def committe(solver, solver_name, intervals, reps):
np.random.seed()
X, y = util.basic_data()
polls = util.add_noise(y)
curr_labels = np.random.choice(range(len(X)), size=4, replace=False)
X_train = X[curr_labels]
square_errors = np.zeros([2, len(intervals)])
for i in range(len(intervals)):
print("interval: ", intervals[i])
for j in range(reps):
while len(curr_labels) <= intervals[i]:
next_points = next_countys(solver, curr_labels, X, polls)
curr_labels = np.append(curr_labels, next_points)
curr_labels = curr_labels[:intervals[i]]
preds = solver(X, X[curr_labels], polls[curr_labels])
square_errors[:, i] += util.square_error(y, preds)
square_errors[:, i] /= reps
square_errors = np.vstack(
(square_errors.mean(axis=0), util.performance(solver, intervals,
reps).mean(axis=0)))
util.plot("committe",
intervals / len(X),
square_errors,
legend=[solver_name, "random"],
x_label="% counties",
y_label="MSE",
title="Committe")
def experiment(args, logger, out_dir):
"""
Main method that removes training instances ordered by
different methods and measure their impact on a random
set of test instances.
"""
# start timer
begin = time.time()
# create random number generator
rng = np.random.default_rng(args.rs)
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# use a fraction of the train data
if args.train_frac < 1.0 and args.train_frac > 0.0:
n_train_samples = int(X_train.shape[0] * args.train_frac)
train_indices = rng.choice(X_train.shape[0], size=n_train_samples, replace=False)
X_train, y_train = X_train[train_indices], y_train[train_indices]
# select a (stratified) subset of test instances uniformly at random
_, X_test_sub, _, y_test_sub = train_test_split(X_test, y_test,
test_size=args.n_test,
random_state=args.rs,
stratify=y_test)
# display dataset statistics
logger.info('\nno. train instances: {:,}'.format(X_train.shape[0]))
logger.info('no. test instances: {:,}'.format(X_test_sub.shape[0]))
logger.info('no. features: {:,}'.format(X_train.shape[1]))
logger.info('pos. label % (test): {:.1f}%\n'.format(np.sum(y_test) / y_test.shape[0] * 100))
# train a tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
# select a subset of test instances, half from the neg. class and half from the pos. class
model_pred_test = model.predict(X_test)
neg_test_indices = np.where(model_pred_test == 0)[0]
pos_test_indices = np.where(model_pred_test == 1)[0]
neg_test_indices = rng.choice(neg_test_indices, size=int(args.n_test / 2), replace=False)
pos_test_indices = rng.choice(pos_test_indices, size=int(args.n_test / 2), replace=False)
test_indices = np.concatenate([neg_test_indices, pos_test_indices])
X_test_sub, y_test_sub = X_test[test_indices], y_test[test_indices]
util.performance(model, X_test_sub, y_test_sub, logger=logger, name='Test')
# compute how many samples to remove before a checkpoint
if args.train_frac_to_remove >= 1.0:
n_checkpoint = int(args.train_frac_to_remove)
elif args.train_frac_to_remove > 0:
n_checkpoint = int(args.train_frac_to_remove * X_train.shape[0] / args.n_checkpoints)
else:
raise ValueError('invalid train_frac_to_remove: {}'.format(args.train_frac_to_remove))
# sort train instances, then remove, retrain, and re-evaluate
train_indices = sort_train_instances(args, model, X_train, y_train, X_test_sub, y_test_sub, rng, logger=logger)
result = measure_performance(train_indices, n_checkpoint, args.n_checkpoints,
clf, X_train, y_train, X_test_sub, y_test_sub, logger=logger)
# save results
result['max_rss'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result['total_time'] = time.time() - begin
np.save(os.path.join(out_dir, 'results.npy'), result)
plt.savefig(os.path.join(out_dir, 'special_ckpt.pdf'), bbox_inches='tight')
# display results
logger.info('\nResults:\n{}'.format(result))
logger.info('\nsaving results to {}...'.format(os.path.join(out_dir, 'results.npy')))
def experiment(args, logger, out_dir):
"""
Main method that removes training instances ordered by
different methods and measure their impact on a random
set of test instances.
