def main():
parser = argparse.ArgumentParser()
parser.add_argument('--results-path', default='./bucket/results/')
parser.add_argument('--data')
parser.add_argument('--target')
parser.add_argument('--data-path', default='./bucket/data/')
parser.add_argument('--verbose', '-v', action='count')
parser.add_argument('--test-size', type=float, default=0.1)
parser.add_argument('--n-iter', type=int, default=1)
parser.add_argument('--n-folds', default=10, type=n_folds_parser)
parser.add_argument('--clf')
args = parser.parse_args()
result = {}
result.update(args.__dict__)
if args.verbose:
for key in result:
print('# {}: {}'.format(key, result[key]))
print('# Start: ' + datetime.now().strftime("%d/%m/%y %H:%M:%S"))
result['selections'] = []
experiment_id = hash(json.dumps(result) + str(np.random.rand(10)))
result_file = join(args.results_path, 'fs_{}.json'.format(experiment_id))
if args.verbose:
print('Results will be saved to {}'.format(result_file))
data, factors = load(args.data, data_path=args.data_path, log=result)
target = factors[args.target]
clf, param_grid = choose_classifier(args.clf, result, args.verbose)
feature_names = data.columns
split = StratifiedShuffleSplit(target,
n_iter=args.n_iter,
test_size=args.test_size)
n_features = data.shape[1]
n_features_to_select = 1
support_ = np.ones(n_features, dtype=np.bool)
ranking_ = np.ones(n_features, dtype=np.int)
# Elimination
t0 = time()
d0 = datetime.now()
while np.sum(support_) > n_features_to_select:
step = 10**int(np.log10(np.sum(support_) - 1))
odd_step = np.sum(support_) - step * (np.sum(support_) // step)
if odd_step > 0:
step = odd_step
if args.verbose:
print('[{}] Selecting best {:d} features.'.format(
datetime.now() - d0,
np.sum(support_) - step))
# Remaining features
features = np.arange(n_features)[support_]
coef_ = None
test_scores = []
for train, test in split:
# Rank the remaining features
if args.n_folds == 'loo':
cv = LeaveOneOut(len(train))
else:
cv = args.n_folds
estimator = GridWithCoef(clf, param_grid, cv=cv)
estimator.fit(data.iloc[train, features], target.iloc[train])
if coef_ is None:
coef_ = safe_sqr(estimator.coef_)
else:
coef_ += safe_sqr(estimator.coef_)
test_scores.append(
estimator.score(data.iloc[test, features], target.iloc[test]))
if coef_.ndim > 1:
ranks = np.argsort(coef_.sum(axis=0))
else:
ranks = np.argsort(coef_)
# for sparse case ranks is matrix
ranks = np.ravel(ranks)
# Eliminate the worse features
threshold = min(step, np.sum(support_) - n_features_to_select)
support_[features[ranks][:threshold]] = False
ranking_[np.logical_not(support_)] += 1
result['selections'].append({
'scores':
test_scores,
'n_features':
np.sum(support_),
'features':
feature_names[support_].tolist()
})
with open(result_file, 'w') as f:
json.dump(result,
f,
sort_keys=True,
indent=2,
separators=(',', ': '))
if args.verbose:
print('# OK')
all_features = mrmr_results['subsets'][-1]['features']
result['experiments'] = [{
'iteration': 0,
'subsets': []
}]
d0 = datetime.now()
current_size = 10
while current_size <= max_features:
if args.verbose:
print('[{}] Fitting with {} features.'.format(datetime.now() - d0, current_size))
sys.stdout.flush()
features = all_features[:current_size]
grid = GridWithCoef(clf, param_grid, cv=args.n_folds)
grid.fit(train_data.iloc[:, features], train_target)
# Save results for current set of features
result['experiments'][-1]['subsets'].append({
'n_features': current_size,
'features': data.columns[features].tolist(),
'best_params': grid.best_params_,
'train': {
'y_true': train_target.tolist(),
'y_pred': grid.predict(train_data.iloc[:, features]).tolist()
},
'test': {
