Module Expr Str ImportFrom alias Import alias ImportFrom alias ImportFrom alias alias Import alias identifier ImportFrom alias ImportFrom alias identifier ImportFrom alias alias ImportFrom alias alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias alias alias alias ImportFrom alias alias ImportFrom alias alias alias ImportFrom alias alias ImportFrom alias alias ImportFrom alias alias ImportFrom alias Assign NameStore ListLoad Str Str Str Str Str Assign NameStore AttributeLoad Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr attr FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr Num NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad keyword NameLoad Assign NameStore CompareEq NameLoad Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore SubscriptLoad NameLoad Index NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num NameLoad body Expr Str If CompareGt NameLoad Num body Print BinOpMod Str TupleLoad BinOpAdd NameLoad Num NameLoad If AttributeLoad NameLoad attr body Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num If CompareIs NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr TupleLoad NameLoad keyword AttributeLoad NameLoad attr orelse Assign NameStore Call AttributeLoad NameLoad attr Assign NameStore Call NameLoad AttributeLoad NameLoad attr NameLoad Assign NameStore Call NameLoad NameLoad keyword NameLoad AugAssignMult NameStore NameLoad If CompareEq NameLoad Str body With Call AttributeLoad NameLoad attr body Expr Call AttributeLoad NameLoad attr Str NameLoad AugAssignMult NameStore Call NameLoad Str NameLoad NameLoad orelse If CompareEq NameLoad Str body AugAssignMult NameStore Call NameLoad Str NameLoad NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad keyword NameLoad keyword NameLoad orelse Expr Call AttributeLoad NameLoad attr NameLoad NameLoad keyword NameLoad keyword NameLoad Return NameLoad decorator_list ClassDef bases Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Call NameLoad NameLoad NameLoad Num NameLoad Num NameLoad NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list NameLoad FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call Call NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword Str GeneratorExp Call Call NameLoad NameLoad NameLoad Str NameLoad keyword NameLoad comprehension NameStore AttributeLoad NameLoad attr Return AttributeLoad Call AttributeLoad NameLoad attr NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call Call NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword Str GeneratorExp Call Call NameLoad NameLoad NameLoad Str NameLoad keyword NameLoad comprehension NameStore AttributeLoad NameLoad attr Assign NameStore ListLoad Num Expr Call AttributeLoad NameLoad attr ListComp SubscriptLoad AttributeLoad NameLoad attr Index Num comprehension NameStore NameLoad Assign NameStore Call AttributeLoad Call AttributeLoad NameLoad attr NameLoad attr Return TupleLoad Call AttributeLoad Call NameLoad NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad keyword Str keyword NameLoad Assign NameStore Call NameLoad NameLoad keyword Str keyword NameLoad keyword NameLoad If Call NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr Assign TupleStore NameStore AttributeStore NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad If BoolOpAnd CompareEq AttributeLoad NameLoad attr Num CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num Num body Expr Call NameLoad Str NameLoad keyword Num If CompareEq AttributeLoad NameLoad attr Num body Assign NameStore Call AttributeLoad NameLoad attr NameLoad TupleLoad Num Num Assign AttributeStore NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad If BoolOpOr CompareNotEq Call NameLoad NameLoad Str NameLoad NameLoad UnaryOpNot AttributeLoad AttributeLoad NameLoad attr attr body Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword NameLoad If CompareIsNot NameLoad NameLoad body If CompareIsNot NameLoad NameLoad body Assign NameStore BinOpMult NameLoad NameLoad orelse Assign NameStore NameLoad Expr Call AttributeLoad NameLoad attr If BoolOpAnd UnaryOpNot AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Raise Call NameLoad Str Assign NameStore Call NameLoad AttributeLoad NameLoad attr If UnaryOpNot AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr ListLoad Assign NameStore BinOpSub AttributeLoad NameLoad attr Call NameLoad AttributeLoad NameLoad attr If CompareLt NameLoad Num body Raise Call NameLoad BinOpMod Str TupleLoad AttributeLoad NameLoad attr Call NameLoad AttributeLoad NameLoad attr orelse If CompareEq NameLoad Num body Expr Call NameLoad Str orelse If BoolOpAnd AttributeLoad NameLoad attr CompareGt Call NameLoad AttributeLoad NameLoad attr Num body Expr Call AttributeLoad NameLoad attr NameLoad keyword Call NameLoad AttributeLoad NameLoad attr Assign NameStore ListLoad For NameStore Call NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr keyword NameLoad Expr Call AttributeLoad NameLoad attr keyword Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call Call NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword Str GeneratorExp Call Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Call NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr comprehension TupleStore NameStore NameStore Call NameLoad NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad If AttributeLoad NameLoad attr body Expr Call AttributeLoad NameLoad attr NameLoad NameLoad If BoolOpAnd Call NameLoad NameLoad Str CompareEq AttributeLoad NameLoad attr Num body Assign AttributeStore NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num Assign AttributeStore NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str decorator_list NameLoad FunctionDef arguments args NameParam NameParam defaults body Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str If BoolOpOr CompareIs AttributeLoad NameLoad attr NameLoad CompareEq Call NameLoad AttributeLoad NameLoad attr Num body Raise Call NameLoad Str Return Call AttributeLoad SubscriptLoad AttributeLoad NameLoad attr Index Num attr NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam defaults body Expr Str If BoolOpOr CompareIs AttributeLoad NameLoad attr NameLoad CompareEq Call NameLoad AttributeLoad NameLoad attr Num body Raise Call NameLoad Str Assign NameStore Call Call NameLoad keyword AttributeLoad NameLoad attr keyword Str GeneratorExp Call Call NameLoad NameLoad NameLoad Str comprehension NameStore AttributeLoad NameLoad attr Return BinOpDiv Call NameLoad NameLoad Call NameLoad AttributeLoad NameLoad attr decorator_list NameLoad decorator_list ClassDef bases Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Call NameLoad NameLoad NameLoad Num NameLoad Num NameLoad NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad decorator_list NameLoad FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assign NameStore Call NameLoad NameLoad keyword NameLoad keyword Str Assign NameStore AttributeLoad NameLoad attr Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore ListLoad Assign NameStore Num Assign NameStore ListLoad For NameStore Call NameLoad AttributeLoad NameLoad attr body Expr Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr TupleLoad NameLoad SubscriptLoad NameLoad Index NameLoad For NameStore AttributeLoad NameLoad attr body Assign NameStore Call NameLoad AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad ExtSlice Index NameLoad Slice keyword NameLoad If CompareEq AttributeLoad NameLoad attr Num body Assign NameStore ListLoad NameLoad For NameStore Call NameLoad AttributeLoad NameLoad attr body AugAssignAdd SubscriptStore SubscriptLoad NameLoad Index NameLoad ExtSlice Index NameLoad Slice SubscriptLoad NameLoad Index NameLoad For NameStore Call NameLoad AttributeLoad NameLoad attr body If Call AttributeLoad CompareEq Call AttributeLoad SubscriptLoad NameLoad Index NameLoad attr keyword Num Num attr body Expr Call NameLoad Str Assign NameStore BinOpDiv SubscriptLoad NameLoad Index NameLoad SubscriptLoad Call AttributeLoad SubscriptLoad NameLoad Index NameLoad attr keyword Num ExtSlice Slice Index AttributeLoad NameLoad attr Expr Call AttributeLoad NameLoad attr NameLoad AugAssignAdd NameStore Call AttributeLoad NameLoad attr CompareEq SubscriptLoad NameLoad ExtSlice Slice Index NameLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index NameLoad keyword Num keyword Num If CompareEq AttributeLoad NameLoad attr Num body Assign AttributeStore NameLoad attr SubscriptLoad NameLoad Index Num