"""
# start timer
begin = time.time()
# create random number generator
rng = np.random.default_rng(args.rs)
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# train a tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
util.performance(model, X_test, y_test, logger=logger, name='Test')
# select a subset of test instances uniformly at random
if args.start_pred == -1:
# select an instance at random
if args.n_test == 1:
test_indices = rng.choice(X_test.shape[0],
size=args.n_test,
replace=False)
X_test_sub, y_test_sub = X_test[test_indices], y_test[test_indices]
# use the entire test set
elif args.n_test <= 0:
X_test_sub, y_test_sub = X_test, y_test
# use a stratified sample of the test set
else:
n_test = 1.0 if args.n_test == -1 else args.n_test
_, X_test_sub, _, y_test_sub = train_test_split(
X_test,
y_test,
test_size=n_test,
random_state=args.rs,
stratify=y_test)
# select a subset of test instances of the desired predicted label uniformly at random
elif args.start_pred in [0, 1]:
# use the entire test set
if args.n_test <= 0:
X_test_sub, y_test_sub = X_test, y_test
# select a specified no. instances from the specified class
else:
model_pred = model.predict(X_test)
label_indices = np.where(model_pred == args.start_pred)[0]
test_indices = rng.choice(label_indices,
size=args.n_test,
replace=False)
X_test_sub, y_test_sub = X_test[test_indices], y_test[test_indices]
else:
raise ValueError('unknown start_pred: {}'.format(args.start_pred))
# display dataset statistics
logger.info('\nno. train instances: {:,}'.format(X_train.shape[0]))
logger.info('no. test instances: {:,}'.format(X_test_sub.shape[0]))
logger.info('no. features: {:,}\n'.format(X_train.shape[1]))
# sort train instances, then remove, retrain, and re-evaluate
result = measure_performance(args,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
rng,
out_dir,
logger=logger)
# save results
result['max_rss'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result['total_time'] = time.time() - begin
np.save(os.path.join(out_dir, 'results.npy'), result)
# display results
logger.info('\nResults:\n{}'.format(result))
logger.info('\nsaving results to {}...'.format(
os.path.join(out_dir, 'results.npy')))
def experiment(args, logger, out_dir, seed):
"""
Main method comparing performance of tree ensembles and svm models.
"""
# start experiment timer
begin = time.time()
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
logger.info('no. train: {:,}'.format(X_train.shape[0]))
logger.info('no. test: {:,}'.format(X_test.shape[0]))
logger.info('no. features: {:,}'.format(X_train.shape[1]))
# tune on a fraction of the training data
if not args.no_tune:
if args.tune_frac < 1.0:
sss = StratifiedShuffleSplit(n_splits=1,
test_size=2,
train_size=args.tune_frac,
random_state=args.rs)
tune_indices, _ = list(sss.split(X_train, y_train))[0]
X_train_sub, y_train_sub = X_train[tune_indices], y_train[
tune_indices]
logger.info('tune instances: {:,}'.format(X_train_sub.shape[0]))
else:
X_train_sub, y_train_sub = X_train, y_train
else:
X_train_sub, y_train_sub = X_train, y_train
# get model
model, param_grid = get_model(args, cat_indices=cat_indices)
logger.info('\nmodel: {}, param_grid: {}'.format(args.model, param_grid))
# tune the model
start = time.time()
if not args.no_tune:
skf = StratifiedKFold(n_splits=args.cv,
shuffle=True,
random_state=args.rs)
gs = GridSearchCV(model,
param_grid,
scoring=args.scoring,
cv=skf,
verbose=args.verbose)
gs = gs.fit(X_train_sub, y_train_sub)
cols = ['mean_fit_time', 'mean_test_score', 'rank_test_score']
cols += ['param_{}'.format(param) for param in param_grid.keys()]
df = pd.DataFrame(gs.cv_results_)
logger.info('gridsearch results:')
logger.info(df[cols].sort_values('rank_test_score'))
model = clone(gs.best_estimator_)
logger.info('best params: {}'.format(gs.best_params_))
tune_time = time.time() - start
logger.info('tune time: {:.3f}s'.format(tune_time))
# train model
start = time.time()
model = model.fit(X_train, y_train)
train_time = time.time() - start
logger.info('train time: {:.3f}s'.format(train_time))
# evaluate
auc, acc, ap, ll = util.performance(model,
X_test,
y_test,
logger,
