From 3befb7dbb9e97723afa4856436cf387e4935f945 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Mon, 26 Jul 2021 15:24:27 +0100 Subject: [PATCH 01/11] WIP - refactoring example runs --- .../adult-resampling-ensemble.py | 91 +---------- .../adult-subsample-ensemble.py | 91 +---------- .../artificial_1-resampling-ensemble.py | 90 +---------- .../artificial_2-resampling-ensemble.py | 90 +---------- .../artificial_3-resampling-ensemble.py | 89 +---------- .../artificial_4-resampling-ensemble.py | 90 +---------- .../artificial_5-resampling-ensemble.py | 89 +---------- .../artificial_6-resampling-ensemble.py | 89 +---------- .../artificial_7-resampling-ensemble.py | 88 +---------- ...generated-data-synthpop-disclosure-risk.py | 39 +++-- .../privbayes-adult-ensemble.py | 108 +------------ .../privbayes-artificial_1-ensemble.py | 100 +----------- .../privbayes-artificial_2-ensemble.py | 100 +----------- .../privbayes-artificial_3-ensemble.py | 101 +----------- .../privbayes-artificial_4-ensemble.py | 102 +----------- .../privbayes-artificial_5-ensemble.py | 101 +----------- .../privbayes-artificial_6-ensemble.py | 103 +----------- .../privbayes-artificial_7-ensemble.py | 101 +----------- examples/utils.py | 147 ++++++++++++++++++ reproduce_experiments.md | 58 +++++++ 20 files changed, 324 insertions(+), 1543 deletions(-) create mode 100644 examples/utils.py create mode 100644 reproduce_experiments.md diff --git a/examples/adult-resampling-ensemble/adult-resampling-ensemble.py b/examples/adult-resampling-ensemble/adult-resampling-ensemble.py index 9c732235..9f3c4ed3 100644 --- a/examples/adult-resampling-ensemble/adult-resampling-ensemble.py +++ b/examples/adult-resampling-ensemble/adult-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'adult-resampling-ensemble' def input_json(random_state): return { @@ -81,84 +80,6 @@ def input_json(random_state): }, } - - -def filename_stem(i): - return f"adult-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/adult-subsample-ensemble/adult-subsample-ensemble.py b/examples/adult-subsample-ensemble/adult-subsample-ensemble.py index 122a1ea2..4e013807 100644 --- a/examples/adult-subsample-ensemble/adult-subsample-ensemble.py +++ b/examples/adult-subsample-ensemble/adult-subsample-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'adult-subsample-ensemble' def input_json(random_state, sample_frac): return { @@ -61,84 +60,6 @@ def input_json(random_state, sample_frac): } -def filename_stem(i): - return f"adult-subsample-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-s", - "--sample-fractions", - dest="sample_fracs", - required=True, - help="The list of fraction of samples used", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.sample_fracs.strip('[]').split(','))), columns=["random_state", "sample_frac"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.SUBSAMPLING) \ No newline at end of file diff --git a/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py b/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py index a8cca0ce..f3d05330 100644 --- a/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py +++ b/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_1-resampling-ensemble' def input_json(random_state): return { @@ -56,84 +55,7 @@ def input_json(random_state): }, } - - -def filename_stem(i): - return f"artificial_1-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) + run(input_json, FILESTEM) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py b/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py index d7b33e45..d31b378c 100644 --- a/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py +++ b/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_2-resampling-ensemble' def input_json(random_state): return { @@ -57,83 +56,6 @@ def input_json(random_state): } - -def filename_stem(i): - return f"artificial_2-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) + run(input_json, FILESTEM) \ No newline at end of file diff --git a/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py b/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py index 29a592c5..15a786e9 100644 --- a/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py +++ b/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_3-resampling-ensemble' def input_json(random_state): return { @@ -56,84 +55,8 @@ def input_json(random_state): }, } - - -def filename_stem(i): - return f"artificial_3-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) + run(input_json, FILESTEM) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py b/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py index d02dcfed..4b847124 100644 --- a/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py +++ b/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_4-resampling-ensemble' def input_json(random_state): return { @@ -57,83 +56,6 @@ def input_json(random_state): } - -def filename_stem(i): - return f"artificial_4-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) + run(input_json, FILESTEM) diff --git a/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py b/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py index f1d9f7df..a48318a1 100644 --- a/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py +++ b/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_5-resampling-ensemble' def input_json(random_state): return { @@ -66,84 +65,8 @@ def input_json(random_state): }, } - - -def filename_stem(i): - return f"artificial_5-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) + run(input_json, FILESTEM) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py b/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py index 703d7822..6bc56ae1 100644 --- a/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py +++ b/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_6-resampling-ensemble' def input_json(random_state): return { @@ -66,84 +65,8 @@ def input_json(random_state): }, } - - -def filename_stem(i): - return f"artificial_6-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) + run(input_json, FILESTEM) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py b/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py index 6fe17e2f..9fecdfc0 100644 --- a/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py +++ b/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run + +FILESTEM = 'artificial_7-resampling-ensemble' def input_json(random_state): return { @@ -55,82 +54,7 @@ def input_json(random_state): } -def filename_stem(i): - return f"artificial_7-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) + run(input_json, FILESTEM) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/demo-generated-data-synthpop-disclosure-risk.py b/examples/demo-generated-data-synthpop-disclosure-risk.py index ae6b0485..773d847b 100644 --- a/examples/demo-generated-data-synthpop-disclosure-risk.py +++ b/examples/demo-generated-data-synthpop-disclosure-risk.py @@ -22,13 +22,17 @@ def handle_cmdline_args(): handling the command line arguments""" parser = argparse.ArgumentParser( - description='Run pipeline for each .json files in the input directory ' - 'and plot the results. ') + description="Run pipeline for each .json files in the input directory " + "and plot the results. " + ) parser.add_argument( - '-i', dest='indirectory', required=False, + "-i", + dest="indirectory", + required=False, default="parameter_files/generated-synthpop-dr", - help='The directory containing all input json files.') + help="The directory containing all input json files.", + ) args = parser.parse_args() return args @@ -47,10 +51,10 @@ def run_pipeline(): Note: Can take several minutes depending on number of files and method used. Not all output is printed to the console. If you want to see all the output, you can run make from bash.""" - cmd = ''' + cmd = """ cd .. make - ''' + """ subprocess.check_output(cmd, shell=True) @@ -72,7 +76,9 @@ def main(): privacy_dict = json.load(privacy_file) privacy_scores.append(privacy_dict["EMRi_norm"]) - with open(os.path.join(synth_path, f, "sklearn_classifiers.json")) as utility_file: + with open( + os.path.join(synth_path, f, "sklearn_classifiers.json") + ) as utility_file: utility_dict = json.load(utility_file) print(utility_file) utility_scores.append(utility_dict["Overall"]["f1_diff"]) @@ -81,18 +87,17 @@ def main(): print(f"Utility scores: {1-np.array(utility_scores)}") sns.set_palette("hls", 8) - ax = sns.scatterplot(x=1-np.array(utility_scores), y=1-np.array(privacy_scores), s=70) - - plt.title('Utility/Privacy plot') - plt.xlabel('Utility') - plt.ylabel('Privacy') - plt.grid(linestyle='--', linewidth=0.5) + ax = sns.scatterplot( + x=1 - np.array(utility_scores), y=1 - np.array(privacy_scores), s=70 + ) + + plt.title("Utility/Privacy plot") + plt.xlabel("Utility") + plt.ylabel("Privacy") + plt.grid(linestyle="--", linewidth=0.5) ax.set(xlim=(0, 1.03), ylim=(0, 1.03)) plt.show() -if __name__ == '__main__': +if __name__ == "__main__": main() - - - diff --git a/examples/privbayes-adult-ensemble/privbayes-adult-ensemble.py b/examples/privbayes-adult-ensemble/privbayes-adult-ensemble.py index d299ec51..bdf604fa 100644 --- a/examples/privbayes-adult-ensemble/privbayes-adult-ensemble.py +++ b/examples/privbayes-adult-ensemble/privbayes-adult-ensemble.py @@ -1,11 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'privbayes-adult-ensemble' def input_json(random_state, epsilon, k): @@ -65,100 +63,6 @@ def input_json(random_state, epsilon, k): }, } - -def filename_stem(i): - return f"privbayes-adult-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define epsilon for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) \ No newline at end of file diff --git a/examples/privbayes-artificial_1-ensemble/privbayes-artificial_1-ensemble.py b/examples/privbayes-artificial_1-ensemble/privbayes-artificial_1-ensemble.py index e3c604ec..a7a2ad17 100644 --- a/examples/privbayes-artificial_1-ensemble/privbayes-artificial_1-ensemble.py +++ b/examples/privbayes-artificial_1-ensemble/privbayes-artificial_1-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-artificial_1-ensemble' def input_json(random_state, epsilon, k): return { @@ -49,93 +46,6 @@ def input_json(random_state, epsilon, k): } -def filename_stem(i): - return f"privbayes-artificial_1-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/examples/privbayes-artificial_2-ensemble/privbayes-artificial_2-ensemble.py b/examples/privbayes-artificial_2-ensemble/privbayes-artificial_2-ensemble.py index 44f3e84e..b04df0be 100644 --- a/examples/privbayes-artificial_2-ensemble/privbayes-artificial_2-ensemble.py +++ b/examples/privbayes-artificial_2-ensemble/privbayes-artificial_2-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-artificial_2-ensemble' def input_json(random_state, epsilon, k): return { @@ -49,93 +46,6 @@ def input_json(random_state, epsilon, k): } -def filename_stem(i): - return f"privbayes-artificial_2-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/examples/privbayes-artificial_3-ensemble/privbayes-artificial_3-ensemble.py b/examples/privbayes-artificial_3-ensemble/privbayes-artificial_3-ensemble.py index 644d945c..7d4b8023 100644 --- a/examples/privbayes-artificial_3-ensemble/privbayes-artificial_3-ensemble.py +++ b/examples/privbayes-artificial_3-ensemble/privbayes-artificial_3-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-artificial_3-ensemble' def input_json(random_state, epsilon, k): return { @@ -48,94 +45,6 @@ def input_json(random_state, epsilon, k): } } - -def filename_stem(i): - return f"privbayes-artificial_3-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/examples/privbayes-artificial_4-ensemble/privbayes-artificial_4-ensemble.py b/examples/privbayes-artificial_4-ensemble/privbayes-artificial_4-ensemble.py index 2d023085..780c3385 100644 --- a/examples/privbayes-artificial_4-ensemble/privbayes-artificial_4-ensemble.py +++ b/examples/privbayes-artificial_4-ensemble/privbayes-artificial_4-ensemble.py @@ -1,11 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'privbayes-artificial_4-ensemble' def input_json(random_state, epsilon, k): @@ -48,94 +46,6 @@ def input_json(random_state, epsilon, k): } } - -def filename_stem(i): - return f"privbayes-artificial_4-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/examples/privbayes-artificial_5-ensemble/privbayes-artificial_5-ensemble.py b/examples/privbayes-artificial_5-ensemble/privbayes-artificial_5-ensemble.py index 47671345..1626c6db 100644 --- a/examples/privbayes-artificial_5-ensemble/privbayes-artificial_5-ensemble.py +++ b/examples/privbayes-artificial_5-ensemble/privbayes-artificial_5-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-artificial_5-ensemble' def input_json(random_state, epsilon, k): return { @@ -48,94 +45,6 @@ def input_json(random_state, epsilon, k): } } - -def filename_stem(i): - return f"privbayes-artificial_5-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) \ No newline at end of file diff --git a/examples/privbayes-artificial_6-ensemble/privbayes-artificial_6-ensemble.py b/examples/privbayes-artificial_6-ensemble/privbayes-artificial_6-ensemble.py index 0f32a625..bfccde06 100644 --- a/examples/privbayes-artificial_6-ensemble/privbayes-artificial_6-ensemble.py +++ b/examples/privbayes-artificial_6-ensemble/privbayes-artificial_6-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-artificial_6-ensemble' def input_json(random_state, epsilon, k): return { @@ -47,95 +44,7 @@ def input_json(random_state, epsilon, k): "categorical_enconding": "labels" } } - - -def filename_stem(i): - return f"privbayes-artificial_6-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - + if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/examples/privbayes-artificial_7-ensemble/privbayes-artificial_7-ensemble.py b/examples/privbayes-artificial_7-ensemble/privbayes-artificial_7-ensemble.py index ea2b57d7..27d6477b 100644 --- a/examples/privbayes-artificial_7-ensemble/privbayes-artificial_7-ensemble.py +++ b/examples/privbayes-artificial_7-ensemble/privbayes-artificial_7-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-artificial_7-ensemble' def input_json(random_state, epsilon, k): return { @@ -48,94 +45,6 @@ def input_json(random_state, epsilon, k): } } - -def filename_stem(i): - return f"privbayes-artificial_7-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/examples/utils.py b/examples/utils.py new file mode 100644 index 00000000..f4911741 --- /dev/null +++ b/examples/utils.py @@ -0,0 +1,147 @@ +from typing import Callable, Any, Dict +import argparse +import json +import subprocess +import pandas as pd +from itertools import product +from pathlib import Path +from enum import Enum + +QUIPP_DIR = Path(__file__).parent.parent +InputJSON = Callable[[], Dict[str, Any]] + + +class SynthMethod(Enum): + + PRIVBAYES = 1 + RESAMPLING = 2 + SUBSAMPLING = 3 + + +def filename_stem(i: int, stem: str) -> str: + return f"{stem}-{i:04}" + + +def input_path(i: int, stem: str): + if not (QUIPP_DIR / "run-inputs").exists(): + print("Could not find run-inputs dir") + exit() + return QUIPP_DIR / "run-inputs" / f"{filename_stem(i, stem)}.json" + + +def write_input_file( + i: int, + filestem: str, + input_json: InputJSON, + params: Dict[str, Any], + force: bool = False, +): + fname = input_path(i, filestem) + run_input = json.dumps(input_json(**params), indent=4) + if force or not fname.exists(): + print(f"Writing {fname}") + print(fname.exists()) + with open(fname, "w") as input_file: + input_file.write(run_input) + + +def read_json(fname): + with open(fname) as f: + return json.load(f) + + +def handle_cmdline_args(synth_method: SynthMethod): + parser = argparse.ArgumentParser( + description="Generate (optionally run and postprocess) an ensemble of run inputs" + ) + + parser.add_argument( + "-n", + "--num-replicas", + dest="nreplicas", + required=True, + type=int, + help="The number of replicas to generate", + ) + + parser.add_argument( + "-r", + "--run", + default=False, + action="store_true", + help="Run (via make) and postprocess?", + ) + + parser.add_argument( + "-f", + "--force-write", + dest="force", + default=False, + action="store_true", + help="Write out input files, even if they exist", + ) + + if synth_method == SynthMethod.PRIVBAYES: + + parser.add_argument( + "-e", + "--epsilons", + dest="epsilons", + required=True, + help="Define list of epsilons for the requested run", + ) + + parser.add_argument( + "-k", + "--parents", + dest="k", + required=True, + type=int, + help="Define k (number of parents) for the requested run", + ) + + if synth_method == SynthMethod.SUBSAMPLING: + + parser.add_argument( + "-s", + "--sample-fractions", + dest="sample_fracs", + required=True, + help="The list of fraction of samples used", + ) + + args = parser.parse_args() + return args + + +def run( + input_json: Callable[[], Dict[str, Any]], filestem: str, synth_method: SynthMethod +): + + args = handle_cmdline_args(synth_method) + + random_states = range(args.nreplicas) + + if synth_method == SynthMethod.PRIVBAYES: + all_params = pd.DataFrame( + data=product( + random_states, + map(float, args.epsilons.strip("[]").split(",")), + [args.k], + ), + columns=["random_state", "epsilon", "k"], + ) + elif synth_method == SynthMethod.RESAMPLING: + all_params = pd.DataFrame(data=random_states, columns=["random_state"]) + else: + raise TypeError("Not implemented for this type") + + for i, params in all_params.iterrows(): + print(dict(params)) + write_input_file(i, filestem, input_json, dict(params), force=args.force) + + if args.run: + all_targets = [ + f"run-{filename_stem(i, filestem)}" for i, _ in all_params.iterrows() + ] + subprocess.run(["make", "-k", "-j72",] + all_targets) diff --git a/reproduce_experiments.md b/reproduce_experiments.md new file mode 100644 index 00000000..8c5861f9 --- /dev/null +++ b/reproduce_experiments.md @@ -0,0 +1,58 @@ +## Docker +The easiest way to use the pipeline is with [Docker](https://www.docker.com/). Get the official image with + +```bash +docker pull turinginst/quipp-env:base +``` + +To run the pipeline use +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base make +``` + +# Experiments + + +## Artificial 1 + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_1-ensemble/privbayes-artificial_1-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +### Artificial 2 + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_2-ensemble/privbayes-artificial_2-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +### Artificial 3 + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_3-ensemble/privbayes-artificial_3-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +### Artificial 4 + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_4-ensemble/privbayes-artificial_4-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +### Adult + +**PrivBayes** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-adult-ensemble/privbayes-adult-ensemble.py -n 25 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +``` + +**Resampling** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/adult-resampling-ensemble/adult-resampling-ensemble.py -n 50 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +``` + +**Subsample** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/adult-subsample-ensemble/adult-subsample-ensemble.py -n 25 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +``` +### Household Poverty + +### Polish Survey \ No newline at end of file From 219e4d92e52ca44caad3959ace5db9010dec7a33 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Mon, 26 Jul 2021 15:25:10 +0100 Subject: [PATCH 02/11] WIP - update README --- README.md | 55 +++++++++++++++++++++++++++++++------------------------ 1 file changed, 31 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index d86d6992..dcb4edb0 