content: """
  Testing for the forest module (sklearn.ensemble.forest).
  """
  
  # Authors: Gilles Louppe,
  #          Brian Holt,
  #          Andreas Mueller,
  #          Arnaud Joly
  # License: BSD 3 clause
  
  import pickle
  from collections import defaultdict
  from itertools import product
  
  import numpy as np
  from scipy.sparse import csr_matrix, csc_matrix, coo_matrix
  
  from sklearn.utils.testing import assert_almost_equal
  from sklearn.utils.testing import assert_array_almost_equal
  from sklearn.utils.testing import assert_array_equal
  from sklearn.utils.testing import assert_equal
  from sklearn.utils.testing import assert_false, assert_true
  from sklearn.utils.testing import assert_less, assert_greater
  from sklearn.utils.testing import assert_greater_equal
  from sklearn.utils.testing import assert_raises
  from sklearn.utils.testing import assert_warns
  from sklearn.utils.testing import ignore_warnings
  
  from sklearn import datasets
  from sklearn.decomposition import TruncatedSVD
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.ensemble import ExtraTreesRegressor
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble import RandomTreesEmbedding
  from sklearn.grid_search import GridSearchCV
  from sklearn.svm import LinearSVC
  from sklearn.utils.validation import check_random_state
  
  from sklearn.tree.tree import SPARSE_SPLITTERS
  
  # toy sample
  X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
  y = [-1, -1, -1, 1, 1, 1]
  T = [[-1, -1], [2, 2], [3, 2]]
  true_result = [-1, 1, 1]
  
  # also load the iris dataset
  # and randomly permute it
  iris = datasets.load_iris()
  rng = check_random_state(0)
  perm = rng.permutation(iris.target.size)
  iris.data = iris.data[perm]
  iris.target = iris.target[perm]
  
  # also load the boston dataset
  # and randomly permute it
  boston = datasets.load_boston()
  perm = rng.permutation(boston.target.size)
  boston.data = boston.data[perm]
  boston.target = boston.target[perm]
  
  FOREST_CLASSIFIERS = {
      "ExtraTreesClassifier": ExtraTreesClassifier,
      "RandomForestClassifier": RandomForestClassifier,
  }
  
  FOREST_REGRESSORS = {
      "ExtraTreesRegressor": ExtraTreesRegressor,
      "RandomForestRegressor": RandomForestRegressor,
  }
  
  FOREST_TRANSFORMERS = {
      "RandomTreesEmbedding": RandomTreesEmbedding,
  }
  
  FOREST_ESTIMATORS = dict()
  FOREST_ESTIMATORS.update(FOREST_CLASSIFIERS)
  FOREST_ESTIMATORS.update(FOREST_REGRESSORS)
  FOREST_ESTIMATORS.update(FOREST_TRANSFORMERS)
  
  
  def check_classification_toy(name):
      """Check classification on a toy dataset."""
      ForestClassifier = FOREST_CLASSIFIERS[name]
  
      clf = ForestClassifier(n_estimators=10, random_state=1)
      clf.fit(X, y)
      assert_array_equal(clf.predict(T), true_result)
      assert_equal(10, len(clf))
  
      clf = ForestClassifier(n_estimators=10, max_features=1, random_state=1)
      clf.fit(X, y)
      assert_array_equal(clf.predict(T), true_result)
      assert_equal(10, len(clf))
  
      # also test apply
      leaf_indices = clf.apply(X)
      assert_equal(leaf_indices.shape, (len(X), clf.n_estimators))
  
  
  def test_classification_toy():
      for name in FOREST_CLASSIFIERS:
          yield check_classification_toy, name
  
  
  def check_iris_criterion(name, criterion):
      # Check consistency on dataset iris.
      ForestClassifier = FOREST_CLASSIFIERS[name]
  
      clf = ForestClassifier(n_estimators=10, criterion=criterion,
                             random_state=1)
      clf.fit(iris.data, iris.target)
      score = clf.score(iris.data, iris.target)
      assert_greater(score, 0.9, "Failed with criterion %s and score = %f"
                                 % (criterion, score))
  
      clf = ForestClassifier(n_estimators=10, criterion=criterion,
                             max_features=2, random_state=1)
      clf.fit(iris.data, iris.target)
      score = clf.score(iris.data, iris.target)
      assert_greater(score, 0.5, "Failed with criterion %s and score = %f"
                                 % (criterion, score))
  
  
  def test_iris():
      for name, criterion in product(FOREST_CLASSIFIERS, ("gini", "entropy")):
          yield check_iris_criterion, name, criterion
  
  
  def check_boston_criterion(name, criterion):
      # Check consistency on dataset boston house prices.
      ForestRegressor = FOREST_REGRESSORS[name]
  
