23All, ngrams23All$modality=='Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage')
IASTM1 <- IASTM[,-c(1:3)]
IASTM2 <- subset(IASTM1, colSums(IASTM1)!=0)
IASTM2 <- IASTM1[,colSums(IASTM1) >= 1]
contrainstrument <- grep('[.].*[.].*',colnames(IASTM2))
instrumentContra <- IASTM2[,contrainstrument]
instrumentBenefit <- IASTM2[,-contrainstrument]
benefits_instrument <- gsub('[.]',' ', colnames(instrumentBenefit), perl=TRUE)
contra_instrument <- gsub('[.]',' ', colnames(instrumentContra), perl=TRUE)Trigger Point Therapy
TPT <- subset(ngrams23All, ngrams23All$modality=='Trigger Point Therapy')
TPT1 <- TPT[,-c(1:3)]
TPT2 <- subset(TPT1, colSums(TPT1)!=0)
TPT2 <- TPT1[,colSums(TPT1) >= 1]
contratpt <- grep('[.].*[.].*',colnames(TPT2))
tptContra <- TPT2[,contratpt]
tptBenefit <- TPT2[,-contratpt]
benefits_tpt <- gsub('[.]',' ', colnames(tptBenefit), perl=TRUE)
contra_tpt <- gsub('[.]',' ', colnames(tptContra), perl=TRUE)Massage Gun Therapy
massageGun <- subset(ngrams23All, ngrams23All$modality=='Massage Gun Therapy')
massageGun1 <- massageGun[,-c(1:3)]
massageGun2 <- subset(massageGun1, colSums(massageGun1)!=0)
massageGun2 <- massageGun1[,colSums(massageGun1) >= 1]
contramassagegun <- grep('[.].*[.].*',colnames(massageGun2))
massagegunContra <- massageGun2[,contramassagegun]
massagegunBenefit <- massageGun2[,-contramassagegun]
benefits_massagegun <- gsub('[.]',' ', colnames(massagegunBenefit), perl=TRUE)
contra_massagegun <- gsub('[.]',' ', colnames(massagegunContra), perl=TRUE)Lymphatic Drainage Massage
Lymphatic <- subset(ngrams23All, ngrams23All$modality=='Lymphatic Drainage Massage')
Lymphatic1 <- Lymphatic[,-c(1:3)]
Lymphatic2 <- subset(Lymphatic1, colSums(Lymphatic1)!=0)
Lymphatic2 <- Lymphatic1[,colSums(Lymphatic1) >= 1]
contralymphatic <- grep('[.].*[.].*',colnames(Lymphatic2))
lymphaticContra <- Lymphatic2[,contralymphatic]
lymphaticBenefit <- Lymphatic2[,-contralymphatic]
benefits_lymphatic <- gsub('[.]',' ', colnames(lymphaticBenefit), perl=TRUE)
contra_lymphatic <- gsub('[.]',' ', colnames(lymphaticContra), perl=TRUE)Reflexology Massage
Reflexology <- subset(ngrams23All, ngrams23All$modality=='Reflexology Massage')
Reflexology1 <- Reflexology[,-c(1:3)]
Reflexology2 <- subset(Reflexology1, colSums(Reflexology1)!=0)
Reflexology2 <- Reflexology1[,colSums(Reflexology1) >= 1]
contrareflexology <- grep('[.].*[.].*',colnames(Reflexology2))
reflexologyContra <- Reflexology2[,contrareflexology]
reflexologyBenefit <- Reflexology2[,-contrareflexology]
benefits_reflexology <- gsub('[.]',' ', colnames(reflexologyBenefit), perl=TRUE)
contra_reflexology <- gsub('[.]',' ', colnames(reflexologyContra), perl=TRUE)Craniosacral Massage
Craniosacral <- subset(ngrams23All, ngrams23All$modality=='Craniosacral Massage')
Craniosacral1 <- Craniosacral[,-c(1:3)]
Craniosacral2 <- subset(Craniosacral1, colSums(Craniosacral1)!=0)
Craniosacral2 <- Craniosacral1[,colSums(Craniosacral1) >= 1]
contracraniosacral <- grep('[.].*[.].*',colnames(Craniosacral2))
craniosacralContra <- Craniosacral2[,contracraniosacral]
craniosacralBenefit <- Craniosacral2[,-contracraniosacral]
benefits_craniosacral <- gsub('[.]',' ', colnames(craniosacralBenefit), perl=TRUE)
contra_craniosacral <- gsub('[.]',' ', colnames(craniosacralContra), perl=TRUE)Cannabidiol (CBD) Massage Balm
CBD <- subset(ngrams23All, ngrams23All$modality=='Cannabidiol (CBD) Massage Balm')
CBD1 <- CBD[,-c(1:3)]
CBD2 <- subset(CBD1, colSums(CBD1)!=0)
CBD2 <- CBD1[,colSums(CBD1) >= 1]
contracbd <- grep('[.].*[.].*',colnames(CBD2))
cbdContra <- CBD2[,contracbd]
cbdBenefit <- CBD2[,-contracbd]
benefits_cbd <- gsub('[.]',' ', colnames(cbdBenefit), perl=TRUE)
contra_cbd <- gsub('[.]',' ', colnames(cbdContra), perl=TRUE)Benefits of each modality as lists: benefits_cbd,benefits_craniosacral,benefits_reflexology,benefits_lymphatic,benefits_massagegun,benefits_tpt,benefits_instrument,benefits_DT,benefits_swedish,benefits_aroma,benefits_stretch,benefits_cold,benefits_freeze,benefits_sports,benefits_cup,benefits_hs,benefits_shiatsu,benefits_prenatal,benefits_myofascial
Contraindications of each modality as lists: contra_cbd,contra_craniosacral,contra_reflexology,contra_lymphatic,contra_massagegun,contra_tpt,contra_instrument,contra_DT,contra_swedish,contra_aroma,contra_stretch,contra_cold,contra_freeze, contra_sports,contra_cup,contra_hs,contra_shiatsu,contra_prenatal,contra_myofascial
benefits_cbd## [1] "arthritis stress" "associated nerve" "chronic pain" "help chronic"
## [5] "nerve pain" "pain arthritis" "pain associated"contra_cbd## [1] "aneurism history cancer" "autoimmune disease psychosis"
## [3] "breathing medication could" "breathing ragged breathing"
## [5] "breathing trouble breathing" "cancer autoimmune disease"
## [7] "cannabidiol allergy cbd" "could interact cannabidiol"
## [9] "dehydration fever rash" "difficulty breathing ragged"
## [11] "disease psychosis numbness" "disorder nausea epilepsy"
## [13] "epilepsy pregnant aneurism" "fever rash infection"
## [15] "history cancer autoimmune" "infection mental disorder"
## [17] "interact cannabidiol allergy" "limb limb numbness"
## [19] "limb numbness nerve" "medication could interact"
## [21] "mental disorder nausea" "nausea epilepsy pregnant"
## [23] "nerve pain difficulty" "nerve tingling nerve"
## [25] "numbness limb limb" "numbness nerve tingling"
## [27] "pain difficulty breathing" "pregnant aneurism history"
## [29] "psychosis numbness limb" "ragged breathing trouble"
## [31] "rash infection mental" "tingling nerve pain"
## [33] "trouble breathing medication"The above demonstrates the benefits of CBD as a list of double word pairs or bigrams with the stop words stripped, and the bottom list is the longer list of trigrams of three word groups for the contraindications for CBD with the stopwords stripped. What we now want is a way to get the user the have a user input that will scan the list of contraindications for each massage modality, and if it is in the list of a modality, then it will be excluded from the list of available massage modalities for the user.
Looking at the above list the contraindications are grouped together that are different health conditions like psychosis (and) numbness (in the) limb. Users don’t want to scan a list of 500 health conditions or even more than 10, but any health conditions they have will have to be reported before scheduling a massage so that it isn’t cancelled or booked for the wrong modality. Lets assume the user honestly includes every possible health condition and history of their health conditions for serious medical conditions, then we want this program to scan those groups of words and find the modalities the user absolutely should not have, so that a list of available massage modalities are provided for the user to select the best one for benefits.Some users might not spell the same words or use the same words to describe the same sort of health condition. Like psychosis, I caught my own spelling error as pyschosis and fixed it. Also, the mental disorder or psychosis wouldn’t be a health condition one’s self would put down, only someone scheduling the massage for the person, like a child of a dementia patient, or parent, for similar mental disorders. There are quite a bit of those, and no massage therapist wants to put their professional or livelihood on the line to massage someone with mental disorders if the client will have a break down in session of some sort, like a relapse or yelling, or similar actions people aren’t capable of dealing with on a professional level. Massage therapists are not mental healthcare workers, but the massage does produce benefits that improve mental functions, therefore they cannot handle a client having a break down or threatening their safety if it occurs. So it is best to have these types of health conditions resolved or alerted to, so that a bystander for the client with a mental disorder can be nearby to handle the situation should it occur, or wait until the mental health has improved for the client before scheduling a massage. Lets not compete with God here, lest he or she put us in a comatose state and be like, ‘oh you know my thoughts and any of my children’s thoughts, well see how you produce machine learning on thoughts now, but, etc.’ So, we will not manually scan these words in the list and pick them out, but lets perhaps take that same user input off our trigrams from our model built on contrainidcations, and assume that we can select any bigram word pairs from these trigrams and then add any modality the user input pulls up into a list of modalities to exclude. Some will not be good bigrams, like ‘disease pyschosis’ but others like ‘autoimmune disease’ are good bigrams for reported health conditions. Because someone with autoimmune disease will not want a painful and debillitating flare up if a massage modality activated those symptoms, and the client came to realize that he or she did not report it beforehand. Other words like,pregnant, fever, and rash would be best as unigrams. So we should create an ngram of unigrams and bigrams from each list of contraindications.
We can use string literals and list apply methods to extract these uni and bi gram word pairs from our trigrams of contraindications.
cbd_split <- strsplit(contra_cbd,split=' ')
cbd_split1 <- lapply(cbd_split, '[',1)
cbd_split1b <- as.character(cbd_split1)
cbd_split1b## [1] "aneurism" "autoimmune" "breathing" "breathing" "breathing"
## [6] "cancer" "cannabidiol" "could" "dehydration" "difficulty"
## [11] "disease" "disorder" "epilepsy" "fever" "history"
## [16] "infection" "interact" "limb" "limb" "medication"
## [21] "mental" "nausea" "nerve" "nerve" "numbness"
## [26] "numbness" "pain" "pregnant" "psychosis" "ragged"
## [31] "rash" "tingling" "trouble"cat('\n','\n')cbd_split2 <- lapply(cbd_split, '[',2)
cbd_split2b <- as.character(cbd_split2)
cbd_split2b## [1] "history" "disease" "medication" "ragged" "trouble"
## [6] "autoimmune" "allergy" "interact" "fever" "breathing"
## [11] "psychosis" "nausea" "pregnant" "rash" "cancer"
## [16] "mental" "cannabidiol" "limb" "numbness" "could"
## [21] "disorder" "epilepsy" "pain" "tingling" "limb"
## [26] "nerve" "difficulty" "aneurism" "numbness" "breathing"
## [31] "infection" "nerve" "breathing"cat('\n','\n')cbd_split3 <- lapply(cbd_split, '[',3)
cbd_split3b <- as.character(cbd_split3)
cbd_split3b## [1] "cancer" "psychosis" "could" "breathing" "breathing"
## [6] "disease" "cbd" "cannabidiol" "rash" "ragged"
## [11] "numbness" "epilepsy" "aneurism" "infection" "autoimmune"
## [16] "disorder" "allergy" "numbness" "nerve" "interact"
## [21] "nausea" "pregnant" "difficulty" "nerve" "limb"
## [26] "tingling" "breathing" "history" "limb" "trouble"
## [31] "mental" "pain" "medication"cat('\n','\n')Above is our list of unigrams for CBD contraindications. Lets get the bigrams.