'y_true': test_target.tolist(),
'y_pred': grid.predict(test_data.iloc[:, features]).tolist()
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--results-path', default='./bucket/results/')
parser.add_argument('--data')
parser.add_argument('--target')
parser.add_argument('--tissue',
type=lambda x: re.sub(r'[\"\']', '', x) if x is not None else None)
parser.add_argument('--data-path', default='./bucket/data/')
parser.add_argument('--verbose', '-v', action='count')
parser.add_argument('--test-size', type=float, default=0.1)
parser.add_argument('--n-iter', type=int, default=1)
parser.add_argument('--n-folds', default=10, type=n_folds_parser)
parser.add_argument('--clf')
parser.add_argument('--filter', default='anova')
args = parser.parse_args()
result = {}
result.update(args.__dict__)
start_time = datetime.now().strftime("%d/%m/%y %H:%M:%S")
result['start_time'] = start_time
if args.verbose:
for key in result:
print('# {}: {}'.format(key, result[key]))
print('# Running in: ' + gethostname())
print('# Start: ' + start_time)
sys.stdout.flush()
load_params = {}
if args.data == 'epi_ad':
load_params = {'read_original': True, 'skip_pickle': True}
data, factors = load(args.data, data_path=args.data_path, log=result, **load_params)
if args.tissue:
data = data[factors['source tissue'] == args.tissue]
factors = factors[factors['source tissue'] == args.tissue]
target = factors[args.target]
target_num = LabelEncoder().fit_transform(target)
clf, param_grid = choose_classifier(args.clf, result, args.verbose)
score_params = {}
preprocessor = None
if args.filter == 'anova':
score_features = anova
elif args.filter == 'infogain_10':
score_features = relevance
score_params = {'bins': 10}
elif args.filter == 'infogain_exp':
preprocessor = ExpressionDiscretizer()
score_features = relevance
score_params = {'bins': 3}
elif args.filter == 'chi2':
preprocessor = ExpressionDiscretizer()
score_features = chi_squared
else:
raise ValueError('Filter {} unknown.'.format(args.filter))
experiment_id = hash(json.dumps(result) + str(np.random.rand(10, 1)))
result_file = join(args.results_path, '{}_{}.json'.format(args.filter, experiment_id))
if args.verbose:
print('Results will be saved to {}'.format(result_file))
sys.stdout.flush()
split = StratifiedShuffleSplit(target, n_iter=args.n_iter, test_size=args.test_size)
n_features = data.shape[1]
n_features_to_select = 9
d0 = datetime.now()
result['experiments'] = []
for i, (train, test) in enumerate(split):
if args.verbose:
print('### ITERATION {}'.format(i))
if preprocessor:
preprocessor.fit(data.iloc[train, :])
train_data = preprocessor.transform(data.iloc[train, :])
test_data = preprocessor.transform(data.iloc[test, :])
else:
train_data = data.iloc[train, :]
test_data = data.iloc[test, :]
scores_ = score_features(train_data, target_num[train], **score_params)
result['experiments'].append({
'iteration': i,
'train_samples_label': data.index[train].tolist(),
'train_samples_idx': train.tolist(),
'scores': scores_.tolist()
})
if args.verbose:
print('[{}] Features scored.'.format(datetime.now() - d0))
sys.stdout.flush()
result['experiments'][-1]['subsets'] = []
current_size = n_features
sorted_features = np.argsort(scores_)
for step in subset_sizes(n_features, n_features_to_select):
if args.verbose:
print('[{}] Fitting with {} features.'.format(datetime.now() - d0, current_size))
sys.stdout.flush()
features = sorted_features[-current_size:]
grid = GridWithCoef(clf, param_grid, cv=args.n_folds)
grid.fit(train_data.iloc[:, features], target.iloc[train])
# Save results for current set of features
result['experiments'][-1]['subsets'].append({