      clf = ForestRegressor(n_estimators=5, criterion=criterion, random_state=1)
      clf.fit(boston.data, boston.target)
      score = clf.score(boston.data, boston.target)
      assert_greater(score, 0.95, "Failed with max_features=None, criterion %s "
                                  "and score = %f" % (criterion, score))
  
      clf = ForestRegressor(n_estimators=5, criterion=criterion,
                            max_features=6, random_state=1)
      clf.fit(boston.data, boston.target)
      score = clf.score(boston.data, boston.target)
      assert_greater(score, 0.95, "Failed with max_features=6, criterion %s "
                                  "and score = %f" % (criterion, score))
  
  
  def test_boston():
      for name, criterion in product(FOREST_REGRESSORS, ("mse", )):
          yield check_boston_criterion, name, criterion
  
  
  def check_regressor_attributes(name):
      # Regression models should not have a classes_ attribute.
      r = FOREST_REGRESSORS[name](random_state=0)
      assert_false(hasattr(r, "classes_"))
      assert_false(hasattr(r, "n_classes_"))
  
      r.fit([[1, 2, 3], [4, 5, 6]], [1, 2])
      assert_false(hasattr(r, "classes_"))
      assert_false(hasattr(r, "n_classes_"))
  
  
  def test_regressor_attributes():
      for name in FOREST_REGRESSORS:
          yield check_regressor_attributes, name
  
  
  def check_probability(name):
      # Predict probabilities.
      ForestClassifier = FOREST_CLASSIFIERS[name]
      with np.errstate(divide="ignore"):
          clf = ForestClassifier(n_estimators=10, random_state=1, max_features=1,
                                 max_depth=1)
          clf.fit(iris.data, iris.target)
          assert_array_almost_equal(np.sum(clf.predict_proba(iris.data), axis=1),
                                    np.ones(iris.data.shape[0]))
          assert_array_almost_equal(clf.predict_proba(iris.data),
                                    np.exp(clf.predict_log_proba(iris.data)))
  
  
  def test_probability():
      for name in FOREST_CLASSIFIERS:
          yield check_probability, name
  
  
  def check_importances(name, X, y):
      # Check variable importances.
  
      ForestClassifier = FOREST_CLASSIFIERS[name]
      for n_jobs in [1, 2]:
          clf = ForestClassifier(n_estimators=10, n_jobs=n_jobs)
          clf.fit(X, y)
          importances = clf.feature_importances_
          n_important = np.sum(importances > 0.1)
          assert_equal(importances.shape[0], 10)
          assert_equal(n_important, 3)
  
          X_new = clf.transform(X, threshold="mean")
          assert_less(0 < X_new.shape[1], X.shape[1])
  
          # Check with sample weights
          sample_weight = np.ones(y.shape)
          sample_weight[y == 1] *= 100
  
          clf = ForestClassifier(n_estimators=50, n_jobs=n_jobs, random_state=0)
          clf.fit(X, y, sample_weight=sample_weight)
          importances = clf.feature_importances_
          assert_true(np.all(importances >= 0.0))
  
          clf = ForestClassifier(n_estimators=50, n_jobs=n_jobs, random_state=0)
          clf.fit(X, y, sample_weight=3 * sample_weight)
          importances_bis = clf.feature_importances_
          assert_almost_equal(importances, importances_bis)
  
  
  def test_importances():
      X, y = datasets.make_classification(n_samples=1000, n_features=10,
                                          n_informative=3, n_redundant=0,
                                          n_repeated=0, shuffle=False,
                                          random_state=0)
  
      for name in FOREST_CLASSIFIERS:
          yield check_importances, name, X, y
  
  
  def check_unfitted_feature_importances(name):
      assert_raises(ValueError, getattr, FOREST_ESTIMATORS[name](random_state=0),
                    "feature_importances_")
  
  
  def test_unfitted_feature_importances():
      for name in FOREST_ESTIMATORS:
          yield check_unfitted_feature_importances, name
  
  
  def check_oob_score(name, X, y, n_estimators=20):
      # Check that oob prediction is a good estimation of the generalization
      # error.
      # Proper behavior
      est = FOREST_ESTIMATORS[name](oob_score=True, random_state=0,
                                    n_estimators=n_estimators, bootstrap=True)
      n_samples = X.shape[0]
      est.fit(X[:n_samples // 2, :], y[:n_samples // 2])
      test_score = est.score(X[n_samples // 2:, :], y[n_samples // 2:])
  
      if name in FOREST_CLASSIFIERS:
          assert_less(abs(test_score - est.oob_score_), 0.1)
      else:
          assert_greater(test_score, est.oob_score_)
          assert_greater(est.oob_score_, .8)
  
      # Check warning if not enough estimators
      with np.errstate(divide="ignore", invalid="ignore"):
          est = FOREST_ESTIMATORS[name](oob_score=True, random_state=0,
                                        n_estimators=1, bootstrap=True)
          assert_warns(UserWarning, est.fit, X, y)
  
  
  def test_oob_score():
      for name in FOREST_CLASSIFIERS:
          yield check_oob_score, name, iris.data, iris.target
  
