def compute(self, verbose: bool = False, lineage: bool = False) -> \
Union[float, np.array, Tuple[Union[float, np.array], str]]:
if self._result_var is None or self._lineage_trace is None:
self._script = DMLScript(self.sds_context)
self._script.build_code(self)
if verbose:
print("SCRIPT:")
print(self._script.dml_script)
if lineage:
result_variables, self._lineage_trace = self._script.execute(
lineage)
else:
result_variables = self._script.execute(lineage)
if self.output_type == OutputType.DOUBLE:
self._result_var = result_variables.getDouble(
self._script.out_var_name[0])
elif self.output_type == OutputType.MATRIX:
self._result_var = matrix_block_to_numpy(
self.sds_context.java_gateway.jvm,
result_variables.getMatrixBlock(
self._script.out_var_name[0]))
elif self.output_type == OutputType.LIST:
self._result_var = []
for idx, v in enumerate(self._script.out_var_name):
if (self._output_types == None):
self._result_var.append(
matrix_block_to_numpy(
self.sds_context.java_gateway.jvm,
result_variables.getMatrixBlock(v)))
elif (self._output_types[idx] == OutputType.MATRIX):
self._result_var.append(
matrix_block_to_numpy(
self.sds_context.java_gateway.jvm,
result_variables.getMatrixBlock(v)))
else:
self._result_var.append(
result_variables.getDouble(
self._script.out_var_name[idx]))
if verbose:
for x in self.sds_context.get_stdout():
print(x)
for y in self.sds_context.get_stderr():
print(y)
if lineage:
return self._result_var, self._lineage_trace
else:
return self._result_var
def compute(self, verbose: bool = False, lineage: bool = False) -> \
Union[float, np.array, Tuple[Union[float, np.array], str]]:
if self._result_var is None or self._lineage_trace is None:
self._script = DMLScript(self.sds_context)
self._script.build_code(self)
if lineage:
result_variables, self._lineage_trace = self._script.execute(
lineage)
else:
result_variables = self._script.execute(lineage)
if self.output_type == OutputType.DOUBLE:
self._result_var = result_variables.getDouble(
self._script.out_var_name)
elif self.output_type == OutputType.MATRIX:
self._result_var = matrix_block_to_numpy(
self.sds_context.java_gateway.jvm,
result_variables.getMatrixBlock(self._script.out_var_name))
if verbose:
print(self._script.dml_script)
# TODO further info
if lineage:
return self._result_var, self._lineage_trace
else:
return self._result_var
def _parse_output_result_variables(self, result_variables):
result_var = []
jvmV = self.sds_context.java_gateway.jvm
for idx, v in enumerate(self._script.out_var_name):
out_type = self._outputs[idx].output_type
if out_type == OutputType.MATRIX:
result_var.append(
matrix_block_to_numpy(jvmV,
result_variables.getMatrixBlock(v)))
elif out_type == OutputType.FRAME:
result_var.append(
frame_block_to_pandas(jvmV,
result_variables.getFrameBlock(v)))
elif out_type == OutputType.DOUBLE:
result_var.append(result_variables.getDouble(v))
else:
raise NotImplementedError("Not Implemented Support of type" +
out_type)
return result_var
def _parse_output_result_variables(self, result_variables):
return matrix_block_to_numpy(
self.sds_context.java_gateway.jvm,
result_variables.getMatrixBlock(self._script.out_var_name[0]))
def convert_back_and_forth(self, array):
matrix_block = numpy_to_matrix_block(self.jvm, array)
# use the ability to call functions on matrix_block.
returned = matrix_block_to_numpy(self.jvm, matrix_block)
self.assertTrue(np.allclose(array, returned))
def __parse_output_result_matrix(self, result_variables, var_name):
return matrix_block_to_numpy(self.sds_context.java_gateway.jvm,
result_variables.getMatrixBlock(var_name))