:mod:`quartic_sdk.model.BaseSpectralModel`
==========================================

.. py:module:: quartic_sdk.model.BaseSpectralModel


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   quartic_sdk.model.BaseSpectralModel.BaseSpectralModel



.. class:: 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

   Parameters
   ----------
   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

   Attributes
   ----------
   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")

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

   Examples
   --------
   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)

   .. method:: 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)

      :param client:          Quartic APIClient
      :param output_tag_name: name for Prediction output tag
      :param feature_wavelengths: Feature wavelengths of spectral_tag used in model
      :param spectral_tag:    Spectral tag_id
      :param target_tag:      Target tag id to specify the parent of current soft tag
      :param test_df:         Test input dataframe to validate input and
                              prediction output in agreement with Quartic AI Platform
      :param ml_node:         Optional - ML Node ID if deployment of model needs to be done to specific node
      :future_window:         Optional - time in ms(int)
      :return:                None on successfully storing the model to the Quartic AI Platform


   .. method:: predict(self, input_df: pandas.DataFrame) -> pandas.Series
      :abstractmethod:

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