          # csc matrix
          yield check_oob_score, name, csc_matrix(iris.data), iris.target
  
          # non-contiguous targets in classification
          yield check_oob_score, name, iris.data, iris.target * 2 + 1
  
      for name in FOREST_REGRESSORS:
          yield check_oob_score, name, boston.data, boston.target, 50
  
          # csc matrix
          yield check_oob_score, name, csc_matrix(boston.data), boston.target, 50
  
  
  def check_oob_score_raise_error(name):
      ForestEstimator = FOREST_ESTIMATORS[name]
  
      if name in FOREST_TRANSFORMERS:
          for oob_score in [True, False]:
              assert_raises(TypeError, ForestEstimator, oob_score=oob_score)
  
          assert_raises(NotImplementedError, ForestEstimator()._set_oob_score,
                        X, y)
  
      else:
          # Unfitted /  no bootstrap / no oob_score
          for oob_score, bootstrap in [(True, False), (False, True),
                                       (False, False)]:
              est = ForestEstimator(oob_score=oob_score, bootstrap=bootstrap,
                                    random_state=0)
              assert_false(hasattr(est, "oob_score_"))
  
          # No bootstrap
          assert_raises(ValueError, ForestEstimator(oob_score=True,
                                                    bootstrap=False).fit, X, y)
  
  
  def test_oob_score_raise_error():
      for name in FOREST_ESTIMATORS:
          yield check_oob_score_raise_error, name
  
  
  def check_gridsearch(name):
      forest = FOREST_CLASSIFIERS[name]()
      clf = GridSearchCV(forest, {'n_estimators': (1, 2), 'max_depth': (1, 2)})
      clf.fit(iris.data, iris.target)
  
  
  def test_gridsearch():
      # Check that base trees can be grid-searched.
      for name in FOREST_CLASSIFIERS:
          yield check_gridsearch, name
  
  
  def check_parallel(name, X, y):
      """Check parallel computations in classification"""
      ForestEstimator = FOREST_ESTIMATORS[name]
      forest = ForestEstimator(n_estimators=10, n_jobs=3, random_state=0)
  
      forest.fit(X, y)
      assert_equal(len(forest), 10)
  
      forest.set_params(n_jobs=1)
      y1 = forest.predict(X)
      forest.set_params(n_jobs=2)
      y2 = forest.predict(X)
      assert_array_almost_equal(y1, y2, 3)
  
  
  def test_parallel():
      for name in FOREST_CLASSIFIERS:
          yield check_parallel, name, iris.data, iris.target
  
      for name in FOREST_REGRESSORS:
          yield check_parallel, name, boston.data, boston.target
  
  
  def check_pickle(name, X, y):
      # Check pickability.
  
      ForestEstimator = FOREST_ESTIMATORS[name]
      obj = ForestEstimator(random_state=0)
      obj.fit(X, y)
      score = obj.score(X, y)
      pickle_object = pickle.dumps(obj)
  
      obj2 = pickle.loads(pickle_object)
      assert_equal(type(obj2), obj.__class__)
      score2 = obj2.score(X, y)
      assert_equal(score, score2)
  
  
  def test_pickle():
      for name in FOREST_CLASSIFIERS:
          yield check_pickle, name, iris.data[::2], iris.target[::2]
  
      for name in FOREST_REGRESSORS:
          yield check_pickle, name, boston.data[::2], boston.target[::2]
  
  
  def check_multioutput(name):
      # Check estimators on multi-output problems.
  
      X_train = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-2, 1],
                 [-1, 1], [-1, 2], [2, -1], [1, -1], [1, -2]]
      y_train = [[-1, 0], [-1, 0], [-1, 0], [1, 1], [1, 1], [1, 1], [-1, 2],
                 [-1, 2], [-1, 2], [1, 3], [1, 3], [1, 3]]
      X_test = [[-1, -1], [1, 1], [-1, 1], [1, -1]]
      y_test = [[-1, 0], [1, 1], [-1, 2], [1, 3]]
  
      est = FOREST_ESTIMATORS[name](random_state=0, bootstrap=False)
      y_pred = est.fit(X_train, y_train).predict(X_test)
      assert_array_almost_equal(y_pred, y_test)
  
      if name in FOREST_CLASSIFIERS:
          with np.errstate(divide="ignore"):
              proba = est.predict_proba(X_test)
              assert_equal(len(proba), 2)
              assert_equal(proba[0].shape, (4, 2))
              assert_equal(proba[1].shape, (4, 4))
  
              log_proba = est.predict_log_proba(X_test)
              assert_equal(len(log_proba), 2)
              assert_equal(log_proba[0].shape, (4, 2))
              assert_equal(log_proba[1].shape, (4, 4))
  
  
  def test_multioutput():
      for name in FOREST_CLASSIFIERS:
          yield check_multioutput, name
  
      for name in FOREST_REGRESSORS:
          yield check_multioutput, name
  
  
  def check_classes_shape(name):
      # Test that n_classes_ and classes_ have proper shape.
      ForestClassifier = FOREST_CLASSIFIERS[name]
  
