def test_binomial_and_multinomial_loss(global_random_seed):
"""Test that multinomial loss with n_classes = 2 is the same as binomial loss."""
rng = np.random.RandomState(global_random_seed)
n_samples = 20
binom = HalfBinomialLoss()
multinom = HalfMultinomialLoss(n_classes=2)
y_train = rng.randint(0, 2, size=n_samples).astype(np.float64)
raw_prediction = rng.normal(size=n_samples)
raw_multinom = np.empty((n_samples, 2))
raw_multinom[:, 0] = -0.5 * raw_prediction
raw_multinom[:, 1] = 0.5 * raw_prediction
assert_allclose(
binom.loss(y_true=y_train, raw_prediction=raw_prediction),
multinom.loss(y_true=y_train, raw_prediction=raw_multinom),
)
def test_multinomial_loss():
# test if the multinomial loss and gradient computations are consistent
X, y = iris.data, iris.target.astype(np.float64)
n_samples, n_features = X.shape
n_classes = len(np.unique(y))
rng = check_random_state(42)
weights = rng.randn(n_features, n_classes)
intercept = rng.randn(n_classes)
sample_weights = rng.randn(n_samples)
np.abs(sample_weights, sample_weights)
# compute loss and gradient like in multinomial SAG
dataset, _ = make_dataset(X, y, sample_weights, random_state=42)
loss_1, grad_1 = _multinomial_grad_loss_all_samples(
dataset, weights, intercept, n_samples, n_features, n_classes)
# compute loss and gradient like in multinomial LogisticRegression
loss = LinearModelLoss(
base_loss=HalfMultinomialLoss(n_classes=n_classes),
fit_intercept=True,
)
weights_intercept = np.vstack((weights, intercept)).T
loss_2, grad_2 = loss.loss_gradient(weights_intercept,
X,
y,
l2_reg_strength=0.0,
sample_weight=sample_weights)
grad_2 = grad_2[:, :-1].T
# comparison
assert_array_almost_equal(grad_1, grad_2)
assert_almost_equal(loss_1, loss_2)
def test_multinomial_coef_shape(fit_intercept):
"""Test that multinomial LinearModelLoss respects shape of coef."""
loss = LinearModelLoss(base_loss=HalfMultinomialLoss(),
fit_intercept=fit_intercept)
n_samples, n_features = 10, 5
X, y, coef = random_X_y_coef(linear_model_loss=loss,
n_samples=n_samples,
n_features=n_features,
seed=42)
s = np.random.RandomState(42).randn(*coef.shape)
l, g = loss.loss_gradient(coef, X, y)
g1 = loss.gradient(coef, X, y)
g2, hessp = loss.gradient_hessian_product(coef, X, y)
h = hessp(s)
assert g.shape == coef.shape
assert h.shape == coef.shape
assert_allclose(g, g1)
assert_allclose(g, g2)
coef_r = coef.ravel(order="F")
s_r = s.ravel(order="F")
l_r, g_r = loss.loss_gradient(coef_r, X, y)
g1_r = loss.gradient(coef_r, X, y)
g2_r, hessp_r = loss.gradient_hessian_product(coef_r, X, y)
h_r = hessp_r(s_r)
assert g_r.shape == coef_r.shape
assert h_r.shape == coef_r.shape
assert_allclose(g_r, g1_r)
assert_allclose(g_r, g2_r)
assert_allclose(g, g_r.reshape(loss.base_loss.n_classes, -1, order="F"))
assert_allclose(h, h_r.reshape(loss.base_loss.n_classes, -1, order="F"))
def test_multinomial_loss_fit_intercept_only():
"""Test that fit_intercept_only returns the mean functional for CCE."""