name=args.model)
# save results
result = {}
result['model'] = args.model
result['auc'] = auc
result['acc'] = acc
result['ap'] = ap
result['ll'] = ll
result['tune_time'] = tune_time
result['train_time'] = train_time
result['max_rss'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result['tune_frac'] = args.tune_frac
if args.model in ['cb', 'rf']:
result['n_estimators'] = gs.best_params_['n_estimators']
result['max_depth'] = gs.best_params_['max_depth']
np.save(os.path.join(out_dir, 'results.npy'), result)
# Macs show this in bytes, unix machines show this in KB
logger.info('max_rss: {:,}'.format(result['max_rss']))
logger.info('total time: {:.3f}s'.format(time.time() - begin))
logger.info('saving results to {}...'.format(
os.path.join(out_dir, 'results.npy')))
from lda import lda
if __name__ == '__main__':
filepath = os.path.join('Yale_Face_Database', 'Training')
H, W = 231, 195
X, y = imread(filepath, H, W)
eigenvalues_pca, eigenvectors_pca, X_mean = pca(X, num_dim=31)
X_pca = eigenvectors_pca.T @ (X - X_mean)
eigenvalues_lda, eigenvectors_lda = lda(X_pca, y)
# Transform matrix
U = eigenvectors_pca @ eigenvectors_lda
print('U shape: {}'.format(U.shape))
# show top 25 eigenface
show_eigenface(U, 25, H, W)
# reduce dim (projection)
Z = U.T @ X
# recover
X_recover = U @ Z + X_mean
show_reconstruction(X, X_recover, 10, H, W)
# accuracy
filepath = os.path.join('Yale_Face_Database', 'Testing')
X_test, y_test = imread(filepath, H, W)
acc = performance(X_test, y_test, Z, y, U, X_mean, 5)
print('acc: {:.2f}%'.format(acc * 100))
def experiment(args, logger, out_dir):
logger.info('\nDATA')
start = time.time()
in_dir = os.path.join(args.data_dir, args.dataset,
'fold_{}'.format(args.fold))
# read in feature data
logger.info('reading in data...')
X_train = load_npz(
os.path.join(in_dir,
'{}_train.npz'.format(args.feature_type))).tocsr()
X_val = load_npz(
os.path.join(in_dir, '{}_val.npz'.format(args.feature_type))).tocsr()
X_test = load_npz(
os.path.join(in_dir, '{}_test.npz'.format(args.feature_type))).tocsr()
# read in label data
train_df = pd.read_csv(os.path.join(in_dir, 'y_train.csv'))
val_df = pd.read_csv(os.path.join(in_dir, 'y_val.csv'))
test_df = pd.read_csv(os.path.join(in_dir, 'y_test.csv'))
# filter out transductive test indices
if args.test_type == 'inductive':
indices = np.load(os.path.join(in_dir, 'inductive_indices.npz'))
val_df = val_df[val_df['com_id'].isin(indices['val'])]
test_df = test_df[test_df['com_id'].isin(indices['test'])]
X_val = X_val[val_df.index]
X_test = X_test[test_df.index]
# extract label data
y_train = train_df['label'].to_numpy()
y_val = val_df['label'].to_numpy()
y_test = test_df['label'].to_numpy()
# extract identifier data
target_ids_train = train_df['com_id'].to_numpy()
target_ids_val = val_df['com_id'].to_numpy()
target_ids_test = test_df['com_id'].to_numpy()
logger.info('\ntrain instances: X: {}, y: {}'.format(
X_train.shape, y_train.shape))
logger.info('val instances: X: {}, y: {}'.format(X_val.shape,
y_val.shape))
logger.info('test instances: X: {}, y: {}'.format(X_test.shape,
y_test.shape))
logger.info('total time: {:.3f}s'.format(time.time() - start))
# train
logger.info('\nTRAIN')
start = time.time()
# setup models
model = _get_model(args, data_dir=in_dir, logger=logger)
if args.eggs:
model = model.fit(X_train, y_train, target_ids_train, X_val, y_val,
target_ids_val)
else:
model = model.fit(X_train, y_train)
logger.info('total time: {:.3f}s'.format(time.time() - start))
# predict
logger.info('\nPREDICT')
start = time.time()
if args.eggs:
proba = model.predict_proba(X_test, target_ids_test)[:, 1]
else:
proba = model.predict_proba(X_test)[:, 1]
auc, ap = util.performance(y_test, proba, logger=logger, name='model')
logger.info('total time: {:.3f}s'.format(time.time() - start))
# save results
result = {'auc': auc, 'ap': ap}
result['target_id'] = target_ids_test
result['label'] = y_test
result['yhat'] = proba
np.save(os.path.join(out_dir, 'result.npy'), result)
def experiment(args, logger, out_dir):
"""
Main method that trains a tree ensemble, then compares the
runtime of different methods to explain a single test instance.
