content: 
  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.utils.testing import assert_false
  
  from sklearn.preprocessing.imputation import Imputer
  from sklearn.pipeline import Pipeline
  from sklearn.model_selection import GridSearchCV
  from sklearn import tree
  from sklearn.random_projection import sparse_random_matrix
  
  
  def _check_statistics(X, X_true,
                        strategy, statistics, missing_values):
      """Utility function for testing imputation for a given strategy.
  
      Test:
          - along the two axes
          - with dense and sparse arrays
  
      Check that:
          - the statistics (mean, median, mode) are correct
          - the missing values are imputed correctly"""
  
      err_msg = "Parameters: strategy = %s, missing_values = %s, " \
                "axis = {0}, sparse = {1}" % (strategy, missing_values)
  
      # Normal matrix, axis = 0
      imputer = Imputer(missing_values, strategy=strategy, axis=0)
      X_trans = imputer.fit(X).transform(X.copy())
      assert_array_equal(imputer.statistics_, statistics,
                         err_msg.format(0, False))
      assert_array_equal(X_trans, X_true, err_msg.format(0, False))
  
      # Normal matrix, axis = 1
      imputer = Imputer(missing_values, strategy=strategy, axis=1)
      imputer.fit(X.transpose())
      if np.isnan(statistics).any():
          assert_raises(ValueError, imputer.transform, X.copy().transpose())
      else:
          X_trans = imputer.transform(X.copy().transpose())
          assert_array_equal(X_trans, X_true.transpose(),
                             err_msg.format(1, False))
  
      # Sparse matrix, axis = 0
      imputer = Imputer(missing_values, strategy=strategy, axis=0)
      imputer.fit(sparse.csc_matrix(X))
      X_trans = imputer.transform(sparse.csc_matrix(X.copy()))
  
      if sparse.issparse(X_trans):
          X_trans = X_trans.toarray()
  
      assert_array_equal(imputer.statistics_, statistics,
                         err_msg.format(0, True))
      assert_array_equal(X_trans, X_true, err_msg.format(0, True))
  
      # Sparse matrix, axis = 1
      imputer = Imputer(missing_values, strategy=strategy, axis=1)
      imputer.fit(sparse.csc_matrix(X.transpose()))
      if np.isnan(statistics).any():
          assert_raises(ValueError, imputer.transform,
                        sparse.csc_matrix(X.copy().transpose()))
      else:
          X_trans = imputer.transform(sparse.csc_matrix(X.copy().transpose()))
  
          if sparse.issparse(X_trans):
              X_trans = X_trans.toarray()
  
          assert_array_equal(X_trans, X_true.transpose(),
                             err_msg.format(1, True))
  
  
  def test_imputation_shape():
      # Verify the shapes of the imputed matrix for different strategies.
      X = np.random.randn(10, 2)
      X[::2] = np.nan
  
      for strategy in ['mean', 'median', 'most_frequent']:
          imputer = Imputer(strategy=strategy)
          X_imputed = imputer.fit_transform(X)
          assert_equal(X_imputed.shape, (10, 2))
          X_imputed = imputer.fit_transform(sparse.csr_matrix(X))
          assert_equal(X_imputed.shape, (10, 2))
  
  
  def test_imputation_mean_median_only_zero():
      # Test imputation using the mean and median strategies, when
      # missing_values == 0.
      X = np.array([
          [np.nan, 0, 0, 0, 5],
          [np.nan, 1, 0, np.nan, 3],
          [np.nan, 2, 0, 0, 0],
          [np.nan, 6, 0, 5, 13],
      ])
  
      X_imputed_mean = np.array([
          [3, 5],
          [1, 3],
          [2, 7],
          [6, 13],
      ])
      statistics_mean = [np.nan, 3, np.nan, np.nan, 7]
  
      # Behaviour of median with NaN is undefined, e.g. different results in
      # np.median and np.ma.median
      X_for_median = X[:, [0, 1, 2, 4]]
      X_imputed_median = np.array([
          [2, 5],
          [1, 3],
          [2, 5],
          [6, 13],
      ])
      statistics_median = [np.nan, 2, np.nan, 5]
  