orelse Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr BinOpDiv NameLoad AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Call NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore NameLoad If CompareIsNot AttributeLoad NameLoad attr NameLoad body Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign AttributeStore NameLoad attr ListLoad Assign AttributeStore NameLoad attr ListLoad Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr For NameStore Call NameLoad AttributeLoad NameLoad attr body Assign TupleStore NameStore SubscriptStore NameLoad ExtSlice Slice Index NameLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad ExtSlice Slice Index NameLoad keyword NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore NameLoad If CompareIsNot AttributeLoad NameLoad attr NameLoad body Assign NameStore TupleLoad Str Str Str Str If Call NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr body If CompareNotIn AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If CompareEq AttributeLoad NameLoad attr Str body Expr Call NameLoad Str NameLoad If AttributeLoad NameLoad attr body Expr Call NameLoad Str If BoolOpOr CompareNotIn AttributeLoad NameLoad attr ListLoad Str Str UnaryOpNot AttributeLoad NameLoad attr body If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Str orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Str orelse Assign NameStore AttributeLoad NameLoad attr With Call AttributeLoad NameLoad attr body If CompareEq NameLoad Str body Expr Call AttributeLoad NameLoad attr Str NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq AttributeLoad NameLoad attr Num body Return Call AttributeLoad AttributeLoad NameLoad attr attr Call AttributeLoad NameLoad attr NameLoad keyword Num keyword Num orelse Assign NameStore SubscriptLoad AttributeLoad SubscriptLoad NameLoad Index Num attr Index Num Assign NameStore Call AttributeLoad NameLoad attr TupleLoad NameLoad AttributeLoad NameLoad attr For NameStore Call NameLoad AttributeLoad NameLoad attr body Assign SubscriptStore NameLoad ExtSlice Slice Index NameLoad Call AttributeLoad SubscriptLoad AttributeLoad NameLoad attr Index NameLoad attr Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index NameLoad keyword Num keyword Num Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign TupleStore NameStore NameStore NameStore Call NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call Call NameLoad keyword NameLoad keyword AttributeLoad NameLoad attr keyword Str GeneratorExp Call Call NameLoad NameLoad NameLoad Str NameLoad keyword NameLoad comprehension NameStore AttributeLoad NameLoad attr Assign NameStore SubscriptLoad NameLoad Index Num If CompareEq AttributeLoad NameLoad attr Num body For NameStore Call NameLoad Num Call NameLoad NameLoad body AugAssignAdd NameStore SubscriptLoad NameLoad Index NameLoad AugAssignDiv NameStore Call NameLoad AttributeLoad NameLoad attr orelse For NameStore Call NameLoad Num Call NameLoad NameLoad body For NameStore Call NameLoad AttributeLoad NameLoad attr body AugAssignAdd SubscriptStore NameLoad Index NameLoad SubscriptLoad SubscriptLoad NameLoad Index NameLoad Index NameLoad For NameStore Call NameLoad AttributeLoad NameLoad attr body AugAssignDiv SubscriptStore NameLoad Index NameLoad AttributeLoad NameLoad attr Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq AttributeLoad NameLoad attr Num body Return Call AttributeLoad NameLoad attr NameLoad orelse For NameStore Call NameLoad AttributeLoad NameLoad attr body Assign SubscriptStore NameLoad Index NameLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index NameLoad Return NameLoad decorator_list decorator_list ClassDef bases Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Call NameLoad NameLoad NameLoad Num NameLoad Num NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad decorator_list NameLoad FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign TupleStore NameStore NameStore NameStore Call NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call Call NameLoad keyword NameLoad keyword AttributeLoad NameLoad attr keyword Str GeneratorExp Call Call NameLoad NameLoad NameLoad Str NameLoad keyword NameLoad comprehension NameStore AttributeLoad NameLoad attr Assign NameStore BinOpDiv Call NameLoad NameLoad Call NameLoad AttributeLoad NameLoad attr Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assign NameStore Call NameLoad NameLoad keyword NameLoad keyword Str Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call AttributeLoad NameLoad attr TupleLoad NameLoad AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr TupleLoad NameLoad AttributeLoad NameLoad attr For NameStore AttributeLoad NameLoad attr body Assign NameStore Call NameLoad AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad ExtSlice Index NameLoad Slice keyword NameLoad If CompareEq AttributeLoad NameLoad attr Num body Assign NameStore SubscriptLoad NameLoad ExtSlice Slice Index AttributeLoad NameLoad attr AugAssignAdd SubscriptStore NameLoad ExtSlice Index NameLoad Slice NameLoad AugAssignAdd SubscriptStore NameLoad ExtSlice Index NameLoad Slice Num If Call AttributeLoad CompareEq NameLoad Num attr body Expr Call NameLoad Str Assign SubscriptStore NameLoad Index CompareEq NameLoad Num Num AugAssignDiv NameStore NameLoad Assign AttributeStore NameLoad attr NameLoad If CompareEq AttributeLoad NameLoad attr Num body Assign AttributeStore NameLoad attr Call AttributeLoad AttributeLoad NameLoad attr attr TupleLoad NameLoad Assign AttributeStore NameLoad attr Num For NameStore Call NameLoad AttributeLoad NameLoad attr body AugAssignAdd AttributeStore NameLoad attr Call NameLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad AugAssignDiv AttributeStore NameLoad attr AttributeLoad NameLoad attr decorator_list decorator_list ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Str NameLoad Num Num Num Str NameLoad NameLoad NameLoad Num NameLoad Num NameLoad NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword Call NameLoad keyword NameLoad keyword TupleLoad Str Str Str Str Str Str Str Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list decorator_list ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Str NameLoad Num Num Num Str NameLoad NameLoad NameLoad Num NameLoad Num NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword Call NameLoad keyword NameLoad keyword TupleLoad Str Str Str Str Str Str Str Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list decorator_list ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Str NameLoad Num Num Num Str NameLoad NameLoad NameLoad Num NameLoad Num NameLoad NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword Call NameLoad keyword NameLoad keyword TupleLoad Str Str Str Str Str Str Str Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list decorator_list ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Str NameLoad Num Num Num Str NameLoad NameLoad NameLoad Num NameLoad Num NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword Call NameLoad keyword NameLoad keyword TupleLoad Str Str Str Str Str Str Str Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list decorator_list ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Num Num Num Num NameLoad NameLoad Num NameLoad Num NameLoad body Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword Call NameLoad keyword NameLoad keyword TupleLoad Str Str Str Str Str Str Str Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr Str Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr Num Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Raise Call NameLoad Str decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad NameLoad body Expr Str Expr Call AttributeLoad NameLoad attr NameLoad NameLoad keyword NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad keyword ListLoad Str keyword NameLoad If Call NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr Assign NameStore Call NameLoad AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr keyword SubscriptLoad AttributeLoad NameLoad attr Index Num Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr NameLoad NameLoad keyword NameLoad Assign AttributeStore NameLoad attr Call NameLoad keyword AttributeLoad NameLoad attr Return Call AttributeLoad AttributeLoad NameLoad attr attr Call AttributeLoad NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Return Call AttributeLoad AttributeLoad NameLoad attr attr Call AttributeLoad NameLoad attr NameLoad decorator_list decorator_list
N/A N/A Forest of trees-based ensemble methods