cbd_bi1 <- paste(cbd_split1b,cbd_split2b)
cbd_bi1## [1] "aneurism history" "autoimmune disease" "breathing medication"
## [4] "breathing ragged" "breathing trouble" "cancer autoimmune"
## [7] "cannabidiol allergy" "could interact" "dehydration fever"
## [10] "difficulty breathing" "disease psychosis" "disorder nausea"
## [13] "epilepsy pregnant" "fever rash" "history cancer"
## [16] "infection mental" "interact cannabidiol" "limb limb"
## [19] "limb numbness" "medication could" "mental disorder"
## [22] "nausea epilepsy" "nerve pain" "nerve tingling"
## [25] "numbness limb" "numbness nerve" "pain difficulty"
## [28] "pregnant aneurism" "psychosis numbness" "ragged breathing"
## [31] "rash infection" "tingling nerve" "trouble breathing"cat('\n\n')cbd_bi2 <- paste(cbd_split2b,cbd_split3b)
cbd_bi2## [1] "history cancer" "disease psychosis" "medication could"
## [4] "ragged breathing" "trouble breathing" "autoimmune disease"
## [7] "allergy cbd" "interact cannabidiol" "fever rash"
## [10] "breathing ragged" "psychosis numbness" "nausea epilepsy"
## [13] "pregnant aneurism" "rash infection" "cancer autoimmune"
## [16] "mental disorder" "cannabidiol allergy" "limb numbness"
## [19] "numbness nerve" "could interact" "disorder nausea"
## [22] "epilepsy pregnant" "pain difficulty" "tingling nerve"
## [25] "limb limb" "nerve tingling" "difficulty breathing"
## [28] "aneurism history" "numbness limb" "breathing trouble"
## [31] "infection mental" "nerve pain" "breathing medication"cat('\n\n')cbd_bi3 <- paste(cbd_split3b,cbd_split1b)
cbd_bi3## [1] "cancer aneurism" "psychosis autoimmune" "could breathing"
## [4] "breathing breathing" "breathing breathing" "disease cancer"
## [7] "cbd cannabidiol" "cannabidiol could" "rash dehydration"
## [10] "ragged difficulty" "numbness disease" "epilepsy disorder"
## [13] "aneurism epilepsy" "infection fever" "autoimmune history"
## [16] "disorder infection" "allergy interact" "numbness limb"
## [19] "nerve limb" "interact medication" "nausea mental"
## [22] "pregnant nausea" "difficulty nerve" "nerve nerve"
## [25] "limb numbness" "tingling numbness" "breathing pain"
## [28] "history pregnant" "limb psychosis" "trouble ragged"
## [31] "mental rash" "pain tingling" "medication trouble"cat('\n\n')The following is a list of all the unique unigrams in the CBD contraindication trigrams.
uni_cbd <- unique(c(cbd_split1b, cbd_split2b, cbd_split3b))
uni_cbd## [1] "aneurism" "autoimmune" "breathing" "cancer" "cannabidiol"
## [6] "could" "dehydration" "difficulty" "disease" "disorder"
## [11] "epilepsy" "fever" "history" "infection" "interact"
## [16] "limb" "medication" "mental" "nausea" "nerve"
## [21] "numbness" "pain" "pregnant" "psychosis" "ragged"
## [26] "rash" "tingling" "trouble" "allergy" "cbd"The following is a list of all the unique bigrams of the CBD contraindications trigrams.
bi_cbd <- unique(c(cbd_bi1, cbd_bi2, cbd_bi3))
bi_cbd## [1] "aneurism history" "autoimmune disease" "breathing medication"
## [4] "breathing ragged" "breathing trouble" "cancer autoimmune"
## [7] "cannabidiol allergy" "could interact" "dehydration fever"
## [10] "difficulty breathing" "disease psychosis" "disorder nausea"
## [13] "epilepsy pregnant" "fever rash" "history cancer"
## [16] "infection mental" "interact cannabidiol" "limb limb"
## [19] "limb numbness" "medication could" "mental disorder"
## [22] "nausea epilepsy" "nerve pain" "nerve tingling"
## [25] "numbness limb" "numbness nerve" "pain difficulty"
## [28] "pregnant aneurism" "psychosis numbness" "ragged breathing"
## [31] "rash infection" "tingling nerve" "trouble breathing"
## [34] "allergy cbd" "cancer aneurism" "psychosis autoimmune"
## [37] "could breathing" "breathing breathing" "disease cancer"
## [40] "cbd cannabidiol" "cannabidiol could" "rash dehydration"
## [43] "ragged difficulty" "numbness disease" "epilepsy disorder"
## [46] "aneurism epilepsy" "infection fever" "autoimmune history"
## [49] "disorder infection" "allergy interact" "nerve limb"
## [52] "interact medication" "nausea mental" "pregnant nausea"
## [55] "difficulty nerve" "nerve nerve" "tingling numbness"
## [58] "breathing pain" "history pregnant" "limb psychosis"
## [61] "trouble ragged" "mental rash" "pain tingling"
## [64] "medication trouble"The trigrams are already uniquely identified, because they were column names, and only unique column names can be used but also they were the trigrams that had count values out of all contraindications within the massage modalities’ contraindications, for this modality.
So, we will combine this as the list of 1-3 ngrams for our CBD contraindications.
CBD_contraindications <- c(contra_cbd, bi_cbd, uni_cbd)
CBD_contraindications## [1] "aneurism history cancer" "autoimmune disease psychosis"
## [3] "breathing medication could" "breathing ragged breathing"
## [5] "breathing trouble breathing" "cancer autoimmune disease"
## [7] "cannabidiol allergy cbd" "could interact cannabidiol"
## [9] "dehydration fever rash" "difficulty breathing ragged"
## [11] "disease psychosis numbness" "disorder nausea epilepsy"
## [13] "epilepsy pregnant aneurism" "fever rash infection"
## [15] "history cancer autoimmune" "infection mental disorder"
## [17] "interact cannabidiol allergy" "limb limb numbness"
## [19] "limb numbness nerve" "medication could interact"
## [21] "mental disorder nausea" "nausea epilepsy pregnant"
## [23] "nerve pain difficulty" "nerve tingling nerve"
## [25] "numbness limb limb" "numbness nerve tingling"
## [27] "pain difficulty breathing" "pregnant aneurism history"
## [29] "psychosis numbness limb" "ragged breathing trouble"
## [31] "rash infection mental" "tingling nerve pain"
## [33] "trouble breathing medication" "aneurism history"
## [35] "autoimmune disease" "breathing medication"
## [37] "breathing ragged" "breathing trouble"
## [39] "cancer autoimmune" "cannabidiol allergy"
## [41] "could interact" "dehydration fever"
## [43] "difficulty breathing" "disease psychosis"
## [45] "disorder nausea" "epilepsy pregnant"
## [47] "fever rash" "history cancer"
## [49] "infection mental" "interact cannabidiol"
## [51] "limb limb" "limb numbness"
## [53] "medication could" "mental disorder"
## [55] "nausea epilepsy" "nerve pain"
## [57] "nerve tingling" "numbness limb"
## [59] "numbness nerve" "pain difficulty"
## [61] "pregnant aneurism" "psychosis numbness"
## [63] "ragged breathing" "rash infection"
## [65] "tingling nerve" "trouble breathing"
## [67] "allergy cbd" "cancer aneurism"
## [69] "psychosis autoimmune" "could breathing"
## [71] "breathing breathing" "disease cancer"
## [73] "cbd cannabidiol" "cannabidiol could"
## [75] "rash dehydration" "ragged difficulty"
## [77] "numbness disease" "epilepsy disorder"
## [79] "aneurism epilepsy" "infection fever"
## [81] "autoimmune history" "disorder infection"
## [83] "allergy interact" "nerve limb"
## [85] "interact medication" "nausea mental"
## [87] "pregnant nausea" "difficulty nerve"
## [89] "nerve nerve" "tingling numbness"
## [91] "breathing pain" "history pregnant"
## [93] "limb psychosis" "trouble ragged"
## [95] "mental rash" "pain tingling"
## [97] "medication trouble" "aneurism"
## [99] "autoimmune" "breathing"
## [101] "cancer" "cannabidiol"
## [103] "could" "dehydration"
## [105] "difficulty" "disease"
## [107] "disorder" "epilepsy"
## [109] "fever" "history"
## [111] "infection" "interact"
## [113] "limb" "medication"
## [115] "mental" "nausea"
## [117] "nerve" "numbness"
## [119] "pain" "pregnant"
## [121] "psychosis" "ragged"
## [123] "rash" "tingling"
## [125] "trouble" "allergy"
## [127] "cbd"Looking at the list above, it is clear that if any of these unigrams are in a user input, then the modality will get excluded. We won’t want this to occur, because somebody using dialogue consisting of ‘pain’ or ‘could’ would not get any recommendations for massage. And, also, you might be wondering why not do this earlier when pulling the ngrams and setting to (1,3), but recall this is a list of trigrams for contraindications and a list of bigrams for benefits that were put together in the table, and when using regex to pull the bigrams from the trigrams, we were able to separate the bigrams with one ‘[.]’ and the trigrams with two ‘[.]’.
We can do the similar with the benefits of CBD list of bigrams to create the unigrams to combine with the bigrams for CBD benefits, then pull out the unigrams in common between the two and favor those single words or unigrams as benefits. Same for the bigrams in common, we will pull those out to assign them to benefits. A possible problem is that the bigram could be a contraindication and the same for the unigrams. We will examine that after building those lists.
Lets get the unigrams of benefits of CBD.
benefit_uni <- strsplit(benefits_cbd, split=" ")
benefit_uni1 <- lapply(benefit_uni,'[',1)
benefit_uni1b <- as.character(benefit_uni1)
benefit_uni2 <- lapply(benefit_uni,'[',2)
benefit_uni2b <- as.character(benefit_uni2)
uni_benefits <- unique(c(benefit_uni1b,benefit_uni2b))
benefits_cbd1 <- c(uni_benefits, benefits_cbd)
benefits_cbd1## [1] "arthritis" "associated" "chronic" "help"
## [5] "nerve" "pain" "stress" "arthritis stress"
## [9] "associated nerve" "chronic pain" "help chronic" "nerve pain"
## [13] "pain arthritis" "pain associated"Lets now get the list of tokens in both benefits and contraindications.
both_cbd <- CBD_contraindications %in% benefits_cbd1
length(both_cbd)## [1] 127both_cbd## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSEcbd1 <- CBD_contraindications[both_cbd]
length(cbd1)## [1] 3cbd1## [1] "nerve pain" "nerve" "pain"The above words are included in both the benefits of CBD and the contraindications of CBD. So, judging from the three words, ‘nerve pain’, ‘nerve’, and ‘pain,’ we will exclude these as markers for contraindications for CBD oil during massage, as they are more likely benefits.
CBD_contraindications1 <- CBD_contraindications[both_cbd==FALSE]
length(CBD_contraindications1)## [1] 124CBD_contraindications1## [1] "aneurism history cancer" "autoimmune disease psychosis"
## [3] "breathing medication could" "breathing ragged breathing"
## [5] "breathing trouble breathing" "cancer autoimmune disease"
## [7] "cannabidiol allergy cbd" "could interact cannabidiol"
## [9] "dehydration fever rash" "difficulty breathing ragged"
## [11] "disease psychosis numbness" "disorder nausea epilepsy"
## [13] "epilepsy pregnant aneurism" "fever rash infection"
## [15] "history cancer autoimmune" "infection mental disorder"
## [17] "interact cannabidiol allergy" "limb limb numbness"
## [19] "limb numbness nerve" "medication could interact"
## [21] "mental disorder nausea" "nausea epilepsy pregnant"
## [23] "nerve pain difficulty" "nerve tingling nerve"
## [25] "numbness limb limb" "numbness nerve tingling"
## [27] "pain difficulty breathing" "pregnant aneurism history"
## [29] "psychosis numbness limb" "ragged breathing trouble"
## [31] "rash infection mental" "tingling nerve pain"
## [33] "trouble breathing medication" "aneurism history"
## [35] "autoimmune disease" "breathing medication"
## [37] "breathing ragged" "breathing trouble"
## [39] "cancer autoimmune" "cannabidiol allergy"
## [41] "could interact" "dehydration fever"
## [43] "difficulty breathing" "disease psychosis"
## [45] "disorder nausea" "epilepsy pregnant"
## [47] "fever rash" "history cancer"
## [49] "infection mental" "interact cannabidiol"
## [51] "limb limb" "limb numbness"
## [53] "medication could" "mental disorder"
## [55] "nausea epilepsy" "nerve tingling"
## [57] "numbness limb" "numbness nerve"
## [59] "pain difficulty" "pregnant aneurism"
## [61] "psychosis numbness" "ragged breathing"
## [63] "rash infection" "tingling nerve"
## [65] "trouble breathing" "allergy cbd"
## [67] "cancer aneurism" "psychosis autoimmune"
## [69] "could breathing" "breathing breathing"
## [71] "disease cancer" "cbd cannabidiol"
## [73] "cannabidiol could" "rash dehydration"
## [75] "ragged difficulty" "numbness disease"
## [77] "epilepsy disorder" "aneurism epilepsy"
## [79] "infection fever" "autoimmune history"
## [81] "disorder infection" "allergy interact"
## [83] "nerve limb" "interact medication"
## [85] "nausea mental" "pregnant nausea"
## [87] "difficulty nerve" "nerve nerve"
## [89] "tingling numbness" "breathing pain"
## [91] "history pregnant" "limb psychosis"
## [93] "trouble ragged" "mental rash"
## [95] "pain tingling" "medication trouble"
## [97] "aneurism" "autoimmune"
## [99] "breathing" "cancer"
## [101] "cannabidiol" "could"
## [103] "dehydration" "difficulty"
## [105] "disease" "disorder"
## [107] "epilepsy" "fever"
## [109] "history" "infection"
## [111] "interact" "limb"
## [113] "medication" "mental"
## [115] "nausea" "numbness"
## [117] "pregnant" "psychosis"
## [119] "ragged" "rash"
## [121] "tingling" "trouble"
## [123] "allergy" "cbd"We could create a data table of the benefits of each modality, the contraindications, and/or both tokens to pull from or keep them as lists to pull from within our program. But not only do we already have those tables, because we made our 19 modality subsets from them, but we would have to merge each one to the other or join creating Nulls. So we will keep them as lists that have been filtered to be better than the orignal lists that removed as much ambiguity as possible between benefits and contraindications. So, now we will create these filtered lists for the other 18 modalities.
