Johnp/resource estimate script

scripts/compute_sponsor_json_resource_estimates.py

@@ -0,0 +1,242 @@
+#!/usr/bin/env python3
+
+# Copyright 2024 L3Harris Technologies, Inc.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+import argparse
+import datetime
+from pathlib import Path
+import json
+from urllib.parse import urlparse
+import uuid
+import sys
+
+import numpy as np
+import pandas as pd
+
+
+
+import logging
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+console_handler = logging.StreamHandler()
+file_handler = logging.FileHandler(
+    "compute_sponsor_json_resource_estimates.log.txt",
+    delay=False
+)
+formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
+handlers = [console_handler , file_handler]
+for h in handlers:
+    h.setFormatter(formatter)
+    logger.addHandler(h)
+
+
+
+
+
+def main(args):
+
+
+
+    overall_start_time = datetime.datetime.now()
+    logging.info(f"===============================================")
+    logging.info(f"Starting compute_sponsor_json_resource_estimates...")
+    logging.info(f"overall start time: {overall_start_time}")
+    logging.info(f"aggregated labels: {args.aggregated_solver_labels}")
+    logging.info(f"utility estimate file: {args.utility_estimate_xlsx}")
+    logging.info(f"solver UUID: {args.solver_uuid}")
+    solver_uuid = args.solver_uuid
+
+
+    logging.info(f"reading in: {args.aggregated_solver_labels}")
+    aggregated_solver_labels = pd.read_csv(args.aggregated_solver_labels)
+
+    logging.info(f"reading in: {args.utility_estimate_xlsx}")
+    try:
+        utility_estimate_xlsx = pd.read_excel(args.utility_estimate_xlsx)
+    except Exception as e:
+        logging.error(f'Error: {e}', exc_info=True)
+        logging.info(f"attempting to read the file as .csv... ")
+        utility_estimate_xlsx = pd.read_csv(args.utility_estimate_xlsx)
+
+    utility_estimate_xlsx["task_uuid"] = utility_estimate_xlsx["task_uuid"].str.strip()
+    logging.info(f"{len(utility_estimate_xlsx)} lines in the excel sheet.")
+
+    # filter 
+    solver_labels = aggregated_solver_labels[aggregated_solver_labels["solver_uuid"]==solver_uuid]
+    solver_labels["task_uuid"] = solver_labels["task_uuid"].str.strip()
+
+    logging.info(f"number of entries in solver labels (for the solver): {len(solver_labels)}")
+
+
+    # Inner join on the utility estimate xlsx and aggregated_solver_labels
+    #===============================================================
+    data = pd.merge(
+        solver_labels,
+        utility_estimate_xlsx,
+        on="task_uuid",
+        how="inner"
+        # how=inner only match rows that by task_uuid that exist in either file (possibly fewer rows).
+        # how=outer fill in NaN when merging if uuids missing from either file.
+    )
+    data.to_csv("artifact.resource_estimate_data.csv", index=False)
+
+
+
+    # Create a resource_estimate.json file for each problem_instance
+    #===============================================================
+    problem_instance_uuid_list = data["problem_instance_uuid"].unique()
+    for problem_instance_uuid in problem_instance_uuid_list:
+        resource_estimate = {} # init
+        resource_estimate["id"] = problem_instance_uuid
+        resource_estimate["category"] = "industrial"
+        resource_estimate["size"] = None # TODO: number of orbitals?
+        resource_estimate["task"] = "ground_state_energy_estimation"
+
+
+
+        df_filtered = data[data["problem_instance_uuid"]==problem_instance_uuid]
+
+
+        problem_instance_short_name = df_filtered["problem_instance_short_name"].iloc[0]
+        logging.info(f"problem_instance_short_name: {problem_instance_short_name}")
+        logging.info(f"problem_instance_uuid: {problem_instance_uuid}")
+        resource_estimate["name"] = problem_instance_short_name
+
+        aggregate_value = 0 # init
+        aggregate_value_min = 0 # init
+        aggregate_value_max = 0 # init
+
+        resource_estimate["tasks"] = [] # init
+        task_uuid_list = df_filtered["task_uuid"].values
+        for task_uuid in task_uuid_list:
+            logging.info(f"task_uuid: {task_uuid}")
+
+            df_filtered = data[data["task_uuid"]==task_uuid]
+            assert len(df_filtered) == 1, "possible issue #44:  may have more than one task_uuid in sheet."