Those methods include random forests and extremely randomized trees.

The module structure is the following:

- The ``BaseForest`` base class implements a common ``fit`` method for all
  the estimators in the module. The ``fit`` method of the base ``Forest``
  class calls the ``fit`` method of each sub-estimator on random samples
  (with replacement, a.k.a. bootstrap) of the training set.

  The init of the sub-estimator is further delegated to the
  ``BaseEnsemble`` constructor.

- The ``ForestClassifier`` and ``ForestRegressor`` base classes further
  implement the prediction logic by computing an average of the predicted
  outcomes of the sub-estimators.

- The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived
  classes provide the user with concrete implementations of
  the forest ensemble method using classical, deterministic
  ``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as
  sub-estimator implementations.

- The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived
  classes provide the user with concrete implementations of the
  forest ensemble method using the extremely randomized trees
  ``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as
  sub-estimator implementations.

Single and multi-output problems are both handled.

 __future__ division N/A warnings warnings warn abc ABCMeta abstractmethod N/A numpy np scipy.sparse issparse scipy.sparse hstack sparse_hstack base ClassifierMixin RegressorMixin externals.joblib Parallel delayed externals six feature_selection.from_model _LearntSelectorMixin metrics r2_score preprocessing OneHotEncoder tree DecisionTreeClassifier DecisionTreeRegressor ExtraTreeClassifier ExtraTreeRegressor tree._tree DTYPE DOUBLE utils check_random_state check_array compute_sample_weight exceptions DataConversionWarning NotFittedError base BaseEnsemble _partition_estimators utils.fixes bincount parallel_helper utils.multiclass check_classification_targets N/A __all__ N/A RandomForestClassifier RandomForestRegressor ExtraTreesClassifier ExtraTreesRegressor RandomTreesEmbedding N/A MAX_INT N/A N/A N/A np iinfo N/A np int32 max _generate_sample_indices N/A N/A random_state n_samples N/A N/A N/A Private function used to _parallel_build_trees function. N/A random_instance N/A check_random_state random_state N/A sample_indices N/A N/A random_instance randint 0 n_samples n_samples N/A sample_indices N/A _generate_unsampled_indices N/A N/A random_state n_samples N/A N/A N/A Private function used to forest._set_oob_score function. N/A sample_indices N/A _generate_sample_indices random_state n_samples N/A sample_counts N/A bincount sample_indices minlength n_samples N/A unsampled_mask N/A sample_counts 0 N/A indices_range N/A N/A np arange n_samples N/A unsampled_indices N/A indices_range N/A unsampled_mask N/A unsampled_indices N/A _parallel_build_trees N/A N/A tree forest X y sample_weight tree_idx n_trees verbose class_weight N/A 0 None N/A N/A Private function used to fit a single tree in parallel. N/A N/A verbose 1 N/A N/A N/A building tree %d of %d N/A N/A tree_idx 1 n_trees N/A N/A forest bootstrap N/A N/A n_samples N/A N/A X shape N/A 0 N/A N/A sample_weight None N/A N/A curr_sample_weight N/A N/A np ones N/A n_samples dtype N/A np float64 N/A N/A curr_sample_weight N/A N/A sample_weight copy N/A indices N/A _generate_sample_indices N/A tree random_state n_samples N/A sample_counts N/A bincount indices minlength n_samples N/A curr_sample_weight sample_counts N/A N/A class_weight subsample N/A N/A N/A N/A warnings catch_warnings N/A N/A N/A N/A warnings simplefilter ignore DeprecationWarning N/A curr_sample_weight N/A compute_sample_weight auto y indices N/A N/A N/A class_weight balanced_subsample N/A N/A curr_sample_weight N/A compute_sample_weight balanced y indices N/A N/A N/A tree fit X y sample_weight curr_sample_weight check_input False N/A N/A N/A N/A tree fit X y sample_weight sample_weight check_input False N/A tree N/A BaseForest N/A N/A N/A six with_metaclass ABCMeta BaseEnsemble _LearntSelectorMixin N/A N/A Base class for forests of trees.

    Warning: This class should not be used directly. Use derived classes
    instead.
     __init__ N/A N/A self base_estimator n_estimators estimator_params bootstrap oob_score n_jobs random_state verbose warm_start class_weight N/A 10 N/A tuple False False 1 None 0 False None N/A N/A N/A N/A N/A super BaseForest self __init__ base_estimator base_estimator n_estimators n_estimators estimator_params estimator_params N/A N/A self bootstrap bootstrap N/A N/A self oob_score oob_score N/A N/A self n_jobs n_jobs N/A N/A self random_state random_state N/A N/A self verbose verbose N/A N/A self warm_start warm_start N/A N/A self class_weight class_weight N/A abstractmethod apply N/A N/A self X N/A N/A N/A Apply trees in the forest to X, return leaf indices.

        Parameters
        ----------
        X : array-like or sparse matrix, shape = [n_samples, n_features]
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        X_leaves : array_like, shape = [n_samples, n_estimators]
            For each datapoint x in X and for each tree in the forest,
            return the index of the leaf x ends up in.
         N/A X N/A N/A self _validate_X_predict X N/A results N/A N/A Parallel n_jobs N/A self n_jobs verbose N/A self verbose backend threading N/A N/A N/A delayed parallel_helper tree apply X check_input False N/A tree N/A self estimators_ N/A N/A N/A N/A np array results T N/A decision_path N/A N/A self X N/A N/A N/A Return the decision path in the forest

        Parameters
        ----------
        X : array-like or sparse matrix, shape = [n_samples, n_features]
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        indicator : sparse csr array, shape = [n_samples, n_nodes]
            Return a node indicator matrix where non zero elements
            indicates that the samples goes through the nodes.

        n_nodes_ptr : array of size (n_estimators + 1, )
            The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
            gives the indicator value for the i-th estimator.
         N/A X N/A N/A self _validate_X_predict X N/A indicators N/A N/A Parallel n_jobs N/A self n_jobs verbose N/A self verbose backend threading N/A N/A N/A delayed parallel_helper tree decision_path X check_input False N/A tree N/A self estimators_ N/A n_nodes N/A 0 N/A N/A N/A n_nodes extend N/A N/A N/A i shape N/A 1 N/A i indicators N/A n_nodes_ptr N/A N/A N/A N/A np array n_nodes cumsum N/A N/A N/A N/A N/A sparse_hstack indicators tocsr n_nodes_ptr N/A fit N/A N/A self X y sample_weight N/A None N/A N/A Build a forest of trees from the training set (X, y).

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            The training input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csc_matrix``.

        y : array-like, shape = [n_samples] or [n_samples, n_outputs]
            The target values (class labels in classification, real numbers in
            regression).

        sample_weight : array-like, shape = [n_samples] or None
            Sample weights. If None, then samples are equally weighted. Splits
            that would create child nodes with net zero or negative weight are
            ignored while searching for a split in each node. In the case of
            classification, splits are also ignored if they would result in any
            single class carrying a negative weight in either child node.