"""
# start timer
begin = time.time()
# create random number generator
rng = np.random.default_rng(args.rs)
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
logger.info('\nno. train instances: {:,}'.format(X_train.shape[0]))
logger.info('no. test instances: {:,}'.format(X_test.shape[0]))
logger.info('no. features: {:,}\n'.format(X_train.shape[1]))
# train a tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
util.performance(model, X_test, y_test, logger=logger, name='Test')
# randomly pick a test instances to explain
test_ndx = rng.choice(y_test.shape[0], size=1, replace=False)
# TREX
if 'klr' in args.method or 'svm' in args.method:
result = trex_method(args,
model,
test_ndx,
X_train,
y_train,
X_test,
logger=logger)
# Leaf Influence
elif 'leaf_influence' in args.method and args.model == 'cb':
result = leaf_influence_method(args,
model,
test_ndx,
X_train,
y_train,
X_test,
y_test,
logger=logger)
# MAPLE
elif args.method == 'maple':
result = maple_method(args,
model,
test_ndx,
X_train,
y_train,
X_test,
logger=logger)
# TEKNN
elif 'knn' in args.method:
result = teknn_method(args,
model,
test_ndx,
X_train,
y_train,
X_test,
logger=logger)
else:
raise ValueError('method {} unknown!'.format(args.method))
# save results
result['max_rss'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result['total_time'] = time.time() - begin
np.save(os.path.join(out_dir, 'results.npy'), result)
# display results
logger.info('\nResults:\n{}'.format(result))
logger.info('\nsaving results to {}...'.format(
os.path.join(out_dir, 'results.npy')))
def experiment(args, logger, out_dir):
"""
Cleaning Experiment:
1) Train a tree ensemble.
2) Flip a percentage of train labels.
3) Prioritize train instances to be checked using various methods.
4) Check and correct any flipped train labels.
5) Compute how effective each method is at cleaning the data.
"""
# start timer
begin = time.time()
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# use a subset of the training data
if args.train_frac < 1.0 and args.train_frac > 0.0:
n_train = int(X_train.shape[0] * args.train_frac)
X_train, y_train = X_train[:n_train], y_train[:n_train]
logger.info('\nno. train instances: {:,}'.format(len(X_train)))
logger.info('no. test instances: {:,}'.format(len(X_test)))
logger.info('no. features: {:,}'.format(X_train.shape[1]))
# add noise
y_train_noisy, noisy_indices = flip_labels(y_train, k=args.flip_frac, seed=args.rs, logger=logger)
noisy_indices = np.array(sorted(noisy_indices))
logger.info('no. noisy labels: {:,}'.format(noisy_indices.shape[0]))
# train a tree ensemble on the clean and noisy labels
model = clone(clf).fit(X_train, y_train)
model_noisy = clone(clf).fit(X_train, y_train_noisy)
# show model performance before and after noise
logger.info('\nBefore noise:')
util.performance(model, X_train, y_train, logger=logger, name='Before, Train')
util.performance(model, X_test, y_test, logger=logger, name='Before, Test')
logger.info('\nAfter noise:')
util.performance(model_noisy, X_train, y_train_noisy, logger=logger, name='After, Noisy Train')
util.performance(model_noisy, X_train, y_train, logger=logger, name='After, Clean Train')
util.performance(model_noisy, X_test, y_test, logger=logger, name='After, Test')
# check predictive performance before and after noise
acc_clean, auc_clean = score(model, X_test, y_test)
acc_noisy, auc_noisy = score(model_noisy, X_test, y_test)
# find how many corrupted / non-corrupted labels were incorrectly predicted
predicted_labels = model_noisy.predict(X_train).flatten()
incorrect_indices = np.where(y_train_noisy != predicted_labels)[0]
incorrect_noisy_indices = np.intersect1d(noisy_indices, incorrect_indices)
logger.info('\nno. incorrectly predicted noisy train instances: {:,}'.format(incorrect_noisy_indices.shape[0]))
logger.info('no. incorrectly predicted train instances: {:,}'.format(incorrect_indices.shape[0]))
# total no. instances to check and no. instances to check between checkpoints
n_check = int(y_train.shape[0] * args.check_pct)
n_checkpoint = int(n_check / args.n_checkpoints)
# random
if args.method == 'random':
result = random_method(args, noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# TREX
elif 'klr' in args.method or 'svm' in args.method:
result = trex_method(args, model_noisy, y_train_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# tree-esemble loss
elif args.method == 'tree_loss':
result = tree_loss_method(args, model_noisy, y_train_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# Leaf Influence
elif 'leaf_influence' in args.method and args.model == 'cb':
result = leaf_influence_method(args, model_noisy, y_train_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# MAPLE
elif args.method == 'maple':
result = maple_method(args, model_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# TEKNN
elif 'knn' in args.method:
result = teknn_method(args, model_noisy, y_train_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# Tree Prototype
elif args.method == 'tree_prototype':
result = tree_prototype_method(args, model_noisy, y_train_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
# MMD Prototype
elif args.method == 'mmd_prototype':
result = mmd_prototype_method(args, model_noisy, y_train_noisy,
noisy_indices, n_check, n_checkpoint,
clf, X_train, y_train, X_test, y_test,
acc_noisy, auc_noisy, logger=logger)
else:
raise ValueError('unknown method {}'.format(args.method))
# save results
result['max_rss'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result['total_time'] = time.time() - begin
result['acc_clean'] = acc_clean
result['auc_clean'] = auc_clean
np.save(os.path.join(out_dir, 'results.npy'), result)
# display results
logger.info('\nResults:\n{}'.format(result))
def experiment(args, logger, out_dir):
"""
Main method that removes training instances ordered by
different methods and measure their impact on a random
set of test instances.