      _check_statistics(X, X_imputed_mean, "mean", statistics_mean, 0)
      _check_statistics(X_for_median, X_imputed_median, "median",
                        statistics_median, 0)
  
  
  def safe_median(arr, *args, **kwargs):
      # np.median([]) raises a TypeError for numpy >= 1.10.1
      length = arr.size if hasattr(arr, 'size') else len(arr)
      return np.nan if length == 0 else np.median(arr, *args, **kwargs)
  
  
  def safe_mean(arr, *args, **kwargs):
      # np.mean([]) raises a RuntimeWarning for numpy >= 1.10.1
      length = arr.size if hasattr(arr, 'size') else len(arr)
      return np.nan if length == 0 else np.mean(arr, *args, **kwargs)
  
  
  def test_imputation_mean_median():
      # Test imputation using the mean and median strategies, when
      # missing_values != 0.
      rng = np.random.RandomState(0)
  
      dim = 10
      dec = 10
      shape = (dim * dim, dim + dec)
  
      zeros = np.zeros(shape[0])
      values = np.arange(1, shape[0] + 1)
      values[4::2] = - values[4::2]
  
      tests = [("mean", "NaN", lambda z, v, p: safe_mean(np.hstack((z, v)))),
               ("mean", 0, lambda z, v, p: np.mean(v)),
               ("median", "NaN", lambda z, v, p: safe_median(np.hstack((z, v)))),
               ("median", 0, lambda z, v, p: np.median(v))]
  
      for strategy, test_missing_values, true_value_fun in tests:
          X = np.empty(shape)
          X_true = np.empty(shape)
          true_statistics = np.empty(shape[1])
  
          # Create a matrix X with columns
          #    - with only zeros,
          #    - with only missing values
          #    - with zeros, missing values and values
          # And a matrix X_true containing all true values
          for j in range(shape[1]):
              nb_zeros = (j - dec + 1 > 0) * (j - dec + 1) * (j - dec + 1)
              nb_missing_values = max(shape[0] + dec * dec
                                      - (j + dec) * (j + dec), 0)
              nb_values = shape[0] - nb_zeros - nb_missing_values
  
              z = zeros[:nb_zeros]
              p = np.repeat(test_missing_values, nb_missing_values)
              v = values[rng.permutation(len(values))[:nb_values]]
  
              true_statistics[j] = true_value_fun(z, v, p)
  
              # Create the columns
              X[:, j] = np.hstack((v, z, p))
  
              if 0 == test_missing_values:
                  X_true[:, j] = np.hstack((v,
                                            np.repeat(
                                                true_statistics[j],
                                                nb_missing_values + nb_zeros)))
              else:
                  X_true[:, j] = np.hstack((v,
                                            z,
                                            np.repeat(true_statistics[j],
                                                      nb_missing_values)))
  
              # Shuffle them the same way
              np.random.RandomState(j).shuffle(X[:, j])
              np.random.RandomState(j).shuffle(X_true[:, j])
  
          # Mean doesn't support columns containing NaNs, median does
          if strategy == "median":
              cols_to_keep = ~np.isnan(X_true).any(axis=0)
          else:
              cols_to_keep = ~np.isnan(X_true).all(axis=0)
  
          X_true = X_true[:, cols_to_keep]
  
          _check_statistics(X, X_true, strategy,
                            true_statistics, test_missing_values)
  
  
  def test_imputation_median_special_cases():
      # Test median imputation with sparse boundary cases
      X = np.array([
          [0, np.nan, np.nan],  # odd: implicit zero
          [5, np.nan, np.nan],  # odd: explicit nonzero
          [0, 0, np.nan],    # even: average two zeros
          [-5, 0, np.nan],   # even: avg zero and neg
          [0, 5, np.nan],    # even: avg zero and pos
          [4, 5, np.nan],    # even: avg nonzeros
          [-4, -5, np.nan],  # even: avg negatives
          [-1, 2, np.nan],   # even: crossing neg and pos
      ]).transpose()
  
      X_imputed_median = np.array([
          [0, 0, 0],
          [5, 5, 5],
          [0, 0, 0],
          [-5, 0, -2.5],
          [0, 5, 2.5],
          [4, 5, 4.5],
          [-4, -5, -4.5],
          [-1, 2, .5],
      ]).transpose()
      statistics_median = [0, 5, 0, -2.5, 2.5, 4.5, -4.5, .5]
  
      _check_statistics(X, X_imputed_median, "median",
                        statistics_median, 'NaN')
  
  
  def test_imputation_most_frequent():
      # Test imputation using the most-frequent strategy.
      X = np.array([
          [-1, -1, 0, 5],
          [-1, 2, -1, 3],
          [-1, 1, 3, -1],
          [-1, 2, 3, 7],
      ])
  
