Training a model requires various parameters, depending on the model type. Here we present examples of regression, time series, and OpenAI models.
Here are some useful links:
Regression Model
Let’s look at an example of how to create and train a simple regression model, and then, use it for making predictions.
// Defining training options
const regressionTrainingOptions = {
select: 'SELECT * FROM demo_data.home_rentals',
integration: 'example_db'
};
try {
// Creating and training a model
// The returned promise resolves when the model is created, NOT when training is actually complete
let homeRentalPriceModel = await MindsDB.Models.trainModel(
'home_rentals_model',
'rental_price',
'mindsdb',
regressionTrainingOptions);
console.log('created a model');
// Waiting for the training to be complete
while (homeRentalPriceModel.status !== 'complete' && homeRentalPriceModel.status !== 'error') {
homeRentalPriceModel = await MindsDB.Models.getModel('home_rentals_model', 'mindsdb');
}
// Checking model's status
console.log('Model status: ' + homeRentalPriceModel.status);
// Defining query options
const queryOptions = {
where: [
'sqft = 823',
'location = "good"',
'neighborhood = "downtown"',
'days_on_market = 10'
]
}
// Querying for a single prediction
const rentalPricePrediction = await homeRentalPriceModel.query(queryOptions);
console.log('Predicted value:');
console.log(rentalPricePrediction.value);
console.log('Prediction insights:');
console.log(rentalPricePrediction.explain);
console.log('Input data:');
console.log(rentalPricePrediction.data);
} catch (error) {
// Something went wrong training or querying
console.log(error);
}
Time Series Model
As time series models require more parameters, let’s go over an example of how to create and train a time series model, and then, use it for making batch predictions.
// Defining training options
const timeSeriesTrainingOptions = {
integration: 'example_db',
select: 'SELECT * FROM demo_data.house_sales',
orderBy: 'saledate',
groupBy: 'bedrooms',
window: 8,
horizon: 4
}
try {
// Creating and training a model
let houseSalesForecastModel = await MindsDB.Models.trainModel(
'house_sales_model',
'ma',
'mindsdb',
timeSeriesTrainingOptions);
console.log('created a model');
// Waiting for the training to be complete
while (houseSalesForecastModel.status !== 'complete' && houseSalesForecastModel.status !== 'error') {
houseSalesForecastModel = await MindsDB.Models.getModel('house_sales_model', 'mindsdb');
}
// Checking model's status
console.log('Model status: ' + houseSalesForecastModel.status);
// Describing a model
const modelDescription = await houseSalesForecastModel.describe();
console.log('Model description:');
console.log(modelDescription);
// Defining query options
const queryOptions = {
// Join model to this data source
join: 'example_db.demo_data.house_sales',
// When using batch queries, the 't' alias is used for the joined data source ('t' is short for training/test)
// The 'm' alias is used for the trained model to be queried
where: ['t.saledate > LATEST', 't.bedrooms = 2'],
limit: 4
}
// Querying for batch predictions
const rentalPriceForecasts = await houseSalesForecastModel.batchQuery(queryOptions);
console.log('Batch predictions:');
rentalPriceForecasts.forEach(f => {
console.log(f.value);
console.log(f.explain);
console.log(f.data);
})
} catch (error) {
// Something went wrong training or predicting.
console.log(error);
}
OpenAI Model
Here is how to create and deploy an OpenAI model from JavaScript code:
// Defining training options
const trainingOptions = {
using: {engine: 'openai', prompt_template: 'describe the sentiment of the reviews strictly as "positive", "neutral", or "negative". "I love the product":positive "It is a scam":negative "{{review}}.":'}
};
try {
// Creating and training a model
let openai_js = await MindsDB.Models.trainModel(
'openai_js',
'sentiment',
'mindsdb',
trainingOptions);
console.log('created a model');
// Waiting for the training to be complete
while (openai_js.status !== 'complete' && openai_js.status !== 'error') {
openai_js = await MindsDB.Models.getModel('openai_js', 'mindsdb');
}
// Checking model's status
console.log('Model status: ' + openai_js.status);
// Defining query options
const queryOptions = {
where: [
'review = \'It is ok.\''
]
}
// Querying for a single prediction
openai_js = await openai_js.query(queryOptions);
console.log('Predicted value:');
console.log(openai_js.value);
console.log('Prediction insights:');
console.log(openai_js.explain);
console.log('Input data:');
console.log(openai_js.data);
} catch (error) {
// Something went wrong training or querying
console.log(error);
}
