About ads available in Oracle Data Science Cloud.

Conclusion

Regression if the type of Series specified as target is float. Classification if int.

Decision logic

Package location

Let's take a look at the code of the ads package.


import sys

sys.path

['/tmp/dask-worker-space/worker-_4k3q1mv',
 '/home/datascience/conda/mlcpuv1/lib/python36.zip',
 '/home/datascience/conda/mlcpuv1/lib/python3.6',
 '/home/datascience/conda/mlcpuv1/lib/python3.6/lib-dynload',
 '',
 '/home/datascience/conda/mlcpuv1/lib/python3.6/site-packages', <=here
 '/home/datascience/conda/mlcpuv1/lib/python3.6/site-packages/IPython/extensions',
 '/home/datascience/.ipython']

Determined by the variable "ml_task_type".

ads/automl/driver.py


def get_ml_task_type(X, y, classes):
    target_type = TypeDiscoveryDriver().discover(y.name, y)
    if isinstance(target_type, DiscreteTypedFeature):
        if len(classes) == 2:
            if helper.is_text_data(X):
                ml_task_type = utils.ml_task_types.BINARY_TEXT_CLASSIFICATION
            else:
                ml_task_type = utils.ml_task_types.BINARY_CLASSIFICATION
        else:
            if helper.is_text_data(X):
                ml_task_type = utils.ml_task_types.MULTI_CLASS_TEXT_CLASSIFICATION
            else:
                ml_task_type = utils.ml_task_types.MULTI_CLASS_CLASSIFICATION
    elif isinstance(target_type, ContinuousTypedFeature):
        ml_task_type = utils.ml_task_types.REGRESSION
    else:
        raise TypeError("AutoML for target type ({0}) is not yet available"
                                   .format(target_type.meta_data["type"]))
    return ml_task_type

TypeDiscoveryDriver (). Determined by the type of discover (y.name, y)

from ads.type_discovery.type_discovery_driver import TypeDiscoveryDriver

ads/automl/type_discovery/type_discovery_driver.py

class TypeDiscoveryDriver:

    #
    # takes a pandas series
    #
    def discover(self, name, s, is_target=False):
　　　　　：
　　　　　：

        if is_target and ContinuousDetector._target_is_continuous(s):
            return ContinuousTypedFeature.build(name, s)

Regression if ContinuousDetector._target_is_continuous (s) is True

from ads.type_discovery.continuous_detector import ContinuousDetector

ads/automl/type_discovery/continuous_detector.py


class ContinuousDetector(AbstractTypeDiscoveryDetector):

    @staticmethod
    def _target_is_continuous(series):
        if str(series.dtype) in ['float16', 'float32', 'float64']:
            return True # treat target variable as continuous
        elif str(series.dtype) in ['int16', 'int32', 'int64']:
            if series.nunique() >= 20:
                return True # treat target variable as continuous

        return False