rng = np.random.RandomState(0)
n_classes = 4
loss = HalfMultinomialLoss(n_classes=n_classes)
# Same logic as test_specific_fit_intercept_only. Here inverse link
# function = softmax and link function = log - symmetry term.
y_train = rng.randint(0, n_classes + 1, size=100).astype(np.float64)
baseline_prediction = loss.fit_intercept_only(y_true=y_train)
assert baseline_prediction.shape == (n_classes, )
p = np.zeros(n_classes, dtype=y_train.dtype)
for k in range(n_classes):
p[k] = (y_train == k).mean()
assert_allclose(baseline_prediction, np.log(p) - np.mean(np.log(p)))
assert_allclose(baseline_prediction[None, :], loss.link.link(p[None, :]))
for y_train in (np.zeros(shape=10), np.ones(shape=10)):
y_train = y_train.astype(np.float64)
baseline_prediction = loss.fit_intercept_only(y_true=y_train)
assert baseline_prediction.dtype == y_train.dtype
assert_all_finite(baseline_prediction)
def test_multinomial_loss_ground_truth():
# n_samples, n_features, n_classes = 4, 2, 3
n_classes = 3
X = np.array([[1.1, 2.2], [2.2, -4.4], [3.3, -2.2], [1.1, 1.1]])
y = np.array([0, 1, 2, 0], dtype=np.float64)
lbin = LabelBinarizer()
Y_bin = lbin.fit_transform(y)
weights = np.array([[0.1, 0.2, 0.3], [1.1, 1.2, -1.3]])
intercept = np.array([1.0, 0, -0.2])
sample_weights = np.array([0.8, 1, 1, 0.8])
prediction = np.dot(X, weights) + intercept
logsumexp_prediction = logsumexp(prediction, axis=1)
p = prediction - logsumexp_prediction[:, np.newaxis]
loss_1 = -(sample_weights[:, np.newaxis] * p * Y_bin).sum()
diff = sample_weights[:, np.newaxis] * (np.exp(p) - Y_bin)
grad_1 = np.dot(X.T, diff)
loss = LinearModelLoss(
base_loss=HalfMultinomialLoss(n_classes=n_classes),
fit_intercept=True,
)
weights_intercept = np.vstack((weights, intercept)).T
loss_2, grad_2 = loss.loss_gradient(weights_intercept,
X,
y,
l2_reg_strength=0.0,
sample_weight=sample_weights)
grad_2 = grad_2[:, :-1].T
assert_almost_equal(loss_1, loss_2)
assert_array_almost_equal(grad_1, grad_2)
# ground truth
loss_gt = 11.680360354325961
grad_gt = np.array([[-0.557487, -1.619151, +2.176638],
[-0.903942, +5.258745, -4.354803]])
assert_almost_equal(loss_1, loss_gt)
assert_array_almost_equal(grad_1, grad_gt)
(HalfTweedieLoss(power=-3), [0.1, 100], [-np.inf, np.inf]),
(HalfTweedieLoss(power=0), [0.1, 100], [-np.inf, np.inf]),
(HalfTweedieLoss(power=1.5), [0.1, 100], [-np.inf, -3, -0.1, np.inf]),
(HalfTweedieLoss(power=2), [0.1, 100], [-np.inf, -3, -0.1, 0, np.inf]),
(HalfTweedieLoss(power=3), [0.1, 100], [-np.inf, -3, -0.1, 0, np.inf]),
(HalfTweedieLossIdentity(power=-3), [0.1, 100], [-np.inf, np.inf]),
(HalfTweedieLossIdentity(power=0), [-3, -0.1, 0, 0.1,
100], [-np.inf, np.inf]),
(HalfTweedieLossIdentity(power=1.5), [0.1,
100], [-np.inf, -3, -0.1, np.inf]),
(HalfTweedieLossIdentity(power=2), [0.1,
100], [-np.inf, -3, -0.1, 0, np.inf]),
(HalfTweedieLossIdentity(power=3), [0.1,
100], [-np.inf, -3, -0.1, 0, np.inf]),
(HalfBinomialLoss(), [0.1, 0.5, 0.9], [-np.inf, -1, 2, np.inf]),
(HalfMultinomialLoss(), [], [-np.inf, -1, 1.1, np.inf]),
]