        Returns
        -------
        self : object
            Returns self.
         N/A X N/A check_array X accept_sparse csc dtype DTYPE N/A y N/A check_array y accept_sparse csc ensure_2d False dtype None N/A N/A issparse X N/A N/A N/A N/A X sort_indices N/A N/A n_samples N/A self n_features_ N/A X shape N/A y N/A N/A np atleast_1d y N/A N/A N/A N/A y ndim 2 N/A N/A N/A y shape N/A 1 1 N/A N/A N/A warn A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). DataConversionWarning stacklevel 2 N/A N/A N/A y ndim 1 N/A N/A y N/A N/A np reshape y N/A -1 1 N/A N/A self n_outputs_ N/A N/A y shape N/A 1 N/A N/A y expanded_class_weight N/A N/A self _validate_y_class_weight y N/A N/A N/A N/A getattr y dtype None DOUBLE N/A N/A N/A y flags contiguous N/A N/A y N/A N/A np ascontiguousarray y dtype DOUBLE N/A N/A expanded_class_weight None N/A N/A N/A sample_weight None N/A N/A sample_weight N/A sample_weight expanded_class_weight N/A N/A sample_weight expanded_class_weight N/A N/A N/A self _validate_estimator N/A N/A N/A N/A self bootstrap N/A self oob_score N/A N/A N/A ValueError Out of bag estimation only available if bootstrap=True N/A random_state N/A check_random_state N/A self random_state N/A N/A N/A self warm_start N/A N/A N/A self estimators_ N/A N/A n_more_estimators N/A N/A self n_estimators N/A len N/A self estimators_ N/A N/A n_more_estimators 0 N/A N/A N/A ValueError N/A n_estimators=%d must be larger or equal to len(estimators_)=%d when warm_start==True N/A N/A self n_estimators N/A len N/A self estimators_ N/A N/A N/A n_more_estimators 0 N/A N/A N/A warn Warm-start fitting without increasing n_estimators does not fit new trees. N/A N/A N/A N/A self warm_start N/A N/A len N/A self estimators_ 0 N/A N/A N/A N/A random_state randint MAX_INT size N/A len N/A self estimators_ N/A trees N/A N/A i N/A range n_more_estimators N/A N/A tree N/A N/A self _make_estimator append False N/A N/A N/A tree set_params random_state N/A N/A random_state randint MAX_INT N/A N/A N/A trees append tree N/A trees N/A N/A Parallel n_jobs N/A self n_jobs verbose N/A self verbose backend threading N/A N/A N/A delayed _parallel_build_trees t self X y sample_weight i N/A len trees verbose N/A self verbose class_weight N/A self class_weight N/A N/A i t N/A enumerate trees N/A N/A N/A N/A self estimators_ extend trees N/A N/A self oob_score N/A N/A N/A N/A self _set_oob_score X y N/A N/A N/A hasattr self classes_ N/A N/A self n_outputs_ 1 N/A N/A N/A self n_classes_ N/A N/A self n_classes_ N/A 0 N/A N/A self classes_ N/A N/A self classes_ N/A 0 N/A self N/A _set_oob_score N/A N/A self X y N/A N/A N/A Calculate out of bag predictions and score. N/A abstractmethod _validate_y_class_weight N/A N/A self y N/A N/A N/A N/A y None N/A _validate_X_predict N/A N/A self X N/A N/A N/A Validate X whenever one tries to predict, apply, predict_proba N/A N/A N/A N/A self estimators_ None N/A N/A len N/A self estimators_ 0 N/A N/A N/A NotFittedError Estimator not fitted, call `fit` before exploiting the model. N/A N/A N/A N/A N/A self estimators_ N/A 0 _validate_X_predict X check_input True N/A feature_importances_ N/A N/A self N/A N/A N/A Return the feature importances (the higher, the more important the
           feature).

        Returns
        -------
        feature_importances_ : array, shape = [n_features]
         N/A N/A N/A N/A self estimators_ None N/A N/A len N/A self estimators_ 0 N/A N/A N/A NotFittedError Estimator not fitted, call `fit` before `feature_importances_`. N/A all_importances N/A N/A Parallel n_jobs N/A self n_jobs backend threading N/A N/A N/A delayed getattr tree feature_importances_ N/A tree N/A self estimators_ N/A N/A N/A sum all_importances N/A len N/A self estimators_ N/A property N/A ForestClassifier N/A N/A N/A six with_metaclass ABCMeta BaseForest ClassifierMixin N/A N/A Base class for forest of trees-based classifiers.

    Warning: This class should not be used directly. Use derived classes
    instead.
     __init__ N/A N/A self base_estimator n_estimators estimator_params bootstrap oob_score n_jobs random_state verbose warm_start class_weight N/A 10 N/A tuple False False 1 None 0 False None N/A N/A N/A N/A N/A super ForestClassifier self __init__ base_estimator n_estimators n_estimators estimator_params estimator_params bootstrap bootstrap oob_score oob_score n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start class_weight class_weight N/A abstractmethod _set_oob_score N/A N/A self X y N/A N/A N/A Compute out-of-bag score N/A X N/A check_array X dtype DTYPE accept_sparse csr N/A n_classes_ N/A self n_classes_ N/A n_samples N/A N/A y shape N/A 0 N/A oob_decision_function N/A N/A oob_score 0.0 N/A predictions N/A N/A k N/A range N/A self n_outputs_ N/A N/A N/A N/A predictions append N/A N/A np zeros N/A n_samples N/A n_classes_ N/A k N/A estimator N/A self estimators_ N/A N/A unsampled_indices N/A _generate_unsampled_indices N/A estimator random_state n_samples N/A p_estimator N/A N/A estimator predict_proba N/A X N/A N/A unsampled_indices N/A check_input False N/A N/A N/A self n_outputs_ 1 N/A N/A p_estimator N/A p_estimator N/A k N/A range N/A self n_outputs_ N/A N/A N/A N/A predictions N/A k N/A N/A unsampled_indices N/A N/A p_estimator N/A k N/A k N/A range N/A self n_outputs_ N/A N/A N/A N/A N/A N/A N/A N/A predictions N/A k sum axis 1 0 any N/A N/A N/A warn Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates. N/A decision N/A N/A predictions N/A k N/A N/A N/A N/A predictions N/A k sum axis 1 N/A N/A N/A N/A np newaxis N/A N/A N/A oob_decision_function append decision N/A oob_score N/A N/A np mean N/A N/A y N/A N/A N/A k N/A N/A np argmax N/A predictions N/A k axis 1 axis 0 N/A N/A N/A self n_outputs_ 1 N/A N/A N/A self oob_decision_function_ N/A oob_decision_function N/A 0 N/A N/A N/A self oob_decision_function_ oob_decision_function N/A N/A self oob_score_ N/A oob_score N/A self n_outputs_ N/A _validate_y_class_weight N/A N/A self y N/A N/A N/A N/A check_classification_targets y N/A y N/A N/A np copy y N/A expanded_class_weight None N/A N/A N/A self class_weight None N/A N/A y_original N/A N/A np copy y N/A N/A self classes_ N/A N/A N/A self n_classes_ N/A N/A y_store_unique_indices N/A N/A np zeros N/A y shape dtype N/A np int N/A k N/A range N/A self n_outputs_ N/A N/A N/A classes_k N/A y_store_unique_indices N/A N/A N/A k N/A N/A np unique N/A y N/A N/A N/A k return_inverse True N/A N/A N/A N/A self classes_ append classes_k N/A N/A N/A N/A self n_classes_ append N/A N/A classes_k shape N/A 0 N/A y y_store_unique_indices N/A N/A N/A self class_weight None N/A N/A valid_presets N/A auto balanced subsample balanced_subsample N/A N/A isinstance N/A self class_weight N/A six string_types N/A N/A N/A N/A self class_weight valid_presets N/A N/A N/A ValueError N/A Valid presets for class_weight include "balanced" and "balanced_subsample". Given "%s". N/A self class_weight N/A N/A N/A self class_weight subsample N/A N/A N/A warn class_weight='subsample' is deprecated in 0.17 andwill be removed in 0.19. It was replaced by class_weight='balanced_subsample' using the balancedstrategy. DeprecationWarning N/A N/A self warm_start N/A N/A N/A warn class_weight presets "balanced" or "balanced_subsample" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use "balanced" weights, use compute_class_weight("balanced", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter. N/A N/A N/A N/A self class_weight N/A subsample balanced_subsample N/A N/A self bootstrap N/A N/A N/A N/A self class_weight subsample N/A N/A class_weight auto N/A N/A N/A N/A self class_weight balanced_subsample N/A N/A class_weight balanced N/A N/A class_weight N/A self class_weight N/A N/A N/A warnings catch_warnings N/A N/A N/A class_weight auto N/A N/A N/A N/A warnings simplefilter ignore DeprecationWarning N/A expanded_class_weight N/A compute_sample_weight class_weight y_original N/A N/A y expanded_class_weight N/A predict N/A N/A self X N/A N/A N/A Predict class for X.

        The predicted class of an input sample is a vote by the trees in
        the forest, weighted by their probability estimates. That is,
        the predicted class is the one with highest mean probability
        estimate across the trees.