As an aside, who here has watched the reintroduced film on Amazon Prime from 2008, ‘Rainman Twins-..Savante autistics Kay and…’? I don’t remember the name, but easily Googled. I paid $2.99 to watch it yesterday thinking it was new, but only to me. Sometimes, just like neural networkds old stuff isn’t good at that time, but can be re-introduced 10-30 years later for its benefits. The point is, if you did watch this, and if you know the back story of neural networks, you will find that those twins were savante autistics who could recall on the spot with no more than one second to recall the day of the week of any date, the food they ate, the clothes worn, etc. and were viewed and harassed as ‘retards’ until the film came out with Dustin HOffman praising and encouraging savante’s to be found and appreciated. They needed to have routine, and marked all the bells, buzzards, and host clothings during every episode of the 100,000 Pyramid show, and went calmly nuts when it ended. If they were to do the following, they could be of better use. We are going to do just that, and think to ourselves how those twins could have been put to better work. So as we build our next filtered lists of the remaining 18 benefits and contraindications for each modality. We will also be thinking about how we are going to build this program.
benefits_craniosacral,contra_craniosacral
cranio_split <- strsplit(contra_craniosacral,split=' ')
cranio_split1 <- lapply(cranio_split, '[',1)
cranio_split1b <- as.character(cranio_split1)
cranio_split2 <- lapply(cranio_split, '[',2)
cranio_split2b <- as.character(cranio_split2)
cranio_split3 <- lapply(cranio_split, '[',3)
cranio_split3b <- as.character(cranio_split3)
cranio_bi1 <- paste(cranio_split1b,cranio_split2b)
cranio_bi2 <- paste(cranio_split2b,cranio_split3b)
cranio_bi3 <- paste(cranio_split3b,cranio_split1b)
uni_cranio <- unique(c(cranio_split1b, cranio_split2b, cranio_split3b))
bi_cranio <- unique(c(cranio_bi1, cranio_bi2, cranio_bi3))
Cranio_contraindications <- c(contra_craniosacral, bi_cranio, uni_cranio)
csbenefit_uni <- strsplit(benefits_craniosacral, split=" ")
csbenefit_uni1 <- lapply(csbenefit_uni,'[',1)
csbenefit_uni1b <- as.character(csbenefit_uni1)
csbenefit_uni2 <- lapply(csbenefit_uni,'[',2)
csbenefit_uni2b <- as.character(csbenefit_uni2)
csuni_benefits <- unique(c(csbenefit_uni1b,csbenefit_uni2b))
benefits_cranio1 <- c(csuni_benefits, benefits_craniosacral) #
both_cranio <- Cranio_contraindications %in% benefits_cranio1
cranio1 <- Cranio_contraindications[both_cranio] #
Cranio_contraindications1 <- Cranio_contraindications[both_cranio==FALSE] #benefits_reflexology,contra_reflexology
reflex_split <- strsplit(contra_reflexology,split=' ')
reflex_split1 <- lapply(reflex_split, '[',1)
reflex_split1b <- as.character(reflex_split1)
reflex_split2 <- lapply(reflex_split, '[',2)
reflex_split2b <- as.character(reflex_split2)
reflex_split3 <- lapply(reflex_split, '[',3)
reflex_split3b <- as.character(reflex_split3)
reflex_bi1 <- paste(reflex_split1b,reflex_split2b)
reflex_bi2 <- paste(reflex_split2b,reflex_split3b)
reflex_bi3 <- paste(reflex_split3b,reflex_split1b)
uni_reflex <- unique(c(reflex_split1b, reflex_split2b, reflex_split3b))
bi_reflex <- unique(c(reflex_bi1, reflex_bi2, reflex_bi3))
Reflex_contraindications <- c(contra_reflexology, bi_reflex, uni_reflex)
rfxbenefit_uni <- strsplit(benefits_reflexology, split=" ")
rfxbenefit_uni1 <- lapply(rfxbenefit_uni,'[',1)
rfxbenefit_uni1b <- as.character(rfxbenefit_uni1)
rfxbenefit_uni2 <- lapply(rfxbenefit_uni,'[',2)
rfxbenefit_uni2b <- as.character(rfxbenefit_uni2)
rfxuni_benefits <- unique(c(rfxbenefit_uni1b,rfxbenefit_uni2b))
benefits_reflex1 <- c(rfxuni_benefits, benefits_reflexology) #
both_reflex <- Reflex_contraindications %in% benefits_reflex1
reflex1 <- Reflex_contraindications[both_reflex] #
Reflex_contraindications1 <- Reflex_contraindications[both_reflex==FALSE] #benefits_lymphatic, contra_lymphatic
lymph_split <- strsplit(contra_lymphatic,split=' ')
lymph_split1 <- lapply(lymph_split, '[',1)
lymph_split1b <- as.character(lymph_split1)
lymph_split2 <- lapply(lymph_split, '[',2)
lymph_split2b <- as.character(lymph_split2)
lymph_split3 <- lapply(lymph_split, '[',3)
lymph_split3b <- as.character(lymph_split3)
lymph_bi1 <- paste(lymph_split1b,lymph_split2b)
lymph_bi2 <- paste(lymph_split2b,lymph_split3b)
lymph_bi3 <- paste(lymph_split3b,lymph_split1b)
uni_lymph <- unique(c(lymph_split1b, lymph_split2b, lymph_split3b))
bi_lymph <- unique(c(lymph_bi1, lymph_bi2, lymph_bi3))
Lymph_contraindications <- c(contra_lymphatic, bi_lymph, uni_lymph)
lymphbenefit_uni <- strsplit(benefits_lymphatic, split=" ")
lymphbenefit_uni1 <- lapply(lymphbenefit_uni,'[',1)
lymphbenefit_uni1b <- as.character(lymphbenefit_uni1)
lymphbenefit_uni2 <- lapply(lymphbenefit_uni,'[',2)
lymphbenefit_uni2b <- as.character(lymphbenefit_uni2)
lymphuni_benefits <- unique(c(lymphbenefit_uni1b,lymphbenefit_uni2b))
benefits_lymph1 <- c(lymphuni_benefits, benefits_lymphatic) #
both_lymph <- Lymph_contraindications %in% benefits_lymph1
lymph1 <- Lymph_contraindications[both_lymph] #
Lymph_contraindications1 <- Lymph_contraindications[both_lymph==FALSE] #benefits_massagegun, contra_massagegun
mgn_split <- strsplit(contra_massagegun,split=' ')
mgn_split1 <- lapply(mgn_split, '[',1)
mgn_split1b <- as.character(mgn_split1)
mgn_split2 <- lapply(mgn_split, '[',2)
mgn_split2b <- as.character(mgn_split2)
mgn_split3 <- lapply(mgn_split, '[',3)
mgn_split3b <- as.character(mgn_split3)
mgn_bi1 <- paste(mgn_split1b,mgn_split2b)
mgn_bi2 <- paste(mgn_split2b,mgn_split3b)
mgn_bi3 <- paste(mgn_split3b,mgn_split1b)
uni_mgn <- unique(c(mgn_split1b, mgn_split2b, mgn_split3b))
bi_mgn <- unique(c(mgn_bi1, mgn_bi2, mgn_bi3))
Mgn_contraindications <- c(contra_massagegun, bi_mgn, uni_mgn)
mgnbenefit_uni <- strsplit(benefits_massagegun, split=" ")
mgnbenefit_uni1 <- lapply(mgnbenefit_uni,'[',1)
mgnbenefit_uni1b <- as.character(mgnbenefit_uni1)
mgnbenefit_uni2 <- lapply(mgnbenefit_uni,'[',2)
mgnbenefit_uni2b <- as.character(mgnbenefit_uni2)
mgnuni_benefits <- unique(c(mgnbenefit_uni1b,mgnbenefit_uni2b))
benefits_mgn1 <- c(mgnuni_benefits, benefits_massagegun) #
both_mgn <- Mgn_contraindications %in% benefits_mgn1
mgn1 <- Mgn_contraindications[both_mgn] #
Mgn_contraindications1 <- Mgn_contraindications[both_mgn==FALSE] #benefits_tpt, contra_tpt
tpt_split <- strsplit(contra_tpt,split=' ')
tpt_split1 <- lapply(tpt_split, '[',1)
tpt_split1b <- as.character(tpt_split1)
tpt_split2 <- lapply(tpt_split, '[',2)
tpt_split2b <- as.character(tpt_split2)
tpt_split3 <- lapply(tpt_split, '[',3)
tpt_split3b <- as.character(tpt_split3)
tpt_bi1 <- paste(tpt_split1b,tpt_split2b)
tpt_bi2 <- paste(tpt_split2b,tpt_split3b)
tpt_bi3 <- paste(tpt_split3b,tpt_split1b)
uni_tpt <- unique(c(tpt_split1b, tpt_split2b, tpt_split3b))
bi_tpt <- unique(c(tpt_bi1, tpt_bi2, tpt_bi3))
TPT_contraindications <- c(contra_tpt, bi_tpt, uni_tpt)
tptbenefit_uni <- strsplit(benefits_tpt, split=" ")
tptbenefit_uni1 <- lapply(tptbenefit_uni,'[',1)
tptbenefit_uni1b <- as.character(tptbenefit_uni1)
tptbenefit_uni2 <- lapply(tptbenefit_uni,'[',2)
tptbenefit_uni2b <- as.character(tptbenefit_uni2)
tptuni_benefits <- unique(c(tptbenefit_uni1b,tptbenefit_uni2b))
benefits_tpt1 <- c(tptuni_benefits, benefits_tpt) #
both_tpt <- TPT_contraindications %in% benefits_tpt1
tpt1 <- TPT_contraindications[both_tpt] #
TPT_contraindications1 <- TPT_contraindications[both_tpt==FALSE] #benefits_instrument, contra_instrument
instrument_split <- strsplit(contra_instrument,split=' ')
instrument_split1 <- lapply(instrument_split, '[',1)
instrument_split1b <- as.character(instrument_split1)
instrument_split2 <- lapply(instrument_split, '[',2)
instrument_split2b <- as.character(instrument_split2)
instrument_split3 <- lapply(instrument_split, '[',3)
instrument_split3b <- as.character(instrument_split3)
instrument_bi1 <- paste(instrument_split1b,instrument_split2b)
instrument_bi2 <- paste(instrument_split2b,instrument_split3b)
instrument_bi3 <- paste(instrument_split3b,instrument_split1b)
uni_instrument <- unique(c(instrument_split1b, instrument_split2b, instrument_split3b))
bi_instrument <- unique(c(instrument_bi1, instrument_bi2, instrument_bi3))
Instrument_contraindications <- c(contra_instrument, bi_instrument, uni_instrument)
instrumentbenefit_uni <- strsplit(benefits_instrument, split=" ")
instrumentbenefit_uni1 <- lapply(instrumentbenefit_uni,'[',1)
instrumentbenefit_uni1b <- as.character(instrumentbenefit_uni1)
instrumentbenefit_uni2 <- lapply(instrumentbenefit_uni,'[',2)
instrumentbenefit_uni2b <- as.character(instrumentbenefit_uni2)
instrumentuni_benefits <- unique(c(instrumentbenefit_uni1b,instrumentbenefit_uni2b))
benefits_instrument1 <- c(instrumentuni_benefits, benefits_instrument) #
both_instrument <- Instrument_contraindications %in% benefits_instrument1
instrument1 <- Instrument_contraindications[both_instrument] #
Instrument_contraindications1 <- Instrument_contraindications[both_instrument==FALSE] #benefits_DT, contra_DT
DT_split <- strsplit(contra_DT,split=' ')
DT_split1 <- lapply(DT_split, '[',1)
DT_split1b <- as.character(DT_split1)
DT_split2 <- lapply(DT_split, '[',2)
DT_split2b <- as.character(DT_split2)
DT_split3 <- lapply(DT_split, '[',3)
DT_split3b <- as.character(DT_split3)
DT_bi1 <- paste(DT_split1b,DT_split2b)
DT_bi2 <- paste(DT_split2b,DT_split3b)
DT_bi3 <- paste(DT_split3b,DT_split1b)
uni_DT <- unique(c(DT_split1b, DT_split2b, DT_split3b))
bi_DT <- unique(c(DT_bi1, DT_bi2, DT_bi3))
DT_contraindications <- c(contra_DT, bi_DT, uni_DT)
DTbenefit_uni <- strsplit(benefits_DT, split=" ")
DTbenefit_uni1 <- lapply(DTbenefit_uni,'[',1)
DTbenefit_uni1b <- as.character(DTbenefit_uni1)
DTbenefit_uni2 <- lapply(DTbenefit_uni,'[',2)
DTbenefit_uni2b <- as.character(DTbenefit_uni2)
DTuni_benefits <- unique(c(DTbenefit_uni1b,DTbenefit_uni2b))
benefits_DT1 <- c(DTuni_benefits, benefits_DT) #
