class documentation

The DummyDataBatchSource is a data source that generates random data for a given request. This can be useful for testing and development purposes.

It will use the data types and constraints defined on a feature to generate the data.

```python from aligned import feature_view, Int64, String, DummyDataBatchSource

@feature_view(
source=RandomDataSource(),

) class MyView:

passenger_id = Int64().as_entity() survived = Bool() age = Float().lower_bound(0).upper_bound(100) name = String() sex = String().accepted_values(["male", "female"])

```

Class Method multi_source_features_for Undocumented
Static Method with_values Undocumented
Method __init__ Undocumented
Method all_between_dates Undocumented
Method all_data Undocumented
Method depends_on Undocumented
Async Method insert Undocumented
Method job_group_key A key defining which sources can be grouped together in one request.
Async Method overwrite Undocumented
Async Method schema Returns the schema for the data source
Async Method upsert Undocumented
Async Method write_polars Undocumented
Class Variable type_name Undocumented
Instance Variable default_data_size Undocumented
Instance Variable raw_partial_data Undocumented
Instance Variable seed Undocumented
Property partial_data Undocumented

Inherited from CodableBatchDataSource:

Class Method _deserialize Undocumented
Method _serialize Undocumented

Inherited from BatchDataSource (via CodableBatchDataSource):

Method __hash__ Undocumented
Method all Undocumented
Method all_columns Undocumented
Async Method feature_view_code Setup the code needed to represent the data source as a feature view
Method features_for Undocumented
Method filter Undocumented
Async Method freshness .table("my_table") .freshness()
Method location_id Undocumented
Method source_id An id that identifies a source from others.
Method tags Undocumented
Method transform_with_polars Undocumented
Method with_loaded_at Undocumented
Method with_view Undocumented

Inherited from DataFileReference (via CodableBatchDataSource, BatchDataSource):

Async Method read_pandas Undocumented
Async Method to_lazy_polars Undocumented
Async Method to_pandas Undocumented
Async Method to_polars Undocumented
Async Method write_pandas Undocumented
@staticmethod
def with_values(values: dict[str, object], seed: int | None = None) -> RandomDataSource: (source)

Undocumented

def __init__(self, default_data_size: int = 10000, seed: int | None = None, partial_data: pl.DataFrame | None = None): (source)

Undocumented

def all_between_dates(self, request: RetrivalRequest, start_date: datetime, end_date: datetime) -> RetrivalJob: (source)
def all_data(self, request: RetrivalRequest, limit: int | None = None) -> RetrivalJob: (source)
def depends_on(self) -> set[FeatureLocation]: (source)
async def insert(self, job: RetrivalJob, request: RetrivalRequest): (source)
def job_group_key(self) -> str: (source)

A key defining which sources can be grouped together in one request.

async def overwrite(self, job: RetrivalJob, request: RetrivalRequest): (source)
async def schema(self) -> dict[str, FeatureType]: (source)

Returns the schema for the data source

`python source = FileSource.parquet_at('test_data/titanic.parquet') schema = await source.schema() >>> {'passenger_id': FeatureType(name='int64'), ...} `

Returns:
dict[str, FeatureType]: A dictionary containing the column name and the feature type
async def upsert(self, job: RetrivalJob, request: RetrivalRequest): (source)
async def write_polars(self, df: pl.LazyFrame): (source)
default_data_size: int = (source)

Undocumented

raw_partial_data: dict[str, list] = (source)

Undocumented

Undocumented

@property
partial_data: pl.DataFrame = (source)

Undocumented