Matrix
A Matrix is represented either by an OperationNode, or the derived class Matrix.
Matrices are the most fundamental objects SystemDS operates on.
Although it is possible to generate matrices with the function calls or object construction specified below,
the recommended way is to use the methods defined on SystemDSContext.
- class systemds.operator.Matrix(sds_context, operation: str, unnamed_input_nodes: Union[str, Iterable[Union[DAGNode, str, int, float, bool]]] = None, named_input_nodes: Dict[str, Union[DAGNode, str, int, float, bool]] = None, local_data: array = None, brackets: bool = False)
- __init__(sds_context, operation: str, unnamed_input_nodes: Union[str, Iterable[Union[DAGNode, str, int, float, bool]]] = None, named_input_nodes: Dict[str, Union[DAGNode, str, int, float, bool]] = None, local_data: array = None, brackets: bool = False) Matrix
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.
- cbind(other) Matrix
Column-wise matrix concatenation, by concatenating the second matrix as additional columns to the first matrix. :param: The other matrix to bind to the right hand side. :return: The OperationNode containing the concatenated matrices/frames.
- cholesky(safe: bool = False) Matrix
Computes the Cholesky decomposition of a symmetric, positive definite matrix
- Parameters
safe – default value is False, if flag is True additional checks to ensure that the matrix is symmetric positive definite are applied, if False, checks will be skipped
- Returns
the OperationNode representing this operation
- 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) array
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
- max(axis: Optional[int] = None) OperationNode
Calculate max of matrix.
- Parameters
axis – can be 0 or 1 to do either row or column aggregation
- Returns
Matrix representing operation
- mean(axis: Optional[int] = None) OperationNode
Calculate mean of matrix.
- Parameters
axis – can be 0 or 1 to do either row or column means
- Returns
Matrix representing operation
- min(axis: Optional[int] = None) OperationNode
Calculate max of matrix.
- Parameters
axis – can be 0 or 1 to do either row or column aggregation
- Returns
Matrix representing operation
- order(by: int = 1, decreasing: bool = False, index_return: bool = False) Matrix
Sort by a column of the matrix X in increasing/decreasing order and returns either the index or data
- Parameters
by – sort matrix by this column number
decreasing – If true the matrix will be sorted in decreasing order
index_return – If true, the index numbers will be returned
- Returns
the OperationNode representing this operation
- pass_python_data_to_prepared_script(sds, 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
- rbind(other) Matrix
Row-wise matrix concatenation, by concatenating the second matrix as additional rows to the first matrix. :param: The other matrix to bind to the right hand side :return: The OperationNode containing the concatenated matrices/frames.
- replace(pattern: Union[DAGNode, str, int, float, bool], replacement: Union[DAGNode, str, int, float, bool]) Matrix
Replace all values with replacement value
- round() Matrix
round all values to nearest natural number
- Returns
The Matrix representing the result of this operation
- sum(axis: Optional[int] = None) OperationNode
Calculate sum of matrix.
- Parameters
axis – can be 0 or 1 to do either row or column sums
- Returns
Matrix representing operation
- to_one_hot(num_classes: int) Matrix
OneHot encode the matrix.
It is assumed that there is only one column to encode, and all values are whole numbers > 0
- Parameters
num_classes – The number of classes to encode into. max value contained in the matrix must be <= num_classes
- Returns
The OperationNode containing the oneHotEncoded values
- to_string(**kwargs: Dict[str, Union[DAGNode, str, int, float, bool]]) Scalar
Converts the input to a string representation. :return: Scalar containing the string.
- var(axis: Optional[int] = None) OperationNode
Calculate variance of matrix.
- Parameters
axis – can be 0 or 1 to do either row or column vars
- Returns
Matrix representing operation