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generate_data.py
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generate_data.py
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"""
run.py - A PostHocExplainerEvaluation file
Copyright (C) 2020 Zach Carmichael
"""
import warnings
import os
from math import sqrt
from joblib import Parallel
from joblib import delayed
from tqdm.auto import tqdm
import sympy as sp
from sympy import stats
import numpy as np
from posthoceval.expl_utils import CompatUnpickler
from posthoceval.data_generation import sample
from posthoceval.utils import tqdm_parallel
def generate_data(out_filename, symbols, domains, n_samples, seed):
if os.path.isfile(out_filename):
warnings.warn(f'{out_filename} already exists, skipping generating '
f'data for the related model')
return
# Note: uniform distributions only supported with this code
# TODO: hard-coded...
low = -1
high = +1
distribution = []
constraints = {}
for symbol in symbols:
domain = domains.get(symbol)
if domain is None:
# no domain provided (possible that symbol is a dummy variable in
# expression)
U = stats.Uniform('U', low, high)
distribution.append(U)
continue
interval = sp.Interval(low, high)
valid_interval = sp.Intersection(interval, domain)
non_interval = False
if isinstance(valid_interval, sp.Interval):
intervals = [valid_interval]
elif (isinstance(valid_interval, sp.Union) and
all(isinstance(i, sp.Interval) for i in valid_interval.args)):
# naive special case optimization...
intervals = valid_interval.args
non_interval = True
else:
# failed to find optimization...
U = stats.Uniform('U', low, high)
distribution.append(U)
constraints[symbol] = valid_interval.contains(U)
continue
# otherwise
left = None
left_open = None
right = None
right_open = None
for i in intervals:
if left is None or i.left > left:
left = i.left
left_open = i.left_open
if right is None or i.right > right:
right = i.right
right_open = i.right_open
# number-fudging
left = float(left) # noqa
right = float(right) # noqa
if left_open:
left = np.nextafter(left, +1, dtype=np.float32)
if right_open:
right = np.nextafter(right, -1, dtype=np.float32)
U = stats.Uniform('U', left, right)
distribution.append(U)
if non_interval:
constraints[symbol] = valid_interval.contains(U)
a = sample(
variables=symbols,
distribution=distribution,
n_samples=n_samples,
constraints=constraints,
seed=seed,
)
np.savez_compressed(out_filename, data=a)
def run(out_dir, expr_filename, n_samples, scale_samples, n_jobs, seed):
out_dir_full = os.path.join(
out_dir, os.path.basename(expr_filename).rsplit('.', 1)[0])
os.makedirs(out_dir_full, exist_ok=True)
print('Loading', expr_filename, '(this may take a while)')
with open(expr_filename, 'rb') as f:
expr_data = CompatUnpickler(f).load()
print('Will save compressed numpy arrays to', out_dir_full)
n_expr = len(expr_data)
with tqdm_parallel(tqdm(desc='Data Generation', total=n_expr)) as pbar:
def jobs():
nonlocal seed
for i, expr_result in enumerate(expr_data):
symbols = expr_result.symbols
domains = expr_result.domains
n_samples_job = n_samples
if scale_samples:
# higher-dim may increase variance of the output of
# generated expressions
n_samples_job *= round(sqrt(len(symbols)))
out_filename = os.path.join(out_dir_full, str(i)) + '.npz'
yield delayed(generate_data)(
out_filename, symbols, domains, n_samples_job, seed)
# increment seed (don't have same RNG state per job)
seed += 1
if n_jobs == 1:
# TODO: this doesn't update tqdm
[f(*a, **kw) for f, a, kw in jobs()]
else:
Parallel(n_jobs=n_jobs)(jobs())
if __name__ == '__main__':
import argparse
def main():
parser = argparse.ArgumentParser( # noqa
description='Generate data and save to file',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
'expr_filename', help='Filename of the expression pickle'
)
parser.add_argument(
'--n-samples', '-n', default=500, type=int,
help='Number of samples'
)
parser.add_argument(
'--no-scale-samples', action='store_true',
help='Do not scale number of samples by number of dimensions'
)
default_out_dir = os.path.join(
os.path.dirname(__file__), 'experiment_data', 'data')
parser.add_argument(
'--out-dir', '-O', default=default_out_dir,
help='Output directory to save generated data'
)
parser.add_argument(
'--n-jobs', '-j', default=-1, type=int,
help='Number of jobs to use in generation'
)
parser.add_argument(
'--seed', default=42, type=int,
help='Seed for reproducibility. Technically the starting seed '
'from which each seed is derived per job'
)
args = parser.parse_args()
run(out_dir=args.out_dir,
expr_filename=args.expr_filename,
n_jobs=args.n_jobs,
n_samples=args.n_samples,
scale_samples=not args.no_scale_samples,
seed=args.seed)
main()