:mod:`quartic_sdk.model.BaseQuarticModel`
=========================================

.. py:module:: quartic_sdk.model.BaseQuarticModel


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

Classes
~~~~~~~

.. autoapisummary::

   quartic_sdk.model.BaseQuarticModel.BaseQuarticModel



.. class:: BaseQuarticModel(name: str, description: str = '', log_level: str = 'INFO')


   A Base Class Model for Wrapping User 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 model 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(BaseQuarticModel):
       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('my_model_output', [Tag('A'), Tag('B')], Tag('C'), input_data, None)

   .. method:: save(self, client, output_tag_name: str, feature_tags: List[int], target_tag: int, test_df: pandas.DataFrame, ml_node: int = None)

      :param client:          Quartic APIClient
      :param output_tag_name: name for Prediction output tag
      :param feature_tags:    Feature Tag ids used in the model
      :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
      :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


   .. method:: with_window(duration)

      This is decorator method for window support
      User can decorate predict method with this decorator for window support
      :param duration: window duration in sec
      :return:    None


   .. method:: moving_window_predict(self, input_df: pandas.DataFrame, previous_df: pandas.DataFrame)

      This method calls predict for with window model along with respective window data for each row in input_df.
      :param input_df: input dataframe
      :param previous_df: previous dataframe for with window model
      :return: pandas series of predictions along with timestamps respective to input_df records