      X_true = np.array([
          [2, 0, 5],
          [2, 3, 3],
          [1, 3, 3],
          [2, 3, 7],
      ])
  
      # scipy.stats.mode, used in Imputer, doesn't return the first most
      # frequent as promised in the doc but the lowest most frequent. When this
      # test will fail after an update of scipy, Imputer will need to be updated
      # to be consistent with the new (correct) behaviour
      _check_statistics(X, X_true, "most_frequent", [np.nan, 2, 3, 3], -1)
  
  
  def test_imputation_pipeline_grid_search():
      # Test imputation within a pipeline + gridsearch.
      pipeline = Pipeline([('imputer', Imputer(missing_values=0)),
                           ('tree', tree.DecisionTreeRegressor(random_state=0))])
  
      parameters = {
          'imputer__strategy': ["mean", "median", "most_frequent"],
          'imputer__axis': [0, 1]
      }
  
      l = 100
      X = sparse_random_matrix(l, l, density=0.10)
      Y = sparse_random_matrix(l, 1, density=0.10).toarray()
      gs = GridSearchCV(pipeline, parameters)
      gs.fit(X, Y)
  
  
  def test_imputation_pickle():
      # Test for pickling imputers.
      import pickle
  
      l = 100
      X = sparse_random_matrix(l, l, density=0.10)
  
      for strategy in ["mean", "median", "most_frequent"]:
          imputer = Imputer(missing_values=0, strategy=strategy)
          imputer.fit(X)
  
          imputer_pickled = pickle.loads(pickle.dumps(imputer))
  
          assert_array_equal(imputer.transform(X.copy()),
                             imputer_pickled.transform(X.copy()),
                             "Fail to transform the data after pickling "
                             "(strategy = %s)" % (strategy))
  
  
  def test_imputation_copy():
      # Test imputation with copy
      X_orig = sparse_random_matrix(5, 5, density=0.75, random_state=0)
  
      # copy=True, dense => copy
      X = X_orig.copy().toarray()
      imputer = Imputer(missing_values=0, strategy="mean", copy=True)
      Xt = imputer.fit(X).transform(X)
      Xt[0, 0] = -1
      assert_false(np.all(X == Xt))
  
      # copy=True, sparse csr => copy
      X = X_orig.copy()
      imputer = Imputer(missing_values=X.data[0], strategy="mean", copy=True)
      Xt = imputer.fit(X).transform(X)
      Xt.data[0] = -1
      assert_false(np.all(X.data == Xt.data))
  
      # copy=False, dense => no copy
      X = X_orig.copy().toarray()
      imputer = Imputer(missing_values=0, strategy="mean", copy=False)
      Xt = imputer.fit(X).transform(X)
      Xt[0, 0] = -1
      assert_array_equal(X, Xt)
  
      # copy=False, sparse csr, axis=1 => no copy
      X = X_orig.copy()
      imputer = Imputer(missing_values=X.data[0], strategy="mean",
                        copy=False, axis=1)
      Xt = imputer.fit(X).transform(X)
      Xt.data[0] = -1
      assert_array_equal(X.data, Xt.data)
  
      # copy=False, sparse csc, axis=0 => no copy
      X = X_orig.copy().tocsc()
      imputer = Imputer(missing_values=X.data[0], strategy="mean",
                        copy=False, axis=0)
      Xt = imputer.fit(X).transform(X)
      Xt.data[0] = -1
      assert_array_equal(X.data, Xt.data)
  
      # copy=False, sparse csr, axis=0 => copy
      X = X_orig.copy()
      imputer = Imputer(missing_values=X.data[0], strategy="mean",
                        copy=False, axis=0)
      Xt = imputer.fit(X).transform(X)
      Xt.data[0] = -1
      assert_false(np.all(X.data == Xt.data))
  
      # copy=False, sparse csc, axis=1 => copy
      X = X_orig.copy().tocsc()
      imputer = Imputer(missing_values=X.data[0], strategy="mean",
                        copy=False, axis=1)
      Xt = imputer.fit(X).transform(X)
      Xt.data[0] = -1
      assert_false(np.all(X.data == Xt.data))
  
      # copy=False, sparse csr, axis=1, missing_values=0 => copy
      X = X_orig.copy()
      imputer = Imputer(missing_values=0, strategy="mean",
                        copy=False, axis=1)
      Xt = imputer.fit(X).transform(X)
      assert_false(sparse.issparse(Xt))
  
      # Note: If X is sparse and if missing_values=0, then a (dense) copy of X is
      # made, even if copy=False.
  