+
+
+            task_results = {} # init
+            task_results["id"] =  task_uuid
+            task_results["name"] = df_filtered["instance_data_object_url"].values[0]
+
+            task_results["value"] = df_filtered["Utility NPV $"].values[0]
+            task_results["value_ci"] = []
+            task_results["value_ci"].append(df_filtered["Utility NVP lower bound $"].values[0])
+            task_results["value_ci"].append(df_filtered["Utility NVP upper bound $"].values[0])
+
+            logging.info(f"value: {task_results['value']}")
+            aggregate_value += task_results["value"]
+            aggregate_value_min += task_results["value_ci"][0]
+            aggregate_value_max += task_results["value_ci"][1]
+
+
+            task_results["implementation"] = "Qubitized QPE with double factorization"
+
+            if df_filtered["attempted"].values[0]:
+                task_results["repetitions"] = df_filtered["num_shots"].values[0]
+                task_results["logical-abstract"] = {}
+                task_results["logical-abstract"]["num_qubits"] = df_filtered["num_logical_qubits"].values[0]
+                task_results["logical-abstract"]["t_count"] = df_filtered["num_T_gates_per_shot"].values[0]
+            else:
+                # did not attempt resource estimation.  probably due to number of orbitals.
+                task_results["comment"] = "did not attempt resource estimation for this task."
+                task_results["repetitions"] = None
+                task_results["logical-abstract"] = {}
+                task_results["logical-abstract"]["num_qubits"] = None
+                task_results["logical-abstract"]["t_count"] = None
+
+
+            # add to building list of all tasks for the problem_instance
+            resource_estimate["tasks"].append(task_results)
+
+
+        # write out JSON for each problem_instance
+        resource_estimate["value"] = aggregate_value
+        resource_estimate["value_ci"] = [aggregate_value_min, aggregate_value_max]
+
+        resource_estimate_file_name = f"resource_estimate.gsee.{problem_instance_short_name}.{problem_instance_uuid}.json"
+        with open(resource_estimate_file_name, "w") as output_file:
+            json.dump(resource_estimate, output_file, indent=4)
+        logging.info(f"wrote file: {resource_estimate_file_name}")
+
+
+
+
+
+
+
+
+
+
+    # Print overall time.
+    #===============================================================
+    overall_stop_time = datetime.datetime.now()
+    logging.info(f"done.")
+    logging.info(f"overall start time: {overall_start_time}")
+    logging.info(f"overall stop time: {overall_stop_time}")
+    logging.info(f"run time (seconds): {(overall_stop_time - overall_start_time).total_seconds()}")
+
+
+
+
+
+
+
+
+
+
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="""
+            a script to calculate `resource_estimate.json` file per the 
+            sponsor-requested schema.
+            outputs of the `performance_metrics` script:  `aggregated_solver_labels_<date>.csv`
+            and a `utility_estimation.xlsx` spreadsheet.
+            F
+        """
+    )
+
+    parser.add_argument(
+        "--aggregated_solver_labels", 
+        type=str, 
+        required=True,
+        help="Typically `aggregated_solver_labels_<date>.csv`, which is an ephemeral output from the `performance_metrics` script."
+    )
+
+    parser.add_argument(
+        "--utility_estimate_xlsx", 
+        type=str, 
+        required=True,
+        help="The excel sheet with the latest utility estimate figures."
+    )
+
+    parser.add_argument(
+        "--solver_uuid", 
+        type=str, 
+        required=True,
+        help="""
+            The UUID of the resource estimation software.
+        """
+    )
+
+    args = parser.parse_args()
+    main(args)
+
+

scripts/compute_sponsor_json_resource_estimates_cli_args.txt

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+
+# note date stamp on some file names.  update as necessary.
+
+
+./compute_sponsor_json_resource_estimates.py --aggregated_solver_labels aggregated_solver_labels_2024-12-10-07-10.csv --utility_estimate_xlsx GSEE-HC_utility_estimates_all_instances_task_uuids_v2.csv --solver_uuid f2d73e1f-3058-43c4-a634-b6c267c84ff1
+
+