==============
Productionize
==============

Configure
---------

After having set up ``ads opctl`` on your desired machine using ``ads opctl configure``, you are ready to begin you anomaly detection applicaiton. At a bare minimum, you will need to provide the following details about the data:

- Path to the input data (input_data)
- Name of the Datetime column (datetime_column)
- Name of the Target column (target_column)


These details exactly match the initial anomaly.yaml file generated by running ``ads operator init --type anomaly``:

.. code-block:: yaml

    kind: operator
    type: anomaly
    version: v1
    spec:
        datetime_column:
            name: Date
        input_data:
            url: data.csv
        target_column: target


Optionally, you are able to specify much more. The most common additions are:

- Path to the validation data, which has all of the columns of the input_data plus an ``anomaly`` column. (validation_data)
- Path to test data, in the event you want to evaluate the selected model on a test set (test_data)
- List of column names that index different timeseries within the data, such as a product_ID or some other such series (target_category_columns)
- Path to the output directory, where the operator will place the outliers.csv, report.html, and other artifacts produced from the run (output_directory)

An extensive list of parameters can be found in the ``YAML Schema`` section.


Run
---

Once written, run the anomaly.yaml file:

.. code-block:: bash

    ads operator run -f anomaly.yaml


Interpret Results
-----------------

The anomaly detection operator produces many output files: ``outliers.csv``,  ``report.html``, and optionally ``inliers.csv``.

We will go through each of these output files in turn.

**outliers.csv**

This file contains the entire historical dataset with the following columns:

* Date: Time series data
* Series: Categorical or numerical index
* Target Column: Input data
* Score: This will give a score from 0-1 of how anomalous a datapoint is

**report.html**

The report.html file is designed differently for each model type. Generally, it contains a summary of the input and validation data, a plot of the target from input data overlaid with red dots for anomalous values, analysis of the models used, and details about the model components. It also includes a receipt YAML file, providing a fully detailed version of the original anomaly.yaml file.

**Metrics.csv**

The metrics file includes relevant metrics calculated on the training set.


Examples
--------

**Simple Example**

The simplest yaml file is generated by the ``ads operator init --type anomaly`` and looks like the following:

.. code-block:: yaml

    kind: operator
    type: anomaly
    version: v1
    spec:
        datetime_column:
            name: Date
        input_data:
            url: data.csv
        model: auto
        target_column: target


**Typical Example**

A typical anomaly detection application may have the following fields:

.. code-block:: yaml

    kind: operator
    type: anomaly
    version: v1
    spec:
        input_data:
            connect_args:
                user: XXX
                password: YYY
                dsn: "localhost/orclpdb"
            sql: 'SELECT Series, Total, time FROM live_data'
        datetime_column:
            name: time
            format: "%H:%M:%S"
        model: "auto"
        output_directory:
            url: results
        target_category_columns:
            - Series
        target_column: Total
        test_data:
            url: oci://bucket@namespace/test_data.csv


**Complex Example**

The yaml can also be maximally stated as follows:

.. code-block:: yaml

    kind: operator
    type: anomaly
    version: v1
    spec:
        input_data:
            connect_args:
                user: XXX
                password: YYY
                dsn: "localhost/orclpdb"
            sql: 'SELECT Store_ID, Sales, Date FROM live_data'
        validation_data: 
            url: oci://bucket@namespace/additional_data.csv
            columns:
                - Date
                - Store_ID
                - v1
                - v3
                - v4
        output_directory:
            url: results
        test_data:
            url: test_data.csv
        target_category_columns:
            - Store_ID
        target_column: Sales
        datetime_column:
            format: "%d/%m/%y"
            name: Date
        model: automlx
        model_kwargs:
            time_budget: 100 
        preprocessing: true
        generate_metrics: true
        generate_report: true
        metrics_filename: metrics.csv
        report_filename: report.html
        report_theme: light
        test_metrics_filename: test_metrics.csv