482 lines
15 KiB
Python
482 lines
15 KiB
Python
"""
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Ridge regression
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"""
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# Author: Mathieu Blondel <mathieu@mblondel.org>
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# License: Simplified BSD
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import numpy as np
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from .base import LinearModel
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from ..utils.extmath import safe_sparse_dot
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from ..utils import safe_asanyarray
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from ..preprocessing import LabelBinarizer
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from ..grid_search import GridSearchCV
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class Ridge(LinearModel):
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"""
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Ridge regression.
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Parameters
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----------
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alpha : float
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Small positive values of alpha improve the conditioning of the
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problem and reduce the variance of the estimates.
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Alpha corresponds to (2*C)^-1 in other linear models such as
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LogisticRegression or LinearSVC.
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fit_intercept : boolean
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Whether to calculate the intercept for this model. If set
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to false, no intercept will be used in calculations
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(e.g. data is expected to be already centered).
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Examples
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--------
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>>> from scikits.learn.linear_model import Ridge
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>>> import numpy as np
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>>> n_samples, n_features = 10, 5
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>>> np.random.seed(0)
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>>> y = np.random.randn(n_samples)
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>>> X = np.random.randn(n_samples, n_features)
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>>> clf = Ridge(alpha=1.0)
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>>> clf.fit(X, y)
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Ridge(alpha=1.0, fit_intercept=True)
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"""
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def __init__(self, alpha=1.0, fit_intercept=True):
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self.alpha = alpha
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self.fit_intercept = fit_intercept
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def fit(self, X, y, sample_weight=1.0, solver="default", **params):
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"""
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Fit Ridge regression model
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Parameters
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----------
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X : numpy array of shape [n_samples,n_features]
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Training data
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y : numpy array of shape [n_samples]
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Target values
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sample_weight : float or numpy array of shape [n_samples]
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Sample weight
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solver : 'default' | 'cg'
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Solver to use in the computational routines. 'default'
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will use the standard scipy.linalg.solve function, 'cg'
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will use the a conjugate gradient solver as found in
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scipy.sparse.linalg.cg.
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Returns
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-------
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self : returns an instance of self.
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"""
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self._set_params(**params)
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self.solver = solver
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X = safe_asanyarray(X, dtype=np.float)
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y = np.asanyarray(y, dtype=np.float)
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X, y, Xmean, ymean = \
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LinearModel._center_data(X, y, self.fit_intercept)
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import scipy.sparse as sp
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if sp.issparse(X):
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self._solve_sparse(X, y, sample_weight)
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else:
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self._solve_dense(X, y, sample_weight)
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self._set_intercept(Xmean, ymean)
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return self
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def _solve_dense(self, X, y, sample_weight):
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n_samples, n_features = X.shape
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if n_features > n_samples or \
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isinstance(sample_weight, np.ndarray) or \
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sample_weight != 1.0:
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# kernel ridge
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# w = X.T * inv(X X^t + alpha*Id) y
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A = np.dot(X, X.T)
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A.flat[::n_samples + 1] += self.alpha * sample_weight
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self.coef_ = np.dot(X.T, self._solve(A, y))
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else:
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# ridge
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# w = inv(X^t X + alpha*Id) * X.T y
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A = np.dot(X.T, X)
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A.flat[::n_features + 1] += self.alpha
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self.coef_ = self._solve(A, np.dot(X.T, y))
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def _solve_sparse(self, X, y, sample_weight):
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n_samples, n_features = X.shape
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import scipy.sparse as sp
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if n_features > n_samples or \
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isinstance(sample_weight, np.ndarray) or \
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sample_weight != 1.0:
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I = sp.lil_matrix((n_samples, n_samples))
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I.setdiag(np.ones(n_samples) * self.alpha * sample_weight)
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c = self._solve(X * X.T + I, y)
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self.coef_ = X.T * c
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else:
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I = sp.lil_matrix((n_features, n_features))
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I.setdiag(np.ones(n_features) * self.alpha)
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self.coef_ = self._solve(X.T * X + I, X.T * y)
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def _solve(self, A, b):
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if self.solver == "cg":
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# this solver cannot handle a 2-d b.
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from scipy.sparse import linalg as sp_linalg
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sol, error = sp_linalg.cg(A, b)
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if error:
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raise ValueError("Failed with error code %d" % error)
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return sol
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else:
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import scipy.sparse as sp
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# we are working with dense symmetric positive A
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if sp.issparse(A):
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A = A.todense()
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from scipy import linalg
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return linalg.solve(A, b, sym_pos=True, overwrite_a=True)
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class RidgeClassifier(Ridge):
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"""Classifier using Ridge regression
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Parameters
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----------
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alpha : float
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Small positive values of alpha improve the conditioning of the
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problem and reduce the variance of the estimates.
