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evaluator.py
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evaluator.py
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import json
import csv
import glob
import numpy as np
import os
import statistics
import logging
import ntpath
from scipy import spatial
logger = logging.getLogger("idebench")
class Evaluator:
def __init__(self, options):
self.options = options
def evaluate(self, workflow_path):
self.file_name = workflow_path
logger.info("starting evaluation")
result_json = None
try:
with open(workflow_path, "r") as json_data:
result_json = json.load(json_data)
except Exception as e:
print("couldn't load file %s" % (workflow_path))
print(e)
return
args = result_json["args"]
groundtruth = gt_folder_path = gt_path = None
try:
gt_folder_path = os.path.join(self.options.gt_folder, args["driver_name"], args["settings_dataset"], args["settings_size"])
gt_path = os.path.join(gt_folder_path, args["settings_workflow"] + ".json")
except:
pass
if gt_path:
with open(gt_path, "r") as json_data:
groundtruth = json.load(json_data)["results"]
with open("reports/%s.csv" % os.path.splitext(ntpath.basename(workflow_path))[0], 'w') as fp:
w = csv.DictWriter(fp, [
"db_query_id",
"file_name",
"event_id",
"dataset",
"dataset_size",
"driver",
"viz_name",
"workflow",
"expected_start_time",
"actual_start_time",
"actual_end_time",
"duration",
"dropped",
"think_time",
"time_requirement",
"time_violated",
"num_binning_dimensions",
"binning_type",
"num_invalid_bins",
"num_bins_out_of_margin",
"num_bins_delivered",
"num_bins_in_gt",
"missing_bins",
"dissimilarity",
"num_aggregates_per_bin",
"aggregate_type",
"bias",
"rel_error_avg",
"rel_error_stdev",
"rel_error_min",
"rel_error_max",
"margin_avg",
"margin_stdev",
"margin_min",
"margin_max",
"margin_ratio",
"progress",], delimiter=",", lineterminator="\n")
w.writeheader()
operations = result_json["results"]
operation_counter = 0
for op_id in operations.keys():
rel_error = 0
margins = []
invalid_bins = []
missing_bins = 0
out_of_margin_count = 0
if groundtruth and op_id in groundtruth and groundtruth[op_id]["output"]:
gt_output = groundtruth[op_id]["output"]
op_output = operations[op_id]["output"]
rel_error = self.compute_mean_relative_error(op_output, gt_output)
missing_bins = self.compute_missing_bins(op_output, gt_output)
invalid_bins = self.compute_invalid_bins(op_output, gt_output)
operation_counter += 1
operation = operations[op_id]
op_eval_result = {}
op_eval_result["db_query_id"] = operation["id"]
op_eval_result["file_name"] = self.file_name
op_eval_result["event_id"] = operation["event_id"]
op_eval_result["dataset"] = args["settings_dataset"]
op_eval_result["dataset_size"] = args["settings_size"]
op_eval_result["viz_name"] = operation["viz_name"]
op_eval_result["think_time"] = args["settings_thinktime"]
op_eval_result["time_requirement"] = args["settings_time_requirement"]
op_eval_result["driver"] = args["driver_name"]
op_eval_result["workflow"] = args["settings_workflow"]
op_eval_result["expected_start_time"] = operation["expected_start_time"]
op_eval_result["actual_start_time"] = operation["start_time"]
op_eval_result["actual_end_time"] = operation["end_time"]
op_eval_result["duration"] = operation["end_time"] - operation["start_time"]
op_eval_result["dropped"] = operation["dropped"]
if "time_violated" in operation:
op_eval_result["time_violated"] = operation["time_violated"]
elif "timedout" in operation:
op_eval_result["time_violated"] = operation["timedout"]
else:
raise Exception()
op_eval_result["num_invalid_bins"] = len(invalid_bins)
op_eval_result["binning_type"] = operation["binning_type"]
op_eval_result["aggregate_type"] = operation["aggregate_type"]
op_eval_result["num_bins_in_gt"] = 0
op_eval_result["missing_bins"] = "%.5f" % missing_bins if missing_bins else 0
op_eval_result["dissimilarity"] = 0
op_eval_result["num_bins_out_of_margin"] = "%i" % out_of_margin_count
op_eval_result["num_aggregates_per_bin"] = operation["num_aggregates_per_bin"]
op_eval_result["num_binning_dimensions"] = operation["num_binning_dimensions"]
op_eval_result["progress"] = "%.5f" % operation["progress"]
op_eval_result["bias"] = 0
#op_eval_result["rel_error_stdev"] = 0
#op_eval_result["rel_error_min"] = 0
#op_eval_result["rel_error_max"] = 0
op_eval_result["rel_error_avg"] = "%.5f" % float(rel_error)
op_eval_result["margin_stdev"] = "%.5f" % statistics.stdev(margins) if len(margins) > 1 else 0.0
op_eval_result["margin_min"] = "%.5f" % min(margins) if len(margins) > 0 else 0.0
op_eval_result["margin_max"] = "%.5f" % max(margins) if len(margins) > 0 else 0.0
op_eval_result["margin_avg"] = "%.5f" % float(sum(margins) / float(len(margins))) if len(margins) > 0 else 0.0
op_eval_result["margin_ratio"] = "%.5f" % float(len(operation["margins"]) / len(operation["output"])) if operation["margins"] and len(operation["output"]) > 0 else 1
w.writerow(op_eval_result)
logger.info("evalation done")
logger.info(operation_counter)
def compute_missing_bins(self, op_output, gt_output):
return 1 - len(op_output.keys()) / len(gt_output.keys()) if len(gt_output.keys()) > 0 else 0
def compute_mean_relative_error(self, op_output, gt_output):
gt_values = []
diff_sum = 0
for gt_bin_identifier, gt_aggregate_results in gt_output.items():
if gt_bin_identifier in op_output:
op_bin_value = op_output[gt_bin_identifier]
gt_bin_value = gt_aggregate_results[0]
delta = op_bin_value - gt_bin_value
diff_sum += (delta * delta)
gt_values.append(gt_bin_value)
else:
pass # ignore missing bins for mean relative error
return np.sqrt(diff_sum) / (np.linalg.norm(np.array(gt_values)))
def compute_invalid_bins(self, op_output, gt_output):
invalid_bins = []
for operation_id in op_output.keys():
if operation_id not in gt_output:
invalid_bins.append(operation_id)
return invalid_bins