def main():
parser = argparse.ArgumentParser(
description="Worker script for the case study.")
# Experiment settings
parser.add_argument("min_feature", type=int, help="Min Feature ID")
parser.add_argument(
"max_feature",
type=int,
help="Max Feature ID. Set to -1 to go to the max feature ID.",
)
parser.add_argument("--nthreads",
type=int,
default=4,
help="Number of parallel workers to run.")
parser.add_argument("--fdr_threshold",
type=float,
default=0.2,
help="Target false discovery rate.")
# Get the arguments from the command line
args = parser.parse_args()
dargs = vars(args)
torch.set_num_threads(1) # bad torch, no biscuit
# Load the data and the model
print("Loading data")
X_drug, y_drug, features, ccle_features, elastic_model = load_plx4720()
if args.max_feature == -1:
args.max_feature = len(ccle_features)
# Get the weights to use in feature ranking
model_weights = get_model_weights(elastic_model)
# Build all the jobs
jobs = [(
feat_idx,
X_drug,
y_drug,
elastic_model,
features,
ccle_features,
model_weights,
) for feat_idx in range(args.min_feature,
min(args.max_feature + 1, len(ccle_features)))]
print("Running {} jobs".format(len(jobs)))
# Run in parallel (okay this is processes not threads, but who's counting?)
with Pool(args.nthreads, initializer=seed_fn) as pool:
p_values = np.array(pool.map(run_parallel, jobs))
def main():
parser = argparse.ArgumentParser(
description='Worker script for the case study.')
# Experiment settings
descriptors = [
'Bakery', 'Sour', 'Intensity', 'Sweet', 'Burnt', 'Pleasantness',
'Fish', 'Fruit', 'Garlic', 'Spices', 'Cold', 'Acid', 'Warm', 'Musky',
'Sweaty', 'Ammonia', 'Decayed', 'Wood', 'Grass', 'Flower', 'Chemical'
]
parser.add_argument('min_feature', type=int, help='Min Feature ID')
parser.add_argument('max_feature', type=int, help='Max Feature ID')
parser.add_argument('--descriptor',
choices=descriptors,
default='Bakery',
help='The descriptor type to get p-values for.')
parser.add_argument('--nthreads',
type=int,
default=4,
help='Number of parallel workers to run.')
parser.add_argument('--fdr_threshold',
type=float,
default=0.2,
help='Target false discovery rate.')
parser.add_argument(
'--importance_threshold',
type=float,
default=1e-3,
help=
'Minimum heuristic feature importance to make a feature test-worthy.')
# Get the arguments from the command line
args = parser.parse_args()
dargs = vars(args)
torch.set_num_threads(1) # bad torch, no biscuit
# Load the data and the model
print('Loading data')
X, Y, descriptors, target_features = load_olfaction()
features = X.columns
print('Loading model')
# Get the model and data specifically for this descriptor class
x, y, forest_model = load_or_fit_model(args.descriptor, X, Y)
# Handle an idiosyncracy of multiprocessing with sklearn random forests
for m in forest_model.models:
m.n_jobs = 1
# Get the weights to use in feature ranking
model_weights = get_model_weights(forest_model)
print('Total: {}'.format(len(X.columns)))
# Build all the jobs
jobs = [
(target_feature, x, y, features, forest_model, model_weights,
args.descriptor)
for target_feature, importance in zip(
X.columns[args.min_feature:args.max_feature +
1], model_weights[args.min_feature:args.max_feature + 1])
if importance >= args.importance_threshold
]
print('Running {} jobs'.format(len(jobs)))
# Run in parallel (okay this is processes not threads, but who's counting?)
with Pool(args.nthreads, initializer=seed_fn) as pool:
p_values = np.array(pool.map(run_parallel, jobs))
# Multiple testing correction via Benjamini-Hochberg at a 20% FDR
discoveries = bh(p_values, args.fdr_threshold)
discovery_genes = features[discoveries]
discovery_p = p_values[discoveries]
discovery_weights = model_weights[discoveries]
# Print the discoveries along with their model weights and p-values
order = np.argsort(discovery_weights)[::-1]
print('')
print('Molecular Feature & Model Weight & $p$-value \\\\')
for g, w, p in zip(discovery_genes[order], discovery_weights[order],
discovery_p[order]):
print('{} & {} & {} \\\\'.format(g, w, p))
def main():
parser = argparse.ArgumentParser(
description="Worker script for the case study.")
