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[MRG] Simplify linear models #2444

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[MRG] Simplify linear models #2444

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6b05f14
MAINT get rid of decision_function in linear regression
larsmans Sep 16, 2013
276c758
ENH sparsify and densify methods for CD models
larsmans Sep 16, 2013
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19 changes: 2 additions & 17 deletions sklearn/linear_model/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,8 +126,8 @@ class LinearModel(six.with_metaclass(ABCMeta, BaseEstimator)):
def fit(self, X, y):
"""Fit model."""

def decision_function(self, X):
"""Decision function of the linear model.
def predict(self, X):
"""Predict using the linear model.

Parameters
----------
Expand All @@ -143,21 +143,6 @@ def decision_function(self, X):
return safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_

def predict(self, X):
"""Predict using the linear model

Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.

Returns
-------
C : array, shape = (n_samples,)
Returns predicted values.
"""
return self.decision_function(X)

_center_data = staticmethod(center_data)

def _set_intercept(self, X_mean, y_mean, X_std):
Expand Down
27 changes: 6 additions & 21 deletions sklearn/linear_model/coordinate_descent.py
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Expand Up @@ -14,7 +14,7 @@
import numpy as np
from scipy import sparse

from .base import LinearModel, _pre_fit
from .base import LinearModel, _pre_fit, SparseCoefMixin
from ..base import RegressorMixin
from .base import center_data
from ..utils import array2d, atleast2d_or_csc, deprecated
Expand Down Expand Up @@ -445,7 +445,7 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
# ElasticNet model


class ElasticNet(LinearModel, RegressorMixin):
class ElasticNet(LinearModel, RegressorMixin, SparseCoefMixin):
"""Linear Model trained with L1 and L2 prior as regularizer

Minimizes the objective function::
Expand Down Expand Up @@ -625,27 +625,12 @@ def fit(self, X, y, coef_init=None):

@property
def sparse_coef_(self):
""" sparse representation of the fitted coef """
"""Sparse representation of the fitted coef_."""
warnings.warn("sparse_coef_ is deprecated and will be removed in 0.17."
" Call the sparsify method instead.",
DeprecationWarning)
return sparse.csr_matrix(self.coef_)

def decision_function(self, X):
"""Decision function of the linear model

Parameters
----------
X : numpy array or scipy.sparse matrix of shape (n_samples, n_features)

Returns
-------
T : array, shape = (n_samples,)
The predicted decision function
"""
if sparse.isspmatrix(X):
return np.ravel(safe_sparse_dot(self.coef_, X.T, dense_output=True)
+ self.intercept_)
else:
return super(ElasticNet, self).decision_function(X)


###############################################################################
# Lasso model
Expand Down
2 changes: 0 additions & 2 deletions sklearn/linear_model/tests/test_least_angle.py
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Expand Up @@ -399,8 +399,6 @@ def test_multitarget():
for estimator in (linear_model.LassoLars(), linear_model.Lars()):
estimator.fit(X, Y)
Y_pred = estimator.predict(X)
Y_dec = estimator.decision_function(X)
assert_array_almost_equal(Y_pred, Y_dec)
alphas, active, coef, path = (estimator.alphas_, estimator.active_,
estimator.coef_, estimator.coef_path_)
for k in range(n_targets):
Expand Down