# y_pred and y_true do not always have the same domain (valid value range).
# Hence, we define extra sets of parameters for each of them.
Y_TRUE_PARAMS = [ # type: ignore
# (loss, [y success], [y fail])
(HalfPoissonLoss(), [0], []),
(HalfTweedieLoss(power=-3), [-100, -0.1, 0], []),
(HalfTweedieLoss(power=0), [-100, 0], []),
(HalfTweedieLoss(power=1.5), [0], []),
(HalfTweedieLossIdentity(power=-3), [-100, -0.1, 0], []),
(HalfTweedieLossIdentity(power=0), [-100, 0], []),
(HalfTweedieLossIdentity(power=1.5), [0], []),
(HalfBinomialLoss(), [0, 1], []),
(HalfMultinomialLoss(), [0.0, 1.0, 2], []),
]
def __logistic_regression_path(
X,
y,
pos_class=None,
Cs=10,
fit_intercept=True,
max_iter=100,
tol=1e-4,
verbose=0,
solver='lbfgs',
coef=None,
class_weight=None,
dual=False,
penalty='l2',
intercept_scaling=1.,
multi_class='warn',
random_state=None,
check_input=True,
max_squared_sum=None,
sample_weight=None,
l1_ratio=None,
n_threads=1,
):
"""Compute a Logistic Regression model for a list of regularization
parameters.
This is an implementation that uses the result of the previous model
to speed up computations along the set of solutions, making it faster
than sequentially calling LogisticRegression for the different parameters.
Note that there will be no speedup with liblinear solver, since it does
not handle warm-starting.
Read more in the :ref:`User Guide <logistic_regression>`.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Input data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Input data, target values.
pos_class : int, None
The class with respect to which we perform a one-vs-all fit.
If None, then it is assumed that the given problem is binary.
Cs : int | array-like, shape (n_cs,)
List of values for the regularization parameter or integer specifying
the number of regularization parameters that should be used. In this
case, the parameters will be chosen in a logarithmic scale between
1e-4 and 1e4.
fit_intercept : bool
Whether to fit an intercept for the model. In this case the shape of
the returned array is (n_cs, n_features + 1).
max_iter : int
Maximum number of iterations for the solver.
tol : float
Stopping criterion. For the newton-cg and lbfgs solvers, the iteration
will stop when ``max{|g_i | i = 1, ..., n} <= tol``
where ``g_i`` is the i-th component of the gradient.
verbose : int
For the liblinear and lbfgs solvers set verbose to any positive
number for verbosity.
solver : {'lbfgs', 'newton-cg', 'liblinear', 'sag', 'saga'}
Numerical solver to use.
coef : array-like, shape (n_features,), default None
Initialization value for coefficients of logistic regression.
Useless for liblinear solver.
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
dual : bool
Dual or primal formulation. Dual formulation is only implemented for
l2 penalty with liblinear solver. Prefer dual=False when
n_samples > n_features.
penalty : str, 'l1', 'l2', or 'elasticnet'
Used to specify the norm used in the penalization. The 'newton-cg',
'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is
only supported by the 'saga' solver.
intercept_scaling : float, default 1.
Useful only when the solver 'liblinear' is used
and self.fit_intercept is set to True. In this case, x becomes
[x, self.intercept_scaling],
i.e. a "synthetic" feature with constant value equal to
intercept_scaling is appended to the instance vector.
The intercept becomes ``intercept_scaling * synthetic_feature_weight``.
Note! the synthetic feature weight is subject to l1/l2 regularization
as all other features.
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased.
multi_class : str, {'ovr', 'multinomial', 'auto'}, default: 'ovr'
If the option chosen is 'ovr', then a binary problem is fit for each
label. For 'multinomial' the loss minimised is the multinomial loss fit
across the entire probability distribution, *even when the data is
binary*. 'multinomial' is unavailable when solver='liblinear'.
'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
and otherwise selects 'multinomial'.
.. versionadded:: 0.18
Stochastic Average Gradient descent solver for 'multinomial' case.