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Alpha corresponds to (2*C)^-1 in other linear models such as
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LogisticRegression or LinearSVC.
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fit_intercept : boolean
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Whether to calculate the intercept for this model. If set
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to false, no intercept will be used in calculations
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(e.g. data is expected to be already centered).
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Note
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----
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For multi-class classification, n_class classifiers are trained in
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a one-versus-all approach.
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"""
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def fit(self, X, y):
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"""
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Fit Ridge regression model.
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Parameters
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----------
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X : numpy array of shape [n_samples,n_features]
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Training data
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y : numpy array of shape [n_samples]
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Target values
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Returns
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-------
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self : returns an instance of self.
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"""
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self.label_binarizer = LabelBinarizer()
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Y = self.label_binarizer.fit_transform(y)
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Ridge.fit(self, X, Y)
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return self
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def decision_function(self, X):
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return Ridge.predict(self, X)
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def predict(self, X):
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"""
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Predict target values according to the fitted model.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples]
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"""
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Y = self.decision_function(X)
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return self.label_binarizer.inverse_transform(Y)
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class _RidgeGCV(LinearModel):
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"""
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Ridge regression with built-in Generalized Cross-Validation, i.e.
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efficient Leave-One-Out cross-validation.
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This class is not intended to be used directly. Use RidgeCV instead.
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Notes
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-----
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We want to solve (K + alpha*Id)c = y,
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where K = X X^T is the kernel matrix.
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Let G = (K + alpha*Id)^-1.
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Dual solution: c = Gy
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Primal solution: w = X^T c
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Compute eigendecomposition K = Q V Q^T.
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Then G = Q (V + alpha*Id)^-1 Q^T,
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where (V + alpha*Id) is diagonal.
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It is thus inexpensive to inverse for many alphas.
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Let loov be the vector of prediction values for each example
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when the model was fitted with all examples but this example.
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loov = (KGY - diag(KG)Y) / diag(I-KG)
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Let looe be the vector of prediction errors for each example
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when the model was fitted with all examples but this example.
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looe = y - loov = c / diag(G)
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Reference
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---------
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http://cbcl.mit.edu/projects/cbcl/publications/ps/MIT-CSAIL-TR-2007-025.pdf
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http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
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"""
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def __init__(self, alphas=[0.1, 1.0, 10.0], fit_intercept=True,
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score_func=None, loss_func=None):
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self.alphas = np.asanyarray(alphas)
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self.fit_intercept = fit_intercept
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self.score_func = score_func
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self.loss_func = loss_func
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def _pre_compute(self, X, y):
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# even if X is very sparse, K is usually very dense
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K = safe_sparse_dot(X, X.T, dense_output=True)
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from scipy import linalg
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v, Q = linalg.eigh(K)
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return K, v, Q
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def _errors(self, v, Q, y, alpha):
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G = np.dot(np.dot(Q, np.diag(1.0 / (v + alpha))), Q.T)
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c = np.dot(G, y)
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G_diag = np.diag(G)
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# handle case when y is 2-d
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G_diag = G_diag if len(y.shape) == 1 else G_diag[:, np.newaxis]
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return (c / G_diag) ** 2, c
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def _values(self, K, v, Q, y, alpha):
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n_samples = y.shape[0]
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G = np.dot(np.dot(Q, np.diag(1.0 / (v + alpha))), Q.T)
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c = np.dot(G, y)
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KG = np.dot(K, G)
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#KG = np.dot(np.dot(Q, np.diag(v / (v + alpha))), Q.T)
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KG_diag = np.diag(KG)
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denom = np.ones(n_samples) - KG_diag
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if len(y.shape) == 2:
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# handle case when y is 2-d
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KG_diag = KG_diag[:, np.newaxis]
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denom = denom[:, np.newaxis]
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num = np.dot(KG, y) - KG_diag * y
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return num / denom, c
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def fit(self, X, y, sample_weight=1.0):
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"""Fit Ridge regression model
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Parameters
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----------
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X : numpy array of shape [n_samples, n_features]
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Training data
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y : numpy array of shape [n_samples] or [n_samples, n_responses]
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Target values
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sample_weight : float or numpy array of shape [n_samples]
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Sample weight
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Returns
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-------
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self : Returns self.
