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Update rsrm.py to fit new scikit learn 0.24 requirements #492

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Merged
mihaic merged 1 commit into from
Feb 19, 2021
Merged

Update rsrm.py to fit new scikit learn 0.24 requirements #492

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14 changes: 7 additions & 7 deletions brainiak/funcalign/rsrm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,7 @@ class RSRM(BaseEstimator, TransformerMixin):
def __init__(self, n_iter=10, features=50, gamma=1.0, rand_seed=0):
self.n_iter = n_iter
self.features = features
self.lam = gamma
self.gamma = gamma
self.rand_seed = rand_seed

def fit(self, X):
Expand All @@ -123,7 +123,7 @@ def fit(self, X):
logger.info('Starting RSRM')

# Check that the regularizer value is positive
if 0.0 >= self.lam:
if 0.0 >= self.gamma:
raise ValueError("Gamma parameter should be positive.")

# Check the number of subjects
Expand Down Expand Up @@ -216,7 +216,7 @@ def _transform_new_data(self, X, subject):
R = None
for i in range(self.n_iter):
R = self.w_[subject].T.dot(X - S)
S = self._shrink(X - self.w_[subject].dot(R), self.lam)
S = self._shrink(X - self.w_[subject].dot(R), self.gamma)
return R, S

def transform_subject(self, X):
Expand Down Expand Up @@ -249,7 +249,7 @@ def transform_subject(self, X):
s = np.zeros_like(X)
for i in range(self.n_iter):
w = self._update_transform_subject(X, s, self.r_)
s = self._shrink(X - w.dot(self.r_), self.lam)
s = self._shrink(X - w.dot(self.r_), self.gamma)

return w, s

Expand Down Expand Up @@ -286,17 +286,17 @@ def _rsrm(self, X):
R = self._update_shared_response(X, S, W, features)

if logger.isEnabledFor(logging.INFO):
objective = self._objective_function(X, W, R, S, self.lam)
objective = self._objective_function(X, W, R, S, self.gamma)
logger.info('Objective function %f' % objective)

# Main loop
for i in range(self.n_iter):
W = self._update_transforms(X, S, R)
S = self._update_individual(X, W, R, self.lam)
S = self._update_individual(X, W, R, self.gamma)
R = self._update_shared_response(X, S, W, features)
# Print objective function every iteration
if logger.isEnabledFor(logging.INFO):
objective = self._objective_function(X, W, R, S, self.lam)
objective = self._objective_function(X, W, R, S, self.gamma)
logger.info('Objective function %f' % objective)

return W, R, S
Expand Down