SVM-SGD Wrapper

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This is a wrapper function for the Linear SVM with Stochastic Gradient Descent , designed to be passed into Bayesian optimization .  It takes in an array of four hyperparameters: a regularization coefficient, initial learning rate, weight decay parameter, and number of iterations to perform stochastic gradient descent.  The data , which is a collection of SMS messages to be classified as "spam" or "ham", is supplied manually, broken into training, validation, and test data (training and validation are used here).  A model is trained with the given hyperparameters on the training data, and the output is the classification error on the validation data.   This algorithm is designed to be used as a demonstration for Bayesian optimization.  Stochastic gradient descent shines most on larger scale data, but this job will finish in enough time to allow Bayesian optimization with a "reasonable" number of jobs to finish in a "reasonable" amount of time.  

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1. Type your input

2. See the result

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3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/mheimann/SVMSGDWrapper/0.1.0
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://mheimann/SVMSGDWrapper/0.1.0 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://mheimann/SVMSGDWrapper/0.1.0");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://mheimann/SVMSGDWrapper/0.1.0")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mheimann/SVMSGDWrapper/0.1.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mheimann/SVMSGDWrapper/0.1.0")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mheimann/SVMSGDWrapper/0.1.0')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("mheimann/SVMSGDWrapper/0.1.0")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mheimann/SVMSGDWrapper/0.1.0')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo('mheimann/SVMSGDWrapper/0.1.0');
let response = algo.pipe(input);
View Rust Docs
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