# Experiment settings
descriptors = [
"Bakery",
"Sour",
"Intensity",
"Sweet",
"Burnt",
"Pleasantness",
"Fish",
"Fruit",
"Garlic",
"Spices",
"Cold",
"Acid",
"Warm",
"Musky",
"Sweaty",
"Ammonia",
"Decayed",
"Wood",
"Grass",
"Flower",
"Chemical",
]
parser.add_argument("min_feature", type=int, help="Min Feature ID")
parser.add_argument("max_feature", type=int, help="Max Feature ID")
parser.add_argument(
"--descriptor",
choices=descriptors,
default="Bakery",
help="The descriptor type to get p-values for.",
)
parser.add_argument("--nthreads",
type=int,
default=4,
help="Number of parallel workers to run.")
parser.add_argument("--fdr_threshold",
type=float,
default=0.2,
help="Target false discovery rate.")
parser.add_argument(
"--importance_threshold",
type=float,
default=1e-3,
help=
"Minimum heuristic feature importance to make a feature test-worthy.",
)
# Get the arguments from the command line
args = parser.parse_args()
dargs = vars(args)
torch.set_num_threads(1) # bad torch, no biscuit
# Load the data and the model
print("Loading data")
X, Y, descriptors, target_features = load_olfaction()
features = X.columns
print("Loading model")
# Get the model and data specifically for this descriptor class
x, y, forest_model = load_or_fit_model(args.descriptor, X, Y)
# Handle an idiosyncracy of multiprocessing with sklearn random forests
for m in forest_model.models:
m.n_jobs = 1
# Get the weights to use in feature ranking
model_weights = get_model_weights(forest_model)
print("Total: {}".format(len(X.columns)))
# Build all the jobs
jobs = [(target_feature, x, y, features, forest_model, model_weights,
args.descriptor) for target_feature, importance in zip(
X.columns[args.min_feature:args.max_feature + 1],
model_weights[args.min_feature:args.max_feature + 1],
) if importance >= args.importance_threshold]
print("Running {} jobs".format(len(jobs)))
# Run in parallel (okay this is processes not threads, but who's counting?)
with Pool(args.nthreads, initializer=seed_fn) as pool:
p_values = np.array(pool.map(run_parallel, jobs))
# Multiple testing correction via Benjamini-Hochberg at a 20% FDR
discoveries = bh(p_values, args.fdr_threshold)
discovery_genes = features[discoveries]
discovery_p = p_values[discoveries]
discovery_weights = model_weights[discoveries]
# Print the discoveries along with their model weights and p-values
order = np.argsort(discovery_weights)[::-1]
print("")
print("Molecular Feature & Model Weight & $p$-value \\\\")
for g, w, p in zip(discovery_genes[order], discovery_weights[order],
discovery_p[order]):
print("{} & {} & {} \\\\".format(g, w, p))
import os
import argparse
import torch
from main import load_plx4720, run_hrt, CvModel, get_model_weights
from pyhrt.utils import bh
if __name__ == '__main__':
fdr_threshold = 0.2
importance_threshold = 1e-3
# Load the data and the model
print('Loading data')
X_drug, y_drug, features, ccle_features, elastic_model = load_plx4720()
# Get the weights to use in feature ranking
model_weights = get_model_weights(elastic_model)
all_p_path = 'data/p.npy'
if os.path.exists(all_p_path):
p_values = np.load(all_p_path)
else:
p_values = np.full(len(ccle_features), np.nan)
for feat_idx in range(len(ccle_features)):
if not np.isnan(p_values[feat_idx]):
continue
p_path = 'data/p_{}.npy'.format(feat_idx)
# Check if we have already computed this p-value
if os.path.exists(p_path):
p_values[feat_idx] = np.load(p_path)
def main():
parser = argparse.ArgumentParser(
description='Worker script for the case study.')
