77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
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from sklearn.utils import check_X_y, check_random_state
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from sklearn.linear_model import Lasso
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from scipy.sparse import issparse
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from scipy import sparse
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def _rescale_data(x, weights):
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if issparse(x):
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size = weights.shape[0]
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weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size))
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x_rescaled = x * weight_dia
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else:
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x_rescaled = x * (1 - weights)
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return x_rescaled
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class RandomizedLasso(Lasso):
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"""
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Randomized version of scikit-learns Lasso class.
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Randomized LASSO is a generalization of the LASSO. The LASSO penalises
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the absolute value of the coefficients with a penalty term proportional
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to `alpha`, but the randomized LASSO changes the penalty to a randomly
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chosen value in the range `[alpha, alpha/weakness]`.
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Parameters
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----------
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weakness : float
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Weakness value for randomized LASSO. Must be in (0, 1].
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See also
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--------
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sklearn.linear_model.LogisticRegression : learns logistic regression models
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using the same algorithm.
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"""
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def __init__(self, weakness=0.5, alpha=1.0, fit_intercept=True,
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precompute=False, copy_X=True, max_iter=1000,
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tol=1e-4, warm_start=False, positive=False,
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random_state=None, selection='cyclic'):
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self.weakness = weakness
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super(RandomizedLasso, self).__init__(
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alpha=alpha, fit_intercept=fit_intercept, precompute=precompute, copy_X=copy_X,
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max_iter=max_iter, tol=tol, warm_start=warm_start,
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positive=positive, random_state=random_state,
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selection=selection)
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def fit(self, X, y):
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"""Fit the model according to the given training data.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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The training input samples.
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y : array-like, shape = [n_samples]
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The target values.
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"""
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if not isinstance(self.weakness, float) or not (0.0 < self.weakness <= 1.0):
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raise ValueError('weakness should be a float in (0, 1], got %s' % self.weakness)
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X, y = check_X_y(X, y, accept_sparse=True)
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n_features = X.shape[1]
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weakness = 1. - self.weakness
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random_state = check_random_state(self.random_state)
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weights = weakness * random_state.randint(0, 1 + 1, size=(n_features,))
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# TODO: I am afraid this will do double normalization if set to true
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#X, y, _, _ = _preprocess_data(X, y, self.fit_intercept, normalize=self.normalize, copy=False,
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# sample_weight=None, return_mean=False)
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# TODO: Check if this is a problem if it happens before standardization
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X_rescaled = _rescale_data(X, weights)
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return super(RandomizedLasso, self).fit(X_rescaled, y)
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