aurora: Logistic Regression

Build your trainable Logistic Regression

LogisticRegression lr = new LogisticRegressionBuilder()
  .inputSize(3) // the input size - must be the same as the input array length
  .outputSize(1) // the output size - must be the same as the output array length
  .learningRate(0.1) // define the learning rate
  .epochs(1_000_000) // define the epochs
  .activation(ActivationFunction.SIGMOID) // define the activation function - ActivationFunction.SIGMOID by default
  .printing(Bar.CLASSIC) // define the printing - Bar.CLASSIC by default
  .build();

Train your Logistic Regression

Create a Data Set:

double[][] inputs = new double[][] { // 3 input size
  {0, 0, 1},
  {0, 1, 1},
  {1, 0, 1},
  {1, 1, 1}
};

double[][] outputs = new double[][] { // 1 output size
  {0},
  {1},
  {1},
  {0}
};

Train the model:

lr.train(inputs, outputs);

Printing: Printing comes with attributes, providing information about the training process:

• Loss: Represents the decreasing error which means the model is improving
• Stage: Represents the current stage in the training process, which goes to 100 when it is completed
• Accuracy: Represents the accuracy score of the model, whereas you may use HyperparameterTuning for the best score
• Epoch: Represents the current iteration

[##################=============] Loss: 3.7998585945554775E-14 | Stage: 57 | Accuracy: 0.999999999999962 | Epoch: 570000 (\)

Predict

double[] output = lr.predict(new double[] {1, 0, 0}); // output should be close to 0.9 since we trained the output to be 1

Evaluate your model

Create a Test Set:

Map<double[], double[]> data = new HashMap<>();
data.put(new double[] {0, 0, 1}, new double[] {0});
data.put(new double[] {0, 1, 1}, new double[] {0});
data.put(new double[] {1, 0, 1}, new double[] {1});
data.put(new double[] {1, 1, 1}, new double[] {1});
TestSet testSet = new TestSet(data);

Evaluate:

Evaluation eval = lr.evaluate(testSet);
eval.printSummary();

Prints:

Evaluation Summary of Type: Logistic Regression
 Accuracy: 1.0
 Precision: 1.0
 Recall: 1.0
 F1 Score: 1.0

Deploy your model

Save your Logistic Regression Model:

Model model = lr.toModel();
model.save("my_directory");

Load a pre-trained model

Load from file:

LogisticRegressionModel model = null;
try (ModelLoader loader = new ModelLoader(new File("my_directory"))) {
  model = loader.load(LogisticRegressionModel.class);
}

Load from URL:

LogisticRegressionModel model = null;
try (ModelLoader loader = new ModelLoader(new URL("my_model_url"))) {
  model = loader.load(LogisticRegressionModel.class);
} catch (MalformedURLException e) {
  throw new RuntimeException(e);
}