.. versionchanged:: 0.20
Default will change from 'ovr' to 'auto' in 0.22.
random_state : int, RandomState instance or None, optional, default None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`. Used when ``solver`` == 'sag' or
'liblinear'.
check_input : bool, default True
If False, the input arrays X and y will not be checked.
max_squared_sum : float, default None
Maximum squared sum of X over samples. Used only in SAG solver.
If None, it will be computed, going through all the samples.
The value should be precomputed to speed up cross validation.
sample_weight : array-like, shape(n_samples,) optional
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
l1_ratio : float or None, optional (default=None)
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
combination of L1 and L2.
Returns
-------
coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1)
List of coefficients for the Logistic Regression model. If
fit_intercept is set to True then the second dimension will be
n_features + 1, where the last item represents the intercept. For
``multiclass='multinomial'``, the shape is (n_classes, n_cs,
n_features) or (n_classes, n_cs, n_features + 1).
Cs : ndarray
Grid of Cs used for cross-validation.
n_iter : array, shape (n_cs,)
Actual number of iteration for each Cs.
Notes
-----
You might get slightly different results with the solver liblinear than
with the others since this uses LIBLINEAR which penalizes the intercept.
.. versionchanged:: 0.19
The "copy" parameter was removed.
"""
if isinstance(Cs, numbers.Integral):
Cs = np.logspace(-4, 4, Cs)
solver = _check_solver(solver, penalty, dual)
# Preprocessing.
if check_input:
if sklearn_check_version('1.1'):
X = check_array(
X,
accept_sparse='csr',
dtype=np.float64,
accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
)
else:
X = check_array(
X,
accept_sparse='csr',
dtype=np.float64,
accept_large_sparse=solver != 'liblinear',
)
y = check_array(y, ensure_2d=False, dtype=None)
check_consistent_length(X, y)
_, n_features = X.shape
classes = np.unique(y)
random_state = check_random_state(random_state)
multi_class = _check_multi_class(multi_class, solver, len(classes))
if pos_class is None and multi_class != 'multinomial':
if (classes.size > 2):
raise ValueError('To fit OvR, use the pos_class argument')
# np.unique(y) gives labels in sorted order.
pos_class = classes[1]
_patching_status = PatchingConditionsChain(
"sklearn.linear_model.LogisticRegression.fit")
_dal_ready = _patching_status.and_conditions([
(solver in ['lbfgs',
'newton-cg'], f"'{solver}' solver is not supported. "
"Only 'lbfgs' and 'newton-cg' solvers are supported."),
(not sparse.issparse(X),
"X is sparse. Sparse input is not supported."),
(sample_weight is None, "Sample weights are not supported."),
(class_weight is None, "Class weights are not supported.")
])
if not _dal_ready:
if sklearn_check_version('0.24'):
sample_weight = _check_sample_weight(sample_weight,
X,
dtype=X.dtype,
copy=True)
else:
sample_weight = _check_sample_weight(sample_weight,
X,
dtype=X.dtype)
# If class_weights is a dict (provided by the user), the weights
# are assigned to the original labels. If it is "balanced", then
# the class_weights are assigned after masking the labels with a OvR.
le = LabelEncoder()
if (isinstance(class_weight, dict) or multi_class == 'multinomial') and \
not _dal_ready:
class_weight_ = compute_class_weight(class_weight,
classes=classes,
y=y)
if not np.allclose(class_weight_, np.ones_like(class_weight_)):
sample_weight *= class_weight_[le.fit_transform(y)]
# For doing a ovr, we need to mask the labels first. for the
# multinomial case this is not necessary.
if multi_class == 'ovr':
y_bin = np.ones(y.shape, dtype=X.dtype)
if sklearn_check_version('1.1'):
mask = (y == pos_class)
y_bin = np.ones(y.shape, dtype=X.dtype)
# for compute_class_weight
if solver in ["lbfgs", "newton-cg"]:
# HalfBinomialLoss, used for those solvers, represents y in [0, 1] instead
# of in [-1, 1].
mask_classes = np.array([0, 1])
y_bin[~mask] = 0.0
else:
mask_classes = np.array([-1, 1])
y_bin[~mask] = -1.0
else:
mask_classes = np.array([-1, 1])
mask = (y == pos_class)
y_bin[~mask] = -1.