"""
# start timer
begin = time.time()
# create random number generator
rng = np.random.default_rng(args.rs)
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# use a fraction of the train data
if args.train_frac < 1.0 and args.train_frac > 0.0:
n_train_samples = int(X_train.shape[0] * args.train_frac)
train_indices = rng.choice(X_train.shape[0],
size=n_train_samples,
replace=False)
X_train, y_train = X_train[train_indices], y_train[train_indices]
# train a tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
# select an ambiguously predicted test instance
proba = model.predict_proba(X_test)[:, 1]
sorted_indices = np.argsort(np.abs(proba - 0.5))
test_indices = sorted_indices[:1] # shape=(1,)
X_test_sub, y_test_sub = X_test[test_indices], y_test[test_indices]
# display dataset statistics
logger.info('\nno. train instances: {:,}'.format(X_train.shape[0]))
logger.info('no. test instances: {:,}'.format(X_test_sub.shape[0]))
logger.info('no. features: {:,}\n'.format(X_train.shape[1]))
logger.info('pos. label % (test): {:.1f}%\n'.format(
np.sum(y_test) / y_test.shape[0] * 100))
# sort train instances
exc_indices, inh_indices = trex_method(args,
model,
X_train,
y_train,
X_test_sub,
logger=logger)
ran_indices = rng.choice(np.arange(X_train.shape[0]),
size=X_train.shape[0],
replace=False)
ran_pos_indices = np.where(y_train == 1)[0]
ran_neg_indices = np.where(y_train == 0)[0]
rng.shuffle(ran_pos_indices)
rng.shuffle(ran_neg_indices)
# remove, retrain, and re-evaluate
logger.info('\nremoving most excitatory train instances...')
exc_result = measure_performance(args,
exc_indices,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
logger=logger)
logger.info('\nremoving most inhibitory train instances...')
inh_result = measure_performance(args,
inh_indices,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
logger=logger)
logger.info('\nremoving train instances uniformly at random...')
ran_result = measure_performance(args,
ran_indices,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
logger=logger)
if args.extra_methods:
logger.info('\nremoving positive train instances at random...')
ran_pos_result = measure_performance(args,
ran_pos_indices,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
logger=logger)
logger.info('\nremoving negative train instances at random...')
ran_neg_result = measure_performance(args,
ran_neg_indices,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
logger=logger)
# matplotlib settings
util.plot_settings(fontsize=13)
# inches
width = 4.8 # Machine Learning journal
height = get_height(width=width, subplots=(1, 1))
fig, ax = plt.subplots(figsize=(width * 1.65, height * 1.0))
# plot results
l1 = ax.errorbar(exc_result['remove_pct'],
exc_result['proba'],
color='blue',
linestyle='--',
marker='.',
label='Most excitatory')
l2 = ax.errorbar(inh_result['remove_pct'],
inh_result['proba'],
color='green',
linestyle='--',
marker='+',
label='Most inhibitory')
l3 = ax.errorbar(ran_result['remove_pct'],
ran_result['proba'],
color='red',
linestyle='-',
marker='*',
label='Random')
lines = [l1, l2, l3]
labels = ['Most excitatory', 'Most inhibitory', 'Random']
if args.extra_methods:
l4 = ax.errorbar(ran_pos_result['remove_pct'],
ran_pos_result['proba'],
color='cyan',
linestyle=':',
marker='1',
label='Pos. random')
l5 = ax.errorbar(ran_neg_result['remove_pct'],
ran_neg_result['proba'],
color='orange',
linestyle=':',
marker='2',
label='Neg. random')
lines += [l4, l5]
labels += ['Random (pos. only)', 'Random (neg. only)']
ax.set_xlabel('Train data removed (%)')
ax.set_ylabel('Predicted probability')
ax.set_ylim(0, 1)
# adjust legend
fig.legend(tuple(lines),
tuple(labels),
loc='left',
ncol=1,
bbox_to_anchor=(1.0, 0.85),
title='Removal Ordering')
plt.tight_layout()
fig.subplots_adjust(right=0.65)
# save plot
plt.savefig(os.path.join(out_dir, 'probas.pdf'), bbox_inches='tight')
# display results
logger.info('\nsaving results to {}/...'.format(os.path.join(out_dir)))
logger.info('total time: {:.3f}s'.format(time.time() - begin))
def experiment(args, logger, out_dir):
# start timer
begin = time.time()
# create random number generator
rng = np.random.default_rng(args.rs)
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing,
mismatch=True)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# display dataset statistics