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,26 @@ The current draft QUiPP report can be found in [`doc`](doc/), with a pdf available [here](https://github.com/alan-turing-institute/QUIPP-pipeline/releases). -## Installation +## Install + +### Docker +The easiest way to use the pipeline is with [Docker](https://www.docker.com/). Get the official image with + +```bash +docker pull turinginst/quipp-env:base +``` + +To run the pipeline use +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base make +``` + +### Local + +Clone and checkout + + + - Clone the repository `git clone git@github.com:alan-turing-institute/QUIPP-pipeline.git` @@ -51,7 +70,7 @@ More detail on setting this up can be found - Various parts of this code and its dependencies are written in Python, R, C++ and Bash. - It has been tested with - - python 3.6 + - python 3.8 - R 3.6 - gcc 9.3 - bash 3.2 @@ -62,6 +81,16 @@ More detail on setting this up can be found ### Installing the dependencies +We recommend using [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) with Python 3.8. + +```bash +conda create -n quipp python=3.8 +``` + +```bash +conda activate quipp +``` + #### R and Python dependencies To install all of the python and R dependencies, run the following @@ -94,29 +123,7 @@ environmental variable `SGFROOT` to point to this location. That is, in bash, - either ```export PATH=$PATH:/path/to/sgf/bin```, - or ```export SGFROOT=/path/to/sgf/bin``` -#### Forked DataSynthesizer - -We use the PrivBayes implementation within the DataSynthesizer fork found [here](https://github.com/gmingas/DataSynthesizer). -In order to install it, clone the above repository locally, go to its root directory and run `pip install .` - -#### Forked synthetic_data_release -We use the PATE-GAN implementation within the `synthetic_data_release` fork found [here](https://github.com/kasra-hosseini/synthetic_data_release). -In order to use PATE-GAN in QUIPP: -1. create a new directory: - -```bash -cd /path/to/QUIPP-pipeline -mkdir libs -``` - -2. Clone the above repository inside `libs` directory created in the previous step: - -```bash -# from /path/to/QUIPP-pipeline -cd libs -git clone https://github.com/kasra-hosseini/synthetic_data_release.git -``` ## Top-level directory contents From 5650965da60b1aca6c38d366824f07792b950432 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Tue, 27 Jul 2021 13:54:18 +0100 Subject: [PATCH 03/11] fix feature importance bug --- metrics/utility-metrics/feature_importance.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/metrics/utility-metrics/feature_importance.py b/metrics/utility-metrics/feature_importance.py index 37321217..eca61b49 100644 --- a/metrics/utility-metrics/feature_importance.py +++ b/metrics/utility-metrics/feature_importance.py @@ -147,11 +147,11 @@ def featuretools_importances(df, data_meta, utility_params_ft, rs): fm = fm.dropna() # replace +,-,*,/,< with words to avoid errors later on when using pulp for optimisation - fm.columns = [col.replace("<", "LESSTHAN") for col in - [col.replace("/", "DIV") for col in - [col.replace("*", "MULT") for col in - [col.replace("-", "SUB") for col in - [col.replace("+", "ADD") for col in + fm.columns = [col.replace(" < ", "LESSTHAN") for col in + [col.replace(" / ", "DIV") for col in + [col.replace(" * ", "MULT") for col in + [col.replace(" - ", "SUB") for col in + [col.replace(" + ", "ADD") for col in fm.columns]]]]] for col in fm: From 201d4b6a66c637e3fa08c388b2220b1446124d0a Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Tue, 27 Jul 2021 14:51:30 +0100 Subject: [PATCH 04/11] Instructions for running experiments and refactoring examples --- .../household-resampling-ensemble.py | 90 ++-------------- .../household-subsample-ensemble.py | 92 ++-------------- .../polish-resampling-ensemble.py | 89 ++------------- .../polish-subsample-ensemble.py | 91 ++-------------- .../privbayes-household-ensemble.py | 102 +----------------- .../privbayes-polish-ensemble.py | 102 ++---------------- .../reproduce_experiments.md | 46 +++++++- 7 files changed, 78 insertions(+), 534 deletions(-) rename reproduce_experiments.md => examples/reproduce_experiments.md (53%) diff --git a/examples/household-resampling-ensemble/household-resampling-ensemble.py b/examples/household-resampling-ensemble/household-resampling-ensemble.py index b1a9de73..76bf40b3 100644 --- a/examples/household-resampling-ensemble/household-resampling-ensemble.py +++ b/examples/household-resampling-ensemble/household-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'household-resampling-ensemble' def input_json(random_state): return { @@ -94,83 +93,6 @@ def input_json(random_state): } } - -def filename_stem(i): - return f"household-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/household-subsample-ensemble/household-subsample-ensemble.py b/examples/household-subsample-ensemble/household-subsample-ensemble.py index ff3e673f..f853ab60 100644 --- a/examples/household-subsample-ensemble/household-subsample-ensemble.py +++ b/examples/household-subsample-ensemble/household-subsample-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'household-subsample-ensemble' def input_json(random_state, sample_frac): return { @@ -57,85 +56,6 @@ def input_json(random_state, sample_frac): } } - -def filename_stem(i): - return f"household-subsample-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-s", - "--sample-fractions", - dest="sample_fracs", - required=True, - help="The list of fractions of samples used", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.sample_fracs.strip('[]').split(','))), columns=["random_state", "sample_frac"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.SUBSAMPLING) \ No newline at end of file diff --git a/examples/polish-resampling-ensemble/polish-resampling-ensemble.py b/examples/polish-resampling-ensemble/polish-resampling-ensemble.py index 1d451d0a..00d32d29 100644 --- a/examples/polish-resampling-ensemble/polish-resampling-ensemble.py +++ b/examples/polish-resampling-ensemble/polish-resampling-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'polish-resampling-ensemble' def input_json(random_state): return { @@ -67,82 +66,6 @@ def input_json(random_state): -def filename_stem(i): - return f"polish-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/polish-subsample-ensemble/polish-subsample-ensemble.py b/examples/polish-subsample-ensemble/polish-subsample-ensemble.py index 7bf539a1..7a42c482 100644 --- a/examples/polish-subsample-ensemble/polish-subsample-ensemble.py +++ b/examples/polish-subsample-ensemble/polish-subsample-ensemble.py @@ -1,10 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod + +FILESTEM = 'polish-subsample-ensemble' def input_json(random_state, sample_frac): return { @@ -46,84 +45,6 @@ def input_json(random_state, sample_frac): } -def filename_stem(i): - return f"polish-subsample-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-s", - "--sample-fractions", - dest="sample_fracs", - required=True, - help="The list of fractions of samples used", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.sample_fracs.strip('[]').split(','))), columns=["random_state", "sample_frac"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.SUBSAMPLING) diff --git a/examples/privbayes-household-ensemble/privbayes-household-ensemble.py b/examples/privbayes-household-ensemble/privbayes-household-ensemble.py index 1db7a3c7..9147d891 100644 --- a/examples/privbayes-household-ensemble/privbayes-household-ensemble.py +++ b/examples/privbayes-household-ensemble/privbayes-household-ensemble.py @@ -1,12 +1,9 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-household-ensemble' def input_json(random_state, epsilon, k): return { @@ -62,95 +59,6 @@ def input_json(random_state, epsilon, k): } } - -def filename_stem(i): - return f"privbayes-household-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), - columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) \ No newline at end of file diff --git a/examples/privbayes-polish-ensemble/privbayes-polish-ensemble.py b/examples/privbayes-polish-ensemble/privbayes-polish-ensemble.py index 6243a5de..f7b76989 100644 --- a/examples/privbayes-polish-ensemble/privbayes-polish-ensemble.py +++ b/examples/privbayes-polish-ensemble/privbayes-polish-ensemble.py @@ -1,13 +1,11 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product +import sys from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils import run, SynthMethod +FILESTEM = 'privbayes-polish-ensemble' + def input_json(random_state, epsilon, k): return { "enabled": True, @@ -50,94 +48,6 @@ def input_json(random_state, epsilon, k): } } - -def filename_stem(i): - return f"privbayes-polish-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) + run(input_json, FILESTEM, SynthMethod.PRIVBAYES) diff --git a/reproduce_experiments.md b/examples/reproduce_experiments.md similarity index 53% rename from reproduce_experiments.md rename to examples/reproduce_experiments.md index 8c5861f9..b34e8b98 100644 --- a/reproduce_experiments.md +++ b/examples/reproduce_experiments.md @@ -46,13 +46,53 @@ docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp- **Resampling** ```bash -docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/adult-resampling-ensemble/adult-resampling-ensemble.py -n 50 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/adult-resampling-ensemble/adult-resampling-ensemble.py -n 50 -f -r ``` **Subsample** ```bash -docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/adult-subsample-ensemble/adult-subsample-ensemble.py -n 25 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/adult-subsample-ensemble/adult-subsample-ensemble.py -n 25 -f -r ``` + ### Household Poverty +You must download the Household poverty data following the instructions [here]( +generators/household_poverty/data/README.md). + +Then preprocess the data: + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base make generated-data +``` -### Polish Survey \ No newline at end of file +**PrivBayes** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-household-ensemble/privbayes-household-ensemble.py -n 25 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +``` + +**Resampling** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/household-resampling-ensemble/household-resampling-ensemble.py -n 50 -f -r +``` + +**Subsample** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/household-subsample-ensemble/household-subsample-ensemble.py -n 25 -f -r +``` + +### Polish Survey + + +**PrivBayes** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-polish-ensemble/privbayes-polish-ensemble.py -n 25 -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -f -r +``` + +**Resampling** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/polish-resampling-ensemble/polish-resampling-ensemble.py -n 50 -f -r +``` + +**Subsample** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/polish-subsample-ensemble/polish-subsample-ensemble.py -n 25 -f -r +``` \ No newline at end of file From bff263d44e22768f09c57fdf5054fb0ff7ec2ca6 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Tue, 27 Jul 2021 16:26:49 +0100 Subject: [PATCH 05/11] Update reproduce_experiments.md --- examples/reproduce_experiments.md | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/examples/reproduce_experiments.md b/examples/reproduce_experiments.md index b34e8b98..bbba73b8 100644 --- a/examples/reproduce_experiments.md +++ b/examples/reproduce_experiments.md @@ -1,16 +1,23 @@ -## Docker -The easiest way to use the pipeline is with [Docker](https://www.docker.com/). Get the official image with +# Reproduce experiments + +This document explains how to reproduce all experimental runs and plots. + +## Requirements +The easiest way to use the pipeline is with [Docker](https://www.docker.com/). Get the official image ```bash docker pull turinginst/quipp-env:base ``` -To run the pipeline use +# Experiments + +Check out the [QUIPP-pipeline](https://github.com/alan-turing-institute/QUIPP-pipeline) repository and set as the current directory + ```bash -docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base make +git clone https://github.com/alan-turing-institute/QUIPP-pipeline.git && cd QUIPP-pipeline ``` -# Experiments +All experiments can then be run with the docker image and all results will be saved to `/synth-output`, which is mounted to the docker container. ## Artificial 1 From d9c8c83c422040d0ed9d37d88a11b6802fcb35ed Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Wed, 28 Jul 2021 11:39:40 +0100 Subject: [PATCH 06/11] update reproduce_experiments.md --- examples/reproduce_experiments.md | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/examples/reproduce_experiments.md b/examples/reproduce_experiments.md index bbba73b8..8c43f124 100644 --- a/examples/reproduce_experiments.md +++ b/examples/reproduce_experiments.md @@ -22,28 +22,55 @@ All experiments can then be run with the docker image and all results will be sa ## Artificial 1 +**PrivBayes** ```bash docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_1-ensemble/privbayes-artificial_1-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r ``` +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py -n 25 -f -r +``` + ### Artificial 2 +**PrivBayes** ```bash docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_2-ensemble/privbayes-artificial_2-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r ``` +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py -n 25 -f -r +``` + ### Artificial 3 +**PrivBayes** ```bash docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_3-ensemble/privbayes-artificial_3-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r ``` +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py -n 25 -f -r +``` ### Artificial 4 +**PrivBayes** ```bash docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_4-ensemble/privbayes-artificial_4-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r ``` +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py -n 25 -f -r +``` + ### Adult **PrivBayes** From c3b44de3aa99a8f931e8d4c8c63bdc0b9a45b162 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Wed, 28 Jul 2021 11:58:55 +0100 Subject: [PATCH 07/11] Add synthpop version to pipeline test --- .github/workflows/run-synth-pipeline.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/run-synth-pipeline.yml b/.github/workflows/run-synth-pipeline.yml index a71be0a9..cc79a958 100644 --- a/.github/workflows/run-synth-pipeline.yml +++ b/.github/workflows/run-synth-pipeline.yml @@ -18,5 +18,7 @@ jobs: password: ${{ secrets.DOCKER_PAC }} - name: Run pipeline with privbayes-adult - run: docker run -v $GITHUB_WORKSPACE:/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base make run-privbayes-adult + - + name: Run pipeline with Synthpop + run: docker run -v $GITHUB_WORKSPACE:/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py -n 1 -f -r \ No newline at end of file From 23bc9139dd9356afa08f0543184557a747e6fef9 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Wed, 28 Jul 2021 12:07:26 +0100 Subject: [PATCH 08/11] Remove framingham --- datasets/framingham/framingham.csv | 1 - datasets/framingham/framingham_cleaned.csv | 4241 ----------------- datasets/framingham/framingham_cleaned.json | 69 - .../framingham-bootstrap-ensemble.py | 153 - .../framingham-resampling-ensemble.py | 177 - .../framingham-subsample-ensemble.py | 155 - .../privbayes-framingham-ensemble.py | 169 - 7 files changed, 4965 deletions(-) delete mode 100644 datasets/framingham/framingham.csv delete mode 100644 datasets/framingham/framingham_cleaned.csv delete mode 100644 datasets/framingham/framingham_cleaned.json delete mode 100644 examples/framingham-bootstrap-ensemble/framingham-bootstrap-ensemble.py delete mode 100644 examples/framingham-resampling-ensemble/framingham-resampling-ensemble.py delete mode 100644 examples/framingham-subsample-ensemble/framingham-subsample-ensemble.py delete mode 100644 examples/privbayes-framingham-ensemble/privbayes-framingham-ensemble.py diff --git a/datasets/framingham/framingham.csv b/datasets/framingham/framingham.csv deleted file mode 100644 index 82ae1584..00000000 --- a/datasets/framingham/framingham.csv +++ /dev/null @@ -1 +0,0 @@ -male,age,education,currentSmoker,cigsPerDay,BPMeds,prevalentStroke,prevalentHyp,diabetes,totChol,sysBP,diaBP,BMI,heartRate,glucose,TenYearCHD 1,39,4,0,0,0,0,0,0,195,106,70,26.97,80,77,0 0,46,2,0,0,0,0,0,0,250,121,81,28.73,95,76,0 1,48,1,1,20,0,0,0,0,245,127.5,80,25.34,75,70,0 0,61,3,1,30,0,0,1,0,225,150,95,28.58,65,103,1 0,46,3,1,23,0,0,0,0,285,130,84,23.1,85,85,0 0,43,2,0,0,0,0,1,0,228,180,110,30.3,77,99,0 0,63,1,0,0,0,0,0,0,205,138,71,33.11,60,85,1 0,45,2,1,20,0,0,0,0,313,100,71,21.68,79,78,0 1,52,1,0,0,0,0,1,0,260,141.5,89,26.36,76,79,0 1,43,1,1,30,0,0,1,0,225,162,107,23.61,93,88,0 0,50,1,0,0,0,0,0,0,254,133,76,22.91,75,76,0 0,43,2,0,0,0,0,0,0,247,131,88,27.64,72,61,0 1,46,1,1,15,0,0,1,0,294,142,94,26.31,98,64,0 0,41,3,0,0,1,0,1,0,332,124,88,31.31,65,84,0 0,39,2,1,9,0,0,0,0,226,114,64,22.35,85,NA,0 0,38,2,1,20,0,0,1,0,221,140,90,21.35,95,70,1 1,48,3,1,10,0,0,1,0,232,138,90,22.37,64,72,0 0,46,2,1,20,0,0,0,0,291,112,78,23.38,80,89,1 0,38,2,1,5,0,0,0,0,195,122,84.5,23.24,75,78,0 1,41,2,0,0,0,0,0,0,195,139,88,26.88,85,65,0 0,42,2,1,30,0,0,0,0,190,108,70.5,21.59,72,85,0 0,43,1,0,0,0,0,0,0,185,123.5,77.5,29.89,70,NA,0 0,52,1,0,0,0,0,0,0,234,148,78,34.17,70,113,0 0,52,3,1,20,0,0,0,0,215,132,82,25.11,71,75,0 1,44,2,1,30,0,0,1,0,270,137.5,90,21.96,75,83,0 1,47,4,1,20,0,0,0,0,294,102,68,24.18,62,66,1 0,60,1,0,0,0,0,0,0,260,110,72.5,26.59,65,NA,0 1,35,2,1,20,0,0,1,0,225,132,91,26.09,73,83,0 0,61,3,0,0,0,0,1,0,272,182,121,32.8,85,65,1 0,60,1,0,0,0,0,0,0,247,130,88,30.36,72,74,0 1,36,4,1,35,0,0,0,0,295,102,68,28.15,60,63,0 1,43,4,1,43,0,0,0,0,226,115,85.5,27.57,75,75,0 0,59,1,0,0,0,0,1,0,209,150,85,20.77,90,88,1 1,61,NA,1,5,0,0,0,0,175,134,82.5,18.59,72,75,1 1,54,1,1,20,0,0,1,0,214,147,74,24.71,96,87,0 1,37,2,0,0,0,0,1,0,225,124.5,92.5,38.53,95,83,0 1,56,NA,0,0,0,0,0,0,257,153.5,102,28.09,72,75,0 1,52,1,0,0,0,0,1,1,178,160,98,40.11,75,225,0 0,42,1,1,1,0,0,1,0,233,153,101,28.93,60,90,0 1,36,3,0,0,0,0,0,0,180,111,73,27.78,71,80,0 0,43,2,1,10,0,0,0,0,243,116.5,80,26.87,68,78,0 0,41,2,1,1,0,0,0,0,237,122,78,23.28,75,74,0 0,52,1,0,0,1,0,1,0,NA,148,92,25.09,70,NA,1 1,54,2,0,0,0,0,0,0,195,132,83.5,26.21,75,100,0 0,53,3,0,0,1,0,1,1,311,206,92,21.51,76,215,1 0,49,2,0,0,0,0,0,0,208,96,63,20.68,65,98,0 0,65,1,0,0,0,0,1,0,252,179.5,114,30.47,90,87,0 1,46,1,1,20,0,0,0,0,261,119,77.5,23.59,75,74,0 0,63,2,1,40,0,0,0,0,179,116,69,22.15,95,75,1 1,36,3,1,20,NA,0,1,0,194,139,93,24.33,80,62,0 0,63,1,1,3,0,0,1,0,267,156.5,92.5,27.1,60,79,1 1,51,4,0,0,0,0,0,0,216,112,66,23.47,90,95,0 0,47,2,1,20,0,0,0,0,237,130,78,19.66,80,75,0 0,62,1,0,0,0,0,0,0,240,145,82.5,28.27,63,75,0 0,39,2,1,20,0,0,0,0,209,115,75,22.54,90,NA,0 0,46,1,1,10,0,0,0,0,250,116,71,20.35,88,94,0 0,54,1,1,9,0,0,0,1,266,114,76,17.61,88,55,0 1,49,1,1,2,0,0,1,0,255,143.5,81,25.65,75,80,0 1,44,2,0,0,0,0,0,0,185,115,69,22.29,65,82,0 0,40,4,1,20,0,0,0,0,205,158,102,25.45,75,87,1 1,56,4,1,20,0,0,0,0,270,121,79,23.58,95,93,0 0,67,1,0,0,0,0,1,0,254,157,89,24.25,60,74,0 1,53,1,1,20,0,0,0,0,220,123.5,75,19.64,78,73,0 0,57,1,1,3,0,0,0,0,235,126.5,80,24.88,83,72,0 1,57,1,0,0,0,0,0,0,220,136,84,26.84,75,64,1 0,63,1,0,0,0,0,1,0,252,154,87,28.6,72,45,0 0,62,1,0,0,0,0,1,1,212,190,99,29.64,100,202,0 1,38,1,1,20,0,0,0,0,223,107,73,23.01,65,78,0 0,47,2,1,20,0,0,0,0,300,112.5,60,20.13,76,83,0 0,52,4,0,0,0,0,0,0,302,110,67.5,23.51,63,87,0 0,63,4,0,0,0,0,0,0,248,164.5,76,29.35,70,NA,0 1,42,3,1,10,0,0,0,0,175,116,72.5,28.61,63,95,0 0,37,NA,0,0,0,0,0,0,200,119,79,33.29,67,87,0 0,41,1,1,20,0,0,1,0,189,150,106,33.8,85,75,0 0,44,2,1,10,0,0,0,0,221,110,76,22.16,64,83,1 0,59,1,0,0,0,0,1,0,258,138.5,85,34.55,65,103,1 