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        y : array of shape = [n_samples] or [n_samples, n_outputs]
            The predicted classes.
         N/A proba N/A N/A self predict_proba X N/A N/A N/A self n_outputs_ 1 N/A N/A N/A N/A N/A self classes_ take N/A N/A np argmax proba axis 1 axis 0 N/A N/A n_samples N/A N/A N/A proba N/A 0 shape N/A 0 N/A predictions N/A N/A np zeros N/A n_samples N/A self n_outputs_ N/A k N/A range N/A self n_outputs_ N/A N/A N/A predictions N/A N/A N/A k N/A N/A N/A N/A self classes_ N/A k take N/A N/A np argmax N/A proba N/A k axis 1 axis 0 N/A predictions N/A predict_proba N/A N/A self X N/A N/A N/A Predict class probabilities for X.

        The predicted class probabilities of an input sample is computed as
        the mean predicted class probabilities of the trees in the forest. The
        class probability of a single tree is the fraction of samples of the same
        class in a leaf.

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        p : array of shape = [n_samples, n_classes], or a list of n_outputs
            such arrays if n_outputs > 1.
            The class probabilities of the input samples. The order of the
            classes corresponds to that in the attribute `classes_`.
         N/A X N/A N/A self _validate_X_predict X N/A N/A n_jobs _ _ N/A _partition_estimators N/A self n_estimators N/A self n_jobs N/A all_proba N/A N/A Parallel n_jobs n_jobs verbose N/A self verbose backend threading N/A N/A N/A delayed parallel_helper e predict_proba X check_input False N/A e N/A self estimators_ N/A proba N/A all_proba N/A 0 N/A N/A N/A self n_outputs_ 1 N/A N/A j N/A range 1 N/A len all_proba N/A N/A proba N/A all_proba N/A j N/A proba N/A len N/A self estimators_ N/A N/A j N/A range 1 N/A len all_proba N/A N/A k N/A range N/A self n_outputs_ N/A N/A N/A proba N/A k N/A N/A all_proba N/A j N/A k N/A k N/A range N/A self n_outputs_ N/A N/A N/A proba N/A k N/A self n_estimators N/A proba N/A predict_log_proba N/A N/A self X N/A N/A N/A Predict class log-probabilities for X.

        The predicted class log-probabilities of an input sample is computed as
        the log of the mean predicted class probabilities of the trees in the
        forest.

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        p : array of shape = [n_samples, n_classes], or a list of n_outputs
            such arrays if n_outputs > 1.
            The class probabilities of the input samples. The order of the
            classes corresponds to that in the attribute `classes_`.
         N/A proba N/A N/A self predict_proba X N/A N/A N/A self n_outputs_ 1 N/A N/A N/A N/A np log proba N/A N/A k N/A range N/A self n_outputs_ N/A N/A N/A proba N/A k N/A N/A np log N/A proba N/A k N/A proba N/A N/A ForestRegressor N/A N/A N/A six with_metaclass ABCMeta BaseForest RegressorMixin N/A N/A Base class for forest of trees-based regressors.

    Warning: This class should not be used directly. Use derived classes
    instead.
     __init__ N/A N/A self base_estimator n_estimators estimator_params bootstrap oob_score n_jobs random_state verbose warm_start N/A 10 N/A tuple False False 1 None 0 False N/A N/A N/A N/A N/A super ForestRegressor self __init__ base_estimator n_estimators n_estimators estimator_params estimator_params bootstrap bootstrap oob_score oob_score n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start N/A abstractmethod predict N/A N/A self X N/A N/A N/A Predict regression target for X.

        The predicted regression target of an input sample is computed as the
        mean predicted regression targets of the trees in the forest.

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        y : array of shape = [n_samples] or [n_samples, n_outputs]
            The predicted values.
         N/A X N/A N/A self _validate_X_predict X N/A N/A n_jobs _ _ N/A _partition_estimators N/A self n_estimators N/A self n_jobs N/A all_y_hat N/A N/A Parallel n_jobs n_jobs verbose N/A self verbose backend threading N/A N/A N/A delayed parallel_helper e predict X check_input False N/A e N/A self estimators_ N/A y_hat N/A N/A sum all_y_hat N/A len N/A self estimators_ N/A y_hat N/A _set_oob_score N/A N/A self X y N/A N/A N/A Compute out-of-bag scores N/A X N/A check_array X dtype DTYPE accept_sparse csr N/A n_samples N/A N/A y shape N/A 0 N/A predictions N/A N/A np zeros N/A n_samples N/A self n_outputs_ N/A n_predictions N/A N/A np zeros N/A n_samples N/A self n_outputs_ N/A estimator N/A self estimators_ N/A N/A unsampled_indices N/A _generate_unsampled_indices N/A estimator random_state n_samples N/A p_estimator N/A N/A estimator predict N/A X N/A N/A unsampled_indices N/A check_input False N/A N/A N/A self n_outputs_ 1 N/A N/A p_estimator N/A p_estimator N/A N/A N/A N/A np newaxis N/A N/A predictions N/A N/A unsampled_indices N/A p_estimator N/A N/A n_predictions N/A N/A unsampled_indices N/A 1 N/A N/A N/A N/A n_predictions 0 any N/A N/A N/A warn Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates. N/A N/A n_predictions N/A N/A n_predictions 0 1 N/A predictions n_predictions N/A N/A self oob_prediction_ predictions N/A N/A N/A self n_outputs_ 1 N/A N/A N/A self oob_prediction_ N/A N/A N/A self oob_prediction_ reshape N/A n_samples N/A N/A self oob_score_ 0.0 N/A k N/A range N/A self n_outputs_ N/A N/A N/A self oob_score_ N/A r2_score N/A y N/A N/A N/A k N/A predictions N/A N/A N/A k N/A N/A self oob_score_ N/A self n_outputs_ N/A N/A RandomForestClassifier N/A ForestClassifier N/A N/A A random forest classifier.

    A random forest is a meta estimator that fits a number of decision tree
    classifiers on various sub-samples of the dataset and use averaging to
    improve the predictive accuracy and control over-fitting.
    The sub-sample size is always the same as the original
    input sample size but the samples are drawn with replacement if
    `bootstrap=True` (default).

    Read more in the :ref:`User Guide <forest>`.

    Parameters
    ----------
    n_estimators : integer, optional (default=10)
        The number of trees in the forest.

    criterion : string, optional (default="gini")
        The function to measure the quality of a split. Supported criteria are
        "gini" for the Gini impurity and "entropy" for the information gain.
        Note: this parameter is tree-specific.

    max_features : int, float, string or None, optional (default="auto")
        The number of features to consider when looking for the best split:

        - If int, then consider `max_features` features at each split.
        - If float, then `max_features` is a percentage and
          `int(max_features * n_features)` features are considered at each
          split.
        - If "auto", then `max_features=sqrt(n_features)`.
        - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
        - If "log2", then `max_features=log2(n_features)`.
        - If None, then `max_features=n_features`.

