class SparsePCAImpl():
def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01, max_iter=1000, tol=1e-08, method='lars', n_jobs=None, U_init=None, V_init=None, verbose=False, random_state=None, normalize_components=False):
self._hyperparams = {
'n_components': n_components,
'alpha': alpha,
'ridge_alpha': ridge_alpha,
'max_iter': max_iter,
'tol': tol,
'method': method,
'n_jobs': n_jobs,
'U_init': U_init,
'V_init': V_init,
'verbose': verbose,
'random_state': random_state,
'normalize_components': normalize_components}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def SPCA(model_data, components=None, transform_data=None):
t0 = time()
spca = SparsePCA(n_components=components)
if transform_data == None:
projection = spca.fit_transform(model_data)
else:
spca.fit(model_data)
projection = spca.transform(transform_data)
print "Sparse PCA Time: %0.3f" % (time() - t0)
return projection
def SPCA(model_data, components = None, transform_data = None):
t0 = time()
spca = SparsePCA(n_components=components)
if transform_data == None:
projection = spca.fit_transform(model_data)
else:
spca.fit(model_data)
projection = spca.transform(transform_data)
print "Sparse PCA Time: %0.3f" % (time() - t0)
return projection