0,44,1,0,0,0,0,0,0,202,155,85,24.04,83,68,0 0,44,3,1,20,NA,0,0,0,213,115,72.5,21.16,80,89,0 0,45,1,0,0,0,0,1,0,183,151,101,45.8,80,63,0 1,41,2,1,43,0,0,1,0,274,152,90,30.58,85,65,0 1,60,1,1,20,0,0,1,0,170,179,94,26.52,90,83,0 1,39,2,0,0,0,0,1,0,285,155,110,32.51,84,70,1 0,53,2,0,0,0,0,0,0,210,138,86.5,22.49,88,87,0 0,52,1,1,15,0,0,0,0,170,124,78,26.03,75,82,0 0,61,1,0,0,0,0,1,0,210,182,101,29.35,70,83,0 0,36,2,1,15,0,0,0,0,197,113,72.5,22.73,70,65,0 0,62,1,0,0,0,0,0,0,261,138,82,23.89,65,77,0 0,61,2,1,1,0,0,1,0,326,200,104,38.46,57,78,0 1,41,4,1,43,0,0,0,0,252,124,86,28.56,100,70,0 1,41,2,0,0,0,0,0,0,274,121,61.5,25.42,80,76,0 1,53,1,1,20,0,0,0,0,188,138,89,18.23,60,75,0 1,39,2,1,15,0,0,0,0,256,132.5,80,24.8,75,97,0 0,51,1,0,0,0,0,0,0,244,102,71.5,27.38,71,77,0 0,66,1,0,0,0,0,1,0,311,154,80,28.55,60,104,0 1,60,4,1,30,0,0,0,0,243,126,79,28.57,80,65,1 0,65,3,0,0,0,0,0,0,193,123,76.5,29.33,60,96,0 0,63,4,1,20,0,0,0,1,239,134,80,26.64,88,126,0 0,40,2,0,0,0,0,0,0,205,100,60,NA,60,72,1 0,56,1,0,0,0,0,1,0,296,180,90,23.72,75,120,0 0,56,1,1,15,0,0,0,0,269,121,75,22.36,50,66,0 0,47,2,1,20,0,0,0,0,220,132.5,87,27.98,65,75,0 0,60,3,0,0,0,0,1,0,275,141,84,29.66,75,105,0 0,45,3,1,9,0,0,0,0,268,110,64,20.68,63,71,0 0,48,1,0,0,0,0,1,0,265,145,77,24.23,74,64,0 0,42,1,1,20,0,0,0,0,173,100,63,23.25,65,99,0 0,63,1,0,0,0,0,0,0,273,135,82,26.76,85,56,0 0,42,4,0,0,0,0,0,0,250,115,79,26.93,65,79,0 1,40,1,1,43,0,0,1,0,290,138,90,27.54,85,73,1 0,66,1,0,0,0,0,1,0,278,187,88,40.52,90,84,1 1,49,1,1,20,0,0,0,0,197,123,69,29.62,76,60,0 0,67,1,0,0,0,0,1,0,264,139,80,25.75,75,87,0 1,51,3,0,0,0,0,1,0,214,145,92.5,26.09,70,NA,0 1,48,4,0,0,0,0,0,0,233,138,88.5,23.62,86,68,0 0,64,1,0,0,0,0,1,0,282,158,105,24.37,75,71,0 0,41,1,0,0,0,0,1,0,265,136,98,42.15,90,NA,0 1,50,1,0,0,0,0,1,0,257,127,82,32.23,75,117,1 1,60,1,0,0,0,0,1,0,278,160.5,96,26.4,55,75,1 0,37,2,1,5,0,0,0,0,185,100,68,18.38,70,72,0 1,36,4,0,0,0,0,0,0,210,112,85.5,21.93,71,77,0 1,50,3,1,40,0,0,1,0,175,157,88,25.09,88,85,0 0,39,4,0,0,0,0,0,0,241,105,75,26.12,68,87,0 0,46,1,1,3,0,0,0,0,237,112,70,20.2,75,62,0 1,66,3,0,0,0,0,0,0,288,109,71,29.29,80,80,0 0,46,1,0,0,0,0,1,0,200,135,97,36.81,100,97,0 0,55,2,0,0,0,0,1,0,183,158,86,24.45,70,102,0 1,41,2,0,0,0,0,0,0,223,112.5,80,29.44,75,58,0 1,44,2,0,0,0,0,0,0,244,128,77,25.95,75,90,0 1,38,4,0,0,0,0,0,0,235,118,77,25.87,60,82,0 1,43,1,1,15,0,0,0,0,210,115,77.5,25.1,70,68,0 0,41,2,0,0,0,0,0,0,213,112,73,24.81,62,74,0 1,53,1,1,30,0,0,0,0,244,106,67.5,21.84,88,65,0 1,43,4,1,NA,0,0,0,0,222,109.5,69,25.5,75,NA,0 1,59,2,1,30,0,0,0,0,303,134,89,24.07,78,75,0 0,56,2,1,20,0,0,0,0,246,128,64,25.54,92,92,0 0,50,3,1,15,0,0,0,0,150,121,84,28.69,75,88,0 1,46,1,1,30,0,0,0,0,270,131,81,26.4,75,83,0 0,43,2,0,0,0,0,0,0,266,131,81,24.03,100,94,0 1,61,2,0,0,0,0,0,0,246,124,70,25.63,55,78,1 0,41,3,1,30,0,0,1,0,187,154,100,20.5,66,78,1 1,49,4,1,NA,0,0,0,0,256,127.5,81.5,28.21,93,85,1 0,55,1,0,0,0,0,0,0,286,138,82,24.27,80,90,1 1,43,1,1,40,0,0,0,0,240,100,72.5,24.3,70,65,0 1,40,3,0,0,0,0,0,0,154,117.5,72.5,26.82,80,87,0 0,57,1,0,0,0,0,1,0,266,151,95,38.39,96,109,1 0,56,1,1,3,0,0,1,0,279,136,94,32.99,50,102,0 1,48,4,0,0,0,0,1,0,293,149,100,31.61,87,76,1 0,59,1,1,1,0,0,1,0,259,141,86,25.97,70,86,0 0,47,3,0,0,0,0,1,0,219,153,98,22.02,80,92,0 1,64,4,0,0,0,0,0,0,210,123,81,26.49,60,75,1 0,54,1,0,0,0,0,1,0,230,180.5,106.5,28.92,72,64,0 0,64,1,0,0,0,0,0,0,320,130,77,26.24,70,74,0 0,61,1,0,0,0,0,0,0,220,142,93,23.37,80,79,0 0,61,1,0,0,0,0,0,0,312,136.5,76,31.13,71,85,1 0,66,1,0,0,0,0,1,0,214,212,104,25.32,57,84,1 0,38,3,1,3,1,0,1,0,NA,125,80,22.79,98,NA,0 1,39,1,1,40,0,0,0,0,209,134,82,28.34,70,75,0 0,58,2,0,0,0,0,1,0,195,153,80.5,23.36,60,73,0 0,49,1,0,0,0,0,1,0,265,150,77.5,21.83,96,107,0 0,49,3,0,0,1,0,1,0,254,191,124.5,28.35,78,54,0 0,46,3,0,0,0,0,0,0,165,108,81,24.19,80,72,0 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0,51,2,1,10,0,0,0,0,255,102.5,64.5,24.14,65,71,0 1,48,2,1,17,0,0,0,0,218,113,79,27.33,62,73,0 1,43,1,1,20,0,0,0,0,179,129,81,19.05,63,77,0 0,65,1,0,0,0,0,0,0,212,94,62,25.83,80,88,0 0,65,3,0,0,0,0,1,0,216,163,102,30.12,91,73,0 1,56,2,1,20,0,0,1,0,205,210,130,25.49,95,127,1 0,61,1,1,25,0,0,0,0,NA,147.5,72.5,26.58,83,NA,0 1,45,2,1,9,0,0,1,0,188,132,91,28.04,70,77,0 0,61,3,0,0,0,0,0,0,260,112.5,75,21.97,60,74,0 0,53,1,0,0,0,0,1,0,180,184.5,110.5,27.49,62,74,0 0,65,1,0,0,0,0,0,0,221,141,82.5,29.48,80,93,0 0,34,2,1,10,0,0,0,0,159,92.5,70,22.15,65,68,0 1,46,1,1,9,0,0,1,0,216,126,85,30.1,80,70,0 1,49,3,1,20,0,0,1,0,193,172,105,19.7,90,77,1 0,63,1,0,0,0,0,0,0,229,120,82,25.58,78,73,0 1,55,4,1,20,0,0,0,0,268,128,87,28.57,70,87,0 1,42,2,1,9,0,0,0,0,205,110,73,22.4,61,66,0 0,44,1,1,5,0,0,0,0,214,102,68,32.82,88,80,0 1,49,2,1,9,0,0,0,0,193,115,79,21.86,48,88,0 1,60,NA,1,20,0,0,0,0,212,138.5,87.5,19.54,64,73,1 0,47,4,1,20,0,0,0,0,261,120,77.5,23.26,85,NA,0 1,49,3,1,20,0,0,0,0,189,132,76,20.26,75,60,0 0,52,3,1,20,0,0,0,0,248,128,74,21.84,65,79,0 0,41,2,0,0,0,0,0,0,185,125,88,24.88,107,75,0 1,36,4,1,20,0,0,0,0,155,127.5,77.5,30.2,56,61,0 0,50,2,1,9,0,0,1,0,161,145,89,20.3,66,81,0 1,59,2,1,20,0,0,1,0,266,165,99,29.07,96,82,1 1,44,4,1,43,0,0,0,0,275,127,82.5,27.43,77,76,0 0,49,3,0,0,0,0,0,0,224,126,82.5,27.49,100,67,0 1,51,2,0,0,0,0,0,0,223,117.5,77.5,27.78,68,97,0 0,56,3,1,20,0,0,1,0,325,160,97.5,23.4,85,86,1 1,38,2,0,0,0,0,0,0,278,136.5,87,29.84,91,75,0 1,66,2,0,0,0,0,1,0,124,138,91,32.33,75,96,1 0,54,2,0,0,0,0,0,0,253,127,68.5,22.54,70,76,0 0,48,2,1,25,0,0,0,0,250,103,76.5,23.25,72,66,0 0,63,2,1,30,1,0,1,0,213,172,95,27.68,50,67,0 1,63,2,1,20,0,0,1,0,264,129.5,82.5,24.91,88,83,0 0,50,4,1,30,0,0,1,0,214,176,113,22.17,68,71,0 1,51,1,0,0,0,0,1,0,205,125,94,29.11,72,90,0 1,48,3,0,0,0,0,0,0,243,116,82,26.09,80,85,0 0,60,1,0,0,0,0,1,0,295,162,85,32.76,75,71,0 1,48,3,1,20,0,0,0,0,187,102,69,24.24,68,72,0 1,58,3,0,0,0,0,0,0,189,136,86,23.97,71,60,0 0,44,1,0,0,0,0,0,0,242,124,72.5,23.07,67,83,0 0,39,2,1,20,0,0,0,0,236,127,78,17.51,75,76,0 0,37,2,1,9,0,0,0,0,185,115,76,23.55,85,80,0 1,56,2,1,20,0,0,0,0,270,109,75,24.58,67,64,0 1,59,3,0,0,0,0,1,0,285,128,91,28.23,70,80,0 0,53,2,0,0,0,0,1,0,185,202.5,85,24.65,107,104,0 1,59,2,0,0,0,0,0,0,299,120.5,78,25.45,60,100,1 1,65,1,0,0,0,1,1,0,266,140,100,29.36,80,77,1 0,39,2,1,9,0,0,0,0,254,112.5,75,22.91,72,NA,0 0,58,1,0,0,1,0,1,0,290,145,88,36.65,60,86,0 0,40,2,1,20,0,0,0,0,261,112,67,21.83,75,61,0 1,37,2,1,9,0,0,0,0,237,102,72,19.68,67,83,0 1,64,1,0,0,0,0,1,0,189,156,69,21.68,66,100,0 1,62,1,0,0,1,0,1,0,208,140,97.5,27.27,70,121,0 1,35,NA,0,0,0,0,0,0,275,132.5,79,34.04,75,80,0 1,48,4,1,20,0,0,1,0,259,135,90,20.72,102,81,1 1,37,4,1,16,0,0,0,0,212,120,72,23.51,75,80,0 1,52,2,0,0,0,1,0,0,202,136,94,29.93,83,67,1 1,51,2,1,20,0,0,0,0,227,139,74,29.29,80,67,0 0,66,1,1,1,1,0,1,0,261,154,97,32.6,70,81,0 0,54,3,0,0,0,0,1,0,239,142.5,83.5,30.47,83,84,0 1,55,1,0,0,0,0,1,0,165,166,101,24.8,65,90,1 1,57,1,1,20,0,0,0,0,198,128,85,28.18,85,99,0 1,39,1,1,20,0,0,0,0,232,122.5,78.5,26.11,80,73,0 1,59,1,0,0,0,0,0,0,252,146,92,27.88,68,80,1 0,55,3,0,0,0,0,0,0,260,136.5,87.5,25.41,75,60,0 1,42,3,1,20,0,0,0,0,230,112,66,25.31,80,69,0 0,45,2,0,0,0,0,0,0,260,98,74,19.16,73,76,0 0,48,2,1,20,0,0,0,0,271,130,84,21.97,120,85,0 0,50,2,1,10,0,0,0,0,220,122,80,24.22,75,72,0 0,37,1,1,20,0,0,0,0,200,112.5,68,25.87,65,67,0 0,57,1,0,0,0,0,0,0,273,131,84,22.99,90,76,0 1,57,1,1,20,0,0,1,0,235,150,95.5,27.56,78,73,0 0,42,2,1,30,0,0,0,0,201,141,84.5,26.58,90,97,0 0,67,2,1,15,0,0,1,0,371,166,85,25.35,100,86,0 1,44,4,0,0,0,0,0,0,201,120,81,26.49,70,78,0 0,59,2,1,15,0,0,0,0,309,141,77.5,25.97,70,79,0 1,47,4,0,0,0,0,0,0,190,120,81,24.54,60,73,0 0,59,1,1,10,1,0,1,0,312,175,82,39.82,120,85,1 1,46,3,0,0,0,0,1,0,300,146,98.5,30.41,60,79,0 0,39,1,0,0,0,0,0,0,227,138,89,26.74,95,60,0 1,45,2,0,0,0,0,0,0,215,104,72,30.34,70,79,0 1,53,1,0,0,0,0,0,0,235,130,80,28.15,84,78,1 0,67,1,1,3,0,0,1,0,270,137.5,72.5,35.01,85,73,0 1,57,1,0,0,0,0,0,1,300,121,74,28.09,80,155,1 0,46,2,1,15,0,0,0,0,232,115,70,25.18,75,59,1 1,45,1,1,1,0,0,1,0,277,140,84,28.74,69,74,0 1,60,1,0,0,0,0,1,0,270,145,81,29.37,75,73,0 1,53,1,0,0,0,0,1,0,219,141,105,26.86,62,60,0 1,43,1,1,20,0,0,0,1,309,124,85,26.91,70,215,0 0,45,2,1,7,0,0,0,0,199,124,78,21.94,85,78,0 0,68,3,1,20,0,0,1,0,258,158,94,31.64,80,84,1 0,57,2,0,0,0,0,0,0,199,117,83,24.76,95,82,0 1,59,2,0,0,0,0,1,0,242,144,87.5,28.7,72,NA,0 0,54,2,0,0,1,0,1,0,199,159,102,22.91,66,93,0 0,44,2,1,10,0,0,0,0,223,96,59,23.82,67,87,0 0,50,1,0,0,0,0,1,0,212,169,117,27.08,100,68,0 0,39,2,1,20,0,0,0,0,206,102.5,65,19.8,80,85,0 0,43,2,1,15,1,0,1,0,172,149,82,22.35,60,64,0 1,42,1,1,NA,0,0,0,0,226,119,80,25.29,62,98,0 0,36,1,0,0,0,0,0,0,211,100,61.5,22.19,60,73,0 1,54,2,0,0,0,0,0,0,334,133.5,80,23.4,85,77,0 0,41,2,0,0,0,0,0,0,197,113,70,23.78,65,90,0 1,60,4,0,0,0,0,0,0,280,114,82,23.96,70,84,1 0,63,NA,0,0,0,0,1,0,246,151,88,25.94,69,73,0 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0,58,1,0,0,0,0,1,0,268,170,104,24.51,70,75,0 0,57,1,0,0,0,0,1,0,382,133,77,24.27,75,81,0 0,52,1,0,0,NA,0,1,0,252,208,136,25.79,92,82,0 0,58,3,0,0,0,0,1,0,241,143.5,85.5,23.96,96,NA,1 1,51,4,1,10,0,0,0,0,231,110,72,21.19,75,81,0 1,49,1,1,20,0,0,0,0,288,128,89,35.96,73,75,0 0,64,1,1,10,0,0,1,0,NA,160,98,26.24,85,NA,0 0,52,1,0,0,0,0,0,0,211,129,73,29.09,62,117,0 1,41,3,1,20,0,0,1,0,240,166,71,35.53,90,57,0 0,47,1,1,9,0,0,0,0,214,118,72,24.08,60,NA,0 1,44,4,1,25,0,0,0,0,210,103,78,34.89,62,77,0 1,40,2,1,30,1,0,1,0,287,141,86,27.42,80,94,0 0,39,1,0,0,0,0,0,0,205,139.5,87,20.7,67,85,0 0,38,2,1,20,0,0,0,0,195,116,72,24.45,75,90,0 1,64,1,0,0,0,0,1,0,188,191,106,37.38,82,84,0 1,44,2,1,30,0,0,0,0,240,134,88,31.62,70,61,0 1,53,4,0,0,0,0,0,0,241,113,84,28.27,62,73,0 0,43,2,0,0,0,0,0,0,308,110,70,24.83,55,83,0 0,53,2,0,0,0,0,0,0,213,133,89,17.89,75,73,0 0,60,3,0,0,0,0,1,0,232,152.5,85,23.03,85,123,1 0,48,2,1,20,0,0,0,0,211,130,73,19.72,82,NA,1 0,48,1,0,0,0,0,0,0,225,132,80,21.35,60,74,0 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0,64,1,0,0,0,0,1,0,255,147.5,93,25.16,95,84,0 