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Machine Learning With Go

You're reading from  Machine Learning With Go

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Pages 304 pages
Edition 1st Edition
Languages
Toc

Table of Contents (11) Chapters close

Preface 1. Gathering and Organizing Data 2. Matrices, Probability, and Statistics 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

JSON

In a world in which the majority of data is accessed via the web, and most engineering organizations implement some number of microservices, we are going to encounter data in JSON format fairly frequently. We may only need to deal with it when pulling some random data from an API, or it might actually be the primary data format that drives our analytics and machine learning workflows.

Typically, JSON is used when ease of use is the primary goal of data interchange. Since JSON is human readable, it is easy to debug if something breaks. Remember that we want to maintain the integrity of our data handling as we process data with Go, and part of that process is ensuring that, when possible, our data is interpretable and readable. JSON turns out to be very useful in achieving these goals (which is why it is also used for logging, in many cases).

Go offers really great JSON functionality in its standard library with encoding/json. We will utilize this standard library functionality throughout the book.

Parsing JSON

To understand how to parse (that is, unmarshal) JSON data in Go, we will be using some data from the Citi Bike API (https://www.citibikenyc.com/system-data), a bike-sharing service operating in New York City. Citi Bike provides frequently updated operational information about its network of bike sharing stations in JSON format at https://gbfs.citibikenyc.com/gbfs/en/station_status.json:

{
"last_updated": 1495252868,
"ttl": 10,
"data": {
"stations": [
{
"station_id": "72",
"num_bikes_available": 10,
"num_bikes_disabled": 3,
"num_docks_available": 26,
"num_docks_disabled": 0,
"is_installed": 1,
"is_renting": 1,
"is_returning": 1,
"last_reported": 1495249679,
"eightd_has_available_keys": false
},
{
"station_id": "79",
"num_bikes_available": 0,
"num_bikes_disabled": 0,
"num_docks_available": 33,
"num_docks_disabled": 0,
"is_installed": 1,
"is_renting": 1,
"is_returning": 1,
"last_reported": 1495248017,
"eightd_has_available_keys": false
},

etc...

{
"station_id": "3464",
"num_bikes_available": 1,
"num_bikes_disabled": 3,
"num_docks_available": 53,
"num_docks_disabled": 0,
"is_installed": 1,
"is_renting": 1,
"is_returning": 1,
"last_reported": 1495250340,
"eightd_has_available_keys": false
}
]
}
}

To parse the import and this type of data in Go, we first need to import encoding/json (along with a couple of other things from a standard library, such as net/http, because we are going to pull this data off of the previously mentioned website). We will also define struct that mimics the structure of the JSON shown in the preceding code:

import (
"encoding/json"
"fmt"
"io/ioutil"
"log"
"net/http"
)

// citiBikeURL provides the station statuses of CitiBike bike sharing stations.
const citiBikeURL = "https://gbfs.citibikenyc.com/gbfs/en/station_status.json"

// stationData is used to unmarshal the JSON document returned form citiBikeURL.
type stationData struct {
LastUpdated int `json:"last_updated"`
TTL int `json:"ttl"`
Data struct {
Stations []station `json:"stations"`
} `json:"data"`
}

// station is used to unmarshal each of the station documents in stationData.
type station struct {
ID string `json:"station_id"`
NumBikesAvailable int `json:"num_bikes_available"`
NumBikesDisabled int `json:"num_bike_disabled"`
NumDocksAvailable int `json:"num_docks_available"`
NumDocksDisabled int `json:"num_docks_disabled"`
IsInstalled int `json:"is_installed"`
IsRenting int `json:"is_renting"`
IsReturning int `json:"is_returning"`
LastReported int `json:"last_reported"`
HasAvailableKeys bool `json:"eightd_has_available_keys"`
}

Note a couple of things here: (i) we have followed Go idioms by avoiding the struct field name with underscores, but (ii) we have utilized the json struct tags to label the struct fields with the corresponding expected fields in the JSON data.

Note, to properly parse JSON data, the struct fields need to be exported fields. That is, the fields need to begin with a capital letter. encoding/json does cannot view fields using reflect unless they are exported.

Now we can get the JSON data from the URL and unmarshal it into a new stationData value. This will produce a struct variable with the respective fields filled with the data in the tagged JSON data fields. We can check it by printing out some data associated with one of the stations:

// Get the JSON response from the URL.
response, err := http.Get(citiBikeURL)
if err != nil {
log.Fatal(err)
}
defer response.Body.Close()

// Read the body of the response into []byte.
body, err := ioutil.ReadAll(response.Body)
if err != nil {
log.Fatal(err)
}

// Declare a variable of type stationData.
var sd stationData

// Unmarshal the JSON data into the variable.
if err := json.Unmarshal(body, &sd); err != nil {
log.Fatal(err)
}

// Print the first station.
fmt.Printf("%+v\n\n", sd.Data.Stations[0])

When we run this, we can see that our struct contains the parsed data from the URL:

$ go build
$ ./myprogram
{ID:72 NumBikesAvailable:11 NumBikesDisabled:0 NumDocksAvailable:25 NumDocksDisabled:0 IsInstalled:1 IsRenting:1 IsReturning:1 LastReported:1495252934 HasAvailableKeys:false}

JSON output

Now let's say that we have the Citi Bike station data in our stationData struct value and we want to save that data out to a file. We can do this with json.marshal:

// Marshal the data.
outputData, err := json.Marshal(sd)
if err != nil {
log.Fatal(err)
}

// Save the marshalled data to a file.
if err := ioutil.WriteFile("citibike.json", outputData, 0644); err != nil {
log.Fatal(err)
}
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Machine Learning With Go
Published in: Sep 2017 Publisher: Packt ISBN-13: 9781785882104
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