      # Classification, single output
      clf = ForestClassifier(random_state=0).fit(X, y)
  
      assert_equal(clf.n_classes_, 2)
      assert_array_equal(clf.classes_, [-1, 1])
  
      # Classification, multi-output
      _y = np.vstack((y, np.array(y) * 2)).T
      clf = ForestClassifier(random_state=0).fit(X, _y)
  
      assert_array_equal(clf.n_classes_, [2, 2])
      assert_array_equal(clf.classes_, [[-1, 1], [-2, 2]])
  
  
  def test_classes_shape():
      for name in FOREST_CLASSIFIERS:
          yield check_classes_shape, name
  
  
  def test_random_trees_dense_type():
      # Test that the `sparse_output` parameter of RandomTreesEmbedding
      # works by returning a dense array.
  
      # Create the RTE with sparse=False
      hasher = RandomTreesEmbedding(n_estimators=10, sparse_output=False)
      X, y = datasets.make_circles(factor=0.5)
      X_transformed = hasher.fit_transform(X)
  
      # Assert that type is ndarray, not scipy.sparse.csr.csr_matrix
      assert_equal(type(X_transformed), np.ndarray)
  
  
  def test_random_trees_dense_equal():
      # Test that the `sparse_output` parameter of RandomTreesEmbedding
      # works by returning the same array for both argument values.
  
      # Create the RTEs
      hasher_dense = RandomTreesEmbedding(n_estimators=10, sparse_output=False,
                                          random_state=0)
      hasher_sparse = RandomTreesEmbedding(n_estimators=10, sparse_output=True,
                                           random_state=0)
      X, y = datasets.make_circles(factor=0.5)
      X_transformed_dense = hasher_dense.fit_transform(X)
      X_transformed_sparse = hasher_sparse.fit_transform(X)
  
      # Assert that dense and sparse hashers have same array.
      assert_array_equal(X_transformed_sparse.toarray(), X_transformed_dense)
  
  
  def test_random_hasher():
      # test random forest hashing on circles dataset
      # make sure that it is linearly separable.
      # even after projected to two SVD dimensions
      # Note: Not all random_states produce perfect results.
      hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
      X, y = datasets.make_circles(factor=0.5)
      X_transformed = hasher.fit_transform(X)
  
      # test fit and transform:
      hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
      assert_array_equal(hasher.fit(X).transform(X).toarray(),
                         X_transformed.toarray())
  
      # one leaf active per data point per forest
      assert_equal(X_transformed.shape[0], X.shape[0])
      assert_array_equal(X_transformed.sum(axis=1), hasher.n_estimators)
      svd = TruncatedSVD(n_components=2)
      X_reduced = svd.fit_transform(X_transformed)
      linear_clf = LinearSVC()
      linear_clf.fit(X_reduced, y)
      assert_equal(linear_clf.score(X_reduced, y), 1.)
  
  
  def test_random_hasher_sparse_data():
      X, y = datasets.make_multilabel_classification(return_indicator=True,
                                                     random_state=0)
      hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
      X_transformed = hasher.fit_transform(X)
      X_transformed_sparse = hasher.fit_transform(csc_matrix(X))
      assert_array_equal(X_transformed_sparse.toarray(), X_transformed.toarray())
  
  
  def test_parallel_train():
      rng = check_random_state(12321)
      n_samples, n_features = 80, 30
      X_train = rng.randn(n_samples, n_features)
      y_train = rng.randint(0, 2, n_samples)
  
      clfs = [
          RandomForestClassifier(n_estimators=20, n_jobs=n_jobs,
                                 random_state=12345).fit(X_train, y_train)
          for n_jobs in [1, 2, 3, 8, 16, 32]
      ]
  
      X_test = rng.randn(n_samples, n_features)
      probas = [clf.predict_proba(X_test) for clf in clfs]
      for proba1, proba2 in zip(probas, probas[1:]):
          assert_array_almost_equal(proba1, proba2)
  
  
  def test_distribution():
      rng = check_random_state(12321)
  
      # Single variable with 4 values
      X = rng.randint(0, 4, size=(1000, 1))
      y = rng.rand(1000)
      n_trees = 500
  
      clf = ExtraTreesRegressor(n_estimators=n_trees, random_state=42).fit(X, y)
  
      uniques = defaultdict(int)
      for tree in clf.estimators_:
          tree = "".join(("%d,%d/" % (f, int(t)) if f >= 0 else "-")
                         for f, t in zip(tree.tree_.feature,
                                         tree.tree_.threshold))
  
          uniques[tree] += 1
  
      uniques = sorted([(1. * count / n_trees, tree)
                        for tree, count in uniques.items()])
  
