Model Creation

QuarticSDK allows the users to deploy two types of models via the Quartic AI platform. It allows deployment of normal tag-based Machine Learning models, that use the tags as the feature variables, as well as the deployment of wavelength based models for spectral tags that use spectral tag wavelengths as the feature variables. This article explains how to create, wrap, and deploy both kinds of models to the Quartic AI Platform.

BaseSpectralModel

BaseSpectralModel is a base class for all the ML models that can be deployed to the Platform, which use the spectral tag wavelengths as features for the deployed ML model. Users must extend this class and implement the predict method to make the ML model compatible to deploy in the Quartic AI Platform. The available methods are as follows:

__init__

The method has following parameters for initialization:

• name (mandatory): Enter a unique name for the model

• description (optional): Describe the model. The default value for the field is ''

• log_level (optional): Select one of the log level for the model: 'CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'. The default value is INFO.

Note: While implementing an instance of BaseSpectralModel, users must call super().init with above parameters.

.save

This is a private method used to save the model to the Quartic AI Platform.

Note: While implementing an instance of BaseSpectralModel, users must call super().init with above parameters.

Warning: Do not override this method.

• client (mandatory): Refers to an instance of APIClient

• output_tag_name (mandatory): Refers to a unique name for the prediction results

• feature_wavelengths (mandatory): Refers to a list of spectral tag wavelengths that are as features in the model

• spectral_tag (mandatory): Refers to the spectral tag whose wavelengths are used for deploying the model

• target_tag (mandatory): Refers to the tag that has been assigned to an asset the ML model will use this asset as a parent of the current prediction output

• test_df (mandatory): Refers to the test dataframe that validates the model prediction results and ensures compatibility with the Quartic AI Platform

• ml_node (optional): Refers to the Ml Node ID for deploying model to a particular node.

Note:  1. The Quartic AI Platform selects the best ML node depending on CPU and memory utilization at the time of deployment (by default). To deploy a model to a particular node, specify ml_node={node_id}; for example, ml_node=1 . 2. The save_method takes a sample of test data frame to validate the model._

.predict

The method has the following parameters for running the predictions of a ML model:

• input_df (mandatory): Refers to the dataframe on which the model is being evaluated

Note:  1. Users must override this method to transform and run predictions for the model created. 2. input_df is expected to have tag IDs as the column names. If model is trained with tag names instead, a transformation step must be added and used in predict method to convert the tag IDs in input dataframe into tag names._

Example

import pandas as pd
from quartic_sdk.model import BaseSpectralModel
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

class ExampleModel(BaseSpectralModel):
    def __init__(self):
        self.model = RandomForestRegressor()
        super().__init__("Sample Model", description='This is a simple model to give a quick introduction on creating and deploying models to the Quartic AI Platform.')

    def train(self, X, y):
        self.model.fit(X, y)

    def predict(self, input_df):
        predictions = self.model.predict(input_df)
        return pd.Series(predictions)

quartic_model = ExampleModel()
quartic_model.train(X_train, y_train) # Training data extracted from data loaded from the Quartic AI Platform
quartic_model.save(client=api_client, output_tag_name="Prediction Result",
                   feature_tags=[1,2,3], # tags that are used in the X variable or features, for example 1,2,3
                   target_tag = 3, # tag that specifies a relationship for prediction, for example, 3
                   test_df = X_train
                   )

Note: Any intermediate steps that are used for model training outside the wrapper must be included in wrapper for applying similar set of transformations during prediction.