        Note: the search for a split does not stop until at least one
        valid partition of the node samples is found, even if it requires to
        effectively inspect more than ``max_features`` features.

    max_depth : integer or None, optional (default=None)
        The maximum depth of the tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.
        Ignored if ``max_leaf_nodes`` is not None.

    min_samples_split : int, float, optional (default=2)
        The minimum number of samples required to split an internal node:

        - If int, then consider `min_samples_split` as the minimum number.
        - If float, then `min_samples_split` is a percentage and
          `ceil(min_samples_split * n_samples)` are the minimum
          number of samples for each split.

    min_samples_leaf : int, float, optional (default=1)
        The minimum number of samples required to be at a leaf node:

        - If int, then consider `min_samples_leaf` as the minimum number.
        - If float, then `min_samples_leaf` is a percentage and
          `ceil(min_samples_leaf * n_samples)` are the minimum
          number of samples for each node.

    min_weight_fraction_leaf : float, optional (default=0.)
        The minimum weighted fraction of the input samples required to be at a
        leaf node.

    max_leaf_nodes : int or None, optional (default=None)
        Grow trees with ``max_leaf_nodes`` in best-first fashion.
        Best nodes are defined as relative reduction in impurity.
        If None then unlimited number of leaf nodes.
        If not None then ``max_depth`` will be ignored.

    bootstrap : boolean, optional (default=True)
        Whether bootstrap samples are used when building trees.

    oob_score : bool
        Whether to use out-of-bag samples to estimate
        the generalization accuracy.

    n_jobs : integer, optional (default=1)
        The number of jobs to run in parallel for both `fit` and `predict`.
        If -1, then the number of jobs is set to the number of cores.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    verbose : int, optional (default=0)
        Controls the verbosity of the tree building process.

    warm_start : bool, optional (default=False)
        When set to ``True``, reuse the solution of the previous call to fit
        and add more estimators to the ensemble, otherwise, just fit a whole
        new forest.

    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional
        Weights associated with classes in the form ``{class_label: weight}``.
        If not given, all classes are supposed to have weight one. For
        multi-output problems, a list of dicts can be provided in the same
        order as the columns of y.

        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``

        The "balanced_subsample" mode is the same as "balanced" except that
        weights are computed based on the bootstrap sample for every tree
        grown.

        For multi-output, the weights of each column of y will be multiplied.

        Note that these weights will be multiplied with sample_weight (passed
        through the fit method) if sample_weight is specified.

    Attributes
    ----------
    estimators_ : list of DecisionTreeClassifier
        The collection of fitted sub-estimators.

    classes_ : array of shape = [n_classes] or a list of such arrays
        The classes labels (single output problem), or a list of arrays of
        class labels (multi-output problem).

    n_classes_ : int or list
        The number of classes (single output problem), or a list containing the
        number of classes for each output (multi-output problem).

    n_features_ : int
        The number of features when ``fit`` is performed.

    n_outputs_ : int
        The number of outputs when ``fit`` is performed.

    feature_importances_ : array of shape = [n_features]
        The feature importances (the higher, the more important the feature).

    oob_score_ : float
        Score of the training dataset obtained using an out-of-bag estimate.

    oob_decision_function_ : array of shape = [n_samples, n_classes]
        Decision function computed with out-of-bag estimate on the training
        set. If n_estimators is small it might be possible that a data point
        was never left out during the bootstrap. In this case,
        `oob_decision_function_` might contain NaN.

    References
    ----------

    .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.

    See also
    --------
    DecisionTreeClassifier, ExtraTreesClassifier
     __init__ N/A N/A self n_estimators criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes bootstrap oob_score n_jobs random_state verbose warm_start class_weight N/A 10 gini None 2 1 0.0 auto None True False 1 None 0 False None N/A N/A N/A N/A N/A super RandomForestClassifier self __init__ base_estimator N/A DecisionTreeClassifier n_estimators n_estimators estimator_params N/A criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes random_state bootstrap bootstrap oob_score oob_score n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start class_weight class_weight N/A N/A self criterion criterion N/A N/A self max_depth max_depth N/A N/A self min_samples_split min_samples_split N/A N/A self min_samples_leaf min_samples_leaf N/A N/A self min_weight_fraction_leaf min_weight_fraction_leaf N/A N/A self max_features max_features N/A N/A self max_leaf_nodes max_leaf_nodes N/A N/A RandomForestRegressor N/A ForestRegressor N/A N/A A random forest regressor.

    A random forest is a meta estimator that fits a number of classifying
    decision trees on various sub-samples of the dataset and use averaging
    to improve the predictive accuracy and control over-fitting.
    The sub-sample size is always the same as the original
    input sample size but the samples are drawn with replacement if
    `bootstrap=True` (default).

    Read more in the :ref:`User Guide <forest>`.

    Parameters
    ----------
    n_estimators : integer, optional (default=10)
        The number of trees in the forest.

    criterion : string, optional (default="mse")
        The function to measure the quality of a split. The only supported
        criterion is "mse" for the mean squared error.

    max_features : int, float, string or None, optional (default="auto")
        The number of features to consider when looking for the best split:

        - If int, then consider `max_features` features at each split.
        - If float, then `max_features` is a percentage and
          `int(max_features * n_features)` features are considered at each
          split.
        - If "auto", then `max_features=n_features`.
        - If "sqrt", then `max_features=sqrt(n_features)`.
        - If "log2", then `max_features=log2(n_features)`.
        - If None, then `max_features=n_features`.

        Note: the search for a split does not stop until at least one
        valid partition of the node samples is found, even if it requires to
        effectively inspect more than ``max_features`` features.

    max_depth : integer or None, optional (default=None)
        The maximum depth of the tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.
        Ignored if ``max_leaf_nodes`` is not None.

    min_samples_split : int, float, optional (default=2)
        The minimum number of samples required to split an internal node:

        - If int, then consider `min_samples_split` as the minimum number.
        - If float, then `min_samples_split` is a percentage and
          `ceil(min_samples_split * n_samples)` are the minimum
          number of samples for each split.

    min_samples_leaf : int, float, optional (default=1)
        The minimum number of samples required to be at a leaf node:

        - If int, then consider `min_samples_leaf` as the minimum number.
        - If float, then `min_samples_leaf` is a percentage and
          `ceil(min_samples_leaf * n_samples)` are the minimum
          number of samples for each node.

    min_weight_fraction_leaf : float, optional (default=0.)
        The minimum weighted fraction of the input samples required to be at a
        leaf node.

    max_leaf_nodes : int or None, optional (default=None)
        Grow trees with ``max_leaf_nodes`` in best-first fashion.
        Best nodes are defined as relative reduction in impurity.
        If None then unlimited number of leaf nodes.
        If not None then ``max_depth`` will be ignored.

    bootstrap : boolean, optional (default=True)
        Whether bootstrap samples are used when building trees.

    oob_score : bool, optional (default=False)
        whether to use out-of-bag samples to estimate
        the R^2 on unseen data.

    n_jobs : integer, optional (default=1)
        The number of jobs to run in parallel for both `fit` and `predict`.
        If -1, then the number of jobs is set to the number of cores.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    verbose : int, optional (default=0)
        Controls the verbosity of the tree building process.

    warm_start : bool, optional (default=False)
        When set to ``True``, reuse the solution of the previous call to fit
        and add more estimators to the ensemble, otherwise, just fit a whole
        new forest.

    Attributes
    ----------
    estimators_ : list of DecisionTreeRegressor
        The collection of fitted sub-estimators.

    feature_importances_ : array of shape = [n_features]
        The feature importances (the higher, the more important the feature).

    n_features_ : int
        The number of features when ``fit`` is performed.

    n_outputs_ : int
        The number of outputs when ``fit`` is performed.

    oob_score_ : float
        Score of the training dataset obtained using an out-of-bag estimate.

    oob_prediction_ : array of shape = [n_samples]
        Prediction computed with out-of-bag estimate on the training set.

    References
    ----------

    .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.

    See also
    --------
    DecisionTreeRegressor, ExtraTreesRegressor
     __init__ N/A N/A self n_estimators criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes bootstrap oob_score n_jobs random_state verbose warm_start N/A 10 mse None 2 1 0.0 auto None True False 1 None 0 False N/A N/A N/A N/A N/A super RandomForestRegressor self __init__ base_estimator N/A DecisionTreeRegressor n_estimators n_estimators estimator_params N/A criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes random_state bootstrap bootstrap oob_score oob_score n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start N/A N/A self criterion criterion N/A N/A self max_depth max_depth N/A N/A self min_samples_split min_samples_split N/A N/A self min_samples_leaf min_samples_leaf N/A N/A self min_weight_fraction_leaf min_weight_fraction_leaf N/A N/A self max_features max_features N/A N/A self max_leaf_nodes max_leaf_nodes N/A N/A ExtraTreesClassifier N/A ForestClassifier N/A N/A An extra-trees classifier.