logger.info('\nno. train instances: {:,}'.format(X_train.shape[0]))
logger.info('no. test instances: {:,}'.format(X_test.shape[0]))
logger.info('no. features: {:,}'.format(X_train.shape[1]))
logger.info('\npos. label % (train): {:.1f}%'.format(
np.sum(y_train) / y_train.shape[0] * 100))
logger.info('pos. label % (test): {:.1f}%\n'.format(
np.sum(y_test) / y_test.shape[0] * 100))
# train tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
util.performance(model, X_test, y_test, logger=logger, name='Test')
# train surrogate model
params = {
'C': args.C,
'n_neighbors': args.n_neighbors,
'tree_kernel': args.tree_kernel
}
surrogate = trex.train_surrogate(model=model,
surrogate='klr',
X_train=X_train,
y_train=y_train,
val_frac=args.tune_frac,
metric=args.metric,
seed=args.rs,
params=params,
logger=logger)
# extract predictions
start = time.time()
model_pred = model.predict(X_test)
model_proba = model.predict_proba(X_test)[:, 1]
logger.info('predicting...{:.3f}s'.format(time.time() - start))
# pick a test instance in which the person is <= 17 from the Adult dataset
indices = np.where(X_test[:, args.age_ndx] <= 17)[0]
test_ndx = rng.choice(indices)
age_test_val = X_test[test_ndx][args.age_ndx]
x_test = X_test[[test_ndx]]
# show prediction for this test instance
s = '\ntest: {}, actual: {}, proba.: {:.3f}, age: {:.0f}'
logger.info(
s.format(test_ndx, y_test[test_ndx], model_proba[test_ndx],
age_test_val))
# sort based on similarity-influence
if 'sim' in args.surrogate:
# compute influence based on predicted labels
attributions = surrogate.similarity(x_test)
pred_label = model.predict(x_test)
# put positive weight if similar instances have the same label as the predicted test label
for i in range(x_test.shape[0]):
attributions[i] = np.where(y_train == pred_label[i],
attributions[i], attributions[i] * -1)
attributions = attributions.sum(axis=0)
attribution_indices = np.argsort(attributions)[::-1]
# sort training instances by most influential to the predicted label
else:
attributions = surrogate.pred_influence(x_test,
model_pred[[test_ndx]])[0]
attribution_indices = np.argsort(attributions)[::-1]
# sort training instances by most similar to the test instance
sim = surrogate.similarity(x_test)[0]
sim_indices = np.argsort(sim)[::-1]
# get instance weights
alpha = surrogate.get_alpha()
# 1. show most influential training instances
logger.info(
'\nTop {:,} most influential samples to the predicted label...'.format(
args.topk_inf))
show_instances(args, X_train, alpha, sim, attributions, y_train,
attribution_indices, args.topk_inf, logger)
# 2a. compute aggregate surrogate contribution of most influential train instances
attr_all = np.sum(np.abs(attributions))
attr_pos = np.sum(np.where(attributions > 0, attributions,
0)) # sum of pos. attributions only
attr_topk_inf = np.sum(
np.abs(attributions[attribution_indices][:args.topk_inf]))
attr_topk_inf_pct = attr_topk_inf / attr_all * 100
attr_topk_inf_pos_pct = attr_topk_inf / attr_pos * 100
# 2b. display aggregate attributions
s1 = '\nattribution % of top {:,} infuential instances: {:.2f}%'
s2 = 'attribution % of top {:,} infuential instances for pos. attributions: {:.2f}%'
logger.info(s1.format(args.topk_inf, attr_topk_inf_pct))
logger.info(s2.format(args.topk_inf, attr_topk_inf_pos_pct))
# 3. compute change in predicted probability after REMOVING the most influential instances
s = 'test: {}, actual: {}, proba.: {:.3f}, age: {:.0f}'
logger.info('\nRemoving top {:,} influential instances..'.format(
args.topk_inf))
new_X_train = np.delete(X_train,
attribution_indices[:args.topk_inf],
axis=0)
new_y_train = np.delete(y_train, attribution_indices[:args.topk_inf])
new_model = clone(clf).fit(new_X_train, new_y_train)
util.performance(model,
new_X_train,
new_y_train,
logger=logger,
name='Train')
util.performance(model, X_test, y_test, logger=logger, name='Test')
logger.info(
s.format(test_ndx, y_test[test_ndx],
new_model.predict_proba(X_test)[:, 1][test_ndx],
age_test_val))
# 4a. compute change in predicted probability after FLIPPING the labels of the most influential instances
s1 = 'test: {}, actual: {}, proba.: {:.3f}, age: {:.0f}'
s2 = '\n{:,} out of the top {:,} most influential instances have age <= 17'
logger.info(
'\nFixing ONLY corrupted labels of the top {:,} influential instances..'
