Basic Values

The basic_values feature is an observation-level feature that represents the actual observation values, such as temperature and salinity. Any columns in the input dataset can be specified as the basic_values feature.

Configuration: Setup

To include the basic_values feature in your training and classification datasets, the value basic_values needs to be specified in the feature_sets section.

feature_sets:
  - name: feature_set_1
    features:
      - basic_values

Configuration: Parameters

The basic_values feature requires two mandatory parameters: col_names and stats_set.

The col_names parameter specifies the column names in the input dataset that will be used as the basic_values feature.
The stats_set parameter specifies how the feature values are normalized. aiqclib currently supports raw and min_max as normalization methods. The name value in stats_set must correspond to a name in the feature_stats_sets section.

feature_param_sets:
  - name: feature_set_1_param_set_1
    params:
      - feature: basic_values
        col_names: [ temp, psal, pres ]
        stats_set: { type: min_max, name: basic_values3 }

Configuration: Normalization

If the normalization method is not set to raw, the summary statistics specified here will be used for normalization.

feature_stats_sets:
  - name: feature_set_1_stats_set_1
    min_max:
      - name: basic_values3
        stats: { temp: { min: 0, max: 20 },
                 psal: { min: 0, max: 20 },
                 pres: { min: 0, max: 200 } }

Note

aiqclib offers helper functions to calculate summary statistics (like min/max values). Please refer to the Feature Normalization guide for details.