    This class implements a meta estimator that fits a number of
    randomized decision trees (a.k.a. extra-trees) on various sub-samples
    of the dataset and use averaging to improve the predictive accuracy
    and control over-fitting.

    Read more in the :ref:`User Guide <forest>`.

    Parameters
    ----------
    n_estimators : integer, optional (default=10)
        The number of trees in the forest.

    criterion : string, optional (default="gini")
        The function to measure the quality of a split. Supported criteria are
        "gini" for the Gini impurity and "entropy" for the information gain.

    max_features : int, float, string or None, optional (default="auto")
        The number of features to consider when looking for the best split:

        - If int, then consider `max_features` features at each split.
        - If float, then `max_features` is a percentage and
          `int(max_features * n_features)` features are considered at each
          split.
        - If "auto", then `max_features=sqrt(n_features)`.
        - If "sqrt", then `max_features=sqrt(n_features)`.
        - If "log2", then `max_features=log2(n_features)`.
        - If None, then `max_features=n_features`.

        Note: the search for a split does not stop until at least one
        valid partition of the node samples is found, even if it requires to
        effectively inspect more than ``max_features`` features.

    max_depth : integer or None, optional (default=None)
        The maximum depth of the tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.
        Ignored if ``max_leaf_nodes`` is not None.

    min_samples_split : int, float, optional (default=2)
        The minimum number of samples required to split an internal node:

        - If int, then consider `min_samples_split` as the minimum number.
        - If float, then `min_samples_split` is a percentage and
          `ceil(min_samples_split * n_samples)` are the minimum
          number of samples for each split.

    min_samples_leaf : int, float, optional (default=1)
        The minimum number of samples required to be at a leaf node:

        - If int, then consider `min_samples_leaf` as the minimum number.
        - If float, then `min_samples_leaf` is a percentage and
          `ceil(min_samples_leaf * n_samples)` are the minimum
          number of samples for each node.

    min_weight_fraction_leaf : float, optional (default=0.)
        The minimum weighted fraction of the input samples required to be at a
        leaf node.

    max_leaf_nodes : int or None, optional (default=None)
        Grow trees with ``max_leaf_nodes`` in best-first fashion.
        Best nodes are defined as relative reduction in impurity.
        If None then unlimited number of leaf nodes.
        If not None then ``max_depth`` will be ignored.

    bootstrap : boolean, optional (default=False)
        Whether bootstrap samples are used when building trees.

    oob_score : bool, optional (default=False)
        Whether to use out-of-bag samples to estimate
        the generalization accuracy.

    n_jobs : integer, optional (default=1)
        The number of jobs to run in parallel for both `fit` and `predict`.
        If -1, then the number of jobs is set to the number of cores.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    verbose : int, optional (default=0)
        Controls the verbosity of the tree building process.

    warm_start : bool, optional (default=False)
        When set to ``True``, reuse the solution of the previous call to fit
        and add more estimators to the ensemble, otherwise, just fit a whole
        new forest.

    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional
        Weights associated with classes in the form ``{class_label: weight}``.
        If not given, all classes are supposed to have weight one. For
        multi-output problems, a list of dicts can be provided in the same
        order as the columns of y.

        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``

        The "balanced_subsample" mode is the same as "balanced" except that weights are
        computed based on the bootstrap sample for every tree grown.

        For multi-output, the weights of each column of y will be multiplied.

        Note that these weights will be multiplied with sample_weight (passed
        through the fit method) if sample_weight is specified.

    Attributes
    ----------
    estimators_ : list of DecisionTreeClassifier
        The collection of fitted sub-estimators.

    classes_ : array of shape = [n_classes] or a list of such arrays
        The classes labels (single output problem), or a list of arrays of
        class labels (multi-output problem).

    n_classes_ : int or list
        The number of classes (single output problem), or a list containing the
        number of classes for each output (multi-output problem).

    feature_importances_ : array of shape = [n_features]
        The feature importances (the higher, the more important the feature).

    n_features_ : int
        The number of features when ``fit`` is performed.

    n_outputs_ : int
        The number of outputs when ``fit`` is performed.

    oob_score_ : float
        Score of the training dataset obtained using an out-of-bag estimate.

    oob_decision_function_ : array of shape = [n_samples, n_classes]
        Decision function computed with out-of-bag estimate on the training
        set. If n_estimators is small it might be possible that a data point
        was never left out during the bootstrap. In this case,
        `oob_decision_function_` might contain NaN.

    References
    ----------

    .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
           Machine Learning, 63(1), 3-42, 2006.

    See also
    --------
    sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble.
    RandomForestClassifier : Ensemble Classifier based on trees with optimal
        splits.
     __init__ N/A N/A self n_estimators criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes bootstrap oob_score n_jobs random_state verbose warm_start class_weight N/A 10 gini None 2 1 0.0 auto None False False 1 None 0 False None N/A N/A N/A N/A N/A super ExtraTreesClassifier self __init__ base_estimator N/A ExtraTreeClassifier n_estimators n_estimators estimator_params N/A criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes random_state bootstrap bootstrap oob_score oob_score n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start class_weight class_weight N/A N/A self criterion criterion N/A N/A self max_depth max_depth N/A N/A self min_samples_split min_samples_split N/A N/A self min_samples_leaf min_samples_leaf N/A N/A self min_weight_fraction_leaf min_weight_fraction_leaf N/A N/A self max_features max_features N/A N/A self max_leaf_nodes max_leaf_nodes N/A N/A ExtraTreesRegressor N/A ForestRegressor N/A N/A An extra-trees regressor.

    This class implements a meta estimator that fits a number of
    randomized decision trees (a.k.a. extra-trees) on various sub-samples
    of the dataset and use averaging to improve the predictive accuracy
    and control over-fitting.

    Read more in the :ref:`User Guide <forest>`.

    Parameters
    ----------
    n_estimators : integer, optional (default=10)
        The number of trees in the forest.

    criterion : string, optional (default="mse")
        The function to measure the quality of a split. The only supported
        criterion is "mse" for the mean squared error.

    max_features : int, float, string or None, optional (default="auto")
        The number of features to consider when looking for the best split:

        - If int, then consider `max_features` features at each split.
        - If float, then `max_features` is a percentage and
          `int(max_features * n_features)` features are considered at each
          split.
        - If "auto", then `max_features=n_features`.
        - If "sqrt", then `max_features=sqrt(n_features)`.
        - If "log2", then `max_features=log2(n_features)`.
        - If None, then `max_features=n_features`.

        Note: the search for a split does not stop until at least one
        valid partition of the node samples is found, even if it requires to
        effectively inspect more than ``max_features`` features.

    max_depth : integer or None, optional (default=None)
        The maximum depth of the tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.
        Ignored if ``max_leaf_nodes`` is not None.

    min_samples_split : int, float, optional (default=2)
        The minimum number of samples required to split an internal node:

        - If int, then consider `min_samples_split` as the minimum number.
        - If float, then `min_samples_split` is a percentage and
          `ceil(min_samples_split * n_samples)` are the minimum
          number of samples for each split.

    min_samples_leaf : int, float, optional (default=1)
        The minimum number of samples required to be at a leaf node:

        - If int, then consider `min_samples_leaf` as the minimum number.
        - If float, then `min_samples_leaf` is a percentage and
          `ceil(min_samples_leaf * n_samples)` are the minimum
          number of samples for each node.

    min_weight_fraction_leaf : float, optional (default=0.)
        The minimum weighted fraction of the input samples required to be at a
        leaf node.

    max_leaf_nodes : int or None, optional (default=None)
        Grow trees with ``max_leaf_nodes`` in best-first fashion.
        Best nodes are defined as relative reduction in impurity.
        If None then unlimited number of leaf nodes.
        If not None then ``max_depth`` will be ignored.

    bootstrap : boolean, optional (default=False)
        Whether bootstrap samples are used when building trees.

    oob_score : bool, optional (default=False)
        Whether to use out-of-bag samples to estimate the R^2 on unseen data.

    n_jobs : integer, optional (default=1)
        The number of jobs to run in parallel for both `fit` and `predict`.
        If -1, then the number of jobs is set to the number of cores.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    verbose : int, optional (default=0)
        Controls the verbosity of the tree building process.

    warm_start : bool, optional (default=False)
        When set to ``True``, reuse the solution of the previous call to fit
        and add more estimators to the ensemble, otherwise, just fit a whole
        new forest.