      # On a single variable problem where X_0 has 4 equiprobable values, there
      # are 5 ways to build a random tree. The more compact (0,1/0,0/--0,2/--) of
      # them has probability 1/3 while the 4 others have probability 1/6.
  
      assert_equal(len(uniques), 5)
      assert_greater(0.20, uniques[0][0])  # Rough approximation of 1/6.
      assert_greater(0.20, uniques[1][0])
      assert_greater(0.20, uniques[2][0])
      assert_greater(0.20, uniques[3][0])
      assert_greater(uniques[4][0], 0.3)
      assert_equal(uniques[4][1], "0,1/0,0/--0,2/--")
  
      # Two variables, one with 2 values, one with 3 values
      X = np.empty((1000, 2))
      X[:, 0] = np.random.randint(0, 2, 1000)
      X[:, 1] = np.random.randint(0, 3, 1000)
      y = rng.rand(1000)
  
      clf = ExtraTreesRegressor(n_estimators=100, max_features=1,
                                random_state=1).fit(X, y)
  
      uniques = defaultdict(int)
      for tree in clf.estimators_:
          tree = "".join(("%d,%d/" % (f, int(t)) if f >= 0 else "-")
                         for f, t in zip(tree.tree_.feature,
                                         tree.tree_.threshold))
  
          uniques[tree] += 1
  
      uniques = [(count, tree) for tree, count in uniques.items()]
      assert_equal(len(uniques), 8)
  
  
  def check_max_leaf_nodes_max_depth(name, X, y):
      # Test precedence of max_leaf_nodes over max_depth.
      ForestEstimator = FOREST_ESTIMATORS[name]
      est = ForestEstimator(max_depth=1, max_leaf_nodes=4,
                            n_estimators=1).fit(X, y)
      assert_greater(est.estimators_[0].tree_.max_depth, 1)
  
      est = ForestEstimator(max_depth=1, n_estimators=1).fit(X, y)
      assert_equal(est.estimators_[0].tree_.max_depth, 1)
  
  
  def test_max_leaf_nodes_max_depth():
      X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
      for name in FOREST_ESTIMATORS:
          yield check_max_leaf_nodes_max_depth, name, X, y
  
  
  def check_min_samples_leaf(name, X, y):
      # Test if leaves contain more than leaf_count training examples
      ForestEstimator = FOREST_ESTIMATORS[name]
  
      # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
      # by setting max_leaf_nodes
      for max_leaf_nodes in (None, 1000):
          est = ForestEstimator(min_samples_leaf=5,
                                max_leaf_nodes=max_leaf_nodes,
                                random_state=0)
          est.fit(X, y)
          out = est.estimators_[0].tree_.apply(X)
          node_counts = np.bincount(out)
          # drop inner nodes
          leaf_count = node_counts[node_counts != 0]
          assert_greater(np.min(leaf_count), 4,
                         "Failed with {0}".format(name))
  
  
  def test_min_samples_leaf():
      X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
      X = X.astype(np.float32)
      for name in FOREST_ESTIMATORS:
          yield check_min_samples_leaf, name, X, y
  
  
  def check_min_weight_fraction_leaf(name, X, y):
      # Test if leaves contain at least min_weight_fraction_leaf of the
      # training set
      ForestEstimator = FOREST_ESTIMATORS[name]
      rng = np.random.RandomState(0)
      weights = rng.rand(X.shape[0])
      total_weight = np.sum(weights)
  
      # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
      # by setting max_leaf_nodes
      for max_leaf_nodes in (None, 1000):
          for frac in np.linspace(0, 0.5, 6):
              est = ForestEstimator(min_weight_fraction_leaf=frac,
                                    max_leaf_nodes=max_leaf_nodes,
                                    random_state=0)
              if isinstance(est, (RandomForestClassifier,
                                  RandomForestRegressor)):
                  est.bootstrap = False
              est.fit(X, y, sample_weight=weights)
              out = est.estimators_[0].tree_.apply(X)
              node_weights = np.bincount(out, weights=weights)
              # drop inner nodes
              leaf_weights = node_weights[node_weights != 0]
              assert_greater_equal(
                  np.min(leaf_weights),
                  total_weight * est.min_weight_fraction_leaf,
                  "Failed with {0} "
                  "min_weight_fraction_leaf={1}".format(
                      name, est.min_weight_fraction_leaf))
  
  
  def test_min_weight_fraction_leaf():
      X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
      X = X.astype(np.float32)
      for name in FOREST_ESTIMATORS:
          yield check_min_weight_fraction_leaf, name, X, y
  
