Why an API ?!
APIs allow machine learning models to be implemented in many different ways in production connecting the model and its results with so many apps in the whole system. also APIs allow the model to speak to other languages so we can benefit from every language in the field in which it succeeds. as we’re going to see now, a model developed in R and used from within Python and Command Line
we’ll be using the acute inflammations dataset downloaded from https://archive.ics.uci.edu/ml/machine-learning-databases/acute/ the dataset is about patients with some symptoms and the goal is to predict whether the patient suffer from Acute inflammation of urinary bladder or acute nephritises.
Github Link: https://github.com/MNoorFawi/creating-api-for-machine-learning-model
Data Preparation
data <- read.table('diagnosis.data', fileEncoding="UTF-16", dec=",")
names(data) <- c('temperature', 'nausea', 'lumbar_pain',
'urine_pushing', 'micturition_pain',
'burning_swelling_urethra', 'd1', 'd2')
diagnosis <- factor(ifelse(
data$d1 == 'no' & data$d2 == 'no', 'none',
ifelse(data$d2 == 'yes' & data$d1 == 'yes', 'both',
ifelse(data$d1 == 'yes' & data$d2 == 'no',
'urinary', 'nephritis'))))
data$diagnosis <- diagnosis
data <- data[, -c(7:8)]
head(data)
## temperature nausea lumbar_pain urine_pushing micturition_pain
## 1 35.5 no yes no no
## 2 35.9 no no yes yes
## 3 35.9 no yes no no
## 4 36.0 no no yes yes
## 5 36.0 no yes no no
## 6 36.0 no yes no no
## burning_swelling_urethra diagnosis
## 1 no none
## 2 yes urinary
## 3 no none
## 4 yes urinary
## 5 no none
## 6 no none
str(data)
## 'data.frame': 120 obs. of 7 variables:
## $ temperature : num 35.5 35.9 35.9 36 36 36 36.2 36.2 36.3 36.6 ...
## $ nausea : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ lumbar_pain : Factor w/ 2 levels "no","yes": 2 1 2 1 2 2 1 2 1 1 ...
## $ urine_pushing : Factor w/ 2 levels "no","yes": 1 2 1 2 1 1 2 1 2 2 ...
## $ micturition_pain : Factor w/ 2 levels "no","yes": 1 2 1 2 1 1 2 1 2 2 ...
## $ burning_swelling_urethra: Factor w/ 2 levels "no","yes": 1 2 1 2 1 1 2 1 2 2 ...
## $ diagnosis : Factor w/ 4 levels "both","nephritis",..: 3 4 3 4 3 3 4 3 4 4 ...
Exploring the data visually
library(reshape) library(ggplot2) ggplot(data, aes(x = temperature, fill = diagnosis)) + geom_density(alpha = 0.6, color = 'white', size = 0.5) + scale_fill_brewer(palette = 'Set1') + theme_minimal() + facet_wrap(~ diagnosis, ncol = 1)
melted <- melt(data[, -1], id.vars = 'diagnosis') ggplot(melted, aes(x = diagnosis, fill = value)) + geom_bar(position = 'dodge', color = 'black') + facet_wrap(~ variable) + scale_fill_brewer(palette = 'Pastel1') + theme(legend.position = c(0.8, 0.2))
Now as we have an idea of the data in our hands, let’s fit the model. we’ll fit a decision tree model.
library(rpart) library(rpart.plot) tree_model <- rpart(diagnosis ~ ., data = data) tree_pred <- predict(tree_model, newdata = data, type = 'class') ## measuring model accuracy mean(tree_pred == data$diagnosis)
## [1] 1
## plotting the model
rpart.plot(tree_model)
our model is such a great one.
let’s create the API using the plumber package first we save the model to load it later in the script of the API.
save(tree_model, file = 'tree_model.RData')
now we write an R script for the API naming it tree_api.R.