    Attributes
    ----------
    estimators_ : list of DecisionTreeRegressor
        The collection of fitted sub-estimators.

    feature_importances_ : array of shape = [n_features]
        The feature importances (the higher, the more important the feature).

    n_features_ : int
        The number of features.

    n_outputs_ : int
        The number of outputs.

    oob_score_ : float
        Score of the training dataset obtained using an out-of-bag estimate.

    oob_prediction_ : array of shape = [n_samples]
        Prediction computed with out-of-bag estimate on the training set.

    References
    ----------

    .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
           Machine Learning, 63(1), 3-42, 2006.

    See also
    --------
    sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble.
    RandomForestRegressor: Ensemble regressor using trees with optimal splits.
     __init__ N/A N/A self n_estimators criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes bootstrap oob_score n_jobs random_state verbose warm_start N/A 10 mse None 2 1 0.0 auto None False False 1 None 0 False N/A N/A N/A N/A N/A super ExtraTreesRegressor self __init__ base_estimator N/A ExtraTreeRegressor n_estimators n_estimators estimator_params N/A criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes random_state bootstrap bootstrap oob_score oob_score n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start N/A N/A self criterion criterion N/A N/A self max_depth max_depth N/A N/A self min_samples_split min_samples_split N/A N/A self min_samples_leaf min_samples_leaf N/A N/A self min_weight_fraction_leaf min_weight_fraction_leaf N/A N/A self max_features max_features N/A N/A self max_leaf_nodes max_leaf_nodes N/A N/A RandomTreesEmbedding N/A BaseForest N/A N/A An ensemble of totally random trees.

    An unsupervised transformation of a dataset to a high-dimensional
    sparse representation. A datapoint is coded according to which leaf of
    each tree it is sorted into. Using a one-hot encoding of the leaves,
    this leads to a binary coding with as many ones as there are trees in
    the forest.

    The dimensionality of the resulting representation is
    ``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``,
    the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.

    Read more in the :ref:`User Guide <random_trees_embedding>`.

    Parameters
    ----------
    n_estimators : int
        Number of trees in the forest.

    max_depth : int
        The maximum depth of each tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.
        Ignored if ``max_leaf_nodes`` is not None.

    min_samples_split : int, float, optional (default=2)
        The minimum number of samples required to split an internal node:

        - If int, then consider `min_samples_split` as the minimum number.
        - If float, then `min_samples_split` is a percentage and
          `ceil(min_samples_split * n_samples)` is the minimum
          number of samples for each split.

    min_samples_leaf : int, float, optional (default=1)
        The minimum number of samples required to be at a leaf node:

        - If int, then consider `min_samples_leaf` as the minimum number.
        - If float, then `min_samples_leaf` is a percentage and
          `ceil(min_samples_leaf * n_samples)` is the minimum
          number of samples for each node.

    min_weight_fraction_leaf : float, optional (default=0.)
        The minimum weighted fraction of the input samples required to be at a
        leaf node.

    max_leaf_nodes : int or None, optional (default=None)
        Grow trees with ``max_leaf_nodes`` in best-first fashion.
        Best nodes are defined as relative reduction in impurity.
        If None then unlimited number of leaf nodes.
        If not None then ``max_depth`` will be ignored.

    sparse_output : bool, optional (default=True)
        Whether or not to return a sparse CSR matrix, as default behavior,
        or to return a dense array compatible with dense pipeline operators.

    n_jobs : integer, optional (default=1)
        The number of jobs to run in parallel for both `fit` and `predict`.
        If -1, then the number of jobs is set to the number of cores.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    verbose : int, optional (default=0)
        Controls the verbosity of the tree building process.

    warm_start : bool, optional (default=False)
        When set to ``True``, reuse the solution of the previous call to fit
        and add more estimators to the ensemble, otherwise, just fit a whole
        new forest.

    Attributes
    ----------
    estimators_ : list of DecisionTreeClassifier
        The collection of fitted sub-estimators.

    References
    ----------
    .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
           Machine Learning, 63(1), 3-42, 2006.
    .. [2] Moosmann, F. and Triggs, B. and Jurie, F.  "Fast discriminative
           visual codebooks using randomized clustering forests"
           NIPS 2007

     __init__ N/A N/A self n_estimators max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes sparse_output n_jobs random_state verbose warm_start N/A 10 5 2 1 0.0 None True 1 None 0 False N/A N/A N/A N/A N/A super RandomTreesEmbedding self __init__ base_estimator N/A ExtraTreeRegressor n_estimators n_estimators estimator_params N/A criterion max_depth min_samples_split min_samples_leaf min_weight_fraction_leaf max_features max_leaf_nodes random_state bootstrap False oob_score False n_jobs n_jobs random_state random_state verbose verbose warm_start warm_start N/A N/A self criterion mse N/A N/A self max_depth max_depth N/A N/A self min_samples_split min_samples_split N/A N/A self min_samples_leaf min_samples_leaf N/A N/A self min_weight_fraction_leaf min_weight_fraction_leaf N/A N/A self max_features 1 N/A N/A self max_leaf_nodes max_leaf_nodes N/A N/A self sparse_output sparse_output N/A _set_oob_score N/A N/A self X y N/A N/A N/A N/A NotImplementedError OOB score not supported by tree embedding N/A fit N/A N/A self X y sample_weight N/A None None N/A N/A Fit estimator.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)
            The input samples. Use ``dtype=np.float32`` for maximum
            efficiency. Sparse matrices are also supported, use sparse
            ``csc_matrix`` for maximum efficiency.

        Returns
        -------
        self : object
            Returns self.

         N/A N/A N/A self fit_transform X y sample_weight sample_weight N/A self N/A fit_transform N/A N/A self X y sample_weight N/A None None N/A N/A Fit estimator and transform dataset.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)
            Input data used to build forests. Use ``dtype=np.float32`` for
            maximum efficiency.

        Returns
        -------
        X_transformed : sparse matrix, shape=(n_samples, n_out)
            Transformed dataset.
         N/A X N/A check_array X accept_sparse N/A csc ensure_2d False N/A N/A issparse X N/A N/A N/A N/A X sort_indices N/A rnd N/A check_random_state N/A self random_state N/A y N/A N/A rnd uniform size N/A N/A X shape N/A 0 N/A N/A N/A N/A super RandomTreesEmbedding self fit X y sample_weight sample_weight N/A N/A self one_hot_encoder_ N/A OneHotEncoder sparse N/A self sparse_output N/A N/A N/A N/A self one_hot_encoder_ fit_transform N/A N/A self apply X N/A transform N/A N/A self X N/A N/A N/A Transform dataset.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)
            Input data to be transformed. Use ``dtype=np.float32`` for maximum
            efficiency. Sparse matrices are also supported, use sparse
            ``csr_matrix`` for maximum efficiency.

        Returns
        -------
        X_transformed : sparse matrix, shape=(n_samples, n_out)
            Transformed dataset.
         N/A N/A N/A N/A self one_hot_encoder_ transform N/A N/A self apply X N/A N/A