  
  def check_sparse_input(name, X, X_sparse, y):
      ForestEstimator = FOREST_ESTIMATORS[name]
  
      dense = ForestEstimator(random_state=0, max_depth=2).fit(X, y)
      sparse = ForestEstimator(random_state=0, max_depth=2).fit(X_sparse, y)
  
      assert_array_almost_equal(sparse.apply(X), dense.apply(X))
  
      if name in FOREST_CLASSIFIERS or name in FOREST_REGRESSORS:
          assert_array_almost_equal(sparse.predict(X), dense.predict(X))
          assert_array_almost_equal(sparse.feature_importances_,
                                    dense.feature_importances_)
  
      if name in FOREST_CLASSIFIERS:
          assert_array_almost_equal(sparse.predict_proba(X),
                                    dense.predict_proba(X))
          assert_array_almost_equal(sparse.predict_log_proba(X),
                                    dense.predict_log_proba(X))
  
      if name in FOREST_TRANSFORMERS:
          assert_array_almost_equal(sparse.transform(X).toarray(),
                                    dense.transform(X).toarray())
          assert_array_almost_equal(sparse.fit_transform(X).toarray(),
                                    dense.fit_transform(X).toarray())
  
  
  def test_sparse_input():
      X, y = datasets.make_multilabel_classification(return_indicator=True,
                                                     random_state=0,
                                                     n_samples=40)
  
      for name, sparse_matrix in product(FOREST_ESTIMATORS,
                                         (csr_matrix, csc_matrix, coo_matrix)):
          yield check_sparse_input, name, X, sparse_matrix(X), y
  
  
  def check_memory_layout(name, dtype):
      # Check that it works no matter the memory layout
  
      est = FOREST_ESTIMATORS[name](random_state=0, bootstrap=False)
  
      # Nothing
      X = np.asarray(iris.data, dtype=dtype)
      y = iris.target
      assert_array_equal(est.fit(X, y).predict(X), y)
  
      # C-order
      X = np.asarray(iris.data, order="C", dtype=dtype)
      y = iris.target
      assert_array_equal(est.fit(X, y).predict(X), y)
  
      # F-order
      X = np.asarray(iris.data, order="F", dtype=dtype)
      y = iris.target
      assert_array_equal(est.fit(X, y).predict(X), y)
  
      # Contiguous
      X = np.ascontiguousarray(iris.data, dtype=dtype)
      y = iris.target
      assert_array_equal(est.fit(X, y).predict(X), y)
  
      if est.base_estimator.splitter in SPARSE_SPLITTERS:
          # csr matrix
          X = csr_matrix(iris.data, dtype=dtype)
          y = iris.target
          assert_array_equal(est.fit(X, y).predict(X), y)
  
          # csc_matrix
          X = csc_matrix(iris.data, dtype=dtype)
          y = iris.target
          assert_array_equal(est.fit(X, y).predict(X), y)
  
          # coo_matrix
          X = coo_matrix(iris.data, dtype=dtype)
          y = iris.target
          assert_array_equal(est.fit(X, y).predict(X), y)
  
      # Strided
      X = np.asarray(iris.data[::3], dtype=dtype)
      y = iris.target[::3]
      assert_array_equal(est.fit(X, y).predict(X), y)
  
  
  def test_memory_layout():
      for name, dtype in product(FOREST_CLASSIFIERS, [np.float64, np.float32]):
          yield check_memory_layout, name, dtype
  
      for name, dtype in product(FOREST_REGRESSORS, [np.float64, np.float32]):
          yield check_memory_layout, name, dtype
  
  
  def check_1d_input(name, X, X_2d, y):
      ForestEstimator = FOREST_ESTIMATORS[name]
      assert_raises(ValueError, ForestEstimator(random_state=0).fit, X, y)
  
      est = ForestEstimator(random_state=0)
      est.fit(X_2d, y)
  
      if name in FOREST_CLASSIFIERS or name in FOREST_REGRESSORS:
          assert_raises(ValueError, est.predict, X)
  
  
  def test_1d_input():
      X = iris.data[:, 0].ravel()
      X_2d = iris.data[:, 0].reshape((-1, 1))
      y = iris.target
  
      for name in FOREST_ESTIMATORS:
          yield check_1d_input, name, X, X_2d, y
  
  
  def check_class_weights(name):
      # Check class_weights resemble sample_weights behavior.
      ForestClassifier = FOREST_CLASSIFIERS[name]
  
      # Iris is balanced, so no effect expected for using 'balanced' weights
      clf1 = ForestClassifier(random_state=0)
      clf1.fit(iris.data, iris.target)
      clf2 = ForestClassifier(class_weight='balanced', random_state=0)
      clf2.fit(iris.data, iris.target)
      assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
  
