Source code for qci_client.optimization.data_converter

  • # Copyright 2023-2024, Quantum Computing Incorporated
  • """Functions for data conversion."""
  • import logging
  • import time
  • import networkx as nx
  • import numpy as np
  • import scipy.sparse as sp
  • from qci_client.optimization import utilities
  • from qci_client.optimization import enum
  • # We want to limit the memory size of each uploaded chunk to be safely below the max of 15 * MebiByte (~15MB).
  • # See https://git.qci-dev.com/qci-dev/qphoton-files-api/-/blob/main/service/files.go#L32.
  • MEMORY_MAX: int = 8 * 1000000 # 8MB
  • def data_to_json(*, file: ) -> dict: # pylint: disable=too-many-branches
  • """
  • Converts data in file input into JSON-serializable dictionary that can be passed to Qatalyst REST API
  • Args:
  • file: file dictionary whose data of type numpy.ndarray, scipy.sparse.spmatrix, or networkx.Graph is to be converted
  • Returns:
  • file dictionary with JSON-serializable data
  • """
  • start_time_s = time.perf_counter()
  • file_config, file_type = utilities.get_file_config(file=file)
  • if file_type not in enum.FILE_TYPES_JOB_INPUTS:
  • input_file_types = [
  • input_file_type.value for input_file_type in enum.FILE_TYPES_JOB_INPUTS
  • ]
  • input_file_types.sort()
  • raise AssertionError(
  • f"unsupported file type, must be one of
  • )
  • data = file["file_config"][file_type.value]["data"]
  • if file_type == enum.FileType.GRAPH:
  • if not isinstance(data, nx.Graph):
  • raise AssertionError(
  • f"file type '
  • )
  • file_config = {
  • **nx.node_link_data(data),
  • "num_edges": data.number_of_edges(),
  • "num_nodes": data.number_of_nodes(),
  • }
  • elif file_type in enum.FILE_TYPES_JOB_INPUTS_MATRIX:
  • if isinstance(data, nx.Graph):
  • raise AssertionError(
  • f"file type '
  • )
  • data_ls = []
  • if sp.isspmatrix_dok(data):
  • for idx, val in zip(data.keys(), data.values()):
  • # dok type has trouble subsequently serializing to json without type
  • # casts. For example, uint16 and float32 cause problems.
  • data_ls.append({"i": int(idx[0]), "j": int(idx[1]), "val": float(val)})
  • elif sp.isspmatrix(data) or isinstance(data, np.ndarray):
  • data = sp.coo_matrix(data)
  • for i, j, val in zip(
  • data.row.tolist(), data.col.tolist(), data.data.tolist()
  • ):
  • data_ls.append({"i": i, "j": j, "val": val})
  • else:
  • raise ValueError(
  • f"file type '
  • f"scipy.sparse.spmatrix data types, got '
  • )
  • file_config = {"data": data_ls}
  • rows, cols = data.get_shape()
  • if file_type == enum.FileType.CONSTRAINTS:
  • # Constraints matrix is [A | -b]
  • file_config.update({"num_constraints": rows, "num_variables": cols - 1})
  • else:
  • # This works for hamiltonians, qubos, and objectives.
  • file_config["num_variables"] = rows
  • else:
  • # Polynomial file types do not require translation.
  • file_config = file["file_config"][file_type.value]
  • logging.debug(
  • "Time to convert data to json: %s s.", time.perf_counter() - start_time_s
  • )
  • return {
  • "file_name": file.get("file_name", f"),
  • "file_config": {file_type.value: file_config},
  • }