def loadFromFile(filename, dataset, verbose=False, run_pca=True, explain_rat=4., ret_var=False):
# TODO: arg check
tissue_name = os.path.basename(filename).split('.')[0]
tissue = Tissue(tissue_name, dataset)
tissue_file = open(filename, 'r')
patient_ids = tissue_file.readline().strip().split('\t')[4:]
for patient_id in patient_ids:
if patient_id not in dataset.patients:
patient = Patient(patient_id)
dataset.addPatient(patient)
patient = dataset.patients[patient_id]
patient.addTissue(tissue)
tissue._rows[patient_id] = tissue.numPatients
tissue._patients[patient_id] = patient
# print 'got patients'
raw_t = [[float(val_str)
for val_str in line.strip().split('\t')[4:]]
for line in tissue_file]
# print 'got data'
val = np.array(raw_t).T
var_exp = 0.
if run_pca:
pca_model = PCA(n_components=50, copy=False)
pca_model.fit_transform(val)
#cov = val.T.dot(val)/(len(raw_t))
#U, W, _ = np.linalg.svd(cov)
#cum_var = np.cumsum(W**2)
#cum_var = cum_var/cum_var[-1]
cum_var = np.cumsum(pca_model.explained_variance_ratio_)
explained_ratio = [float(cum_var[i])/float(i+1)
for i in range(len(cum_var))]
best_dim = 0
for dim in range(len(cum_var)):
if explained_ratio[dim]*len(patient_ids) > explain_rat:
best_dim = dim
n_components = best_dim+1
n_components = max(n_components, 8)
#val = val.dot(U[:,:n_components])
val = val[:,:n_components]
var_exp = cum_var[n_components-1]
if verbose:
print tissue_name + ' has {} components to explain {}% variance for {} patients'.format(n_components, 100.*var_exp, len(patient_ids))
elif verbose:
print tissue_name + ' parsed'
tissue._value = val
if ret_var:
return tissue, var_exp
else:
return tissue