# for compute_class_weight
if class_weight == "balanced" and not _dal_ready:
class_weight_ = compute_class_weight(class_weight,
classes=mask_classes,
y=y_bin)
if not np.allclose(class_weight_, np.ones_like(class_weight_)):
sample_weight *= class_weight_[le.fit_transform(y_bin)]
if _dal_ready:
w0 = np.zeros(n_features + 1, dtype=X.dtype)
y_bin[~mask] = 0.
else:
w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype)
else:
if sklearn_check_version('1.1'):
if solver in ["sag", "saga", "lbfgs", "newton-cg"]:
# SAG, lbfgs and newton-cg multinomial solvers need LabelEncoder,
# not LabelBinarizer, i.e. y as a 1d-array of integers.
# LabelEncoder also saves memory compared to LabelBinarizer, especially
# when n_classes is large.
if _dal_ready:
Y_multi = le.fit_transform(y).astype(X.dtype, copy=False)
else:
le = LabelEncoder()
Y_multi = le.fit_transform(y).astype(X.dtype, copy=False)
else:
# For liblinear solver, apply LabelBinarizer, i.e. y is one-hot encoded.
lbin = LabelBinarizer()
Y_multi = lbin.fit_transform(y)
if Y_multi.shape[1] == 1:
Y_multi = np.hstack([1 - Y_multi, Y_multi])
else:
if solver not in ['sag', 'saga']:
if _dal_ready:
Y_multi = le.fit_transform(y).astype(X.dtype, copy=False)
else:
lbin = LabelBinarizer()
Y_multi = lbin.fit_transform(y)
if Y_multi.shape[1] == 1:
Y_multi = np.hstack([1 - Y_multi, Y_multi])
else:
# SAG multinomial solver needs LabelEncoder, not LabelBinarizer
le = LabelEncoder()
Y_multi = le.fit_transform(y).astype(X.dtype, copy=False)
if _dal_ready:
w0 = np.zeros((classes.size, n_features + 1),
order='C',
dtype=X.dtype)
else:
w0 = np.zeros((classes.size, n_features + int(fit_intercept)),
order='F',
dtype=X.dtype)
if coef is not None:
# it must work both giving the bias term and not
if multi_class == 'ovr':
if coef.size not in (n_features, w0.size):
raise ValueError(
'Initialization coef is of shape %d, expected shape '
'%d or %d' % (coef.size, n_features, w0.size))
if _dal_ready:
w0[-coef.size:] = \
np.roll(coef, 1, -1) if coef.size != n_features else coef
else:
w0[:coef.size] = coef
else:
# For binary problems coef.shape[0] should be 1, otherwise it
# should be classes.size.
n_classes = classes.size
if n_classes == 2:
n_classes = 1
if coef.shape[0] != n_classes or \
coef.shape[1] not in (n_features, n_features + 1):
raise ValueError(
'Initialization coef is of shape (%d, %d), expected '
'shape (%d, %d) or (%d, %d)' %
(coef.shape[0], coef.shape[1], classes.size, n_features,
classes.size, n_features + 1))
if _dal_ready:
w0[:, -coef.shape[1]:] = \
np.roll(coef, 1, -1) if coef.shape[1] != n_features else coef
else:
if n_classes == 1:
w0[0, :coef.shape[1]] = -coef
w0[1, :coef.shape[1]] = coef
else:
w0[:, :coef.shape[1]] = coef
C_daal_multiplier = 1
# commented out because this is Py3 feature
#def _map_to_binary_logistic_regression():
# nonlocal C_daal_multiplier
# nonlocal w0
# C_daal_multiplier = 2
# w0 *= 2
if multi_class == 'multinomial':