1,45,2,0,0,0,0,0,0,279,143.5,90,21.07,90,87,0 1,47,2,1,20,0,0,0,0,210,131,74,18.88,60,86,0 1,54,4,0,0,0,0,0,0,226,122.5,77.5,27.93,92,71,0 0,42,1,1,20,0,0,0,0,171,111,81,29.47,75,77,0 0,52,4,0,0,0,0,1,0,240,139,80,22,68,NA,0 1,46,1,0,0,0,0,0,0,220,119,80,25.86,75,95,0 0,55,1,0,0,0,0,1,0,208,190,130,56.8,90,86,1 0,40,2,1,20,0,0,0,0,250,104,74,18.75,90,NA,0 1,44,2,1,20,0,0,0,0,211,145,88,23.39,60,79,0 1,52,3,1,30,0,0,0,0,248,119,82,26.55,65,100,0 0,65,3,0,0,0,0,0,0,344,120,75,25.41,87,98,0 0,51,1,0,0,0,0,1,0,285,151.5,79,26.91,84,76,0 0,42,1,0,0,0,0,0,0,158,127.5,90,31.75,120,92,0 1,42,2,1,20,0,0,0,0,272,125.5,80.5,25.35,66,71,0 1,58,1,1,15,0,0,0,0,175,83.5,58,29.66,95,115,0 0,40,1,1,10,0,0,0,0,209,96,67,23.46,52,70,0 1,41,3,1,30,0,0,1,0,292,152,73,25.21,75,112,0 0,57,2,0,0,1,0,1,1,318,161,90,38.11,68,112,0 1,55,NA,0,0,0,0,0,0,222,129,86.5,33.76,95,92,1 0,65,1,0,0,1,0,1,0,230,159,87,22.91,70,65,1 0,59,2,0,0,0,0,0,0,364,142,84,26.24,67,70,0 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0,43,3,1,5,0,0,0,0,214,121,84,24.68,80,74,0 1,62,1,0,0,0,0,0,0,157,130,87,28.73,72,47,0 0,57,4,1,9,0,0,0,0,382,140,94,21.2,98,70,0 1,39,1,1,20,0,0,0,0,228,122.5,87,31.6,100,73,0 1,46,2,1,20,0,0,0,0,179,111,80,20.87,72,76,0 1,49,1,1,20,0,0,1,0,270,160.5,106.5,30.33,75,65,0 0,44,2,1,3,0,0,0,0,239,103,67,26.58,66,73,0 0,62,4,0,0,0,0,0,0,288,118.5,71,26.18,68,87,0 1,55,1,1,20,0,0,1,0,281,165,108,24.14,75,66,0 1,58,4,1,9,0,0,0,0,229,140,89.5,25.96,80,83,0 1,53,1,1,11,1,0,1,0,228,132.5,55,19.97,90,83,1 1,46,2,1,30,0,0,0,0,250,123,76,21.66,55,78,0 0,62,1,0,0,0,0,1,0,258,162,97.5,30.53,76,87,0 1,52,4,0,0,0,0,0,0,225,132,88,23.35,72,69,0 1,41,2,1,20,0,0,0,0,212,112,63.5,25.2,80,76,1 1,44,1,1,40,0,0,0,0,158,150.5,87,21.44,75,98,1 0,59,1,0,0,0,0,0,0,280,135.5,72.5,22.38,96,NA,0 0,58,1,0,0,0,0,1,0,241,153,106,26.94,98,84,0 0,51,4,1,10,0,0,0,0,240,112,83,24.1,75,77,0 1,63,1,1,9,0,0,0,0,188,113,82,29.01,74,93,0 0,42,3,0,0,0,0,0,0,204,108,70.5,27.71,75,65,0 1,44,1,0,0,0,0,1,0,229,142,92,25.21,72,76,0 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1,63,1,0,0,0,0,1,0,222,159,90,21.9,80,95,1 0,50,1,1,3,0,0,1,0,238,158,74,35.68,69,98,0 1,38,2,0,0,0,0,1,0,256,123,92,25.42,62,82,0 0,35,2,1,1,0,0,0,0,194,100.5,69,17.92,75,73,0 1,56,1,0,0,0,0,1,0,254,166,107,21.97,75,83,0 1,45,NA,1,20,0,0,0,0,200,113,75.5,21.51,88,72,0 0,40,2,0,0,0,0,0,0,185,111,79,22.9,75,81,0 0,35,2,1,20,0,0,0,0,168,83.5,55,16.71,79,63,1 0,67,2,0,0,0,0,0,0,292,144,89,21.93,96,NA,0 0,62,2,0,0,1,0,1,0,298,248,130,37.1,96,77,1 0,61,4,0,0,0,0,0,0,233,126,73,23.16,74,62,0 1,54,1,1,3,0,0,0,0,173,121,79,26.21,75,68,0 1,38,1,1,30,0,0,0,0,281,111,72.5,27.22,95,80,0 0,63,1,0,0,0,1,1,0,273,152,70,19.69,80,79,1 0,60,3,0,0,0,0,0,0,259,155,90,27.94,68,95,0 0,49,1,1,1,0,0,0,0,185,108,70,20.13,107,58,0 1,53,2,0,0,0,0,1,0,325,172.5,112.5,28.47,75,74,0 1,50,1,0,0,0,0,0,0,204,122,81,32.22,63,73,0 1,62,1,1,6,0,0,1,0,237,163,94,25.62,85,84,1 0,55,1,0,0,0,0,0,0,235,123,81,31.44,75,78,0 0,46,2,0,0,0,0,0,0,221,108,73,20.06,73,85,0 0,40,2,1,20,0,0,0,0,242,112.5,62.5,27.65,75,70,0 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0,49,2,0,0,0,0,0,0,308,128,78,24.82,80,70,0 0,41,3,0,0,0,0,0,0,166,128.5,74.5,21.02,81,NA,0 0,55,1,0,0,0,0,1,0,212,161,85,28.26,66,63,1 1,38,3,1,20,0,0,0,0,270,120,75,23.76,83,96,0 0,54,2,1,15,0,0,1,0,262,230,110,24.76,93,97,0 0,40,3,1,9,0,0,0,0,180,118,86.5,22.69,89,67,0 1,50,NA,1,2,0,0,0,0,238,121,85,25.31,88,NA,0 1,54,1,1,20,0,0,0,0,235,143,75,32.99,85,102,0 1,66,2,0,0,0,0,0,1,189,140,71,27.56,70,119,0 1,63,1,0,0,0,0,1,0,178,155,79,25.9,55,61,0 1,52,1,0,0,0,0,1,0,170,153,102.5,29.35,91,67,0 0,48,3,0,0,0,0,0,0,202,146.5,79,22.19,82,95,0 0,38,3,1,25,0,0,0,0,205,117.5,77,21.44,100,NA,0 1,54,3,1,25,0,0,0,0,208,137.5,82.5,25.58,75,63,1 0,38,1,0,0,0,0,0,0,182,138,72,21.67,85,108,0 1,65,4,1,25,0,0,0,0,215,147.5,95,29.08,82,88,1 1,53,2,0,0,0,0,0,0,196,129,80,26.32,75,89,0 0,44,2,1,15,0,0,0,0,244,101,66,25.38,85,76,0 0,47,3,1,20,0,0,0,0,NA,120,77.5,23.39,82,NA,0 0,47,1,0,0,0,0,1,0,190,162,85,30.59,65,80,0 0,57,1,0,0,0,0,1,0,285,197,72,23.41,68,78,1 0,44,4,1,5,0,0,0,0,231,133,89,29.29,70,83,0 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0,49,3,0,0,0,0,0,0,203,125,70,22.52,72,NA,0 1,61,1,0,0,0,0,0,0,238,119,78,25.36,80,75,0 1,52,3,0,0,0,0,1,0,232,154,92,26.51,90,74,0 0,56,4,0,0,0,0,1,0,246,136,87,26.21,80,72,1 1,54,4,1,30,NA,0,1,0,234,152.5,80,23.5,75,75,0 0,34,3,0,0,0,0,0,0,227,102,68,24.96,80,75,0 1,58,3,0,0,1,1,1,0,241,164,97,32.18,65,54,0 0,54,2,1,20,1,0,1,0,325,170,107,25.07,90,64,0 0,48,1,0,0,0,0,1,0,224,152.5,90,29.8,67,85,0 0,59,1,0,0,0,0,0,0,254,116,71,25.48,75,98,1 1,53,2,0,0,0,0,0,1,234,113,68,24.8,76,108,1 1,43,2,0,0,0,0,0,0,210,127.5,82.5,27.94,78,80,0 1,54,3,1,30,0,0,1,0,267,141.5,91,25.36,92,87,1 0,48,1,0,0,0,0,0,0,274,110,84,22.51,110,78,0 0,48,3,0,0,0,0,0,0,262,125,80,25.23,85,72,0 0,46,3,1,10,0,0,0,0,205,115,75,19.48,55,78,0 1,43,2,1,40,0,0,1,0,188,137,87,26.28,75,73,0 0,53,4,0,0,0,0,1,0,263,173,89,23.03,65,82,1 1,51,1,1,40,0,0,1,0,227,162.5,104,34.97,90,65,1 0,52,2,1,20,0,0,0,0,410,105,67.5,27.33,75,90,0 0,43,4,0,0,0,0,0,0,223,100,70,22.73,63,68,0 1,65,1,1,6,0,0,1,0,238,146,86,29.47,75,66,0 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1,46,1,1,20,0,0,0,0,288,131,81,25.94,90,80,0 1,52,2,1,30,0,0,0,0,222,108,70,23.09,72,61,0 1,68,1,0,0,0,0,1,0,276,127,66.5,25.78,75,104,1 1,45,3,1,20,0,0,0,0,207,111,72,21.49,67,87,0 0,62,1,0,0,0,0,0,0,266,124,69,22.9,66,82,1 0,53,2,0,0,0,0,1,0,279,167,101,30.85,90,87,0 0,42,3,0,0,0,0,0,0,215,111,72,25.38,77,55,0 0,65,1,0,0,1,0,1,0,342,168.5,98.5,23.87,75,60,0 1,63,1,0,0,0,0,1,1,260,155.5,98,30.08,67,109,1 0,49,3,1,15,0,0,0,0,267,107,74,27.8,65,75,0 0,52,2,0,0,0,0,0,0,209,111,79,23.63,96,70,0 0,45,1,0,0,0,0,0,0,219,123.5,80.5,24.45,65,83,0 1,53,1,1,3,0,0,0,0,218,120,80,29.87,90,73,1 0,62,3,1,20,1,0,1,1,358,215,110,37.62,110,368,1 1,40,3,1,30,0,0,0,0,190,110,70,24.63,60,72,0 1,57,1,0,0,1,0,1,0,321,192.5,113,25.94,63,90,1 0,41,1,1,NA,0,0,0,0,171,135,82.5,24.35,79,82,0 1,37,4,1,30,0,0,0,0,170,111,74,26,80,67,0 0,46,2,1,15,0,0,0,1,233,106,60,20.84,75,348,0 1,38,2,1,30,0,0,1,0,266,149,91,31.08,69,61,0 1,48,2,1,40,0,0,0,0,228,110,70,21.47,70,76,0 0,62,1,0,0,0,0,0,0,242,137,75,30.51,60,78,0 0,55,4,0,0,0,0,1,0,260,172.5,100.5,32.27,58,72,1 1,68,1,0,0,0,0,0,0,237,130,62,33.52,60,82,0 1,45,1,0,0,0,0,0,0,213,130,80,27.25,68,75,0 1,58,1,1,20,0,0,0,0,290,124,76,21.65,63,81,0 0,46,1,0,0,0,0,0,0,199,102,56,21.96,80,84,0 0,43,2,1,20,0,0,0,0,163,104.5,65,17.84,75,71,1 0,43,1,1,20,0,0,0,0,204,133,86.5,26.01,100,79,0 0,38,4,0,0,0,0,0,0,254,120.5,76,22.73,75,78,0 0,64,3,0,0,0,0,1,0,200,151,90,21.99,70,87,0 0,67,3,1,5,1,0,1,0,245,169,82,26.05,122,122,0 1,45,4,1,10,0,0,1,0,255,111,80,30.92,72,80,0 1,37,2,1,20,0,0,0,0,220,130,83,26.71,68,NA,0 1,45,3,0,0,0,0,0,0,347,157,98,26.63,88,80,0 1,48,1,0,0,0,0,1,0,259,147.5,87.5,25.1,65,73,1 1,61,3,1,30,0,0,0,0,228,123,88,26.88,95,67,0 1,52,NA,0,0,0,0,0,1,232,130,74,32.52,95,248,1 0,64,2,0,0,0,0,1,0,279,172,87,24.01,80,70,0 0,57,1,0,0,0,0,1,0,277,148,89.5,31.82,86,89,0 1,45,1,1,20,0,0,1,0,256,144,96,26.6,75,83,0 0,46,2,1,20,0,0,0,0,291,124,81,22.02,78,NA,1 1,46,NA,0,0,0,0,0,0,188,135,95,26.84,60,78,0 1,38,4,1,15,0,0,0,0,248,110,61,22.17,85,55,1 0,56,1,1,40,0,0,1,0,214,147,65,17.68,110,87,1 0,59,1,0,0,0,0,1,0,244,160,85,29.41,72,85,0 0,48,3,0,0,0,0,1,0,205,140,99,24.41,100,92,0 1,61,1,0,0,0,0,0,0,300,127,75,24.93,48,NA,0 1,40,4,1,40,0,0,0,0,334,120,77,22.66,82,92,0 1,41,2,0,0,0,0,0,0,246,111.5,67,18.76,65,60,0 0,45,1,0,0,0,0,0,0,285,120,80,27.45,84,93,0 1,59,1,1,43,0,0,0,0,162,117,73,25.01,96,87,0 0,58,2,0,0,0,0,1,0,345,188,102,28.89,95,NA,0 1,47,4,0,0,0,0,1,0,270,152.5,108,26.09,90,88,1 0,39,4,0,0,0,0,0,0,181,112,71,21.8,77,67,0 1,65,4,0,0,0,0,0,0,205,106,73,23.14,66,87,0 1,37,1,1,30,0,0,0,0,189,127,85,32.35,88,84,0 1,40,2,0,0,0,0,0,0,192,128,81,25.41,75,76,0 0,53,1,0,0,0,0,1,0,272,165,95,28.92,100,100,0 0,38,3,1,20,0,0,0,0,167,105,70,19.76,80,80,0 0,57,4,0,0,0,0,1,0,236,164,100,25.45,90,67,0 1,42,1,1,20,0,0,0,0,143,119,73,22.04,60,70,0 0,39,2,0,0,0,0,0,0,202,100.5,67,21.33,63,NA,0 0,39,4,1,25,0,0,0,0,200,110,68,20.24,79,62,0 0,65,1,0,0,0,0,1,1,304,139,75,25.12,82,116,0 0,46,1,1,10,0,0,0,0,263,110,65,27.27,84,73,0 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-1,68,1,0,0,0,0,1,0,176.0,168.0,97.0,23.14,60.0,79.0,1 -1,50,1,1,1,0,0,1,0,313.0,179.0,92.0,25.97,66.0,86.0,1 -1,51,3,1,43,0,0,0,0,207.0,126.5,80.0,19.71,65.0,68.0,0 -0,48,2,1,20,0,0,0,0,248.0,131.0,72.0,22.0,84.0,86.0,0 -0,44,1,1,15,0,0,0,0,210.0,126.5,87.0,19.16,86.0,81.96365524402907,0 -0,52,2,0,0,0,0,0,0,269.0,133.5,83.0,21.47,80.0,107.0,0 -1,40,3,0,0,0,0,1,0,185.0,141.0,98.0,25.6,67.0,72.0,0 -0,39,3,1,30,0,0,0,0,196.0,133.0,86.0,20.91,85.0,80.0,0 diff --git a/datasets/framingham/framingham_cleaned.json b/datasets/framingham/framingham_cleaned.json deleted file mode 100644 index 63d2c5e5..00000000 --- a/datasets/framingham/framingham_cleaned.json +++ /dev/null @@ -1,69 +0,0 @@ -{ - "columns": [ - { - "name": "male", - "type": "Categorical" - }, - { - "name": "age", - "type": "DiscreteNumerical" - }, - { - "name": "education", - "type": "DiscreteNumerical" - }, - { - "name": "currentSmoker", - "type": "Categorical" - }, - { - "name": "cigsPerDay", - "type": "DiscreteNumerical" - }, - { - "name": "BPMeds", - "type": "Categorical" - }, - { - "name": "prevalentStroke", - "type": "Categorical" - }, - { - "name": "prevalentHyp", - "type": "Categorical" - }, - { - "name": "diabetes", - "type": "Categorical" - }, - { - "name": "totChol", - "type": "ContinuousNumerical" - }, - { - "name": "sysBP", - "type": "ContinuousNumerical" - }, - { - "name": "diaBP", - "type": "ContinuousNumerical" - }, - { - "name": "BMI", - "type": "ContinuousNumerical" - }, - { - "name": "heartRate", - "type": "ContinuousNumerical" - }, - { - "name": "glucose", - "type": "ContinuousNumerical" - }, - { - "name": "TenYearCHD", - "type": "Categorical" - } - - ] -} \ No newline at end of file diff --git a/examples/framingham-bootstrap-ensemble/framingham-bootstrap-ensemble.py b/examples/framingham-bootstrap-ensemble/framingham-bootstrap-ensemble.py deleted file mode 100644 index cceae12a..00000000 --- a/examples/framingham-bootstrap-ensemble/framingham-bootstrap-ensemble.py +++ /dev/null @@ -1,153 +0,0 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product -from pathlib import Path - -def input_json(random_state): - return { - "enabled": True, - "dataset": "datasets/framingham/framingham_cleaned", - "synth-method": "bootstrap", - "parameters": { - "enabled": True, - "num_samples_to_synthesize": -1, - "random_state": int(random_state), - }, - "privacy_parameters_disclosure_risk": { - "enabled": False, - "num_samples_intruder": 5000, - "vars_intruder": ["gender", "age", "neighborhood"], - }, - "utility_parameters_classifiers": { - "enabled": False, - "classifier": { - "LogisticRegression": {"mode": "main", "params_main": {"max_iter": 1000}} - }, - }, - "utility_parameters_correlations": {"enabled": True}, - "utility_parameters_feature_importance": { - "enabled": True, - "label_column": "TenYearCHD", - "aggPrimitives": [ - "mean", "max", "min" - ], - "tranPrimitives": [], - "max_depth": 2, - "features_to_exclude": [], - "drop_na": "rows", - "normalized_entities": [ - {"new_entity_id": "edu", - "index": "education", - "make_time_index": False - }, - {"new_entity_id": "smoking", - "index": "cigsPerDay", - "additional_variables": ["currentSmoker"], - "make_time_index": False - }, - {"new_entity_id": "sex", - "index": "male", - "make_time_index": False - }, - {"new_entity_id": "lifeyears", - "index": "age", - "make_time_index": False - }, - {"new_entity_id": "hypertension", - "index": "prevalentHyp", - "make_time_index": False - }, - {"new_entity_id": "diabetic", - "index": "diabetes", - "make_time_index": False - } - ], - "categorical_enconding": "labels", - "compute_shapley": True, - "skip_feature_engineering": False - } - } - - -def filename_stem(i): - return f"framingham-bootstrap-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - -if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/framingham-resampling-ensemble/framingham-resampling-ensemble.py b/examples/framingham-resampling-ensemble/framingham-resampling-ensemble.py deleted file mode 100644 index 8ff2eb0f..00000000 --- a/examples/framingham-resampling-ensemble/framingham-resampling-ensemble.py +++ /dev/null @@ -1,177 +0,0 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product -from pathlib import Path - -def input_json(random_state): - return { - "enabled": True, - "dataset": "datasets/framingham/framingham_cleaned", - "synth-method": "synthpop", - "parameters": { - "enabled": True, - "num_samples_to_fit": -1, - "num_samples_to_synthesize": -1, - "num_datasets_to_synthesize": 1, - "random_state": int(random_state), - "vars_sequence": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], - "synthesis_methods": [ - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - "sample", - ], - "proper": False, - "tree_minbucket": 1, - }, - "privacy_parameters_disclosure_risk": { - "enabled": False, - "num_samples_intruder": 5000, - "vars_intruder": ["gender", "age", "neighborhood"], - }, - "utility_parameters_classifiers": { - "enabled": False, - "classifier": { - "LogisticRegression": {"mode": "main", "params_main": {"max_iter": 1000}} - }, - }, - "utility_parameters_correlations": {"enabled": False}, - "utility_parameters_feature_importance": { - "enabled": True, - "label_column": "TenYearCHD", - "aggPrimitives": [ - "mean", "max", "min" - ], - "tranPrimitives": [], - "max_depth": 2, - "features_to_exclude": [], - "drop_na": True, - "normalized_entities": [ - {"new_entity_id": "edu", - "index": "education", - "make_time_index": False - }, - {"new_entity_id": "smoking", - "index": "cigsPerDay", - "additional_variables": ["currentSmoker"], - "make_time_index": False - }, - {"new_entity_id": "sex", - "index": "male", - "make_time_index": False - }, - {"new_entity_id": "lifeyears", - "index": "age", - "make_time_index": False - }, - {"new_entity_id": "hypertension", - "index": "prevalentHyp", - "make_time_index": False - }, - {"new_entity_id": "diabetic", - "index": "diabetes", - "make_time_index": False - } - ], - "categorical_enconding": "labels", - "compute_shapley": True, - "skip_feature_engineering": False - } - } - - - -def filename_stem(i): - return f"framingham-resampling-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def feature_importance_path(i): - return Path( - f"../../synth-output/{filename_stem(i)}/utility_feature_importance.json" - ) - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - args = parser.parse_args() - return args - - -if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=random_states, columns=["random_state"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j", "-C../.."] + all_targets) diff --git a/examples/framingham-subsample-ensemble/framingham-subsample-ensemble.py b/examples/framingham-subsample-ensemble/framingham-subsample-ensemble.py deleted file mode 100644 index d184efe9..00000000 --- a/examples/framingham-subsample-ensemble/framingham-subsample-ensemble.py +++ /dev/null @@ -1,155 +0,0 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import pandas as pd -from itertools import product -from pathlib import Path - -def input_json(random_state, sample_frac): - return { - "enabled": True, - "dataset": "datasets/framingham/framingham_cleaned", - "synth-method": "subsample", - "parameters": { - "enabled": True, - "frac_samples_to_synthesize": sample_frac, - "random_state": int(random_state), - }, - "privacy_parameters_disclosure_risk": { - "enabled": False, - "num_samples_intruder": 5000, - "vars_intruder": ["gender", "age", "neighborhood"], - }, - "utility_parameters_classifiers": { - "enabled": False, - "classifier": { - "LogisticRegression": {"mode": "main", "params_main": {"max_iter": 1000}} - }, - }, - "utility_parameters_correlations": {"enabled": False}, - "utility_parameters_feature_importance": { - "enabled": True, - "label_column": "TenYearCHD", - "aggPrimitives": [ - "mean", "max", "min" - ], - "tranPrimitives": [], - "max_depth": 2, - "features_to_exclude": [], - "drop_na": True, - "normalized_entities": [ - {"new_entity_id": "edu", - "index": "education", - "make_time_index": False - }, - {"new_entity_id": "smoking", - "index": "cigsPerDay", - "additional_variables": ["currentSmoker"], - "make_time_index": False - }, - {"new_entity_id": "sex", - "index": "male", - "make_time_index": False - }, - {"new_entity_id": "lifeyears", - "index": "age", - "make_time_index": False - }, - {"new_entity_id": "hypertension", - "index": "prevalentHyp", - "make_time_index": False - }, - {"new_entity_id": "diabetic", - "index": "diabetes", - "make_time_index": False - } - ], - "categorical_enconding": "labels", - "compute_shapley": True, - "skip_feature_engineering": False - } - } - - -def filename_stem(i): - return f"framingham-subsample-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-s", - "--sample-fractions", - dest="sample_fracs", - required=True, - help="The list of fractions of samples used", - ) - - args = parser.parse_args() - return args - - -if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.sample_fracs.strip('[]').split(','))), columns=["random_state", "sample_frac"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-j72", "-C../.."] + all_targets) diff --git a/examples/privbayes-framingham-ensemble/privbayes-framingham-ensemble.py b/examples/privbayes-framingham-ensemble/privbayes-framingham-ensemble.py deleted file mode 100644 index 551d5547..00000000 --- a/examples/privbayes-framingham-ensemble/privbayes-framingham-ensemble.py +++ /dev/null @@ -1,169 +0,0 @@ -import argparse -import json -import matplotlib.pyplot as plt -import subprocess -import seaborn as sns -import pandas as pd -from itertools import product -from pathlib import Path - - -def input_json(random_state, epsilon, k): - return { - "enabled": True, - "dataset": "datasets/framingham/framingham_cleaned", - "synth-method": "PrivBayes", - "parameters": { - "enabled": True, - "num_samples_to_synthesize": 4240, - "random_state": int(random_state), - "category_threshold": 20, - "epsilon": epsilon, - "k": int(k), - "keys": [], - "histogram_bins": 10, - "preconfigured_bn": {}, - "save_description": False - }, - "privacy_parameters_disclosure_risk": {"enabled": False}, - "utility_parameters_classifiers": { - "enabled": False, - "classifier": { - "LogisticRegression": {"mode": "main", "params_main": {"max_iter": 1000}} - } - }, - "utility_parameters_correlations": {"enabled": False}, - "utility_parameters_feature_importance": { - "enabled": True, - "label_column": "TenYearCHD", - "aggPrimitives": [ - "mean", "max", "min" - ], - "tranPrimitives": [], - "max_depth": 2, - "features_to_exclude": [], - "drop_na": True, - "normalized_entities": [ - {"new_entity_id": "edu", - "index": "education", - "make_time_index": False - }, - {"new_entity_id": "smoking", - "index": "cigsPerDay", - "additional_variables": ["currentSmoker"], - "make_time_index": False - }, - {"new_entity_id": "sex", - "index": "male", - "make_time_index": False - }, - {"new_entity_id": "lifeyears", - "index": "age", - "make_time_index": False - }, - {"new_entity_id": "hypertension", - "index": "prevalentHyp", - "make_time_index": False - }, - {"new_entity_id": "diabetic", - "index": "diabetes", - "make_time_index": False - } - ], - "categorical_enconding": "labels", - "compute_shapley": True, - "skip_feature_engineering": False - } - } - - -def filename_stem(i): - return f"privbayes-framingham-ensemble-{i:04}" - - -def input_path(i): - return Path(f"../../run-inputs/{filename_stem(i)}.json") - - -def write_input_file(i, params, force=False): - fname = input_path(i) - run_input = json.dumps(input_json(**params), indent=4) - if