# Experiment settings
descriptors = [
'Bakery', 'Sour', 'Intensity', 'Sweet', 'Burnt', 'Pleasantness',
'Fish', 'Fruit', 'Garlic', 'Spices', 'Cold', 'Acid', 'Warm', 'Musky',
'Sweaty', 'Ammonia', 'Decayed', 'Wood', 'Grass', 'Flower', 'Chemical'
]
parser.add_argument('--descriptor',
choices=descriptors,
default='Bakery',
help='The descriptor type to get p-values for.')
parser.add_argument('--fdr_threshold',
type=float,
default=0.2,
help='Target false discovery rate.')
parser.add_argument(
'--importance_threshold',
type=float,
default=1e-3,
help=
'Minimum heuristic feature importance to make a feature test-worthy.')
# Get the arguments from the command line
args = parser.parse_args()
dargs = vars(args)
torch.set_num_threads(1) # bad torch, no biscuit
# Load the data and the model
print('Loading data')
X, Y, descriptors, target_features = load_olfaction()
features = X.columns
print('Loading model')
# Get the model and data specifically for this descriptor class
x, y, forest_model = load_or_fit_model(args.descriptor, X, Y)
# Handle an idiosyncracy of multiprocessing with sklearn random forests
for m in forest_model.models:
m.n_jobs = 1
# Get the weights to use in feature ranking
model_weights = get_model_weights(forest_model)
print('Total: {}'.format(len(X.columns)))
all_p_path = 'data/p_{}.npy'.format(args.descriptor)
if os.path.exists(all_p_path):
p_values = np.load(all_p_path)
else:
p_values = np.full(len(features), np.nan)
for feat_idx in range(len(features)):
if not np.isnan(p_values[feat_idx]):
continue
p_path = 'data/p_{}_{}.npy'.format(args.descriptor, feat_idx)
# Check if we have already computed this p-value
if os.path.exists(p_path):
p_values[feat_idx] = np.load(p_path)
# print('p-value for {}: {}'.format(features[feat_idx], p_values[feat_idx]))
continue
# Check if the model assigns zero weight to this feature such that it can be ignored
if np.abs(model_weights[feat_idx]) < args.importance_threshold:
# print('p-value for {}: 1 (0 weight in model)'.format(features[feat_idx]))
p_values[feat_idx] = 1
continue
print('************ Missing p-value for {} ************'.format(
feat_idx))
# Save the aggregated results
np.save(all_p_path, p_values)
# Print the top-ranked features by their heuristic weight
for rank, (target_feature,
importance) in enumerate(target_features[args.descriptor]):
p_value = p_values[features.get_loc(target_feature)]
print('{} & {:.4f} & {:.4f} \\\\'.format(
target_feature.replace('\'', ''), importance, p_value))
if np.any(np.isnan(p_values)):
print('{} NaN p-values!'.format(np.isnan(p_values).sum()))
missing = np.where(np.isnan(p_values))[0]
print(missing)
print([features[idx] for idx in missing])
print('Setting to 1')
p_values[np.isnan(p_values)] = 1
# Only consider features with substantial heuristic feature importance
important = model_weights >= args.importance_threshold
p_important = p_values[important]
print('{} features above importance threshold'.format(important.sum()))
# Multiple testing correction via Benjamini-Hochberg at a 20% FDR
# discoveries = bh(p_values, args.fdr_threshold) # Test all features
discoveries = np.arange(len(features))[important][bh(
p_important, args.fdr_threshold)] # Test important features
discovery_features = features[discoveries]
discovery_p = p_values[discoveries]
discovery_weights = model_weights[discoveries]
# Print the discoveries along with their model weights and p-values
order = np.argsort(np.abs(discovery_weights))[::-1]
print('')
print('Molecular Feature & Model Weight & $p$-value \\\\')
for f, w, p in zip(discovery_features[order], discovery_weights[order],
discovery_p[order]):
print('{} & {:.4f} & {:.4f} \\\\'.format(f, w, p))