      # Make a multi-output problem with three copies of Iris
      iris_multi = np.vstack((iris.target, iris.target, iris.target)).T
      # Create user-defined weights that should balance over the outputs
      clf3 = ForestClassifier(class_weight=[{0: 2., 1: 2., 2: 1.},
                                            {0: 2., 1: 1., 2: 2.},
                                            {0: 1., 1: 2., 2: 2.}],
                              random_state=0)
      clf3.fit(iris.data, iris_multi)
      assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_)
      # Check against multi-output "balanced" which should also have no effect
      clf4 = ForestClassifier(class_weight='balanced', random_state=0)
      clf4.fit(iris.data, iris_multi)
      assert_almost_equal(clf3.feature_importances_, clf4.feature_importances_)
  
      # Inflate importance of class 1, check against user-defined weights
      sample_weight = np.ones(iris.target.shape)
      sample_weight[iris.target == 1] *= 100
      class_weight = {0: 1., 1: 100., 2: 1.}
      clf1 = ForestClassifier(random_state=0)
      clf1.fit(iris.data, iris.target, sample_weight)
      clf2 = ForestClassifier(class_weight=class_weight, random_state=0)
      clf2.fit(iris.data, iris.target)
      assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
  
      # Check that sample_weight and class_weight are multiplicative
      clf1 = ForestClassifier(random_state=0)
      clf1.fit(iris.data, iris.target, sample_weight ** 2)
      clf2 = ForestClassifier(class_weight=class_weight, random_state=0)
      clf2.fit(iris.data, iris.target, sample_weight)
      assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
  
  
  def test_class_weights():
      for name in FOREST_CLASSIFIERS:
          yield check_class_weights, name
  
  
  def check_class_weight_balanced_and_bootstrap_multi_output(name):
      # Test class_weight works for multi-output"""
      ForestClassifier = FOREST_CLASSIFIERS[name]
      _y = np.vstack((y, np.array(y) * 2)).T
      clf = ForestClassifier(class_weight='balanced', random_state=0)
      clf.fit(X, _y)
      clf = ForestClassifier(class_weight=[{-1: 0.5, 1: 1.}, {-2: 1., 2: 1.}],
                             random_state=0)
      clf.fit(X, _y)
      # smoke test for subsample and balanced subsample
      clf = ForestClassifier(class_weight='balanced_subsample', random_state=0)
      clf.fit(X, _y)
      clf = ForestClassifier(class_weight='subsample', random_state=0)
      ignore_warnings(clf.fit)(X, _y)
  
  
  def test_class_weight_balanced_and_bootstrap_multi_output():
      for name in FOREST_CLASSIFIERS:
          yield check_class_weight_balanced_and_bootstrap_multi_output, name
  
  
  def check_class_weight_errors(name):
      # Test if class_weight raises errors and warnings when expected.
      ForestClassifier = FOREST_CLASSIFIERS[name]
      _y = np.vstack((y, np.array(y) * 2)).T
  
      # Invalid preset string
      clf = ForestClassifier(class_weight='the larch', random_state=0)
      assert_raises(ValueError, clf.fit, X, y)
      assert_raises(ValueError, clf.fit, X, _y)
  
      # Warning warm_start with preset
      clf = ForestClassifier(class_weight='auto', warm_start=True,
                             random_state=0)
      assert_warns(UserWarning, clf.fit, X, y)
      assert_warns(UserWarning, clf.fit, X, _y)
  
      # Not a list or preset for multi-output
      clf = ForestClassifier(class_weight=1, random_state=0)
      assert_raises(ValueError, clf.fit, X, _y)
  
      # Incorrect length list for multi-output
      clf = ForestClassifier(class_weight=[{-1: 0.5, 1: 1.}], random_state=0)
      assert_raises(ValueError, clf.fit, X, _y)
  
  
  def test_class_weight_errors():
      for name in FOREST_CLASSIFIERS:
          yield check_class_weight_errors, name
  
  
  def check_warm_start(name, random_state=42):
      # Test if fitting incrementally with warm start gives a forest of the
      # right size and the same results as a normal fit.
      X, y = datasets.make_hastie_10_2(n_samples=20, random_state=1)
      ForestEstimator = FOREST_ESTIMATORS[name]
      clf_ws = None
      for n_estimators in [5, 10]:
          if clf_ws is None:
              clf_ws = ForestEstimator(n_estimators=n_estimators,
                                       random_state=random_state,
                                       warm_start=True)
          else:
              clf_ws.set_params(n_estimators=n_estimators)
          clf_ws.fit(X, y)
          assert_equal(len(clf_ws), n_estimators)
  
      clf_no_ws = ForestEstimator(n_estimators=10, random_state=random_state,
                                  warm_start=False)
      clf_no_ws.fit(X, y)
  
      assert_equal(set([tree.random_state for tree in clf_ws]),
                   set([tree.random_state for tree in clf_no_ws]))
  
      assert_array_equal(clf_ws.apply(X), clf_no_ws.apply(X),
                         err_msg="Failed with {0}".format(name))
  