# fmin_l_bfgs_b and newton-cg accepts only ravelled parameters.
if solver in ['lbfgs', 'newton-cg']:
if _dal_ready and classes.size == 2:
w0 = w0[-1:, :]
if sklearn_check_version('1.1'):
w0 = w0.ravel(order="F")
else:
w0 = w0.ravel()
target = Y_multi
loss = None
if sklearn_check_version('1.1'):
loss = LinearModelLoss(
base_loss=HalfMultinomialLoss(n_classes=classes.size),
fit_intercept=fit_intercept,
)
if solver == 'lbfgs':
if _dal_ready:
if classes.size == 2:
# _map_to_binary_logistic_regression()
C_daal_multiplier = 2
w0 *= 2
daal_extra_args_func = _daal4py_logistic_loss_extra_args
else:
daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args
func = _daal4py_loss_and_grad
else:
if sklearn_check_version('1.1') and loss is not None:
func = loss.loss_gradient
else:
def func(x, *args):
return _multinomial_loss_grad(x, *args)[0:2]
elif solver == 'newton-cg':
if _dal_ready:
if classes.size == 2:
# _map_to_binary_logistic_regression()
C_daal_multiplier = 2
w0 *= 2
daal_extra_args_func = _daal4py_logistic_loss_extra_args
else:
daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args
func = _daal4py_loss_
grad = _daal4py_grad_
hess = _daal4py_grad_hess_
else:
if sklearn_check_version('1.1') and loss is not None:
func = loss.loss
grad = loss.gradient
hess = loss.gradient_hessian_product # hess = [gradient, hessp]
else:
def func(x, *args):
return _multinomial_loss(x, *args)[0]
def grad(x, *args):
return _multinomial_loss_grad(x, *args)[1]
hess = _multinomial_grad_hess
warm_start_sag = {'coef': w0.T}
else:
target = y_bin
if solver == 'lbfgs':
if _dal_ready:
func = _daal4py_loss_and_grad
daal_extra_args_func = _daal4py_logistic_loss_extra_args
else:
if sklearn_check_version('1.1'):
loss = LinearModelLoss(base_loss=HalfBinomialLoss(),
fit_intercept=fit_intercept)
func = loss.loss_gradient
else:
func = _logistic_loss_and_grad
elif solver == 'newton-cg':
if _dal_ready:
daal_extra_args_func = _daal4py_logistic_loss_extra_args
func = _daal4py_loss_
grad = _daal4py_grad_
hess = _daal4py_grad_hess_
else:
if sklearn_check_version('1.1'):
loss = LinearModelLoss(base_loss=HalfBinomialLoss(),
fit_intercept=fit_intercept)
func = loss.loss
grad = loss.gradient
hess = loss.gradient_hessian_product # hess = [gradient, hessp]
else:
func = _logistic_loss
def grad(x, *args):
return _logistic_loss_and_grad(x, *args)[1]
hess = _logistic_grad_hess
warm_start_sag = {'coef': np.expand_dims(w0, axis=1)}
coefs = list()
n_iter = np.zeros(len(Cs), dtype=np.int32)
for i, C in enumerate(Cs):
if solver == 'lbfgs':
if _dal_ready:
extra_args = daal_extra_args_func(classes.size,
w0,
X,
target,
0.,
1. /
(2 * C * C_daal_multiplier),
fit_intercept,
value=True,
gradient=True,
hessian=False)
else:
if sklearn_check_version('1.1'):
l2_reg_strength = 1.0 / C
extra_args = (X, target, sample_weight, l2_reg_strength,
n_threads)
else:
extra_args = (X, target, 1. / C, sample_weight)
iprint = [-1, 50, 1, 100,
101][np.searchsorted(np.array([0, 1, 2, 3]), verbose)]
opt_res = optimize.minimize(func,
w0,
method="L-BFGS-B",