.format(args.topk_inf))
new_X_train = X_train.copy()
new_y_train = y_train.copy()
# 4b. fixing a portion of the corrupted training instances
temp_indices = np.where(
X_train[attribution_indices][:args.topk_inf][:, args.age_ndx] <= 17)[0]
age17_topk_inf_indices = attribution_indices[temp_indices]
new_y_train[age17_topk_inf_indices] = 0
# 4c. fit new model and re-evaluate
new_model = clone(clf).fit(new_X_train, new_y_train)
util.performance(new_model,
new_X_train,
new_y_train,
logger=logger,
name='Train')
util.performance(new_model, X_test, y_test, logger=logger, name='Test')
logger.info(
s1.format(test_ndx, y_test[test_ndx],
new_model.predict_proba(X_test)[:, 1][test_ndx],
age_test_val))
logger.info(s2.format(age17_topk_inf_indices.shape[0], args.topk_inf))
# 5. show most similar training instances
logger.info(
'\nTop {:,} most similar samples to the predicted label...'.format(
args.topk_sim))
show_instances(args, X_train, alpha, sim, attributions, y_train,
sim_indices, args.topk_sim, logger)
# 6. of the most similar train instances, compute how many have age <= 17
num_age17_topk = np.where(
X_train[sim_indices][:args.topk_sim][:,
args.age_ndx] <= 17)[0].shape[0]
logger.info(
'\nTop {:,} most similar train instances with age <= 17: {:,}'.format(
args.topk_sim, num_age17_topk))
# 7. plot similarity of train instances against their instance weights
logger.info('\nplotting similarity vs. weights...')
plot_similarity(args, alpha, sim, out_dir, logger)
# 8. no. train instances with age <= 17 and an alpha coefficient < 0
neg_alpha_indices = np.where(attributions < 0)[0]
num_age17_neg_alpha = np.where(
X_train[neg_alpha_indices][:, args.age_ndx] <= 17)[0].shape[0]
logger.info('\nno. instances with age <= 17 and alpha < 0: {:,}'.format(
num_age17_neg_alpha))
# 9. no. train instances with age <= 17 and an alpha coefficient >= 0
pos_alpha_indices = np.where(attributions >= 0)[0]
num_age17_pos_alpha = np.where(
X_train[pos_alpha_indices][:, args.age_ndx] <= 17)[0].shape[0]
logger.info('no. instances with age <= 17 and alpha >= 0: {:,}'.format(
num_age17_pos_alpha))
# 10. no. train instances with age <= 17, similarity > thershold and an alpha coefficient < 0
s = 'no. instances with age <= 17, sim > {:.2f} and alpha >= 0: {:,}'
neg_alpha_indices = np.where((attributions < 0)
& (sim > args.sim_thresh))[0]
num_age17_sim_neg_alpha = np.where(
X_train[neg_alpha_indices][:, args.age_ndx] <= 17)[0].shape[0]
logger.info(s.format(args.sim_thresh, num_age17_sim_neg_alpha))
# 11. no. train instances with age <= 17, similarity > thershold and an alpha coefficient >= 0
s = 'no. instances with age <= 17, sim > {:.2f} and alpha >= 0: {:,}'
pos_alpha_indices = np.where((attributions >= 0)
& (sim > args.sim_thresh))[0]
num_age17_sim_pos_alpha = np.where(
X_train[pos_alpha_indices][:, args.age_ndx] <= 17)[0].shape[0]
logger.info(s.format(args.sim_thresh, num_age17_sim_pos_alpha))
# display total time
logger.info('\ntotal time: {:.3f}s'.format(time.time() - begin))
def experiment(args, logger, out_dir):
"""
Main method that removes training instances ordered by
different methods and measure their impact on a random
set of test instances.