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"""
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X = safe_asanyarray(X, dtype=np.float)
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y = np.asanyarray(y, dtype=np.float)
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n_samples = X.shape[0]
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X, y, Xmean, ymean = LinearModel._center_data(X, y, self.fit_intercept)
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K, v, Q = self._pre_compute(X, y)
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n_y = 1 if len(y.shape) == 1 else y.shape[1]
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M = np.zeros((n_samples * n_y, len(self.alphas)))
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C = []
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error = self.score_func is None and self.loss_func is None
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for i, alpha in enumerate(self.alphas):
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if error:
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out, c = self._errors(v, Q, y, sample_weight * alpha)
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else:
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out, c = self._values(K, v, Q, y, sample_weight * alpha)
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M[:, i] = out.ravel()
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C.append(c)
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if error:
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best = M.mean(axis=0).argmin()
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else:
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func = self.score_func if self.score_func else self.loss_func
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out = [func(y.ravel(), M[:, i]) for i in range(len(self.alphas))]
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best = np.argmax(out) if self.score_func else np.argmin(out)
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self.best_alpha = self.alphas[best]
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self.dual_coef_ = C[best]
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self.coef_ = safe_sparse_dot(X.T, self.dual_coef_)
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self._set_intercept(Xmean, ymean)
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return self
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class RidgeCV(LinearModel):
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"""
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Ridge regression with built-in cross-validation.
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By default, it performs Generalized Cross-Validation, which is a form of
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efficient Leave-One-Out cross-validation. Currently, only the n_features >
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n_samples case is handled efficiently.
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Parameters
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----------
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alphas: numpy array of shape [n_alpha]
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Array of alpha values to try.
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Small positive values of alpha improve the conditioning of the
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problem and reduce the variance of the estimates.
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Alpha corresponds to (2*C)^-1 in other linear models such as
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LogisticRegression or LinearSVC.
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fit_intercept : boolean
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Whether to calculate the intercept for this model. If set
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to false, no intercept will be used in calculations
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(e.g. data is expected to be already centered).
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loss_func: callable, optional
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function that takes 2 arguments and compares them in
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order to evaluate the performance of prediciton (small is good)
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if None is passed, the score of the estimator is maximized
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score_func: callable, optional
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function that takes 2 arguments and compares them in
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order to evaluate the performance of prediciton (big is good)
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if None is passed, the score of the estimator is maximized
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See also
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--------
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Ridge
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"""
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def __init__(self, alphas=np.array([0.1, 1.0, 10.0]), fit_intercept=True,
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score_func=None, loss_func=None, cv=None):
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self.alphas = alphas
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self.fit_intercept = fit_intercept
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self.score_func = score_func
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self.loss_func = loss_func
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self.cv = cv
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def fit(self, X, y, sample_weight=1.0, **params):
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"""Fit Ridge regression model
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Parameters
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----------
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X : numpy array of shape [n_samples, n_features]
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Training data
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y : numpy array of shape [n_samples] or [n_samples, n_responses]
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Target values
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sample_weight : float or numpy array of shape [n_samples]
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Sample weight
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cv : cross-validation generator, optional
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If None, Generalized Cross-Validationn (efficient Leave-One-Out)
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will be used.
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Returns
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-------
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self : Returns self.
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"""
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self._set_params(**params)
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if self.cv is None:
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estimator = _RidgeGCV(self.alphas, self.fit_intercept,
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self.score_func, self.loss_func)
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estimator.fit(X, y, sample_weight=sample_weight)
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self.best_alpha = estimator.best_alpha
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else:
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parameters = {'alpha': self.alphas}
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# FIXME: sample_weight must be split into training/validation data
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# too!
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#fit_params = {'sample_weight' : sample_weight}
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fit_params = {}
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gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept),
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parameters, fit_params=fit_params, cv=self.cv)
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gs.fit(X, y)
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estimator = gs.best_estimator
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self.best_alpha = gs.best_estimator.alpha
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self.coef_ = estimator.coef_
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self.intercept_ = estimator.intercept_
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return self
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class RidgeClassifierCV(RidgeCV):
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def fit(self, X, y, sample_weight=1.0, class_weight={}, **params):
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"""
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Fit the ridge classifier.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples
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and n_features is the number of features.
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y : array-like, shape = [n_samples]
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Target values.
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class_weight : dict, optional
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Weights associated with classes in the form
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{class_label : weight}. If not given, all classes are
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supposed to have weight one.
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sample_weight : float or numpy array of shape [n_samples]
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Sample weight
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Returns
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-------
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self : object
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Returns self.
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"""
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self._set_params(**params)
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sample_weight2 = np.array([class_weight.get(k, 1.0) for k in y])
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self.label_binarizer = LabelBinarizer()
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Y = self.label_binarizer.fit_transform(y)
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RidgeCV.fit(self, X, Y,
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sample_weight=sample_weight * sample_weight2,
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cv=self.cv)
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return self
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def decision_function(self, X):
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return RidgeCV.predict(self, X)
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def predict(self, X):
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Y = self.decision_function(X)
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return self.label_binarizer.inverse_transform(Y)
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