Operation Node

An OperationNode represents an operation that executes in SystemDS. Most methods are overloaded for OperationNode. This means that they return an OperationNode.

To get the result from an OperationNode you simply call .compute() on it, thereby getting the numpy or pandas equivalent result.

Note

All operations are lazily evaluated, meaning before calling .compute() nothing will be executed in SystemDS. Therefore errors will not immediately be recognized while constructing an sequence of operators.

class systemds.operator.OperationNode(sds_context: SystemDSContext, operation: str, unnamed_input_nodes: str | Iterable[DAGNode | str | int | float | bool] = None, named_input_nodes: Dict[str, DAGNode | str | int | float | bool] = None, output_type: OutputType = OutputType.MATRIX, is_python_local_data: bool = False, brackets: bool = False)

A Node representing an operation in SystemDS

__init__(sds_context: SystemDSContext, operation: str, unnamed_input_nodes: str | Iterable[DAGNode | str | int | float | bool] = None, named_input_nodes: Dict[str, DAGNode | str | int | float | bool] = None, output_type: OutputType = OutputType.MATRIX, is_python_local_data: bool = False, brackets: bool = False)

Create general OperationNode

Parameters:

  • sds_context – The SystemDS context for performing the operations

  • operation – The name of the DML function to execute

  • unnamed_input_nodes – inputs identified by their position, not name

  • named_input_nodes – inputs with their respective parameter name

  • output_type – type of the output in DML (double, matrix etc.)

  • is_python_local_data – if the data is local in python e.g. Numpy arrays

  • number_of_outputs – If set to other value than 1 then it is expected that this operation node returns multiple values. If set remember to set the output_types value as well.

  • output_types – The types of output in a multi output scenario. Default is None, and means every multi output is a matrix.

code_line(var_name: str, unnamed_input_vars: Sequence[str], named_input_vars: Dict[str, str]) → str

Generates the DML code line equal to the intended action of this node.

Parameters:

  • var_name – Name of DML-variable this nodes result should be saved in

  • unnamed_input_vars – all strings representing the unnamed parameters

  • named_input_vars – all strings representing the named parameters (name value pairs)

Returns:

the DML code line that is equal to this operation

compute(verbose: bool = False, lineage: bool = False) → float | array | Tuple[float | array, str]

Get result of this operation. Builds the dml script and executes it in SystemDS, before this method is called all operations are only building the DAG without actually executing (lazy evaluation).

Parameters:

  • verbose – Can be activated to print additional information such as created DML-Script

  • lineage – Can be activated to print lineage trace till this node

Returns:

the output as an python builtin data type or numpy array

get_lineage_trace() → str

Get the lineage trace for this node.

Returns:

Lineage trace

pass_python_data_to_prepared_script(jvm: JVMView, var_name: str, prepared_script: JavaObject) → None

Passes data from python to the prepared script object.

Parameters:

  • jvm – the java virtual machine object

  • var_name – the variable name the data should get in java

  • prepared_script – the prepared script

print(**kwargs: Dict[str, DAGNode | str | int | float | bool]) → OperationNode

Prints the given Operation Node. There is no return on calling. To get the returned string look at the stdout of SystemDSContext.

write(destination: str, format: str = 'binary', **kwargs: Dict[str, DAGNode | str | int | float | bool]) → OperationNode

Write input to disk. The written format is easily read by SystemDSContext.read(). There is no return on write.

Parameters: