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0.8.x (to be cp to master as well) PLS -- use string comparisons (instead of identity checking) and few spell fixes #164

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Merged
merged 2 commits into from
May 12, 2011
Merged

0.8.x (to be cp to master as well) PLS -- use string comparisons (instead of identity checking) and few spell fixes #164

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47cbfbf
FIX: strings are not necessarily singletones + catch mistakes earlier
yarikoptic May 12, 2011
bbbe225
DOC: minor spellings fixes in pls.py
yarikoptic May 12, 2011
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29 changes: 16 additions & 13 deletions scikits/learn/pls.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06):
# Inner loop of the Wold algo.
while True:
# 1.1 Update u: the X weights
if mode is "B":
if mode == "B":
if X_pinv is None:
X_pinv = linalg.pinv(X) # compute once pinv(X)
u = np.dot(X_pinv, y_score)
Expand All @@ -38,7 +38,7 @@ def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06):
x_score = np.dot(X, u)

# 2.1 Update v: the Y weights
if mode is "B":
if mode == "B":
if Y_pinv is None:
Y_pinv = linalg.pinv(Y) # compute once pinv(Y)
v = np.dot(Y_pinv, x_score)
Expand Down Expand Up @@ -95,16 +95,16 @@ def _center_scale_xy(X, Y, scale=True):
class _PLS(BaseEstimator):
"""Partial Least Square (PLS)

We use the therminology defined by [Wegelin et al. 2000].
We use the terminology defined by [Wegelin et al. 2000].
This implementation uses the PLS Wold 2 blocks algorithm or NIPALS which is
based on two nested loops:
(i) The outer loop iterate over compoments.
(i) The outer loop iterate over components.
(ii) The inner loop estimates the loading vectors. This can be done
with two algo. (a) the inner loop of the original NIPALS algo or (b) a
SVD on residuals cross-covariance matrices.

This implementation provides:
- PLS regression, ie., PLS 2 blocks, mode A, with asymetric deflation.
- PLS regression, ie., PLS 2 blocks, mode A, with asymmetric deflation.
A.k.a. PLS2, with multivariate response or PLS1 with univariate response.
- PLS canonical, ie., PLS 2 blocks, mode A, with symetric deflation.
- CCA, ie., PLS 2 blocks, mode B, with symetric deflation.
Expand Down Expand Up @@ -167,7 +167,7 @@ class _PLS(BaseEstimator):
Y block to latents rotations.

coefs: array, [p, q]
The coeficients of the linear model: Y = X coefs + Err
The coefficients of the linear model: Y = X coefs + Err

References
----------
Expand Down Expand Up @@ -227,7 +227,7 @@ def fit(self, X, Y, **params):
'has %s' % (X.shape[0], Y.shape[0]))
if self.n_components < 1 or self.n_components > p:
raise ValueError('invalid number of components')
if self.algorithm is "svd" and self.mode is "B":
if self.algorithm == "svd" and self.mode == "B":
raise ValueError('Incompatible configuration: mode B is not '
'implemented with svd algorithm')
if not self.deflation_mode in ["canonical", "regression"]:
Expand All @@ -250,12 +250,15 @@ def fit(self, X, Y, **params):
for k in xrange(self.n_components):
#1) weights estimation (inner loop)
# -----------------------------------
if self.algorithm is "nipals":
if self.algorithm == "nipals":
u, v = _nipals_twoblocks_inner_loop(
X=Xk, Y=Yk, mode=self.mode,
max_iter=self.max_iter, tol=self.tol)
if self.algorithm is "svd":
elif self.algorithm == "svd":
u, v = _svd_cross_product(X=Xk, Y=Yk)
else:
raise ValueError("Got algorithm %s when only 'svd' "
"and 'nipals' are known" % self.algorithm)
# compute scores
x_score = np.dot(Xk, u)
y_score = np.dot(Yk, v)
Expand All @@ -273,11 +276,11 @@ def fit(self, X, Y, **params):
x_loadings = np.dot(Xk.T, x_score) / np.dot(x_score.T, x_score)
# - substract rank-one approximations to obtain remainder matrix
Xk -= np.dot(x_score, x_loadings.T)
if self.deflation_mode is "canonical":
if self.deflation_mode == "canonical":
# - regress Yk's on y_score, then substract rank-one approx.
y_loadings = np.dot(Yk.T, y_score) / np.dot(y_score.T, y_score)
Yk -= np.dot(y_score, y_loadings.T)
if self.deflation_mode is "regression":
if self.deflation_mode == "regression":
# - regress Yk's on x_score, then substract rank-one approx.
y_loadings = np.dot(Yk.T, x_score) / np.dot(x_score.T, x_score)
Yk -= np.dot(x_score, y_loadings.T)
Expand All @@ -301,8 +304,8 @@ def fit(self, X, Y, **params):
else:
self.y_rotations_ = np.ones(1)

if True or self.deflation_mode is "regression":
# Estimate regression coeficient
if True or self.deflation_mode == "regression":
# Estimate regression coefficient
# Regress Y on T
# Y = TQ' + Err,
# Then express in function of X
Expand Down