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[MRG] Add Penalty factors for each coefficient in enet ( see #11566) #11671

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[MRG] Add Penalty factors for each coefficient in enet ( see #11566) #11671

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3e71c4b
Implement penalty.factor of glmnet R package to have different penalt…
doaa-altarawy Jul 24, 2018
dbeffd5
Add tests for l1_weights in enet (#11566)
doaa-altarawy Jul 24, 2018
9d4c12e
Fix flake8 and docstring errors
doaa-altarawy Jul 24, 2018
67764c8
Fix flake8, long line
doaa-altarawy Jul 25, 2018
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2 changes: 1 addition & 1 deletion sklearn/covariance/graph_lasso_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -223,7 +223,7 @@ def graphical_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=1e-4,
coefs = -(precision_[indices != idx, idx]
/ (precision_[idx, idx] + 1000 * eps))
coefs, _, _, _ = cd_fast.enet_coordinate_descent_gram(
coefs, alpha, 0, sub_covariance,
coefs, alpha, 0, np.empty(0), sub_covariance,
row, row, max_iter, enet_tol,
check_random_state(None), False)
else:
Expand Down
28 changes: 20 additions & 8 deletions sklearn/linear_model/cd_fast.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -143,6 +143,7 @@ cdef extern from "cblas.h":
@cython.cdivision(True)
def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
floating alpha, floating beta,
np.ndarray[floating, ndim=1] l1_weights,
np.ndarray[floating, ndim=2, mode='fortran'] X,
np.ndarray[floating, ndim=1, mode='c'] y,
int max_iter, floating tol,
Expand Down Expand Up @@ -201,6 +202,8 @@ def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
cdef unsigned int f_iter
cdef UINT32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
cdef UINT32_t* rand_r_state = &rand_r_state_seed
cdef floating w_alpha = alpha
cdef unsigned int use_l1_weight = l1_weights.shape[0] > 0

cdef floating *X_data = <floating*> X.data
cdef floating *y_data = <floating*> y.data
Expand All @@ -224,6 +227,9 @@ def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
w_max = 0.0
d_w_max = 0.0
for f_iter in range(n_features): # Loop over coordinates
if use_l1_weight:
w_alpha = l1_weights[f_iter]

if random:
ii = rand_int(n_features, rand_r_state)
else:
Expand All @@ -245,7 +251,7 @@ def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
if positive and tmp < 0:
w[ii] = 0.0
else:
w[ii] = (fsign(tmp) * fmax(fabs(tmp) - alpha, 0)
w[ii] = (fsign(tmp) * fmax(fabs(tmp) - w_alpha, 0)
/ (norm_cols_X[ii] + beta))

if w[ii] != 0.0:
Expand Down Expand Up @@ -284,8 +290,8 @@ def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
# w_norm2 = np.dot(w, w)
w_norm2 = dot(n_features, w_data, 1, w_data, 1)

if (dual_norm_XtA > alpha):
const = alpha / dual_norm_XtA
if (dual_norm_XtA > w_alpha):
const = w_alpha / dual_norm_XtA
A_norm2 = R_norm2 * (const ** 2)
gap = 0.5 * (R_norm2 + A_norm2)
else:
Expand All @@ -295,7 +301,7 @@ def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
l1_norm = asum(n_features, w_data, 1)

# np.dot(R.T, y)
gap += (alpha * l1_norm
gap += (w_alpha * l1_norm
- const * dot(n_samples, R_data, 1, y_data, n_tasks)
+ 0.5 * beta * (1 + const ** 2) * (w_norm2))

Expand Down Expand Up @@ -529,6 +535,7 @@ def sparse_enet_coordinate_descent(floating [:] w,
@cython.wraparound(False)
@cython.cdivision(True)
def enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
np.ndarray[floating, ndim=1] l1_weights,
np.ndarray[floating, ndim=2, mode='c'] Q,
np.ndarray[floating, ndim=1, mode='c'] q,
np.ndarray[floating, ndim=1] y,
Expand Down Expand Up @@ -581,6 +588,8 @@ def enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
cdef unsigned int f_iter
cdef UINT32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
cdef UINT32_t* rand_r_state = &rand_r_state_seed
cdef floating w_alpha = alpha
cdef unsigned int use_l1_weight = l1_weights.shape[0] > 0

cdef floating y_norm2 = np.dot(y, y)
cdef floating* w_ptr = <floating*>&w[0]
Expand All @@ -599,6 +608,9 @@ def enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
w_max = 0.0
d_w_max = 0.0
for f_iter in range(n_features): # Loop over coordinates
if use_l1_weight:
w_alpha = l1_weights[f_iter]

if random:
ii = rand_int(n_features, rand_r_state)
else:
Expand All @@ -619,7 +631,7 @@ def enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
if positive and tmp < 0:
w[ii] = 0.0
else:
w[ii] = fsign(tmp) * fmax(fabs(tmp) - alpha, 0) \
w[ii] = fsign(tmp) * fmax(fabs(tmp) - w_alpha, 0) \
/ (Q[ii, ii] + beta)

if w[ii] != 0.0:
Expand Down Expand Up @@ -659,16 +671,16 @@ def enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
# w_norm2 = np.dot(w, w)
w_norm2 = dot(n_features, &w[0], 1, &w[0], 1)

if (dual_norm_XtA > alpha):
const = alpha / dual_norm_XtA
if (dual_norm_XtA > w_alpha):
const = w_alpha / dual_norm_XtA
A_norm2 = R_norm2 * (const ** 2)
gap = 0.5 * (R_norm2 + A_norm2)
else:
const = 1.0
gap = R_norm2

