caikit.interfaces.ts.data_model.backends.base

Base classes to share between data model backends

Attributes

`log`
`error`

Classes

`UncachedBackendMixin`	Intermediate base class that disables attribute caching
`StrictFieldBackendMixin`	Intermediate base class that raises attribute errors for unknown fields
`TimeSeriesBackendBase`	Abstract base class for all backends of the central TimeSeries data model
`MultiTimeSeriesBackendBase`	Abstract base class for all backends of the central MultiTimeSeries data model

Module Contents

caikit.interfaces.ts.data_model.backends.base.log[source]

caikit.interfaces.ts.data_model.backends.base.error

class caikit.interfaces.ts.data_model.backends.base.UncachedBackendMixin[source]

Bases: caikit.core.data_model.data_backends.DataModelBackendBase

Intermediate base class that disables attribute caching

cache_attribute(*_, **__) → bool[source]: Never cache attributes

class caikit.interfaces.ts.data_model.backends.base.StrictFieldBackendMixin[source]

Bases: caikit.core.data_model.data_backends.DataModelBackendBase

Intermediate base class that raises attribute errors for unknown fields

get_attribute(data_model_class: Type, name: str) → Any[source]: Base implementation that raises an AttributeError on bad attr names. It should be called after object-specific logic.

class caikit.interfaces.ts.data_model.backends.base.TimeSeriesBackendBase[source]

Bases: UncachedBackendMixin, StrictFieldBackendMixin

Abstract base class for all backends of the central TimeSeries data model type

abstract as_pandas() → Tuple[pandas.DataFrame, str, Iterable[str]][source]

All backends must implement the ability to coerce their underlying data into a pandas DataFrame and provide the pointers to the timeseries source and value source(s)

Returns:

df: pd.DataFrame: The data frame itself
timestamp_source: str: The column name (or None) indicating where the timestamp sequence can be found
value_source: Iterable[str]: The names of the columns holding value sequences

class caikit.interfaces.ts.data_model.backends.base.MultiTimeSeriesBackendBase[source]

Bases: UncachedBackendMixin, StrictFieldBackendMixin

Abstract base class for all backends of the central MultiTimeSeries data model type

abstract as_pandas() → Tuple[pandas.DataFrame, Iterable[str], str, Iterable[str]][source]

All backends must implement the ability to coerce their underlying data into a pandas DataFrame and provide the pointers to the timeseries source and value source(s)

Returns:

df: pd.DataFrame: The data frame itself
key_source: Iterable[str]: the names of the columns holding key values
timestamp_source: str: The column name (or None) indicating where the timestamp sequence can be found
value_source: Iterable[str]: The names of the columns holding value sequences