both_DT <- DT_contraindications %in% benefits_DT1
DT1 <- DT_contraindications[both_DT] #
DT_contraindications1 <- DT_contraindications[both_DT==FALSE] #benefits_swedish, contra_swedish
swedish_split <- strsplit(contra_swedish,split=' ')
swedish_split1 <- lapply(swedish_split, '[',1)
swedish_split1b <- as.character(swedish_split1)
swedish_split2 <- lapply(swedish_split, '[',2)
swedish_split2b <- as.character(swedish_split2)
swedish_split3 <- lapply(swedish_split, '[',3)
swedish_split3b <- as.character(swedish_split3)
swedish_bi1 <- paste(swedish_split1b,swedish_split2b)
swedish_bi2 <- paste(swedish_split2b,swedish_split3b)
swedish_bi3 <- paste(swedish_split3b,swedish_split1b)
uni_swedish <- unique(c(swedish_split1b, swedish_split2b, swedish_split3b))
bi_swedish <- unique(c(swedish_bi1, swedish_bi2, swedish_bi3))
swedish_contraindications <- c(contra_swedish, bi_swedish, uni_swedish)
swedishbenefit_uni <- strsplit(benefits_swedish, split=" ")
swedishbenefit_uni1 <- lapply(swedishbenefit_uni,'[',1)
swedishbenefit_uni1b <- as.character(swedishbenefit_uni1)
swedishbenefit_uni2 <- lapply(swedishbenefit_uni,'[',2)
swedishbenefit_uni2b <- as.character(swedishbenefit_uni2)
swedishuni_benefits <- unique(c(swedishbenefit_uni1b,swedishbenefit_uni2b))
benefits_swedish1 <- c(swedishuni_benefits, benefits_swedish) #
both_swedish <- swedish_contraindications %in% benefits_swedish1
swedish1 <- swedish_contraindications[both_swedish] #
swedish_contraindications1 <- swedish_contraindications[both_swedish==FALSE] #benefits_aroma, contra_aroma
aroma_split <- strsplit(contra_aroma,split=' ')
aroma_split1 <- lapply(aroma_split, '[',1)
aroma_split1b <- as.character(aroma_split1)
aroma_split2 <- lapply(aroma_split, '[',2)
aroma_split2b <- as.character(aroma_split2)
aroma_split3 <- lapply(aroma_split, '[',3)
aroma_split3b <- as.character(aroma_split3)
aroma_bi1 <- paste(aroma_split1b,aroma_split2b)
aroma_bi2 <- paste(aroma_split2b,aroma_split3b)
aroma_bi3 <- paste(aroma_split3b,aroma_split1b)
uni_aroma <- unique(c(aroma_split1b, aroma_split2b, aroma_split3b))
bi_aroma <- unique(c(aroma_bi1, aroma_bi2, aroma_bi3))
Aroma_contraindications <- c(contra_aroma, bi_aroma, uni_aroma)
aromabenefit_uni <- strsplit(benefits_aroma, split=" ")
aromabenefit_uni1 <- lapply(aromabenefit_uni,'[',1)
aromabenefit_uni1b <- as.character(aromabenefit_uni1)
aromabenefit_uni2 <- lapply(aromabenefit_uni,'[',2)
aromabenefit_uni2b <- as.character(aromabenefit_uni2)
aromauni_benefits <- unique(c(aromabenefit_uni1b,aromabenefit_uni2b))
benefits_aroma1 <- c(aromauni_benefits, benefits_aroma) #
both_aroma <- Aroma_contraindications %in% benefits_aroma1
aroma1 <- Aroma_contraindications[both_aroma] #
Aroma_contraindications1 <- Aroma_contraindications[both_aroma==FALSE] #benefits_stretch, contra_stretch
stretch_split <- strsplit(contra_stretch,split=' ')
stretch_split1 <- lapply(stretch_split, '[',1)
stretch_split1b <- as.character(stretch_split1)
stretch_split2 <- lapply(stretch_split, '[',2)
stretch_split2b <- as.character(stretch_split2)
stretch_split3 <- lapply(stretch_split, '[',3)
stretch_split3b <- as.character(stretch_split3)
stretch_bi1 <- paste(stretch_split1b,stretch_split2b)
stretch_bi2 <- paste(stretch_split2b,stretch_split3b)
stretch_bi3 <- paste(stretch_split3b,stretch_split1b)
uni_stretch <- unique(c(stretch_split1b, stretch_split2b, stretch_split3b))
bi_stretch <- unique(c(stretch_bi1, stretch_bi2, stretch_bi3))
stretch_contraindications <- c(contra_stretch, bi_stretch, uni_stretch)
stretchbenefit_uni <- strsplit(benefits_stretch, split=" ")
stretchbenefit_uni1 <- lapply(stretchbenefit_uni,'[',1)
stretchbenefit_uni1b <- as.character(stretchbenefit_uni1)
stretchbenefit_uni2 <- lapply(stretchbenefit_uni,'[',2)
stretchbenefit_uni2b <- as.character(stretchbenefit_uni2)
stretchuni_benefits <- unique(c(stretchbenefit_uni1b,stretchbenefit_uni2b))
benefits_stretch1 <- c(stretchuni_benefits, benefits_stretch) #
both_stretch <- stretch_contraindications %in% benefits_stretch1
stretch1 <- stretch_contraindications[both_stretch] #
stretch_contraindications1 <- stretch_contraindications[both_stretch==FALSE] #benefits_cold, contra_cold
cold_split <- strsplit(contra_cold,split=' ')
cold_split1 <- lapply(cold_split, '[',1)
cold_split1b <- as.character(cold_split1)
cold_split2 <- lapply(cold_split, '[',2)
cold_split2b <- as.character(cold_split2)
cold_split3 <- lapply(cold_split, '[',3)
cold_split3b <- as.character(cold_split3)
cold_bi1 <- paste(cold_split1b,cold_split2b)
cold_bi2 <- paste(cold_split2b,cold_split3b)
cold_bi3 <- paste(cold_split3b,cold_split1b)
uni_cold <- unique(c(cold_split1b, cold_split2b, cold_split3b))
bi_cold <- unique(c(cold_bi1, cold_bi2, cold_bi3))
cold_contraindications <- c(contra_cold, bi_cold, uni_cold)
coldbenefit_uni <- strsplit(benefits_cold, split=" ")
coldbenefit_uni1 <- lapply(coldbenefit_uni,'[',1)
coldbenefit_uni1b <- as.character(coldbenefit_uni1)
coldbenefit_uni2 <- lapply(coldbenefit_uni,'[',2)
coldbenefit_uni2b <- as.character(coldbenefit_uni2)
colduni_benefits <- unique(c(coldbenefit_uni1b,coldbenefit_uni2b))
benefits_cold1 <- c(colduni_benefits, benefits_cold) #
both_cold <- cold_contraindications %in% benefits_cold1
cold1 <- cold_contraindications[both_cold] #
cold_contraindications1 <- cold_contraindications[both_cold==FALSE] #benefits_freeze, contra_freeze
freeze_split <- strsplit(contra_freeze,split=' ')
freeze_split1 <- lapply(freeze_split, '[',1)
freeze_split1b <- as.character(freeze_split1)
freeze_split2 <- lapply(freeze_split, '[',2)
freeze_split2b <- as.character(freeze_split2)
freeze_split3 <- lapply(freeze_split, '[',3)
freeze_split3b <- as.character(freeze_split3)
freeze_bi1 <- paste(freeze_split1b,freeze_split2b)
freeze_bi2 <- paste(freeze_split2b,freeze_split3b)
freeze_bi3 <- paste(freeze_split3b,freeze_split1b)
uni_freeze <- unique(c(freeze_split1b, freeze_split2b, freeze_split3b))
bi_freeze <- unique(c(freeze_bi1, freeze_bi2, freeze_bi3))
Freeze_contraindications <- c(contra_freeze, bi_freeze, uni_freeze)
freezebenefit_uni <- strsplit(benefits_freeze, split=" ")
freezebenefit_uni1 <- lapply(freezebenefit_uni,'[',1)
freezebenefit_uni1b <- as.character(freezebenefit_uni1)
freezebenefit_uni2 <- lapply(freezebenefit_uni,'[',2)
freezebenefit_uni2b <- as.character(freezebenefit_uni2)
freezeuni_benefits <- unique(c(freezebenefit_uni1b,freezebenefit_uni2b))
benefits_freeze1 <- c(freezeuni_benefits, benefits_freeze) #
both_freeze <- Freeze_contraindications %in% benefits_freeze1
freeze1 <- Freeze_contraindications[both_freeze] #
Freeze_contraindications1 <- Freeze_contraindications[both_freeze==FALSE] #benefits_sports, contra_sports
sports_split <- strsplit(contra_sports,split=' ')
sports_split1 <- lapply(sports_split, '[',1)
sports_split1b <- as.character(sports_split1)
sports_split2 <- lapply(sports_split, '[',2)
sports_split2b <- as.character(sports_split2)
sports_split3 <- lapply(sports_split, '[',3)
sports_split3b <- as.character(sports_split3)
sports_bi1 <- paste(sports_split1b,sports_split2b)
sports_bi2 <- paste(sports_split2b,sports_split3b)
sports_bi3 <- paste(sports_split3b,sports_split1b)
uni_sports <- unique(c(sports_split1b, sports_split2b, sports_split3b))
bi_sports <- unique(c(sports_bi1, sports_bi2, sports_bi3))
Sports_contraindications <- c(contra_sports, bi_sports, uni_sports)
sportsbenefit_uni <- strsplit(benefits_sports, split=" ")
sportsbenefit_uni1 <- lapply(sportsbenefit_uni,'[',1)
sportsbenefit_uni1b <- as.character(sportsbenefit_uni1)
sportsbenefit_uni2 <- lapply(sportsbenefit_uni,'[',2)
sportsbenefit_uni2b <- as.character(sportsbenefit_uni2)
sportsuni_benefits <- unique(c(sportsbenefit_uni1b,sportsbenefit_uni2b))
benefits_sports1 <- c(sportsuni_benefits, benefits_sports) #
both_sports <- Sports_contraindications %in% benefits_sports1
sports1 <- Sports_contraindications[both_sports] #
Sports_contraindications1 <- Sports_contraindications[both_sports==FALSE] #benefits_cup, contra_cup
cup_split <- strsplit(contra_cup,split=' ')
cup_split1 <- lapply(cup_split, '[',1)
cup_split1b <- as.character(cup_split1)
cup_split2 <- lapply(cup_split, '[',2)
cup_split2b <- as.character(cup_split2)
cup_split3 <- lapply(cup_split, '[',3)
cup_split3b <- as.character(cup_split3)
cup_bi1 <- paste(cup_split1b,cup_split2b)
cup_bi2 <- paste(cup_split2b,cup_split3b)
cup_bi3 <- paste(cup_split3b,cup_split1b)
uni_cup <- unique(c(cup_split1b, cup_split2b, cup_split3b))
bi_cup <- unique(c(cup_bi1, cup_bi2, cup_bi3))
Cupping_contraindications <- c(contra_cup, bi_cup, uni_cup)
cuppingbenefit_uni <- strsplit(benefits_cup, split=" ")
cuppingbenefit_uni1 <- lapply(cuppingbenefit_uni,'[',1)
cuppingbenefit_uni1b <- as.character(cuppingbenefit_uni1)
cuppingbenefit_uni2 <- lapply(cuppingbenefit_uni,'[',2)
cuppingbenefit_uni2b <- as.character(cuppingbenefit_uni2)
cuppinguni_benefits <- unique(c(cuppingbenefit_uni1b,cuppingbenefit_uni2b))
benefits_cup1 <- c(cuppinguni_benefits, benefits_cup) #
both_cup <- Cupping_contraindications %in% benefits_cup1
cup1 <- Cupping_contraindications[both_cup] #
Cupping_contraindications1 <- Cupping_contraindications[both_cup==FALSE] #benefits_hs, contra_hs
HotStone_split <- strsplit(contra_hs,split=' ')
HotStone_split1 <- lapply(HotStone_split, '[',1)
HotStone_split1b <- as.character(HotStone_split1)
HotStone_split2 <- lapply(HotStone_split, '[',2)
HotStone_split2b <- as.character(HotStone_split2)
HotStone_split3 <- lapply(HotStone_split, '[',3)
HotStone_split3b <- as.character(HotStone_split3)
HotStone_bi1 <- paste(HotStone_split1b,HotStone_split2b)
HotStone_bi2 <- paste(HotStone_split2b,HotStone_split3b)
HotStone_bi3 <- paste(HotStone_split3b,HotStone_split1b)
uni_HotStone <- unique(c(HotStone_split1b, HotStone_split2b, HotStone_split3b))
bi_HotStone <- unique(c(HotStone_bi1, HotStone_bi2, HotStone_bi3))
HotStone_contraindications <- c(contra_hs, bi_HotStone, uni_HotStone)
hsbenefit_uni <- strsplit(benefits_hs, split=" ")
