Skip to content

Latest commit

 

History

History
104 lines (80 loc) · 4.13 KB

sum.rst

File metadata and controls

104 lines (80 loc) · 4.13 KB
Raw

Summation

Just The Code

.. nbimport::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cells: 1 3 4 6

Walkthrough

The Summation value calculation is straightforward. For each :term:`chunk<Data Chunk>` NannyML calculates the sum for all selected numerical columns. The resulting values from the reference :ref:`data<data-drift-periods>` chunks are used to calculate the alert :term:`thresholds<Threshold>`. The sum value results from the analysis chunks are compared against those thresholds and generate alerts if applicable.

We begin by loading the :ref:`synthetic car loan dataset<dataset-synthetic-binary-car-loan>` provided by the NannyML package.

.. nbimport::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cells: 1


.. nbtable::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cell: 2

The :class:`~nannyml.stats.sum.calculator.SummaryStatsSumCalculator` class implements the functionality needed for sum values calculations. We need to instantiate it with appropriate parameters:

  • column_names: A list with the names of columns to be evaluated.
  • timestamp_column_name (Optional): The name of the column in the reference data that contains timestamps.
  • chunk_size (Optional): The number of observations in each chunk of data used. Only one chunking argument needs to be provided. For more information about :term:`chunking<Data Chunk>` configurations check out the :ref:`chunking tutorial<chunking>`.
  • chunk_number (Optional): The number of chunks to be created out of data provided for each :ref:`period<data-drift-periods>`.
  • chunk_period (Optional): The time period based on which we aggregate the provided data in order to create chunks.
  • chunker (Optional): A NannyML :class:`~nannyml.chunk.Chunker` object that will handle the aggregation provided data in order to create chunks.
  • threshold (Optional): The threshold strategy used to calculate the alert threshold limits. For more information about thresholds, check out the :ref:`thresholds tutorial<thresholds>`.
.. nbimport::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cells: 3

Next, the :meth:`~nannyml.base.AbstractCalculator.fit` method needs to be called on the reference data, which provides the baseline that the analysis data will be compared with for :term:`alert<Alert>` generation. Then the :meth:`~nannyml.base.AbstractCalculator.calculate` method will calculate the data quality results on the data provided to it.

The results can be filtered to only include a certain data period, method or column by using the filter method. You can evaluate the result data by converting the results into a DataFrame, by calling the :meth:`~nannyml.base.AbstractResult.to_df` method. By default this will return a DataFrame with a multi-level index. The first level represents the column, the second level represents resulting information such as the data quality metric values, the alert thresholds or the associated sampling error.

.. nbimport::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cells: 4


.. nbtable::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cell: 5

More information on accessing the information contained in the :class:`~nannyml.stats.sum.result.Result` can be found on the :ref:`working_with_results` page.

The next step is visualizing the results, which is done using the :meth:`~nannyml.stats.sum.result.Result.plot` method. It is recommended to filter results for each column and plot separately.

.. nbimport::
    :path: ./example_notebooks/Tutorial - Stats - Sum.ipynb
    :cells: 6

Insights

We see that only the car_value column exhibits a change in sum value.

What Next

We can also inspect the dataset for other Summary Statistics such as :ref:`sum_stats_std`. We can also look for any :term:`Data Drift` present in the dataset using :ref:`data-drift` functionality of NannyML.