force or not fname.exists(): - print(f"Writing {fname}") - with open(fname, "w") as input_file: - input_file.write(run_input) - - -def read_json(fname): - with open(fname) as f: - return json.load(f) - - -def handle_cmdline_args(): - parser = argparse.ArgumentParser( - description="Generate (optionally run and postprocess) an ensemble of run inputs" - ) - - parser.add_argument( - "-n", - "--num-replicas", - dest="nreplicas", - required=True, - type=int, - help="The number of replicas to generate", - ) - - parser.add_argument( - "-r", - "--run", - default=False, - action="store_true", - help="Run (via make) and postprocess?", - ) - - parser.add_argument( - "-f", - "--force-write", - dest="force", - default=False, - action="store_true", - help="Write out input files, even if they exist", - ) - - parser.add_argument( - "-e", - "--epsilons", - dest="epsilons", - required=True, - help="Define list of epsilons for the requested run", - ) - - parser.add_argument( - "-k", - "--parents", - dest="k", - required=True, - type=int, - help="Define k (number of parents) for the requested run", - ) - - args = parser.parse_args() - return args - - -if __name__ == "__main__": - args = handle_cmdline_args() - - random_states = range(args.nreplicas) - - all_params = pd.DataFrame( - data=product(random_states, map(float, args.epsilons.strip('[]').split(',')), [args.k]), columns=["random_state", "epsilon", "k"] - ) - - for i, params in all_params.iterrows(): - print(dict(params)) - write_input_file(i, dict(params), force=args.force) - - if args.run: - all_targets = [f"run-{filename_stem(i)}" for i, _ in all_params.iterrows()] - subprocess.run(["make", "-k", "-j72", "-C../.."] + all_targets) From 6351780cd46605c35f2850efcaa3f10fa5fe9b3c Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Wed, 28 Jul 2021 12:08:42 +0100 Subject: [PATCH 09/11] fix artificial examples --- .../artificial_1-resampling-ensemble.py | 4 ++-- .../artificial_2-resampling-ensemble.py | 4 ++-- .../artificial_3-resampling-ensemble.py | 4 ++-- .../artificial_4-resampling-ensemble.py | 4 ++-- .../artificial_5-resampling-ensemble.py | 4 ++-- .../artificial_6-resampling-ensemble.py | 4 ++-- .../artificial_7-resampling-ensemble.py | 4 ++-- 7 files changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py b/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py index f3d05330..47262f69 100644 --- a/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py +++ b/examples/artificial_1-resampling-ensemble/artificial_1-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_1-resampling-ensemble' @@ -57,5 +57,5 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py b/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py index d31b378c..bb613328 100644 --- a/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py +++ b/examples/artificial_2-resampling-ensemble/artificial_2-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_2-resampling-ensemble' @@ -58,4 +58,4 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) \ No newline at end of file + run(input_json, FILESTEM, SynthMethod.RESAMPLING) \ No newline at end of file diff --git a/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py b/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py index 15a786e9..1525ae57 100644 --- a/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py +++ b/examples/artificial_3-resampling-ensemble/artificial_3-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_3-resampling-ensemble' @@ -58,5 +58,5 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py b/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py index 4b847124..7441cb54 100644 --- a/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py +++ b/examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_4-resampling-ensemble' @@ -58,4 +58,4 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py b/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py index a48318a1..9769493f 100644 --- a/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py +++ b/examples/artificial_5-resampling-ensemble/artificial_5-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_5-resampling-ensemble' @@ -68,5 +68,5 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py b/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py index 6bc56ae1..ba1b819c 100644 --- a/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py +++ b/examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_6-resampling-ensemble' @@ -68,5 +68,5 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) diff --git a/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py b/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py index 9fecdfc0..27a33a49 100644 --- a/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py +++ b/examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py @@ -1,7 +1,7 @@ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) -from utils import run +from utils import run, SynthMethod FILESTEM = 'artificial_7-resampling-ensemble' @@ -56,5 +56,5 @@ def input_json(random_state): if __name__ == "__main__": - run(input_json, FILESTEM) + run(input_json, FILESTEM, SynthMethod.RESAMPLING) From f7d3cd616fbb10248cee88c38de81f9ebde0f519 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Wed, 28 Jul 2021 16:56:22 +0100 Subject: [PATCH 10/11] Update reproduce_experiments.md with instructions for reproducing plots --- datasets/framingham/README.md | 19 ----------- examples/reproduce_experiments.md | 55 +++++++++++++++++++++++++++++-- 2 files changed, 53 insertions(+), 21 deletions(-) delete mode 100644 datasets/framingham/README.md diff --git a/datasets/framingham/README.md b/datasets/framingham/README.md deleted file mode 100644 index d2cb5df7..00000000 --- a/datasets/framingham/README.md +++ /dev/null @@ -1,19 +0,0 @@ -"Framingham Heart Study" dataset by found in Kaggle (https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset), -used under [CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) modified by filling NA values with the column means - -The dataset is not used for commercial purposes. - -Note: Might want to replace this with the details from https://biolincc.nhlbi.nih.gov/studies/framcohort/ , due to time constraints I am using the version available freely on Kaggle rather than waiting for approval, but I believe this is the true original source of the data. - -Instructions to clean (also included in `datasets-raw/framingham/clean.py`): - -```{python} -import pandas as pd - -raw_df = pd.read_csv("framingham.csv") - -df = raw_df.fillna(raw_df.mean()) -df[["cigsPerDay", "age", "education", "BPMeds"]] = df[["cigsPerDay", "age", "education", "BPMeds"]].astype(int) - -df.to_csv("../../datasets/framinghamframingham_cleaned.csv", index=False) -``` diff --git a/examples/reproduce_experiments.md b/examples/reproduce_experiments.md index 8c43f124..bffb7dba 100644 --- a/examples/reproduce_experiments.md +++ b/examples/reproduce_experiments.md @@ -9,7 +9,7 @@ The easiest way to use the pipeline is with [Docker](https://www.docker.com/). G docker pull turinginst/quipp-env:base ``` -# Experiments +## Experiments Check out the [QUIPP-pipeline](https://github.com/alan-turing-institute/QUIPP-pipeline) repository and set as the current directory @@ -71,6 +71,45 @@ docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp- docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_4-resampling-ensemble/artificial_4-resampling-ensemble.py -n 25 -f -r ``` +### Artificial 5 + +**PrivBayes** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_5-ensemble/privbayes-artificial_5-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_-resampling-ensemble/artificial_5-resampling-ensemble.py -n 25 -f -r +``` + +### Artificial 6 + +**PrivBayes** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_6-ensemble/privbayes-artificial_6-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_6-resampling-ensemble/artificial_6-resampling-ensemble.py -n 25 -f -r +``` + +### Artificial 7 + +**PrivBayes** +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/privbayes-artificial_7-ensemble/privbayes-artificial_7-ensemble.py -n 25 -f -k 3 -e 0.0001,0.001,0.01,0.1,0.4,1.0,4.0,10.0 -r +``` + +**Resampling** + +```bash +docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/artificial_7-resampling-ensemble/artificial_7-resampling-ensemble.py -n 25 -f -r +``` + ### Adult **PrivBayes** @@ -129,4 +168,16 @@ docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp- **Subsample** ```bash docker run -v $(pwd):/quipp-pipeline --workdir /quipp-pipeline turinginst/quipp-env:base python examples/polish-subsample-ensemble/polish-subsample-ensemble.py -n 25 -f -r -``` \ No newline at end of file +``` + +## Plotting + +All experimental outputs will now be in `./synth-outputs`. To recreate the paper's figures you must checkout the [QUIPP-feature-importance-plots](https://github.com/alan-turing-institute/QUIPP-feature-importance-plots) repo and install following instructions in the README. + +You can then create the plots with + +```bash +poetry run python panel_plots.py +``` + +where `` is the absolute path of `./synth-outputs`. \ No newline at end of file From 79b70220ae90506516f2c3b0d861e1f7a7bdf610 Mon Sep 17 00:00:00 2001 From: Oscar Giles Date: Wed, 28 Jul 2021 16:59:28 +0100 Subject: [PATCH 11/11] fix typo --- examples/reproduce_experiments.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/reproduce_experiments.md b/examples/reproduce_experiments.md index bffb7dba..feec4db1 100644 --- a/examples/reproduce_experiments.md +++ b/examples/reproduce_experiments.md @@ -177,7 +177,7 @@ All experimental outputs will now be in `./synth-outputs`. To recreate the paper You can then create the plots with ```bash -poetry run python panel_plots.py +poetry run python panel_plots.py --base-path ``` where `` is the absolute path of `./synth-outputs`. \ No newline at end of file