# The call to dasum is equivalent to the L1 norm of w
gap += (alpha * asum(n_features, &w[0], 1) -
gap += (w_alpha * asum(n_features, &w[0], 1) -
const * y_norm2 + const * q_dot_w +
0.5 * beta * (1 + const ** 2) * w_norm2)

Expand Down
56 changes: 41 additions & 15 deletions sklearn/linear_model/coordinate_descent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,7 +140,7 @@ def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,

Where::

||W||_21 = \\sum_i \\sqrt{\\sum_j w_{ij}^2}
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}

i.e. the sum of norm of each row.

Expand Down Expand Up @@ -268,21 +268,21 @@ def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
precompute='auto', Xy=None, copy_X=True, coef_init=None,
verbose=False, return_n_iter=False, positive=False,
check_input=True, **params):
check_input=True, l1_weights=None, **params):
"""Compute elastic net path with coordinate descent

The elastic net optimization function varies for mono and multi-outputs.

For mono-output tasks it is::

1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ alpha * l1_ratio * l1_weights * ||w||_1
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I think the l1_weights should be inside the L1 norm: ||l1_weights*w||__1

+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2

For multi-output tasks it is::

(1 / (2 * n_samples)) * ||Y - XW||^Fro_2
+ alpha * l1_ratio * ||W||_21
+ alpha * l1_ratio * l1_weights * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2

Where::
Expand Down Expand Up @@ -347,6 +347,12 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
Skip input validation checks, including the Gram matrix when provided
assuming there are handled by the caller when check_input=False.

l1_weights : array, shape (n_features, ), optional
Apply separate weight to penalties of each coefficient in the L1 term.
If not provided, no weighting is used (the default).
For example, if the weight of a feature is Zero, it means it's not
penalized at all, and that feature will always be there in the model.

**params : kwargs
keyword arguments passed to the coordinate descent solver.

Expand Down Expand Up @@ -454,6 +460,10 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
for i, alpha in enumerate(alphas):
l1_reg = alpha * l1_ratio * n_samples
l2_reg = alpha * (1.0 - l1_ratio) * n_samples
if l1_weights is not None:
l1_weights_ = np.asfortranarray(l1_weights * l1_reg, dtype=X.dtype)
else:
l1_weights_ = np.asfortranarray([], dtype=X.dtype)
if not multi_output and sparse.isspmatrix(X):
model = cd_fast.sparse_enet_coordinate_descent(
coef_, l1_reg, l2_reg, X.data, X.indices,
Expand All @@ -469,12 +479,12 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
precompute = check_array(precompute, dtype=X.dtype.type,
order='C')
model = cd_fast.enet_coordinate_descent_gram(
coef_, l1_reg, l2_reg, precompute, Xy, y, max_iter,
tol, rng, random, positive)
coef_, l1_reg, l2_reg, l1_weights_, precompute, Xy, y,
max_iter, tol, rng, random, positive)
elif precompute is False:
model = cd_fast.enet_coordinate_descent(
coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random,
positive)
coef_, l1_reg, l2_reg, l1_weights_, X, y, max_iter, tol, rng,
random, positive)
else:
raise ValueError("Precompute should be one of True, False, "
"'auto' or array-like. Got %r" % precompute)
Expand Down Expand Up @@ -599,6 +609,12 @@ class ElasticNet(LinearModel, RegressorMixin):
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.

l1_weights : array, shape (n_features, ), optional
Apply separate weight to penalties of each coefficient in the L1 term.
If not provided, no weighting is used (the default).
For example, if the weight of a feature is Zero, it means it's not
penalized at all, and that feature will always be there in the model.

Attributes
----------
coef_ : array, shape (n_features,) | (n_targets, n_features)
Expand All @@ -624,8 +640,9 @@ class ElasticNet(LinearModel, RegressorMixin):
>>> regr = ElasticNet(random_state=0)
>>> regr.fit(X, y)
ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,
max_iter=1000, normalize=False, positive=False, precompute=False,
random_state=0, selection='cyclic', tol=0.0001, warm_start=False)
l1_weights=None, max_iter=1000, normalize=False, positive=False,
precompute=False, random_state=0, selection='cyclic', tol=0.0001,
warm_start=False)
>>> print(regr.coef_) # doctest: +ELLIPSIS
[18.83816048 64.55968825]
>>> print(regr.intercept_) # doctest: +ELLIPSIS
Expand All @@ -652,7 +669,7 @@ class ElasticNet(LinearModel, RegressorMixin):
def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
normalize=False, precompute=False, max_iter=1000,
copy_X=True, tol=1e-4, warm_start=False, positive=False,
random_state=None, selection='cyclic'):
random_state=None, selection='cyclic', l1_weights=None):
self.alpha = alpha
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
Expand All @@ -665,6 +682,7 @@ def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
self.positive = positive
self.random_state = random_state
self.selection = selection
self.l1_weights = l1_weights