"""
# start timer
begin = time.time()
# create random number generator
rng = np.random.default_rng(args.rs)
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# train a tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
util.performance(model, X_test, y_test, logger=logger, name='Test')
# compute loss of each test instance
proba = model.predict_proba(X_test)
losses = np.abs([proba[i][1] - y_test[i] for i in range(proba.shape[0])])
# select instances with an L1 loss >= 0.9
test_indices = np.where(losses >= 0.9)[0]
n_pos = y_test[test_indices].sum()
logger.info(
'\nNo. test instances w/ L1 loss >= 0.9: {:,}, no. pos.: {:,}'.format(
len(test_indices), n_pos))
for i, ndx in enumerate(test_indices[:args.n_test]):
logger.info('\n\n[#{:,}] Test {}, loss: {:.3f}'.format(
i + 1, ndx, losses[ndx]))
X_test_sub = X_test[[ndx]]
y_test_sub = y_test[[ndx]]
instance_dir = os.path.join(out_dir, 'test_{}'.format(i))
os.makedirs(instance_dir, exist_ok=True)
# display dataset statistics
logger.info('\nno. train instances: {:,}'.format(X_train.shape[0]))
logger.info('no. test instances: {:,}'.format(X_test_sub.shape[0]))
logger.info('no. features: {:,}'.format(X_train.shape[1]))
# sort train instances, then remove, retrain, and re-evaluate
result = measure_performance(args,
clf,
X_train,
y_train,
X_test_sub,
y_test_sub,
rng,
instance_dir,
logger=logger)
# save results
result['max_rss'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result['total_time'] = time.time() - begin
np.save(os.path.join(instance_dir, 'results.npy'), result)
# display results
logger.info('\nResults:\n{}'.format(result))
logger.info('\nsaving results to {}...'.format(
os.path.join(instance_dir, 'results.npy')))
def experiment(args, logger, out_dir):
# start timer
begin = time.time()
# get data
data = util.get_data(args.dataset,
data_dir=args.data_dir,
preprocessing=args.preprocessing)
X_train, X_test, y_train, y_test, feature, cat_indices = data
logger.info('\ntrain instances: {:,}'.format(X_train.shape[0]))
logger.info('test instances: {:,}'.format(X_test.shape[0]))
logger.info('no. features: {:,}'.format(X_train.shape[1]))
# get tree-ensemble
clf = util.get_model(args.model,
n_estimators=args.n_estimators,
max_depth=args.max_depth,
random_state=args.rs,
cat_indices=cat_indices)
# train a tree ensemble
model = clone(clf).fit(X_train, y_train)
util.performance(model, X_train, y_train, logger=logger, name='Train')
util.performance(model, X_test, y_test, logger=logger, name='Test')
# store indexes of different subgroups
train_neg = np.where(y_train == 0)[0]
train_pos = np.where(y_train == 1)[0]
# test_neg = np.where(y_test == 0)[0]
# test_pos = np.where(y_test == 1)[0]
# transform features to tree kernel space
logger.info('\ntransforming features into tree kernel space...')
extractor = trex.TreeExtractor(model, tree_kernel=args.tree_kernel)
start = time.time()
X_train_alt = extractor.transform(X_train)
logger.info('train transform time: {:.3f}s'.format(time.time() - start))
start = time.time()
X_test_alt = extractor.transform(X_test)
logger.info('test transform time: {:.3f}s'.format(time.time() - start))
# reduce dimensionality on original and tree feature spaces
logger.info('\nembed original features into a lower dimensional space')
X_train, X_test = reduce_and_embed(args, X_train, X_test, logger)
logger.info('\nembed tree kernel features into a lower dimensional space')
X_train_alt, X_test_alt = reduce_and_embed(args, X_train_alt, X_test_alt,
logger)
# separating embedded points into train and test
# n_train = len(y_train)
# train_neg_embed = X_embed[:n_train][train_neg]
# train_pos_embed = X_embed[:n_train][train_pos]
# test_neg_embed = X_embed[n_train:][test_neg]
# test_pos_embed = X_embed[n_train:][test_pos]
# save original feature space results
np.save(os.path.join(out_dir, 'train_negative'), X_train[train_neg])
np.save(os.path.join(out_dir, 'train_positive'), X_train[train_pos])
# save tree kenel space results
np.save(os.path.join(out_dir, 'train_tree_negative'),
X_train_alt[train_neg])
np.save(os.path.join(out_dir, 'train_tree_positive'),
X_train_alt[train_pos])