hsbenefit_uni1 <- lapply(hsbenefit_uni,'[',1)
hsbenefit_uni1b <- as.character(hsbenefit_uni1)
hsbenefit_uni2 <- lapply(hsbenefit_uni,'[',2)
hsbenefit_uni2b <- as.character(hsbenefit_uni2)
hsuni_benefits <- unique(c(hsbenefit_uni1b,hsbenefit_uni2b))
benefits_HotStone1 <- c(hsuni_benefits, benefits_hs) #
both_HotStone <- HotStone_contraindications %in% benefits_HotStone1
HotStone1 <- HotStone_contraindications[both_HotStone] #
HotStone_contraindications1 <- HotStone_contraindications[both_HotStone==FALSE] #benefits_shiatsu, contra_shiatsu
shiatsu_split <- strsplit(contra_shiatsu,split=' ')
shiatsu_split1 <- lapply(shiatsu_split, '[',1)
shiatsu_split1b <- as.character(shiatsu_split1)
shiatsu_split2 <- lapply(shiatsu_split, '[',2)
shiatsu_split2b <- as.character(shiatsu_split2)
shiatsu_split3 <- lapply(shiatsu_split, '[',3)
shiatsu_split3b <- as.character(shiatsu_split3)
shiatsu_bi1 <- paste(shiatsu_split1b,shiatsu_split2b)
shiatsu_bi2 <- paste(shiatsu_split2b,shiatsu_split3b)
shiatsu_bi3 <- paste(shiatsu_split3b,shiatsu_split1b)
uni_shiatsu <- unique(c(shiatsu_split1b, shiatsu_split2b, shiatsu_split3b))
bi_shiatsu <- unique(c(shiatsu_bi1, shiatsu_bi2, shiatsu_bi3))
Shiatsu_contraindications <- c(contra_shiatsu, bi_shiatsu, uni_shiatsu)
shiatsubenefit_uni <- strsplit(benefits_shiatsu, split=" ")
shiatsubenefit_uni1 <- lapply(shiatsubenefit_uni,'[',1)
shiatsubenefit_uni1b <- as.character(shiatsubenefit_uni1)
shiatsubenefit_uni2 <- lapply(shiatsubenefit_uni,'[',2)
shiatsubenefit_uni2b <- as.character(shiatsubenefit_uni2)
shiatsuuni_benefits <- unique(c(shiatsubenefit_uni1b,shiatsubenefit_uni2b))
benefits_shiatsu1 <- c(shiatsuuni_benefits, benefits_shiatsu) #
both_shiatsu <- Shiatsu_contraindications %in% benefits_shiatsu1
shiatsu1 <- Shiatsu_contraindications[both_shiatsu] #
Shiatsu_contraindications1 <- Shiatsu_contraindications[both_shiatsu==FALSE] #benefits_prenatal, contra_prenatal
prenatal_split <- strsplit(contra_prenatal,split=' ')
prenatal_split1 <- lapply(prenatal_split, '[',1)
prenatal_split1b <- as.character(prenatal_split1)
prenatal_split2 <- lapply(prenatal_split, '[',2)
prenatal_split2b <- as.character(prenatal_split2)
prenatal_split3 <- lapply(prenatal_split, '[',3)
prenatal_split3b <- as.character(prenatal_split3)
prenatal_bi1 <- paste(prenatal_split1b,prenatal_split2b)
prenatal_bi2 <- paste(prenatal_split2b,prenatal_split3b)
prenatal_bi3 <- paste(prenatal_split3b,prenatal_split1b)
uni_prenatal <- unique(c(prenatal_split1b, prenatal_split2b, prenatal_split3b))
bi_prenatal <- unique(c(prenatal_bi1, prenatal_bi2, prenatal_bi3))
Prenatal_contraindications <- c(contra_prenatal, bi_prenatal, uni_prenatal)
prenatalbenefit_uni <- strsplit(benefits_prenatal, split=" ")
prenatalbenefit_uni1 <- lapply(prenatalbenefit_uni,'[',1)
prenatalbenefit_uni1b <- as.character(prenatalbenefit_uni1)
prenatalbenefit_uni2 <- lapply(prenatalbenefit_uni,'[',2)
prenatalbenefit_uni2b <- as.character(prenatalbenefit_uni2)
prenataluni_benefits <- unique(c(prenatalbenefit_uni1b,prenatalbenefit_uni2b))
benefits_prenatal1 <- c(prenataluni_benefits, benefits_prenatal) #
both_prenatal <- Prenatal_contraindications %in% benefits_prenatal1
prenatal1 <- Prenatal_contraindications[both_prenatal] #
Prenatal_contraindications1 <- Prenatal_contraindications[both_prenatal==FALSE] #benefits_myofascial, contra_myofascial
myofascial_split <- strsplit(contra_myofascial,split=' ')
myofascial_split1 <- lapply(myofascial_split, '[',1)
myofascial_split1b <- as.character(myofascial_split1)
myofascial_split2 <- lapply(myofascial_split, '[',2)
myofascial_split2b <- as.character(myofascial_split2)
myofascial_split3 <- lapply(myofascial_split, '[',3)
myofascial_split3b <- as.character(myofascial_split3)
myofascial_bi1 <- paste(myofascial_split1b,myofascial_split2b)
myofascial_bi2 <- paste(myofascial_split2b,myofascial_split3b)
myofascial_bi3 <- paste(myofascial_split3b,myofascial_split1b)
uni_myofascial <- unique(c(myofascial_split1b, myofascial_split2b, myofascial_split3b))
bi_myofascial <- unique(c(myofascial_bi1, myofascial_bi2, myofascial_bi3))
Myofascial_contraindications <- c(contra_myofascial, bi_myofascial, uni_myofascial)
myofascialbenefit_uni <- strsplit(benefits_myofascial, split=" ")
myofascialbenefit_uni1 <- lapply(myofascialbenefit_uni,'[',1)
myofascialbenefit_uni1b <- as.character(myofascialbenefit_uni1)
myofascialbenefit_uni2 <- lapply(myofascialbenefit_uni,'[',2)
myofascialbenefit_uni2b <- as.character(myofascialbenefit_uni2)
myofascialuni_benefits <- unique(c(myofascialbenefit_uni1b,myofascialbenefit_uni2b))
benefits_myofascial1 <- c(myofascialuni_benefits, benefits_myofascial) #
both_myofascial <- Myofascial_contraindications %in% benefits_myofascial1
myofascial1 <- Myofascial_contraindications[both_myofascial] #
Myofascial_contraindications1 <- Myofascial_contraindications[both_myofascial==FALSE] #Now that we have our lists of better contraindications and better benefits for each massage modality, and the tokens commen to both, we should start building our recommender system for massage that first excludes any modality that has the tokens for contraindications based on a user input, then use the user input to recommend a massage or many massages based on the tokens that fit into the benefits of each modality.
We should first start by tokenizing a list the same way we did with the model that produced these tokens. We did this in python, and we could go back to python, or we could tokenize in R with the same packages used in an earlier text mining script called SentimentAnalysisReviewsMixedBusinessModels2.Rmd (reviewsYelp2 desktop folder) from the tm, tidytext, textstem, stringr, dplyr, and tidyverse R packages.
We will lemmatize the tokens of the benefits and contraindications each separately and also the Description and sideEffects to possibly use later.
modes <- read.csv('MassageModalities2.csv', sep=',', header=TRUE, na.strings=c('',' ','NA'))
colnames(modes)[1] <- 'modality'
modes$lemmaDescription <- lemmatize_strings(modes$Description, dictionary=lexicon::hash_lemmas)
modes$lemmaBenefits <- lemmatize_strings(modes$benefits, dictionary=lexicon::hash_lemmas)
modes$lemmaContraindications <- lemmatize_strings(modes$contraindications,
dictionary=lexicon::hash_lemmas)
modes$lemmaSideEffects <- lemmatize_strings(modes$sideEffects, dictionary=lexicon::hash_lemmas)Then we will take the bigrams of the lemmatized benefits, and trigrams of the lemmatized contraindications.
lemmaTable <- tibble(line=1:456, Modality=modes$modality,
Description=modes$lemmaDescription,
SideEffects=modes$lemmaSideEffects,
Benefits=modes$lemmaBenefits,
Contraindications=modes$lemmaContraindications
)
bigram_df <- lemmaTable %>% unnest_tokens(BenefitsBigram, Benefits, token='ngrams',n=2)
bigram_df2 <- bigram_df %>% count(BenefitsBigram, sort=TRUE)
bigram_separate <- bigram_df2 %>%
separate(BenefitsBigram, c('word1','word2'), sep=' ')
bigram_noStops <- bigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
bigram_counts <- bigram_noStops %>% count(word1,word2,sort=TRUE)
trigram_df <- lemmaTable %>% unnest_tokens(ContraindicationsTrigram,Contraindications,
token='ngrams',n=3)
trigram_df2 <- trigram_df %>% count(ContraindicationsTrigram, sort=TRUE)
trigram_separate <- trigram_df2 %>% separate(ContraindicationsTrigram,
c('word1','word2','word3'), sep=' ')
trigram_noStops <- trigram_separate %>% filter(!word1 %in% stop_words$word)%>%
filter(!word2 %in% stop_words$word) %>% filter(!word3 %in% stop_words$word)
trigram_noStops_counts <- trigram_noStops %>% count(word1,word2,word3, sort=TRUE)colnames(bigram_counts)## [1] "word1" "word2" "n"bigram_counts## # A tibble: 163 x 3
## word1 word2 n
## <chr> <chr> <int>
## 1 ache improve 1
## 2 ache relieve 1
## 3 acute pain 1
## 4 adhesion heal 1
## 5 adhesion improve 1
## 6 adhesion increase 1
## 7 alleviate headache 1
## 8 anxiety stress 1
## 9 arthritis chronic 1
## 10 arthritis tendonitis 1
## # ... with 153 more rowscolnames(trigram_noStops_counts)## [1] "word1" "word2" "word3" "n"trigram_noStops_counts## # A tibble: 191 x 4
## word1 word2 word3 n
## <chr> <chr> <chr> <int>
## 1 acute cranium bleed 1
## 2 anemia blood disorder 1
## 3 anemia diabetes blood 1
## 4 aneurism history cancer 1
## 5 aneurism history psychosis 1
## 6 application site fever 1
## 7 arnold chiari acute 1
## 8 arthritis neuropathic pain 1
## 9 autoimmune disease psychosis 1
## 10 blood clot diabetes 1
## # ... with 181 more rowsquadgram_description <- lemmaTable %>% unnest_tokens(DescriptionQuadgram,Description,
token='ngrams',n=4)
quadgram_description## # A tibble: 46,944 x 6
## line Modality SideEffects Benefits Contraindications DescriptionQuad~
## <int> <fct> <chr> <chr> <chr> <chr>
## 1 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ traditional spa~
## 2 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ spa or clinic m~
## 3 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ or clinic massa~
## 4 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ clinic massage ~
## 5 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ massage with ha~
## 6 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ with hand palm ~
## 7 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ hand palm elbow~
## 8 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ palm elbow fore~
## 9 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ elbow forearm o~
## 10 1 Swedish ~ if no contrai~ improve t~ dehydration, feve~ forearm of mass~
## # ... with 46,934 more rowsThe tidytext has the great tokenization feature, but doesn’t allow groups of more than one ngram, such as setting ngrams to a list like n=c(1,4), as the python 3 nltk package does. But it can be worked around if needed to get those values separately or as we did with string literals earlier when filtering the tokenized words for better tokens of benefits and contraindications for massage.
