text: # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
  # License: BSD 3 clause
  
  import numpy as np
  from scipy import sparse
  
  from sklearn.utils.testing import assert_equal
  from sklearn.utils.testing import assert_array_equal
  from sklearn.utils.testing import assert_raises
  
  from sklearn.linear_model.randomized_l1 import (lasso_stability_path,
                                                  RandomizedLasso,
                                                  RandomizedLogisticRegression)
  from sklearn.datasets import load_diabetes, load_iris
  from sklearn.feature_selection import f_regression, f_classif
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model.base import center_data
  
  diabetes = load_diabetes()
  X = diabetes.data
  y = diabetes.target
  X = StandardScaler().fit_transform(X)
  X = X[:, [2, 3, 6, 7, 8]]
  
  # test that the feature score of the best features
  F, _ = f_regression(X, y)
  
  
  def test_lasso_stability_path():
      # Check lasso stability path
      # Load diabetes data and add noisy features
      scaling = 0.3
      coef_grid, scores_path = lasso_stability_path(X, y, scaling=scaling,
                                                    random_state=42,
                                                    n_resampling=30)
  
      assert_array_equal(np.argsort(F)[-3:],
                         np.argsort(np.sum(scores_path, axis=1))[-3:])
  
  
  def test_randomized_lasso():
      # Check randomized lasso
      scaling = 0.3
      selection_threshold = 0.5
  
      # or with 1 alpha
      clf = RandomizedLasso(verbose=False, alpha=1, random_state=42,
                            scaling=scaling,
                            selection_threshold=selection_threshold)
      feature_scores = clf.fit(X, y).scores_
      assert_array_equal(np.argsort(F)[-3:], np.argsort(feature_scores)[-3:])
  
      # or with many alphas
      clf = RandomizedLasso(verbose=False, alpha=[1, 0.8], random_state=42,
                            scaling=scaling,
                            selection_threshold=selection_threshold)
      feature_scores = clf.fit(X, y).scores_
      assert_equal(clf.all_scores_.shape, (X.shape[1], 2))
      assert_array_equal(np.argsort(F)[-3:], np.argsort(feature_scores)[-3:])
  
      X_r = clf.transform(X)
      X_full = clf.inverse_transform(X_r)
      assert_equal(X_r.shape[1], np.sum(feature_scores > selection_threshold))
      assert_equal(X_full.shape, X.shape)
  
      clf = RandomizedLasso(verbose=False, alpha='aic', random_state=42,
                            scaling=scaling)
      feature_scores = clf.fit(X, y).scores_
      assert_array_equal(feature_scores, X.shape[1] * [1.])
  
      clf = RandomizedLasso(verbose=False, scaling=-0.1)
      assert_raises(ValueError, clf.fit, X, y)
  
      clf = RandomizedLasso(verbose=False, scaling=1.1)
      assert_raises(ValueError, clf.fit, X, y)
  
  
  def test_randomized_logistic():
      # Check randomized sparse logistic regression
      iris = load_iris()
      X = iris.data[:, [0, 2]]
      y = iris.target
      X = X[y != 2]
      y = y[y != 2]
  
      F, _ = f_classif(X, y)
  
      scaling = 0.3
      clf = RandomizedLogisticRegression(verbose=False, C=1., random_state=42,
                                         scaling=scaling, n_resampling=50,
                                         tol=1e-3)
      X_orig = X.copy()
      feature_scores = clf.fit(X, y).scores_
      assert_array_equal(X, X_orig)   # fit does not modify X
      assert_array_equal(np.argsort(F), np.argsort(feature_scores))
  
      clf = RandomizedLogisticRegression(verbose=False, C=[1., 0.5],
                                         random_state=42, scaling=scaling,
                                         n_resampling=50, tol=1e-3)
      feature_scores = clf.fit(X, y).scores_
      assert_array_equal(np.argsort(F), np.argsort(feature_scores))
  
  
  def test_randomized_logistic_sparse():
      # Check randomized sparse logistic regression on sparse data
      iris = load_iris()
      X = iris.data[:, [0, 2]]
      y = iris.target
      X = X[y != 2]
      y = y[y != 2]
  
      # center here because sparse matrices are usually not centered
      # labels should not be centered
      X, _, _, _, _ = center_data(X, y, True, True)
  
      X_sp = sparse.csr_matrix(X)
  
      F, _ = f_classif(X, y)
  
      scaling = 0.3
      clf = RandomizedLogisticRegression(verbose=False, C=1., random_state=42,
                                         scaling=scaling, n_resampling=50,
                                         tol=1e-3)
      feature_scores = clf.fit(X, y).scores_
      clf = RandomizedLogisticRegression(verbose=False, C=1., random_state=42,
                                         scaling=scaling, n_resampling=50,
                                         tol=1e-3)
      feature_scores_sp = clf.fit(X_sp, y).scores_
      assert_array_equal(feature_scores, feature_scores_sp)
  