  
  def test_warm_start():
      for name in FOREST_ESTIMATORS:
          yield check_warm_start, name
  
  
  def check_warm_start_clear(name):
      # Test if fit clears state and grows a new forest when warm_start==False.
      X, y = datasets.make_hastie_10_2(n_samples=20, random_state=1)
      ForestEstimator = FOREST_ESTIMATORS[name]
      clf = ForestEstimator(n_estimators=5, max_depth=1, warm_start=False,
                            random_state=1)
      clf.fit(X, y)
  
      clf_2 = ForestEstimator(n_estimators=5, max_depth=1, warm_start=True,
                              random_state=2)
      clf_2.fit(X, y)  # inits state
      clf_2.set_params(warm_start=False, random_state=1)
      clf_2.fit(X, y)  # clears old state and equals clf
  
      assert_array_almost_equal(clf_2.apply(X), clf.apply(X))
  
  
  def test_warm_start_clear():
      for name in FOREST_ESTIMATORS:
          yield check_warm_start_clear, name
  
  
  def check_warm_start_smaller_n_estimators(name):
      # Test if warm start second fit with smaller n_estimators raises error.
      X, y = datasets.make_hastie_10_2(n_samples=20, random_state=1)
      ForestEstimator = FOREST_ESTIMATORS[name]
      clf = ForestEstimator(n_estimators=5, max_depth=1, warm_start=True)
      clf.fit(X, y)
      clf.set_params(n_estimators=4)
      assert_raises(ValueError, clf.fit, X, y)
  
  
  def test_warm_start_smaller_n_estimators():
      for name in FOREST_ESTIMATORS:
          yield check_warm_start_smaller_n_estimators, name
  
  
  def check_warm_start_equal_n_estimators(name):
      # Test if warm start with equal n_estimators does nothing and returns the
      # same forest and raises a warning.
      X, y = datasets.make_hastie_10_2(n_samples=20, random_state=1)
      ForestEstimator = FOREST_ESTIMATORS[name]
      clf = ForestEstimator(n_estimators=5, max_depth=3, warm_start=True,
                            random_state=1)
      clf.fit(X, y)
  
      clf_2 = ForestEstimator(n_estimators=5, max_depth=3, warm_start=True,
                              random_state=1)
      clf_2.fit(X, y)
      # Now clf_2 equals clf.
  
      clf_2.set_params(random_state=2)
      assert_warns(UserWarning, clf_2.fit, X, y)
      # If we had fit the trees again we would have got a different forest as we
      # changed the random state.
      assert_array_equal(clf.apply(X), clf_2.apply(X))
  
  
  def test_warm_start_equal_n_estimators():
      for name in FOREST_ESTIMATORS:
          yield check_warm_start_equal_n_estimators, name
  
  
  def check_warm_start_oob(name):
      # Test that the warm start computes oob score when asked.
      X, y = datasets.make_hastie_10_2(n_samples=20, random_state=1)
      ForestEstimator = FOREST_ESTIMATORS[name]
      # Use 15 estimators to avoid 'some inputs do not have OOB scores' warning.
      clf = ForestEstimator(n_estimators=15, max_depth=3, warm_start=False,
                            random_state=1, bootstrap=True, oob_score=True)
      clf.fit(X, y)
  
      clf_2 = ForestEstimator(n_estimators=5, max_depth=3, warm_start=False,
                              random_state=1, bootstrap=True, oob_score=False)
      clf_2.fit(X, y)
  
      clf_2.set_params(warm_start=True, oob_score=True, n_estimators=15)
      clf_2.fit(X, y)
  
      assert_true(hasattr(clf_2, 'oob_score_'))
      assert_equal(clf.oob_score_, clf_2.oob_score_)
  
      # Test that oob_score is computed even if we don't need to train
      # additional trees.
      clf_3 = ForestEstimator(n_estimators=15, max_depth=3, warm_start=True,
                              random_state=1, bootstrap=True, oob_score=False)
      clf_3.fit(X, y)
      assert_true(not(hasattr(clf_3, 'oob_score_')))
  
      clf_3.set_params(oob_score=True)
      ignore_warnings(clf_3.fit)(X, y)
  
      assert_equal(clf.oob_score_, clf_3.oob_score_)
  
  
  def test_warm_start_oob():
      for name in FOREST_CLASSIFIERS:
          yield check_warm_start_oob, name
      for name in FOREST_REGRESSORS:
          yield check_warm_start_oob, name
  
  
  def test_dtype_convert():
      classifier = RandomForestClassifier()
      CLASSES = 15
      X = np.eye(CLASSES)
      y = [ch for ch in 'ABCDEFGHIJKLMNOPQRSTU'[:CLASSES]]
  
      result = classifier.fit(X, y).predict(X)
      assert_array_equal(result, y)