We aren’t much concerned with counts, as this goal was to get the contraindications, from 24 identical samples for each of 19 massage modality benefits or contraindications, so the counts are not necessary because there won’t be much variance if any, and it was only if there was a count greater than zero that interested this script’s focus. But it is useful to have the counts listed above for each ngram.
Right now, the goal is to build the same extracted tokens as we did earlier in python then use a function to wrap around a user input that will tokenize the input and transform it into the same matrices as was done in python. I am not sure how or what programs to use in R to do that at the moment and won’t spend time searching to finish this task. So it is tempting to switch back to python using R’s reticulate library to run python script in the RStudio console. But the whole purpose of this section is to build a program that will:
1.) Use input from a user and tokenize the input by: a.) lemmatizing the input b.) extracting the: i.) unigrams ii.) bigrams iii.) trigrams
2.) Then take those lemmatized ngrams, and: a.) compare to our tokenized list of contraindications for all modalities b.) create a list of every modality the input is a contraindication for c.) create a list of available massages by: i.) removing any modality in 2.b for the user to choose from ii.) this will be a separate list of available modalities
3.) take the uni and bi gram lemmatized tokens of the user input in 1.b then: a.) compare to the list of available modalities in 2.c.ii. and make a list b.) from this list, any of the tokens that match the user input tokens: i.) add to a list of recommended massage modality ii.) here the more tokens per modality is needed from user input tokens to select the modality with the highest counts of tokens for benefits iii.) If there is a tie for massage modality recommended, then output all available massages and: -also provide the massage modality description -also provide the massage modality side effects
This doesn’t work in Rmarkdown.
# userRequest <- readline(prompt="Please tell us what makes you want a massage today, such as how your feeling, and briefly if you have a headache, pain, a cold, congestion, allergies, health history, pregnant, epileptic, had a history of seizures or aneurisms, or anything else you can think of so that we can recommend a massage for you that isn't contraindicated for your needs. ")
#
# print(userRequest)We will use a program defined request.
userInput <- function(input){
input <- as.character(paste(input))
input
}
print('Please explain what brings you in for a massage today, such as if you want to relax, its a gift, you have a headache, stressed at work, pain in the body, sore workouts, and briefly what your health is like and history of health.')## [1] "Please explain what brings you in for a massage today, such as if you want to relax, its a gift, you have a headache, stressed at work, pain in the body, sore workouts, and briefly what your health is like and history of health."input1 <- userInput('I have had headaches off and on, have seasonal allergies, my sleep is good, I have a history of stroke, and am on blood pressure medicine.')
input2 <- userInput('I just want to relax, and get massages regularly at a local massage place. I don\'t have any pre-existing health conditions and like a deep pressure with focus on my upper back. I am over a cold that I had two weeks ago. I have a sunburn on my face, that you could avoid.')
input3 <- userInput('I have leukemia, but want cupping done. My neighbor says it is good for me and I never tried it. Also, I need medium to deep pressure and don\'t want a light massage.')
input1## [1] "I have had headaches off and on, have seasonal allergies, my sleep is good, I have a history of stroke, and am on blood pressure medicine."input2## [1] "I just want to relax, and get massages regularly at a local massage place. I don't have any pre-existing health conditions and like a deep pressure with focus on my upper back. I am over a cold that I had two weeks ago. I have a sunburn on my face, that you could avoid."input3## [1] "I have leukemia, but want cupping done. My neighbor says it is good for me and I never tried it. Also, I need medium to deep pressure and don't want a light massage."The problem with using the function is that escape characters need to be used. When using the readline() there is no need to worry about that.The following won’t display because it is set to FALSE for eval= in the r chunk header, but shows what it could be in a program.
input1 <- readline('What makes you want a massage today? please explain your sleep, stress, health, health history, pressure used to, and last massage.')Now to tokenize these user inputs. We need to create three functions that will get the uni, bi, and tri grams.
uni <- as.character(c(' '))
unitoken <- function(input){
lemm1 <- lemmatize_strings(input, dictionary=lexicon::hash_lemmas)
Lemm1 <- tibble(line=1, unigram=lemm1)
unigram <- unnest_tokens(Lemm1,userUnigram, unigram,token='ngrams',n=1)
uniNoStops <- filter(unigram, !userUnigram %in% stop_words$word)
uniNoStops1 <- as.character(paste(uniNoStops$userUnigram))
uniNoStops1
}
unoInput1 <- unitoken(input1)
unoInput1## [1] "headache" "seasonal" "allergy" "sleep" "history" "stroke" "blood"
## [8] "pressure" "medicine"bitoken <- function(input){
lemm2 <- lemmatize_strings(input, dictionary=lexicon::hash_lemmas)
Lemm2 <- tibble(line=1, bigram=lemm2)
bigram <- unnest_tokens(Lemm2,userBigram, bigram,token='ngrams',n=2)
bigram_separate <- bigram %>% separate(userBigram,
c('word1','word2'), sep=' ')
bigram_noStops <- bigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
bigram <- as.character(paste(bigram_noStops$word1,bigram_noStops$word2))
bigram
}
dosInput1 <- bitoken(input1)
dosInput1## [1] "seasonal allergy" "blood pressure" "pressure medicine"tritoken <- function(input){
lemm3 <- lemmatize_strings(input, dictionary=lexicon::hash_lemmas)
Lemm3 <- tibble(line=1, trigram=lemm3)
trigram <- unnest_tokens(Lemm3,userTrigram, trigram, token='ngrams',n=3)
trigram_separate <- trigram %>% separate(userTrigram, c('word1','word2','word3'), sep=' ')
trigram_noStops <- trigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word3 %in% stop_words$word)
trigram <- as.character(paste(trigram_noStops$word1,trigram_noStops$word2,
trigram_noStops$word3))
trigram
}
tresInput1 <- tritoken(input1)
tresInput1## [1] "blood pressure medicine"Combine into one function the uni, bi, and tri grams.
uniBiTriTokens <- function(input){
lemm1 <- lemmatize_strings(input, dictionary=lexicon::hash_lemmas)
Lemm1 <- tibble(line=1, unigram=lemm1)
unigram <- unnest_tokens(Lemm1,userUnigram, unigram,token='ngrams',n=1)
uniNoStops <- filter(unigram, !userUnigram %in% stop_words$word)
ungram <- uniNoStops$userUnigram
Lemm2 <- tibble(line=1, bigram=lemm1)
bigram <- unnest_tokens(Lemm2,userBigram, bigram,token='ngrams',n=2)
bigram_separate <- bigram %>% separate(userBigram,
c('word1','word2'), sep=' ')
bigram_noStops <- bigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
bigram <- as.character(paste(bigram_noStops$word1,bigram_noStops$word2))
Lemm3 <- tibble(line=1, trigram=lemm1)
trigram <- unnest_tokens(Lemm3,userTrigram, trigram, token='ngrams',n=3)
trigram_separate <- trigram %>% separate(userTrigram, c('word1','word2','word3'), sep=' ')
trigram_noStops <- trigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word3 %in% stop_words$word)
trigram <- as.character(paste(trigram_noStops$word1,
trigram_noStops$word2,trigram_noStops$word3))
bt <- append(bigram,trigram, after=length(bigram))
uniBiTriToken <-append(ungram, bt, after=length(ungram))
uniBiTriToken
}
unDosTresGram <- uniBiTriTokens(input1)
unDosTresGram## [1] "headache" "seasonal"
## [3] "allergy" "sleep"
## [5] "history" "stroke"
## [7] "blood" "pressure"
## [9] "medicine" "seasonal allergy"
## [11] "blood pressure" "pressure medicine"
## [13] "blood pressure medicine"We have our list and functio that will produce the bigrams, trigrams, and unigrams of a single user input. Now we need to compare this list of preprocessed, lemmatized, ngram tokens to the lists of tokens in each contraindication for each massage modality. And build a list of modalities this user input is not recommended. From there, we will compare that list of available modalities, to the list of tokens in the benefits of those modalities, and only keep those modalities the user input is recommended based on his or her input.
As an aside, this would probably be similar to the automated prompts to most telecommunications and major credit cards like Capital One or Macy’s, where a user might possibly get lost, know what they want but don’t know how to input according to these tokens. That would not be a desired outcome of this model. But textual recommender, not based on voice to text recommendations. So the better the user inputs his or her health and massage goals the better the results, but also the better fine tuned our benefits and contraindications for how a user would input for example, a heart condition or symptoms related to health conditions, like a fever could vary user to user. As a refresher, our named lists of contraindications within each modality are:
Our function that separates the user input into bi-tri-uni-tokens is called uniBiTriTokens(), with the user input as the only argument as a string. We stored the first input as unDosTresGram. This is the list we need to compare to every modality list above to see if any of the tokens are in that list, and create a list that will be a list of unavailable massages.
unDosTresGram## [1] "headache" "seasonal"
## [3] "allergy" "sleep"
## [5] "history" "stroke"
## [7] "blood" "pressure"
## [9] "medicine" "seasonal allergy"
## [11] "blood pressure" "pressure medicine"
## [13] "blood pressure medicine"CBD_contraindications1## [1] "aneurism history cancer" "autoimmune disease psychosis"
## [3] "breathing medication could" "breathing ragged breathing"
## [5] "breathing trouble breathing" "cancer autoimmune disease"
## [7] "cannabidiol allergy cbd" "could interact cannabidiol"
## [9] "dehydration fever rash" "difficulty breathing ragged"
## [11] "disease psychosis numbness" "disorder nausea epilepsy"
## [13] "epilepsy pregnant aneurism" "fever rash infection"
## [15] "history cancer autoimmune" "infection mental disorder"
## [17] "interact cannabidiol allergy" "limb limb numbness"
## [19] "limb numbness nerve" "medication could interact"
## [21] "mental disorder nausea" "nausea epilepsy pregnant"
## [23] "nerve pain difficulty" "nerve tingling nerve"
## [25] "numbness limb limb" "numbness nerve tingling"
## [27] "pain difficulty breathing" "pregnant aneurism history"
## [29] "psychosis numbness limb" "ragged breathing trouble"
## [31] "rash infection mental" "tingling nerve pain"
## [33] "trouble breathing medication" "aneurism history"
## [35] "autoimmune disease" "breathing medication"
## [37] "breathing ragged" "breathing trouble"
## [39] "cancer autoimmune" "cannabidiol allergy"
## [41] "could interact" "dehydration fever"
## [43] "difficulty breathing" "disease psychosis"
## [45] "disorder nausea" "epilepsy pregnant"
## [47] "fever rash" "history cancer"
## [49] "infection mental" "interact cannabidiol"
## [51] "limb limb" "limb numbness"
## [53] "medication could" "mental disorder"
## [55] "nausea epilepsy" "nerve tingling"
## [57] "numbness limb" "numbness nerve"
## [59] "pain difficulty" "pregnant aneurism"
## [61] "psychosis numbness" "ragged breathing"
## [63] "rash infection" "tingling nerve"
## [65] "trouble breathing" "allergy cbd"
## [67] "cancer aneurism" "psychosis autoimmune"
## [69] "could breathing" "breathing breathing"
## [71] "disease cancer" "cbd cannabidiol"
## [73] "cannabidiol could" "rash dehydration"
## [75] "ragged difficulty" "numbness disease"
## [77] "epilepsy disorder" "aneurism epilepsy"
## [79] "infection fever" "autoimmune history"
## [81] "disorder infection" "allergy interact"
## [83] "nerve limb" "interact medication"
## [85] "nausea mental" "pregnant nausea"
## [87] "difficulty nerve" "nerve nerve"
## [89] "tingling numbness" "breathing pain"
## [91] "history pregnant" "limb psychosis"
## [93] "trouble ragged" "mental rash"
## [95] "pain tingling" "medication trouble"
## [97] "aneurism" "autoimmune"
## [99] "breathing" "cancer"
## [101] "cannabidiol" "could"
## [103] "dehydration" "difficulty"
## [105] "disease" "disorder"
## [107] "epilepsy" "fever"
## [109] "history" "infection"
## [111] "interact" "limb"
## [113] "medication" "mental"
## [115] "nausea" "numbness"
## [117] "pregnant" "psychosis"
## [119] "ragged" "rash"
## [121] "tingling" "trouble"
## [123] "allergy" "cbd"The above shows the list of tokens in the user input, then the list of contraindications for CBD as a massage modality.