'n_features': current_size,
'features': data.columns[features].tolist(),
'best_params': grid.best_params_,
'train': {
'y_true': target.iloc[train].tolist(),
'y_pred': grid.predict(train_data.iloc[:, features]).tolist()
},
'test': {
'y_true': target.iloc[test].tolist(),
'y_pred': grid.predict(test_data.iloc[:, features]).tolist()
}
})
# Store results
with open(result_file, 'w') as f:
json.dump(result, f, sort_keys=True, indent=2, separators=(',', ': '))
current_size -= step
if args.verbose:
print('# OK')
sys.stdout.flush()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--results-path', default='./bucket/results/')
parser.add_argument('--data')
parser.add_argument('--target')
parser.add_argument('--data-path', default='./bucket/data/')
parser.add_argument('--verbose', '-v', action='count')
parser.add_argument('--test-size', type=float, default=0.1)
parser.add_argument('--n-iter', type=int, default=1)
parser.add_argument('--n-folds', default=10, type=n_folds_parser)
parser.add_argument('--clf')
args = parser.parse_args()
result = {}
result.update(args.__dict__)
if args.verbose:
for key in result:
print('# {}: {}'.format(key, result[key]))
print('# Start: ' + datetime.now().strftime("%d/%m/%y %H:%M:%S"))
result['selections'] = []
experiment_id = hash(json.dumps(result) + str(np.random.rand(10)))
result_file = join(args.results_path, 'fs_{}.json'.format(experiment_id))
if args.verbose:
print('Results will be saved to {}'.format(result_file))
data, factors = load(args.data, data_path=args.data_path, log=result)
target = factors[args.target]
clf, param_grid = choose_classifier(args.clf, result, args.verbose)
feature_names = data.columns
split = StratifiedShuffleSplit(target, n_iter=args.n_iter, test_size=args.test_size)
n_features = data.shape[1]
n_features_to_select = 1
support_ = np.ones(n_features, dtype=np.bool)
ranking_ = np.ones(n_features, dtype=np.int)
# Elimination
t0 = time()
d0 = datetime.now()
while np.sum(support_) > n_features_to_select:
step = 10 ** int(np.log10(np.sum(support_) - 1))
odd_step = np.sum(support_) - step * (np.sum(support_) // step)
if odd_step > 0:
step = odd_step
if args.verbose:
print('[{}] Selecting best {:d} features.'
.format(datetime.now() - d0, np.sum(support_) - step))
# Remaining features
features = np.arange(n_features)[support_]
coef_ = None
test_scores = []
for train, test in split:
# Rank the remaining features
if args.n_folds == 'loo':
cv = LeaveOneOut(len(train))
else:
cv = args.n_folds
estimator = GridWithCoef(clf, param_grid, cv=cv)
estimator.fit(data.iloc[train, features], target.iloc[train])
if coef_ is None:
coef_ = safe_sqr(estimator.coef_)
else:
coef_ += safe_sqr(estimator.coef_)
test_scores.append(estimator.score(data.iloc[test, features], target.iloc[test]))
if coef_.ndim > 1:
ranks = np.argsort(coef_.sum(axis=0))
else:
ranks = np.argsort(coef_)
# for sparse case ranks is matrix
ranks = np.ravel(ranks)
# Eliminate the worse features
threshold = min(step, np.sum(support_) - n_features_to_select)
support_[features[ranks][:threshold]] = False
ranking_[np.logical_not(support_)] += 1
result['selections'].append({
'scores': test_scores,
'n_features': np.sum(support_),
'features': feature_names[support_].tolist()
})
with open(result_file, 'w') as f:
json.dump(result, f, sort_keys=True, indent=2, separators=(',', ': '))
if args.verbose:
print('# OK')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--results-path', default='./bucket/results/')
parser.add_argument('--data')
parser.add_argument('--target')
parser.add_argument('--tissue',
type=lambda x: re.sub(r'[\"\']', '', x)
if x is not None else None)
