Module Contents



A Base Class Model for Wrapping Spectral Models into Quartic Deployments.

class quartic_sdk.model.BaseSpectralModel.BaseSpectralModel(name: str, description: str = '', log_level: str = 'INFO')

A Base Class Model for Wrapping Spectral Models into Quartic Deployments. User needs to inherit this class and override the predict method with all the post model training steps such as, preprocessing, prediction, postprocessing the pandas dataframe passed to :func: predict during real time prediction.

Note: Please do not overwrite method :func: save as it contains utilities to validate and deploy models to the Quartic AI Platform

name : Name of the model to be saved in Quartic AI Platform description : Description of the current model log_level : Log Level for logs created/executed during run time i.e. during real time predictions

name : Name of the model to be saved in Quartic AI Platform description : description of model log_level : Log level log : Logger instance which can be used to set run time logs ex: self.log.info(“Example Log”)

save : private save method to save deploy model to the Quartic AI Platform predict : abstract predict method which needs to overridden

class MyModel(BaseSpectralModel):
def __init__(self, model):

self.model = model super().__init__(‘MyModel’, ‘model description’, ‘INFO’)

def preprocess(self, input_df):

transformed_df = custom_transform(input_df) return transformed_df

def postprocess(self, input_df):

transformed_df = custom_transform_post(input_df) return transformed_df

def predict(self, input_df):

pre_transformed_df = self.preprocess(input_df) prediction_df = self.model.predict(pre_transformed_df) self.log.info(“Test Log”) return postprocess(prediction_df)[‘output_column’] # pandas Series

lr = LinearRegression() lr.train(input_data) my_model = MyModel(lr) my_model.save(client, ‘my_spectralmodel_output’, [‘1460000.0’,’1460001.0’,’1460002.0’], Tag(‘Spectral’), Tag(‘Target’), input_data, None, 12)

save(self, client, output_tag_name: str, feature_wavelengths: List[str], spectral_tag: int, target_tag: int, test_df: pandas.DataFrame, ml_node: int = None, future_window: int = None)
  • client – Quartic APIClient

  • output_tag_name – name for Prediction output tag

  • feature_wavelengths – Feature wavelengths of spectral_tag used in model

  • spectral_tag – Spectral tag_id

  • target_tag – Target tag id to specify the parent of current soft tag

  • test_df – Test input dataframe to validate input and prediction output in agreement with Quartic AI Platform

  • ml_node – Optional - ML Node ID if deployment of model needs to be done to specific node


Optional - time in ms(int)


None on successfully storing the model to the Quartic AI Platform

abstract predict(self, input_df: pandas.DataFrame)pandas.Series

Abstract method for custom predict method :param input_df: Input Data frame for prediction :return: Returns pd