library(jsonlite)
library(plumber)
## load the model
load('tree_model.RData')
## writing a POST request, we can write get request with the same syntax
#* @post /diagnose
diagnose <- function(
temperature, nausea, lumbar_pain, urine_pushing,
micturition_pain, burning_swelling_urethra) {
data <- list(
temperature = temperature, nausea = nausea,
lumbar_pain = lumbar_pain, urine_pushing = urine_pushing,
micturition_pain = micturition_pain,
burning_swelling_urethra = burning_swelling_urethra
)
diagnosis <- predict(tree_model, data, type = 'class')
d <- data.frame(diagnosis)
lst <- list(results = d)
return(lst)
}
## get method to extract "variable importance" from the model
#* @get /vimp
vimp <- function(){
importance <- round(tree_model$variable.importance, 2)
variable <- names(importance)
varimp <- data.frame(variable, importance)
rownames(varimp) <- NULL
list(varimp)
}
now we have everything done. let’s experiment it.
library(plumber)
r <- plumb('tree_api.R')
r$run(port = 8080)
now we have the API running on port 8080. let’s call it from within python
import json
import requests
import pandas as pd
import ast
## new data
data = {"temperature" : [36, 35, 35.9, 37],
"nausea": ['yes', 'no', 'no', 'yes'],
"lumbar_pain": ['no', 'yes', 'yes', 'no'],
"urine_pushing": ['no', 'no', 'yes', 'yes'],
"micturition_pain": ['no', 'yes', 'yes', 'no'],
'burning_swelling_urethra': ['yes', 'no', 'yes', 'yes']}
data = json.dumps(data)
## writing the data to disk to use it later with command line
with open('data.txt', 'w') as f:
json.dump(ast.literal_eval(data), f)
headers = {'content-type': 'application/json'}
url = 'http://localhost:8080/diagnose'
r = requests.post(url, data = data, headers = headers)
d = json.loads(r.content)
d
## converting it to pandas DataFrame to further analysis
pd.DataFrame(d['results'])
## {'results':
## [{'diagnosis': 'none'},
## {'diagnosis': 'none'},
## {'diagnosis': 'none'},
## {'diagnosis': 'urinary'}]}
## diagnosis
## 0 none
## 1 none
## 2 none
## 3 urinary
well, it works fine. now we have a model implemented with R and its results can be used with python.
let’s experiment it with the command line.
curl -s -X POST localhost:8080/diagnose \
-d @data.txt -H 'Content:Type: application/json'
## {"results":
## "[{"diagnosis":"none"},{"diagnosis":"none"},
## {"diagnosis":"none"},{"diagnosis":"urinary"}]"}
To operate over it using some command line tools, for example to transform this json output into csv format.
curl -s -X POST localhost:8080/diagnose \ -d @data.txt -H 'Content:Type: application/json' \ | jq -r '.[]' | json2csv | csvlook ## | diagnosis | ## | --------- | ## | none | ## | none | ## | none | ## | urinary |
We can also send a get request to get the importance of each variable in the model.
curl -s localhost:8080/vimp
# [
# [
# {
# "variable": "temperature",
# "importance": 53.52
# },
# {
# "variable": "urine_pushing",
# "importance": 41.02
# },
# {
# "variable": "lumbar_pain",
# "importance": 26.75
# },
# {
# "variable": "nausea",
# "importance": 15.27
# },
# {
# "variable": "burning_swelling_urethra",
# "importance": 5.98
# },
# {
# "variable": "micturition_pain",
# "importance": 0.53
# }
# ]
# ]
curl -s localhost:8080/vimp | jq -r '.[]' | json2csv | csvlook
# | variable | importance |
# | ------------------------ | ---------- |
# | temperature | 53.52 |
# | urine_pushing | 41.02 |
# | lumbar_pain | 26.75 |
# | nausea | 15.27 |
# | burning_swelling_urethra | 5.98 |
# | micturition_pain | 0.53 |
Our model is working fine from everywhere else not only R where it was implemented.
We can connect the model with whatever application so easily now.
N.B. many forms can be returned using plumber but I prefer a dictionary of list of dictionaries because it makes more sense to have a name for the whole data especially when the data sent and returned are of many variables.
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