parser.add_argument('--data-path', default='./bucket/data/')
parser.add_argument('--verbose', '-v', action='count')
parser.add_argument('--test-size', type=float, default=0.1)
parser.add_argument('--n-iter', type=int, default=1)
parser.add_argument('--n-folds', default=10, type=n_folds_parser)
parser.add_argument('--clf')
parser.add_argument('--filter', default='anova')
args = parser.parse_args()
result = {}
result.update(args.__dict__)
start_time = datetime.now().strftime("%d/%m/%y %H:%M:%S")
result['start_time'] = start_time
if args.verbose:
for key in result:
print('# {}: {}'.format(key, result[key]))
print('# Running in: ' + gethostname())
print('# Start: ' + start_time)
sys.stdout.flush()
load_params = {}
if args.data == 'epi_ad':
load_params = {'read_original': True, 'skip_pickle': True}
data, factors = load(args.data,
data_path=args.data_path,
log=result,
**load_params)
if args.tissue:
data = data[factors['source tissue'] == args.tissue]
factors = factors[factors['source tissue'] == args.tissue]
target = factors[args.target]
target_num = LabelEncoder().fit_transform(target)
clf, param_grid = choose_classifier(args.clf, result, args.verbose)
score_params = {}
preprocessor = None
if args.filter == 'anova':
score_features = anova
elif args.filter == 'infogain_10':
score_features = relevance
score_params = {'bins': 10}
elif args.filter == 'infogain_exp':
preprocessor = ExpressionDiscretizer()
score_features = relevance
score_params = {'bins': 3}
elif args.filter == 'chi2':
preprocessor = ExpressionDiscretizer()
score_features = chi_squared
else:
raise ValueError('Filter {} unknown.'.format(args.filter))
experiment_id = hash(json.dumps(result) + str(np.random.rand(10, 1)))
result_file = join(args.results_path,
'{}_{}.json'.format(args.filter, experiment_id))
if args.verbose:
print('Results will be saved to {}'.format(result_file))
sys.stdout.flush()
split = StratifiedShuffleSplit(target,
n_iter=args.n_iter,
test_size=args.test_size)
n_features = data.shape[1]
n_features_to_select = 9
d0 = datetime.now()
result['experiments'] = []
for i, (train, test) in enumerate(split):
if args.verbose:
print('### ITERATION {}'.format(i))
if preprocessor:
preprocessor.fit(data.iloc[train, :])
train_data = preprocessor.transform(data.iloc[train, :])
test_data = preprocessor.transform(data.iloc[test, :])
else:
train_data = data.iloc[train, :]
test_data = data.iloc[test, :]
scores_ = score_features(train_data, target_num[train], **score_params)
result['experiments'].append({
'iteration':
i,
'train_samples_label':
data.index[train].tolist(),
'train_samples_idx':
train.tolist(),
'scores':
scores_.tolist()
})
if args.verbose:
print('[{}] Features scored.'.format(datetime.now() - d0))
sys.stdout.flush()
result['experiments'][-1]['subsets'] = []
current_size = n_features
sorted_features = np.argsort(scores_)
for step in subset_sizes(n_features, n_features_to_select):
if args.verbose:
print('[{}] Fitting with {} features.'.format(
datetime.now() - d0, current_size))
sys.stdout.flush()
features = sorted_features[-current_size:]
grid = GridWithCoef(clf, param_grid, cv=args.n_folds)
grid.fit(train_data.iloc[:, features], target.iloc[train])
# Save results for current set of features
result['experiments'][-1]['subsets'].append({
'n_features':
current_size,
'features':
data.columns[features].tolist(),
'best_params':
grid.best_params_,
'train': {
'y_true': target.iloc[train].tolist(),
'y_pred': grid.predict(train_data.iloc[:,
features]).tolist()
},
'test': {
'y_true': target.iloc[test].tolist(),
'y_pred': grid.predict(test_data.iloc[:,
features]).tolist()
}
})