testCBD_contra <- ifelse(unDosTresGram %in% CBD_contraindications1,1,0)
l <- length(unDosTresGram)
s <- sum(testCBD_contra)
l## [1] 13s## [1] 2notlikelyCBD <- round(s/l,2)
notlikelyCBD## [1] 0.15unDosTresGram[testCBD_contra>0]## [1] "allergy" "history"input1## [1] "I have had headaches off and on, have seasonal allergies, my sleep is good, I have a history of stroke, and am on blood pressure medicine."We can see from above that the history is important, but it says stroke, which is a term not used as a heart condition from the contraindications listed for CBD or any of the modalities, because it listed blood pressure, blood clots, heart condition, and circulatory problems. So, adding in ‘stroke’ would be a good contraindication to add in. This is an example of dialect wording for heart diseases. There is probably a setting in one of these natural language processing libraries that accounts for synonyms. We will make a note of this for a later version. $%^
Lets look at the contraindications for CBD to see how the input compares.
cbd <- subset(modes, modes$modality=='Cannabidiol (CBD) Massage Balm')[1,4]
cbd## [1] dehydration, fever, rashes, infection, some mental disorders, nausea, epilepsy, pregnant, aneurism history, cancer, autoimmune disease, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing, medications that could interact with cannabidiol, allergies to CBD
## 19 Levels: dehydration, asthma, allergies to fragrances, epilepsy, fever, infection, heart condition, neuropathy, pregnant, breast feeding, open wounds, skin rashes, eczema, psoriasis, sunburn, sensitive skin, sensitive to smells, pregnant, aneurism history, cancer, psychosis, numbness in limbs, limb numbness, nerve tingling, nerve pain, difficulty breathing, ragged breathing, trouble breathing, ...We can see that heart condition is in the list, but not blood pressure nor stroke. So, we should add those two conditions to our original data table and update in a new version. For now, since is is 15%, it is questionable, and with a doctor’s note it should be fine. CBD oil is safe at the levels sold by stores in CA < 0.003% or 3/1000 parts of the product. So, we will keep it from our list of unavailable modalities for this user.
Lets create a function to do just this for all 19 massage modalities. It will return a percent of not recommended that the modality is a contraindication for the user. The input would be the output of the user input from the uniBiTriTokens stored as a variable, so that the variable would be the input to this function, we will name nonRecommended().
notRecommended <- function(uniBiTriTokensOutputVar){
testCBD <- ifelse(uniBiTriTokensOutputVar %in% CBD_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCBD)
cbd <- round(s/l,2)
testCranio <- ifelse(uniBiTriTokensOutputVar %in% Cranio_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCranio)
craniosacral <- round(s/l,2)
testReflex <- ifelse(uniBiTriTokensOutputVar %in% Reflex_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testReflex)
reflexology <- round(s/l,2)
testLymph <- ifelse(uniBiTriTokensOutputVar %in% Lymph_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testLymph)
lymphatic <- round(s/l,2)
testMGN <- ifelse(uniBiTriTokensOutputVar %in% Mgn_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMGN)
massagegun <- round(s/l,2)
testIASTM <- ifelse(uniBiTriTokensOutputVar %in% Instrument_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testIASTM)
IASTM <- round(s/l,2)
testDT <- ifelse(uniBiTriTokensOutputVar %in% DT_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testDT)
deepTissue <- round(s/l,2)
testSwedish <- ifelse(uniBiTriTokensOutputVar %in% swedish_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSwedish)
swedish <- round(s/l,2)
testAroma <- ifelse(uniBiTriTokensOutputVar %in% Aroma_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testAroma)
aromatherapy <- round(s/l,2)
testStretch <- ifelse(uniBiTriTokensOutputVar %in% stretch_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testStretch)
stretch <- round(s/l,2)
testTPT <- ifelse(uniBiTriTokensOutputVar %in% TPT_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testTPT)
triggerPoint <- round(s/l,2)
testHS <- ifelse(uniBiTriTokensOutputVar %in% HotStone_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testHS)
hotStone <- round(s/l,2)
testCups <- ifelse(uniBiTriTokensOutputVar %in% Cupping_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCups)
cupping <- round(s/l,2)
testSports <- ifelse(uniBiTriTokensOutputVar %in% Sports_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSports)
sports <- round(s/l,2)
testFreeze <- ifelse(uniBiTriTokensOutputVar %in% Freeze_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testFreeze)
biofreeze <- round(s/l,2)
testCold <- ifelse(uniBiTriTokensOutputVar %in% cold_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCold)
coldStone <- round(s/l,2)
testShiatsu <- ifelse(uniBiTriTokensOutputVar %in% Shiatsu_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testShiatsu)
shiatsu <- round(s/l,2)
testPreg <- ifelse(uniBiTriTokensOutputVar %in% Prenatal_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testPreg)
prenatal <- round(s/l,2)
testMyo <- ifelse(uniBiTriTokensOutputVar %in% Myofascial_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMyo)
myofascial <- round(s/l,2)
names <- c("Cannabidiol (CBD) Massage Balm","Craniosacral Massage","Reflexology Massage","Lymphatic Drainage Massage","Massage Gun Therapy","Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage", "Deep tissue Massage","Swedish Massage","Aromatherapy","Stretching","Trigger Point Therapy","Hot Stone Therapy Massage", "Cupping Therapy","Sports Massage","Biofreeze Muscle Pain Relief Gel","Cold Stone Therapy","Shiatsu Massage","Prenatal Massage","Myofascial Massage")
probs <- c(cbd,craniosacral,reflexology,lymphatic,massagegun,IASTM,deepTissue,
swedish,aromatherapy,stretch,triggerPoint,hotStone,cupping,sports,
biofreeze,coldStone,shiatsu,prenatal,myofascial)
probabilities <- as.data.frame(cbind(names,probs))
colnames(probabilities) <- c('modality','probability')
probabilities$probability <- as.numeric(paste(probabilities$probability))
probabilities1 <- subset(probabilities,
probabilities$probability>min(probabilities$probability))
probabilities1
}nr <- notRecommended(unDosTresGram)
nr## modality
## 1 Cannabidiol (CBD) Massage Balm
## 2 Craniosacral Massage
## 6 Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage
## 9 Aromatherapy
## 13 Cupping Therapy
## 15 Biofreeze Muscle Pain Relief Gel
## 16 Cold Stone Therapy
## 18 Prenatal Massage
## probability
## 1 0.15
## 2 0.15
## 6 0.31
## 9 0.15
## 13 0.31
## 15 0.38
## 16 0.31
## 18 0.15Now that we have the list of massages not to recommend for this user’s input, we can compare those tokens in the user input to the benefits tokens for each massage modality not in the modalities not recommended.
NR <- as.character(paste(nr$modality))
NR## [1] "Cannabidiol (CBD) Massage Balm"
## [2] "Craniosacral Massage"
## [3] "Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage"
## [4] "Aromatherapy"
## [5] "Cupping Therapy"
## [6] "Biofreeze Muscle Pain Relief Gel"
## [7] "Cold Stone Therapy"
## [8] "Prenatal Massage"modesList <- as.character(paste(unique(modes$modality)))
availModes <- !modesList %in% NR
availModes## [1] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [13] TRUE FALSE FALSE TRUE FALSE FALSE FALSEavailable <- modesList[availModes]
available## [1] "Swedish Massage" "Shiatsu Massage"
## [3] "Sports Massage" "Lymphatic Drainage Massage"
## [5] "Reflexology Massage" "Trigger Point Therapy"
## [7] "Myofascial Massage" "Deep tissue Massage"
## [9] "Hot Stone Therapy Massage" "Massage Gun Therapy"
## [11] "Stretching"Now that we have our avaiable massage modalities, lets get the best recommended massages from this list to recommend a massage for our user’s input tokens.The uniBiTriTokens output that was stored as a variable is used, and inside this function, modesRecommended(), it will calculate all probability recommended massages based on our filtered benefits lists of each 19 modes, then return a list of available massages that aren’t in our notRecommended list and with a higher probability based on the user input tokens to be recommended compared with each modality’s benefits. We will modify the function above but replace the contraindications for each modality with the benefits. The benefits lists built earlier are: - benefits_cbd1 - benefits_cranio1 - benefits_reflex1
- benefits_lymph1 - benefits_mgn1 - benefits_tpt1 - benefits_instrument1 - benefits_DT1 - benefits_swedish1 - benefits_aroma1 - benefits_stretch1 - benefits_cold1 - benefits_freeze1 - benefits_sports1 - benefits_cup1 - benefits_HotStone1 - benefits_shiatsu1 - benefits_prenatal1 - benefits_myofascial1
The stored variable for our input, uniBiTriTokensOutputVar, is unDosTresGrams.
modesRecommended <- function(uniBiTriTokensOutputVar){
testCBD <- ifelse(uniBiTriTokensOutputVar %in% benefits_cbd1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCBD)
cbd <- round(s/l,2)
testCranio <- ifelse(uniBiTriTokensOutputVar %in% benefits_cranio1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCranio)
craniosacral <- round(s/l,2)
testReflex <- ifelse(uniBiTriTokensOutputVar %in% benefits_reflex1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testReflex)
reflexology <- round(s/l,2)
testLymph <- ifelse(uniBiTriTokensOutputVar %in% benefits_lymph1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testLymph)
lymphatic <- round(s/l,2)
testMGN <- ifelse(uniBiTriTokensOutputVar %in% benefits_mgn1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMGN)
massagegun <- round(s/l,2)
testIASTM <- ifelse(uniBiTriTokensOutputVar %in% benefits_instrument1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testIASTM)
IASTM <- round(s/l,2)
testDT <- ifelse(uniBiTriTokensOutputVar %in% benefits_DT1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testDT)
deepTissue <- round(s/l,2)
testSwedish <- ifelse(uniBiTriTokensOutputVar %in% benefits_swedish1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSwedish)
swedish <- round(s/l,2)
testAroma <- ifelse(uniBiTriTokensOutputVar %in% benefits_aroma1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testAroma)
aromatherapy <- round(s/l,2)
testStretch <- ifelse(uniBiTriTokensOutputVar %in% benefits_stretch1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testStretch)
stretch <- round(s/l,2)
testTPT <- ifelse(uniBiTriTokensOutputVar %in% benefits_tpt1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testTPT)
triggerPoint <- round(s/l,2)
testHS <- ifelse(uniBiTriTokensOutputVar %in% benefits_HotStone1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testHS)
hotStone <- round(s/l,2)
testCups <- ifelse(uniBiTriTokensOutputVar %in% benefits_cup1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCups)
cupping <- round(s/l,2)
testSports <- ifelse(uniBiTriTokensOutputVar %in% benefits_sports1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSports)
sports <- round(s/l,2)
testFreeze <- ifelse(uniBiTriTokensOutputVar %in% benefits_freeze1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testFreeze)
biofreeze <- round(s/l,2)
testCold <- ifelse(uniBiTriTokensOutputVar %in% benefits_cold1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCold)
coldStone <- round(s/l,2)
testShiatsu <- ifelse(uniBiTriTokensOutputVar %in% benefits_shiatsu1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testShiatsu)
shiatsu <- round(s/l,2)
testPreg <- ifelse(uniBiTriTokensOutputVar %in% benefits_prenatal1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testPreg)
prenatal <- round(s/l,2)
testMyo <- ifelse(uniBiTriTokensOutputVar %in% benefits_myofascial1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMyo)
myofascial <- round(s/l,2)
names <- c("Cannabidiol (CBD) Massage Balm","Craniosacral Massage","Reflexology Massage","Lymphatic Drainage Massage","Massage Gun Therapy","Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage", "Deep tissue Massage","Swedish Massage","Aromatherapy","Stretching","Trigger Point Therapy","Hot Stone Therapy Massage", "Cupping Therapy","Sports Massage","Biofreeze Muscle Pain Relief Gel","Cold Stone Therapy","Shiatsu Massage","Prenatal Massage","Myofascial Massage")
probs <- c(cbd,craniosacral,reflexology,lymphatic,massagegun,IASTM,deepTissue,
swedish,aromatherapy,stretch,triggerPoint,hotStone,cupping,sports,
biofreeze,coldStone,shiatsu,prenatal,myofascial)
probabilities <- as.data.frame(cbind(names,probs))
colnames(probabilities) <- c('modality','probability')
probabilities$probability <- as.numeric(paste(probabilities$probability))
probabilities0 <- probabilities %>% filter(probabilities$modality %in% available)
probabilities01 <- probabilities0[order(probabilities0$probability, decreasing=TRUE),]
print(probabilities01)
probs <- as.character(paste(probabilities01$modality))
print('Your top choices for your recommended massage modalities based on your user input are: ')
# print(probs[1])
# print(probs[2])
# print(probs[3])
# print('Thank you.')
probs[1:3]
}reccMode <- modesRecommended(unDosTresGram)## modality probability
## 1 Reflexology Massage 0.08
## 4 Deep tissue Massage 0.08
## 5 Swedish Massage 0.08
## 6 Stretching 0.08
## 7 Trigger Point Therapy 0.08
## 8 Hot Stone Therapy Massage 0.08
## 9 Sports Massage 0.08
## 10 Shiatsu Massage 0.08
## 2 Lymphatic Drainage Massage 0.00
## 3 Massage Gun Therapy 0.00
## 11 Myofascial Massage 0.00
## [1] "Your top choices for your recommended massage modalities based on your user input are: "reccMode## [1] "Reflexology Massage" "Deep tissue Massage" "Swedish Massage"That last function returns Deep tissue massage, and earlier we found that it shouldn’t because it wasn’t recognizing the blood pressure medication or heart condition, and so the underlying data was changed to fix this issue. Because a deep tissue massage is a contraindication for a person with a heart condition among other health issues. Even if their muscles are super dense and thick and even if they are used to it and fall asleep in session. It could dislodge any embolisms, clots, or any other factor after the massage that affects the person’s heart or circulatory system.
