def main(argv):
# rma, drug, stress = load_data()
rma, label = data.load_mdd_data()
alpha_range = np.logspace(-2, 7, 10)
l1_ratio_range = np.arange(0., 1., 0.1)
en_param_grid = dict(alpha=alpha_range, l1_ratio=l1_ratio_range)
c_range = np.logspace(-2, 7, 10)
gamma_range = np.logspace(-6, 3, 10)
svm_param_grid = dict(gamma=gamma_range, C=c_range)
logit_param_grid = dict(C=c_range)
test_size = float(argv[1])
n_iter = int(argv[2])
n_folds = int(argv[3])
target = argv[4]
classifier = argv[5]
pca_components = int(argv[7])
log = {
'target': target,
'std_select': {
'n_feat': pca_components
},
'split': {
'type': 'StratifiedShuffleSplit',
'n_iter': n_iter,
'test_size': test_size
},
'cross_val': {
'n_folds': n_folds
},
'classifier': classifier
}
# if target == 'drug':
# target = drug
# else:
# target = stress
# pca = PCA(n_components=pca_components)
pca = SelectStd(n_features=pca_components)
if classifier == 'svm':
clf = SVC()
param_grid = svm_param_grid
grid_search = True
elif classifier == 'en':
clf = SGDClassifier(loss='log', penalty='elasticnet', n_jobs=1)
param_grid = en_param_grid
grid_search = True
elif classifier == 'logit':
clf = LogisticRegression()
param_grid = logit_param_grid
grid_search = True
timer = Timer()
print('\nStarting...' + ' '.join(argv))
pprint(log)
split = StratifiedShuffleSplit(label[target],
n_iter=n_iter,
test_size=test_size)
if grid_search:
clf = GridSearchCV(clf, param_grid=param_grid, cv=n_folds, n_jobs=1)
pipeline = Pipeline([('pca', pca), ('clf', clf)])
accuracy = cross_val_score(pipeline,
rma,
y=label[target],
scoring='accuracy',
cv=split,
n_jobs=n_iter,
verbose=1)
print('\n{}: Accuracy: {:.2%} +/- {:.2%}'.format(timer.elapsed(),
np.nanmean(accuracy),
np.nanstd(accuracy)))
log['results'] = {
'accuracy': {
'scores': accuracy.tolist(),
'mean': accuracy.mean(),
'std': accuracy.std()
}
}
log['time'] = timer.elapsed()
# results = [dict(log, accuracy=acc) for acc in accuracy]
# log_results(results)
# save_results(results, folder=argv[6], filename='results_new.json')
save_experiment(log, folder=argv[6])
def main(argv):
# rma, drug, stress = load_data()
rma, label = data.load_mdd_data()
alpha_range = np.logspace(-2, 7, 10)
l1_ratio_range = np.arange(0.0, 1.0, 0.1)
en_param_grid = dict(alpha=alpha_range, l1_ratio=l1_ratio_range)
c_range = np.logspace(-2, 7, 10)
gamma_range = np.logspace(-6, 3, 10)
svm_param_grid = dict(gamma=gamma_range, C=c_range)
logit_param_grid = dict(C=c_range)
test_size = float(argv[1])
n_iter = int(argv[2])
n_folds = int(argv[3])
target = argv[4]
classifier = argv[5]
pca_components = int(argv[7])
log = {
"target": target,
"pca": {"n_components": pca_components},
"split": {"type": "StratifiedShuffleSplit", "n_iter": n_iter, "test_size": test_size},
"cross_val": {"n_folds": n_folds},
"classifier": classifier,
}
# if target == 'drug':
# target = drug
# else:
# target = stress
pca = PCA(n_components=pca_components)
if classifier == "svm":
clf = SVC()
param_grid = svm_param_grid
grid_search = True
elif classifier == "en":
clf = SGDClassifier(loss="log", penalty="elasticnet", n_jobs=1)
param_grid = en_param_grid
grid_search = True
elif classifier == "logit":
clf = LogisticRegression()
param_grid = logit_param_grid
grid_search = True
timer = Timer()
print("\nStarting..." + " ".join(argv))
pprint(log)
split = StratifiedShuffleSplit(label[target], n_iter=n_iter, test_size=test_size)
if grid_search:
clf = GridSearchCV(clf, param_grid=param_grid, cv=n_folds, n_jobs=1)
pipeline = Pipeline([("pca", pca), ("clf", clf)])
accuracy = cross_val_score(pipeline, rma, y=label[target], scoring="accuracy", cv=split, n_jobs=n_iter, verbose=1)
print("\n{}: Accuracy: {:.2%} +/- {:.2%}".format(timer.elapsed(), np.nanmean(accuracy), np.nanstd(accuracy)))
log["results"] = {"accuracy": {"scores": accuracy.tolist(), "mean": accuracy.mean(), "std": accuracy.std()}}
log["time"] = timer.elapsed()
# results = [dict(log, accuracy=acc) for acc in accuracy]
# log_results(results)
# save_results(results, folder=argv[6], filename='results_new.json')
save_experiment(log, folder=argv[6])
print(result)
if args.data == 'epi_ad':
betas, factors = load_epi_ad_data(log=result, verbose=args.verbose)
elif args.data == 'mdd':
betas, factors = load_mdd_data(log=result, verbose=args.verbose)
else:
result['error'] = '{} not a valid dataset.'.format(args.tissue)
if args.verbose:
print('{} not a valid dataset.'.format(args.tissue))
if args.tissue is not None:
condition = factors['source tissue'] == args.tissue
if condition.sum() == 0:
result['error'] = '{} is not a valid tissue.'.format(args.tissue)
save_experiment(result, folder=args.results_path, filename=None, error=True,
verbose=args.verbose)
sys.exit()
betas = betas[condition]
factors = factors[condition]
target = factors[args.target]
split = StratifiedShuffleSplit(target, n_iter=args.n_iter, test_size=args.test_size)
result['split'] = {
'type': 'StratifiedShuffleSplit',
'n_iter': args.n_iter,
'test_size': args.test_size
}
result['cross_val'] = {'n_folds': args.n_folds}
if args.data == 'epi_ad':
betas, factors = load_epi_ad_data(log=result, verbose=args.verbose)
elif args.data == 'mdd':
betas, factors = load_mdd_data(log=result, verbose=args.verbose)
else:
result['error'] = '{} not a valid dataset.'.format(args.tissue)
if args.verbose:
print('{} not a valid dataset.'.format(args.tissue))
if args.tissue is not None:
condition = factors['source tissue'] == args.tissue
if condition.sum() == 0:
result['error'] = '{} is not a valid tissue.'.format(args.tissue)
save_experiment(result,
folder=args.results_path,
filename=None,
error=True,
verbose=args.verbose)
sys.exit()
betas = betas[condition]
factors = factors[condition]
target = factors[args.target]
split = StratifiedShuffleSplit(target,
n_iter=args.n_iter,
test_size=args.test_size)
result['split'] = {
'type': 'StratifiedShuffleSplit',
'n_iter': args.n_iter,
'test_size': args.test_size