# Store results
with open(result_file, 'w') as f:
json.dump(result,
f,
sort_keys=True,
indent=2,
separators=(',', ': '))
current_size -= step
if args.verbose:
print('# OK')
sys.stdout.flush()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--results-path', default='./bucket/results/')
parser.add_argument('--data')
parser.add_argument('--tissue',
type=lambda x: re.sub(r'[\"\']', '', x)
if x is not None else None)
parser.add_argument('--target')
parser.add_argument('--data-path', default='./bucket/data/')
parser.add_argument('--verbose', '-v', action='count')
parser.add_argument('--test-size', type=float, default=0.1)
parser.add_argument('--n-iter', type=int, default=1)
parser.add_argument('--n-folds', default=10, type=n_folds_parser)
parser.add_argument('--clf')
args = parser.parse_args()
result = {}
result.update(args.__dict__)
start_time = datetime.now().strftime("%d/%m/%y %H:%M:%S")
result['start_time'] = start_time
if args.verbose:
for key in result:
print('# {}: {}'.format(key, result[key]))
print('# Running in: ' + gethostname())
print('# Start: ' + start_time)
experiment_id = hash(json.dumps(result) + str(np.random.rand(10, 1)))
result_file = join(args.results_path, 'rfe_{}.json'.format(experiment_id))
if args.verbose:
print('Results will be saved to {}'.format(result_file))
load_params = {}
if args.data == 'epi_ad':
load_params = {'read_original': True, 'skip_pickle': True}
data, factors = load(args.data,
data_path=args.data_path,
log=result,
**load_params)
if args.tissue:
data = data[factors['source tissue'] == args.tissue]
factors = factors[factors['source tissue'] == args.tissue]
target = factors[args.target]
clf, param_grid = choose_classifier(args.clf, result, args.verbose)
split = StratifiedShuffleSplit(target,
n_iter=args.n_iter,
test_size=args.test_size)
n_features = data.shape[1]
n_features_to_select = 9
preprocess_steps = [('scaler', StandardScaler())]
# RFE
d0 = datetime.now()
result['experiments'] = []
for i, (train, test) in enumerate(split):
if args.verbose:
print('### ITERATION {}'.format(i))
result['experiments'].append({
'iteration':
i,
'train_samples':
data.index[train].tolist(),
'subsets': []
})
support_ = np.ones(n_features, dtype=np.bool)
ranking_ = np.ones(n_features, dtype=np.int)
for step in subset_sizes(n_features, n_features_to_select):
if args.verbose:
print('[{}] Evaluating with {} features and selecting {}.'.
format(datetime.now() - d0, np.sum(support_),
np.sum(support_) - step))
# Train with current subset
pipeline = preprocess_steps + [
('grid', GridWithCoef(clf, param_grid, cv=args.n_folds))
]
pipeline = Pipeline(pipeline)
features = np.arange(n_features)[support_]
pipeline.fit(data.iloc[train, features], target.iloc[train])
# Save results for current set of features
grid = pipeline.steps[-1][1]
result['experiments'][-1]['subsets'].append({
'n_features':
np.sum(support_),
'features':
data.columns[features].tolist(),
'best_params':
grid.best_params_,
'train': {
'y_true': target.iloc[train].tolist(),
'y_pred': grid.predict(data.iloc[train,
features]).tolist()
},
'test': {
'y_true': target.iloc[test].tolist(),
'y_pred': grid.predict(data.iloc[test, features]).tolist()
}
})
# Select best subset
coef_ = safe_sqr(grid.coef_)
if coef_.ndim > 1:
ranks = np.argsort(coef_.sum(axis=0))
else:
ranks = np.argsort(coef_)
# for sparse case ranks is matrix
ranks = np.ravel(ranks)
# Eliminate the worse features
support_[features[ranks][:step]] = False
ranking_[np.logical_not(support_)] += 1
# Store results
with open(result_file, 'w') as f:
json.dump(result,
f,
sort_keys=True,
indent=2,
separators=(',', ': '))
if args.verbose:
print('# OK')