Aside: many clients do want deep tissue massage and have had stints in their body, but are healthy otherwise, because they work out 3x or more a week, have good blood pressure, lowered their stress and anger issues if they had them prior to heart disease diagnosis, and have a better diet, and these people have had regular massages. This system should still not recommend a deep tissue massage for this person. They can bring a doctor’s note, email, text, or fax, but this program also won’t account for that. There will be variations of this program.
Lets make this function look pretty, but not using any pretty packaging of a related design. Just clean it up and combine it to work in an online app.
massageModalityRecommender <- function(input){
lemm1 <- lemmatize_strings(input, dictionary=lexicon::hash_lemmas)
Lemm1 <- tibble(line=1, unigram=lemm1)
unigram <- unnest_tokens(Lemm1,userUnigram, unigram,token='ngrams',n=1)
uniNoStops <- filter(unigram, !userUnigram %in% stop_words$word)
ungram <- uniNoStops$userUnigram
Lemm2 <- tibble(line=1, bigram=lemm1)
bigram <- unnest_tokens(Lemm2,userBigram, bigram,token='ngrams',n=2)
bigram_separate <- bigram %>% separate(userBigram,
c('word1','word2'), sep=' ')
bigram_noStops <- bigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
bigram <- as.character(paste(bigram_noStops$word1,bigram_noStops$word2))
Lemm3 <- tibble(line=1, trigram=lemm1)
trigram <- unnest_tokens(Lemm3,userTrigram, trigram, token='ngrams',n=3)
trigram_separate <- trigram %>% separate(userTrigram, c('word1','word2','word3'), sep=' ')
trigram_noStops <- trigram_separate %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word3 %in% stop_words$word)
trigram <- as.character(paste(trigram_noStops$word1,
trigram_noStops$word2,trigram_noStops$word3))
bt <- append(bigram,trigram, after=length(bigram))
uniBiTriToken <-append(ungram, bt, after=length(ungram))
uniBiTriTokensOutputVar <- uniBiTriToken
testCBD <- ifelse(uniBiTriTokensOutputVar %in% CBD_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCBD)
cbd <- round(s/l,2)
testCranio <- ifelse(uniBiTriTokensOutputVar %in% Cranio_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCranio)
craniosacral <- round(s/l,2)
testReflex <- ifelse(uniBiTriTokensOutputVar %in% Reflex_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testReflex)
reflexology <- round(s/l,2)
testLymph <- ifelse(uniBiTriTokensOutputVar %in% Lymph_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testLymph)
lymphatic <- round(s/l,2)
testMGN <- ifelse(uniBiTriTokensOutputVar %in% Mgn_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMGN)
massagegun <- round(s/l,2)
testIASTM <- ifelse(uniBiTriTokensOutputVar %in% Instrument_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testIASTM)
IASTM <- round(s/l,2)
testDT <- ifelse(uniBiTriTokensOutputVar %in% DT_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testDT)
deepTissue <- round(s/l,2)
testSwedish <- ifelse(uniBiTriTokensOutputVar %in% swedish_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSwedish)
swedish <- round(s/l,2)
testAroma <- ifelse(uniBiTriTokensOutputVar %in% Aroma_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testAroma)
aromatherapy <- round(s/l,2)
testStretch <- ifelse(uniBiTriTokensOutputVar %in% stretch_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testStretch)
stretch <- round(s/l,2)
testTPT <- ifelse(uniBiTriTokensOutputVar %in% TPT_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testTPT)
triggerPoint <- round(s/l,2)
testHS <- ifelse(uniBiTriTokensOutputVar %in% HotStone_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testHS)
hotStone <- round(s/l,2)
testCups <- ifelse(uniBiTriTokensOutputVar %in% Cupping_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCups)
cupping <- round(s/l,2)
testSports <- ifelse(uniBiTriTokensOutputVar %in% Sports_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSports)
sports <- round(s/l,2)
testFreeze <- ifelse(uniBiTriTokensOutputVar %in% Freeze_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testFreeze)
biofreeze <- round(s/l,2)
testCold <- ifelse(uniBiTriTokensOutputVar %in% cold_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCold)
coldStone <- round(s/l,2)
testShiatsu <- ifelse(uniBiTriTokensOutputVar %in% Shiatsu_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testShiatsu)
shiatsu <- round(s/l,2)
testPreg <- ifelse(uniBiTriTokensOutputVar %in% Prenatal_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testPreg)
prenatal <- round(s/l,2)
testMyo <- ifelse(uniBiTriTokensOutputVar %in% Myofascial_contraindications1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMyo)
myofascial <- round(s/l,2)
names <- c("Cannabidiol (CBD) Massage Balm","Craniosacral Massage","Reflexology Massage","Lymphatic Drainage Massage","Massage Gun Therapy","Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage", "Deep tissue Massage","Swedish Massage","Aromatherapy","Stretching","Trigger Point Therapy","Hot Stone Therapy Massage", "Cupping Therapy","Sports Massage","Biofreeze Muscle Pain Relief Gel","Cold Stone Therapy","Shiatsu Massage","Prenatal Massage","Myofascial Massage")
probs <- c(cbd,craniosacral,reflexology,lymphatic,massagegun,IASTM,deepTissue,
swedish,aromatherapy,stretch,triggerPoint,hotStone,cupping,sports,
biofreeze,coldStone,shiatsu,prenatal,myofascial)
probabilities <- as.data.frame(cbind(names,probs))
colnames(probabilities) <- c('modality','probability')
probabilities$probability <- as.numeric(paste(probabilities$probability))
probabilities1 <- subset(probabilities,
probabilities$probability>min(probabilities$probability))
NR <- as.character(paste(probabilities1$modality))
availModes <- !names %in% NR
available <- names[availModes]
testCBD1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_cbd1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCBD1)
cbd1 <- round(s/l,2)
testCranio1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_cranio1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCranio1)
craniosacral1 <- round(s/l,2)
testReflex1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_reflex1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testReflex1)
reflexology1 <- round(s/l,2)
testLymph1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_lymph1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testLymph1)
lymphatic1 <- round(s/l,2)
testMGN1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_mgn1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMGN1)
massagegun1 <- round(s/l,2)
testIASTM1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_instrument1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testIASTM1)
IASTM1 <- round(s/l,2)
testDT1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_DT1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testDT1)
deepTissue1 <- round(s/l,2)
testSwedish1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_swedish1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSwedish1)
swedish1 <- round(s/l,2)
testAroma1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_aroma1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testAroma1)
aromatherapy1 <- round(s/l,2)
testStretch1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_stretch1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testStretch1)
stretch1 <- round(s/l,2)
testTPT1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_tpt1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testTPT1)
triggerPoint1 <- round(s/l,2)
testHS1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_HotStone1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testHS1)
hotStone1 <- round(s/l,2)
testCups1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_cup1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCups1)
cupping1 <- round(s/l,2)
testSports1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_sports1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testSports1)
sports1 <- round(s/l,2)
testFreeze1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_freeze1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testFreeze1)
biofreeze1 <- round(s/l,2)
testCold1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_cold1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testCold1)
coldStone1 <- round(s/l,2)
testShiatsu1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_shiatsu1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testShiatsu1)
shiatsu1 <- round(s/l,2)
testPreg1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_prenatal1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testPreg1)
prenatal1 <- round(s/l,2)
testMyo1 <- ifelse(uniBiTriTokensOutputVar %in% benefits_myofascial1,1,0)
l <- length(uniBiTriTokensOutputVar)
s <- sum(testMyo1)
myofascial1 <- round(s/l,2)
names1 <- c("Cannabidiol (CBD) Massage Balm","Craniosacral Massage","Reflexology Massage","Lymphatic Drainage Massage","Massage Gun Therapy","Instrument Assisted Soft Tissue Mobilization (IASTM) Friction Massage", "Deep tissue Massage","Swedish Massage","Aromatherapy","Stretching","Trigger Point Therapy","Hot Stone Therapy Massage", "Cupping Therapy","Sports Massage","Biofreeze Muscle Pain Relief Gel","Cold Stone Therapy","Shiatsu Massage","Prenatal Massage","Myofascial Massage")
probs <- c(cbd1,craniosacral1,reflexology1,lymphatic1,massagegun1,IASTM1,deepTissue1,
swedish1,aromatherapy1,stretch1,triggerPoint1,hotStone1,cupping1,sports1,
biofreeze1,coldStone1,shiatsu1,prenatal1,myofascial1)
probabilities <- as.data.frame(cbind(names1,probs))
colnames(probabilities) <- c('modality','probability')
probabilities$probability <- as.numeric(paste(probabilities$probability))
probabilities0 <- probabilities %>% filter(probabilities$modality %in% available)
probabilities01 <- probabilities0[order(probabilities0$probability, decreasing=TRUE),]
print(probabilities01)
probs <- as.character(paste(probabilities01$modality))
print('Your top choices for your recommended massage modalities based on your user input are: ')
probs[1:3]
}massageModalityRecommender(input1)## modality probability
## 1 Reflexology Massage 0.08
## 4 Deep tissue Massage 0.08
## 5 Swedish Massage 0.08
## 6 Stretching 0.08
## 7 Trigger Point Therapy 0.08
## 8 Hot Stone Therapy Massage 0.08
## 9 Sports Massage 0.08
## 10 Shiatsu Massage 0.08
## 2 Lymphatic Drainage Massage 0.00
## 3 Massage Gun Therapy 0.00
## 11 Myofascial Massage 0.00
## [1] "Your top choices for your recommended massage modalities based on your user input are: "## [1] "Reflexology Massage" "Deep tissue Massage" "Swedish Massage"Great! The function works as planned. Now, we need to make version 3 with the better data choices that will hopefully make better recommendations. We should monitor the remaining data and possible inputs.
Moving forward, after version 3 ofcourse, there can be many different approaches and models for recommending a massage to a user. These could be off of intake forms checked that return exact ‘keywords’ or tokens that fit the puzzles for contraindications and benefits to fine tune an automated recommendation, the option to add in whether they have a doctor’s not that approves massage for a contraindication a user has could also be included, so that it would essentially delete that word token that flags or marks the contraindicated modality tokens.
As an aside: we could also go through my notes on clients handwritten apart from the franchise client notes that I don’t even know what some of the words are, because I tried cramming the notes onto the page and adding more in after, and those notes only go from 2016-2018 when I decided to stop and focus on graduate work. Those notes explain the person’s massage goals with sometims whether or not they had certain health conditions, if they had body parts avoided like legs or feet due to a health condition like neuropathy or just uncomfortable, if they had injuries, modifications to people with broken bones and injuries, people recently in car wrecks but more than two weeks ago, how frequently they got massages like 3x a week, weekly, biweekly, monthly, and so on, complaints, pressure, allergies, runny nose while face down too long, smells of cigarette smoke, congested while face down, getting over a cold, and so on. Also, if they used aroma, but not including the scent, the front desk would have that information. Those notes were while working at team Massage Green or team Bodycentre Fullerton (btw, Chayla at Bodycentre Fullerton and Diane at Massage Green and Diane at Massage Heights Chino are Bi### to coworkers or team members) and also team Massage Heights Newport Beach.
So lets get to version 3, shall we?