def fit(self, X, y, check_input=True):
"""Fit model with coordinate descent.
Expand Down Expand Up @@ -755,6 +773,7 @@ def fit(self, X, y, check_input=True):
X_offset=X_offset, X_scale=X_scale, return_n_iter=True,
coef_init=coef_[k], max_iter=self.max_iter,
random_state=self.random_state,
l1_weights=self.l1_weights,
selection=self.selection,
check_input=False)
coef_[k] = this_coef[:, 0]
Expand Down Expand Up @@ -1499,6 +1518,12 @@ class ElasticNetCV(LinearModelCV, RegressorMixin):
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.

l1_weights : array, shape (n_features, ), optional
Apply separate weight to penalties of each coefficient in the L1 term.
If not provided, no weighting is used (the default).
For example, if the weight of a feature is Zero, it means it's not
penalized at all, and that feature will always be there in the model.

Attributes
----------
alpha_ : float
Expand Down Expand Up @@ -1534,9 +1559,9 @@ class ElasticNetCV(LinearModelCV, RegressorMixin):
>>> regr = ElasticNetCV(cv=5, random_state=0)
>>> regr.fit(X, y)
ElasticNetCV(alphas=None, copy_X=True, cv=5, eps=0.001, fit_intercept=True,
l1_ratio=0.5, max_iter=1000, n_alphas=100, n_jobs=1,
normalize=False, positive=False, precompute='auto', random_state=0,
selection='cyclic', tol=0.0001, verbose=0)
l1_ratio=0.5, l1_weights=None, max_iter=1000, n_alphas=100,
n_jobs=1, normalize=False, positive=False, precompute='auto',
random_state=0, selection='cyclic', tol=0.0001, verbose=0)
>>> print(regr.alpha_) # doctest: +ELLIPSIS
0.1994727942696716
>>> print(regr.intercept_) # doctest: +ELLIPSIS
Expand Down Expand Up @@ -1583,7 +1608,7 @@ def __init__(self, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
fit_intercept=True, normalize=False, precompute='auto',
max_iter=1000, tol=1e-4, cv='warn', copy_X=True,
verbose=0, n_jobs=1, positive=False, random_state=None,
selection='cyclic'):
selection='cyclic', l1_weights=None):
self.l1_ratio = l1_ratio
self.eps = eps
self.n_alphas = n_alphas
Expand All @@ -1600,6 +1625,7 @@ def __init__(self, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
self.positive = positive
self.random_state = random_state
self.selection = selection
self.l1_weights = l1_weights


###############################################################################
Expand Down
38 changes: 38 additions & 0 deletions sklearn/linear_model/tests/test_coordinate_descent.py
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Expand Up @@ -291,6 +291,44 @@ def test_enet_path():
assert_almost_equal(clf1.alpha_, clf2.alpha_)


@pytest.mark.filterwarnings('ignore: You should specify a value') # 0.22
def test_enet_selective_penalty():
n_features = 200
n_informative_features = 20
# A dataset with small number of samples and large number of features
# with the last n_informative_features as the informative ones
X, y, X_test, y_test = build_dataset(
n_samples=50, n_features=n_features,
n_informative_features=n_informative_features)

# Default weight is 1 for all features, keep l1 penalty
l1_weights = np.ones(n_features)

# Add some prior knowledge, when we know some features are important
# So, we will relax l1 penalty on the last n_informative_features.
# Use any small number, or zero if you are 100% sure of the prior
# knowledge
l1_weights[:n_informative_features-1] *= 0.001

# Run enet with prior knowledge (l1_weights)
clf_with_prior = ElasticNetCV(alphas=[0.01, 0.05, 0.1, 0.5, 1, 1.5],
eps=2e-3, cv=3, l1_weights=l1_weights)

ignore_warnings(clf_with_prior.fit)(X, y)

# This is a model without using any prior knowledge
clf_base = ElasticNetCV(alphas=[0.01, 0.05, 0.1, 0.5, 1, 1.5],
eps=2e-3, cv=3)

ignore_warnings(clf_base.fit)(X, y)

# Accuracy of the model with prior knowledge should be higher
# than the model without prior knowledge for a hard data set
# (much less samples than features)
assert_greater(clf_with_prior.score(X_test, y_test),
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Please use plain assert

clf_base.score(X_test, y_test))


@pytest.mark.filterwarnings('ignore: You should specify a value') # 0.22
def test_path_parameters():
X, y, _, _ = build_dataset()
Expand Down