jac=True,
args=extra_args,
options={
"iprint": iprint,
"gtol": tol,
"maxiter": max_iter
})
n_iter_i = _check_optimize_result(
solver,
opt_res,
max_iter,
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
w0, loss = opt_res.x, opt_res.fun
if _dal_ready and C_daal_multiplier == 2:
w0 /= 2
elif solver == 'newton-cg':
if _dal_ready:
def make_ncg_funcs(f,
value=False,
gradient=False,
hessian=False):
daal_penaltyL2 = 1. / (2 * C * C_daal_multiplier)
_obj_, X_, y_, n_samples = daal_extra_args_func(
classes.size,
w0,
X,
target,
0.,
daal_penaltyL2,
fit_intercept,
value=value,
gradient=gradient,
hessian=hessian)
def _func_(x, *args):
return f(x, _obj_, *args)
return _func_, (X_, y_, n_samples, daal_penaltyL2)
loss_func, extra_args = make_ncg_funcs(func, value=True)
grad_func, _ = make_ncg_funcs(grad, gradient=True)
grad_hess_func, _ = make_ncg_funcs(hess, gradient=True)
w0, n_iter_i = _newton_cg(grad_hess_func,
loss_func,
grad_func,
w0,
args=extra_args,
maxiter=max_iter,
tol=tol)
else:
if sklearn_check_version('1.1'):
l2_reg_strength = 1.0 / C
args = (X, target, sample_weight, l2_reg_strength,
n_threads)
else:
args = (X, target, 1. / C, sample_weight)
w0, n_iter_i = _newton_cg(hess,
func,
grad,
w0,
args=args,
maxiter=max_iter,
tol=tol)
elif solver == 'liblinear':
coef_, intercept_, n_iter_i, = _fit_liblinear(
X,
target,
C,
fit_intercept,
intercept_scaling,
None,
penalty,
dual,
verbose,
max_iter,
tol,
random_state,
sample_weight=sample_weight,
)
if fit_intercept:
w0 = np.concatenate([coef_.ravel(), intercept_])
else:
w0 = coef_.ravel()
elif solver in ['sag', 'saga']:
if multi_class == 'multinomial':
target = target.astype(X.dtype, copy=False)
loss = 'multinomial'
else:
loss = 'log'
# alpha is for L2-norm, beta is for L1-norm
if penalty == 'l1':
alpha = 0.
beta = 1. / C
elif penalty == 'l2':
alpha = 1. / C
beta = 0.
else: # Elastic-Net penalty
alpha = (1. / C) * (1 - l1_ratio)
beta = (1. / C) * l1_ratio
w0, n_iter_i, warm_start_sag = sag_solver(
X,
target,
sample_weight,
loss,
alpha,
beta,
max_iter,
tol,
verbose,
random_state,
False,
max_squared_sum,
warm_start_sag,
is_saga=(solver == 'saga'))
else:
raise ValueError("solver must be one of {'liblinear', 'lbfgs', "
"'newton-cg', 'sag'}, got '%s' instead" % solver)
if multi_class == 'multinomial':
if _dal_ready:
if classes.size == 2:
multi_w0 = w0[np.newaxis, :]
else:
multi_w0 = np.reshape(w0, (classes.size, -1))
else:
if sklearn_check_version('1.1'):
if solver in ["lbfgs", "newton-cg"]:
multi_w0 = np.reshape(w0, (n_classes, -1), order="F")
else:
multi_w0 = w0
else:
n_classes = max(2, classes.size)
multi_w0 = np.reshape(w0, (n_classes, -1))
if n_classes == 2:
multi_w0 = multi_w0[1][np.newaxis, :]
coefs.append(multi_w0.copy())
else:
coefs.append(w0.copy())
n_iter[i] = n_iter_i
if _dal_ready:
if fit_intercept:
for i, ci in enumerate(coefs):
coefs[i] = np.roll(ci, -1, -1)
else:
for i, ci in enumerate(coefs):
coefs[i] = np.delete(ci, 0, axis=-1)
_patching_status.write_log()
return np.array(coefs), np.array(Cs), n_iter